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  • 1. IT & Decision Support Systems in Hospital Supply Chains EGIE 512 Hospital Logistics and Supply Chain Management Nawanan Theera-Ampornpunt, M.D., Ph.D. March 20, 2014 http://www.SlideShare.net/Nawanan
  • 2. 2 2003 M.D. (First-Class Honors) (Ramathibodi) 2009 M.S. in Health Informatics (U of MN) 2011 Ph.D. in Health Informatics (U of MN) 2012 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 SlideShare.net/Nawanan http://groups.google.com/group/ThaiHealthIT Introduction
  • 3. 3 Outline • Healthcare & Information • Health Information Technology • Clinical Decision Making • Clinical Decision Support Systems – Definitions – Types & examples • Issues Related to CDS Implementation • Other Decision Support Systems • Summary
  • 4. 4 Let’s take a look at these pictures...
  • 5. 5 Image Source: Guardian.co.uk Manufacturing
  • 6. 6 Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3 Banking
  • 7. 7 ER - Image Source: nj.com Healthcare (on TV)
  • 8. 8 Healthcare (at an undisclosed hospital)
  • 9. 9 • 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 Why Healthcare Isn’t Like Any Others
  • 10. 10 • Large variations & contextual dependence Input Process Output Patient Presentation Decision- Making Biological Responses Why Healthcare Isn’t Like Any Others
  • 11. 11 Input Process Output Transfer Banking Value-Add - Security - Convenience - Customer Service Location A Location B But...Are We That Different?
  • 12. 12 Input Process Output Assembling Manufacturing Raw Materials Finished Goods Value-Add - Innovation - Design - QC But...Are We That Different?
  • 13. 13 Input Process Output Patient Care Health care Sick Patient Well Patient Value-Add - Technology & medications - Clinical knowledge & skills - Quality of care; process improvement - Information But...Are We That Different?
  • 14. 14 Engineer’s Perspectives • Logistics & Supply Chain (Administrative) • Focus on Processes • Analytical, Systematic Mind • Tracking & Improving – Patient Flow – Materials Flow (Drugs, Documents, Equipments) – Information Flow • Main Objectives – Efficiency – Variability – Traceability Clinician’s Perspectives • Patient Care (Clinical) • Focus on Outcomes • Specialized Clinical Mind • Improving – Patient Care Process – Healthcare Delivery • Main Objectives – Quality • Effectiveness • Safety • Timeliness Engineers & Clinicians
  • 15. 15 Back to something simple...
  • 16. 16 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) What Clinicians Want?
  • 17. 17 • 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. High-Quality Care
  • 18. 18 Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8. Information Is Everywhere in Healthcare
  • 19. 19 Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8. “Information” in Medicine
  • 20. 20 Why We Need ICT in Healthcare? #1: Because information is everywhere in healthcare
  • 21. 21 (IOM, 2001)(IOM, 2000) (IOM, 2011) Landmark IOM Reports
  • 22. 22 • 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 Patient Safety
  • 23. 23 • 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 IOM Reports Summary
  • 24. 24 • Perception errors Image Source: interaction-dynamics.com To Err Is Human 1: Perception
  • 25. 25 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 2: Attention
  • 26. 26 Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University To Err Is Human 3: Memory
  • 27. 27 • 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 To Err Is Human 4: Cognition
  • 28. 28Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr 2;330(7494):781-3. “Everyone makes mistakes. But our reliance on cognitive processes prone to bias makes treatment errors more likely than we think” Cognitive Biases in Healthcare
  • 29. 29 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. Cognitive Biases in Healthcare
  • 30. 30 Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med. 2003 Aug;78(8):775-80. Cognitive Biases in Healthcare
  • 31. 31 • Medication Errors –Drug Allergies –Drug Interactions • Ineffective or inappropriate treatment • Redundant orders • Failure to follow clinical practice guidelines Common Errors
  • 32. 32 Why We Need ICT in Healthcare? #2: Because healthcare is error-prone and technology can help
  • 33. 33 Why We Need ICT in Healthcare? #3: Because access to high-quality patient information improves care
  • 34. 34 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 Health IT
  • 35. 35 Health Information Technology Goal Value-Add Tools Health IT: What’s in a Word?
