VIReC CyberSeminar Series 2006Clinical Informatics

Attributes of An Ideal Informatics System with  Highlights from a State of the Art Conference

Denise M. Hynes, Ph.D., R.N.

Director, VIReCResearch Health Scientist, MCHSPR COE

HSR&D Resource Centers

CiDER = Center for Information Dissemination & Education Resources

HERC  = Health Economics Resource Center

metric = Measurement Excellence & Training Resources Information Center

VIReC = VA Information Resource Center

          - VIReC is one of $ HSR&D Service Resource Centers
          - VIReC
          - Founded 1998
          - Mission:  To improve the quality of research that uses VA data and information systems
          - Goals:  
          - Build Knowledge About Databases & Data Quality
          - Share Knowledge
          - Advocate and Collaborate for Research Needs/Perspectives

Series/Course Objectives
        - To learn about applications in clinical informatics in research and quality improvement efforts
        - VA and non-VA efforts
        - To understand approaches for evaluating clinical informatics and IT interventions

Selected Course Topics
        - Attributes of an Ideal Information System:  Highlights from a State of the Art Conference
        - Approaches for Evaluating Health Informatics Technology and CDSS
        - Implementation of CDSS in Hypertension Management
        - Implementation of a CDSS in Mental Health
        - Using CDSS in Pain Management
        - Informatics Applications for Promoting Collaborative Care

Session Objectives
        - Understand the key aspects to consider in a clinical information system   
        - Understand the desired attributes of a clinical information system
        - Become aware of the barriers and facilitators to use of IT in healthcare and research

IOM Quality Chasm Report, 2001

Predominance of chronic care

Need for evidence-based practice

Critical value of information technology

IOM Committee on Quality of Health Care in America 
        - Point-of-care access to health literature and evidence-based guidelines
        - Computer-assisted decision support systems;
        - Computerized patient clinical data
        - Automation of decisions to reduce errors
        - Electronic communication between providers 
        - Electronic communication between providers and patients

The Institute of Medicine (IOM) has defined eight aspects of chronic care that require informatics support. 

. Similarly, other organizations, such as the Leapfrog Group, have made the business case for investments in IT, such as physician order entry.

Informatics Support for Clinical Practice Guideline Implementation

Pathman,  et al.  Medical Care 1996; 34:873-889
| Step       | Facilitators                                                          | Informatics Support                                                                  |
|------------|-----------------------------------------------------------------------|--------------------------------------------------------------------------------------|
| Awareness  | Priming Activities such as profiling of baseline performance          | Profiling from prescription order and diagnosis database                             |
| Acceptance | Active education such as Academic Detailing; Clinical Opinion Leaders | Present evidence relevant to patient; allow opinion leaders to browse knowledge base |
| Adoption   | Enabling strategies such as incorporation into clinic workflow        | Integration with existing EMR/CPRS                                                   |
| Adherence  | Reinforcing Strategies such as reminders                              | Point-of-care patient-specific advisories & alerts                               |

For each of the steps in Awareness to Adherence, there are potential facilitators, examples of which are shown.  Information technology can provide support to each step.  Examples of how information technology can be used as facilitators to the steps are shown

The theoretical model we use for the path to guideline adherence is the “Awareness to Adherence” model, in which the clinician must

 Awareness of guideline

 Acceptance of guideline 

 Adoption of guideline

 Adherence to guideline

Pathman, D. E., T. R. Konard, et al. (1996).  "The Awareness-to-Adherence Model of the Steps to Clinical Guideline Compliance."  Medical Care 34:873-889

Diffusion of Innovation Theory:  Key Dimensions
        - Nature of the innovation itself  
        - How communication channels can be impacted  
        - Time  
        - Social system  and organizational context

Everett Rogers’ Diffusion of Innovation Theory outlines the process through which an innovation is communicated through certain channels over time among the members of a social system. The key dimensions that must be considered in implementing any new technology include the: 1) innovation itself; 2) communication channels; 3) time; and 4) The social system.  This theoretical framework, or model, provides some of the key characteristics that must be considered in investigating the effective diffusion of IT support for clinical care, (e.g., EHRs, CCRs) through a social system such as the Veterans Health Administration (VHA).

Challenge of Using Information Technology (IT) for QI
        - Integrating new forms of decision support into legacy data systems and electronic record interfaces   
        -  Sociotechnical aspects

		Goldstein, et al., JAMIA 11: 368-76, 2004.

Berg’s Sociotechnical Success
        - Technical success
        -  generates correct recommendations offline
        -  extracts and uses patient data correctly
        -  integrates with CPRS to display for the right
        - Patient,
        - Provider
        - Clinical location
        - Time window
        -  tracks the data needed for research evaluation  
        - Sociological success
        -  clinicians find it usable and useful

Berg, M., Int J Med Inf, 1999. 55(2): p. 87-101.

