ICT Applications for Healthcare

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Information about ICT Applications for Healthcare
Health & Medicine

Published on February 18, 2014

Author: nawanan

Source: slideshare.net

ICT Applications for Healthcare MUICT Seminar Nawanan Theera-Ampornpunt, M.D., Ph.D. Faculty of Medicine Ramathibodi Hospital February 19, 2014 SlideShare.net/Nawanan

A Bit About Myself... 2003 2009 2011 2012 M.D. (First-Class Honors) (Ramathibodi) M.S. in Health Informatics (U of MN) Ph.D. in Health Informatics (U of MN) 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 2

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 3

Let’s take a look at these pictures... 4

Manufacturing Image Source: Guardian.co.uk 5

Banking Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3 6

Healthcare (on TV) ER - Image Source: nj.com 7

Healthcare (At an undisclosed nearby hospital) 8

Why Healthcare Isn’t Like Any Others • 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 9

Back to something simple... 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

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

Information is Everywhere in Healthcare Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8. 13

“Information” in Medicine Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8. 14

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 15

Why We Need ICT in Healthcare? #1: Because information is everywhere in healthcare 16

Landmark IOM Reports (IOM, 2000) (IOM, 2001) (IOM, 2011) 17

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 18

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 19

To Err is Human 1: Attention 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 20

To Err is Human 2: Memory Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University 21

To Err is Human 3: Cognition • Cognitive Errors - Example: Decoy Pricing The Economist Purchase Options • Economist.com subscription • Print subscription • Print & web subscription $59 $125 $125 The Economist Purchase Options • Economist.com subscription • Print & web subscription $59 $125 # of People 16 0 84 # of People 68 32 Ariely (2008) 22

What If This Happens in Healthcare? • It already happens.... (Mamede et al., 2010; Croskerry, 2003; Klein, 2005; Croskerry, 2013) 23

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. 24

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. 25

Cognitive Biases in Healthcare “Everyone makes mistakes. But our reliance on cognitive processes prone to bias makes treatment errors more likely than we think” Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr 2;330(7494):781-3. 26

Common Errors • Medication Errors – Drug Allergies – Drug Interactions • Ineffective or inappropriate treatment • Redundant orders • Failure to follow clinical practice guidelines 27

Why We Need ICT in Healthcare? #2: Because healthcare is error-prone and technology can help 28

Fragmented Healthcare http://www.dplindbenchmark.com/wp-content/uploads/2013/02/HHRI-Our-Health-Care-River.pdf 29

Why We Need ICT in Healthcare? #3: Because access to high-quality patient information improves care 30

Why We Need ICT in Healthcare? #4: Because healthcare at all levels is fragmented & in need of process improvement 31

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 32

Health IT 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 33

eHealth 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 34

eHealth & Health IT eHealth  Health IT Slide adapted from: Boonchai Kijsanayotin 35

More Terms All information about health HMIS HIS eHealth mHealth Telemedicine Slide adapted from: Karl Brown (Rockefeller Foundation), via Boonchai Kijsanayotin 36

Health IT: What’s in a Word? Health Information Technology Goal Value-Add Tools 37

eHealth Components (WHO-ITU Model)  All components are essential  All components should be balanced Slide adapted from: Boonchai Kijsanayotin 38

Thailand’s eHealth: 2010 39 eHealth in Thailand: The current status. Stud Health Technol Inform 2010;160:376–80, Presented at MedInfo2010 South Africa 39

Thailand: Unbalanced Development Slide adapted from: Boonchai Kijsanayotin 40

eHealth Development Model eHealth Applications • Services • Applications • Software Enabling Policies & Strategies • Standards & Interoperability • Capability Building Foundation Policies & Strategies • Leadership & Governance • Legislation & Policy • Strategy & Investment • Infrastructure Slide adapted from: Boonchai Kijsanayotin 41

Thailand’s eHealth Development Slide adapted from: Boonchai Kijsanayotin 42

Thailand’s eHealth Situation     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 43

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 44

Various Forms of Health IT Hospital Information System (HIS) Computerized Provider Order Entry (CPOE) Electronic Health Records (EHRs) Screenshot Images from Faculty of Medicine Ramathibodi Hospital, Mahidol University Picture Archiving and Communication System (PACS) 45

Still Many Other Forms of Health IT Biosurveillance mHealth Personal Health Records (PHRs) and Patient Portals Images from Apple Inc., Geekzone.co.nz, Google, HealthVault.com and American Telecare, Inc. Telemedicine & Telehealth 46

Values of Health IT • Guideline adherence • Better documentation • Practitioner decision making or process of care • Medication safety • Patient surveillance & monitoring • Patient education/reminder 47

Enterprise-wide Hospital IT • • • • • • 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) 48

