Health Informatics: The Next Stethoscope in Healthcare


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Theera-Ampornpunt N. Health informatics: the next “stethoscope” in healthcare. Presented at: Intelligent logistics for innovation hospitals; 2010 Dec 23; Faculty of Engineering, Mahidol University, Thailand. Invited speaker, in Thai.

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Health Informatics: The Next Stethoscope in Healthcare

  1. 1. Health f H lth IInformatics: ti The Next “Stethoscope” in Healthcare p Nawanan Theera-Ampornpunt, MD, MS
  2. 2. Healthcare & Health H lth IT
  3. 3. Manufacturing g Source:
  4. 4. Banking g Source:
  5. 5. Healthcare Source:
  6. 6. Why Healthcare Isn’t Like Any Others?• Life-or-Death• Many & varied stakeholders• Strong professional values• Evolving standards of care• Fragmented, poorly coordinated Fragmented poorly-coordinated systems• Large, ever-growing & changing body of knowledge• High volume, low resources, little time Source:
  7. 7. Why Healthcare Isn’t Like Any Others?• Large variations & contextual dependence g p Input Process Output Patient  Patient Decision‐ Decision Biological  Biological Presentation Making Responses Source:
  8. 8. But...Are We That Different? Banking Input Process Output Transfer Location A Location A Location B Location B Value‐Add ‐ Security ‐CConvenience i ‐ Customer Service
  9. 9. But...Are We That Different? Manufacturing Input Process Output Raw  Raw Assembling Finished  Finished Materials Goods Value‐Add ‐ Innovation ‐ Skills ‐ QA
  10. 10. But...Are We That Different? Healthcare Input Process Output Sick Patient Patient Care Well Patient Value‐Add ‐ Medical technology & medications g ‐ Clinical knowledge & skills ‐ Quality of care; process improvement ‐ Information
  11. 11. Information is Everywhere y
  12. 12. Various Forms of Health ITHospital Information System (HIS) Computerized Provider Order Entry (CPOE) Electronic Health Records Picture Archiving and g (EHRs) Communication System (PACS)
  13. 13. Still Many Other Forms of Health IT Health Information Exchange ( g (HIE)) m-Health m Health BiosurveillancePersonal Health Records (PHRs) Telemedicine &Information Retrieval Telehealth Images from Apple Inc.,, Google,, and American Telecare, Inc.
  14. 14. Why Adopting Health IT? “Go paperless” Go paperless “Computerize” Computerize “Get a HIS” “Digital Hospital” Digital Hospital“Have EMR ”“H EMRs” “Modernize” “Share data” Share data
  15. 15. Some Quotes• “Don’t implement technology just for technology s sake.” Don t technology’s sake• “Don’t make use of excellent technology. Make excellent use of technology.” (Tangwongsan, Supachai. Personal communication, 2005.)• “Health care IT is not a panacea for all that ails medicine.” (Hersh, 2004)• “We worry, however, that [electronic records] are being touted as a panacea for nearly all the ills of modern medicine.” (Hartzband & Groopman, 2008)
  16. 16. Health IT: What’s In A Word?Health GoalInformation  f Value-AddTechnology Tools
  17. 17. Dimensions of Quality Healthcare• Safety• Timeliness• Effectiveness• Efficiency• Equity E it• Patient-centeredness at e t ce te ed ess (IOM, 2001)
  18. 18. Value of Health IT• Guideline G ideline adherence• Better documentation• Practitioner decision making or process of care• Medication safety• Patient surveillance & monitoring• Patient ed cation/reminder education/reminder
  19. 19. Fundamental Theorem of Informatics (Friedman, 2009)
  20. 20. Is There A Role for Health IT? (IOM, 2000)
  21. 21. Landmark IOM Reports (IOM, 2000) (IOM, 2001)
  22. 22. Landmark IOM Reports: Summary• Humans are not perfect and are bound to make errors p• High-light problems in the U.S. health care system that systematically contributes to medical errors and poor quality• Recommends reform that would change how health care works and how technology innovations can help improve quality/safety
  23. 23. Why We Need Health IT• Health care is very complex (and inefficient)• Health care is information-rich• Quality of care depends on timely availability & quality of information• Clinical knowledge body is too large• Short time during a visit• Practice guidelines are put “on-the-shelf”• “To err is human”
  24. 24. To Err Is Human• Perception errors Source:
  25. 25. To Err Is Human• Lack of Attention Source:
  26. 26. To Err Is Human• Decoy Pricing # of The Economist Purchase Options People• subscription $59 16• Print subscription $125 0• Print & web subscription $125 84 # of The Economist Purchase Options People• Economist com subscription $59 68• Print & web subscription $125 32 (Ariely, 2008)
  27. 27. What If This Happens in Healthcare?• It already h l d happens.... (Mamede et al., 2010; Croskerry, 2003; Klein, 2005)• What if health IT can help?
