Overview of Hospital Information Systems
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Overview of Hospital Information Systems Presentation Transcript

  • 1. Overview of Hospital Information Systems Nawanan Theera-Ampornpunt, M.D., Ph.D. Department of Community Medicine Faculty of Medicine Ramathibodi Hospital March 3, 2014 SlideShare.net/Nawanan
  • 2. 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
  • 3. Outline • • • • • • Healthcare & Information Why We Need ICT in Healthcare Health IT Hospital Information Systems Health Information Exchange Q&A 3
  • 4. Let’s start with something simple... 4
  • 5. 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) 5
  • 6. 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. 6
  • 7. Information is Everywhere in Healthcare Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8. 7
  • 8. “Information” in Medicine Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8. 8
  • 9. Why We Need ICT in Healthcare? #1: Because information is everywhere in healthcare 9
  • 10. Landmark IOM Reports (IOM, 2000) (IOM, 2001) (IOM, 2011) 10
  • 11. 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 11
  • 12. 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 12
  • 13. 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 13
  • 14. To Err is Human 2: Memory Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University 14
  • 15. 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) 15
  • 16. 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. 16
  • 17. 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. 17
  • 18. 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. 18
  • 19. Common Errors • Medication Errors – Drug Allergies – Drug Interactions • Ineffective or inappropriate treatment • Redundant orders • Failure to follow clinical practice guidelines 19
  • 20. Why We Need ICT in Healthcare? #2: Because healthcare is error-prone and technology can help 20
  • 21. Why We Need ICT in Healthcare? #3: Because access to high-quality patient information improves care 21
  • 22. 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 22
  • 23. Health IT: What’s in a Word? Health Information Technology Goal Value-Add Tools 23
  • 24. “Health” in “Health IT” • Patient’s Health • Population’s Health • Organization’s Health (Quality, Reputation & Finance) 24
  • 25. 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) 25
  • 26. 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 26
  • 27. Values of Health IT • Guideline adherence • Better documentation • Practitioner decision making or process of care • Medication safety • Patient surveillance & monitoring • Patient education/reminder 27
  • 28. 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) - Finance, Materials Management, Human Resources 28
  • 29. 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 29
  • 30. 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) 30
  • 31. Computerized Provider Order Entry (CPOE) 31
  • 32. Computerized Provider Order Entry (CPOE) Values • No handwriting!!! • Structured data entry: Completeness, clarity, fewer mistakes (?) • No transcription errors! • Streamlines workflow, increases efficiency 32
  • 33. Stages of Medication Process Ordering CPOE Transcription Dispensing Administration Automatic Medication Dispensing Electronic Medication Administration Records (e-MAR) Barcoded Medication Dispensing Barcoded Medication Administration 33
  • 34. 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 34
  • 35. 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 35
  • 36. Example of “Reminders” 36
  • 37. 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) 37
  • 38. Infobuttons Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html 38
  • 39. Other CDS Examples • Pre-defined documents – – – – Order sets, personalized “favorites” Templates for clinical notes Checklists Forms • Can be either computer-based or paper-based 39
  • 40. Order Sets Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm 40
  • 41. Other CDS Examples • Simple UI designed to help clinical decision making – Abnormal lab highlights – Graphs/visualizations for lab results – Filters & sorting functions 41
  • 42. Abnormal Lab Highlights Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html 42
  • 43. Clinical Decision Making PATIENT Perception CLINICIAN Attention Long Term Memory Knowledge Working Memory Data External Memory Knowledge Data Inference DECISION Elson, Faughnan & Connelly (1997) 43
  • 44. Clinical Decision Making PATIENT Perception CLINICIAN Attention Long Term Memory Knowledge Working Memory Data Abnormal lab highlights External Memory Knowledge Data Inference DECISION 44
  • 45. Clinical Decision Making PATIENT Perception CLINICIAN Attention Long Term Memory Knowledge Working Memory Data Drug-Allergy Checks External Memory Knowledge Data Inference DECISION 45
  • 46. 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) 46
  • 47. 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) 47
  • 48. 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) 48
  • 49. 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) 49
  • 50. Unintended Consequences of Health IT Some risks • Alert fatigue 50
  • 51. Workarounds 51
  • 52. Health Information Exchange (HIE) Government Hospital B Hospital A Lab Patient at Home Clinic C 52
  • 53. Outline Healthcare & Information Why We Need ICT in Healthcare Health IT Hospital Information Systems Health Information Exchange • Q&A 53
  • 54. Patients Are Counting on Us... Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/ 54
  • 55. 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 55