Overview of Health IT

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Overview of Health IT

  1. 1. Overview of Health IT Nawanan Theera-Ampornpunt, M.D., Ph.D. Faculty of Medicine Ramathibodi Hospital June 26, 2014 SlideShare.net/Nawanan
  2. 2. 2 Outline • “Information” in Healthcare • Health IT & eHealth • Some Health IT Applications • A Dream for Healthcare • Q&A
  3. 3. 3 Let’s take a look at these pictures...
  4. 4. 4Image Source: Guardian.co.uk Manufacturing
  5. 5. 5Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3 Banking
  6. 6. 6ER - Image Source: nj.com Healthcare (on TV)
  7. 7. 7 Healthcare (At an undisclosed nearby hospital)
  8. 8. 8 • Life-or-Death • Difficult to automate human decisions – Nature of business – Many & varied stakeholders – Evolving standards of care • Fragmented, poorly-coordinated systems • Large, ever-growing & changing body of knowledge • High volume, low resources, little time Why Healthcare Isn’t Like Any Others
  9. 9. 9 Back to something simple...
  10. 10. 10 What Clinicians Want? To treat & to care for their patients to their best abilities, given limited time & resources Image Source: http://en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)
  11. 11. 11 High Quality Care • Safe • Timely • Effective • Patient-Centered • Efficient • Equitable Institute of Medicine, Committee on Quality of Health Care in America. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academy Press; 2001. 337 p.
  12. 12. 12 Information is Everywhere in Healthcare Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.
  13. 13. 13 “Information” in Medicine Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.
  14. 14. 14 Outline “Information” in Healthcare • Health IT & eHealth • Some Health IT Applications • A Dream for Healthcare • Q&A
  15. 15. 15 (IOM, 2001)(IOM, 2000) (IOM, 2011) Landmark IOM Reports
  16. 16. 16 IOM Reports Summary • Humans are not perfect and are bound to make errors • Highlight problems in U.S. health care system that systematically contributes to medical errors and poor quality • Recommends reform • Health IT plays a role in improving patient safety
  17. 17. 17 Image Source: (Left) http://docwhisperer.wordpress.com/2007/05/31/sleepy-heads/ (Right) http://graphics8.nytimes.com/images/2008/12/05/health/chen_600.jpg To Err is Human 1: Attention
  18. 18. 18Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University To Err is Human 2: Memory
  19. 19. 19 To Err is Human 3: Cognition • Cognitive Errors - Example: Decoy Pricing The Economist Purchase Options • Economist.com subscription $59 • Print subscription $125 • Print & web subscription $125 Ariely (2008) 16 0 84 The Economist Purchase Options • Economist.com subscription $59 • Print & web subscription $125 68 32 # of People # of People
  20. 20. 20 • It already happens.... (Mamede et al., 2010; Croskerry, 2003; Klein, 2005; Croskerry, 2013) What If This Happens in Healthcare?
  21. 21. 21 Cognitive Biases in Healthcare Mamede S, van Gog T, van den Berge K, Rikers RM, van Saase JL, van Guldener C, Schmidt HG. Effect of availability bias and reflective reasoning on diagnostic accuracy among internal medicine residents. JAMA. 2010 Sep 15;304(11):1198-203.
  22. 22. 22 Cognitive Biases in Healthcare Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med. 2003 Aug;78(8):775-80.
  23. 23. 23 Cognitive Biases in Healthcare Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr 2;330(7494):781-3. “Everyone makes mistakes. But our reliance on cognitive processes prone to bias makes treatment errors more likely than we think”
  24. 24. 24 • Medication Errors – Drug Allergies – Drug Interactions • Ineffective or inappropriate treatment • Redundant orders • Failure to follow clinical practice guidelines Common Errors
  25. 25. 25 Use of information and communications technology (ICT) in health & healthcare settings Source: The Health Resources and Services Administration, Department of Health and Human Service, USA Slide adapted from: Boonchai Kijsanayotin Health IT
  26. 26. 26 Health Information Technology Goal Value-Add Tools Health IT: What’s in a Word?
  27. 27. 27 Hospital Information System (HIS) Computerized Provider Order Entry (CPOE) Electronic Health Records (EHRs) Picture Archiving and Communication System (PACS) Various Forms of Health IT Screenshot Images from Faculty of Medicine Ramathibodi Hospital, Mahidol University
