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Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
Clinical Decision Support Systems
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Clinical Decision Support Systems

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  • 1. Clinical Decision Support Systems Nawanan Theera-Ampornpunt, M.D., Ph.D. June 14, 2014 http://www.SlideShare.net/Nawanan
  • 2. 2 Outline • What is a Decision? • Clinical Decision Making • Roles of IT in Decision Making • Clinical Decision Support Systems – Definitions – Types & examples – Architecture • Issues Related to CDS Implementation • Summary
  • 3. 3 WHAT IS A DECISION?
  • 4. 4 Wisdom Knowledge Information Data Data-Information-Knowledge- Wisdom (DIKW) Pyramid
  • 5. 5 Wisdom Knowledge Information Data Contextualization/ Interpretation Processing/ Synthesis/ Organization Judgment Data-Information-Knowledge- Wisdom (DIKW) Pyramid
  • 6. 6 Wisdom Knowledge Information Data Contextualization/ Interpretation Processing/ Synthesis/ Organization Judgment 100,000,000 I have 100,000,000 baht in my bank account I am rich!!!!! I should buy a luxury car (and a BIG house)! Example
  • 7. 7 Example: Problem A • Patient A has a blood pressure reading of 170/100 mmHg • Data: 170/100 • Information: BP of Patient A = 170/100 mmHg • Knowledge: Patient A has high blood pressure • Wisdom (or Decision): – Patient A needs to be investigated for cause of HT – Patient A needs to be treated with anti-hypertensives – Patient A needs to be referred to a cardiologist
  • 8. 8 Example: Problem B • Patient B is allergic to penicillin. He was recently prescribed amoxicillin for his sore throat. • Data: Penicillin, amoxicillin, sore throat • Information: – Patient B has penicillin allergy – Patient B was prescribed amoxicillin for his sore throat • Knowledge: – Patient B may have allergic reaction to his prescription • Wisdom (or Decision): – Patient B should not take amoxicillin!!!
  • 9. 9 Decision & Decision Making • Decision – “A choice that you make about something after thinking about it : the result of deciding” (Merriam-Webster Dictionary) • Decision making – “The cognitive process resulting in the selection of a course of action among several alternative scenarios.” (Wikipedia)
  • 10. 10 LET’S TAKE A LOOK AT PATIENT CARE PROCESS
  • 11. 11 Patient Care Image Sources: (Left) Faculty of Medicine Ramathibodi Hospital (Right) /en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)
  • 12. 12 EXERCISE 1 Provide some examples of “decisions” health care providers make
  • 13. 13 Clinical Decisions • Patient Care – What patient history to ask? – What physical examinations to do? – What investigations to order? • Lab tests • Radiologic studies (X-rays, CTs, MRIs, etc.) • Other special investigations (EKG, etc.) – What diagnosis (or possible diagnosis) to make?
  • 14. 14 Clinical Decisions • Patient Care – What treatment to order/perform? • Medications • Surgery/Procedures/Nursing Interventions • Patient Education/Advice for Self-Care • Admission – How should patient be followed-up? – With good or poor response to treatment, what to do next? – With new information, what to do next?
  • 15. 15 Clinical Decisions • Management – How to improve quality of care and clinical operations? – How to allocate limited budget & resources? – What strategies should the hospital pursue & what actions/projects should be done?
  • 16. 16 Clinical Decisions • Public Health – How to improve health of population? – How to investigate/control/prevent disease outbreak? – How to allocate limited budget & resources? – What areas of the country’s public health need attention & what to do with it?
  • 17. 17 CLINICAL DECISION MAKING
  • 18. 18 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
  • 19. 19 PROBLEMS WITH HUMAN’S DECISION MAKING
  • 20. 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
  • 21. 21 Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University To Err is Human 2: Memory
  • 22. 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. 23 • Medical Errors –Drug allergies –Drug interactions • Missing Abnormal Lab Findings • Clinical Practice Guidelines • Bias in Judgment & Decision-Making Common Errors in Healthcare
  • 24. 24 ROLES OF INFORMATION TECHNOLOGY IN DECISION MAKING
  • 25. 25 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
  • 26. 26 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Possible Human Errors Possibility of Human Errors
  • 27. 27 CLINICAL DECISION SUPPORT SYSTEMS (CDS)
  • 28. 28 • Clinical Decision Support (CDS) “is a process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information to improve health and healthcare delivery” (Including both computer-based & non-computer-based CDS) (Osheroff et al., 2012) What Is A CDS?
