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âĒ Computer-based clinical decision support
(CDS): âUse of the computer [ICT] to bring
relevant knowledge to bear on the health
care and well being of a patient.â
(Greenes, 2007)
What Is A CDS?](https://image.slidesharecdn.com/2014-06-14clinicaldecisionsupportsystems-nawanan-140613222114-phpapp01/75/Clinical-Decision-Support-Systems-29-2048.jpg)
Uploaded byNawanan Theera-Ampornpunt
Clinical Decision Support Systems
Clinical decision support systems use information technology to reduce errors and improve clinical decision making by providing clinicians with patient data, knowledge resources, and clinical guidance at the point of care through tools such as diagnostic decision trees, drug databases, alerts, and reminders. However, implementing clinical decision support systems poses challenges around usability, workflow integration, and clarifying the relationship between machine recommendations and human clinical judgment.
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Introduction to the theme of Clinical Decision Support Systems (CDS) and an outline of topics.
Explores definitions of decisions, the DIKW pyramid model illustrating data to wisdom.
Discusses various clinical decisions made in patient care, management, and public health.
Focuses on decision making processes, human errors, and the impact of technology on these.
Describes the concept of CDS and its significance in enhancing healthcare decisions.
Details the various forms of clinical decision support, including expert systems and reminders.
Illustrates how CDS aids decision making with examples like abnormal lab highlights and clinical guidelines.
Discusses implementation challenges, human factors, ethical issues, and the significance of usability.
Summarizes key insights about clinical decision support, emphasizing the importance of effective design.
Speculates on the future of machines in healthcare, emphasizing roles, benefits, and ethical considerations. Provides references related to CDS, including studies and guidelines important for further reading.
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Similar to Clinical Decision Support Systems
Clinical Decision Support Systems
- 1. Clinical Decision Support Systems NawananTheera-Ampornpunt, M.D., Ph.D. June 14, 2014 http://www.SlideShare.net/Nawanan
- 2. 2 Outline âĒ What isa Decision? âĒ Clinical Decision Making âĒ Roles of IT in Decision Making âĒ Clinical Decision Support Systems â Definitions â Types & examples â Architecture âĒ Issues Related to CDS Implementation âĒ Summary
- 3. 3 WHAT IS ADECISION?
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- 6. 6 Wisdom Knowledge Information Data Contextualization/ Interpretation Processing/ Synthesis/ Organization Judgment 100,000,000 I have 100,000,000 bahtin my bank account I am rich!!!!! I should buy a luxury car (and a BIG house)! Example
- 7. 7 Example: Problem A âĒPatient A has a blood pressure reading of 170/100 mmHg âĒ Data: 170/100 âĒ Information: BP of Patient A = 170/100 mmHg âĒ Knowledge: Patient A has high blood pressure âĒ Wisdom (or Decision): â Patient A needs to be investigated for cause of HT â Patient A needs to be treated with anti-hypertensives â Patient A needs to be referred to a cardiologist
- 8. 8 Example: Problem B âĒPatient B is allergic to penicillin. He was recently prescribed amoxicillin for his sore throat. âĒ Data: Penicillin, amoxicillin, sore throat âĒ Information: â Patient B has penicillin allergy â Patient B was prescribed amoxicillin for his sore throat âĒ Knowledge: â Patient B may have allergic reaction to his prescription âĒ Wisdom (or Decision): â Patient B should not take amoxicillin!!!
- 9. 9 Decision & DecisionMaking âĒ Decision â âA choice that you make about something after thinking about it : the result of decidingâ (Merriam-Webster Dictionary) âĒ Decision making â âThe cognitive process resulting in the selection of a course of action among several alternative scenarios.â (Wikipedia)
- 10. 10 LETâS TAKE ALOOK 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 someexamples of âdecisionsâ health care providers make
- 13. 13 Clinical Decisions âĒ PatientCare â What patient history to ask? â What physical examinations to do? â What investigations to order? âĒ Lab tests âĒ Radiologic studies (X-rays, CTs, MRIs, etc.) âĒ Other special investigations (EKG, etc.) â What diagnosis (or possible diagnosis) to make?
- 14. 14 Clinical Decisions âĒ PatientCare â What treatment to order/perform? âĒ Medications âĒ Surgery/Procedures/Nursing Interventions âĒ Patient Education/Advice for Self-Care âĒ Admission â How should patient be followed-up? â With good or poor response to treatment, what to do next? â With new information, what to do next?
- 15. 15 Clinical Decisions âĒ Management âHow to improve quality of care and clinical operations? â How to allocate limited budget & resources? â What strategies should the hospital pursue & what actions/projects should be done?