Berg, M., Rationalizing Medical Work: Decision-Support Techniques and Medical Practices. Inside Technology, ed. W.E. Bijker, W.B. Carlson, and T. Pinch. 1997, Cambridge, Massachusetts: The MIT Press.

Social Challenges of IT in Health
        - Clinicians’ time constraints
        - Strike balance between ease of access to system and ease of ignoring it
        - Balance with interpersonal needs of patients  
        - Variability in comfort with computers
        - And virtually no training time available  
        - Lack of consensus/agreement about the guidelines

State of the Art Conference SummaryAttributes of an Ideal Integrated Informatics System that Supports Implementation of Evidence

	The ideal information management system supports managers, providers and patients to achieve practice and outcomes consistent with evidence 

Attributes of the Ideal Information System
        - Supports knowledge-based decisions 
        - Evaluation/Reporting Capability 
        - Evolves with health care system
        - Accurate/Correct Content 
        - Standardized/Compatible 

*Supports knowledge-based decisions

Imbeds information into routine processes

IS system should allow extraction from live data for patients, providers and managers, with flexible analytic and reporting tools.

IS should support patient centered care, including access to all records, choices, organizational data; system would be accessible to patients and caregivers for info, self-management and shared decision making; information needs to be presented appropriately for patients and caregivers.

IS knowledge systems for clinicians (CR, guidelines, order check system) should have links to external systems for additional information, with ability to tailor amount and type of data retrieved.

Data warehouse allows access to research, clinical care and admin data.

IS should have computational power that brings useful analyses to point of care – e.g., risk adjustment/stratification using population stats; 

IS should support coordination of care; current IS does not: consults are labor intensive process.

Need to capture non-physician care that affects patient outcomes (e.g., nursing processes), all labor data including contract staff, with number of hours (VetPro is physician only) and amount of time spent in direct care.

*Accurate/Right Content

Routine IS content - decision info and pt data - should be constructed in same way and with same quality as a registry – no separate “research” databases. 

Need standard business rules for entering data into the system. In private systems, billing requires detailed records of workload, but not in VA.

Need coded data elements (not free text).

*Standardized/*Compatible

IS should be consistent within VA and with other health care systems;

Interface within and outside VA requires standardized data elements and packages [this might be covered];

Ideally, IS would provide automated interface with other system(s) (e.g., template that will pull data from multiple sources not just CPRS; google-type search for medical info on patients); 

*Evaluation Capability

IS should allow rapid monitoring and feedback for evidence-based practice (most “evidence” is based on small numbers in stylized environment – need to confirm with clinical results); IS needs to confirm both process and outcomes; this is essential for physician buy-in, and to allow organization to decide whether to implement evidence.

	IS should recommend appropriate treatment (reminders), track actual treatment and results of treatment; provide ongoing analysis of effectiveness including search for new relationships to be explored (data mining functions).

	IS should allow ongoing evaluation of physician qualifications and quality of care, can support appropriate staffing and training to meet standards of care; or, system can provide monitoring and assurance that appropriate level of care is being provided, without specific credentialing of individual providers.

*Reporting Function

	Ideal IS will have diverse reporting function: different cohorts, time periods, data elements

Financial reports

Clinical and Patient Education

Administration

IS should be able to roll up reports for daily summary (care management information system).

        - Defined Accountability: organization vs. provider
        - Equitable: “No service/facility left behind”
        - Sustainable 
        - Secure 

*Defined Accountability

With a clinical decision support system, organization takes on accountability for care based on the research/evidence on which practice is based; legal liability could rest with research on best practice or with data systems supporting practice recommendation, not with practitioner. Determining who is responsible at the point of care is a difficult question; current reminders allow options that leave decisions in physicians hands.

	Ideal IS needs criteria for deciding what evidence to apply, automated update of evidence (every VA has a committee that reviews this) – this is a content issue.

	Software tools available for monitoring but completely automated monitoring is probably not desirable.

*Equitable

“No facility left behind”; implementation of best practices should be equitable across disciplines within a facility; resources should not be concentrated in a small set of facilities – some minimum resources at all facilities.

*Sustainable

IS requires planning for finance, training, support, redundancy; a business case is necessary to sustain staff and support of IT activities.

*Secure

        - Usable 
        - Integrated 
        - All electronic
        - flexible
        - transparent to users
        - non intrusive 
        - easily up-datable 

*Usable

Data systems should be:

Integrated

Currently, services are not integrated, some data are never entered into VistA (e.g., imaging, cath data), location of local data entry for tests (cardio, pulmonary, etc.) and consults varies so data cannot be rolled up. Shouldn’t have to “data mine” to find information.

all-electronic – not partly paper records

flexible

transparent to users

non intrusive 

easily updatable

As much as possible, data should be entered automatically; e.g., from instrumentation (“nothing should be entered twice”); 

Clinical encounter data should be entered directly by provider, but data entry should not be a burden to clinical staff.

