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**American Health Information Community (AHIC)**

**Quality Workgroup**

**Analysis of Requirements for Health Information Technology to the
Quality Workgroup's Future Vision for Longitudinal Quality Measurement
and Improvement**

**DRAFT**

**Pre-Decisional Document For Discussion Purposes Only**

**Do Not Distribute**

**Prepared by:**

**Booz Allen Hamilton**

**DOCUMENT CHANGE RECORD**

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  Version       Date            Description
  Number                        

  1.0           8/17/2007       Draft 1 of Requirements Analysis

  2.0           8/27/2007       Draft 2 of Requirements Analysis

  3.0           9/28/2007       Draft 3 of Requirements Analysis

                                

                                

                                

                                
  ------------- --------------- ------------------------------------------

TABLE OF CONTENTS

[**1.** **Introduction 1**](#introduction)

[2. Longitudinal quality Measurement and Improvement: A Case study
4](#longitudinal-quality-measurement-and-improvement-a-case-study)

[2.1 The Current State of Diabetes Management
4](#the-current-state-of-diabetes-management)

[2.2 The Future State of Diabetes Management
5](#the-future-state-of-diabetes-management)

[3. Current and Emerging Longitudinal Quality Assessment Capabilities
8](#current-and-emerging-longitudinal-quality-assessment-capabilities)

[3.1 Current HIT Solutions to Support Longitudinal Patient Care and
Quality Improvement
8](#current-hit-solutions-to-support-longitudinal-patient-care-and-quality-improvement)

[3.2 Emerging HIT Strategies to Enable Longitudinal Patient Care and
Quality Improvement
10](#emerging-hit-strategies-to-enable-longitudinal-patient-care-and-quality-improvement)

[3.3 Emerging HIT Strategies to Enable Cross-Organizational Quality
Measurement and Reporting
12](#emerging-hit-strategies-to-enable-cross-organizational-quality-measurement-and-reporting)

[4. Emerging Future State Requirements
15](#emerging-future-state-requirements)

[4.1 Business Case/Incentive Alignment
16](#business-caseincentive-alignment)

[4.2 Measure Development 18](#measure-development)

[4.3 Data Standards 19](#data-standards)

[4.4 Data Exchange and Aggregation 24](#data-exchange-and-aggregation)

[4.5 Policy 28](#policy)

[5. Unresolved Critical Path Requirements
30](#unresolved-critical-path-requirements)

[5.1 High versus Low EHR Adoption 30](#high-versus-low-ehr-adoption)

[5.2 Use of A Voluntary Unique Patient Identifier versus Matching
Algorithms
31](#use-of-a-voluntary-unique-patient-identifier-versus-matching-algorithms)

[5.3 Locus of Patient Records De-Identification
31](#locus-of-patient-records-de-identification)

[5.4 Locus of Data Aggregation 33](#locus-of-data-aggregation)

[6. Next Steps 35](#next-steps)

[Appendix A: Methods 39](#appendix-a-methods)

[A.1. Key Constraints 40](#a.1.-key-constraints)

[A.2. Key Assumptions and Dependencies
40](#a.2.-key-assumptions-and-dependencies)

[Appendix B: Current Strategies to Assess and Improve Quality
41](#appendix-b-current-strategies-to-assess-and-improve-quality)

[B.1 Current State of HIT to Support Quality at the Point of Care
41](#b.1-current-state-of-hit-to-support-quality-at-the-point-of-care)

[B.2 Current state of HIT To Support Quality Improvement, Measurement
and Reporting
44](#b.2-current-state-of-hit-to-support-quality-improvement-measurement-and-reporting)

[B.2.1 Current State of HIT in the Hospital Quality Measurement and
Reporting Enterprise
44](#b.2.1-current-state-of-hit-in-the-hospital-quality-measurement-and-reporting-enterprise)

[B.2.2 Current State of HIT in the Physician Quality Measurement and
Reporting Enterprise
46](#b.2.2-current-state-of-hit-in-the-physician-quality-measurement-and-reporting-enterprise)

[Appendix C: Glossary 47](#appendix-c-glossary)

[Appendix D: Requirements Analysis Subgroup Participants
52](#appendix-d-requirements-analysis-subgroup-participants)

[Appendix E: Reference Documents 53](#appendix-e-reference-documents)

FIGURES AND TABLES

[Figure 1. Information Flow in the Current State for a Patient with
Diabetes 5](#__RefHeading___Toc178751298)

[Figure 2. Information Flow in the Future State for a Patient with
Diabetes 6](#__RefHeading___Toc178751299)

[Table 1. Key Components of the Future State Vision
16](#__RefHeading___Toc178751300)

[Table 2. Business Case Enablers and Barriers
17](#__RefHeading___Toc178751301)

[Table 3. Business Case-Related Future Requirements
18](#__RefHeading___Toc178751302)

[Table 4. Measure Development Enablers and Barriers
19](#__RefHeading___Toc178751303)

[Table 5. Measure Development-Related Future Requirements
19](#__RefHeading___Toc178751304)

[Table 6. Data Standards Enablers and Barriers
21](#__RefHeading___Toc178751305)

[Table 7. Data Standards-Related Future Requirements
21](#__RefHeading___Toc178751306)

[Table 8. Data Exchange and Aggregation Enablers and Barriers
24](#__RefHeading___Toc178751307)

[Table 9. Data Exchange and Aggregation-Related Requirements
25](#__RefHeading___Toc178751308)

[Table 10. Policy-Related Enablers and Barriers
28](#__RefHeading___Toc178751309)

[Table 11. Policy-Related Future Requirements
29](#__RefHeading___Toc178751310)

[Table 12. Future Scenario: High versus Low EHR Adoption
30](#__RefHeading___Toc178751311)

[Table 13. Future Scenario: Voluntary Unique Patient Identifier versus
Matching Algorithms 31](#__RefHeading___Toc178751312)

[Table 14. Future Scenario: Locus of Patient Record De-Identification
32](#__RefHeading___Toc178751313)

[Table 15. Future Scenario: Locus of Data Aggregation
33](#__RefHeading___Toc178751314)

[Figure 3. Recommendation Topic Idea Overlap with the Quality
Information Flow 38](#__RefHeading___Toc178751315)

[Figure 4. Requirements Analysis Methods
40](#__RefHeading___Toc178751316)

[Table 16. Specific Objectives Needed to Achieve the Next Generation of
Valuable CDS 43](#__RefHeading___Toc178751317)

# Introduction

Today, effective coordination and delivery of care across care settings
is limited by medical records (both paper and electronic) that are
setting-specific and by an outdated reliance on manual processes to
communicate relevant health care information across care settings.
Clinicians have limited access to critical clinical information on
individual patients beyond the records of the institution in which he or
she provides care. In short, clinicians are effectively "blind" to a
patient's course of treatment once that individual leaves a specific
site of care. These limitations can significantly impact a provider's
ability to make optimal care decisions. They also negatively impact the
ability to transition patients effectively across care settings, an
important issue when considering patients with multiple chronic
conditions. As a result of these issues, the transition of patients
between settings is characterized by a lack of continuity that directly
impacts the quality of care a patient receives.

All stakeholders in the health care industry -- from consumers to
accrediting organizations -- share the need to make informed decisions
about quality of care. There is a sense of passion across all
stakeholder groups for improving the quality of care for all patients.
To that end, each group of stakeholders has initiated actions, largely
specific to a narrow business objective and within an individual health
delivery segment, to meet their need to measure quality of care. Current
segment-specific efforts are important; however, this siloed approach to
quality improvement has a serious unintended consequence in that it
continues to promote fragmented care delivery. At its best, a fragmented
approach cannot address the pervasive challenges related to coordination
of care.

The American Health Information Community[^1] (AHIC) chartered the
Quality Workgroup. The Quality Workgroup, chartered in August of 2006,
is charged with determining how health information technology (HIT) can
be used for the development of quality measures helpful to patients and
others in the health care industry. HIT systems are important tools for
data collection that can support performance improvement on those
quality measures. The members of the Quality Workgroup represent
hospitals, hospital associations, health plans, vendor organizations,
employers, consumers, and federal agencies.

-   The Quality Workgroup envisioned a future based on the collection,
    aggregation and analysis of longitudinal data to evaluate and
    improve the quality of patient care across care settings.

Information technology and the sharing of health information across a
network of regional health information entities using data from
electronic health records (EHR), personal health records (PHR), and
strong clinical decision support (CDS) systems will assist providers in
ensuring that the right care is delivered to the right patient - every
time. Consumers and policymakers will use these same systems to
understand how well the nation as a whole and individual providers are
doing in improving care and health status in accordance with the
national, regional, and local priorities.

The Quality Workgroup envisioned a future where transparent reporting of
quality performance and quality improvements is used to inform decisions
made about patient care. That vision is one of a health care system that
is information-driven and patient-focused. Thus, the vision incorporates
an expectation that quality measurement and improvement activities will
evolve from a site-centric focus to a patient-centric focus. This
evolution will require new efforts to collect, aggregate and analyze
longitudinal data to evaluate and improve the quality of patient care
over defined time periods and across care settings. Moreover, the future
state is strongly informed by the perspective that the use of HIT to
improve quality is about both reducing reporting burden and driving
improvements in care when it is delivered. The Quality Workgroup's
vision articulates a future where HIT:

1)  Contributes to an improved care delivery system -- one that is more
    connected, coordinated, safer and efficient;

2)  Helps patients and providers know exactly how they are performing
    relative to standard measures of best-practice quality care (both at
    the point of care and longitudinally); and

3)  Continuously enables quality improvement, including both as it
    relates to clinical care delivery and to cost efficiency of the
    systems supporting care delivery.

As discussed in the Institute of Medicine (IOM) report "*Performance
Measurement, Accelerating Improvement*," quality measurement and
reporting across care settings and of long-term outcomes will require a
shift from the current state of assessing and reporting on the delivery
of care at one point in time in a specific setting to a future state of
assessing quality of care across the spectrum of care settings and over
time.[^2] This shift is vital to ensuring that providers have all of the
information needed to make informed treatment decisions for their
patients. This shift also helps to inform improvements to care delivery
across the spectrum of care settings in which a patient may be seen for
a given illness.

This view point was recognized in the Quality Workgroup's vision, along
with the assertion that HIT plays a critical role in enabling
longitudinal quality assessment and improvement through the integration
of data from multiple sources across multiple institutions. A transition
from sole-source data collection to multi-source data exchange will
occur as the industry increases its use of EHRs and gains experience
with data source integration. This transition will necessarily entail
far more extensive collaboration between the quality enterprise and the
HIT community. Simply digitizing current paper-based records will not be
sufficient. However, current progress is slow and there is an
opportunity to facilitate this transition through the promotion of
effective policies, studies, and other critical activities.

-   The Quality Workgroup identified the capabilities and policies
    needed to realize the envisioned patient-centric quality improvement
    environment and identified key areas for the workgroup to consider
    for future AHIC recommendations.

This document presents the results of an analysis performed by the
Quality Workgroup to define capabilities and policies needed to enable a
health care system where patient-centric care and quality improvement
are the cornerstones, and where reporting and quality improvement is
based on secure and appropriate access to a patient's longitudinal
record of care. The results of the analysis are not intended to
prescribe specific solutions. Rather, they are meant to initiate a
dialogue within the Quality Workgroup that will lead to their next set
of recommendations to the AHIC. Methods used to inform this requirements
analysis are described in Appendix A: Methods.

The document is organized into the following sections:

-   **Section 2, Longitudinal Quality Measurement and Improvement: A
    Case Study**. This section sets the context for understanding the
    requirements of the future quality reporting environment. It
    illustrates the need for HIT that supports access to longitudinal
    care records by presenting a chronic illness-based case study that
    describes how care delivery and quality improvement activities could
    be improved.

-   **Section 3, Current and Emerging Longitudinal Quality Measurement
    and Improvement Strategies**. In this section, we describe the
    current strategies aimed at conducting longitudinal analysis. This
    section also describes emerging strategies to improve the HIT
    infrastructure used to conduct quality improvement.[^3]

-   **Section 4,** **Requirements for Realizing the Envisioned
    Longitudinal Quality Measurement.** This section articulates the
    high-level requirements for realizing the envisioned future state of
    quality measurement. Requirements are organized into the five key
    components and include requirements for the Business Case, Measure
    Development, Data Standards, Data Exchange and Aggregation, and
    Policy to facilitate future Quality Workgroup activities.

-   **Section 5**, **Unresolved Critical Path Requirements**. Section 5
    presents four critical path scenarios that describe various options
    for the future, where future state requirements are unclear or there
    where there is disagreement in the industry on what path the future
    should take.

-   **Section 6, Next Steps.** Finally, Section 6 describes the next
    steps to developing recommendations for the AHIC and Secretary of
    Health and Human Services (HHS) based on the requirements analysis.

# Longitudinal quality Measurement and Improvement: A Case study 

HIT is intended to facilitate the exchange and use of health information
to assist providers and ensure the right care is delivered to the right
patient -- every time. Ideally, all stakeholders would be able to use
information generated by transparent and interoperable HIT systems to
understand how well the health care industry is delivering high quality,
efficient, and effective clinical care. HIT can facilitate this
assessment at the national, regional, local, or provider level. More
importantly, it can facilitate this assessment from a patient-centered
perspective, i.e., one that looks across care settings and over time.

Although HIT has been heralded as a critical enabler in driving quality
care, efficiency and patient safety, the nation is in the early stages
of HIT adoption and HIT penetration across care settings is uneven.
Additionally, the drivers, measures, standards, interoperability
requirements, and policies that are needed to enable effective
HIT-facilitated quality measurement and improvement activities have not
yet been established on a consistent basis across the country. Current
HIT applications have been developed largely in isolation of quality
measurement and improvement organizations, and the latter entities have
not yet incorporated the strengths of health IT into their operational
processes. As a result, the ability to support quality at the point of
care, to measure and report on quality, and, most importantly, to enable
longitudinal analysis of quality data across care settings and over time
is hobbled.

To showcase the need for enabling longitudinal measurement, this section
provides a case study using Diabetes to describing how the care delivery
and quality improvement activities are currently enabled through HIT,
and then describes how both of those functions could be improved if the
envisioned future state is realized.

-   Diabetes provides an example of a chronic illness for which quality
    measurement from a longitudinal perspective would significantly
    increase the ability to manage the patient's treatment and ensure
    high quality care is being delivered.

Diabetes is a chronic illness characterized by complications such as
diabetic foot disorder and diabetic retinopathy, in addition to a number
of co-morbidities such as heart failure and hypertension. It is often
managed by multiple providers through multiple visits, and in a variety
of care settings.

## The Current State of Diabetes Management

In this case study, where a multi-disciplinary management team is caring
for a diabetic patient, it is critical for each provider to have a
comprehensive clinical picture of the patient. In today's system, a
provider has access only to the information that resides in that care
setting/institution as stored in a patient's medical record and to other
information relayed directly by the patient. Primary care of diabetic
patients requires access to and management of complex clinical
information. In today's environment, the provider typically may not be
able to access the course of treatment prescribed by other providers who
have seen the patient, the outcome of that treatment, the additional
medications prescribed by those providers, and the details surrounding
other complications for which that patient may be seeing specialists or
other providers of care. Additionally, in most cases the primary care
provider has no way of knowing that a patient has been admitted for
acute care unless this information is relayed directly by the patient.
Also, the clinical details associated with an emergency department visit
or hospitalization may not be available. A comprehensive clinical
picture is critical to informing clinical decision-making. Absent a full
clinical picture, tests and procedures may be duplicated or diagnoses
delayed.

Over the course of time, an individual patient may be routinely seen at
his or her primary care provider's practice, and the medical record at
that provider practice would reflect key indicators such as the
patient's glucose management, current medications, and the development
of any complications. In the event of an acute event such as diabetic
ketoacidosis, the patient may present at an emergency department and
subsequently be admitted to the hospital. For this event, the patient
could have a separate medical record that reflects only the care
delivered from arrival to discharge from the hospital.

Today, the level of information shared from this hospital to the
patient's primary care provider is inconsistent and reliant upon a
number of manual processes. Likewise, for care and treatment of the
patient's kidney failure or heart disease, the patient may be followed
by a nephrologist and a cardiologist respectively. The level of
information shared among these providers, as well as the availability of
information about the hospitalization, is also inconsistent and often
not available in a comprehensive manner to optimize clinical
decision-making.

The quality measurement and reporting of the care and treatment for this
diabetic patient also occur in a silo today. Individual providers may
report quality indicators that are related to the management of this
diabetic patient, while the health plan computes separate and distinct
quality indicators. Figure 1 below illustrates sample information flows
representing one current state of diabetes care.

[]{#__RefHeading___Toc178751298 .anchor}Figure 1. Information Flow in
the Current State for a Patient with Diabetes

![](media/image2.wmf){width="6.497916666666667in"
height="3.3618055555555557in"}

## The Future State of Diabetes Management

A key aspect of the ideal future state, as articulated in the Quality
Workgroup's vision, is that the national quality enterprise will be one
in which patient-centric care is a reality and HIT is used to enable the
collection, aggregation, and analysis of longitudinal data to evaluate
and improve the quality of patient care over defined time periods and
across care settings.

The ability to collect and evaluate longitudinal data is critical to
providing effective care to patients. Patients with chronic illnesses
and, in many cases, those with acute illnesses, are usually seen by
multiple providers, often concurrently. Coordination of care through the
transfer of critical clinical information in the patient's medical
history across settings is vital to ensuring that each provider and
patient is able to make informed decisions regarding the patient's care
and treatment. The evaluation of medical treatment from a longitudinal
perspective is also crucial to ensuring improvements to the system in a
manner that puts the patient first.

Figure 2 presents an example of a diabetes patient in the future state
who is seeking care for his or her condition over time and shows the
associated flows of data that would result. The patient is managed by a
multi-disciplinary team for eye and foot exams, and for monitoring blood
levels, such as HbA1c, as well as for management of any number of
co-morbid conditions. Additionally, the patient experiences a
hospitalization due to diabetes-related complications such as heart
disease, stroke, kidney failure, blindness, and/or circulation problems.
Information flows interoperably between providers and from providers to
the patient (including the patient's personal health record (PHR)), to
the pharmacy, to quality organizations and to health information
exchanges (HIEs).

[]{#__RefHeading___Toc178751299 .anchor}Figure 2. Information Flow in
the Future State for a Patient with Diabetes

![](media/image3.wmf){width="6.423611111111111in"
height="3.6180555555555554in"}

In the future state, the primary care provider would have access to
important clinical history from across the care settings at which the
patient is being seen. The provider would be armed with the knowledge of
what medications had been prescribed by other providers, and would be
made aware of any emergency room visits or hospitalizations that took
place. This knowledge transfer would be facilitated through
interoperable HIT systems including EHRs, PHRs, administrative systems,
lab systems, and pharmacy systems. The ability to view information
across care settings provides the primary care provider and the patient
with a more thorough and detailed understanding of how well the patient
is managing his or her illness and what treatment has been prescribed in
the past. This ability to view information across care settings will
ultimately help the provider make better decisions on what course of
action to take to help control the diabetes.

-   The longitudinal approach to care, facilitated by HIT, can inform
    system-wide improvement to the quality and efficiency of care.

As the case study illustrates, from a quality measurement perspective,
several aspects of the future approach can help inform system-wide
improvements to the quality and efficiency of care delivery for diabetic
patients. These include:

-   The ability to assess the processes, outcomes, and efficiency with
    which care is provided to a diabetes patient across care settings at
    which he or she may be seen;

-   The aggregation of that information to understand relevant
    frequencies, incidence, co-morbidities, outcomes of care processes
    and costs; and

-   The identification of critical areas of strength and deficiency.

An added benefit of quality assessment from a longitudinal perspective
is that improvements upstream in the patient's treatment trajectory can
result in better outcomes that help reduce costs and improve
efficiencies downstream as treatment traditionally becomes more
expensive and more complicated. This is particularly true of a chronic
condition such as diabetes, which often includes a myriad of other
co-morbidities.

An adequate business case for organizations to invest in the
infrastructure and programming needed to make longitudinal analysis a
reality is fundamental to achieving the future vision. The existence of
policies and procedures to facilitate the ability to conduct
longitudinal analyses are also critical. The ability to view relevant
information from the patient's PHR, as recorded in the EHRs and
administrative systems of the various care delivery institutions at
which the patient is seen, and from the pharmacy systems that are used
by the patient, are essential to ensuring that the patient is treated in
the most appropriate manner possible. Interoperable HIT and HIE
facilitate this exchange of information and are foundational components
to facilitating effective care coordination and achieving the best
possible care delivery. Last, the development of longitudinal measures,
the integration of these measures into the HIT systems of the future,
and the ability to exchange and aggregate the measure outputs in order
to analyze them and inform quality improvement efforts are also
necessary for achieving the future state.

