Transcript Medical Systems - University of Connecticut

TM
HIEx : Health Link Information Exchange
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Review the elements of, and differences between
health information technology and health information
exchange
Relate the importance of HIE to primary care
physicians for both practice management and clinical
information
Develop an understanding of the functionalities in the
HIExTM system, and how this provides a flexible
infrastructure for a cross-disciplinary Regional Health
Information Organization (RHIO)
Excerpted from From Presentation by:
David R. Little, Katherine L. Cauley, and Mary M.
Crimmins – Wright State Univ. Medical School
See:
http://pciwg.amia.org/pmwiki/PapersAndPresentations/HomePage
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Objectives of Effort
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Personal health information
 Continuity of care
 Coordination of care
Family and community
information
Public Health,
Epidemiology
Consultants
Demographic
& Family
Data
Service
Agencies
Primary Care
Physician
Record
Ancillary
Providers
Hospitals
Schools
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Overall Architecture and Technologies
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Scalable multi-tier application architecture
Microsoft SQL database
Supports source and time stamps and log tables to
assure audit functions.
Fully customizable role based access for each data
element.
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Current components of HIEx™
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Demographic and individual health status information
Contacts module for emergency contacts, caseworkers,
PC physicians, guarantors, etc.
Electronic Medicaid and PRC applications
Referrals module with workflow history
Scanned documents
Reporting on individual productivity
Full audit trail for all transactions
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Welcome Screen for HIEx
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© Wright State University, Boonshoft School of Medicine
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Tracking Patients
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© Wright State University, Boonshoft School of Medicine
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Tracking Household
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© Wright State University, Boonshoft School of Medicine
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Detailed Data on Household Members
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© Wright State University, Boonshoft School of Medicine
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More Details on Household
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© Wright State University, Boonshoft School of Medicine
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© Wright State University, Boonshoft School of Medicine
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Referrals module Provides Tracking
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Service utilization patterns are recorded
 Source of referrals
 For example one uninsured family presents at two
hospitals
 The first referral for Medicaid would be recorded from
hospital A and the second from hospital B.
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Community Health Advocates track the progress of
each referral.
 The system displays the history of the progress.
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Tracking Referrals for a Patient
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© Wright State University, Boonshoft School of Medicine
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Scanned documents module adds flexibility
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Designed to capture documentation from paper
Examples include:
 Immunization records
 Birth certificates
 Driver’s license or other identity documents
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Tracking Scanned Documents
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© Wright State University, Boonshoft School of Medicine
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Massachusetts eHealth Collaborative
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Presentation by David W. Bates, MD, MSc, 2005
http://pciwg.amia.org/presentations/MaEHCShortAMIA_files/frame.html
Three-Fold Objective:
 Tools for Health care
 Incorporation into Clinical Practice
 Sustained Usage over Time
Pilot in Different Communities
Collect Experiences
Look at Larger Scale Roll out
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eHealth Collaborative Vision
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Three Areas of Activity for Pilots
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EHRs and Selection Process
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Physician EHR Selections
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Patient Interactions – Opting In Process
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Patient Interactions – Opting In Process
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Patient Interactions – Opting Out Process
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Comments Options
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Knowledge Management and Clinical
Decision Support
Thomas Agresta MD
Associate Professor and Director of Medical Informatics
Department of Family Medicine
University of Connecticut School of Medicine
July 12, 2007
Physicians’ Track
© content developed by Society of Teachers of Family Medicine
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Current Definition of CDS
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Providing clinicians, patients or individuals with
knowledge and person-specific or population
information, intelligently filtered or presented at
appropriate times to foster better health processes,
better individual patient care, and better population
health.
From:
A Roadmap for National Action on Clinical Decision
Support
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© content developed by Society of Teachers of Family Medicine
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Computerized Clinical Decision Support?
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Need machine interpretable data (Standards Help)
 Lab values in standardized formats - K+ (LOINC)
 Patients with specific conditions – Afib (ICDM 9,
SnoMed CT)
Need to monitor for condition (Event Monitor)
 Order for a medication – Digoxin (RxNorm)
 Event Monitor watches the EMR for a specific
event that “triggers” specific program
 Can be internal to forms, or “watching” as a separate
program
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Need “Rules” to guide response
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© content developed by Society of Teachers of Family Medicine
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Example of Architecture
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© content developed by Society of Teachers of Family Medicine
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History of CDS
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1970’s – Artificial Intelligence
 AAP Help – Leeds University – diagnosis
abdominal pain – Bayesian Model
 Internist 1 – Pittsburgh – Decision Tree diagnosis
aid for complex cases. Relied on Master clinicians
 MYCIN – Rules based antimicrobial diagnosis and
treatment aid. (If then rules)
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© content developed by Society of Teachers of Family Medicine
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History of CDS Cont..
