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Healthcare in the Era of Digital Disruption (January 29, 2020)
The document discusses the challenges and potential of healthcare in the digital age, emphasizing the need for clinical judgment despite advancements in technology such as AI and big data. It highlights the complexity of healthcare delivery, the importance of human touch, and the distinction between merely digitizing processes and achieving true digital transformation. The presenter advocates for developing 'smart hospitals' that leverage technology to enhance patient care while focusing on information and process improvement.
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Healthcare in the Era of Digital Disruption (January 29, 2020)
- 1. 1 Healthcare in theEra of Digital Disruption Nawanan Theera-Ampornpunt, M.D., Ph.D. January 29, 2020 www.SlideShare.net/Nawanan
- 2. 2 2003 M.D. (First-ClassHonors) 2011 Ph.D. (Health Informatics), Univ. of Minnesota Deputy Dean for Operations Lecturer, Department of Clinical Epidemiology & Biostatistics Faculty of Medicine Ramathibodi Hospital Mahidol University Interests: Health IT for Quality of Care, Social Media IT Management, Security & Privacy [email protected] SlideShare.net/Nawanan นวนรรน ธีระอัมพรพันธุ์ (Nawanan Theera-Ampornpunt) Line ID: NawananT Introduction
- 3. 3 What words cometo mind when you hear... Digital Health Transformation
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- 5.
- 6. 6englishmoviez.com Rise of theMachines?
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- 8. 8 “Big data islike teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it...” -- Dan Ariely @danariely (2013) Substitute “Big data” with “AI”, “Blockchain”, “IoT” of your choice. -- Nawanan Theera-Ampornpunt (2018)
- 9. 9 Hype vs. Hope JeremyKemp via http://en.wikipedia.org/wiki/Hype_cycle http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
- 10. 10 Gartner Hype Cycle2017 https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/
- 11.
- 12. 12 A Real-Life PersonalStory of My Failure (as a Doctor and as a Son) in Misdiagnosing My Mom Would AI Help?
- 13. 13 • Nothing iscertain in medicine & health care • Large variations exist in patient presentations, clinical course, underlying genetic codes, patient & provider behaviors, biological responses & social contexts Why Clinical Judgment Is Still Necessary?
- 14. 14 • Most diseasesare not diagnosed by diagnostic criteria, but by patterns of clinical presentation and perceived likelihood of different diseases given available information (differential diagnoses) • Human is good at pattern recognition, while machine is good at logic & computations Why Clinical Judgment Is Still Necessary?
- 15. 15 • Machines are(at best) as good as the input data –Not everything can be digitized or digitally acquired –Not everything digitized is accurate (“Garbage In, Garbage Out”) • Experience, context & human touch matters Why Clinical Judgment Is Still Necessary?
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- 17. 17 Let’s take alook at these pictures...
- 18. 18Image Source: https://en.wikipedia.org/wiki/Industrial_robot(KUKA Roboter GmbH) “Smart” Manufacturing
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- 20. 20ER - ImageSource: nj.com Healthcare (On TV)
- 21. 21 (At an undisclosedhospital) Healthcare (Reality)
- 22. 22 • Life-or-Death • Difficultto 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 (Yet) “Smart”?
- 23. 23 But...Are We ThatDifferent? Input Process Output Transfer Banking Value-Add - Security - Convenience - Customer Service Location A Location B
- 24. 24 Input Process Output Assembling Manufacturing RawMaterials Finished Goods Value-Add - Innovation - Design - QC But...Are We That Different?
- 25. 25 But...Are We ThatDifferent? Input Process Output Patient Care Health care Sick Patient Well Patient Value-Add - Technology & medications - Clinical knowledge & skills - Quality of care; process improvement - Information
- 26. 26 • Large variations& contextual dependence Why Health care Isn’t Like Any Others? Input Process Output Patient Presentation Decision- Making Biological Responses
- 27. 27 “To computerize the hospital” “Togo paperless” “To become a Digital Hospital” “To Have EHRs” Why Adopting Health IT?
- 28. 28 • “Don’t implementtechnology just for 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) Some “Smart” Quotes
- 29. 29 Being Smart #1: StopYour “Drooling Reflex”!!
- 30. 30 Being Smart #2: Focuson Information & Process Improvement, Not Technology
- 31. 31 If not “DigitalHospital” or “Paperless Hospital” Then What Should We Aspire to Be?
- 32.
- 33. 33 So How is a“Smart Hospital” Different from a Digital or Paperless Hospital?
- 34. 34 Smart Healthcare ForPolicymakers? Image Source: http://healthdata.moph.go.th/kpi/2557/ProvinceKpiTopicListAll.php?provincecode=99
- 35. 35 Smart Healthcare ForHealth Promoters? Image Source: http://www.hiso.or.th/hiso/picture/reportHealth/ThaiHealth2014/thai2014_3.pdf
- 36. 36 Smart Healthcare ForClinicians? Image Source: http://www.medscape.com/viewarticle/780298
- 37. 37 Smart Healthcare ForPatients & Consumers? Image Source: Agence France-Presse/Getty Images
- 38. 38 So What ExactlyIs Smart Healthcare? Image Source: http://cdn2.hubspot.net/hub/134568/file-1208368053-jpg/6-blind-men-hans.jpg
- 39. 39https://www.youtube.com/watch?v=gxz9ZVvduGc Connecting People toa Healthy Future With Personalized Care – Kaiser Permanente
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- 41. 41 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) What Clinicians Want?