# Current and Emerging Longitudinal Quality Assessment Capabilities

As described previously, longitudinal analysis of individual health care
encounters across a span of time provides a patient-centered and
comprehensive perspective on care provided. Longitudinal assessments
allow health care professionals to analyze treatments choices, identify
high-risk patients, evaluate the quality of the care, compare provider
performance, and manage costs. Longitudinal views of patient care can be
created by grouping inpatient, outpatient, lab, and pharmaceutical
claims into clinically meaningful episodes of care. These episodes
describe a patient\'s complete course of care and the severity of
illness for a single occurrence of an illness or condition. This
longitudinal perspective can enable more accurate quality of care
measurement and cost assessment.

Current strategies in longitudinal measurement, such as applying
episodes of care groupers to claims data, are challenged by poor data
quality, gaps in data and a primary focus by current episode of care
grouping methodologies on measuring cost rather than adherence to
evidence-based care or clinical outcomes. Said another way, current
episode of care methodologies do not explicitly address appropriateness
of care. The emerging strategies include new and innovative approaches
to episode-based, patient-centered, or longitudinal care tracking and
measurement and are addressing some of the needs to develop standards,
improve the accessibility or availability of required data and expand
the focus to include clinical quality.

To provide context in which to think about future requirements and the
path to get there, this section describes the current state of the
health care industry as it relates to the development of longitudinal
analysis capabilities. Specifically,

-   Section 3.1 describes current state-of-the-art HIT solutions that
    support longitudinal care and quality improvement.

-   Section 3.2 describes emerging solutions and strategies that support
    longitudinal assessment that are being investigated or are in
    development.

-   Section 3.3 presents emerging HIT solutions and strategies that will
    better enable quality measurement and improvement and which can
    provide a strong foundation for longitudinal quality improvement
    activities in the future.

## Current HIT Solutions to Support Longitudinal Patient Care and Quality Improvement

Both the Geisinger Health System (Geisinger) and the Intermountain
Health Care delivery systems operate health IT solutions that offer
clinicians a longitudinal view of their patients.

***Geisinger*** is an integrated delivery system, comprised of
hospitals, group practices, and a health plan that serves a geography
with a population of approximately 2.5 million individuals. Geisinger
has developed a prototype Clinical Decision Intelligence System (CDIS)
to drive quality improvements within their system. The CDIS is the
foundation of their electronic health record system and it integrates
information from a multitude of sources, such as claims, evidence-based
medicine guidelines, patient preferences, formularies, finance and
operations data. A large digital warehouse allows Geisinger to actively
measure clinical trends, identify gaps in care, derive new clinical
knowledge, and give providers, researchers, and patients access to
clinical information.

Geisinger has successfully demonstrated the impact of CDIS on quality
improvement. For example, Geisinger improved care for adults with Type 1
and 2 diabetes through the standardization of clinical practices,
automated identification of diabetics and care plan status, automated
identification of suboptimal care, automatic order set creation,
automatic generation of patient-specific report cards at checkout,
automatic outreach to patients, and an influenza/pneumococcal campaign
-- a chronic disease return visit program. Through these improvements in
clinical processes and the use of HIT, Geisinger was able to improve the
quality of care by shifting the orientation of care delivery from one
that is reactive to one that is anticipatory or proactive. After nine
moths of implementing these programmatic improvements, composite quality
indicators for patients in their diabetes program, which includes over
25,000 patients, saw a 5.5% increase. These gains are expected to
increase further as Geisinger works to systematically standardize these
programs and work to ensure they are being effectively implemented
throughout their health system.

***Intermountain Health Care*** is a health care delivery system of 21
hospitals (2,200 beds), more than 90 outpatient clinics, an employed
physician group, and an insurance plan for patients in Utah and
southeastern Idaho. Intermountain provides more than 50 percent of all
care delivered in the region it serves. Intermountain began developing
electronic medical systems in the 1970's to support their mission of
delivering high-quality patient-centered care. Their current system
contains extensive clinical information with two main components, an EHR
and a clinical data warehouse.

The hospitals currently use a desktop clinical application, the Health
Evaluation through Logical Processing (HELP) system, to conduct
evaluations of and provide recommendations on the care of each patient.
HELP is interfaced to a longitudinal electronic patient record and a
Clinical Data Repository (CDR). In both ambulatory and inpatient
settings, providers enter visit notes, problems, and medications into
the EHR and the information is stored in the clinical data repository.
HELP allows users in either inpatient or ambulatory settings to view
laboratory results, text reports, and radiology images, regardless of
where the care was provided. HELP also contains advanced decision
support capabilities for alerts, and privacy safeguards for protecting
patient data. HELP, which combines the data from acute hospital
encounters within the longitudinal clinical data repository, allows an
authorized provider to see a comprehensive picture of patient health
information.

A distinct feature of Intermountain's EHR system was its method of
development, which utilized an analytic and process-driven approach. The
EHR system was designed to support Intermountain's key clinical
processes in delivering care. Intermountain began by first analyzing
their key processes to identify the few processes that affect the
largest portion of their patients. Once clinical processes were
identified, they followed five steps to create a care process model:

1)  Develop evidence-based best practices with scientific literature.

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4)  Blend evidence-based best practices into work flow in the front
    line.

5)  Generate an outcomes tracking system to track the major intermediate
    and final outcomes of the disease, the financial outcomes and the
    service outcomes.

6)  Build these outcomes measures into the EHR.

7)  Build educational materials for clinicians and patients.

Intermountain uses the data from the EHR to manage the system, to advise
patients, to plan and organize care, to provide accountability, and to
set and achieve goals.

In addition to the efforts of Geisinger and Intermountain Health, other
current efforts to offer clinicians a longitudinal view of their
patients include software technology that integrates administrative data
into patient-centered episodes of care. These episode groupers
categorize episodes of care into 500 or more relatively homogeneous
types of episodes. This categorization can be used to create a clinical
profile of the patient to make more informed decisions for current care
and plan for future health care needs. There are two prominent vendor
products providing episode grouping software, Episode Treatment Groups
(ETGs) by Symmetry/Ingenix, a subsidiary of UnitedHealth Group[^4] and
the Medstat Episode Grouper by Thompson Healthcare. [^5] These systems
vary in the areas of the number of classification groups, clinical
homogeneity of these groups, statistical stability, severity or case-mix
adjustment capabilities, and the structure or shifting of episodes.[^6]

These software programs are primarily used by payers for physician
practice performance measurement. The most common output used to measure
physician performance is total episode cost. Total episode cost reflects
the total amount paid by the patient and/or insurer for all care
provided during an episode. In addition to payers using this data to
compare specific patient, physician average, and peer average episode
costs, payers are beginning to aggregate their data and to provide
accrediting bodies with plan-level reporting of costs. The use of these
software programs provides information about patient, provider, and plan
patterns of resource use which are primarily measures of efficiency.

Availability of data and lack of standards has made the application of
longitudinal quality measurement difficult in many settings. Episode
grouping requires access to inpatient, outpatient, lab, and pharmacy
claims data which limits application of groupers to settings where
providers or payers have access to the entire spectrum of patient claims
data. In addition, since current episode groupers are based solely on
administrative data, they do not include patient characteristics (social
economic status, other health status, or literacy), provider
characteristics (such as location of practice), or other clinical
information (non-administrative), largely due to the unavailability of
these types of data.

## Emerging HIT Strategies to Enable Longitudinal Patient Care and Quality Improvement

There are emerging strategies and ongoing efforts to develop new and
innovative approaches to episode-based, patient-centered, and
longitudinal care tracking and measurement. The three examples of such
efforts described in this section are

-   Medicare Continuity Assessment Record and Evaluation (CARE)

-   Continuity of Care Record (CCR)

-   National Quality Forum (NQF) National Priorities and Goal Setting
    Project.

These efforts are addressing the need to develop standards for
information sharing to facilitate improved delivery of care across care
settings.

#### Medicare Continuity Assessment Record and Evaluation (CARE)

The Deficit Reduction Act of 2005 directed the Centers for Medicare and
Medicaid Services (CMS) to develop a Post-Acute Care (PAC) Payment
Reform Demonstration. The PAC settings currently employ three different
assessment instruments that drive quality measurement and payment:
Minimum Data Set (MDS) for Skilled Nursing Facilities (SNFs), the
Outcome and Assessment Information Set (OASIS) for Home Health Agencies
(HHAs), and the Inpatient Rehabilitation Facility--Patient Assessment
Instrument (IRF-PAI) for Inpatient Rehabilitation Facilities (IRFs).
Currently there is not a standard assessment in the Long Term Care (LTC)
setting. The absence of a standard assessment from hospital discharge
through all settings of post-acute care is believed to be one of the
major contributors to variations in care, patient outcomes, and cost.
There are also disparities in the payment amounts for the same "type" of
patient among the three settings.

As part of the PAC Payment Reform Demonstration, CMS is developing
a web-based assessment tool to be used at hospital discharge through all
post-acute care settings to assess clinical and functional status. This
initiative attempts to address challenges related to longitudinal
measurement, including the lack of standards for information sharing and
the availability of data. The new assessment tool, the CARE, may
eventually replace the current tools, i.e., MDS, OASIS, and IRF-PAI. The
assessment data gathered through CARE would be collected for all
Medicare beneficiaries across the entire span of post-acute care. The
new assessment instrument will document various levels of care needs
including: administrative information, admission information, current
medical items, cognitive status, physical factors, factors affecting
outcomes, and discharge items.

This new tool provides the opportunity to encourage new patient-centered
quality measures that span the continuum of care during the post-acute
phase of an episode, to assess the resources used to treat patients,
patient outcomes independent of clinical setting, and to drive new
pay-for-performance initiatives.

#### The Continuity of Care Record (CCR) and Continuity of Care Document (CCD)

The CCR is a joint effort of ASTM International, the Massachusetts
Medical Society, the Health Information and Systems Society, and the
American Academy of Family Physicians. The aim of this effort is to
improve the continuity of care, reduce medical errors, and ensure a
minimum level of interoperable health information. The CCR will be a
structured transportable set of basic patient information that can be
used in the transfer or referral of patients from one provider or
setting to another. The CCR is addressing barriers in longitudinal
measurement and assessment such as limited standards and data
availability.

The basic information captured in the CCR includes: patient and provider
information, insurance information, health status, recent care, future
care recommendations, and reason for referral or transfer. The
information in the CCR will be completed by relevant clinicians and the
format of the CCR (xml) is readable by both machine and humans. The
developers of the CCR envision that existing EHRs can import and export
the information to and from the CCR to allow a seamless transfer of
essential information across care settings.

The CCR does not include any laboratory or radiology information that
was not included by a clinician. It does not list symptoms but does list
diagnoses and reasons for referral or transfer. It does not include a
chronology of events as would exist in an EHR. The CCR is expected to
prevent medical errors and improve the quality of care by providing
essential information to a new health care provider.

The CCD is the harmonization project of HL7 and ASTM endorsed by the
Health Information Technology Standards Panel (HITSP). CCD represents
the major features of ASTM's CCR and the HL7's Clinical Document
Architecture, and it is used for the exchange of crucial patient data
such as demographics, medications and laboratory results. CCD will
enable the rapid transition of CCR content into the more mainstream
Clinical Document Architecture representation. Assuming progress is made
as planned; it will be included in Version 2.0 of HITSP's
Interoperability Specifications, which are expected to be recognized in
December 2007. If this schedule is adhered to, the effective
implementation date for the standards will be January 2008.[^7]

#### NQF National Priorities and Goal Setting Project

The NQF project *Evaluating Efficiency across Episodes of Care* aims to
develop a comprehensive measurement framework to assess efficiency,
quality, and cost for health care episodes that span care settings. The
objectives of this project include creating clear definitions of
terminology, establishing a discrete set of domains, and providing
guiding principles for implementation. The NQF will:

-   Identify a subset of priority conditions intended to serve as a
    starting point for a national effort to measure, report, and improve
    efficiency across episodes of care.

-   Establish national performance improvement goals---measurable
    targets for two priority conditions---over the next three to five
    years.

-   Lay out a future vision and research agenda for the evolution of a
    measurement and monitoring system focused on episode efficiency.

To date, the committee has drafted a measurement framework for "episode
efficiency" that includes definitions, domains, and guiding principles;
conducted a workshop to further develop the framework among a diverse
group of content experts; and selected two priority conditions to serve
as operational examples: acute myocardial infarction and low back pain.
A third condition, diabetes, is being added.

In the course of these efforts, the NQF identified current
methodological issues related to longitudinal measurement. These issues,
in relation to data, are: accountability, security, quality of data,
data integrity, data collection, aggregation methods, and need for EHR
standards. In longitudinal measurement, challenges remain such as the
lack of a conceptual regulatory/reimbursement framework for shared
accountability within a fragmented delivery system, the inability to
attribute of care to specific providers, and privacy concerns for
sharing data across multiple care settings.

## Emerging HIT Strategies to Enable Cross-Organizational Quality Measurement and Reporting

In addition to the efforts described above, several efforts are under
way to better leverage the potential of HIT to drive quality, efficiency
and performance, although without a specific focus on enabling
patient-centered care. These efforts are nonetheless important in
driving quality improvement and are foundational to developing an
infrastructure upon which longitudinal analysis can occur. For example,
in recent years there have been focused efforts to standardize data
elements and sources; to design EHRs to drive quality care; to align
quality measurement and reporting with clinician workflow through the
evolution of clinical decision support tools,; and to remove current
barriers to EHR adoption. The health care industry is beginning to
address many of the foundational elements necessary to build a health
care system that fully leverages HIT to enable quality, as described
below.

-   **Data Standardization**: There is limited standardization of data
    elements, particularly within EHRs, and EHRs are often customized
    during implementation, resulting in a lack of detailed, standardized
    implementation specifications for collecting data pertaining to
    quality metrics in an EHR. Various efforts are underway to begin to
    standardize data elements within EHRs, as well as to consider data
    standards and the ability to automate data collection in the
    development of new quality measures. The National Health Information
    Network (NHIN) coordinated through the Office of the National
    Coordinator for Health Information Technology (ONC) is predicated on
    a platform of interoperability that requires data standardization.
    To achieve the level of interoperability expected of the NHIN, ONC
    has been coordinating a number of integrated activities. ONC
    developed a Quality Use Case that captures the integration of data
    to support quality measurement, feedback and reporting into EHRs,
    begins to use quality measures to support clinical decision making,
    and allows for the aggregation of quality information across
    multiple providers and entities to support public reporting. The
    Quality Workgroup included recommendations to accelerate the use of
    common data elements to enable automated data collection to support
    a prioritized set of AQA Alliance[^8] (AQA) and Hospital Quality
    Alliance (HQA) measures in its March 2007 recommendations to the
    AHIC. HITSP is focused on the harmonization of data standards to
    meet the requirements outlined in the Quality Use Case and to fill
    the identified gaps for data elements required to automate the
    collection of data to support core sets of AQA and HQA quality
    measures. The standards identified by HITSP will then be used by the
    Certification Commission for Health Information Technology (CCHIT)
    during its 2008 certification cycle.

-   **Data Stewardship:** Consensus on managing and storing aggregated
    patient-indexed data is important to the realization of
    comprehensive HIE. Movement toward comprehensive HIE will rely
    heavily on resolution of a variety of data stewardship issues.
    Together, the AQA and the HHS Agency for Healthcare Research and
    Quality (AHRQ) are working to develop a national data strategy that
    delineates the role and governance model for data stewardship. Most
    recently, AHRQ issued a Request for Information (RFI) to gather
    ideas and thoughts across the industry on requirements for a data
    steward entity. Following the responses to this RFI, due July 2007,
    AHRQ will issue a report in October 2007 on the range of viewpoints
    on data stewardship requirements. The Joint Commission on
    Accreditation of Healthcare Organizations (The Joint Commission) has
    also engaged a broad group of stakeholders to identify the critical
    components of a performance measurement data strategy (this effort
    is still in progress).

-   **EHR Adoption:** Widespread adoption of EHRs is a critical
    component of supporting quality through HIT. Through the CCHIT, AHIC
    is working to promote specific EHR products that meet a core set of
    interoperability requirements. Additionally, policy levers such as
    modifications to anti-kickback laws and programs such as CMS's
    Doctor\'s Office Quality - Information Technology (DOQ-IT) program
    which, among other objectives, supports the adoption and effective
    use of information technology by physicians\' office to improve
    quality and safety, are attempting to address barriers to EHR
    adoption.

-   **HIEs/Regional Health Information Organizations (RHIOs):** There
    are over 100+ local activities that are currently testing local
    applications of HIE. State and community-level efforts in HIE will
    require a national strategy to ensure interoperability and
    consistency to enable the NHIN's "networks of networks." As EHRs
    evolve, adoption of EHRs increases, and a nationwide interoperable
    health information network is built, patient data captured as a
    result of routine care and treatment will be accessible across care
    settings so that clinicians have the right information at the right
    time for clinical decision-making.

-   **Better Quality Information to Improve Care for Medicare
    Beneficiaries (BQI):** The AQA, in conjunction with CMS, has
    implemented six pilots focused on the sharing and use of provider
    data to publicly report on quality of care. The BQI pilots will be
    able to aggregate private payer data with CMS claims data to build a
    more comprehensive picture of provider quality.

-   **Chartered Value Exchanges:** To further advance community
    involvement and participation, HHS is formally supporting the
    establishment of Chartered Value Exchanges that represent a
    multi-stakeholder organization that has taken clear action in its
    community to convene community purchasers, health plans, providers,
    and consumers to advance the four cornerstones of Value-Driven
    Health Care. The four cornerstones include: Interoperable HIT,
    Measure and Publish Quality Information, Measure and Publish Price
    Information, and Promote Quality and Efficiency of Care. Chartered
    Value Exchanges will be invited to participate in a nationwide
    learning network that will be sponsored and maintained by AHRQ. 

# Emerging Future State Requirements

This section articulates future requirements as identified through
interviews, through testimony, and through secondary sources recommended
by subgroup members, interviewees, and referenced through testimony.
These future requirements perform several functions. In some cases, they
directly address barriers in today's environment; in other cases, they
take the form of enablers that currently exist or are being explored
through stand alone or organization specific initiatives; and in yet
other cases, they articulate other important characteristics or features
that are necessary for HIT to effectively enable patient-centric quality
improvement in the future.

The requirements are grouped by five components identified through our
review of relevant testimony, interviews, and literature as critical to
ensuring the national quality enterprise can conduct patient-centric
quality improvement in the future. Table 1 provides a brief overview of
each of these key components in the future state, as articulated in the
Quality Workgroup vision.

For each component, a discussion of the enablers and barriers seen in
the current environment precedes each listing of future requirements.
The requirements are further subdivided into those around which there is
common agreement throughout the literature reviewed, interviews
conducted, and testimony received, and those around which there is less
agreement.

