ICT Applications for Healthcare
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ICT Applications for Healthcare ICT Applications for Healthcare Presentation Transcript

  • ICT Applications for Healthcare MUICT Seminar Nawanan Theera-Ampornpunt, M.D., Ph.D. Faculty of Medicine Ramathibodi Hospital May 28, 2014 SlideShare.net/Nawanan
  • 2 A Bit About Myself... 2003 M.D. (First-Class Honors) (Ramathibodi) 2009 M.S. in Health Informatics (U of MN) 2011 Ph.D. in Health Informatics (U of MN) 2012 Certified HL7 CDA Specialist • Deputy Executive Director for Informatics (CIO/CMIO) Chakri Naruebodindra Medical Institute • Lecturer, Department of Community Medicine Faculty of Medicine Ramathibodi Hospital Mahidol University nawanan.the@mahidol.ac.th http://groups.google.com/group/ThaiHealthIT
  • 3 Outline • Healthcare & Information • Why We Need ICT in Healthcare • Health IT & eHealth • Some ICT Applications • A Dream for Healthcare • Food for Thought for ICT Folks • Q&A
  • 4 Let’s take a look at these pictures...
  • 5Image Source: Guardian.co.uk Manufacturing
  • 6Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3 Banking
  • 7ER - Image Source: nj.com Healthcare (on TV)
  • 8 Healthcare (At an undisclosed nearby hospital)
  • 9 • 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
  • 10 Back to something simple...
  • 11 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)
  • 12 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.
  • 13 Information is Everywhere in Healthcare Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.
  • 14 “Information” in Medicine Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.
  • 15 Outline “Information” in Healthcare • Why We Need ICT in Healthcare • Health IT & eHealth • Some ICT Applications • A Dream for Healthcare • Food for Thought for ICT Folks • Q&A
  • 16 Why We Need ICT in Healthcare? #1: Because information is everywhere in healthcare
  • 17 (IOM, 2001)(IOM, 2000) (IOM, 2011) Landmark IOM Reports
  • 18 Patient Safety • To Err is 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 Health IT Workforce Curriculum Version 3.0/Spring 2012 Introduction to Healthcare and Public Health in the US: Regulating Healthcare - Lecture d
  • 19 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
  • 20 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
  • 21Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University To Err is Human 2: Memory
  • 22 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
  • 23 • It already happens.... (Mamede et al., 2010; Croskerry, 2003; Klein, 2005; Croskerry, 2013) What If This Happens in Healthcare?
  • 24 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.
  • 25 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.
  • 26 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”
  • 27 • Medication Errors – Drug Allergies – Drug Interactions • Ineffective or inappropriate treatment • Redundant orders • Failure to follow clinical practice guidelines Common Errors
  • 28 Why We Need ICT in Healthcare? #2: Because healthcare is error-prone and technology can help
  • 29 Fragmented Healthcare http://www.dplindbenchmark.com/wp-content/uploads/2013/02/HHRI-Our-Health-Care-River.pdf
  • 30 Why We Need ICT in Healthcare? #3: Because access to high-quality patient information improves care
  • 31 Why We Need ICT in Healthcare? #4: Because healthcare at all levels is fragmented & in need of process improvement
  • 32 Outline “Information” in Healthcare Why We Need ICT in Healthcare • Health IT & eHealth • Some ICT Applications • A Dream for Healthcare • Food for Thought for ICT Folks • Q&A
  • 33 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
  • 34 Use of information and communications technology (ICT) for health; Including • Treating patients • Conducting research • Educating the health workforce • Tracking diseases • Monitoring public health. Sources: 1) WHO Global Observatory of eHealth (GOe) (www.who.int/goe) 2) World Health Assembly, 2005. Resolution WHA58.28 Slide adapted from: Mark Landry, WHO WPRO & Boonchai Kijsanayotin eHealth
  • 35 eHealth  Health IT Slide adapted from: Boonchai Kijsanayotin eHealth & Health IT
  • 36 HIS All information about health eHealth HMIS mHealth Tele- medicine Slide adapted from: Karl Brown (Rockefeller Foundation), via Boonchai Kijsanayotin More Terms
  • 37 Health Information Technology Goal Value-Add Tools Health IT: What’s in a Word?