  28. 28. U.S.’s Efforts on Health IT Adoption ? “...We will make wider use of electronic records and We other health information technology, to help control costs and reduce dangerous medical errors.” President George W. Bush Sixth State of the Union Address, January 31, 2006Source: Image Source:
  29. 29. Public Policy in Informatics: A US’s Case1991: IOM s CPR Report published1991: IOM’s CPR Report published 1996: HIPAA enacted 2000‐2001: IOM’s To Err Is Human &  Crossing the Quality Chasm published 2004: George W. Bush’s Executive Order  establishing ONCHIT (ONC) 2009‐2010: ARRA/HITECH Act &  “Meaningful use” regulations Meaningful use regulations
  30. 30. U.S. Adoption of Health IT Ambulatory (Hsiao et al, 2009) Hospitals (Jha et al, 2009) Basic EHRs w/ notes 7.6% Comprehensive EHRs p 1.5% CPOE 17% • U.S. lags behind other Western countries (Schoen et al, 2006;Jha et al, 2008) • Money and misalignment of benefits is the biggest reason
  31. 31. We Need “Change” “...we need to upgrade our medical records by switching from a p p to y g paper an electronic system of record keeping...” President Barack Ob P id t B k Obama June 15, 2009
  32. 32. The Birth of “Meaningful Use” “...Our recovery plan will invest in  yp electronic health records and new technology  that will reduce errors, bring down costs,  ensure privacy, and save lives. ensure privacy and save lives ” President Barack Obama Address to Joint Session of Congress Address to Joint Session of Congress February 24, 2009Source:
  33. 33. American Recovery & Reinvestment Act • Contains HITECH Act ( (Health Information Technology for Economic and gy Clinical Health Act) • ~ 20 billion dollars for Health IT investments • Incentives & penalties for providers
  34. 34. National Leadership Office f th N ti Offi of the Nationall Coordinator for Health Information C di t f H lth I f ti Technology (ONC -- formerly ONCHIT) David Blumenthal, MD, MPP National Coordinator for Health Information Technology (2009 - Present) Photo courtesy of U.S. Department of Health & Human Services
  35. 35. What is in the HITECH Act? (Blumenthal, 2010)
  36. 36. “Meaningful Use” gPumpkin “Meaningful Use” of a Pumpkin Image Source & Idea Courtesy of Pat Wise at HIMSS, Oct. 2009
  37. 37. “Meaningful Use” of Health IT g Stage 1 Stage 1 ‐ Electronic capture of  Better health information ‐ Information sharing Stage 3 St 3 Health ‐ Data reporting Stage 2 Use of  EHRs to to  Use of EHRs improve  to improve  outcomes processes of  care (Blumenthal, 2010)
  38. 38. Health ITApplicationsA li ti
  39. 39. Enterprise-wide Hospital IT• Master Patient Index (MPI)• Admit-Discharge-Transfer (ADT)• Electronic Health Records (EHRs)• C Computerized Ph i i O d E t (CPOE) t i d Physician Order Entry• Clinical Decision Support Systems (CDSSs) pp y• Picture Archiving and Communication System (PACS)• Nursing applications• Enterprise Resource Planning (ERP)) l (
  40. 40. Departmental IT• Pharmacy applications y pp• Laboratory Information System (LIS)• Specialized applications (ER, OR, LR, Anesthesia, Critical Care, Dietary Services, Blood Bank)• Incident management & reporting system
  41. 41. EHRs & HISThe Challenge ‐ Knowing What It Means Electronic Health  Records (EHRs) Hospital Information  Hospital Information System (HIS) Electronic Medical  Records (EMRs) Records (EMRs) Electronic Patient  Records (EPRs) Clinical Information  System (CIS) Personal Health  Computer‐Based  C B d Records (PHRs) Patient Records  (CPRs)
  42. 42. EHR SystemsJust electronic documentation? History  Diag‐ Treat‐ ... & PE nosis mentsOr d th have th l ?O do they h other values?