Departmental IT in Hospitals • 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 49

EHRs & HIS The Challenge - Knowing What It Means Electronic Health Records (EHRs) Hospital Information System (HIS) Electronic Medical Records (EMRs) Electronic Patient Records (EPRs) Computer-Based Patient Records (CPRs) Personal Health Records (PHRs) Clinical Information System (CIS) 50

Computerized Provider Order Entry (CPOE) 51

Computerized Provider Order Entry (CPOE) Values • No handwriting!!! • Structured data entry: Completeness, clarity, fewer mistakes (?) • No transcription errors! • Streamlines workflow, increases efficiency 52

Clinical Decision Support Systems (CDS) • The real place where most of the values of health IT can be achieved (Shortliffe, 1976) – Expert systems • Based on artificial intelligence, machine learning, rules, or statistics • Examples: differential diagnoses, treatment options 53

Clinical Decision Support Systems (CDS) – Alerts & reminders • Based on specified logical conditions • Examples: – Drug-allergy checks – Drug-drug interaction checks – Reminders for preventive services – Clinical practice guideline integration 54

Example of “Reminders” 55

More CDS Examples • Reference information or evidencebased knowledge sources – – – – Drug reference databases Textbooks & journals Online literature (e.g. PubMed) Tools that help users easily access references (e.g. Infobuttons) 56

Infobuttons Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html 57

Other CDS Examples • Pre-defined documents – – – – Order sets, personalized “favorites” Templates for clinical notes Checklists Forms • Can be either computer-based or paper-based 58

Order Sets Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm 59

Other CDS Examples • Simple UI designed to help clinical decision making – Abnormal lab highlights – Graphs/visualizations for lab results – Filters & sorting functions 60

Abnormal Lab Highlights Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html 61

Clinical Decision Making PATIENT Perception CLINICIAN Attention Long Term Memory Knowledge Working Memory Data External Memory Knowledge Data Inference DECISION Elson, Faughnan & Connelly (1997) 62

Clinical Decision Making PATIENT Perception CLINICIAN Attention Long Term Memory Knowledge Working Memory Data Abnormal lab highlights External Memory Knowledge Data Inference DECISION 63

Clinical Decision Making PATIENT Perception CLINICIAN Attention Long Term Memory Knowledge Working Memory Data Drug-Allergy Checks External Memory Knowledge Data Inference DECISION 64

Clinical Decision Making PATIENT Perception CLINICIAN Attention Long Term Memory Knowledge Working Memory Data Drug-Drug Interaction Checks External Memory Knowledge Data Inference DECISION Elson, Faughnan & Connelly (1997) 65

Clinical Decision Making PATIENT Perception CLINICIAN Attention Long Term Memory Knowledge Working Memory Data Clinical Practice Guideline Reminders External Memory Knowledge Data Inference DECISION Elson, Faughnan & Connelly (1997) 66

Clinical Decision Making PATIENT Perception CLINICIAN Attention Long Term Memory Knowledge Working Memory Data External Memory Knowledge Inference Data Diagnostic/Treatment Expert Systems DECISION Elson, Faughnan & Connelly (1997) 67

IBM’s Watson Image Source: socialmediab2b.com 68

Rise of the Machines? Image Source: englishmoviez.com 69

Proper Roles of CDS • CDSS as a replacement or supplement of clinicians? – The demise of the “Greek Oracle” model (Miller & Masarie, 1990) The “Greek Oracle” Model Wrong Assumption The “Fundamental Theorem” Model Correct Assumption Friedman (2009) 70

Unintended Consequences of Health IT Some risks • Alert fatigue 71

Workarounds 72

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 73

Health Information Exchange (HIE) Government Hospital B Hospital A Lab Patient at Home Clinic C 74

Standards & Interoperability in HIE Functional Standards (HL7 EHR Functional Specifications) 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 Unique ID Exchange Standards (HL7 v.2, HL7 v.3 Messaging, HL7 CDA, DICOM) Technical Standards (TCP/IP, encryption, security) Some may be hybrid: e.g. HL7 v.3, HL7 CCD 75

Message Exchange Message Message Government Message Hospital B Hospital A Lab Message Patient at Home Clinic C Message 76

My “Mission in Life” • 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 77

Cloud: To Go or Not To Go? http://www.ega.or.th/Content.aspx?m_id=94 78

Roles of mHealth in Future Healthcare WHO mHealth Report: http://www.who.int/goe/publications/goe_mhealth_web.pdf 79

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 80

Some Food for Thought • 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? 81

Patients Are Counting on Us... Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/ 82

What ICT Will It Be? HAL 9000 Dangerous killer machines Data David Intelligent & helpful robots Intelligent humanistic robots in a human world NS-5 Machines that replace humans for a “better” world 83

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 84

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 85

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