Priority 1: Supports knowledge-based decisions
        - Current Information Systems do not fully utilized knowledge-based decision support
        - Clinical reminders, order check system, notification and alerts
        - Data capture limited to local system
        - Difficult to prioritize
        - Handling of adverse events

CPRS expert/knowledge-based systems: clinical reminders (supports EBP at point of care and can be used by individual providers to list patients with specific care needs and review outcomes, has national roll-up capabilities, but doesn’t capture data outside local system, has limited links with other reference material, and can be difficult to prioritize reminders; many local CRs are not part of national system)

Order check system: real-time system for providers when order is placed; e.g., checks for drug interactions when drug is ordered (but doesn’t supply any details about adverse event; ideally would like full info on AEs and options for treatment/monitoring)

Notifications and Alerts: triggered by pre-defined events, e.g., test results (providers can be flooded with these, can turn them on or off; not linked to other knowledge systems).

Ideally, all these should have links to external systems for additional information, with ability to tailor amount and type of data retrieved. Also useful to be able to roll up reports for daily summary (care management information system). Services (inpt, outpt, ICUs, LTC) are not integrated, some data never entered into VistA (e.g., imaging, cath data), location of local data entry for tests (cardio, pulmonary, etc.) and consults varies so data cannot be rolled up. Shouldn’t have to “data mine” to find information.

Barrier: too much info/provider overload

Strategies:
        - Research on how to prioritize
        - Require research to include data on added value in terms of mortality and morbidity

Barrier: lack of integration

Strategies: 	
        - National patient data record
        - Patient ownership of patient data, guidelines and reminders
        - Common patient identifier
        - Common provider identifier
        - Integration across systems
        - Research on what information users need

Barrier: Operationalizing evidence

Strategies: 	
        - Basic research in managing information complexity
        - Alignment of research priorities with clinical management 
        - Performance measures focused on how much evidence informs practice
        - Regular presentation of significant translation  research findings to senior leadership

Barrier: Threats to provider autonomy

Strategies: 
        - Flexibility in decision support with required feedback about reasons for non-compliance and barriers to compliance
        - Local review of compliance with local solutions (tailored training)
        - Add autonomy in other areas: e.g., guideline input, self-review, link to reference materials

Priority 2: Reporting/Evaluation Functions
        - rapid monitoring and feedback for evidence-based practice 
        - recommend appropriate treatment (reminders), track actual treatment and results, provide ongoing analysis of effectiveness 
        - Support analysis and review at different levels and contexts

IS should allow rapid monitoring and feedback for evidence-based practice (most “evidence” is based on small numbers in stylized environment – need to confirm with clinical results); IS needs to confirm both process and outcomes; this is essential for physician buy-in, and to allow organization to decide whether to implement evidence.

	IS should recommend appropriate treatment (reminders), track actual treatment and results of treatment; provide ongoing analysis of effectiveness including search for new relationships to be explored (data mining functions).

	IS should allow ongoing evaluation of physician qualifications and quality of care, can support appropriate staffing and training to meet standards of care; or, system can provide monitoring and assurance that appropriate level of care is being provided, without specific credentialing of individual providers.

Barrier: Data issues

Strategies: 
        - More automation of data (e.g., link diagnosis to test)
        - Review and monitoring of data quality
        - Linkages to other information in EHR so provider doesn’t have to reenter (e.g., test results)

Barriers:

Limited access to data (outside VA, in other VA systems, mismapped), or data not collected and structured for management perspective, or data not collected at all/collected incorrectly; system (overly)dependent on providers for data entry 

(all data functions)

Barrier: Reporting complexity

Reporting complexity requires specialized knowledge

Strategies: 
        - Use OLAP cubes to simplify user generation of reports

Barrier: System resources

Reporting function on existing patient care system eats system resources, slows response time 

Strategies: 
        - Move reports off system – put analytical tools on a system separate from pt care system

Priority 3:  Information system needs to evolve with health care system
        - Aspects of the information system are based on prior care models  
        - Current methods of data collection may not match business process or support evidence-based practice

Barrier: Emphasis on provider-level activities and provider-entered data 

Strategies:
        - Develop patient-centered data collection methods, core data elements, and system capacity for patient-based health data sets 
        - Encourage basic research on capturing home care data for all stakeholders

Barrier: Emphasis on workload rather than care received by patient 
        - Focus on outcomes (maintaining/improving functional status of the patient), not workload
        - encourage “just in time” rather than “just in case” visits, collect interim data remotely 

Some Summary Remarks

Technology must be tailored to providers’ needs

Fit into workflow in real time

Minimize request for additional information

Meet need for speed

Elicit feedback and respond

Tracking mechanisms needed to support research