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IT Use for Nursing Administration (June 21, 2019)
The document discusses using information technology for healthcare management. It was presented by Nawanan Theera-Ampornpunt from Mahidol University. Nawanan discussed the potential for technology to improve healthcare through more accurate documentation, clinical decision support, and reducing medical errors. However, clinical judgement is still necessary given variations in patients and care. The goal of using IT should be to improve quality, safety, efficiency and patient-centeredness of healthcare.
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IT Use for Nursing Administration (June 21, 2019)
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- 2. 2003 M.D. (First-ClassHonors) 2011 Ph.D. (Health Informatics), Univ. of Minnesota Assistant Dean for Informatics Lecturer, Section for Clinical Epidemiology & Biostatistics Faculty of Medicine Ramathibodi Hospital Mahidol University Interests: Health IT for Quality of Care, Social Media IT Management, Security & Privacy nawanan.the@mahidol.ac.th SlideShare.net/Nawanan นวนรรน ธีระอัมพรพันธุ์ (Nawanan Theera-Ampornpunt) Line ID: NawananT Introduction
- 3. ▪ The Roadto Digital Health Transformation ▪ What is a “Smart Hospital”? ▪ Toward a “Smart” Hospital Outline
- 4. What words cometo mind when you hear... Digital Health Transformation
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- 7. englishmoviez.com Rise of theMachines?
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- 9. “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)
- 10. Hype vs. Hope JeremyKemp via http://en.wikipedia.org/wiki/Hype_cycle http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
- 11. Gartner Hype Cycle2017 https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/
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- 13. A Real-Life PersonalStory of My Failure (as a Doctor and as a Son) in Misdiagnosing My Mom Would AI Help?
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- 15. • 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?
- 16. • 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?
- 17. • 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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- 19. Let’s take alook at these pictures...
- 20. Image Source: https://en.wikipedia.org/wiki/Industrial_robot(KUKA Roboter GmbH) “Smart” Manufacturing
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- 22. ER - ImageSource: nj.com Healthcare (On TV)
- 23. (At an undisclosedhospital) Healthcare (Reality)
- 24. • 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”?
- 25. But...Are We ThatDifferent? Input Process Output Transfer Banking Value-Add - Security - Convenience - Customer Service Location A Location B
- 26. Input Process Output Assembling Manufacturing RawMaterials Finished Goods Value-Add - Innovation - Design - QC But...Are We That Different?
- 27. Input Process Output PatientCare Health care Sick Patient Well Patient Value-Add - Technology & medications - Clinical knowledge & skilled providers - Quality of care; process improvement - Customer service - Information But...Are We That Different?
- 28. • Large variations& contextual dependence Input Process Output Patient Presentation Decision- Making Biological Responses Standardizing Healthcare
- 29. “To computerize the hospital” “Togo paperless” “To become a Digital Hospital” “To Have EHRs” Why Adopting Health IT?
- 30. • “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
- 31. Being Smart #1: StopYour “Drooling Reflex”!!
- 32. Being Smart #2: Focuson Information & Process Improvement, Not Technology
- 33. If not “DigitalHospital” or “Paperless Hospital” Then What Should We Aspire to Be?
- 34.
- 35. So How is a“Smart Hospital” Different from a Digital or Paperless Hospital?
- 36. Smart Healthcare ForPolicymakers? Image Source: http://healthdata.moph.go.th/kpi/2557/ProvinceKpiTopicListAll.php?provincecode=99
- 37. Smart Healthcare ForHealth Promoters? Image Source: http://www.hiso.or.th/hiso/picture/reportHealth/ThaiHealth2014/thai2014_3.pdf
- 38. Smart Healthcare ForClinicians? Image Source: http://www.medscape.com/viewarticle/780298
- 39. Smart Healthcare ForPatients & Consumers? Image Source: Agence France-Presse/Getty Images
- 40. So What ExactlyIs Smart Healthcare? Image Source: http://cdn2.hubspot.net/hub/134568/file-1208368053-jpg/6-blind-men-hans.jpg
- 41. ✓The Road toDigital Health Transformation ▪ What is a “Smart Hospital”? ▪ Toward a “Smart” Hospital Outline
- 42. https://www.youtube.com/watch?v=gxz9ZVvduGc Connecting People toa Healthy Future With Personalized Care – Kaiser Permanente
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- 44. 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?