  43. 43. Functions that Should Be Part of EHR Systems• Computerized Medication Order Entry• Computerized Laboratory Order Entry• Computerized Laboratory Results p y• Physician Notes• Patient Demographics P ti t D hi• Problem Lists• Medication Lists• Discharge Summaries• Diagnostic Test Results• Radiologic Reports (IOM, 2003; Blumenthal et al, 2006)
  44. 44. Computerized Physician Order EntryValues• No handwriting!!!• Structured data entry: Completeness clarity Completeness, clarity, fewer mistakes (?)• No transcription errors!• E point for CDSS Entry i f CDSSs• Streamlines workflow, increases efficiency
  45. 45. Clinical Decision Support Systems (CDSSs)• 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 – Alerts & reminders • Based on specified logical conditions • Examples: drug-allergy checks, drug-drug interaction checks, reminders for preventive services or certain actions (e.g. smoking cessation), clinical practice guideline integration – Evidence-based knowledge sources e.g. drug database, literature – Simple UI designed to help clinical decision making
  46. 46. Clinical Decision Support Systems (CDSSs) PATIENT Perception CLINICIAN Attention Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION From a teaching slide by Don Connelly, 2006
  47. 47. Clinical Decision Support Systems (CDSSs) PATIENT Perception CLINICIAN Abnormal lab Attention highlights Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION
  48. 48. Clinical Decision Support Systems (CDSSs) PATIENT Perception CLINICIAN Drug-Allergy Attention Checks Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION
  49. 49. Clinical Decision Support Systems (CDSSs) PATIENT Drug-Drug Perception Interaction CLINICIAN Checks Attention Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION
  50. 50. Clinical Decision Support Systems (CDSSs) PATIENT Perception Clinical CLINICIAN Practice Guideline Attention Reminders Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION
  51. 51. Clinical Decision Support Systems (CDSSs) PATIENT Perception CLINICIAN Attention Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference Diagnostic/Treatment Expert Systems DECISION
  52. 52. Clinical Decision Support Systems (CDSSs)• CDSS as a supplement or replacement of clinicians? – The demise of the “Greek Oracle” model (Miller & Masarie, 1990) The “Greek Oracle” Model The “Fundamental Theorem” (Friedman, 2009)
  53. 53. Clinical Decision Support Systems (CDSSs)Some risks• Alert fatigue
  54. 54. Workarounds
  55. 55. Health IT for Medication Safety Ordering g Transcription p Dispensing p g Administration Automatic Electronic CPOE C O Medication Medication Dispensing Administration Records (e-MAR) Barcoded Medication Barcoded Dispensing Di i Medication Administration
  56. 56. Health Information Exchange (HIE) Government Hospital A Hospital B Clinic C Lab L b Patient t H P ti t at Home
  57. 57. 4 Quadrants of Health IT Strategic Business Intelligence g HIE CDSS CPOEAdministrative Clinical VMI EHRs ERP LIS ADT Operational (Theera-Ampornpunt [unpublished], 2010)
  58. 58. Health Informatics As A Field
  59. 59. Biomedical/Health Informatics • “[T]he field that is concerned with the optimal use of information, often aided by the use of technology, to improve individual health, health care, public health, health care health and biomedical research” (Hersh, 2009) • “[T]he application of the science of information as data l d t plus meaning t problems of bi di l i to bl f biomedical interest” (Bernstam et al, 2010)
  60. 60. Data-Information-Knowledge-Wisdom Pyramid Wisdom Knowledge Information Data
  61. 61. Task-Oriented View Collection Processing Utilization Communication/ Storage Dissemination/ Presentation
  62. 62. M/B/H Informatics As A Field (Shortliffe, 2002)
  63. 63. M/B/H Informatics and Other Fields Social Sciences  (Psychology,  (Psychology Statistics &  Statistics & Sociology,  Research  Linguistics, Law  Methods Cognitive &  & Ethics) Medical  Decision  Decision Sciences &  Sciences & Science Public Health Engineering Management Computer &  Computer & Biomedical/ Library Science,  Library Science, Information  Health  Information  Science Informatics Retrieval, KM And More!