[]{#__RefHeading___Toc178751300 .anchor}Table 1. Key Components of the
Future State Vision

+---------+------------------------------------------------------------+
| Co      | Future State Description                                   |
| mponent |                                                            |
+=========+============================================================+
| B       | Measures and guidelines that promote efficient,            |
| usiness | coordinated care will help galvanize payer's attempts to   |
| Case/   | implement payment programs that reward effectiveness and   |
| In      | coordination of care. These measures and guidelines will   |
| centive | incentivize consumers to remain engaged and empowered, to  |
| Al      | utilize quality information to strengthen their            |
| ignment | experience, and to take a vested interest in care across   |
|         | settings. Providers will also have access to coordinated   |
|         | guidelines and protocols will be widely used along with    |
|         | the inclusion of multi-practice care processes in quality  |
|         | metrics.                                                   |
+---------+------------------------------------------------------------+
| Measure | Measure developers will collaborate to facilitate measure  |
| Deve    | harmonization. Vendors will collaborate with the NQF and   |
| lopment | quality measurement organizations to encourage development |
|         | and implementation of common conventions and guidelines    |
|         | for measure development. Longitudinal measurement systems  |
|         | will capture the performance of multiple providers caring  |
|         | for a patient, will examine how well care is provided      |
|         | across transitions to different settings (e.g., hospital   |
|         | to nursing home), and most importantly, will evaluate      |
|         | patient outcomes over time.                                |
|         |                                                            |
|         | To support improved coordination of care, the national     |
|         | quality enterprise will encourage the alignment of         |
|         | measures across settings as well as the use of             |
|         | interoperable EHRs and PHRs that allow for measurement of  |
|         | care across time and sites of care. The research community |
|         | will develop guidelines and measures that promote          |
|         | efficient, high quality, coordinated care. The research    |
|         | community will continue to identify gaps and to refine and |
|         | update existing measures as more information is gathered   |
|         | on coordination of care.                                   |
+---------+------------------------------------------------------------+
| Data    | Data collection and aggregation practices will be enabled  |
| St      | by measures that use standard definitions of terms to the  |
| andards | greatest extent possible. These activities will also be    |
|         | enhanced by the structuring or standardization of          |
|         | documented data either through direct entry of structured  |
|         | information or through focused and standardized free-text  |
|         | searching and parsing techniques.                          |
+---------+------------------------------------------------------------+
| Data    | Data exchange and aggregation will be facilitated through  |
| E       | standardized data sets, and the widespread use of          |
| xchange | interoperable HIT, including EHRs. Polices and procedures  |
| and     | related to collection, storage, aggregation, linkage and   |
| Aggr    | transmission of health data will be established with       |
| egation | appropriate protection for legitimate use. The             |
|         | establishment of these policies and procedures will allow  |
|         | for the collection of patient-centered data, aggregated    |
|         | across providers and payers to support longitudinal        |
|         | quality measurement at the patient, provider, provider     |
|         | group, plan, and hospital levels.                          |
+---------+------------------------------------------------------------+
| Policy  | -   A national framework for the use of health data will   |
|         |     include a robust infrastructure of policies,           |
|         |     standards, and best practices to facilitate the broad  |
|         |     and multiple-purpose collection, storage, aggregation, |
|         |     linkage, and transmission of health data with          |
|         |     appropriate protections for legitimate use.            |
|         |     Appropriate confidentiality protections will be in     |
|         |     place for the use of patient data that are in strict   |
|         |     compliance with Health Insurance Portability and       |
|         |     Accountability Act (HIPAA) regulations. Potential      |
|         |     problems of patients opting out of inclusion of their  |
|         |     data in a data repository will be addressed and        |
|         |     impacts on accurately assessing the quality of care on |
|         |     both the national and community levels will be         |
|         |     understood.                                            |
+---------+------------------------------------------------------------+

The requirements articulated in this section of the report are intended
to serve as information points for the Quality Workgroup to consider as
they develop the second set of AHIC recommendations. The requirements
are predicated on a number of assumptions, including:

-   EHR technology is the norm for storing and managing clinical
    information for patients.

-   Standard-setting and certification bodies have been established to
    continually develop and update HIT interoperability and
    methodological standards to enable longitudinal analysis.

-   The national quality agenda is unified and quality measures are
    harmonized across care settings.

-   NQF-endorsed longitudinal measures that facilitate care coordination
    and patient-centric quality improvement are available for use across
    care settings.

## Business Case/Incentive Alignment

The annual savings attributable to widespread EHR adoption, including
savings resulting from improved safety and disease management, are
likely to lie between 7.5 and 30 percent of annual health care
spending.[^9] While the aggregate benefits are enormous, the business
case for investing in HIT to support direct patient care, quality
improvement, and measurement activities is made at the organization
level, whether the organization is a private practice, hospital, health
system, or payer. The decision is based primarily on a review of the
financial benefit of investment in enabling technologies versus the
perceived gains. Providers and hospital systems must balance their
primary objective of delivering high quality care to patients against
the costs related to implementing enabling technologies such as EHRs and
CDS to improve how they deliver care, as well as against the costs
associated with investing in associated quality measurement and
improvement activities. Because longitudinal measurement requires
investment in inter-organizational data capture and management
strategies, payers and integrated delivery models have greater
incentives to make this investment, and have made the most progress as
described in Section 3. Table 2 summarizes the key enablers and barriers
that impact the business case for HIT investment in today's environment.

[]{#__RefHeading___Toc178751301 .anchor}Table 2. Business Case Enablers
and Barriers

+-----------------------------------+-----------------------------------+
| Enablers                          | Barriers                          |
+===================================+===================================+
| -   Demographic shifts due to     | -   Economic pressures -- higher  |
|     increase in elderly and       |     costs of doing business,      |
|     chronically ill populations   |     declining reimbursement,      |
|     and the need to better manage |     limited capital and resources |
|     the care provided to those    |     make investment in            |
|     populations                   |     information technology        |
|                                   |     solutions difficult,          |
| -   The increased recognition of  |     especially at the practice    |
|     the importance of the         |     level                         |
|     transitions between care      |                                   |
|     settings and the coordination | -   The explosion of performance  |
|     of care needed across care    |     measurement efforts and       |
|     settings                      |     associated increased system   |
|                                   |     costs and the burden          |
| -   Organizations that have a     |     associated with data          |
|     clear vision regarding their  |     collection and reporting      |
|     quality and data strategy and |                                   |
|     commitment to implementing    | -   Balancing the demand of       |
|     that vision at the            |     multiple stakeholders when    |
|     organizational level          |     considering data strategies   |
|                                   |     and implementation of those   |
| -   Access to financial resources |     strategies                    |
|     to support data strategy      |                                   |
|                                   | -   Management of the business    |
| -   The development of viable     |     agreements and complex        |
|     business models for regional  |     relationships to allow        |
|     HIE networks that support     |     regional data sharing         |
|     provider EHRs, data           |                                   |
|     aggregators and public        | -   Siloed or                     |
|     reporting entities            |     organization-specific         |
|                                   |     viewpoints when considering   |
| -   Community support of a        |     investment in enabling        |
|     cross-organizaitonal data     |     technologies                  |
|     strategy                      |                                   |
|                                   | -   Provider concerns regarding   |
| -   Vendor support of a           |     attribution and               |
|     cross-organizational data     |     accountability when           |
|     strategy                      |     considering longitudinal      |
|                                   |     measurement                   |
| -   The increasing demand for     |                                   |
|     useful performance            | -   Episode-of-care methodologies |
|     information by consumers,     |     that do not take into account |
|     providers, and other          |     prevention and consumer       |
|     stakeholders                  |     choices                       |
|                                   |                                   |
| -   The desire to advance the     | -   No framework for shared       |
|     medical science/technology    |     accountability beyond         |
|                                   |     integrated systems            |
| -   Informed consumers help drive |                                   |
|     the quality improvement       |                                   |
|     agenda                        |                                   |
+-----------------------------------+-----------------------------------+

As described above, the financial benefits associated with investment in
interoperability are substantial, yet there remains a great need to
improve the business case for organizations to encourage them to invest
in longitudinal analysis and an HIT infrastructure that could enable it.
Creating a multi-stakeholder business case is a critical first step to
stimulate a cultural shift that will advance individual organizations
investment in interoperable HIT the accompanying HIE that is required
for longitudinal analysis.

Table 3 below describes the business, technical, and policy-related
requirements that are necessary for establishing such a business case in
the future state and is followed by a discussion of the areas where
future state requirements remain undefined.

[]{#__RefHeading___Toc178751302 .anchor}Table 3. Business Case-Related
Future Requirements

  ----- ----------------- --------------------------------------------------------- -----------
  ID    Requirement                                                                 Common
        Description                                                                 Agreement

  1.1   Systems or                                                                  Yes
        processes to                                                                
        share, collect,                                                             
        aggregate and                                                               
        report quality,                                                             
        cost of care, and                                                           
        patient                                                                     
        experience data                                                             
        will be designed                                                            
        to minimize cost                                                            
        and burden to                                                               
        consumers,                                                                  
        physicians'                                                                 
        practices, health                                                           
        plans, and data                                                             
        aggregators.                                                                
        *(Based on AQA                                                              
        Data Aggregation                                                            
        Principles                                                                  
        document, AQA                                                               
        Data Sharing and                                                            
        Aggregation                                                                 
        Principles for                                                              
        Performance                                                                 
        Measurement and                                                             
        Reporting                                                                   
        document)*                                                                  

  1.2   Sustainable                                                                 Yes
        business models                                                             
        will be                                                                     
        established for                                                             
        HIE at the                                                                  
        regional, state,                                                            
        and national                                                                
        levels that                                                                 
        protect privacy                                                             
        and enable                                                                  
        appropriate uses                                                            
        of data. *(Based                                                            
        on Quality                                                                  
        Workgroup vision                                                            
        document)*                                                                  

  1.3   Resources                                                                   Yes
        required to                                                                 
        establish,                                                                  
        maintain and                                                                
        contribute data                                                             
        to HIE databases                                                            
        will be fairly                                                              
        distributed among                                                           
        all who benefit.                                                            
        *(Based on AQA                                                              
        Registry                                                                    
        Principles                                                                  
        document)*                                                                  

  1.4   System design,                                                              Yes
        implementation,                                                             
        and use will                                                                
        minimize costs.                                                             
        *(AQA Principles                                                            
        for HIT and                                                                 
        Measurement                                                                 
        Aggregation                                                                 
        document)*                                                                  

  1.5   All participating                                                           Yes
        organizations in                                                            
        a HIE will belong                                                           
        to and comply                                                               
        with agreements                                                             
        of a federation,                                                            
        with clear                                                                  
        agreements                                                                  
        established to                                                              
        engender the                                                                
        trust that is                                                               
        essential to the                                                            
        exchange of                                                                 
        health                                                                      
        information.                                                                
        *(Based on Markle                                                           
        Technology                                                                  
        Principle:                                                                  
        Federation,                                                                 
        located in the                                                              
        Common Framework                                                            
        Overview and                                                                
        Principles                                                                  
        document)*                                                                  

  1.6   Providers will                                                              Yes
        have access to                                                              
        coordinated                                                                 
        guidelines and                                                              
        protocols will be                                                           
        widely used along                                                           
        with the                                                                    
        inclusion of                                                                
        multi-practice                                                              
        care process in                                                             
        quality metrics.                                                            
        (*Addressed in                                                              
        March 2007                                                                  
        Quality Workgroup                                                           
        recommendations                                                             
        to the AHIC)*                                                               

  1.7                     Payment strategies for care provision will reflect shared No
                          accountability across organizations. *(Based on Quality   
                          Workgroup Vision document)*                               

  1.8                     Government, payer, and employer-sponsored initiatives     No
                          shall provide financial incentives to drive adoption of   
                          EHRs and other quality measurement and care coordination  
                          enabling technologies. *(Based on Quality Workgroup       
                          Vision document)*                                         
  ----- ----------------- --------------------------------------------------------- -----------

## Measure Development

Today, longitudinal measures of quality of care are almost non-existent.
Measurement efforts have been driven by what data is currently
available, rather than focusing on what is important to measure. The
challenge in today's environment for developing longitudinal measures
has been the structure of the health care delivery system: fragmentation
across the continuum of care that creates clinical setting-specific
silos in data systems, measure development, and standards definitions.
Currently, measure development activities focus on provider encounters,
occurring in silos. Within these silos, measure developers use differing
standards for evidence-grading, differing approaches to measure
specifications, and have varying capabilities for measure development
and maintenance, resulting in inconsistencies in the way measures are
developed, implemented, and maintained.

Current measurement of quality performance relies largely on claims data
because more robust electronic clinical information is generally lacking
and performance information is insufficient for payers to determine true
under- and over-utilization of services. The market perceives the cost
of quality reporting to be higher than the benefits, but generally
supports movement toward value-based payment.

Despite these challenges in the current system, some integrated delivery
networks (IDNs) and payers are currently aligning and utilizing data
from multiple sources. In addition, there are efforts to standardize a
web-based patient assessment tool to be used across the post-acute
spectrum of care (CMS CARE project; see page 11). Standardization
efforts across the continuum of care are critical for sharing
information between information technology systems and across providers.
EHRs remain the key tool for data collection and provision of
evidence-based care. Table 4 below presents the enablers and disablers
that exist in today's environment as they relate to longitudinal measure
development.

[]{#__RefHeading___Toc178751303 .anchor}Table 4. Measure Development
Enablers and Barriers

+--------------------------------+-------------------------------------+
| Enablers                       | Barriers                            |
+--------------------------------+-------------------------------------+
| -   Use of episodes-of-care    | -   Lack of measures that span the  |
|     groupers that include risk |     entire continuum of care        |
|     adjustment algorithms      |                                     |
|                                | -   Measurement often dictated by   |
| -   Use of standard quality    |     what is measurable rather than  |
|     definitions throughout     |     what should be measured         |
|     industry                   |                                     |
|                                | -   Limited set of national         |
| -   Use of standard algorithms |     consensus measures              |
|     throughout the industry    |                                     |
|                                | -   Robust measures not yet         |
| -   Providers and clinical     |     developed for all provider      |
|     leader engagement in the   |     specialties                     |
|     measure development and    |                                     |
|     endorsement process        | -   Limited clinical detail in      |
|                                |     claims data                     |
|                                |                                     |
|                                | -   Limited standardized clinical   |
|                                |     information across EHR products |
|                                |                                     |
|                                | -   Few integrated or episode-based |
|                                |     metrics                         |
|                                |                                     |
|                                | -   Redundancy in reporting         |
|                                |                                     |
|                                | -   Most measures are aggregated at |
|                                |     patient, provider, or encounter |
|                                |     level                           |
+--------------------------------+-------------------------------------+

Harmonization of existing measures; development of methodologies and
associated measures for assessing quality across the continuum of care;
identification of necessary data elements and integration of data
elements into EHRs are a few of the areas that must be addressed in
order for measure development to effectively support longitudinal and
patient-centric quality improvement. Table 5 below describes business,
technical, and/or policy-related requirements related to measure
development in the future state.

[]{#__RefHeading___Toc178751304 .anchor}Table 5. Measure
Development-Related Future Requirements

  -------------------------------------------------------------------------
  ID    Requirement Description                                 Common
                                                                Agreement
  ----- ------------------------------------------------------- -----------
  2.1   Measure developers will produce harmonized measure sets Yes
        in line with national goals that are defined at the     
        data element level to allow uniform data collection and 
        aggregation across disparate systems. *(Based on        
        Quality Workgroup Vision document)*                     

  2.2   The NQF will establish common conventions and           Yes
        guidelines for measure development and maintenance,     
        including HIT standards and specifications. *(Based on  
        Quality Workgroup Vision document)*                     

  2.3   The NQF, in partnership with measure developers, will   No
        build consensus around national goals for quality       
        measurement that will drive the development of          
        longitudinal measures. *(Based on Quality Workgroup     
        Vision document)*                                       

  2.4   Tested methodologies will be employed to define care    Yes
        processes associated with specific illnesses and to     
        define related longitudinal measures that will help     
        improve the quality of the care delivered through those 
        processes. *(Based on Testimony, Interviews*)           

  2.5   To the greatest extent possible, longitudinal quality   No
        measurement will be structured to accurately reflect    
        all services that are accountable in whole or in part   
        for the performance measured. Attribution to providers  
        or organizations should be explicit and transparent.    
        *(Based on AQA Consumer Principles document)* \[Also    
        included as Requirement 1.6\]                           
  -------------------------------------------------------------------------

## Data Standards

Data standards are an essential component to successful longitudinal
measurement. Standards are needed to ensure interoperability of EHR,
PHR, and other clinical and administrative system products. HITSP, a
public-private partnership focused on developing consensus-based
standards, is the primary organization in today's environment
responsible for the development of these standards at the national
level. Working in partnership with HITSP, CCHIT serves as the nation's
primary recognized certification body for EHR products. Together, both
these organizations help drive the standardization of HIT across the
health care industry. However, in addition to standards that help
promote interoperability for HIT, standards are needed for specific uses
for these systems as they relate to longitudinal quality measurement and
improvement, such as for matching patient records, aggregating data, and
data exchange over time and across care settings.

While progress is being made on the development of standards and common
nomenclature for EHRs, and administrative and clinical HIT systems,
there is a lack of broad application of standards-based data
definitions. Achieving standardization will help facilitate the sharing
and use of clinical information to improve the quality of care delivery.
However, even where standard nomenclatures exist, the determination of
which codes should be used to describe care can vary significantly from
provider to provider. This, in turn, impacts the ability to determine if
the care provided relates to a given episode or clinical condition,
which in turn impacts the ability to accurately capture data related
from a longitudinal perspective. Additionally, clinical documentation
continues to be unstructured, non-standardized and potentially
inaccurate. While we are in the early stages of EHR development and
adoption, there are limitations on the ability of EHRs to facilitate
quality measurement and capture appropriate clinical documentation,
resulting in an opportunity lost to use this valuable information to
facilitate quality improvement. Table 6 below presents information on
how current use of standards, lack of standards, or activities to
develop standards enables or disables longitudinal quality measurement
and improvement.

[]{#__RefHeading___Toc178751305 .anchor}Table 6. Data Standards Enablers
and Barriers

+----------------------------+-----------------------------------------+
| Enablers                   | Barriers                                |
+----------------------------+-----------------------------------------+
| -   CCHIT and HITSP        | -   Lack of standards for data          |
|     potential to promote   |     definition and aggregation          |
|     standards development  |                                         |
|     and certification for  | -   Lack of defined interfaces to allow |
|     health IT as it        |     the aggregation (Vision)            |
|     relates to quality     |                                         |
|     measurement and        | -   Application of non-standard data    |
|     reporting              |     definitions to data sources         |
|                            |                                         |
| -   Increased              | -   Inconsistent formats for data       |
|     standardization of     |     collection both within and across   |
|     performance measures   |     organizations                       |
|     and collection         |                                         |
|     processes through      | -   Application of non-standard data    |
|     measure developer      |     elements, templates, formats across |
|     harmonization efforts  |     different data sources              |
|                            |                                         |
| -   Use of standard data   | -   One-dimensional or fragmented       |
|     aggregation algorithms |     efforts                             |
|     for quality measures   |                                         |
|                            | -   No consensus on appropriate use of  |
| -   Increased levels of    |     taxonomy for many data elements,    |
|     collaboration between  |                                         |
|     providers, purchasers, | -   Unstructured clinical documentation |
|     consumers and          |     and non-standardized nomenclature   |
|     accreditors to produce |                                         |
|     uniform standards for  | -   Use of inaccurate clinical data     |
|     sharing and            |     from manual documentation           |
|     aggregating health     |                                         |
|     data and public        | -   Exclusion of working diagnoses in   |
|     reporting              |     the medical record                  |
|                            |                                         |
| -   Increasing             | -   Gaps in quality management          |
|     availability of        |     capabilities of EHRs                |
|     language processing    |                                         |
|     programs to extract    | -   Lack of ability to capture free     |
|     data from text or      |     text data from an EHR               |
|     narrative reports      |                                         |
|                            | -   Inaccurate capture of               |
| -   The potential for EHRs |     provider-entered data into an       |
|     to serve as a key tool |     electronic health record            |
|     for standardized data  |                                         |
|     collection and         | -   Limited standardized clinical       |
|     provision of evidence  |     information across EHR products     |
|     based care             |                                         |
|                            | -   Lack of patient identified data     |
| -   Software systems help  |     available in large warehouses to    |
|     minimize human and     |     conduct research                    |
|     system error in data   |                                         |
|     entry and clinical     | -   Inability to integrate multiple IT  |
|     documentation          |     systems within an organization      |
|                            |                                         |
| -   Increasing use of      | -   No standards for defining           |
|     technologies and       |     patient-provider relationships      |
|     methodologies for      |                                         |
|     encrypted              | -   No standards for defining visits    |
|     identification to      |     and relevant care processes for an  |
|     enable the data        |     individual episode or illness       |
|     re-identified, while   |                                         |
|     ensuring               | -   Need for a national patient         |
|     confidentiality is     |     identifier or methodology for       |
|     preserved when doing   |     matching patient records            |
|     aggregate or           |                                         |
|     population level       | -   Patient record matching is          |
|     analyses               |     expensive                           |
|                            |                                         |
|                            | -   Lack of widely agreed upon          |
|                            |     standards for data stewardship      |
+----------------------------+-----------------------------------------+

One of the most significant areas where progress needs to be made to
enable the future state is in the area of data standards, however
significant work remains. Standards must ultimately be developed and
established across a number of different areas in order to enable
effective and efficient longitudinal analysis capability. Standards must
cover EHR, PHR, and CDS technologies; the methodologies and algorithms
used to match patient records; ensure interoperability to enable data
exchange and aggregation; and facilitate care coordination and care
delivery. Table 7 below describes business, technical, and/or
policy-related future requirements related to development and use of
data standards in the future state.