- 45. Why Aren’t WeTalk About These Words? http://hcca-act.blogspot.com/2011/07/reflections-on-patient-centred-care.html
- 46. The Goal ofHealth Care The answer is already obvious... “Health” “Care”
- 47. • 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
- 48. Information Is Everywherein Healthcare
- 49. Shortliffe EH. Biomedicalinformatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8. “Information” in Medicine
- 50. 50 50 WHO (2009) Components ofHealth Systems
- 51. 51 51 WHO (2009) WHO HealthSystem Framework
- 52. • Safe –Drug allergies –MedicationReconciliation • Timely –Complete information at point of care • Effective –Better clinical decision-making Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/ Being Smart in Healthcare
- 53. • Efficient –Faster care –Time& cost savings –Reducing unnecessary tests • Equitable –Access to providers & knowledge • Patient-Centered –Empowerment & better self-care Being Smart in Healthcare
- 54. (IOM, 2001)(IOM, 2000)(IOM, 2011) Landmark Institute of Medicine Reports
- 55. • To Erris Human (IOM, 2000) reported that: – 44,000 to 98,000 people die in U.S. hospitals each year as a result of preventable medical mistakes – Mistakes cost U.S. hospitals $17 billion to $29 billion yearly – Individual errors are not the main problem – Faulty systems, processes, and other conditions lead to preventable errors Patient Safety
- 56. • 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
- 57. 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
- 58. Image Source: SuthanSrisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital To Err is Human 2: Memory
- 59. • 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 To Err is Human 3: Cognition
- 60. • It alreadyhappens.... (Mamede et al., 2010; Croskerry, 2003; Klein, 2005; Croskerry, 2013) What If This Happens in Healthcare?
- 61. Klein JG. Fivepitfalls 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” Cognitive Biases in Healthcare
- 62. • Medication Errors –DrugAllergies –Drug Interactions • Ineffective or inappropriate treatment • Redundant orders • Failure to follow clinical practice guidelines Common Errors
- 63. Being Smart #3: “ToErr is Human”
- 64. External Memory Knowledge Data LongTerm Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
- 65. Example of “Alerts& Reminders” Reducing Errors through Alerts & Reminders
- 66. • Guideline adherence •Better documentation • Practitioner decision making or process of care • Medication safety • Patient surveillance & monitoring • Patient education/reminder Documented Values of Health IT
- 67. Being Smart #4: LinkIT Values to Quality (Including Safety)
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- 69. Hospital Information System(HIS) Computerized Physician Order Entry (CPOE) Electronic Health Records (EHRs) Picture Archiving and Communication System (PACS) Various Forms of Health IT
- 70. m-Health Health Information Exchange (HIE) Biosurveillance Telemedicine& Telehealth Images from Apple Inc., Geekzone.co.nz, Google, PubMed.gov, and American Telecare, Inc. Personal Health Records (PHRs) Health IT Beyond Hospitals
- 71. 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
- 72. Ordering Transcription DispensingAdministration CPOE Automatic Medication Dispensing Electronic Medication Administration Records (e-MAR) Barcoded Medication Administration Barcoded Medication Dispensing Health IT for Medication Safety
- 73. 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
- 74. 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
- 75. Hospital A HospitalB Clinic D Policymakers Patient at Home Hospital C HIE Platform Health Information Exchange (HIE)
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- 78. 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
- 79. 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)
- 80. 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
- 81. Healthtech Startup Ecosystem www.facebook.com/HealthTechThailand/No endorsements implied
- 82. ✓The Road toDigital Health Transformation ✓What is a “Smart Hospital”? ▪ Toward a “Smart” Hospital Outline
- 83. WHO & ITU AchievingHealth Information Exchange (HIE)
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- 85. 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
- 86. 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
- 87.
