# Presentation: 184419

## Health Information Exchange:Linking Practitioners and Public Health for Decision Support

- Stephen M. Downs
- Indiana University
- Children’s Health Services Research
- Regenstrief Institute

## Overview

- The Indiana Network for Patient Care (INPC) and the Indiana Health Information Exchange (IHIE)
- Real-time Decision Support for Practitioners – Adaptive Turnaround Documents
- Practitioner Data for Public Health – Disease Surveillance

## Concept

- HIE connects health systems & providers
- Creates opportunities for decision support
- Patterns of care identify opportunities for decision support & quality improvement
- Patients with diabetes need annual urinary microalbumin testing
- Infants with positive newborn screens must be located and treated immediately
- Health departments need to respond to disease outbreaks

## What’s Needed

- A mechanism for 2-way communications with physicians in the INPC
- We have used Adaptive Turnaround Document Technology (ATD) for decision support in our clinics
- Adapting to the INPC

## What is an Adaptive Turnaround Document (ATD)?

- Computer Generated
- Paper
- Delivers tailored information
- Scannable
- Captures structured data
- Computer, scanner, or fax

## Applying ATD Technology on a Regional Basis

- Indiana Network for Patient Care (INPC)
- 21 hospitals
- County and State Health Departments
- RxHub
- Medicaid administrative data
- Over one billion coded results

## Slide 7

## How it was accomplished (technical)

- Federated repository
- Data from difference sources are stored in separate physical files
- Global patient index
- Same data structure and data dictionary
- Data arrive by HL7 (also DICOM, NCPDP)
- Parsed
- Translated

## Docs4Docs - Links to clinical providers

- Results delivery service
- All hospitals send reports through same mechanism (HL7)
- HL7 results messages flow through INPC and are routed to MD in-boxes or faxes
- Eliminates mail costs

**Notes:**
Because patient data is routinely distributed across separate databases and systems, both within and among organizations as separate islands, health care providers are often encumbered by the increased record keeping burden this reality brings (24).  Receiving clinical data and test results in many different formats from multiple information sources brings logistical headaches and occupies time intended for direct patient interaction.  In 2002 DOCS4DOCS® was built to address this problem by efficiently delivering aggregated clinical results to physicians regardless of where they practice throughout the INPC.  This innovative system facilitates standardized automated results delivery by formatting, aggregating and conveying information in a consistent fashion.  It dynamically generates customized reports that a practitioner can quickly review and act upon. Reports can be delivered through multiple mediums, including network printer, web browser, email, or fax.  The system is also capable of serving as a “virtual filing cabinet”, giving practices and practitioners a pre-organized short-term results storage system.  In addition, the system contains auditing functions which allow results that have been reviewed to be fully annotated.

## INPC

**INPC**

**HL7 QUEUE**

**Repository**

**DSS**

**HL7**

**Docs4Docs** ©

## Improving Diabetes Management

- ADA Guidelines
- Microalbumin measured annually
- Monitor HL7 messages for Hgb A1C
- Proxy for diabetes
- Check INPC for microalbumin in the last 12 months
- If none, alert physician

## Slide 12

## Simulating the RuleMicroalbumins & A1Cs in 2005

| Hosp | Pats | Albumin | % | Albumin | % |
| --- | --- | --- | --- | --- | --- |
| 1 | 8430 | 2898 | 34% | 2931 | 35% |
| 2 | 4055 | 168 | 4% | 181 | 4% |
| 3 | 7673 | 1270 | 17% | 1345 | 18% |
| 4 | 10927 | 2647 | 24% | 2683 | 25% |
| 5 | 16239 | 5113 | 31% | 5213 | 32% |
| TOTAL | 47324 | A1Cs | 73482 |  |  |

**201 alerts per day**

## A Test by Any Other Name...

- _Hemaglobin A1C_
- Glycated Hgb
- Glycated Hb
- Glycos Hgb A-1
- Hgb Clycosylated
- HGBA1C:EIA
- HGBA1C:HPLC
- HgbA1C % Ser EIA
- Hgb A1c Bld Qn

- _Microalbumin_
- Microalbomin 24h Ur
- Microalbumin R-Ur Qn
- Microalbomin Timed Urine
- Microalbumin 24h Ur Cnc
- Microalbomin Ur Qn mg/dl
- Albumin Ur Qn Elp

## Another Example:Newborn Screening

- Virtually all newborns in the US
- Blood spot obtained by heelstick
- Tested for inborn errors of metabolism and other conditions
- Early treatment may be life saving

## Expanded Newborn Screening

- Tandem Mass Spectrometry
- Over 50 Conditions
- Prevent Morbidity and Mortality
- Save Costs

## Challenges

- Most Conditions Rare
- MDs Unfamiliar
- Families Need Guidance
- Diagnosis & Treatment Must Be Timely
- Who has the baby?
- No Mechanisms for LTFU

## Solution

- Regional health information network
- Two way communication with providers

## Project Objectives

- Adaptive turnaround document technology to facilitate communication: NBS program, subspecialists, medical home
- Provide “just in time” information to the primary care clinician & family
- Reduce the risk of “missed opportunities” to screen
- Facilitate tracking of children with detected conditions

## HL7

**INPC**

**ISDH**

**ATD**

## Three Ways ATDs Can Enhance the NBS Program

- Just-in-time information to the medical home (physicians and families)
- Prevent missed opportunities to screen
- Long term tracking of children with identified conditions

## Just-in-Time Information for the Primary Care Physician

## X

**X**

**X**

**X**

**X**

**X**

## Generating the ATD

**RULES:**
**IF**   lurelicubwliuc
**THEN**  isjfafkjdf;lksdf
**IF**   lurelicubwliuc
**THEN**  isjfafkjdf;lksdf
**IF**   lurelicubwliuc
**THEN**  isjfafkjdf;lksdf
**IF**   lurelicubwliuc
**THEN**  isjfafkjdf;lksdf
**IF**   lurelicubwliuc
**THEN**  isjfafkjdf;lksdf
**IF**   lurelicubwliuc
**THEN**  isjfafkjdf;lksdf