- 16. 16 Clinical Decisions âĒ PublicHealth â How to improve health of population? â How to investigate/control/prevent disease outbreak? â How to allocate limited budget & resources? â What areas of the countryâs public health need attention & what to do with it?
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- 18. 18 External Memory Knowledge Data LongTerm Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
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- 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: SuthanSrisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University To Err is Human 2: Memory
- 22. 22 To Err isHuman 3: Cognition âĒ Cognitive Errors - Example: Decoy Pricing The Economist Purchase Options âĒ Economist.com subscription $59 âĒ Print subscription $125 âĒ Print & web subscription $125 Ariely (2008) 16 0 84 The Economist Purchase Options âĒ Economist.com subscription $59 âĒ Print & web subscription $125 68 32 # of People # of People
- 23. 23 âĒ Medical Errors âDrugallergies âDrug interactions âĒ Missing Abnormal Lab Findings âĒ Clinical Practice Guidelines âĒ Bias in Judgment & Decision-Making Common Errors in Healthcare
- 24. 24 ROLES OF INFORMATION TECHNOLOGY INDECISION MAKING
- 25. 25 External Memory Knowledge Data LongTerm Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
- 26. 26 External Memory Knowledge Data LongTerm Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Possible Human Errors Possibility of Human Errors
- 27.
- 28. 28 âĒ Clinical DecisionSupport (CDS) âis a process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information to improve health and healthcare deliveryâ (Including both computer-based & non-computer-based CDS) (Osheroff et al., 2012) What Is A CDS?
- 29. 29 âĒ Computer-based clinicaldecision support (CDS): âUse of the computer [ICT] to bring relevant knowledge to bear on the health care and well being of a patient.â (Greenes, 2007) What Is A CDS?
- 30. 30 âĒ The realplace where most of the values of health IT can be achieved âĒ There are a variety of forms and nature of CDS Clinical Decision Support Systems (CDS)
- 31. 31 âĒ Expert systems âBasedon artificial intelligence, machine learning, rules, or statistics âExamples: differential diagnoses, treatment options CDS Examples Shortliffe (1976)
- 32. 32 âĒ Alerts &reminders âBased on specified logical conditions âĒ Drug-allergy checks âĒ Drug-drug interaction checks âĒ Drug-lab interaction checks âĒ Drug-formulary checks âĒ Reminders for preventive services or certain actions (e.g. smoking cessation) âĒ Clinical practice guideline integration (e.g. best practices for chronic disease patients) CDS Examples
- 33.
- 34. 34 âĒ Reference informationor evidence- based knowledge sources âDrug reference databases âTextbooks & journals âOnline literature (e.g. PubMed) âTools that help users easily access references (e.g. Infobuttons) CDS Examples
- 35.
- 36. 36 âĒ Pre-defined documents âOrdersets, personalized âfavoritesâ âTemplates for clinical notes âChecklists âForms âĒ 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 âĒ Simple UIdesigned to help clinical decision making âAbnormal lab highlights âGraphs/visualizations for lab results âFilters & sorting functions CDS Examples
- 39. 39 Abnormal Lab Highlights ImageSource: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
- 40. 40 External Memory Knowledge Data LongTerm 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 LongTerm 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 LongTerm 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 LongTerm 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 LongTerm 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 LongTerm 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 LongTerm 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 LongTerm 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 InferenceEngine Knowledge BaseOther Data âĒ Rules & Parameters âĒ Statistical data âĒ Literature âĒ Etc. âĒ System states âĒ Epidemiological/ surveillance data âĒ Etc. Example of CDS Architecture Other Systems
- 49. 49 ISSUES RELATED TO CDSIMPLEMENTATION
- 50. 50 âĒ How willCDS be implemented in real life? âĒ Will it interfere with user workflow? âĒ Will it be used by users? If not, why? âĒ What user interface design is best? âĒ What are most common user complaints? âĒ Who is responsible if something bad happens? âĒ How to balance reliance on machines & humans Human Factor Issues of CDS
- 51. 51 IBMâs Watson Image Source:socialmediab2b.com
- 52. 52 Image Source: englishmoviez.com Riseof the Machines?
- 53. 53 Issues âĒ CDSS asa supplement or replacement of clinicians? â The demise of the âGreek Oracleâ model (Miller & Masarie, 1990) The âGreek Oracleâ Model The âFundamental Theoremâ Friedman (2009) Human Factor Issues of CDS Wrong Assumption Correct Assumption
- 54. 54 âĒ Features withimproved clinical practice (Kawamoto et al., 2005) â Automatic provision of decision support as part of clinician workflow â Provision of recommendations rather than just assessments â Provision of decision support at the time and location of decision making â Computer based decision support âĒ Usability & impact on productivity Human Factor Issues of CDS
- 55. 55 Issues âĒ Alert sensitivity& alert fatigue Alert Fatigue
- 56. 56 âĒ Liabilities â Cliniciansas âlearned intermediariesâ âĒ Prohibition of certain transactions vs. Professional autonomy (see Strom et al., 2010) Ethical-Legal Issues of CDS
- 57.
