Clinical Decision Support Systems (MUICT Teaching)

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  • 1. Clinical Decision Support Systems ITCS 404 IT for Healthcare Services Nawanan Theera-Ampornpunt, M.D., Ph.D. October 19, 2013 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 • Perception errors Pitfalls of Human Decision Making Image Source: interaction-dynamics.com
  • 21. 21 • Lack of Attention Pitfalls of Human Decision Making Image Source: aafp.org
  • 22. 22 • 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 Pitfalls of Human Decision Making
  • 23. 23 IOM (2000) “To Err Is Human”
  • 24. 24 • Medical Errors –Drug allergies –Drug interactions • Abnormal Lab Findings • Clinical Practice Guidelines • Bias in Judgment & Decision-Making What About Health Care?
  • 25. 25 ROLES OF INFORMATION TECHNOLOGY IN DECISION MAKING
  • 26. 26 EXERCISE 2 Provide some examples on how IT can help reduce errors in clinical decision making
  • 27. 27 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
  • 28. 28 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
  • 29. 29 CLINICAL DECISION SUPPORT SYSTEMS (CDS)
  • 30. 30 • 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?
  • 31. 31 • 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?
  • 32. 32 • 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)
  • 33. 33 • Expert systems –Based on artificial intelligence, machine learning, rules, or statistics –Examples: differential diagnoses, treatment options CDS Examples Shortliffe (1976)
  • 34. 34 • 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
  • 35. 35 Example of “Reminders”
  • 36. 36 • 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
  • 37. 37 Infobuttons Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html
  • 38. 38 • Pre-defined documents –Order sets, personalized “favorites” –Templates for clinical notes –Checklists –Forms • Can be either computer-based or paper-based CDS Examples
  • 39. 39 Order Sets Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm
  • 40. 40 • Simple UI designed to help clinical decision making –Abnormal lab highlights –Graphs/visualizations for lab results –Filters & sorting functions CDS Examples
  • 41. 41 Abnormal Lab Highlights Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
  • 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 Abnormal lab highlights
  • 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 Order Sets
  • 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-Allergy 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 Drug-Drug Interaction Checks
  • 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 Drug-Drug Interaction Checks
  • 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 Clinical Practice Guideline Alerts/Reminders
  • 48. 48 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)
  • 49. 49 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
  • 50. 50 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
  • 51. 51 ISSUES RELATED TO CDS IMPLEMENTATION
  • 52. 52 • 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
  • 53. 53 IBM’s Watson Image Source: socialmediab2b.com
  • 54. 54 Image Source: englishmoviez.com Rise of the Machines?
  • 55. 55 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
  • 56. 56 • 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
  • 57. 57 Issues • Alert sensitivity & alert fatigue Alert Fatigue
  • 58. 58 • Liabilities – Clinicians as “learned intermediaries” • Prohibition of certain transactions vs. Professional autonomy (see Strom et al., 2010) Ethical-Legal Issues of CDS
  • 59. 59 Workarounds
  • 60. 60 • “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
  • 61. 61 Ash et al. (2004) Unintended Consequences of CDS & Health IT
  • 62. 62 • 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
  • 63. 63 • 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
  • 64. 64 • 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
  • 65. 65 • 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
  • 66. 66 • 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
  • 67. 67 • 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
  • 68. 68 • 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
  • 69. 69 • 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
  • 70. 70 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?
  • 71. 71 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.
  • 72. 72 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.
  • 73. 73 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.