IT for MDs (Part 2)
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This document provides an overview of health information technology (IT) applications in hospitals. It discusses enterprise-wide hospital IT systems like electronic health records and computerized physician order entry. It also covers departmental IT systems and how they integrate. A key focus is on clinical decision support systems and their potential to improve patient care through functions like alerts, reminders, and access to evidence-based knowledge sources. The document notes both benefits of health IT, like reducing errors, but also risks such as alert fatigue and potential workarounds or unintended consequences of implementation.
![References
Miller RA, Masarie FE. The demise of the "Greek Oracle" model for medical
diagnostic systems. Methods Inf Med. 1990 Jan;29(1):1‐2.
Rogers EM. Diffusion of innovations. 5th ed. New York City (NY): Free
Press;2003. 551 p.
Riley RT, Lorenzi NM. Gaining physician acceptance of information
technology systems. Med Interface. 1995 Nov;8(11):78‐80, 82‐3.
Theera‐Ampornpunt N. Thai hospitals' adoption of information technology:
a theory development and nationwide survey [dissertation]. Minneapolis
(MN): University of Minnesota; 2011 Dec. 376 p.
56](https://image.slidesharecdn.com/itformds-part2-130216221623-phpapp02/85/IT-for-MDs-Part-2-56-320.jpg)
IT for MDs (Part 2)
- 1. IT for MDs (Part 2) Nawanan Theera‐Ampornpunt, MD, PhD Feb. 20, 2013 Faculty of Medicine Ramathibodi Hospital SlideShare.net/Nawanan
- 2. Recap from Part 1 Information is everywhere in medicine Computerizing health care is difficult because of its complexity Health IT has a role because “To Err Is Human” Health IT is just a tool. Whether it improves patient care and outcomes depends on its design and implementation 2
- 3. Health IT: What’s In A Word? Health Goal Information Value‐Add Technology Tools 3
- 4. Fundamental Theorem of Informatics 4 (Friedman, 2009) (Friedman, 2009)
- 5. Health IT Applications in Hospitals 5
- 6. Enterprise‐wide Hospital IT Master Patient Index (MPI) Admission‐Discharge‐Transfer (ADT) Electronic Health Records (EHRs) Computerized Physician Order Entry (CPOE) Clinical Decision Support Systems (CDSSs) Picture Archiving and Communication System (PACS) Nursing applications Enterprise Resource Planning (ERP) 6
- 7. Departmental IT 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 7
- 8. Hospital Information System Clinical Medical ADT Notes Records Workflow Pharmacy IS Operation Master Patient LIS Theatre Index (MPI) Order CCIS RIS Scheduling Portals Billing PACS 8 Modified from Dr. Artit Ungkanont’s slide
- 9. EHRs & HIS The Challenge ‐ Knowing What It Means Electronic Health Records (EHRs) Hospital Information System Electronic Medical (HIS) Records (EMRs) Electronic Patient Records (EPRs) Clinical Information System (CIS) Personal Health Computer‐Based Records (PHRs) Patient Records (CPRs) 9
- 10. EHR Systems Just electronic documentation? History Diag‐ Treat‐ ... & PE nosis ments Or do they have other values? 10
- 11. Functions that Should Be Part of EHR Systems Computerized Medication Order Entry Computerized Laboratory Order Entry Computerized Laboratory Results Physician Notes Patient Demographics Problem Lists Medication Lists Discharge Summaries Diagnostic Test Results Radiologic Reports 11 (IOM, 2003; Blumenthal et al, 2006)
- 13. Computerized Physician Order Entry (CPOE) Values No handwriting!!! Structured data entry: Completeness, clarity, fewer mistakes (?) No transcription errors! Entry point for CDSSs Streamlines workflow, increases efficiency 13
- 14. Clinical Decision Support Systems (CDSSs) The real place where most of the values of health IT can be achieved Expert systems Based on artificial intelligence, machine learning, rules, or statistics Examples: differential diagnoses, (Shortliffe, 1976) treatment options 14
- 15. Clinical Decision Support Systems (CDSSs) Alerts & reminders Based on specified logical conditions Examples: Drug‐allergy checks Drug‐drug interaction checks Drug‐disease checks Drug‐lab checks Drug‐formulary checks Reminders for preventive services or certain actions (e.g. smoking cessation) Clinical practice guideline integration 15
- 17. Clinical Decision Support Systems (CDSSs) Evidence‐based knowledge sources e.g. drug database, literature Simple UI designed to help clinical decision making E.g., Abnormal Lab Highlights 17
- 18. Clinical Decision Support Systems (CDSSs) PATIENT Perception CLINICIAN Attention Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION 18 From a teaching slide by Don Connelly, 2006
- 19. Clinical Decision Support Systems (CDSSs) PATIENT Perception CLINICIAN Abnormal lab Attention highlights Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION 19
- 20. Clinical Decision Support Systems (CDSSs) PATIENT Perception CLINICIAN Drug‐Allergy Attention Checks Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION 20
- 21. Clinical Decision Support Systems (CDSSs) PATIENT Perception Drug‐Drug CLINICIAN Interaction Attention Checks Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION 21
- 22. Clinical Decision Support Systems (CDSSs) PATIENT Perception Clinical Practice CLINICIAN Guideline Reminders Attention Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION 22
- 23. Clinical Decision Support Systems (CDSSs) PATIENT Perception CLINICIAN Attention Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference Diagnostic/Treatment Expert Systems DECISION 23
- 24. Clinical Decision Support Systems (CDSSs) CDSS as a replacement or supplement of clinicians? The demise of the “Greek Oracle” model (Miller & Masarie, 1990) The “Greek Oracle” Model The “Fundamental Theorem” 24 (Friedman, 2009)