  • 36. 36 • Patient’s Health • Population’s Health • Organization’s Health (Quality, Reputation & Finance) “Health” in Health IT
  • 37. 37 Hospital Information System (HIS) Computerized Provider Order Entry (CPOE) Electronic Health Records (EHRs) Picture Archiving and Communication System (PACS) Screenshot Images from Faculty of Medicine Ramathibodi Hospital, Mahidol University Various Forms of Health IT
  • 38. 38 mHealth Biosurveillance Telemedicine & Telehealth Images from Apple Inc., Geekzone.co.nz, Google, HealthVault.com and American Telecare, Inc. Personal Health Records (PHRs) and Patient Portals Still Many Other Forms of Health IT
  • 39. 39 • Guideline adherence • Better documentation • Practitioner decision making or process of care • Medication safety • Patient surveillance & monitoring • Patient education/reminder Values of Health IT
  • 40. 40 • 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 Enterprise-Wide Hospital IT
  • 41. 41 • 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 Departmental IT in Hospitals
  • 42. 42 • Business Intelligence • Data Mining/ Utilization • MIS • Research Informatics • E-learning • CDSS • HIE • CPOE • PACS • EHRs Enterprise Resource Planning (ERP) • Finance • Materials • HR • ADT • HIS • LIS • RIS Strategic Operational ClinicalAdministrative Position may vary based on local context 4 Ways IT Can Support Hospitals
  • 43. 43 The Challenge - Knowing What It Means Electronic Medical Records (EMRs) Computer-Based Patient Records (CPRs) Electronic Patient Records (EPRs) Electronic Health Records (EHRs) Personal Health Records (PHRs) Hospital Information System (HIS) Clinical Information System (CIS) EHRs & HIS
  • 44. 44 Computerized Physician Order Entry (CPOE)
  • 45. 45 Values • No handwriting!!! • Structured data entry: Completeness, clarity, fewer mistakes (?) • No transcription errors! • Streamlines workflow, increases efficiency Computerized Physician Order Entry (CPOE)
  • 46. 46 Ordering Transcription Dispensing Administration CPOE Automatic Medication Dispensing Electronic Medication Administration Records (e-MAR) Barcoded Medication Administration Barcoded Medication Dispensing Stages of Medication Process
  • 47. 47 CLINICAL DECISION MAKING
  • 48. 48 WHAT IS A DECISION?
  • 49. 49 Wisdom Knowledge Information Data Data-Information-Knowledge- Wisdom (DIKW) Pyramid
  • 50. 50 Wisdom Knowledge Information Data Contextualization/ Interpretation Processing/ Synthesis/ Organization Judgment Data-Information-Knowledge- Wisdom (DIKW) Pyramid
  • 51. 51 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
  • 52. 52 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
  • 53. 53 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!!!
  • 54. 54 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)
  • 55. 55 LET’S TAKE A LOOK AT PATIENT CARE PROCESS
  • 56. 56 Patient Care Image Sources: (Left) Faculty of Medicine Ramathibodi Hospital (Right) /en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)
  • 57. 57 EXERCISE 1 Provide some examples of “decisions” health care providers make
  • 58. 58 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?
  • 59. 59 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?
  • 60. 60 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?
  • 61. 61 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?
  • 62. 62 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
  • 63. 63 IOM (2000) “To Err Is Human”
  • 64. 64 ROLES OF INFORMATION TECHNOLOGY IN DECISION MAKING
  • 65. 65 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
  • 66. 66 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
  • 67. 67 CLINICAL DECISION SUPPORT SYSTEMS (CDS)
  • 68. 68 • 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?
  • 69. 69 • 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?