[]{#__RefHeading___Toc178751306 .anchor}Table 7. Data Standards-Related
Future Requirements

  --------------------------------------------------------------------------------------
  ID                 Requirement Description                                 Common
                                                                             Agreement
  ------------------ ------------------------------------------------------- -----------
  Electronic Health                                                          
  Record Standards                                                           

  3.1                EHR certification will include basic standards to       Yes
                     ensure interoperability with other systems. *(Based on  
                     Quality Workgroup Vision document)*                     

  3.2                EHR certification will include basic standards for that Yes
                     define standardized data elements needed for            
                     longitudinal quality measurement, care management, and  
                     quality improvement. *(Based on Quality Workgroup       
                     Vision document)*                                       

  3.3.               EHR certification will include basic standards for      Yes
                     structured capture of narrative documentation and       
                     create parameters around all free text documentation.   
                     *(Based on Testimony, Interviews, Various               
                     documentation)*                                         

  Personal Health                                                            
  Record Standards                                                           

  3.4                PHR certification will include standards to ensure      Yes
                     interoperability with EHRs and CDS Systems. *(Based on  
                     Quality Workgroup Vision document)*                     

  3.5                PHR certification will include basic standards that     Yes
                     define standardized data elements that could be used    
                     for quality reporting and improvement activities.       
                     *(Based on Testimony, Interviews)*                      

  Clinical Decision                                                          
  Support Standards                                                          

  3.6                CDS system certification will include standards to      Yes
                     ensure interoperability with other systems including    
                     EHRs and PHRs. *(Based on Quality Workgroup Vision      
                     document)*                                              

  3.7                Standard development organizations will establish       Yes
                     standards for CDS that allow for the application of     
                     applied collaborative research into best practice.      
                     *(Based on Quality Workgroup Vision document)*          

  Patient Record                                                             
  Matching Standards                                                         

  3.8                Standards will be developed for recording and storing   Yes
                     patient information relative to name, date of birth,    
                     address and other demographic information that may be   
                     used to match medical records. *(Based on Draft         
                     Unpublished Document)*                                  

  3.9                Standards will be developed for identifying             Yes
                     institutions through which care was delivered so that   
                     information can be matched to medical records. *(Based  
                     on Testimony, Interviews)*                              

  3.10               Standardized criteria, methodologies, and algorithms    Yes
                     will be used to link and match patient records          
                     throughout various linked databases for performance     
                     measurement while protecting patient privacy. *(Based   
                     on Draft Unpublished Document, Testimony, Interviews)*  

  3.11               Methodologies/algorithms for linking and matching       No
                     patient records will use multiple attributes for        
                     matching patients and records. *(Based on Subgroup      
                     Input)*                                                 

  3.12               Methodologies/algorithms for linking and matching       Yes
                     patient records will have little tolerance for error    
                     and shall avoid false positive matches at all costs.    
                     *(Based on Subgroup Input, Markle Technology Principle: 
                     Accuracy located in the Markle Technology Principles    
                     document)*                                              

  3.13               Methodologies/algorithms for linking and matching       Yes
                     patient records will have the capacity for the          
                     correction of common errors in a timely manner. *(Based 
                     on Subgroup Input)*                                     

  3.14               Methodologies/algorithms for linking and matching       Yes
                     patient records will have rapid/near instantaneous      
                     processing times. *(Based on Subgroup Input)*           

  HIT                                                                        
  Interoperability                                                           
  Standards                                                                  

  3.15               Standards for networked technologies will enable near   Yes
                     real time information exchange across systems, care     
                     settings, and among providers, payers, consumers and    
                     other stakeholders as deemed appropriate. (*Based on    
                     Testimony, Interviews, Quality Workgroup Vision         
                     document)*                                              

  3.16               Uniform operating rules and standards for the exchange  Yes
                     and aggregation of quality and efficiency data used in  
                     both the public and private sectors will be established 
                     for the purposes of performance measurement and         
                     reporting. *(Based on AQA Data Sharing and Aggregation  
                     Principles document and National Health Data            
                     Stewardship Entity document)*                           

  3.17               Uniform operating rules and standards for the exchange  Yes
                     and aggregation of quality and efficiency data used in  
                     both the public and private sectors will be established 
                     for the purposes of establishing effective care         
                     coordination across settings. *(Based on Interviews,    
                     Testimony, AQA Data Sharing and Aggregation Principles  
                     document, and National Health Data Stewardship Entity   
                     document)*                                              

  3.18               Open networks, standards, and protocols will ensure     Yes
                     that compatibility, connectivity, and interoperability  
                     characterize the systems used for quality measurement   
                     and improvement. *(Based on AQA Principles for HIT and  
                     Measurement Aggregation document)*                      

  3.19               Stacked data structures and master variable             No
                     dictionaries will be used to manage all systems housing 
                     linkable patient data. *(Based on Testimony)*           

  3.20               Certification for HIT systems will include an           Yes
                     assessment of adherence to established interoperability 
                     standards. *(Based on Quality Workgroup Vision          
                     document)*                                              

  3.21               Certification for HIT systems will include all          Yes
                     technical standards that are adoption ready, including  
                     what is referred to as the "operable" set: HL7 v2.x     
                     data interchange standard, the HL7 Reference            
                     Information Model, the DICOM standard for imaging, the  
                     NCPDP SCRIPT prescription drug 2 information standard,  
                     the LOINC vocabulary for laboratory tests, the          
                     IEEE/CEN/ISO 1073 medical device communication          
                     standard, the ASC X12 administrative transaction        
                     standard, HL7 Data Types, Clinical Document             
                     Architecture, and the HL7 Clinical Context Management   
                     Specification. *(Based on Connecting for Health Key     
                     Findings document)*                                     

  3.22               Government, payer, and employer sponsored initiatives   No
                     will promote the use of open, non-proprietary standards 
                     for interoperability and data exchange. *(Based on      
                     Interviews, Testimony)*                                 

  Care Coordination                                                          
  Standards                                                                  

  3.23               A core set of data elements common to all care settings No
                     will be automatically captured in relevant HIT systems  
                     and transferred across care settings upon provider      
                     request. *(Based on Subgroup Input, Continuity of Care  
                     Concept Paper)*                                         

  3.24               A unique set of data elements to individual care        No
                     settings will be automatically captured in relevant HIT 
                     systems and transferred across care settings upon       
                     provider request. *(Based on Subgroup Input, Continuity 
                     of Care Concept Paper)*                                 

  3.25               Software applications for care management (e.g. EHRs,   Yes
                     practice management systems, registries) will make      
                     standardized quality, performance, and efficiency       
                     measurement a routine by-product of their use. *(Based  
                     on AQA Principles for HIT and Measurement Aggregation   
                     document)*                                              

  3.26               Software applications for care management (e.g., EHRs,  Yes
                     practice management systems, registries) will be        
                     designed to enable the merger of their data with others 
                     for the purpose of facilitating quality improvement     
                     efforts or the production of standardized quality,      
                     performance and efficiency measurement. *(Based on AQA  
                     Principles for HIT and Measurement Aggregation          
                     document)*                                              

  Clinical Registry                                                          
  Standards                                                                  

  3.27               Publicly available protocols that encompass common      Yes
                     nomenclature, data definitions, data collection, sample 
                     size, sampling and data transfer protocols (when        
                     appropriate) and reporting format will be standardized  
                     among institutions reporting to the registry. *(AQA     
                     Registry Principles)*                                   

  3.28               When data on the same procedure(s) is transmitted and   Yes
                     collected by multiple registries (e.g., different       
                     specialty societies collecting data on the same         
                     clinical procedure), data collection and submission     
                     procedures will follow the same data field definitions, 
                     protocols, and methodology. Ideally, national standards 
                     should be used or developed whenever possible. *(AQA    
                     Registry Principles)*                                   

  3.29               Clinical data registries shall be populated with        Yes
                     clinical and administrative data as required to         
                     appropriately measure quality. *(AQA Registry           
                     Principles document)*                                   

  3.30               Data submitted to clinical registries shall enable      Yes
                     comparative reporting to inform choices, quality        
                     improvement or quality assessment. *(AQA Registry       
                     Principles document)*                                   

  3.31               Data sources for clinical registries shall be multi     Yes
                     institutional with agreed upon policies for data        
                     submission. *(AQA Registry Principles document)*        

  3.32               Registries shall have an infrastructure to support and  Yes
                     maintain expansion in data capacity and analysis of     
                     trends. *(AQA Registry Principles document)*            

  3.33               Registries should be able to accept the electronic      Yes
                     transfer of data. *(AQA Registry Principles document)*  

  3.34               Health data registries shall be built to minimize human Yes
                     factor errors and entry. *(AQA Registry Principles      
                     document)*                                              
  --------------------------------------------------------------------------------------

## Data Exchange and Aggregation

While the increased recognition of the value of longitudinal performance
measurement data has prompted the development of quality databases
across care settings, several barriers exist that are significant
barriers to successful implementation of effective data exchange and
aggregation of quality data. Some of these barriers include the limited
availability of the necessary data, the lack of data standards to enable
sharing and aggregation across sources, and the disparate definitions
and coding choices for the care processes associated with specific
episodes.

Information capture is, by the nature of care delivery,
setting-specific, existing in silos. However, even within a unique care
setting, matching and aggregating data from across data sources can be
problematic. Independent health care systems such as Intermountain
Health or IDNs such as Kaiser Permanente have made progress in
integrating and aggregating data across the continuum of care and over
time, and using that data to achieve critical quality improvements.
Collaborations among providers at the local and regional level, RHIOs
and BQIs are exploring locally grown methodologies for effective data
exchange and aggregation across organizations. Quality alliances among
providers, purchasers, consumers and accreditors are beginning to
produce uniform standards for sharing and aggregating health data for
quality improvement and for public reporting. However efforts to enable
these activities from a longitudinal perspective are few and far
between. Disease registries an important source of longitudinal data as
well, however uniform data standards and rules for sharing and
aggregating the information contained within them are not in place, thus
enabling their integration with data from other sources to inform
quality improvement is difficult.

Table 8 below summarizes the key enablers and barriers to data exchange
and aggregation.

[]{#__RefHeading___Toc178751307 .anchor}Table 8. Data Exchange and
Aggregation Enablers and Barriers

+-----------------------------+----------------------------------------+
| Enablers                    | Barriers                               |
+=============================+========================================+
| -   Increased recognition   | -   No single data source to provide a |
|     of the value of         |     complete view of a patient\'s care |
|     performance measurement |     across settings                    |
|     data across care        |                                        |
|     settings                | -   Lack of data standards to support  |
|                             |     data exchange across systems       |
| -   Availability and        |                                        |
|     accessibility of data   | -   Limited access to laboratory data, |
|     from multiple sources   |     PBM data, EHR data both within     |
|                             |     organizations and across           |
| -   Integrated delivery     |     organizations                      |
|     systems, payer, and     |                                        |
|     large health systems'   | -   Continued paper-based data storage |
|     ability and business    |                                        |
|     case to integrate data  | -   Low EHR adoption                   |
|     across settings         |                                        |
|                             | -   Inability to aggregate data from   |
| -   Increasing availability |     across multiple HIT systems due to |
|     of scalable open source |     interoperability issues            |
|     software development to |                                        |
|     reduce costs of         | -   Inability to integrate multiple IT |
|     multiple approaches to  |     systems within an organization due |
|     data aggregation        |     to interoperability issues         |
|                             |                                        |
| -   Standardization across  | -   Costs associated with developing   |
|     HIT systems to enable   |     interfaces between systems         |
|     data exchange between   |                                        |
|     systems and across      | -   Inability to aggregate data from   |
|     providers               |     across multiple care settings      |
|                             |                                        |
| -   EHRs and PHRs role as   | -   Use of proprietary technologies    |
|     key tools for           |                                        |
|     standardized data       | -   Limited technology infrastructure  |
|     collection and the      |     capabilities of those receiving or |
|     provision of evidence   |     viewing the information            |
|     based care              |                                        |
|                             | -   Access to broad band across        |
| -   Collaboration among     |     regional locations, particularly   |
|     local and regional      |     in rural areas                     |
|     health care providers,  |                                        |
|     RHIOs, and BQI sites to | -   HIE operational in less than 10    |
|     explore and test models |     regions across the United States   |
|     for effective data      |                                        |
|     exchange and            | -   Disease registries are not         |
|     aggregation             |     integrated into HIE efforts,       |
|                             |     though they house longitudinal     |
| -   Presidential executive  |     data                               |
|     order requiring Federal |                                        |
|     agencies and their      | -   Current quality metrics do not     |
|     contractors to promote  |     require, and thus do not           |
|     the use of              |     incentivize, reporting across      |
|     interoperable HIT so    |     organizations                      |
|     that data can easily be |                                        |
|     shared.                 | -   Liability issues, need for more    |
|                             |     explicit privacy policy            |
|                             |     development, and lack of business  |
|                             |     case limit exploration of          |
|                             |     effective data exchange and        |
|                             |     aggregation strategies             |
|                             |                                        |
|                             | -   Limited ability to analyze         |
|                             |     cost/utilization across episodes   |
+-----------------------------+----------------------------------------+

Data exchange and aggregation both within and across care institutions
are limited in large part due to the limitations in existing technology
and infrastructure, interoperability standards, the resulting use of
organization or locality-specific methodologies for exchanging and
aggregating data, and the difficulty in developing business agreements
to exchange data across institutions. The table previously presented in
Section 4.3 outlined the requirements related to interoperability
standards necessary to promote data exchange and aggregation. Table 9
describes business, technical, and/or policy-related future requirements
related to other aspects data exchange and aggregation in the future
state, including requirements related to HIE, data stewardship, and the
role of record locator services (RLS).

[]{#__RefHeading___Toc178751308 .anchor}Table 9. Data Exchange and
Aggregation-Related Requirements

  ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  ID             Requirement Description                                                                                                                                                               Common
                                                                                                                                                                                                       Agreement
  -------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -----------
  HIE                                                                                                                                                                                                  

  4.1            Data exchange and aggregation will be compliant with privacy, confidentiality and other applicable rules, while ensuring that providers, plans, other data contributors, and          Yes
                 consumers have necessary and appropriate access to useful information. *(Based on the AQA Data Sharing and Aggregation Principles for Performance Measurement and Reporting           
                 document)*                                                                                                                                                                            

  4.2            Those submitting data to an HIE will be accountable for the accuracy and completeness of their data. *(Based on the AQA Data Sharing and Aggregation Principles for Performance       Yes
                 Measurement and Reporting document)*                                                                                                                                                  

  4.3            HIE will operate under the following principle: Only the minimum number of rules and protocols essential to widespread exchange of health information shall be specified as part of a Yes
                 Common Framework. It is desirable to leave to the local systems those things best handled locally, while specifying at a national level those things required as universal in order   
                 to allow for exchange among subordinate networks. *(Based on Markle, Technology Principle: Make it "Thin," located in the Markle Technology Principles document)*                     

  4.4            Any hardware or software will be used for HIE as long as it conforms to a common framework of essential requirements. The HIE network should support variation and innovation in      Yes
                 response to local needs. The HIE network must be able to scale and evolve over time. *(Based on Markle Technology Principle: Flexibility, located in the Common Framework Overview    
                 and Principles document)*                                                                                                                                                             

  4.5            There will be feedback mechanisms to help organizations to fix or "clean" their data in the event that errors are discovered \[in regards to HIE\]. *(Markle, Technology Principle:   Yes
                 Accuracy, located in the Common Framework Overview and Principles document)*                                                                                                          

  4.6            Aggregation will be compliant with privacy, confidentiality and other applicable rules, while ensuring that providers, plans, other data contributors, and consumers have necessary   Yes
                 and appropriate access to useful information. *(AQA Data Sharing and Aggregation Principles for Performance Measurement and Reporting, located in the Common Framework Overview and   
                 Principles document)*                                                                                                                                                                 

  4.7            Those submitting data will be accountable for the accuracy and completeness of their data. *(AQA Data Sharing and Aggregation Principles for Performance Measurement and Reporting,   Yes
                 located in the Common Framework Overview and Principles document)*                                                                                                                    

  4.8            HIE models will take into account the current structure of the health care system, taking advantage of what is deployed today where possible and appropriate. *(Based on the Markle   Yes
                 Technology Principle: Avoid Rip and Replace, located in the Common Framework Overview and Principles document)*                                                                       

  4.9            HIE will be predicated on a federated model that protects the privacy and security of information and allows accurate and timely access to information. This includes a common set of Yes
                 policies, procedures, and standards to facilitate reliable, efficient sharing of health information among authorized users, is required. These standards or practices specify when    
                 patient information can be shared, which information can be shared, and how the information can be used. *(Based on NHIN RFI Collaborative Response)*                                 

  4.10           HIE will be predicated on a decentralized model that protects the privacy and security of information and allows accurate and timely access to information. It facilitates the        Yes
                 transfer of selected information from one end-point system to another (not necessarily the source system), as is required for providing care and supporting informed patient          
                 participation in care. *(Based on NHIN RFI Collaborative Response)*                                                                                                                   

  4.11           HIEs will develop polices to encourage consumer education around: a) security; b) how it works; c) patient permission; d) who has access; e) the benefits of HIE to the patient and   No
                 physician *(Based on National Committee on Vital Health Statistics (NCVHS) Testimony)*                                                                                                

  Care                                                                                                                                                                                                 
  Coordination                                                                                                                                                                                         
  (through HIE)                                                                                                                                                                                        

  4.12           Clinical and administrative patient data will be transferred across care settings when patients transition from one care setting to the next and shall be made available to the       Yes
                 provider treating the patient. *(Based on Interviews, Testimony, Subgroup Input)*                                                                                                     

  4.13           All providers responsible for providing care to an individual patient will be able to access that patient's clinical and administrative records across sites to ensure a full         Yes
                 (longitudinal) understanding of the patient's medical history and facilitate deliver of appropriate care. *(Based on Interviews, Subgroup Input)*                                     

  4.14           Longitudinal data on patients will be filtered for exclusion criteria for each case identified as eligible for a quality measure. *(Based on Quality Use Case)*                       Yes

  4.15           Clinicians and hospitals will collaborate to share pertinent information in a timely manner that promotes patient safety and quality improvement. *(AQA Reporting Principles          Yes
                 document)*                                                                                                                                                                            

  Data                                                                                                                                                                                                 
  Stewardship                                                                                                                                                                                          

  4.16           The data stewardship entity will be responsible for ensuring all nationally established standards related to data exchange and aggregation are adhered to all institutions and data   No
                 under its purview and shall established more detailed operational standards or rules as appropriate. *(Based on Interviews)*                                                          

  4.17           The data steward entity will be objective in its decision making; weigh carefully the views of its constituents in developing concepts and operating rules and standards; bring about No
                 needed changes in ways that minimizes disruption to current aggregation efforts; review the effects of past decisions and interpret, amend or replace operating rules, standards and  
                 processes in a timely fashion when such action is indicated; follow an open, orderly process for setting policies, operating rules and standards that precludes placing any           
                 particular interest above the interests of the many stakeholders who rely on health care information. *(Based on AQA Data Sharing and Aggregation Principles document and National    
                 Health Data Stewardship Entity document)*                                                                                                                                             

  4.18           The data stewardship entity will address various data aggregation issues including required characteristics of aggregators (e.g., they should be trusted and respected entities),     No
                 transparency of aggregation processes, control and ownership rights of the data, potential liability within data aggregation processes, and issues that arise when competing          
                 aggregation efforts are in a single market area; should ensure that the experience of existing aggregation efforts are leveraged. These responsibilities will be conducted in         
                 accordance with any relevant national standards. *(Based on AQA Data Sharing and Aggregation Principles document and National Health Data Stewardship Entity document)*               

  4.19           The data stewardship entity will set policies, rules and standards for collecting public and private sector data from relevant stakeholders, including providers, employers, health   No
                 insurance plans and others based on an agreed-upon measurement set; should assess the pros and cons of using data derived from administrative data (e.g., claims, pharmacy and lab    
                 data), medical record review and surveys, and develop policies that prioritize data sources based on various dimensions. These responsibilities will be conducted in accordance with  
                 any relevant national standards. *(Based on AQA Data Sharing and Aggregation Principles document and National Health Data Stewardship Entity document)*                               

  4.20           The data stewardship entity will address at what specific level(s) data should be aggregated (e.g., individual physician level or group practice level). When making this             No
                 determination, should consider sample size issues and physician/practice identifier issues. These responsibilities will be conducted in accordance with any relevant national         
                 standards. *(Based on AQA Data Sharing and Aggregation Principles document and National Health Data Stewardship Entity document)*                                                     

  4.21           [The data stewardship entity will set methodological rules and standards for aggregating data, including those addressing risk adjustment, measure weights and sample                 No
                 size.](../../../../../../../../C:%5CDocuments%20and%20Settings%5C507799%5CDesktop%5CReq%20Analysis%20Related%20Info%5CFutureRequirements_20070806.xls#RANGE!E125%23RANGE!E125) These  
                 responsibilities will be conducted in accordance with any relevant national standards. *(Based on AQA Data Sharing and Aggregation Principles document and National Health Data       
                 Stewardship Entity document)*                                                                                                                                                         