- 88. • 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)
- 89. Being Smart #6: Don’tReplace Human Users. Use ICT to Help Them Perform Smarter & Better.
- 90. Some Risks ofClinical Decision Support Systems • Alert Fatigue Unintended Consequences of Health IT
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- 92. Being Smart #7: HealthIT Also Have Risks & Unintended Consequences
- 93. Technology ProcessPeople Balanced Focus ofInformatics
- 94. Being Smart #8: BalanceYour Focus (People, Process, Technology)
- 95. 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
- 96. • โรงพยาบาลจะพัฒนาระบบสารสนเทศของตัวเองให้เข้มแข็งได้อย่างไร • เมื่อไรข้อมูลผู้ป่วยจะเชื่อมถึงกันได้ระหว่างโรงพยาบาลต่างๆโดยเฉพาะข้ามสังกัด และนอก สธ. // ควรมีซอฟต์แวร์เดียวใช้ทั้งประเทศหรือไม่ • มาตรฐานข้อมูล 43 แฟ้มไม่ครอบคลุมการใช้งาน จะปรับปรุงได้อย่างไร • จะเลือกมาตรฐานข้อมูลยาที่เหมาะสมมาใช้งานใน รพ. ได้อย่างไร • โครงสร้างการออกแบบ Data Center ระดับจังหวัด, เขต และประเทศ ควรเป็น อย่างไร (Centralized, Decentralized, Distributed, Hybrid, etc.) • การนาข้อมูลระดับ รพ. มาใช้ประโยชน์ในระดับจังหวัด เขต และประเทศ ควรเป็น อย่างไร และออกแบบ Infrastructure Data Model อย่างไร • Disruptive technology (AI, Blockchain, etc.) ควรมีบทบาทใน Healthcare ไทยอย่างไร มากกว่าการเป็น buzzwords • ความก้าวหน้าและยั่งยืนของกาลังคนด้าน Health IT เมื่อไรจะได้รับการแก้ไข คาถามที่เจอบ่อยๆ ในวงการ Health IT ไทย
- 97. • ระบบข้อมูลการส่งต่อผู้ป่วยควรใช้โปรแกรมใด • ระบบIT การแพทย์ฉุกเฉินมีข้อจากัดการเชื่อมต่อ • ระบบ IT PCC ควรเป็นอย่างไร • ทาอย่างไรจึงจะมีระบบ PHRs ที่ครอบคลุมผู้ป่วยส่วนใหญ่ มีการใช้ ประโยชน์อย่างเต็มที่ • ข้อมูล Precision Medicine & Genomics จะ integrate ในการดูแล ผู้ป่วยอย่างไร • ข้อเสนอ: ควรเป็น Platform เดียวกัน คาถามที่เจอบ่อยๆ ในวงการ Health IT ไทย
- 98. ข้อเสนอของสมาคมเวชสารสนเทศไทย ต่อ รมว.สธ. (ศ.นพ.รัชตะ รัชตะนาวิน) เมื่อ พ.ศ. 2557
- 99. ข้อเสนอของสมาคมเวชสารสนเทศไทย ต่อ รมว.สธ. (ศ.นพ.รัชตะ รัชตะนาวิน) เมื่อ พ.ศ. 2557
- 101. Necessary Standards inHealth IT Functional Semantic Syntactic Technical Standards (TCP/IP, encryption, security) Exchange Standards (HL7 V2, HL7 V3 Messaging, HL7 CDA, HL7 FHIR, DICOM) Vocabularies, Terminologies, Coding Systems (ICD-10, ICD-9, CPT, SNOMED CT, LOINC) Information Models (HL7 V3 RIM, ASTM CCR, HL7 CCD) Standard Data Sets Functional Standards (HL7 EHR Functional Specifications) Some may be hybrid: e.g. HL7 V3, HL7 CCD Unique ID
- 102. Envisioning a SmartHealth Thailand
- 103. 103 My Plea... Less FancyRoofs More Enabling Foundations
- 104.