  • 70. 70 • 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)
  • 71. 71 • Expert systems –Based on artificial intelligence, machine learning, rules, or statistics –Examples: differential diagnoses, treatment options CDS Examples Shortliffe (1976)
  • 72. 72 • 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
  • 73. 73 Example of “Reminders”
  • 74. 74 • 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
  • 75. 75 Infobuttons Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html
  • 76. 76 • Pre-defined documents –Order sets, personalized “favorites” –Templates for clinical notes –Checklists –Forms • Can be either computer-based or paper-based CDS Examples
  • 77. 77 Order Sets Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm
  • 78. 78 • Simple UI designed to help clinical decision making –Abnormal lab highlights –Graphs/visualizations for lab results –Filters & sorting functions CDS Examples
  • 79. 79 Abnormal Lab Highlights Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
  • 80. 80 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
  • 81. 81 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
  • 82. 82 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
  • 83. 83 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
  • 84. 84 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
  • 85. 85 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
  • 86. 86 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)
  • 87. 87 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
  • 88. 88 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
  • 89. 89 ISSUES RELATED TO CDS IMPLEMENTATION
  • 90. 90 • 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
  • 91. 91 IBM’s Watson Image Source: socialmediab2b.com
  • 92. 92 Image Source: englishmoviez.com Rise of the Machines?
  • 93. 93 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
  • 94. 94 • 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
  • 95. 95 Issues • Alert sensitivity & alert fatigue Alert Fatigue
  • 96. 96 • Liabilities – Clinicians as “learned intermediaries” • Prohibition of certain transactions vs. Professional autonomy (see Strom et al., 2010) Ethical-Legal Issues of CDS
  • 97. 97 Workarounds
  • 98. 98 • “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
  • 99. 99 Ash et al. (2004) Unintended Consequences of CDS & Health IT
  • 100. 100 • 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
  • 101. 101 • 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
  • 102. 102 • 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
  • 103. 103 • 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
  • 104. 104 • 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
  • 105. 105 OTHER DECISION SUPPORT SYSTEMS
  • 106. 106 • Provides information needed to manage an organization (e.g. a hospital) effectively and efficiently • A broad category of information systems – Administrative reports – Enterprise resource planning (ERP) – Supply Chain Management (SCM) – Customer Relationship Management (CRM) – Project management tools – Knowledge management tools – Business intelligence (BI) Management Information Systems (MIS)
  • 107. 107 • Allows for – Data analysis – Correlation – Trending – Reporting of data across multiple sources Health IT Workforce Curriculum Version 2.0/Spring 2011 Business Intelligence (BI)
  • 108. 108 • Examples – Clinical and Financial Analytics and Decision Support – Query and Reporting Tools – Data Mining – Online Scoreboards and Dashboards Business Intelligence & Data Warehousing for Healthcare. Clinical Informatics Wiki. 2008. Available from: http://www.informatics- review.com/wiki/index.php/Business_Intelligence_&_Data_Warehousing_for_Healthcare Health IT Workforce Curriculum Version 2.0/Spring 2011 Business Intelligence (BI)
  • 109. 109 Image Source: http://www.hiso.or.th/dashboard/ Data Reporting Systems
  • 110. 110 Image Source: http://www.inetsoft.com/business/solutions/applying_business_intelligence_to_manufacturing/ Business Intelligence (BI)
  • 111. 111 Image Source: https://www.sas.com/technologies/bi/entbiserver/ Business Dashboards
  • 112. 112 • 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
  • 113. 113 • 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
  • 114. 114 • 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 • There are other administrative (non-clinical) decision support systems as well Key Points
  • 115. 115 Intelligent & helpful robots Intelligent humanistic robots in a human world Machines that replace humans for a “better” world HAL 9000 Data David NS-5 Dangerous killer machines What Will The Future Be for Health Care?
  • 116. 116 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.
  • 117. 117 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.
  • 118. 118 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.