  4.22           The data stewardship entity will set data analysis rules and standards. These responsibilities will be conducted in accordance with any relevant national standards. *(Based on AQA   No
                 Data Sharing and Aggregation Principles document and National Health Data Stewardship Entity document)*                                                                               

  4.23           The data stewardship entity will set policies, rules and standards to ensure that the validity of the data submitted is independently audited. These responsibilities will be         No
                 conducted in accordance with any relevant national standards. *(Based on AQA Data Sharing and Aggregation Principles document and National Health Data Stewardship Entity document)*  

  4.24           The data stewardship entity will recommend allowable and non-allowable uses of data, based on current law. These responsibilities will be conducted in accordance with any relevant   No
                 national standards. *(Based on AQA Data Sharing and Aggregation Principles document, National Health Data Stewardship Entity document, and the AHRQ Data Steward Request for          
                 Information)*                                                                                                                                                                         

  4.25           The data stewardship entity will specify who should have access to data and applicable limitations, such as confidentiality and privacy rules; should consider policies which allow   No
                 contributors, including both public and private sector entities, to have access to their own data as well as information which allows them to compare their data against benchmarks.  
                 These responsibilities will be conducted in accordance with any relevant national standards. *(Based on AQA Data Sharing and Aggregation Principles document and National Health Data 
                 Stewardship Entity document)*                                                                                                                                                         

  4.26           The data stewardship entity [will develop guiding principles for public reporting and reporting back information to clinicians. Screening processes to ensure valid reporting also    No
                 should be                                                                                                                                                                             
                 addressed.](../../../../../../../../C:%5CDocuments%20and%20Settings%5C507799%5CDesktop%5CReq%20Analysis%20Related%20Info%5CFutureRequirements_20070806.xls#RANGE!E126%23RANGE!E126)   
                 These responsibilities will be conducted in accordance with any relevant national standards. *(AQA Data Sharing and Aggregation Principles document and National Health Data          
                 Stewardship Entity document)*                                                                                                                                                         

  Record Locator                                                                                                                                                                                       
  Service                                                                                                                                                                                              

  4.27           Record Locator Services will be used to help providers locate relevant records across care institutions for any patients he or she is treating. *(Based on Subgroup Input)*           No

  4.28           Standards will be developed for identifying institutions through which care was delivered so that information can be matched to medical records. *(Based on Interviews, Testimony)*   Yes
                 \[Same as Requirement 3.9\]                                                                                                                                                           

  4.29           Methodologies/algorithms for linking and matching patient records \[through a Record Locator Service\] will have rapid/near instantaneous processing times. *(Based on Subgroup       No
                 Input)* \[Similar to Requirement 3.14\]                                                                                                                                               
  ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

## Policy

National, state, and organizational-level policies play a critical role
in governing today's quality enterprise. Practices related to informed
consent, patient privacy, data identification and de-identification
practices, and information security are governed through HIPAA, common
law, state laws, institutional review boards, data safety and monitoring
boards, and organizational governing committees. The establishment of
policies related to these topics is critical to ensuring the ability of
organizations to exchange data, aggregate it, and share it for different
end uses including longitudinal analysis and patient-centric quality
improvement, while at the same time encouraging transparency, managing
liability and protecting privacy. Current payment practices and business
models do not promote investment in longitudinal and patient-centric
quality improvement across organizational entities. Many argue these
issues can only be resolved with the development of national or state
policies that incentivize organizations to collaborate and exchange
information with a goal of improving care from a patient-centric
perspective.

Pilot and grant programs to invest in the exploration of data exchange
strategies are often initiated through government funding. These include
programs such as RHIOs/HIE's, CMS demonstration projects, BQI, NHIN, and
the Value Exchange program. The results of these programs may serve to
provide the evidence needed to advance or change policies at the
organizational, state, or national level to promote longitudinal and
patient-centric quality improvement. Table 10 outlines policy-based
enablers for quality measurement, specifically longitudinal measurement,
and barriers that could be overcome by the development of such policies
or policy-based initiatives.

[]{#__RefHeading___Toc178751309 .anchor}Table 10. Policy-Related
Enablers and Barriers

+-----------------------------------+-----------------------------------+
| Enablers                          | Barriers                          |
+-----------------------------------+-----------------------------------+
| -   The Presidential executive    | -   Lack of alignment of          |
|     order requiring Federal       |     reimbursement with quality    |
|     agencies and their            |     performance                   |
|     contractors to promote the    |                                   |
|     use of interoperable HIT so   | -   Lack of alignment of          |
|     that data can easily be       |     reimbursement with care       |
|     shared.                       |     coordination                  |
|                                   |                                   |
| -   Mandatory reporting programs  | -   Privacy concerns around the   |
|     at the Federal and state      |     sharing patient identified    |
|     levels that include a robust  |     data across organizations     |
|     list of measures spanning     |                                   |
|     issues of patient safety and  | -   Privacy concerns often        |
|     quality                       |     prevent aggregation of        |
|                                   |     identified patient data       |
| -   Initiation of pilot projects  |                                   |
|     that provide leadership for a | -   The need for clear policies   |
|     national framework (ex: NHIN, |     to define the use of          |
|     Value Exchanges, BQI) and act |     patient-identified data in    |
|     as learning laboratories to   |     large data warehouses         |
|     link public and private data  |                                   |
|     sets and assess clinical      | -   HIE is operational in less    |
|     quality, cost of care and     |     than 10 regions to enable     |
|     patient experience            |     data sharing for Quality      |
|                                   |     Reporting                     |
| -   Organizational policies       |                                   |
|     governing the use of          | -   Gaps in regulations and       |
|     encrypted identifiers so the  |     practices relating to         |
|     receivers of the information  |     privacy/security and          |
|     (providers, hospitals, plans) |     secondary use of data         |
|     can re-identify the data      |                                   |
|     while ensuring                | -   Lack of standardized          |
|     confidentiality is preserved  |     mechanisms for external       |
|     when doing aggregate or       |     reporting including data      |
|     population level analyses     |     stewardship                   |
|                                   |                                   |
|                                   | -   Limited access to broad band  |
|                                   |     across regional locations,    |
|                                   |     particularly in rural areas   |
|                                   |                                   |
|                                   | -   Limited EHR infrastructure    |
|                                   |     within underserved            |
|                                   |     communities without           |
|                                   |     assistance and associations   |
|                                   |     with larger organizations     |
+-----------------------------------+-----------------------------------+

Policies are needed at the organizational, community, state, and
national level to facilitate the ability to conduct longitudinal
analyses through data exchange and aggregation across institutions. They
are also needed to facilitate the use of data (both point of care data
and longitudinal data), while at the same time maintaining appropriate
privacy and security safeguards for the sharing and use patient data.
Many interviewees indicated that lack of a detailed legal framework will
be a critical barrier to enabling longitudinal analysis in the future.
Table 11 describes future requirements related to critical policy issues
that need to be addressed in the future state that have been articulated
by thought-leaders in the health care industry.

[]{#__RefHeading___Toc178751310 .anchor}Table 11. Policy-Related Future
Requirements

  ------ ----------------------------------------------------- ------------
  ID     Requirement Description                               Common
                                                               Agreement

  5.1    All HIEs, including in support of the delivery of     Yes
         care and the conduct of research and public health    
         reporting, will be conducted in an environment of     
         trust, based upon conformance with appropriate        
         requirements for patient privacy, security,           
         confidentiality, integrity, audit, and informed       
         consent. *(Markle, Technology Principle: Privacy and  
         Security, located in the Common Framework Overview    
         and Principles document)*                             

  5.2    Systems or processes to share, collect, aggregate and Yes
         report quality, cost of care and patient experience   
         data will be compliant with privacy, confidentiality  
         and other applicable rules, while ensuring that       
         providers, plans, other data contributors, and        
         consumers have necessary and appropriate access to    
         useful information. *(AQA Data Aggregation Principles 
         document)*                                            

  5.3    There will be a general policy of openness about      Yes
         developments, practices, and policies with respect to 
         personal data. Individuals should be able to know     
         what information exists about them, the purpose of    
         its use, who can access and use it, and where it      
         resides. *(Markle, Policy Principle: Openness and     
         Transparency, located in the Common Framework         
         Overview and Principles document)*                    

  5.4    Individuals will control access to their personal     Yes
         information. (*Markle, Policy Principle: Individual   
         Participation and Control, located in the Common      
         Framework Overview and Principles document)*          

  5.5    A organization (provider or HIE) will incorporate a   Yes
         governing body to develop, maintain, track, and       
         enforce access and control rules *(NCVHS              
         testimonies)*                                         

  5.6    Personal data will not be disclosed, made available,  Yes
         or otherwise used for purposes other than those       
         specified. *(Markle, Policy Principle: Use            
         Limitation, located in the Common Framework Overview  
         and Principles document)*                             

  5.7    Individuals will have the right to: • Have personal   Yes
         data relating to them communicated within a           
         reasonable time (at an affordable charge, if any),    
         and in a form that is readily understandable; • Be    
         given reasons if a request (as described above) is    
         denied, and to be able to challenge such denial; and  
         • Challenge data relating to them and have it         
         rectified, completed, or mended. *(Markle, Policy     
         Principle: Individual Participation and Control,      
         located in the Common Framework Overview and          
         Principles document)*                                 

  5.8    Personal data will be protected by reasonable         Yes
         security safeguards against such risks as loss or     
         unauthorized access, destruction, use, modification,  
         or disclosure. *(Markle, Policy Principle: Security   
         Safeguards and Controls, located in the Common        
         Framework Overview and Principles document)*          

  5.9    Entities in control of personal health data will be   Yes
         held accountable for implementing information use and 
         safeguard practices. *(Markle, Policy Principle:      
         Accountability and Oversight, located in the Common   
         Framework Overview and Principles document)*          

  5.10   Legal and financial remedies will exist to address    Yes
         any security breaches or privacy violations.          
         *(Markle, Policy Principle: Remedies)*                

  5.11   A organization (provider or HIE) will incorporate a   Yes
         governing body to develop, maintain, track, and       
         enforce access and control rules (*Testimony)*        

  5.12   Payment strategies for care provision will reflect    No
         shared accountability across organizations. *(Based   
         on Quality Workgroup Vision document)* \[Also         
         included as Requirement 1.7\]                         

  5.13   Government, payer, and employer-sponsored initiatives No
         will provide financial incentives to drive adoption   
         of EHRs and other quality measurement and care        
         coordination enabling technologies*. (Based on        
         Quality Workgroup Vision document)* \[Also included   
         as 1.8\]                                              
  ------ ----------------------------------------------------- ------------

# Unresolved Critical Path Requirements 

The requirements articulated in Section 4 describe critical features
needed to enable the envisioned future state, one where longitudinal
analysis is commonly used to improve care delivery, and the ability to
collect, share, and report information is facilitated by HIT. However,
as noted throughout the Section 4, there are several areas where there
is a lack of common agreement regarding those requirements or where
requirements have not been articulated. We reviewed these areas and
identified a core set of topic areas that are part of the critical path
to achieving the future state.

This section presents several scenarios that describe various options
for the future state for areas that are part of the critical path to the
future state, and discusses the strengths and weaknesses of each option.
These scenario include:

1)  High versus low EHR Adoption

```{=html}
<!-- -->
```
8)  The use of a voluntary unique patient identifier versus matching
    algorithms

9)  The locus for patient records de-identification

10) The locus of data aggregation.

The intent of this section, as with the requirements summarized in
Section 4, is to foster discussion and dialogue regarding
recommendations that the Quality Workgroup could put forth to help drive
the industry towards the envisioned future state.

## High versus Low EHR Adoption

The requirements in Section 4 are based on an assumption that in the
future state, EHR use is the norm for health care providers across all
care settings. The ability to achieve high penetration of interoperable
EHRs is impacted by many factors; political, financial, technical, and
cultural. Thus, considering the implications of low versus high EHR
penetration can provide valuable input on important interim requirements
to consider. Table 12 presents strengths and weaknesses to consider for
either scenario.

[]{#__RefHeading___Toc178751311 .anchor}Table 12. Future Scenario: High
versus Low EHR Adoption

+-----------------------------------+-----------------------------------+
| Low EHR Adoption                  | High EHR Adoption                 |
+===================================+===================================+
| Description                       |                                   |
+-----------------------------------+-----------------------------------+
| Quality improvement will be based | Quality improvement will be based |
| primarily on a hybrid of claims   | primarily on clinical data.       |
| and clinical data.                |                                   |
+-----------------------------------+-----------------------------------+
| Strengths                         |                                   |
+-----------------------------------+-----------------------------------+
| -   A limited degree of quality   | -   Assessment of quality based   |
|     assessment is possible using  |     on clinical data is the gold  |
|     hybrid claims and clinical    |     standard.                     |
|     data.                         |                                   |
|                                   | -   EHRs can be integrated with   |
| -   For the limited number of     |     other data sources,           |
|     nationally-endorsed quality   |     supporting capabilities such  |
|     measures, which use claims    |     as HIE resulting in more      |
|     data, consistency in          |     comprehensive clinical        |
|     interpretation and            |     information in the hands of   |
|     transparency in reporting has |     clinicians (e.g.,             |
|     already been established.     |     availability of a             |
|                                   |     longitudinal patient record   |
|                                   |     is possible with high EHR     |
|                                   |     adoption) .                   |
+-----------------------------------+-----------------------------------+
| Weaknesses                        |                                   |
+-----------------------------------+-----------------------------------+
| -   Unevenness in measurement     | -   Clinical decision support     |
|     ability due to variations in  |     tools are still evolving, to  |
|     available data sources across |     fully take advantage of high  |
|     organizations.                |     EHR adoption.                 |
|                                   |                                   |
| -   Unable to link disparate data | -   Standards in data transfer,   |
|     sets together to ensure       |     data content and use of data  |
|     richer clinical picture at    |     within EHRs still evolving.   |
|     point of care. Inability to   |                                   |
|     truly assess quality from     |                                   |
|     claims data (though           |                                   |
|     efficiency can be assessed).  |                                   |
|                                   |                                   |
| -   Claims are not always filed   |                                   |
|     for all visits.               |                                   |
|                                   |                                   |
| -   Claims data integrity is      |                                   |
|     sometimes questionable.       |                                   |
+-----------------------------------+-----------------------------------+

## Use of A Voluntary Unique Patient Identifier versus Matching Algorithms

The ability to match patient records is integral to enabling HIE. Use of
a voluntary unique patient identifier is a subject that has come up
repeatedly in today's HIE debates and is one that is filled with many
political overtones. Some states have implemented voluntary patient
identifier programs, however for the most part, existing mechanisms for
patient record matching rely on the use of organization-level
identifiers and matching algorithms. There are opportunities to learn
from public health agencies on methods to match patient records across
disparate sources. By improving accuracy in matching (less false
positive and / or negatives), patient safety is enhanced and the
likelihood for redundant tests is decreased. Additionally, with more
efficient matching, costs for providers and others to maintain accuracy
in their local files decreases.

Table 13 presents an overview of the strengths and weaknesses to both
approaches: use of a voluntary unique patient identifier versus matching
algorithms.

[]{#__RefHeading___Toc178751312 .anchor}Table 13. Future Scenario:
Voluntary Unique Patient Identifier versus Matching Algorithms

+-----------------------------------+-----------------------------------+
| Voluntary Unique Patient          | Matching Algorithms               |
| Identifier                        |                                   |
+===================================+===================================+
| Description                       |                                   |
+-----------------------------------+-----------------------------------+
| Patient-record matching will be   | Patient-record matching will be   |
| accomplished through the use of   | accomplished through the use of   |
| voluntary unique patient          | matching algorithms.              |
| identifiers at the state or       |                                   |
| regional level. Limited matching  |                                   |
| will still be needed.             |                                   |
+-----------------------------------+-----------------------------------+
| Strengths                         |                                   |
+-----------------------------------+-----------------------------------+
| -   Methods for matching will be  | -   Public concern over privacy   |
|     easier to implement because a |     and security will be tempered |
|     unique identifier will be in  |     with use of algorithms and    |
|     use.                          |     established stewardship of    |
|                                   |     data.                         |
| -   Record matching may be        |                                   |
|     possible at the state or      | -   Development of matching       |
|     regional level, depending on  |     algorithms and a core set of  |
|     the level of implementation.  |     matching requirements or      |
|                                   |     principles can be used to     |
| -                                 |     facilitate record matching    |
|                                   |     without significant system    |
|                                   |     changes.                      |
+-----------------------------------+-----------------------------------+
| Weaknesses                        |                                   |
+-----------------------------------+-----------------------------------+
| -   Safeguards for unique patient | -   Accuracy of matching and      |
|     identifier theft and          |     ensuring the "right" patient  |
|     protection are necessary if   |     match is dependent on         |
|     such a program is             |     application of algorithms.    |
|     implemented.                  |                                   |
|                                   | -   Multiple and sophisticated    |
| -   Public concerns over privacy  |     algorithms will need to be    |
|     and security may derail       |     developed and maintained to   |
|     establishment and/or patient  |     accommodate the varied record |
|     participation in voluntary    |     identification methodologies  |
|     unique patient identifier     |     used across diverse systems.  |
|     programs.                     |                                   |
|                                   |                                   |
| -   Information technology system |                                   |
|     changes will be needed across |                                   |
|     any states or regions where   |                                   |
|     an identifier program is      |                                   |
|     implemented.                  |                                   |
+-----------------------------------+-----------------------------------+

## Locus of Patient Records De-Identification

Patient record de-identification is necessary to ensure privacy is
protected when data are used for purposes other than direct clinical
care. However, when data are used for quality measurement purposes, in
order to provide feedback at the patient-level, a re-identification
process must also occur. The locus of that
de-identification/re-identification activity may occur at several
different levels in the future and each option has implications for the
ability to aggregate data across sites and data sources, such as: who is
privy to the information necessary to de-identify and re-identify
records; who can provide patient-level feedback; and how long will the
feedback loop take. Table 14 describes how this activity would occur at
three different levels and also describes strengths and weaknesses
related to each option.