  64. 64. Balanced Focus of Informatics People Techno‐ Process logy
  65. 65. Informatics & EngineeringProcess-focusProcess focus• Industrial Engineering / Operations Research g g p & Management / Business Process ReengineeringTechnology-focus• Computer & Software Engineering• Biomedical Engineering• Electrical Engineering
  66. 66. Summary• Healthcare will benefit from health IT through – Information deliveryy – Process improvement• The world is moving toward health IT• H lth iinformatics needs expertise f engineering & Health f ti d ti from i i other fields• Health informatics will be crucial to future’s healthcare
  67. 67. Let s Let’s Build TheNext Generation’s Healthcare! H lth !
  68. 68. References• Bernstam EV, Smith JW, Johnson TR. What is biomedical informatics? J Biomed Inform. 2010  Feb;43(1):104‐10.• Blumenthal D. Launching HITECH. N Engl J Med. 2010 Feb 4;362(5):382‐5.• Blumenthal D, DesRoches C, Donelan K, Ferris T, Jha A, Kaushal R, Rao S, Rosenbaum S.  Health information technology in the United States: the information base for progress  [Internet]. Princeton (NJ): Robert Wood Johnson Foundation; 2006• Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them.  Acad Med. 2003 Aug;78(8):775‐80. 81 p. Available from:  A d M d 2003 A 78(8) 775 80 81 A il bl f• Friedman CP. A "fundamental theorem" of biomedical informatics. J Am Med Inform Assoc.  2009 Apr;16(2):169 70. 2009 Apr;16(2):169‐70• Hersh W. A stimulus to define informatics and health information technology. BMC Med  Inform Decis Mak. 2009;9:24.• Hsiao C, Beatty PC, Hing ES, Woodwell DA. Electronic medical record/electronic health record  Hsiao C, Beatty PC, Hing ES, Woodwell DA. Electronic medical record/electronic health record use by office‐based physicians: United States, 2008 and preliminary 2009 [Internet]. 2009  [cited 2010 Apr 12]; Available from: emr_ehr.pdf
  69. 69. References• Institute of Medicine, Board on Health Care Services, Committee on Data Standards for  Patient Safety. Key Capabilities of an electronic health record system: letter report [Internet].  f bl f l h lh d l [ ] Washington, DC: National Academy of Sciences; 2003.  31 p. Available from:• Jha AK DesRoches CM Campbell EG Donelan K Rao SR Ferris TG Shields A Rosenbaum S AK, DesRoches CM, Campbell EG, Donelan K, Rao SR, Ferris TG, Shields A, Rosenbaum S,  Blumenthal D. Use of electronic health records in U.S. hospitals. N Engl J Med.  2009;360(16):1628‐38.• Jha AK, Doolan D, Grandt D, Scott T, Bates DW. The use of health information technology in  , , , , gy seven nations. Int J Med Inform. 2008;77(12):848‐54.• Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr  2;330(7494):781‐3.• 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.• Miller RA, Masarie FE Th d i Mill RA M i FE. The demise of the "Greek Oracle" model for medical diagnostic  f h "G k O l " d lf di l di i systems. Methods Inf Med. 1990 Jan;29(1):1‐2. • Schoen C, Osborn R, Huynh PT, Doty M, Puegh J, Zapert K. On the front lines of care: primary  care doctors office systems experiences and views in seven countries Health Aff care doctors’ office systems, experiences, and views in seven countries. Health Aff (Millwood). 2006;25(6):w555‐71.• Shortliffe EH. JBI status report. Journal of Biomedical Informatics. 2002 Oct;35(5‐6):279‐80.