  • 29. 29 • Computer-based clinical decision support (CDS): “Use of the computer [ICT] to bring relevant knowledge to bear on the health care and well being of a patient.” (Greenes, 2007) What Is A CDS?
  • 30. 30 • The real place where most of the values of health IT can be achieved • There are a variety of forms and nature of CDS Clinical Decision Support Systems (CDS)
  • 31. 31 • Expert systems –Based on artificial intelligence, machine learning, rules, or statistics –Examples: differential diagnoses, treatment options CDS Examples Shortliffe (1976)
  • 32. 32 • Alerts & reminders –Based on specified logical conditions • Drug-allergy checks • Drug-drug interaction checks • Drug-lab interaction checks • Drug-formulary checks • Reminders for preventive services or certain actions (e.g. smoking cessation) • Clinical practice guideline integration (e.g. best practices for chronic disease patients) CDS Examples
  • 33. 33 Example of “Reminders”
  • 34. 34 • 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) CDS Examples
  • 35. 35 Infobuttons Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html
  • 36. 36 • Pre-defined documents –Order sets, personalized “favorites” –Templates for clinical notes –Checklists –Forms • Can be either computer-based or paper-based CDS Examples
  • 37. 37 Order Sets Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm
  • 38. 38 • Simple UI designed to help clinical decision making –Abnormal lab highlights –Graphs/visualizations for lab results –Filters & sorting functions CDS Examples
  • 39. 39 Abnormal Lab Highlights Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
  • 40. 40 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Abnormal lab highlights
  • 41. 41 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Order Sets
  • 42. 42 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Drug-Allergy Checks
  • 43. 43 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Drug-Drug Interaction Checks
  • 44. 44 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Drug-Drug Interaction Checks
  • 45. 45 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Clinical Practice Guideline Alerts/Reminders
  • 46. 46 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Integration of Evidence-Based Resources (e.g. drug databases, literature)
  • 47. 47 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Diagnostic/Treatment Expert Systems
  • 48. 48 User User Interface Patient Data Inference Engine Knowledge BaseOther Data • Rules & Parameters • Statistical data • Literature • Etc. • System states • Epidemiological/ surveillance data • Etc. Example of CDS Architecture Other Systems
  • 49. 49 ISSUES RELATED TO CDS IMPLEMENTATION
  • 50. 50 • How will CDS be implemented in real life? • Will it interfere with user workflow? • Will it be used by users? If not, why? • What user interface design is best? • What are most common user complaints? • Who is responsible if something bad happens? • How to balance reliance on machines & humans Human Factor Issues of CDS
  • 51. 51 IBM’s Watson Image Source: socialmediab2b.com
  • 52. 52 Image Source: englishmoviez.com Rise of the Machines?
  • 53. 53 Issues • CDSS as a supplement or replacement of clinicians? – The demise of the “Greek Oracle” model (Miller & Masarie, 1990) The “Greek Oracle” Model The “Fundamental Theorem” Friedman (2009) Human Factor Issues of CDS Wrong Assumption Correct Assumption
  • 54. 54 • Features with improved clinical practice (Kawamoto et al., 2005) – Automatic provision of decision support as part of clinician workflow – Provision of recommendations rather than just assessments – Provision of decision support at the time and location of decision making – Computer based decision support • Usability & impact on productivity Human Factor Issues of CDS
  • 55. 55 Issues • Alert sensitivity & alert fatigue Alert Fatigue
  • 56. 56 • Liabilities – Clinicians as “learned intermediaries” • Prohibition of certain transactions vs. Professional autonomy (see Strom et al., 2010) Ethical-Legal Issues of CDS
  • 57. 57 Workarounds
  • 58. 58 • “Unanticipated and unwanted effect of health IT implementation” (www.ucguide.org) • Resources – www.ucguide.org – Ash et al. (2004) – Campbell et al. (2006) – Koppel et al. (2005) Unintended Consequences of CDS & Health IT
  • 59. 59 Ash et al. (2004) Unintended Consequences of CDS & Health IT
  • 60. 60 • Errors in the process of entering and retrieving information – A human-computer interface that is not suitable for a highly interruptive use context – Causing cognitive overload by overemphasizing structured and “complete” information entry or retrieval • Structure • Fragmentation • Overcompleteness Ash et al. (2004) Unintended Consequences of CDS & Health IT