[]{#__RefHeading___Toc178751313 .anchor}Table 14. Future Scenario: Locus
of Patient Record De-Identification

+----------------------+----------------------+-----------------------+
| Provider / Care      | HIE                  | Quality Organization  |
| Delivery             |                      |                       |
| Organization         |                      |                       |
+======================+======================+=======================+
| Description          |                      |                       |
+----------------------+----------------------+-----------------------+
| Patient data is      | Patient data is      | Patient-identified    |
| de-identified at the | received from care   | data is received from |
| care delivery        | settings and         | HIE for translation   |
| organization prior   | de-identified in a   | into quality          |
| to submitting data   | database or RLS as   | measures. Quality     |
| to an HIE or other   | managed by the HIE.  | organization will     |
| entity for           | HIE will serve as    | serve as the owner of |
| aggregation.         | the owner of the     | the quality           |
|                      | quality improvement  | improvement feedback  |
|                      | feedback loop to     | loop to providers and |
|                      | providers and care   | care delivery         |
|                      | delivery             | organizations.        |
|                      | organizations. HIE   | De-identification of  |
|                      | will also serve as   | data occurs at        |
|                      | the steward for the  | Quality organization  |
|                      | data it maintains.   | prior to reporting.   |
+----------------------+----------------------+-----------------------+
| Strengths            |                      |                       |
+----------------------+----------------------+-----------------------+
| -   Patient privacy  | -   Patient records  | -   Patient records   |
|     protections are  |     can be linked    |     can be linked     |
|     greatest when    |     across care      |     across care       |
|     the only place   |     settings; a      |     settings.         |
|     there is         |     longitudinal     |                       |
|     identified data  |     patient medical  | -   Longitudinal      |
|     is at the care   |     record is        |     analysis is       |
|     delivery         |     possible.        |     possible.         |
|     organization.    |                      |                       |
|                      | -   Longitudinal     | -   Data can be used  |
| -   Data held within |     analysis is      |     support to        |
|     a care delivery  |     possible; data   |     support           |
|     organization is  |     can be used to   |     longitudinal      |
|     bound and        |     support          |     quality           |
|     protected by     |     longitudinal     |     measurement.      |
|     HIPAA            |     quality          |                       |
|     regulations.     |     measurement.     | -   Patient-level     |
|                      |                      |     feedback loop to  |
|                      | -   Established      |     clinicians is     |
|                      |     protocols are    |     streamlined       |
|                      |     uniformly        |     because Quality   |
|                      |     followed by HIEs |     Organizations can |
|                      |     today and data   |     provide feedback  |
|                      |     are              |     directly to the   |
|                      |     appropriately    |     providers and     |
|                      |     safeguarded in   |     care delivery     |
|                      |                      |     organizations     |
|                      |    de-identification |     without going     |
|                      |     and              |     through the HIE   |
|                      |                      |     to re-identify    |
|                      |    re-identification |     data.             |
|                      |     activities.      |                       |
|                      |                      |                       |
|                      | -   Data stewardship |                       |
|                      |     and privacy      |                       |
|                      |     policies are     |                       |
|                      |     already          |                       |
|                      |     established and  |                       |
|                      |     there is         |                       |
|                      |     transparency in  |                       |
|                      |     the process, to  |                       |
|                      |     minimize public  |                       |
|                      |     concerns over    |                       |
|                      |     privacy.         |                       |
|                      |                      |                       |
|                      | -   Submitting       |                       |
|                      |     organizations do |                       |
|                      |     not have burden  |                       |
|                      |     of ensuring      |                       |
|                      |     anonymity of     |                       |
|                      |     patient data.    |                       |
+----------------------+----------------------+-----------------------+
| Weaknesses           |                      |                       |
+----------------------+----------------------+-----------------------+
| -   Ability to link  | -   Patient-level    | -   Additional rules, |
|     patient records  |     feedback may be  |     standards, and    |
|     across care      |     delayed since    |     agreements are    |
|     settings is      |     data must be     |     needed to protect |
|     constrained.     |     de-identified,   |     patient privacy   |
|                      |     sent to the      |     since             |
| -   Ability to use   |     Quality          |     de-identified     |
|     the data to      |     Organization,    |     data will         |
|     support          |     returned in the  |     accessible by     |
|     longitudinal     |     form of usable   |     both the HIE and  |
|     analysis or      |     metrics,         |     Quality           |
|     quality          |     re-identified,   |     Organization.     |
|     measurement is   |     and sent back to |                       |
|     inhibited.       |     the providers    | -   Secondary use of  |
|                      |     and care         |     patient-level     |
| -   Applies          |     delivery         |     clinical data may |
|     additional       |     organizations.   |     be limited when   |
|     burden on        |                      |     managed by        |
|     provider         | -   Mechanisms and   |     Quality           |
|     settings to      |     standards for    |     Organization as   |
|     de-identify data |                      |     compared to HIE.  |
|     prior to         |    de-identification |                       |
|     transmission /   |     and              |                       |
|     submission to    |                      |                       |
|     various          |    re-identification |                       |
|     entities.        |     must further     |                       |
|                      |     evolve to meet   |                       |
| -   Introduces risk  |     industry-wide    |                       |
|     to integrity of  |     needs.           |                       |
|     data             |                      |                       |
|                      |                      |                       |
|    de-identification |                      |                       |
|     and risk of      |                      |                       |
|     security         |                      |                       |
|     breaches when    |                      |                       |
|                      |                      |                       |
|    de-identification |                      |                       |
|     responsibility   |                      |                       |
|     is decentralized |                      |                       |
|     across           |                      |                       |
|     individual care  |                      |                       |
|     delivery         |                      |                       |
|     organizations.   |                      |                       |
+----------------------+----------------------+-----------------------+

## Locus of Data Aggregation

Data aggregation is necessary to develop quality metrics and perform any
degree of population level analysis. The level at which data aggregation
occurs, if at all, has implications for the infrastructure, rules,
policies that are needed and types of analyses that are possible. Table
15 presents the strengths and weaknesses when considering three
different approaches: no aggregation, local/regional aggregation, and
national aggregation.

[]{#__RefHeading___Toc178751314 .anchor}Table 15. Future Scenario: Locus
of Data Aggregation

+----------------------+----------------------+-----------------------+
| None                 | Local/Regional       | National Aggregation  |
|                      | Aggregation          |                       |
+======================+======================+=======================+
| Description          |                      |                       |
+----------------------+----------------------+-----------------------+
| Data are not         | Data aggregation     | Data aggregation      |
| aggregated. An RLS   | occurs at the        | occurs at the         |
| is the primary       | local/state level.   | national level. Data  |
| mechanism for        | Data are stored in   | are stored in a       |
| exchanging data      | multiple regional    | central database.     |
| across sites.        | databases.           |                       |
+----------------------+----------------------+-----------------------+
| Strengths            |                      |                       |
+----------------------+----------------------+-----------------------+
| -   Data can be used | -   Data can be used | -   Data can be used  |
|     to facilitate    |     to facilitate    |     to facilitate     |
|     care delivery.   |     longitudinal     |     longitudinal      |
|                      |     quality          |     quality           |
| -   Provider         |     improvement      |     improvement       |
|     organizations    |     initiatives at   |     initiatives at    |
|     can request and  |     the              |     multiple levels   |
|     manage           |     local/regional   |     (local, regional, |
|     point-to-point   |     level.           |     national)         |
|     transfer and     |                      |                       |
|     receipt of       | -   Strategies for   | -   New opportunities |
|     patient data for |     matching patient |     exist for         |
|     established      |     data can be      |     nationwide        |
|     purposes such as |     established at   |     application of    |
|     medication       |     the              |     rich clinical     |
|     reconciliation.  |     local/regional   |     data such as      |
|                      |     level, building  |     "electronic       |
|                      |     on current       |     epidemiology,"    |
|                      |     local/regional   |     adverse event     |
|                      |     efforts and      |     tracking, and     |
|                      |     leveraging local |     various other     |
|                      |     innovations.     |     secondary uses of |
|                      |                      |     clinical data.    |
|                      | -   Standards for    |                       |
|                      |     data exchange,   |                       |
|                      |     security of      |                       |
|                      |     data,            |                       |
|                      |     established      |                       |
|                      |     sharing          |                       |
|                      |     agreements, and  |                       |
|                      |     patient privacy  |                       |
|                      |     protections may  |                       |
|                      |     be easier to     |                       |
|                      |     establish at the |                       |
|                      |     local/regional   |                       |
|                      |     level; a         |                       |
|                      |     business case    |                       |
|                      |     for doing so may |                       |
|                      |     also be easier   |                       |
|                      |     to establish and |                       |
|                      |     minimize public  |                       |
|                      |     concerns over    |                       |
|                      |     privacy.         |                       |
|                      |                      |                       |
|                      | -   Partnerships in  |                       |
|                      |     quality          |                       |
|                      |     improvement may  |                       |
|                      |     be encouraged at |                       |
|                      |     local level as   |                       |
|                      |     payers,          |                       |
|                      |     providers and    |                       |
|                      |     patients work    |                       |
|                      |     together through |                       |
|                      |     local/regional   |                       |
|                      |     collaboratives.  |                       |
+----------------------+----------------------+-----------------------+
| Weaknesses           |                      |                       |
+----------------------+----------------------+-----------------------+
| -   Data cannot be   | -   National         | -   A national        |
|     easily used to   |                      |     strategy will     |
|     facilitate       |   strategy/framework |     need to be        |
|     longitudinal     |     for alignment is |     developed to      |
|     quality          |     necessary (and   |     match patient     |
|     improvement      |     as yet           |     data.             |
|     initiatives.     |     undeveloped) to  |                       |
|                      |     build from local | -   One set of        |
| -   There is a need  |     efforts and      |     standards will    |
|     for policies and |     enable           |     need to be used   |
|     standards for    |     population       |     to manage data    |
|     security and     |     analyses at      |     exchange, ensure  |
|     data sharing at  |     national level.  |     security of data, |
|     the source       |                      |     establish data    |
|     system or care   | -   There is         |     sharing           |
|     delivery         |     potential for    |     agreements, and   |
|     organization     |     inconsistencies  |     ensure patient    |
|     level.           |     across           |     privacy is        |
|                      |     localities or    |     protected.        |
| -   A back up        |     regions as       |                       |
|     strategy will be |     aggregation      | -   Database scale    |
|     needed if source |     methods may      |     may be an issue.  |
|     systems go down  |     differ.          |                       |
|     or are           |                      | -   The ability to    |
|     unavailable.     | -   Individual       |     manage and        |
|                      |     localities and   |     facilitate data   |
| -   Transfer or      |     regions are      |     exchange may be   |
|     exchange of      |     subject to local |     an issue.         |
|     information is   |     pressures,       |                       |
|     the              |     policies and     | -   Provider and      |
|     responsibility   |     needs and may    |     community         |
|     of the care      |     threaten overall |     pushback for      |
|     delivery         |     standardization  |     patient data      |
|     organization.    |     and application  |     storage outside   |
|                      |     of data.         |     the community.    |
|                      |                      |                       |
|                      |                      | -   Public concerns   |
|                      |                      |     over ownership of |
|                      |                      |     patient data, and |
|                      |                      |     security concerns |
|                      |                      |     over protection   |
|                      |                      |     of that data      |
+----------------------+----------------------+-----------------------+

# Next Steps

Based on the requirements analysis, several topic were identified as
potential areas where the Quality Workgroup may have an opportunity to
develop recommendations. These topics focus on areas where improvements
could facilitate the ability of HIT to support (longitudinal) quality
measurement and improvement. They include areas where there may be an
opportunity to inform changes to the overall environment and also areas
that are more HIT-specific. The list was prioritized by the requirements
analysis subgroup by loosely considering the following three factors:

1)  The Quality Workgroup's ability to affect significant change and/or
    drive the industry

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11) Whether or not the topics are part of the critical path to the
    future state

12) What other initiatives are underway related to the topics.

#### Topic 1: Data Aggregation 

Data aggregation is necessary to develop quality metrics and perform any
degree of population level analysis. The level at which data aggregation
occurs, if at all, has implications for the infrastructure, rules,
policies that are needed and types of analyses that are possible. There
is a lack of such common policies, procedures, and standards for data
aggregation. Other issues related to data aggregation include a
determination of at which locus data aggregation should occur (not at
all, locally/regionally, nationally), and the debate of using a RLS
versus databases that can aggregate data.

Other Efforts to Address Topic:

-   HIEs / Value Exchanges.

-   RHIOs.

#### Topic 2: Minimum Data Set

There is a need to 1) Identify minimum data sets to transfer across care
settings when patients are moved from one care setting to the next and
2) integrate these data sets into EHRs to facilitate the exchange of
information to support care coordination.

Other Efforts to Address Topic:

-   The Joint Commission efforts to identify minimum data sets

-   CMS PAC Payment Reform Demonstration

-   HITSP CCD

-   Massachusetts Medical Society CCR

#### Topic 3: Expand data element standardization to facilitate data exchange and data collection for quality improvement

Need to improve / standardize coding used by labs, radiology, pharmacy,
and other data sources to facilitate information exchange and use of
information in quality assessment and improvement activities. Without
such standardization, aggregating data across systems remains a
challenge and poses significant limitations to the development of a
longitudinal patient record. Need to improve / standardize fields needed
to help structure unstructured clinical documentation and, as a result,
facilitate information exchange and use of information in quality
assessment and improvement activities. Absent such improvements, data
collection will remain a heavy burden, and the integrity of the data
collected may be inconsistent.

Other Efforts to Address Topic:

-   NQF Priority and Goal Setting Project

-   AMA NCQA Vendor Collaborative

#### Topic 4: Patient Record Matching

There is currently a lack of standardized methods for matching
patient-records. Patient matching is necessary enable HIE and to support
longitudinal patient records, yet there are unresolved issues around how
patient records may be matched, by whom, and for what purposes.
Evaluation of common methods may be useful to better understand
strengths and weaknesses of common approaches.

Other Efforts to Address Topic:

-   NHIN Scope of Work

-   NCVHS Ad Hoc Workgroup on Secondary Uses of Health Data

#### Topic 5: The locus of record identification and de-identification

Along with patient matching, identification and de-identification are
important elements in HIE and the development of longitudinal patient
records. Patient record de-identification is necessary to ensure privacy
is protected when data are used for purposes other than direct clinical
care. However, when data are used for quality measurement purposes, in
order to provide feedback at the patient-level, a re-identification
process must also occur. There is a lack of common policies, procedures,
and standards for patient record de/re-identification including a
determination of the locus at which this activity should occur (i.e.,
institution level, HIE level, quality organization level).

Other Efforts to Address Topic:

-   NCHVS Ad Hoc Workgroup on Secondary Uses of Health Data

#### Topic 6: Coding Improvements

Need for common agreement on the specific codes used across systems to
manage specific clinical conditions. Even if the coding is standardized,
different experts will choose different codes when describing a clinical
condition, the procedures used to treat it, or the outcomes associated
with it. Such variations in coding impact the ability to determine if
the care provided relates to a given episode or clinical condition,
which in turn impacts the ability to accurately capture data related
from a longitudinal perspective. Standard coding of conditions is
critical to leveraging EHRs to measure quality.

Other Efforts to Address Topic:

-   NQF Expert Panel

#### Topic 7: Legal Framework for Data Sharing

Need for a more explicit legal framework (i.e., policies) through which
to address patient privacy and consent, information security, and
liability issues related to data sharing across institutions and the
accountability of providers given the availability of data from across
institutions. In the envisioned state of automated quality measurement
and reporting, access to personal health information from EHRs need to
be accomplished in a confidential and secure manner that complies with
privacy requirements and respects consumer decisions regarding access to
their information.

Other Efforts to Address Topic:

-   Other AHIC Workgroups

-   NCVHS Ad Hoc Workgroup on Secondary Uses of Health Data

#### Topic 8: Data Stewardship

There is a lack of clear stewardship role and identification of a
stewardship body to set uniform operating rules and standards for
sharing and aggregating public and private sector health care data. The
Quality Workgroup's vision for quality improvement, quality measurement
and quality reporting relies heavily on the availability and use of
rich, clinical patient data. Access to and use of
personally-identifiable health data must be governed by established
rules and standards, and must engender the trust of the public. For
these reasons addressing the lack of a national or series of regional
data stewards is critical to achieving the Quality Workgroup's vision.

Other Efforts to Address Topic:

-   AHRQ/AQA Data Stewardship RFI.

#### Topic 9: Incentives

There are insufficient financial incentives for quality improvement,
care coordination, EHR adoption and data sharing in today's environment.
The current fee-for-service model incentivizes volume, not quality or,
necessarily, collaboration. Incentives are needed to promote higher
levels of quality across diverse heath care settings and may require
payment reform initiatives. Incentives are not currently aligned to
promote quality in care delivery.

Other Efforts to Address Topic:

-   CMS Physician Group Practice Demonstration.

Figure 3 shows the different points of intersection between the
recommendation topic areas and the flow of information to support
quality improvement and other activities using quality data. The intent
of this figure is to help the Quality Workgroup think through the topic
areas, considering the topic areas that have systemic impact and the
downstream implications of making recommendations on various topic
areas. For instance, recommendations related to the locus of record
de-identification (item number 5) will have an impact on downstream
health information exchange and value exchange.

These topic areas were put forth to the Quality Workgroup at the August
30^th^ meeting to further discussion on recommendations and begin the
consensus development process. The Quality Workgroup considered the
following key questions as it formulates recommendations:

-   Are these topic areas where the Quality Workgroup should put forth
    recommendations?

-   How can the Quality Workgroup advance these areas in the form of
    recommendations?

-   What behaviors or value does the Quality Workgroup hope to achieve?

-   Are there other efforts to address these topic areas that the
    Quality Workgroup should consider when determining if
    recommendations should be made? ...When considering the form of
    those recommendations?

-   Are there other topic areas the Quality Workgroup should also
    consider?

Based on the output of the August 30^th^ meeting, the Quality Workgroup
has begun drafting recommendations to put forth to the AHIC and
Secretary of HHS in November 2007 and January and February of 2008.

[]{#__RefHeading___Toc178751315 .anchor}Figure 3. Recommendation Topic
Idea Overlap with the Quality Information Flow

![](media/image4.png){width="6.375in" height="4.582638888888889in"}

# Appendix A: Methods

The requirements put forth in this document were informed by several
distinct activities that were coordinated by the Quality Workgroup.
These key activities were designed to solicit widespread input from
public and private sector stakeholders. Specifically, the Quality
Workgroup:

1)  Heard testimony through the AHIC Quality Workgroup meetings.

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13) Reviewed relevant testimony presented to the NCVHS Workgroup on
    Quality and the NCVHS Ad Hoc Committee on Secondary Uses of Health
    Data.

14) Conducted an environmental scan that included two key components:

    a.  Information submitted in response to the June 14, 2007, Federal
        Register notice announcing the June 22, 2007, Quality Workgroup
        meeting, which included a list of key questions that the Quality
        Workgroup was interested in obtaining input on from the general
        public.

    b.  Targeted interviews to collect additional information from
        experts who have experience with and insights into issues
        related to the use of HIT in quality measurement and
        improvement.

15) Reviewed key secondary sources published by seminal organizations in
    the fields of HIT, HIE, clinical informatics and quality measurement
    and improvement as recommended by subgroup members and interviewees,
    and as referenced through testimony.

The information collected through these activities was synthesized into
this report and used to inform our understanding of the current
environment and requirements for the future environment. For the current
environment, strategies for quality improvement, quality measurement and
reporting, and longitudinal analysis were identified along with emerging
strategies to address these areas in a more integrated patient-centric
manner. Throughout our review of information, five components emerged as
critical components of a national quality enterprise:

+-----------------------------------+-----------------------------------+
| -   Business Case / Incentive     | -   Data Exchange and Aggregation |
|     Alignment                     |                                   |
|                                   | -   Policy                        |
| -   Measure Development           |                                   |
|                                   |                                   |
| -   Data Standards                |                                   |
+-----------------------------------+-----------------------------------+

The information gathered from these sources was used to identify key
enablers and barriers as they relate to each of these five components.
For the future environment, the identified enablers and barriers
directly informed the identification of requirements for the future
state, and were organized by these same five component areas.
Additionally, areas where there was little or no common agreement for
requirements or where requirements could not be identified were broken
down into scenarios where different options were explored and the
strengths and weaknesses of each option were discussed. The goal of this
analysis is to help the Workgroup determine whether recommendations are
needed to help guide the selection of one option versus another or to
help move the industry towards a particular option, if appropriate.

To guide the development of the report and the synthesis of all of the
information described above, the Quality Workgroup identified a subgroup
of experts comprised of both Quality Workgroup members and external
providers. This expert subgroup met biweekly to provide input on the
information gathered for the analysis, to draft language as needed, and
to direct the development of this report.

Figure 1 describes the full methodology used to conduct the requirements
analysis.

[]{#__RefHeading___Toc178751316 .anchor}Figure 4. Requirements Analysis
Methods

## A.1. Key Constraints

Currently, the nation is in an active learning state in regards to how
HIT can best support quality measurement and improvement. Both public
and private sector initiated efforts are underway to implement business
and HIT solutions to help evaluate and improve the quality of care
delivery, including improved coordination of care across settings. This
requirements analysis is limited by the evolving state of both HIT and
quality improvement in today's environment. Our ability to evaluate the
current state, establish requirements, and develop scenarios that depict
options for the future state are limited by our collective experiences
with the current systems.

## A.2. Key Assumptions and Dependencies

-   This requirements analysis assumes that the future state of quality
    measurement and quality improvement is one based on a
    patient-centric view of care delivery, i.e., one that looks at care
    delivery longitudinally, across care settings and over time.

-   This requirements analysis assumes that the vision of the Quality
    Workgroup has not changed since its completion in January 2007.

-   This requirements analysis assumes that the NHIN will continue to
    move in its current direction; the CCHIT will continue
    certification; and the HITSP will continue to guide the development
    and harmonization of standards.

# Appendix B: Current Strategies to Assess and Improve Quality

## B.1 Current State of HIT to Support Quality at the Point of Care

Clinical Decision Support, or CDS, can be defined as providing pertinent
clinical knowledge or patient data to clinicians and patients, at the
right time and in the right form, to foster better health care processes
and better outcomes for individual patients and populations. CDS
interventions include information delivery mechanisms such as
computerized alerts and reminders, clinical guidance and answers to
questions, order sets, patient data reports and dashboards,
documentation templates, diagnostic support, and clinical workflow
tools. [^10] CDS interventions can direct attention to potential medical
errors of omission and commission, help empower patients, support
development of optimal care plans, help gather and present data needed
to execute those plans, and ensure the best clinical knowledge and
recommendations are used to improve clinical decision-making.^9^ CDS
interventions draw on various sources of knowledge such as clinical
evidence and practice guidelines, and data such as current and
retrospective patient data, population data, and performance data to
inform clinical decision making.