- Database

- Knowledge Base

## Avoiding Missed Opportunities

- Capture HL7 message for neonates

- INPC

- Check against newborn screening reports

- Alert physician if missing or positive

## Probabilistic Matching withHL7 Messages

- MSH|^~\&|RLY_NB|RILEY|NEWBORN|REGEN|20051202070251||ORU^R01|||2.3
- PID|||72360601||Doe^John||20050908155100|M|Jones|White||||||||||||Non Hispanic
- NK1||Doe^Jane|Mother|123 MAIN STREET^^GREENWOOD^IN^46143^^^^JOHNSON|3175350809|||||||||||19791116
- PV1|||||||^SMITH^LYNETTE

- Probabilistic Matching withHL7 Messages

- Infant LName = Doe
- Infant FName = John
- Infant DOB = 9/8/2005
- Infant Sex = Male
- Infant Race = White
- Infant Ethnicity = Non Hispanic

- Mother LName = Doe
- Mother FName = Jane
- Mother Street = 123 Main Street
- Mother City = Greenwood
- Mother State = IN
- Mother Zip = 46143
- Mother County = Johnson
- Mother Phone = 317-535-0809

## 318 un-linked HL7 records

- Riley (100,785 unique records)

- INPC (2,561 total unique records)

- 2,243 linked HL7 records

- 97,960 un-linked Riley records

- 2,825 linked Riley records

- 1,107 un-linked Riley records with Indianapolis address

## Explaining Unmatched Cases

- Manual Review of 300 “children” whose HL7 messages did not match a newborn screen
- 200 “junk” messages
- E.g., did not contain PID and NK1 segments
- 70 messages could be manually linked to an infant with a newborn screen
- Matching algorithm needs refining
- 30 appear not to have had a newborn screen

## Long Term Follow-up Data Collection

## Electronic Lab Reporting

- Reportable disease HL7 messages are sent to ISDH and Local Public Health daily.

## Reportable Condition Processor

- Inbound
- HL7

- Potentially
- Reportable

- Reportable
- Condition

- Reportable
- Conditions
- Databases

- Abnormal flag,
- Organism name, Value above threshold

- Record Count
- as denominator

- E-mail
- Summary

- Realtime

- Daily Batch

- Print
- Reports

- To Public
- Health

- To Infection
- Control

**Notes:**
Generalize what is on the right
What is on the left is difficult to generalize because it has to be integrated with and optimized for performance

## Syndromic Surveillance Basics

- Signal:

- Event:

- Time

- GI Outbreak

**Notes:**
The main theoretical underpinning of detection is signal detection theory, which has relatively familiar concepts. 

The detection problem is conceptualized as concise statement of a detection problem (or of a detection system) might include descriptions of what is to be detected, the types of input signal, and the detector itself. 

Event such as an epidemic that you are trying to detect

Data that contains signal and noise.

Detector that analyzes the data, and when some THRESHOLD is exceeded produces as output an alarm  

The alarms may be true or false alarms and we therefore measure the performance of detection systems using the metrics sensitivity and specificity.

By manipulating the alarm threshold, we can reduce the increase the number of true alarms by lowering the threshold but almost always at the cost of increasing the numbers of false alarms and vice versa.  Usually we are not sure what mix is optimal, so we summarize it with a receiver-operating characteristic curve. 

Timeliness can also be measured by comparing the time of the signal to the time of the vent itself.  Timeliness can also be improved by adjusting the threshold of detection so that all three measures can be improved at the expense of the others.

## Geocode

- Syndrome
- Classify

- Segregate

- ED Data

## Slide 34

## Current PHESS Hospitals (n=72)

- Goal of 78 hospitals by August 2007

- > 6,500 visits per day
- ~ 15 MB data per day

## stmdowns@iupui.edu	

- www.ichsr.org

## Arden SyntaxMedical Logic Module

- Arden SyntaxMedical Logic Module

## Probabilistic Scoring ExampleAGREEMENT RATES

- 2 files, 10 Records each
- Form all possible pairs: 10 x 10 = 100 pairs
- Human review of all 100 record pairs shows that 10 are true-links, 90 are non-links.

**Notes:**
A concept critical to understanding probabilistic scoring is that of AGREEMENT RATES.

Imagine 2 patient files each containing 10 records. If we form the Cartesian product of 

(STRESS THE TERM “AGREEMENT RATES”) as developed by felligi-sunter

## Probabilistic Scoring Example AGREEMENT RATES

- Among the 10 true-links, the _last names_ agreed in 9/10 pairs (e.g. one of the last names was misspelled)
- This represents a 90% AGREEMENT RATE for last name among TRUE LINKS.
- Similarly, among the 90 non-links, last names agreed (by random chance) in 2/90 pairs
- This represents a 2% AGREEMENT RATE for last name among NON-LINKS.

## Probabilistic Scoring Example AGREEMENT RATES

- 90%

- 2%

- Records that agree on last name are 45 times more likely to be a true-link than a non-link

- Weights for each field are combined to form a composite record pair score.
- Field disagreement contributes a negative weight, and reduces the overall record pair score.

## No Change in Workflow

- Since PHESS requires information that is already being collected by the registration system, there is
- no change in work flow, (unless chief complaint is not currently captured)
- The only upfront work involved is setting up connectivity and an HL7 interface.
- On average it takes approximately 11 hours of a network engineer and interface programmer’s time for this project
- Record go live time set on 3/6/2006 – under 3 hrs