  • 61. 61 • Errors in communication & coordination – Misrepresenting collective, interactive work as a linear, clearcut, and predictable workflow • Inflexibility • Urgency • Workarounds • Transfers of patients – Misrepresenting communication as information transfer • Loss of communication • Loss of feedback • Decision support overload • Catching errors Ash et al. (2004) Unintended Consequences of CDS & Health IT
  • 62. 62 • Which type of CDS should be chosen? • What algorithms should be used? • How to “represent” knowledge in the system? • How to update/maintain knowledge base in the system? • How to standardize data/knowledge? • How to implement CDS with good system performance? Technical Issues of CDS
  • 63. 63 • Choosing the right CDSS strategies • Expertise required for proper CDSS design & implementation • Everybody agreeing on the “rules” to be enforced • Evaluation of effectiveness Other Issues
  • 64. 64 • Speed is Everything • Anticipate Needs and Deliver in Real Time • Fit into the User’s Workflow • Little Things (like Usability) Can Make a Big Difference • Recognize that Physicians Will Strongly Resist Stopping • Changing Direction Is Easier than Stopping • Simple Interventions Work Best • Ask for Additional Information Only When You Really Need It • Monitor Impact, Get Feedback, and Respond • Manage and Maintain Your Knowledge-based Systems Bates et al. (2003) “Ten Commandments” for Effective CDS
  • 65. 65 • There are several decisions made in a clinical patient care process • Data leads to information, knowledge, and ultimately, decision & actions • Human clinicians are not perfect and can make mistakes • A clinical decision support systems (CDS) provides support for clinical decision making (to prevent mistakes & provide best patient care) • A CDS can be computer-based or paper-based Key Points
  • 66. 66 • CDS comes in various forms, designs, and architecture • There are many issues related to design, implementation and use of CDS – Technical Issues – Human Factor Issues – Ethical-Legal Issues Key Points
  • 67. 67 • Current mindset: CDS should be used to help, not replace, human providers • Be attentive to workarounds, alert fatigues, and other unintended consequences of CDS – They can cause more danger to patients!! – They may lead users to abandon using CDS (a failure) • There are recommendations on how to best design & implement CDS Key Points
  • 68. 68 Intelligent & helpful machines Machines with a human touch Machines that replace humans HAL 9000 Data David NS-5 Dangerous killer machines What Will The Future Be for Health Care?
  • 69. 69 References • Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc. 2004 Mar-Apr;11(2):104-12. • Ariely D. Predictably irrational: the hidden forces that shape our decisions. New York City (NY): HarperCollins; 2008. 304 p. • Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, Spurr C, Khorasani R, Tanasijevic M, Middleton B. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc. 2003 Nov-Dec;10(6):523-30. • Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc. 2006 Sep-Oct;13(5):547-56. • Elson RB, Faughnan JG, Connelly DP. An industrial process view of information delivery to support clinical decision making: implications for systems design and process measures. J Am Med Inform Assoc. 1997 Jul-Aug;4(4):266-78. • Friedman CP. A "fundamental theorem" of biomedical informatics. J Am Med Inform Assoc. 2009 Apr;16(2):169-170.
  • 70. 70 References • Greenes RA. Clinical decision support: the road ahead. Oxford (UK): Elsevier; 2007. 581 p. • Institute of Medicine, Committee on Quality of Health Care in America. To err is human: building a safer health system. Kohn LT, Corrigan JM, Donaldson MS, editors. Washington, DC: National Academy Press; 2000. 287 p. • Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005 Apr 2;330(7494):765. • Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005 Mar 9;293(10):1197-1203. • Miller RA, Masarie FE. The demise of the "Greek Oracle" model for medical diagnostic systems. Methods Inf Med. 1990 Jan;29(1):1-2. • Osheroff JA, Teich JM, Levick D, Saldana L, Velasco FT, Sittig DF, Rogers KM, Jenders RA. Improving outcomes with clinical decision support: an implementer’s guide. 2nd ed. Chicago (IL): Healthcare Information and Management Systems Society; 2012. 323 p.
  • 71. 71 References • Shortliffe EH. Computer-based medical consultations: MYCIN. New York (NY): Elsevier; 1976. 264 p. • Strom BL, Schinnar R, Aberra F, Bilker W, Hennessy S, Leonard CE, Pifer E. Unintended effects of a computerized physician order entry nearly hard-stop alert to prevent a drug interaction: a randomized controlled trial. Arch Intern Med. 2010 Sep 27;170(17):1578-83.

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