Successful CDS deployment requires accomplishing five critical tasks
correctly, often referred to as the "five CDS rights." It requires
getting the *right* information (i.e., information that is
evidence-based and actionable), to the *right* stakeholder (e.g., a
specific provider or patient) in the *right* format or intervention
type, through the *right* channel (e.g. EHR, PHR, etc.) at the *right*
point in workflow (e.g. when it can be acted upon) to favorably
influence clinical decisions and actions. Improving outcomes with CDS
requires defining specific objectives and tuning these components, often
in a suite of CDS interventions, to address each objective.[^11]

Some pioneering organizations have realized substantial benefits in cost
and quality in focused areas through CDS use. A few of these
organizations, such as Intermountain Healthcare, Geisinger Health
System, Kaiser Permanente, Partners Healthcare, Regenstrief Institute,
and a few others, have reported these results in the literature. CDS is
both commonly used and widely accepted at these organizations as a
result of years of experimentation and intensive effort. In addition, an
increasing number of healthcare organizations are using CDS supplied
with commercial EHR systems and reporting significant improvements as a
result, particularly in adverse event prevention and quality metric
improvement.

At the same time, there is significant variation in the efficiency and
effectiveness of how CDS interventions are used across the nation, and
widespread agreement that we are in early stages of the journey toward
optimal CDS development and deployment. This is primarily because CDS
development to date has occurred in an uncoordinated fashion by
individual CDS developers and health care institutions across the
country, efforts that are characterized by repeated reinvention and
limited ability to leverage and build upon prior experience.
Additionally, standards that could allow for interoperability of CDS
interventions across systems and 'plug and play' efficiency are
generally not available. These advances will likely need to occur in
order to enable widespread general effective use of CDS.

More guidance on best practices for implementers would also help
minimize the current level of rework in CDS implementation. For example,
a broader knowledge base on how to optimize the '5 CDS rights' above for
specific high-priority outcomes would be useful. These best practices
would include:

-   Optimal implementation and management strategies, taking into
    account the workflow and care processes at different types of
    practices and settings;

-   Past experience in combining several CDS strategies to effectively
    target specific clinical conditions;

-   Knowledge of which CDS intervention types best address a given type
    of clinical objective (such as improving preventive care testing);
    and

-   Better association between the quality performance metric
    development and CDS development, so that optimal performance on
    metrics can be clearly defined and facilitated.

A better understanding of these areas provides a solid foundation for
CDS developers to make more effective and better targeted CDS
interventions. It can also help ensure that CDS is integrated
appropriately and effectively into existing HIT systems. Last, it can
also help ensure that CDS is deployed successfully and is well received
by targeted end users on the basis of its design, usefulness, and
integration into existing workflow.

Even with a detailed understanding of the three areas above, there are
other present barriers to widespread adoption and effective use of CDS.
The lack of widespread use of EHRs and the lack of sufficient
interoperability standards hinder the ability to collect data about
individuals, and to pool data from across care settings and
institutions. Many of the more powerful CDS tools rely on clinical data
produced at the point of care to inform information delivery to
providers and patients. Lack of interoperability standards for key data
elements, and for CDS interventions themselves, make this integration
difficult. This, in turn, impacts the ability to provide effective
messaging and alerts to providers and patients that span the continuum
of care and that reflect a more informed longitudinal perspective of the
individual patient's clinical circumstances.

There are also important economic barriers to EHR and CDS adoption,
particularly in small practices where economies of scale are not
available. A growing number of small-practice EHRs and e‑prescribing
systems with some CDS elements are moving into smaller practices, but
this sector still represents both the highest number of total patient
visits and the lowest EHR penetration. Direct programs, such as the
government's Doctor's Office Quality -- Information Technology (DOQ-IT)
program, are seeking to address gaps in both EHR and CDS adoption to
facilitate greater quality and efficiency in these practices.

In 2005 ONC requested the American Medical Informatics Association
(AMIA) to develop a roadmap for national action to accelerate the
successful application of CDS to improving health and care processes and
outcomes. The Roadmap was developed with input of scores of experts and
stakeholders, and was presented to AHIC in June 2006. The Roadmap
outlines a vision of healthcare supported by next generation CDS
capabilities, as well as detailed suggestions for short-term and
longer-term activities to achieve this vision. Table 16, which is an
excerpt from the Roadmap, describes this CDS Vision and related
objectives.

[]{#__RefHeading___Toc178751317 .anchor}Table 16. Specific Objectives
Needed to Achieve the Next Generation of Valuable CDS[^12]

+-----------------------------------------------------------------------+
| The best available clinical knowledge is well organized, accessible   |
| to all, and written, stored and transmitted in a format that makes it |
| easy to build and deploy CDS interventions that deliver knowledge     |
| into the decision-making process.                                     |
|                                                                       |
| -   **Strategic Objective A:** Clinical knowledge and CDS             |
|     interventions are represented in standardized formats (both human |
|     and machine-interpretable) so that a variety of knowledge         |
|     developers can produce information in a way that users can ready  |
|     understand, assess, and apply.                                    |
|                                                                       |
| -   **Strategic Objective B:** Clinical knowledge and CDS             |
|     interventions are collected, organized, and distributed in one or |
|     more services from which users can readily find the specific      |
|     material they need and incorporate it into their own information  |
|     systems and processes.                                            |
|                                                                       |
|     CDS tools are widely implemented, extensively used, and produce   |
|     significant clinical value while making financial and operational |
|     sense to their end users and purchasers.                          |
|                                                                       |
| -   **Strategic Objective C:** Policy, legal, and financial barriers  |
|     are addressed and additional support and enablers created for     |
|     widespread CDS adoption and deployment.                           |
|                                                                       |
| -   **Strategic Objective D:** Clinical adoption and usage of CDS     |
|     interventions are improved by helping clinical knowledge and      |
|     information system producers and implementers design CDS systems  |
|     that are easy to deploy and use and by identifying and            |
|     disseminating best practices for CDS deployment.                  |
|                                                                       |
|     Both CDS interventions and clinical knowledge undergo continuous  |
|     improvement based on feedback, experience, and data that is easy  |
|     to aggregate, assess, and apply.                                  |
|                                                                       |
| -   **Strategic Objective E:** The national experience with CDS is    |
|     assessed and refined by systematically capturing, organizing, and |
|     examining existing deployments. Lessons learned will be shared    |
|     and used to continually enhance implementation best practices.    |
|                                                                       |
| -   **Strategic Objective F:** Care-guiding knowledge is advanced by  |
|     fully leveraging the data available in interoperable EHRs to      |
|     enhance clinical knowledge and improve health management.         |
+-----------------------------------------------------------------------+

Besides the Roadmap, below are several related initiatives underway that
are helping to address the some of the barriers described in the Roadmap
and outlined above. This represents just a small subset, as there are
many other efforts also underway (though perhaps not optimally
coordinated) to in improve the relationship between clinical care
processes, CDS, quality measurement, interoperability, and data
management:

-   **Health Information and Management Systems Society CDS
    implementer's Guide:** This book is a resource designed to help
    healthcare organizations use CDS to measurably improve key
    healthcare outcomes such as the quality, safety, and
    cost-effectiveness of care delivery. It helps organizations
    determine their CDS program's goals and clinical objectives; catalog
    local information systems' capabilities to achieve those goals;
    select the best approach to address the goals with specific CDS
    interventions; develop the interventions; ensure the interventions
    are acceptable to stakeholders and put them into practice; and
    monitor the effectiveness of the CDS program on an ongoing
    basis.[^13]

-   **CMS DOQ-IT care management/CDS project and "DOQ-IT University"
    program:** These programs, as noted above, support the adoption and
    effective use of information technology by physicians\' office to
    improve quality and safety. DOQ-IT seeks to accomplish this by
    promoting greater availability of high quality affordable health
    information technology, including both EHRs and CDS, by providing
    assistance to physician offices in adopting and using such
    technology.[^14]

-   **AHIC Quality Workgroup and Ad Hoc CDS Workgroup:** The formation
    of the AHIC Quality Workgroup and an ad hoc CDS Workgroup have led
    to vision statements and use cases that address the best
    implementation and sharing of CDS, as well as focusing particularly
    on the relationship between HIT and national quality metrics. In
    addressing their charges, several other workgroups are also looking
    at CDS. The ad hoc CDS Workgroup is working to cultivate synergies
    and coordination among these efforts and other related national
    activities.

-   **AHRQ Funded Initiatives:** AHRQ has funded a number of efforts to
    expand and improve CDS locally and nationally. The recent AHRQ CDS
    Service Contract Solicitation is aimed at supporting achievement of
    several of the objectives outlined in the CDS roadmap by funding
    pilots that will implement valuable, scalable, sharable CDS and its
    corresponding infrastructure. Awards for this solicitation are
    expected to be made in October 2007.

-   **CCHIT Certification of CDS Requirement**s: CCHIT has and continues
    addressed CDS in several of its certification requirements, and is
    increasing the focus on CDS as technology continues to mature.

-   **Independent research:** Research activities continue at many
    academic institutions to study new needs for CDS, to determine
    optimal intervention strategies, to reduce any unintended
    consequences, and to explore CDS for an increasing set of clinical
    conditions.

While the timeline is still uncertain, the future should see an
environment in which CDS is widely available through EHRs and other
applications, progressively optimized for usability and value,
continuously shared, and tightly associated with key quality indicators.

## B.2 Current state of HIT To Support Quality Improvement, Measurement and Reporting

In today's environment, there is unevenness in the rigor, transparency,
and scale of quality measurement and reporting across care settings. The
CMS has implemented various setting-specific efforts to collect data to
monitor the quality of care delivered to Medicare beneficiaries. Efforts
such as the Nursing Home, Home Health and Hospital Compare programs
underscore that data collection and quality measurement remain
setting-specific.

Today, there is no unified national agenda for measuring, improving, and
reporting on health care quality. Current measurement efforts are
limited by what is measurable, rather than focused entirely on what is
important to measure. Quality improvement, measure development, and
quality reporting activities focus on provider encounters and thus,
occur in silos. Effective coordination of care across settings and along
the continuum of care is limited by site- and venue-specific medical
records (both paper and electronic) and the manual processes needed
today to communicate relevant health care information. Transitions
between settings are characterized by a lack of continuity of
information, which directly impacts the quality and continuity of care.

Two of the most established quality measurement efforts are within the
hospital and physician practice settings. National efforts to promote
transparency of quality and price information, coupled with growing
support for and attention to the use of HIT to drive quality have aided
in further formalizing these efforts. The sections below describes the
current state of the use of HIT to support hospital and physician
quality measurement and reporting and emerging strategies to help enable
these activities from a cross-setting perspective.

### B.2.1 Current State of HIT in the Hospital Quality Measurement and Reporting Enterprise

To date, EHRs, where implemented, support select aspects of care
delivery but are not generally designed to facilitate the assessment and
improvement in the quality of care delivered. Hospitals today are
required to submit a core set of quality measures to both CMS and the
Joint Commission and to submit additional data to private payers as well
as state and local organizations. Hospitals continue to make significant
investments in time and resources to collect quality data. National
efforts on the part of CMS and the Joint Commission to publicly report a
core set of quality measures have resulted in establishing elements of a
national framework for how HIT may be leveraged to support data
reporting requirements.

There remains a reliance on labor-intensive retrospective chart review
for performance measurement and manual data extraction activities,
limiting the opportunity to make the necessary information available to
drive improvements in care delivery at the point of care. Hospitals
receive feedback reports on quality retrospectively, with a significant
lag (anywhere from 6 to 9 months) from the date of care delivery. The
data used to measure quality are largely derived from medical records,
with a historical focus on what can be measured rather than what ideally
should be measured in the quality of care delivery.

Today, there is not yet a consensus regarding the need for a single set
of national goals. The NQF has established consensus regarding the
development, definition and interpretation of quality measures, and has
begun to develop a process for articulating national goals. Typically,
hospitals utilize vendors to "implement" the agreed-upon quality
measures so that data can be abstracted from individual hospital systems
and from paper medical charts. Hospitals also utilize vendors to provide
performance feedback to providers on their performance. Implementation
includes customized specifications for individual hospital information
systems, testing of algorithms, and transmission of hospital quality
data between hospitals and their vendors, and between vendors and
authorized recipients such as the CMS and the Joint Commission. In
addition to data gathered from the medical record, hospital
administrative records typically used to generate claims are also used
to calculate quality measures.

Typically, vendors collect and submit patient-level data on behalf of
hospitals to both the Joint Commission and to CMS's national data stores
for aggregation and hospital-level public reporting on the quality
measures (a small percentage of hospitals currently collect and submit
data without use of a vendor). Both CMS and the Joint Commission make
national hospital quality measurement results available online.[^15]
Additionally, CMS has linked payment updates to quality measurement
results in order to further incentivize hospitals to drive quality
improvement through measurement and reporting.

To support national reporting of hospital quality measures, consensus
was achieved on a core set of 22 hospital quality measures, standardized
measure specifications were established, and results are able to be
consistently interpreted across the country. In this way, there is some
level of data standardization established for this core set of measures,
and a high-level data flow that is consistently followed in order to
meet the reporting requirements of CMS and the Joint Commission.
Stewardship of the national data store is maintained today by CMS, and
patient-level data stored are not available for other purposes at this
time.

Testimony has indicated that there is still great room for improvement
in EHR adoption rates and the current generation of EHRs does not
adequately support automated quality measurement and reporting.
Therefore, regardless of the presence of an EHR, data collection for
quality measurement and reporting remains largely a manual process in
which specific data elements must be identified and collected by
hospitals. The 22 Hospital Quality Measures, nationally reported today,
were not developed with the vision of automated quality measurement
through EHRs, leaving a significant amount of manual effort required to
successfully collect and submit the necessary information. In order to
capitalize on the potential of health IT to support quality and other
secondary data uses, measures must be developed in tandem with the
critical goal of establishing seamless data collection as a byproduct of
care delivery, thereby diminishing the need for manual data collection,
leveraging EHRs and integrating with provider workflows.

### B.2.2 Current State of HIT in the Physician Quality Measurement and Reporting Enterprise

Historically, physician performance has been measured through
information derived from claims data and housed in administrative
systems. Similar to hospitals, physicians are required to report
disparate sets of quality information to any number of requesting
organizations resulting in multiple, uncoordinated and at times
conflicting reporting initiatives. Today, physicians submit claims data
to a number of payers, including CMS. Physician quality measurement is
derived from this claims data and supplemental information submitted by
the physician using procedure and billing codes which requires some
manual chart review on the part of the providers. CMS can receive data
from physicians for Medicare patients only, and is able to determine
physician quality measurement as limited to those measures that can be
easily derived from claims data with supplemental billing code quality
measure identifiers. Concurrently, the AQA has been collaborating to put
forth richer quality measures for physicians. Many of these measures
have been incorporated into the recent Physician Quality Reporting
Initiative (PQRI), a voluntary quality reporting program initiated by
CMS in 2007 for physicians in which increased payment is tied to
reporting of a core set of physician quality measures.

While organizations such as the AQA work to develop a quality
measurement strategy for physician quality measurement, EHR adoption
among the physician community remains a major obstacle to leveraging HIT
to support quality. Various studies estimate EHR adoption rates from
10-18 percent[^16], and enumerate a variety of barriers that must be
overcome in order to achieve widespread adoption of EHRs within the
physician community. To address such issues in adoption, CMS instituted
the DOQ-IT program which advocates the use of HIT to promote quality
care within physician offices. Through DOQ-IT, CMS is working to make
affordable HIT available to physicians, as well as to provide services
to assist physicians in adopting the new technology.

In the physician environment, accurate quality measurement of individual
providers will require pooling of data from multiple sources, e.g.,
claims data from multiple insurers. Physicians treat patients who
represent a number of different payers; determining a physician's
performance for a specific quality indicator requires access to
information that spans these data sources. For example, in order to
determine the "denominator" of a particular quality indicator, one must
determine across all patients, the total number of diabetic patients
seen by an individual physician. There is a distinct need for HIE
capabilities to enable this pooling of data from multiple sources.

Various efforts are underway to move physician quality measurement, with
its current claims-based and manual chart review limitations, to a more
robust, interoperable system in which physicians are able to access
comprehensive clinical information for decision-making, and receive
feedback with which to refine practice. Pilot efforts across the country
are underway in order to better understand the applicability and needed
capabilities of HIT to support and improve the quality of care delivered
by physicians.

# Appendix C: Glossary

**Aggregate Data:** A summary of individual data; data that is not
longer at the patient level. *Source: Adapted from
http://en.wikipedia.org/wiki/Aggregate_data.*

**Anonymization:** A process of de-associating sensitive attributes from
corresponding identifiers. *Source:
http://www.cse.buffalo.edu/\~szhong/papers/kanony.pdf.*

**AQA Alliance:** A public private partnership formed by the American
Academy of Family Physicians (AAFP), the American College of Physicians
(ACP), America's Health Insurance Plans (AHIP), and the AHRQ to lead an
effort for determining how to most effectively and efficiently improve
performance measurement, data aggregation and reporting in the
ambulatory care setting. The AQA Alliance was formerly known as the
Ambulatory Quality Alliance but has changed their name to AQA Alliance
to reflect its broadened mission, which incorporates all areas of
physician practice. *Source: http://www.aqaalliance.org.*

**Care Process:** A process by which a clinician delivers care to
patients. *Source: Adapted from
http://en.wikipedia.org/wiki/Nursing_process.*

**Care Setting:** The department or location that serves as the direct
point of care (i.e., primary care practice, nursing home, hospital, or
long term care facility) where individuals are evaluated, diseases or
disorder prevented, diagnosed, and treated. *Source: Adapted from
https://leapfrog.medstat.com/pdf/Glossary.pdf.*

**Central Database:** A database where data is collected from a
multitude of independently managed, heterogeneous database systems,
using a consistent format, and stored and accessed in a single database.
*Source: Adapted from Testimony and Interviews.*

**Certification Commission for Healthcare Information Technology:** The
Certification Commission for Healthcare Information Technology or CCHIT
is a recognized certification body for electronic health records and
their networks, and an independent, voluntary, private-sector
initiative. *Source: http://www.cchit.org/about/index.asp.*

**Clinical Decision Support (CDS):** Any system designed to improve
clinical decision making related to diagnostic or therapeutic processes
of care. CDS thus addresses activities ranging from the selection of
drugs or diagnostic tests to the detailed support for optimal drug
dosing and support for resolving diagnostic dilemmas. *Source:
http://psnet.ahrq.gov/glossary.aspx#C and
http://www.amia.org/inside/initiatives/cds/cdswhitepaperforhhs-final2005-03-08.pdf.*

**Data Aggregation:** The process of collecting and consolidating data
from multiple sources in to one location or database. *Source: Adapted
from* *http://download.oracle.com/docs/html/B13970_01/glossary.htm.*

**Data Element:** The smallest and simplest unit of data that imparts
meaningful information, generally corresponding to a field in a database
file or a blank on a paper or electronic form. *Source:
http://www.google.com/url?sa=X&start=1&oi=define&q=http://osulibrary.oregonstate.edu/archives/handbook/definitions/&usg=AFQjCNEBCtFh3I-nEhSNKagVg8T8USlEHw.*

**Data Exchange:** A process of storing, accessing, and transmitting of
data. *Source:
http://www.google.com/url?sa=X&start=0&oi=define&q=http://cedar.web.cern.ch/CEDAR/glossary.html&usg=AFQjCNHBccxf_EIbPhylyN7-RiUgzRtZ3g.*

**Data Sharing:** The ability to share the same data resource with
multiple applications or users. It implies that the data are stored in
one or more servers in a network and there is some software locking
mechanism that prevents the same set of data from being changed by two
people at the same time. *Source:
http://www.pcmag.com/encyclopedia_term/0,2542,t=data+sharing&i=40842,00.asp.*

**Data Standards:** A technical standard that will enable information
systems to exchange clinical systems in a private and secure manner both
within and across institutions. *Source: Adapted from
http://www.connectingforhealth.org/resources/dswg_report_6.5.03.pdf.*

**Data Stewardship:** A data steward has the oversight of the various
uses of health care data and is responsible for setting rules and
standards for sharing and using health care quality measurement.
*Source: http://www.ahrq.gov/qual/aqamtg3.htm#datasharing.*

**Data Use Agreement:** A data use agreement is a written agreement
between one entity and another who is requesting a disclosure of
protected health information (PHI) contained in a limited data set.
*Source: http://www.hhs.gov/ocr/hipaa/guidelines/research.pdf.*

**De-identification:** A process for the removal of identifying
information. Under HIPAA, this means the removal of 18 specific
identifiers: names, geographic subdivisions smaller than state, all
dates and ages over 89, telephone number, fax number, e-mail address,
social security number, medical record number, health plan beneficiary
number, account numbers, license numbers, vehicle identifiers, device
identifiers, URLs, IP addresses, biometric identifiers, full face photo,
and any other unique identifying number, characteristic or code.
*Source: http://aspe.hhs.gov/admnsimp/pl104191.htm.*

**Electronic Health Record (EHR):** A longitudinal electronic record of
patient health information generated in one or more encounters in any
care delivery setting. This information may include patient
demographics, progress notes, problems, medications, vital signs, past
medical history, immunizations, laboratory information, and radiology
reports. *Source: Detailed Quality Use Case;
http://www.hhs.gov/healthit/documents/UseCaseQuality.pdf*

**Encryption:** A process of converting messages or data into a form
that cannot be read without decrypting or deciphering it. *Source:
http://library.ahima.org/xpedio/groups/public/documents/government/bok1_027444.pdf#page%3D4.*

**Episode / Episode of Care:** An interval of care by a health care
facility or provider for a specific medical problem or condition. It may
be continuous or it may consist of a series of intervals marked by one
or more brief separations from care, and can also identify the sequence
of care (e.g., emergency, inpatient, outpatient), thus serving as one
measure of health care provided. *Source: Detailed Quality Use Case;
http://www.hhs.gov/healthit/documents/UseCaseQuality.pdf.*

**Federated Database System:** A federated database system is a
distributed system formed of a collection of independently managed
heterogeneous database systems that allow partial and controlled sharing
of data without affecting existing applications. *Source:
http://www.google.com/url?sa=X&start=0&oi=define&q=http://it.csumb.edu/departments/data/glossary.html&usg=AFQjCNH1kP8agIcn5i_9HHNorIvDFOpI-w.*

**Harmonization:** A process for making identical or minimizing the
differences between standards or related measures of similar scope.
*Source: http://www.ahrq.gov/qual/performance5/perfm5b.htm#Reporting.*

**Health Information Exchange:** A multi-stakeholder entity that enables
the movement of health-related data within state, regional, or
non-jurisdictional participant groups. *Source: Detailed* *Quality Use
Case; http://www.hhs.gov/healthit/documents/UseCaseQuality.pdf.*

**Health Information Technology:** Health information technology allows
comprehensive management of medical information and its secure exchange
between health care consumers and providers. *Source:
http://www.hhs.gov/healthit.*

**Health Information Technology Standards Panel:** The Healthcare
Information Technology Standards Panel or HITSP is a cooperative
partnership between the public and private sectors organized for the
purpose of achieving a widely accepted and useful set of standards to
enable and support widespread interoperability among health care
software applications, as they will interact in a local, regional and
national health information network for the United States. *Source:
http://www.ansi.org/standards_activities/standards_boards_panels/hisb/hitsp.aspx?menuid=3.*

**HIPAA:** Health Insurance Portability and Accountability Act (HIPAA)
of 1996, Public Law 104-191, included "Administrative Simplification"
provisions that required HHS to adopt national standards for electronic
health care transactions. *Source:
http://aspe.hhs.gov/admnsimp/pl104191.htm*.