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1980’s – Some Commercialization
 DxPlain - Uses clinical findings and produces a
ranked list of possible clinical diagnosis.
 Knowledge base includes 5,000 symptoms and 2,200
diseases.
 Still available today - Web based
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QMR – Quick Medical Reference
 Diagnostic Support System – expert consultant
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Turns out Physicians didn’t want / like / need help
with diagnosis most of the time
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© content developed by Society of Teachers of Family Medicine
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Potential Benefits of CDS
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Prevent Errors
 Commission – (drug/allergy interaction)
 Omission – (rapidly respond to critical labs)
Optimize Decision Making
 Optimize choices available (drug formulary)
 Improve compliance with guideline
 Improve compliance complex protocols (Cancer)
 Optimize treatment chronic conditions over time
(HbA1c - diabetes, steroids - asthma)
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© content developed by Society of Teachers of Family Medicine
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Potential Benefits of CDS
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Improve Care Processes
 Documentation of care (allergies, smoking status,
faster more complete diabetes documentation)
 Patient education and empowerment
(communication, patient understanding and self
management)
 Communication among providers (shared, timely
data available to consultant / covering physician)
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© content developed by Society of Teachers of Family Medicine
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Rationale For The Use of CDS
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Mixed overall results – improving with time
CDS effective with other interventions
 Diabetes - care processes & outcomes (Shojania)
Review 100 studies showed 64% improved clinical
outcomes (Garg)
 Improved Screening & Immunizations – ~80%
studies
 Most improved prescribing
 Some decreased hospital length of stay and cost
HIT effects on Quality most with adherence guideline
care, surveillance and monitoring and decreased
medication errors. (Chaudry)
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© content developed by Society of Teachers of Family Medicine
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Diabetes Care – Intelligent Forms
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John Janas M.D.
Forms from Clinical
Content
Consultants
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© content developed by Society of Teachers of Family Medicine
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Alerts and Reminders
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Point of Care
 Drug / drug interactions
 Drug / allergy alerts
 Prompt for disease specific medications
 Preventive services due
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© content developed by Society of Teachers of Family Medicine
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References
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Bates DW et al. Ten Commandments for Effective Clinical Decision Support: Making the
Practice of Evidence Based Medicine a Reality. J Am Med Inform Assoc. 10:523-530, 2003.
Chaudry B, et al. Systematic review: Impact of Heath Information Technology on Quality,
Efficiency and Cost of Medical Care. Ann of Int Med. 144(10): 742-752, 2006
Classen DC. Clinical Decision Support Systems to Improve Clinical Practice and Quality of
Care. JAMA. 280(15)1360-1361, 1998.
Garg AX et al. Effects of Computerized Clinical Decision Support Systems on Physician
Performance and Patient Outcomes. JAMA 293(10)1223-1238, 2005.
Hunt DL et al. Effects of Computer-Based Clinical Decision Support Systems on Physician
Performance and Patient Outcomes. JAMA 290(15)1339-1346, 1998.
Hunt DL et al. Patient-specific evidence-based care recommendations for diabetes mellitus:
development and initial clinic experience with a computerized decision support system. Int J
Med Inform. 51(2-3):127-135, 1998.
Judge J et al. Prescribers' responses to alerts during medication ordering in the long term care
setting. J Am Med Inform Assoc. 13(4):385-90, 2006.
Nagykaldi Z, Mold J. J Am Board of Family Medicine 2007; 20: 188-195
Mcglynn E, Asch S, et al. The Quality of Health Care Delivered to Adults in the United States.
NEJM. 348(26):2635-45. 2003.
Miller RA et al. Clinical Decision Support and Electronic Prescribing Systems: A Time for
Responsible Thought and Action. J Am Med Inform Assoc. 12:403-409, 2005.
Osheroff J, et al. A Roadmap for National Action on Clinical Decision Support
Accessed at http://www.amia.org/inside/initiatives/cds/ on November 26,2006
Osheroff J, et al. Improving Outcomes with Clinical Decision Support: An Implementer’s
Guide. Healthcare Information Management Systems Society. Chicago 2005
Sequist TD et al. A Randomized Trial of Electronic Clinical Reminders to Improve Quality of
Care for Diabetes and Coronary Artery Disease. J Am Med Inform Assoc. 12:431-437, 2005.
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© content developed by Society of Teachers of Family Medicine
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