  28. 28. 28 mHealth Biosurveillance Telemedicine & Telehealth Images from Apple Inc., Geekzone.co.nz, Google, HealthVault.com and American Telecare, Inc. Personal Health Records (PHRs) and Patient Portals Still Many Other Forms of Health IT
  29. 29. 29 • Guideline adherence • Better documentation • Practitioner decision making or process of care • Medication safety • Patient surveillance & monitoring • Patient education/reminder Values of Health IT
  30. 30. 30 • Master Patient Index (MPI) • Admit-Discharge-Transfer (ADT) • Electronic Health Records (EHRs) • Computerized Physician Order Entry (CPOE) • Clinical Decision Support Systems (CDS) • Picture Archiving and Communication System (PACS) • Nursing applications • Enterprise Resource Planning (ERP) Enterprise-wide Hospital IT
  31. 31. 31 • Pharmacy applications • Laboratory Information System (LIS) • Radiology Information System (RIS) • Specialized applications (ER, OR, LR, Anesthesia, Critical Care, Dietary Services, Blood Bank) • Incident management & reporting system Departmental IT in Hospitals
  32. 32. 32 Computerized Provider Order Entry (CPOE)
  33. 33. 33 Values • No handwriting!!! • Structured data entry: Completeness, clarity, fewer mistakes (?) • No transcription errors! • Streamlines workflow, increases efficiency Computerized Provider Order Entry (CPOE)
  34. 34. 34 • The real place where most of the values of health IT can be achieved – Expert systems • Based on artificial intelligence, machine learning, rules, or statistics • Examples: differential diagnoses, treatment options(Shortliffe, 1976) Clinical Decision Support Systems (CDS)
  35. 35. 35 – Alerts & reminders • Based on specified logical conditions • Examples: – Drug-allergy checks – Drug-drug interaction checks – Reminders for preventive services – Clinical practice guideline integration Clinical Decision Support Systems (CDS)
  36. 36. 36 Example of “Reminders”
  37. 37. 37 • Reference information or evidence- based knowledge sources – Drug reference databases – Textbooks & journals – Online literature (e.g. PubMed) – Tools that help users easily access references (e.g. Infobuttons) More CDS Examples
  38. 38. 38Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html Infobuttons
  39. 39. 39 • Pre-defined documents – Order sets, personalized “favorites” – Templates for clinical notes – Checklists – Forms • Can be either computer-based or paper-based Other CDS Examples
  40. 40. 40Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm Order Sets
  41. 41. 41 • Simple UI designed to help clinical decision making – Abnormal lab highlights – Graphs/visualizations for lab results – Filters & sorting functions Other CDS Examples
  42. 42. 42Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html Abnormal Lab Highlights
  43. 43. 43 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
  44. 44. 44 Abnormal lab highlights Clinical Decision Making External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN
  45. 45. 45 Clinical Decision Making External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Drug-Allergy Checks
  46. 46. 46 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making Drug-Drug Interaction Checks
  47. 47. 47 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making Clinical Practice Guideline Reminders
  48. 48. 48 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making Diagnostic/Treatment Expert Systems
  49. 49. 49 • CDSS as a replacement or supplement of clinicians? – The demise of the “Greek Oracle” model (Miller & Masarie, 1990) The “Greek Oracle” Model The “Fundamental Theorem” Model Friedman (2009) Wrong Assumption Correct Assumption Proper Roles of CDS
  50. 50. 50 Some risks • Alert fatigue Unintended Consequences of Health IT
  51. 51. 51 Workarounds
  52. 52. 52 Outline “Information” in Healthcare Health IT & eHealth Some Health IT Applications • A Dream for Healthcare • Q&A
  53. 53. 53 Hospital A Hospital B Clinic C Government Lab Patient at Home Health Information Exchange (HIE)
  54. 54. 54 More Resources • American Medical Informatics Association (AMIA) www.amia.org • International Medical Informatics Association (IMIA) www.imia.org • Thai Medical Informatics Association (TMI) www.tmi.or.th • Asia eHealth Information Network (AeHIN) www.aehin.org • ThaiHealthIT Google Groups Mailing List http://groups.google.com/group/ThaiHealthIT • Thai Health Informatics Academy
  55. 55. 55 Outline “Information” in Healthcare Health IT & eHealth Some Health IT Applications A Dream for Healthcare • Q&A

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