**Hospital Quality Alliance:** A national public-private collaboration
focused on encouraging hospitals to voluntarily collect and publicly
report quality performance information in a consistent, standardized
way. *Source: http://www.hospitalqualityalliance.org.*

**Hybrid Data:** Data from more than one source that are linked by a key
field(s) in each input dataset (i.e., not just claims, but also medical
record, lab data, prescription drug data, and more). *Source: Adapted
from Testimony and Interviews*.

**Interoperability:** The ability to communicate and exchange data
accurately, effectively, securely, and consistently with different
information technology systems, software applications, and networks in
various settings and exchange data such that clinical and operational
purpose and meaning of data are preserved and unaltered. *Source:
Promoting Quality and Efficient Health Care in Federal Government
Administered or Sponsored Health Care Programs. Presidential.*

**The Joint Commission on Accreditation of Healthcare Organizations:**
An independent, not-for-profit organization, the Joint Commission
accredits and certifies nearly 15,000 health care organizations and
programs in the United States. The Joint Commission accreditation and
certification is recognized nationwide as a symbol of quality that
reflects an organization's commitment to meeting certain performance
standards. *Source: http://www.jointcommission.org/AboutUs.*

**Longitudinal Measurement:** The assessment or measurement of clinical
processes or patient outcomes over-time and across settings, from a
longitudinal or episodic perspective*. Source:
http://www.hhs.gov/healthit/ahic/materials/06_07/qual/problem.doc.*

**Matching:** A deliberate process used to link a patient's electronic
records across disparate health information systems. *Source:
http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_028980.hcsp?dDocName=bok1_028980.*

**Measure Developer:** Measure developers refer to those organizations
that are developing quality measures for use across the country. Measure
developers that have developed commonly used measures include, but are
not limited to, organizations such as CMS, JCAHO, AQA, HQA, AHRQ, and
the Society of Thoracic Surgeons. *Source: Adapted from Testimony and
Interviews.*

**National Goals:** A set of goals or objectives set forth and agree
upon by national leaders. *Source: Adapted from Testimony and
Interviews.*

**National Quality Forum:** The National Quality Forum or NQF is a
private, not-for-profit membership organization created to develop and
implement a national strategy for health care quality measurement and
reporting. The organizational members of the NQF will work to promote a
common approach to measuring health care quality and fostering
system-wide capacity for quality improvement. *Source:
http://www.qualityforum.org.*

**Network of Networks:** A local network (a group of interconnected
systems, such as a RHIO) that is connected to other local networks (such
as RHIOs connected with RHIOs). *Source: Adapted from
http://www.ahrq.gov/about/cj2008/hcqo08b.htm.*

**Nationwide Health Information Network:** A network (or networks)that
enables secure access to health care data and real time information
sharing and exchange of health care data among physicians, patients,
hospitals, laboratories and pharmacies, regardless of where the medical
data is located. Source: http://www.gridtoday.com/grid/1221054.html.

**Patient Identifier:** A Patient Identifier is a code (set of
characters) used to uniquely identify a patient within a health register
or a health records system*. Source:
http://www.datadictionaryadmin.scot.nhs.uk/isddd/ISD_DT_TOP_SMR.jsp;jsessionid=38C184434CFCA82EB94B5CAB454B9B0D?pContentID=9718&p_applic=CCC&p_service=Content.show&.*

**Personal Health Information (PHI):** Under HIPAA, PHI is defined as 18
specific identifiers: names, geographic subdivisions smaller than state,
all dates and ages over 89, telephone number, fax number, e-mail
address, social security number, medical record number, health plan
beneficiary number, account numbers, license numbers, vehicle
identifiers, device identifiers, URLs, IP addresses, biometric
identifiers, full face photo, and any other unique identifying number,
characteristic or code. *Source:
http://aspe.hhs.gov/admnsimp/pl104191.htm*.

**Personal Health Record (PHR):** A health record that can be created,
reviewed, annotated, and maintained by the patient or the caregiver. The
personal health record may include any aspect(s) of the health
condition, medications, medical problems, allergies, vaccination
history, visit history, or communications with health care providers.
*Source: Detailed Quality Use Case;
http://www.hhs.gov/healthit/documents/UseCaseQuality.pdf.*

**Pseudonymization:** Pseudonymization is a procedure by which all
person-related data within a data record is replaced by an artificial
identifier that maps one-to-one to the person. The artificial pseudonym
always allows tracking back to data of origin. This is on contrast to
"anonymized" data, where all person-related data that could allow
backtracking has been purged. Pseudonymization is a method in
patient-related data that allows data to be passed securely between
clinical centers. *Source:
http://en.wikipedia.org/wiki/Pseudonymization.*

**Record Locator Service:** A Record Locator Service or RLS holds
information authorized by the patient about where authorized information
can be found, but not the actual information the records may contain. It
thus enables a separation, for reasons of security, privacy, and the
preservation of the autonomy of the participating entities, of the
function of locating authorized records from the function of
transferring them to authorized users. The Record Locator Service could
enable a care professional looking for a specific piece of information
(PCP visit or ER record) to find it rapidly. *Adapted from
http://www.connectingforhealth.org/resources/collaborative_response/appendices/glossary.php.*

**Re-identification:** A process of reconnecting de-identified data to
allow the reintegration of data with personal identifiable information.
*Source: Adapted from http://en.wikipedia.org/wiki/Pseudonymization.*

**Value Exchanges:** An organization selected to facilitate the
collection of provider level measurement across the six IOM aims, and to
use these measures for public reporting, improvement, collaboration,
promotion of interoperable HIT, supporting knowledge transfer, and
conducting evaluations. *Source:
http://www.aqaalliance.org/Files/QASCExpWkgpRecChartTerms02-22-07FINAL.pdf.*

**Vendors:** Individuals or firms hired to provide a specific service or
product (product purchase or fee-for-service). These products or
services include, but are not limited to information technology hardware
and software. *Source: Adapted from
http://www.google.com/url?sa=X&start=2&oi=define&q=http://www.state.nj.us/njded/grants/glossary.shtml&usg=AFQjCNE-Q-7p3k-mB7_NSlgGCha78usdlQ*.

# Appendix D: Requirements Analysis Subgroup Participants

Sarah T. Corley, MD, FACP

Chief Medical Officer

NextGen Healthcare Information Systems, Inc.

Michael D. Hagen, MD\
Vice President, Assessment Methods Development\
American Board of Family Medicine

Dimitra Hannon, JD

Manager, Benefits Management and Strategy

The Boeing Corporation

Phyllis Torda, MA

Vice President, Product Development

National Committee for Quality Assurance

Margaret VanAmringe, MHS

Vice President, Public Policy and Government Relations

The Joint Commission on Accreditation of Healthcare Organizations

Jonathon White, MD

Director of Health Information Technology

Agency for Healthcare Research and Quality

# Appendix E: Reference Documents

This requirements analysis synthesized information across several
sources to inform the description of the current state and the
development of requirements, scenarios for the future. Information
sources reviewed and/or used to provide input into this analysis were
identified based on recommendations made by the subgroup, testimony and
interviews. The full list of sources includes:

-   Agency for Health Care Research and Quality, Report of Proceedings:
    Conference on Health Care Data Collection and Reporting, March 2007

-   American Health Information Community Quality Workgroup, Public
    Meeting Testimony, May 3, 2007

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-   Blue Cross Blue Shield of Illinois

-   Blue Cross Blue Shield Association, Blue Health Intelligence
    Initiative

-   Centers for Medicare and Medicaid Services

-   Geisinger Health System

-   Massachusetts eHealth Collaborative

-   Trinity Health

-   Wisconsin Collaborative for Healthcare Quality

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-   American Health Information Community Quality Workgroup, Public
    Meeting Testimony, June 22, 2007

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-   Kaiser Permanente

-   NQF Expert Panel on Use of EHR Data Elements in AQA and HQA
    Reporting

-   Partners HealthCare

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```
-   American Health Information Community Quality Workgroup, Vision
    document, March 2007

-   American Medical Informatics Association, A Roadmap for National
    Action on Clinical Decision Support, June 13, 2006

-   AQA Alliance, Data Sharing and Aggregation Principles for
    Performance Measurement and Reporting, January 2006

-   AQA Alliance, National Health Data Stewardship Entity, April 2006

-   AQA Alliance, Parameters for Selecting Measures for Physician
    Performance, May 2007

-   AQA Alliance, Principles for Health Information Technology and
    Measurement Aggregation, April 2006

-   AQA Alliance, Principles for Public Reports on Health Care, April
    2006

-   AQA Alliance, Principles for Reporting to Clinicians and Hospitals,
    April 2006

-   AQA Alliance, Principles in the Use of Registries for Enhancing
    Quality of Care through Performance Measurement, October 24, 2006

-   AQA Alliance and AHRQ, RFI on a National Data Stewardship Entity &
    Available Responses, 2007

-   ASTM International, the Massachusetts Medical Society (MMS), the
    Health Information Management and Systems Society (HIMSS), and the
    American Academy of Family Physicians (AAFP), Continuity of Care
    Record Concept Paper, Version 2.1b, No Date

-   Centers for Medicare and Medicaid Services, Physician Focused
    Quality Initiatives. Available at:
    <http://www.cms.hhs.gov/PhysicianFocusedQualInits/>. April 11, 2007

-   Fortham, Dove, Wooster. (2000). Episode Treatment Groupers (ETGs): A
    Patient Classification System for Measuring Outcomes Performance by
    Episode of Illness. Topics in Health Information Management, 21(2),
    51-61

-   HHS Office of the Assistant Secretary for Planning and Evaluation,
    Report on Health Information Exchange in Post-Acute and Long-Term
    Care, February 2007

-   HHS Office of the National Coordinator for Health Information
    Technology, Detailed Quality Use Case, Published June 28, 2007

-   HHS Office of the National Coordinator for Health Information
    Technology**,** RFI on National Health Information Network and
    Available Responses, 2007

-   HIMSS, Electronic Health Record Vendors Association. Notes from
    Chairs. Available at:
    <http://www.himssehrva.org/docs/newsletter/200702_ehrva.htm>.
    February 2007

-   HIMSS, Improving Outcomes with CDS. Available at:
    http://www.himss.org/ASP/topics_cds_workbook.asp?faid=108&tid=14.
    February 13, 2004.

-   Ingenix. Symmetry Episode Treatment. Available at
    <http://www.ingenix.com/Products/Payers/CareHealthManagementPAY/EnterpriseWideDecisionSupport/EpisodeTreatmentGroups/>.
    No Date

-   Institute of Medicine. Performance Measurement, Accelerating
    Improvement. National Academies, Washington, DC 2006

-   Interviews:

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-   Billings Clinic, August 2007

-   Bronx RHIO, August 2007

-   Citizens Memorial Healthcare, August 2007

-   Indiana Health Information Exchange, August 2007

-   Intermountain Healthcare, August 2007

-   Markle Foundation, August 2007

-   National Library of Medicine, August 2007

-   Stratis Health, August 2007

-   University of Pittsburgh Medical Center, August 2007

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-   Jha, A.K., Ferris, T.G., Donelan, K., DesRoches, C., Shields, A.,
    Rosenbaum, S., and Blumenthal, D. (2006) How Common Are Electronic
    Health Records In The United States? A Summary Of The Evidence.
    *Health Affairs: 25 (6) w496 -- w507*

-   Markle Foundation, Connecting for Health, Data Standards Workgroup,
    Clinical Data Exchange Efforts in the United States: An Overview, No
    Date

-   Markle Foundation, Connecting for Health, The Common Framework
    Overview and Principles, June 2003

-   National Committee for Vital Health Statistics, Recommendations on
    Data Linkages to Improve Health Outcomes, June 21, 2007

-   National Committee for Vital Health Statistics, Recommendations on
    the Initial Set of functional requirements for a Nationwide Health
    Information Network, October 30, 2006

-   National Committee for Vital Health Statistics, Updated
    Recommendation on Privacy Laws and Regulations Required to
    Accommodate NHIN Data Sharing Practices, June 21, 2007

-   National Committee for Vital Health Statistics Ad Hoc Committee on
    Secondary Uses of Data, Public Meeting Testimony, July 17-18, 2007

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-   Agency for Healthcare Research and Quality

-   American Medical Informatics Association

-   Dartmouth

-   eHealth Initiative

-   E-Health Sign, LLC

-   Health Information Policy Consulting

-   Health Insurance Reform Project, The George Washington University,
    and the Rapid Learning System

-   McKesson Provider Technologies

-   National Library of Medicine

-   New Jersey e-HIT

-   Siemens Medical Solutions Health Services

-   Stratis Health

-   The Joint Commission

-   The Markle Foundation

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-   National Committee for Vital Health Statistics Ad Hoc Committee on
    Secondary Uses of Data, Public Meeting Testimony, August 1-3, 2007

```{=html}
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```
-   America's Health Insurance Plans

-   American Health Information Management Association

-   American Nursing Association

-   Archimedes Disease Management Model

-   Blue Cross Blue Shield Association

-   caBIG, National Cancer Institute

-   California Office of Statewide Health Planning

-   Centers for Disease Control

-   Fox Systems

-   Genetic Alliance

-   HMO Research Network

-   Institute of Medicine

-   National Quality Forum

-   Oncology Data Alliance / Elekta Inc.

-   Patient Privacy Rights Foundation

-   Solucient

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-   National Committee for Vital Health Statistics Subcommittee on
    Populations, Workgroup on Quality, Public Meeting Testimony, June
    19, 2007

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```
-   Agency for Health Care Research and Quality

-   American Health Information Management Association and the Medical
    Group Management Association

-   Bay Care Health System

-   Cardinal Health (for the Pennsylvania Health Care Cost Containment
    Council)

-   Centers for Medicare and Medicaid Services

-   Department of Veterans Affairs

-   Niagra Health Quality Coalition

-   The Joint Commission

-   Wisconsin Collaborative for Healthcare Quality

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-   National Quality Forum, CEO Survival Guide to Electronic Health
    Record Systems, Available at
    <http://www.nqfexecutiveinstitute.org/executiveinstitute/ehrs_ehrs.cfm>.
    No Date

-   Osheroff J., Pifer E., Teich J., Sittig D., Jenders R.. Improving
    Outcomes with Clinical Decision Support: An Implementers' Guide.
    Healthcare Information Management and Systems Society. 2005

-   Osheroff, J., Teich, J., Middleton, B., Steen, E., Wright, A., and
    Detmer, D. A Roadmap for National Action on Clinical Decision
    Support, HHS Office of the National Coordinator. 2006

-   Thompson Health Care. Medical Episode Grouper. Available at
    <http://www.medstat.com/Products/view/?id=72>. No Date

[^1]: The American Health Information Community (AHIC) is a federal
    advisory body, chartered in 2005 to make recommendations to the
    Secretary of the U.S. Department of Health and Human Services on how
    to accelerate the development and adoption of HIT.

[^2]: Institute of Medicine. Performance Measurement, Accelerating
    Improvement. National Academies, Washington, DC 2006.

[^3]: Additional information regarding current activities related to
    quality improvement, both at the point of care and as they occur
    through our existing quality measurement and reporting systems, is
    included in Appendix B: Current Strategies to Assess and Improve
    Quality.

[^4]: Thompson Health Care. Medical Episode Grouper. Available at
    <http://www.medstat.com/Products/view/?id=72>.

[^5]: Ingenix. Symmetry Episode Treatment. Available at
    <http://www.ingenix.com/Products/Payers/CareHealthManagementPAY/EnterpriseWideDecisionSupport/EpisodeTreatmentGroups/>.

[^6]: Fortham, Dove, Wooster. (2000). Episode Treatment Groupers (ETGs):
    A Patient Classification System for Measuring Outcomes Performance
    by Episode of Illness. Topics in Health Information Management,
    21(2), 51-61.

[^7]: HIMSS, Electronic Health Record Vendors Association. Notes from
    Chairs. February 2007. Available at:
    http://www.himssehrva.org/docs/newsletter/200702_ehrva.htm.

[^8]: The AQA Alliance was formerly known as the Ambulatory Care Quality
    Alliance. The change in name reflects the AQA's expanded mission,
    which incorporates quality improvement targeted to all areas of
    physician practice.

[^9]: NQF, CEO Survival Guide to Electronic Health Record Systems,
    Available at
    <http://www.nqfexecutiveinstitute.org/executiveinstitute/ehrs_ehrs.cfm>.

[^10]: Osheroff, J., Teich, J., Middleton, B., Steen, E., Wright, A.,
    and Detmer, D. A Roadmap for National Action on Clinical decision
    Support, HHS Office of the National Coordinator. 2006.

[^11]: Osheroff J., Pifer E., Teich J., Sittig D., Jenders R.. Improving
    Outcomes with Clinical Decision Support: An Implementers' Guide.
    Healthcare Information Management and Systems Society. 2005.

[^12]: Osheroff, J., Teich, J., Middleton, B., Steen, E., Wright, A.,
    and Detmer, D. A Roadmap for National Action on Clinical Decision
    Support, HHS Office of the National Coordinator. 2006.

[^13]: HIMSS, Improving Outcomes with CDS, February 13, 2004. Available
    at:
    http://www.himss.org/ASP/topics_cds_workbook.asp?faid=108&tid=14.

[^14]: CMS, Physician Focused Quality Initiatives. April 11, 2007.
    Available at: <http://www.cms.hhs.gov/PhysicianFocusedQualInits/>.

[^15]: CMS publishes hospital quality measurement reporting results at
    [www.hospitalcompare.hhs.gov](http://www.hospitalcompare.hhs.gov/).
    The Joint Commission publishes results at
    [http://www.qualitycheck.org](http://www.qualitycheck.org/).

[^16]: **Jha, A.K., Ferris, T.G., Donelan, K., DesRoches, C., Shields,
    A., Rosenbaum, S., and Blumenthal, D. (2006)** How Common Are
    Electronic Health Records In The United States? A Summary Of The
    Evidence. *Health Affairs: 25 (6) w496 -- w507.*