- 25. Health IT for Medication Safety Ordering Transcription Dispensing Administration Automatic Electronic CPOE Medication Medication Dispensing Administration Records (e‐MAR) Barcoded Medication Barcoded Dispensing Medication Administration 25
- 26. Clinical Decision Support Systems (CDSSs) Some risks Alert fatigue 26
- 27. Workarounds 27
- 28. Unintended Consequences of Health IT “Unanticipated and unwanted effect of health IT implementation” (ucguide.org) Resources www.ucguide.org Ash et al. (2004) Campbell et al. (2006) Koppel et al. (2005) 28
- 30. Unintended Consequences of Health IT 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) 30
- 31. Unintended Consequences of Health IT Errors in the communication and coordination process 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) 31
- 32. Unintended Consequences of Health IT Errors in the communication and coordination process 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) 32
- 37. Critical Success Factors in Health IT Projects Communications of plans & progresses Physician & non‐physician user involvement Attention to workflow changes Well‐executed project management Adequate user training Organizational learning Organizational innovativeness Theera‐Ampornpunt (2011) 37
- 38. Health IT Successes & Failures 38 Kaplan & Harris‐Salamone (2009)
- 39. Health IT Successes & Failures What success is Different ideas and definitions of success Need more understanding of different stakeholder views & more longitudinal and qualitative studies of failure What makes it so hard Communication, Workflow, & Quality Difficulties of communicating across different groups makes it harder to identify requirements and understand workflow 39 Kaplan & Harris‐Salamone (2009)
- 40. Health IT Successes & Failures What We Know—Lessons from Experience Provide incentives, remove disincentives Identify and mitigate risks Allow resources and time for training, exposure, and learning to input data Learn from the past and from others 40 Kaplan & Harris‐Salamone (2009)
- 41. Health IT Change Management 41 Lorenzi & Riley (2000)
- 42. Health IT Change Management 42 Lorenzi & Riley (2000)
- 43. Health IT Change Management 43 Lorenzi & Riley (2000)
- 44. Health IT Change Management 44 Lorenzi & Riley (2000)
- 45. Considerations for a successful implementation of CPOE Considerations Motivation for implementation CPOE vision, leadership, and personnel Costs Integration: Workflow, health care processes Value to users/Decision support systems Project management and staging of implementation Technology Training and Support 24 x 7 Learning/Evaluation/Improvement 45 Ash et al. (2003)
- 46. Minimizing MD’s Change Resistance Involve physician champions Create a sense of ownership through communications & involvement Understand their values Be attentive to climate in the organization Provide adequate training & support 46 Ash et al. (2003)
- 47. Reasons for User Involvement Better understanding of needs & requirements Leveraging user expertise about their tasks & how organization functions Assess importance of specific features for prioritization Users better understand project, develop realistic expectations Venues for negotiation, conflict resolution Sense of ownership Pare & Sicotte (2006): Physician ownership important for clinical information systems 47 Ives & Olson (1984)
- 48. Gartner Hype Cycle Image source: Jeremy Kemp via http://en.wikipedia.org/wiki/Hype_cycle 48 http://www.gartner.com/technology/research/methodologies/hype‐cycle.jsp
- 49. Rogers’ Diffusion of Innovations: Adoption Curve Rogers (2003) 49
- 50. Balanced Focus of Informatics People Techno‐ Process logy 50
- 52. Summary Health IT applications vary in their “mechanisms of actions” (improvements in outcomes). Health IT can lead to “unintended consequences” and bad outcomes if poorly designed, implemented, or managed. Realizing benefits of health IT depends on critical success factors, mostly management aspects (including change management, project management) 52
- 53. Q & A... Download Slides SlideShare.net/Nawanan Contacts nawanan.the@mahidol.ac.th www.tc.umn.edu/~theer002 groups.google.com/group/ThaiHealthIT 53
- 54. 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. Ash JS, Stavri PZ, Kuperman GJ. A consensus statement on considerations for a successful CPOE implementation. J Am Med Inform Assoc. 2003 May‐ Jun;10(3):229‐34. Campbell, EM, Sittig DF, Ash JS, et al. Types of Unintended Consequences Related to Computerized Provider Order Entry. J Am Med Inform Assoc. 2006 Sep‐Oct; 13(5): 547‐556. Friedman CP. A "fundamental theorem" of biomedical informatics. J Am Med Inform Assoc. 2009 Apr;16(2):169‐70. 54
- 55. References Ives B, Olson MH. User involvement and MIS success: a review of research. Manage Sci. 1984 May;30(5):586‐603. Kaplan B, Harris‐Salamone KD. Health IT success and failure: recommendations from the literature and an AMIA workshop. J Am Med Inform Assoc. 2009 May‐Jun;16(3):291‐9. Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, Strom BL. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005 Mar 9;293(10):1197‐203. Lorenzi NM, Riley RT. Managing change: an overview. J Am Med Inform Assoc. 2000 Mar‐Apr;7(2):116‐24. 55
- 56. References Miller RA, Masarie FE. The demise of the "Greek Oracle" model for medical diagnostic systems. Methods Inf Med. 1990 Jan;29(1):1‐2. Rogers EM. Diffusion of innovations. 5th ed. New York City (NY): Free Press;2003. 551 p. Riley RT, Lorenzi NM. Gaining physician acceptance of information technology systems. Med Interface. 1995 Nov;8(11):78‐80, 82‐3. Theera‐Ampornpunt N. Thai hospitals' adoption of information technology: a theory development and nationwide survey [dissertation]. Minneapolis (MN): University of Minnesota; 2011 Dec. 376 p. 56