TM HIEx : Health Link Information Exchange CSE 5810 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 SWEA1 Objectives of Effort CSE 5810 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 SWEA2 Overall Architecture and Technologies CSE 5810 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. SWEA3 Current components of HIEx™ CSE 5810 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 SWEA4 Welcome Screen for HIEx CSE 5810 © Wright State University, Boonshoft School of Medicine SWEA5 Tracking Patients CSE 5810 © Wright State University, Boonshoft School of Medicine SWEA6 Tracking Household CSE 5810 © Wright State University, Boonshoft School of Medicine SWEA7 Detailed Data on Household Members CSE 5810 © Wright State University, Boonshoft School of Medicine SWEA8 More Details on Household CSE 5810 © Wright State University, Boonshoft School of Medicine SWEA9 CSE 5810 © Wright State University, Boonshoft School of Medicine SWEA10 Referrals module Provides Tracking CSE 5810 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. Community Health Advocates track the progress of each referral. The system displays the history of the progress. SWEA11 Tracking Referrals for a Patient CSE 5810 © Wright State University, Boonshoft School of Medicine SWEA12 Scanned documents module adds flexibility CSE 5810 Designed to capture documentation from paper Examples include: Immunization records Birth certificates Driver’s license or other identity documents SWEA13 Tracking Scanned Documents CSE 5810 © Wright State University, Boonshoft School of Medicine SWEA14 Massachusetts eHealth Collaborative CSE 5810 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 SWEA15 eHealth Collaborative Vision CSE 5810 SWEA16 Three Areas of Activity for Pilots CSE 5810 SWEA17 EHRs and Selection Process CSE 5810 SWEA18 Physician EHR Selections CSE 5810 SWEA19 Patient Interactions – Opting In Process CSE 5810 SWEA20 Patient Interactions – Opting In Process CSE 5810 SWEA21 Patient Interactions – Opting Out Process CSE 5810 SWEA22 Comments Options CSE 5810 SWEA23 CSE 5810 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 SWEA24 Current Definition of CDS CSE 5810 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 Physicians’ Track © content developed by Society of Teachers of Family Medicine SWEA25 Computerized Clinical Decision Support? CSE 5810 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 Need “Rules” to guide response Physicians’ Track © content developed by Society of Teachers of Family Medicine SWEA26 Example of Architecture CSE 5810 Physicians’ Track © content developed by Society of Teachers of Family Medicine SWEA27 History of CDS CSE 5810 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) Physicians’ Track © content developed by Society of Teachers of Family Medicine SWEA28 History of CDS Cont.. CSE 5810 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 QMR – Quick Medical Reference Diagnostic Support System – expert consultant Turns out Physicians didn’t want / like / need help with diagnosis most of the time Physicians’ Track © content developed by Society of Teachers of Family Medicine SWEA29 Potential Benefits of CDS CSE 5810 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) Physicians’ Track © content developed by Society of Teachers of Family Medicine SWEA30 Potential Benefits of CDS CSE 5810 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) Physicians’ Track © content developed by Society of Teachers of Family Medicine SWEA31 Rationale For The Use of CDS CSE 5810 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) Physicians’ Track © content developed by Society of Teachers of Family Medicine SWEA32 Diabetes Care – Intelligent Forms CSE 5810 John Janas M.D. Forms from Clinical Content Consultants Physicians’ Track © content developed by Society of Teachers of Family Medicine SWEA33 Alerts and Reminders CSE 5810 Point of Care Drug / drug interactions Drug / allergy alerts Prompt for disease specific medications Preventive services due Physicians’ Track © content developed by Society of Teachers of Family Medicine SWEA34 References CSE 5810 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. Physicians’ Track © content developed by Society of Teachers of Family Medicine SWEA35