- 58. 58 âĒ âUnanticipated andunwanted effect of health IT implementationâ (www.ucguide.org) âĒ Resources â www.ucguide.org â Ash et al. (2004) â Campbell et al. (2006) â Koppel et al. (2005) Unintended Consequences of CDS & Health IT
- 59. 59 Ash et al.(2004) Unintended Consequences of CDS & Health IT
- 60. 60 âĒ Errors inthe process of entering and retrieving information â A human-computer interface that is not suitable for a highly interruptive use context â Causing cognitive overload by overemphasizing structured and âcompleteâ information entry or retrieval âĒ Structure âĒ Fragmentation âĒ Overcompleteness Ash et al. (2004) Unintended Consequences of CDS & Health IT
- 61. 61 âĒ Errors incommunication & coordination â Misrepresenting collective, interactive work as a linear, clearcut, and predictable workflow âĒ Inflexibility âĒ Urgency âĒ Workarounds âĒ Transfers of patients â Misrepresenting communication as information transfer âĒ Loss of communication âĒ Loss of feedback âĒ Decision support overload âĒ Catching errors Ash et al. (2004) Unintended Consequences of CDS & Health IT
- 62. 62 âĒ Which typeof CDS should be chosen? âĒ What algorithms should be used? âĒ How to ârepresentâ knowledge in the system? âĒ How to update/maintain knowledge base in the system? âĒ How to standardize data/knowledge? âĒ How to implement CDS with good system performance? Technical Issues of CDS
- 63. 63 âĒ Choosing theright CDSS strategies âĒ Expertise required for proper CDSS design & implementation âĒ Everybody agreeing on the ârulesâ to be enforced âĒ Evaluation of effectiveness Other Issues
- 64. 64 âĒ Speed isEverything âĒ Anticipate Needs and Deliver in Real Time âĒ Fit into the Userâs Workflow âĒ Little Things (like Usability) Can Make a Big Difference âĒ Recognize that Physicians Will Strongly Resist Stopping âĒ Changing Direction Is Easier than Stopping âĒ Simple Interventions Work Best âĒ Ask for Additional Information Only When You Really Need It âĒ Monitor Impact, Get Feedback, and Respond âĒ Manage and Maintain Your Knowledge-based Systems Bates et al. (2003) âTen Commandmentsâ for Effective CDS
- 65. 65 âĒ There areseveral decisions made in a clinical patient care process âĒ Data leads to information, knowledge, and ultimately, decision & actions âĒ Human clinicians are not perfect and can make mistakes âĒ A clinical decision support systems (CDS) provides support for clinical decision making (to prevent mistakes & provide best patient care) âĒ A CDS can be computer-based or paper-based Key Points
- 66. 66 âĒ CDS comesin various forms, designs, and architecture âĒ There are many issues related to design, implementation and use of CDS â Technical Issues â Human Factor Issues â Ethical-Legal Issues Key Points
- 67. 67 âĒ Current mindset:CDS should be used to help, not replace, human providers âĒ Be attentive to workarounds, alert fatigues, and other unintended consequences of CDS â They can cause more danger to patients!! â They may lead users to abandon using CDS (a failure) âĒ There are recommendations on how to best design & implement CDS Key Points
- 68. 68 Intelligent & helpful machines Machines witha 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 âĒ 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. âĒ Ariely D. Predictably irrational: the hidden forces that shape our decisions. New York City (NY): HarperCollins; 2008. 304 p. âĒ 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. âĒ 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. âĒ 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. âĒ Friedman CP. A "fundamental theorem" of biomedical informatics. J Am Med Inform Assoc. 2009 Apr;16(2):169-170.
- 70. 70 References âĒ Greenes RA.Clinical decision support: the road ahead. Oxford (UK): Elsevier; 2007. 581 p. âĒ 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. âĒ 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. âĒ 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. âĒ Miller RA, Masarie FE. The demise of the "Greek Oracle" model for medical diagnostic systems. Methods Inf Med. 1990 Jan;29(1):1-2. âĒ Osheroff JA, Teich JM, Levick D, Saldana L, Velasco FT, Sittig DF, Rogers KM, Jenders RA. Improving outcomes with clinical decision support: an implementerâs guide. 2nd ed. Chicago (IL): Healthcare Information and Management Systems Society; 2012. 323 p.
- 71. 71 References âĒ Shortliffe EH.Computer-based medical consultations: MYCIN. New York (NY): Elsevier; 1976. 264 p. âĒ 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.