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Electronic Health Records: What’s in the pot of gold at the end of the rainbow ?

Electronic Health Records: What’s in the pot of gold at the end of the rainbow ?. Neil S. Calman, MD. The Goals…. Improve Quality, Safety, Efficiency and Reduce Health Disparities Increase Engagement of Patients and Families Improve Care Coordination Improve Population and Public Health

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Electronic Health Records: What’s in the pot of gold at the end of the rainbow ?

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  1. Electronic Health Records:What’s in the pot of gold at the end of the rainbow ? Neil S. Calman, MD

  2. The Goals…. Improve Quality, Safety, Efficiency and Reduce Health Disparities Increase Engagement of Patients and Families Improve Care Coordination Improve Population and Public Health Ensure Privacy and Security Protections of Personal Health Information

  3. Progression of HIT at the Institute ! ! ! Clinical Decision Support 2003 Quality Reporting/ Improvement 2004 EHR/PMS Implementation 2002 HIE/ Patient Portal 2008 EHR = Electronic Health Record PMS = Practice Management System HIE = Health Information Exchange

  4. 1 Supporting population based care

  5. Major Congenital Malformations after First-Trimester Exposure to ACE Inhibitors

  6. Colon Cancer Risk Scoring

  7. 2 Linking to the Nation’s public health surveillance system

  8. t Release The Benefit of Early Detection of Syndromes Symptom Onset Severe Illness Number of Cases 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Days

  9. Patient Address Race / Age / Gender Medical history Provider Location Reason for visit Problem list Temperature Height/weight Respirations Procedures Medications Lab results Diagnoses Single patient visit yields complex EHR data

  10. EHR Fever Blue = ER “flu/fever” Purple = EHR Fever >100 F Red = Flu “A” isolates Violet = Flu “B” isolates

  11. Fever AND respiratory syndrome Blue = ER “flu/fever” Brown = EHR T≥ 100o and Respiratory Syndrome

  12. Institute patient fevers peaked 13 days before ER visits for Fever and Flu – this indicates that health center data may be the first “signal” of an impending epidemic. Patients of the Institute for Urban Family Health Institute fever data responded to Flu B outbreak-ED data did not

  13. 3 Bringing public health information to the point of care in real time

  14. DOH sends alert to community re Legionella cases in Parkchester community in the Bronx Practice Alert in EHR for this center only if patient presents with cough – matched to Legionella order set to assist provider in Dx and Tx

  15. 4 Developing Clinical Decision Supports over aBroad Range of Measures

  16. Clinical Decision Support – Impact on Vaccine Administration in Adults

  17. 10 Take New York Indicators • Have a Regular Doctor or Other Health Care Provider • Be Tobacco-Free • Keep Your Heart Healthy • Know Your HIV Status • Get Help for Depression • Live Free of Dependence on Alcohol and Drugs • Get Checked for Cancer • Get the ImmunizationsYou Need • Make Your Home Safe and Healthy • Have a Healthy Baby

  18. 5 Developing the EHR to monitor quality of care across practice sites

  19. Patients Seen at Least Once by Their Primary Care Provider

  20. Men >35; Women>45 Who have had their cholesterol tested

  21. Men >35; Women>45 Who have had their cholesterol tested

  22. Depression Screen with PHQ2

  23. Depression Screen with PHQ2

  24. Recorded Smoking History

  25. Recorded Smoking History

  26. Pneumococcal Vaccine >65yrs old

  27. Pneumococcal Vaccine >65yrs old

  28. 6 Using EHR data to determine primary care practices that are associated with improved outcomes

  29. Provider Nutritionist Referral Rate vs. Pts Average HgBA1c 22 1 0.9 9 12

  30. 7 Using EHR data to elucidate and help eliminate racial disparities in health outcomes

  31. Last Hemoglobin A1c by Race

  32. Reductions in HgbA1c with Treatment by Race /Language

  33. Five-year Cancer (all sites) Survival Rate SOURCE: CDC/NCHS, Health, United States, 2004

  34. Life Expectancy at Birth SOURCE: CDC/NCHS, Health, United States, 2004

  35. Infant Mortality Rates SOURCE: CDC/NCHS, Health, United States, 2004

  36. Age-adjusted death rates for diseases of the heart per 100,000 males SOURCE: CDC/NCHS, Health, United States, 2004

  37. Dissemination of Results; Credits

  38. Authored Publications • Calman NS, Kitson K, Hauser D “Using Health Information Technology to Improve Health Quality and Safety in Community Health Centers”. Journal of Progress in Community Health Partnerships: Research Education and Action.1(1):83-88. Spring 2007 • Calman, NS, Golub M, Kitson K, Ruddock C. Electronic Health Records: The Use of Technology to Eliminate Racial Disparities in Health Outcomes. In: Medical Informatics: An Executive Primer. Health Information and Management Systems Society, Chicago, IL. Kenneth Ong, MD, Editor. January 2007.

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