- 42. 42 Why Aren’t WeTalk About These Words? http://hcca-act.blogspot.com/2011/07/reflections-on-patient-centred-care.html
- 43. 43 The Goal ofHealth Care The answer is already obvious... “Health” “Care”
- 44. 44 • 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. High Quality Care
- 45. 45 Information Is Everywherein Healthcare
- 46. 46 46 46 WHO (2009) Components ofHealth Systems
- 47. 47 47 47 WHO (2009) WHO HealthSystem Framework
- 48. 48 (IOM, 2001)(IOM, 2000)(IOM, 2011) Landmark Institute of Medicine Reports
- 49. 49 • Humans arenot 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 Summary of These Reports
- 50. 50 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
- 51. 51Image Source: SuthanSrisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital To Err is Human 2: Memory
- 52. 52 • 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 subscription $125 • Print & web subscription $125 68 32 # of People # of People To Err is Human 3: Cognition
- 53. 53 • It alreadyhappens.... (Mamede et al., 2010; Croskerry, 2003; Klein, 2005) • What if health IT can help? What If This Happens in Healthcare?
- 54. 54 • Medication Errors –DrugAllergies –Drug Interactions • Ineffective or inappropriate treatment • Redundant orders • Failure to follow clinical practice guidelines Common Errors
- 55. 55 Being Smart #3: “ToErr is Human”
- 56. 56 External Memory Knowledge Data LongTerm Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
- 57. 57 Example of “Alerts& Reminders” Reducing Errors through Alerts & Reminders
- 58. 58 Being Smart #4: LinkIT Values to Quality (Including Safety)
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- 60. 60 ภาพรวมของงานด้าน Health IT Intra-HospitalIT • Electronic Health Records & Health IT for Quality & Safety • Digital Transformation • AI, Data Analytics • Hospital IT Quality Improvement (HA-IT) Inter-Hospital IT • Health Information Exchange (HIE) Extra-Hospital IT • Patients: Personal Health Records (PHRs) • Public Health: Disease Surveillance & Analytics Patient at Home
- 61. 61 Strategic Operational ClinicalAdministrative LIS Health Information ExchangeBusiness Intelligence Word Processor Social Media PACS PersonalHealth Records Clinical Decision Support Systems Computerized Physician Order Entry Electronic Health Records Admission-Discharge-Transfer Master Patient Index Enterprise Resource Planning Vendor-Managed Inventory Customer Relationship Management 4 Quadrants of Hospital IT
- 62. 62 ภาพรวมของงานด้าน Health IT Intra-HospitalIT • Electronic Health Records & Health IT for Quality & Safety • Digital Transformation • AI, Data Analytics • Hospital IT Quality Improvement (HA-IT) Inter-Hospital IT • Health Information Exchange (HIE) Extra-Hospital IT • Patients: Personal Health Records (PHRs) • Public Health: Disease Surveillance & Analytics Patient at Home
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- 64. 64 Hospital A HospitalB Clinic D Policymakers Patient at Home Hospital C HIE Platform Health Information Exchange (HIE)
- 65. 65 Areas of HealthInformatics Patients & Consumers Providers & Patients Healthcare Managers, Policy- Makers, Payers, Epidemiologists, Researchers Copyright Nawanan Theera-Ampornpunt (2018) Clinical Informatics Public Health Informatics Consumer Health Informatics
- 66. 66 Incarnations of HealthIT Clinical Informatics Public Health Informatics Consumer Health Informatics HIS/CIS EHRs Computerized Physician Order Entry (CPOE) Clinical Decision Support Systems (CDS) (including AI) Closed Loop Medication PACS/RIS LIS Nursing Apps Disease Surveillance (Active/Passive) Business Intelligence & Dashboards Telemedicine Real-time Syndromic Surveillance mHealth for Public Health Workers & Volunteers PHRs Health Information Exchange (HIE) eReferral mHealth for Consumers Wearable Devices Social Media Copyright Nawanan Theera-Ampornpunt (2018)
- 67. 67 Where We AreToday... Copyright Nawanan Theera-Ampornpunt (2018) Clinical Informatics Public Health Informatics Consumer Health Informatics Technology that focuses on the sick, not the healthy Silos of data within hospitalPoor/unstructured data quality Lack of health data outside hospital Poor data integration across hospitals/clinics Poor data integration for monitoring & evaluation Poor data quality (GIGO) Finance leads clinical outcomes Poor IT change management Cybersecurity & privacy risks Few real examples of precision medicine Little access to own health data Poor patient engagement Poor accuracy of wearables Lack of evidence for health values Health literacy Information Behavioral change Few standards Lack of health IT governance
- 68. 68 Healthtech Startup Ecosystem www.facebook.com/HealthTechThailand/No endorsements implied
- 69. 69WHO & ITU AchievingHealth Information Exchange (HIE)
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- 71. 71 Myths • We don’tneed standards • Standards are IT people’s jobs • We should exclude vendors from this • We need the same software to share data • We need to always adopt international standards • We need to always use local standards Theera-Ampornpunt (2011) Myths & Truths about Standards
- 72. 72 Being Smart #5: Gofor Systems that Use Standards, Not a Unified, Conquer-the-World System Image Source: https://www.businessinsider.in/google-let-users-play-with-thanos-destructive- power/articleshow/69054170.cms
- 73. 73 • CDS asa 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 Clinical Decision Support Systems (CDS)
- 74. 74 Being Smart #6: Don’tReplace Human Users. Use ICT to Help Them Perform Smarter & Better.
- 75. 75 Envisioning a SmartHealth Thailand