  • 38  All components are essential  All components should be balanced Slide adapted from: Boonchai Kijsanayotin eHealth Components (WHO-ITU Model)
  • 39 eHealth in Thailand: The current status. Stud Health Technol Inform 2010;160:376–80, Presented at MedInfo2010 South Africa 39 Thailand’s eHealth: 2010
  • 40Slide adapted from: Boonchai Kijsanayotin Thailand: Unbalanced Development
  • 41 eHealth Applications Enabling Policies & Strategies Foundation Policies & Strategies • Services • Applications • Software • Standards & Interoperability • Capability Building • Leadership & Governance • Legislation & Policy • Strategy & Investment • Infrastructure Slide adapted from: Boonchai Kijsanayotin eHealth Development Model
  • 42Slide adapted from: Boonchai Kijsanayotin Thailand’s eHealth Development
  • 43  Silo-type systems  Little integration and interoperability  Mostly aim for administration and management  40% of work-hours spent on managing reports and documents  Lack of national leadership and governance body  Inadequate HIS foundations development Slide adapted from: Boonchai Kijsanayotin Thailand’s eHealth Situation
  • 44 Outline “Information” in Healthcare Why We Need ICT in Healthcare Health IT & eHealth • Some ICT Applications • A Dream for Healthcare • Food for Thought for ICT Folks • Q&A
  • 45 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
  • 46 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
  • 47 • Guideline adherence • Better documentation • Practitioner decision making or process of care • Medication safety • Patient surveillance & monitoring • Patient education/reminder Values of Health IT
  • 48 • 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
  • 49 • 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
  • 50 The Challenge - Knowing What It Means Electronic Medical Records (EMRs) Computer-Based Patient Records (CPRs) Electronic Patient Records (EPRs) Electronic Health Records (EHRs) Personal Health Records (PHRs) Hospital Information System (HIS) Clinical Information System (CIS) EHRs & HIS
  • 51 Computerized Provider Order Entry (CPOE)
  • 52 Values • No handwriting!!! • Structured data entry: Completeness, clarity, fewer mistakes (?) • No transcription errors! • Streamlines workflow, increases efficiency Computerized Provider Order Entry (CPOE)
  • 53 • 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)
  • 54 – 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)
  • 55 Example of “Reminders”
  • 56 • 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
  • 57Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html Infobuttons
  • 58 • Pre-defined documents – Order sets, personalized “favorites” – Templates for clinical notes – Checklists – Forms • Can be either computer-based or paper-based Other CDS Examples
  • 59Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm Order Sets
  • 60 • Simple UI designed to help clinical decision making – Abnormal lab highlights – Graphs/visualizations for lab results – Filters & sorting functions Other CDS Examples
  • 61Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html Abnormal Lab Highlights
  • 62 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
  • 63 Abnormal lab highlights Clinical Decision Making External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN
  • 64 Clinical Decision Making External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Drug-Allergy Checks
  • 65 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
  • 66 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
  • 67 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
  • 68Image Source: socialmediab2b.com IBM’s Watson
  • 69Image Source: englishmoviez.com Rise of the Machines?
  • 70 • 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
  • 71 Some risks • Alert fatigue Unintended Consequences of Health IT
  • 72 Workarounds
  • 73 Outline “Information” in Healthcare Why We Need ICT in Healthcare Health IT & eHealth Some ICT Applications • A Dream for Healthcare • Food for Thought for ICT Folks • Q&A
  • 74 Hospital A Hospital B Clinic C Government Lab Patient at Home Health Information Exchange (HIE)
  • 75 Standards & Interoperability in HIE Technical Standards (TCP/IP, encryption, security) Exchange Standards (HL7 v.2, HL7 v.3 Messaging, HL7 CDA, DICOM) Vocabularies, Terminologies, Coding Systems (ICD-10, ICD-9, CPT, SNOMED CT, LOINC) Information Models (HL7 v.3 RIM, ASTM CCR, HL7 CCD) Standard Data Sets Functional Standards (HL7 EHR Functional Specifications) Some may be hybrid: e.g. HL7 v.3, HL7 CCD Unique ID
  • 76 Hospital A Hospital B Clinic C Government Lab Patient at Home Message Message Message Message Message Message Exchange
  • 77 • As the second formally-trained M.D., Ph.D. in Health Informatics in Thailand, I am driven and socially obligated... • To promote personal & population health through establishment of sustainable foundations for eHealth and strengthening of the field of Biomedical and Health Informatics in Thailand before my end of life. • HIE is at the heart of my life-long dream My “Mission in Life”
  • 78http://www.ega.or.th/Content.aspx?m_id=94 Cloud: To Go or Not To Go?
  • 79WHO mHealth Report: http://www.who.int/goe/publications/goe_mhealth_web.pdf Roles of mHealth in Future Healthcare
  • 80 Outline “Information” in Healthcare Why We Need ICT in Healthcare Health IT & eHealth Some ICT Applications A Dream for Healthcare • Food for Thought for ICT Folks • Q&A
  • 81 • What will the future be for healthcare? • Where’s the roles of ICT professionals in future healthcare? • How to leverage different perspectives & strengths to achieve common goals? • How will we shape future healthcare together? Some Food for Thought
  • 82 Patients Are Counting on Us... Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/
  • 83 Intelligent & helpful robots Intelligent humanistic robots in a human world Machines that replace humans for a “better” world HAL 9000 Data David NS-5 Dangerous killer machines What ICT Will It Be?
  • 84 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
  • 85 Outline “Information” in Healthcare Why We Need ICT in Healthcare Health IT & eHealth Some ICT Applications A Dream for Healthcare Food for Thought for ICT Folks • Q&A