IT for MDs (Part 2)
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IT for MDs (Part 2)

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IT for MDs (Part 2) IT for MDs (Part 2) Presentation Transcript

  • IT for MDs (Part 2) Nawanan Theera‐Ampornpunt, MD, PhD Feb. 20, 2013 Faculty of Medicine Ramathibodi Hospital SlideShare.net/Nawanan
  • 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  implementation2
  • Health IT: What’s In A Word? Health Goal Information Value‐Add Technology Tools3
  • Fundamental Theorem of Informatics4 (Friedman, 2009) (Friedman, 2009)
  • Health IT  Applications  in Hospitals5
  • 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
  • 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 system7
  • Hospital Information System Clinical  Medical  ADT Notes Records Workflow Pharmacy IS Operation  Master  Patient  LIS Theatre Index (MPI) Order CCIS RIS Scheduling Portals Billing PACS8 Modified from Dr. Artit Ungkanont’s slide
  • 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
  • EHR Systems Just electronic documentation? History  Diag‐ Treat‐ ... & PE nosis ments Or do they have other values?10
  • 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 Reports11 (IOM, 2003; Blumenthal et al, 2006)
  • Computerized Physician Order Entry (CPOE)12
  • 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 efficiency13
  • 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 options14
  • 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 integration15
  • Example of “Alerts & Reminders”16
  • 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 Highlights17
  • Clinical Decision Support Systems (CDSSs) PATIENT Perception CLINICIAN Attention Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION18 From a teaching slide by Don Connelly, 2006
  • Clinical Decision Support Systems (CDSSs) PATIENT Perception CLINICIAN Abnormal lab  Attention highlights Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION19
  • Clinical Decision Support Systems (CDSSs) PATIENT Perception CLINICIAN Drug‐Allergy  Attention Checks Long Term Memory External Memory Working Memory Knowledge Data Knowledge Data Inference DECISION20
  • 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 DECISION21
  • 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 DECISION22
  • 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 DECISION23
  • 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)
  • Health IT for Medication Safety Ordering Transcription Dispensing Administration Automatic  Electronic  CPOE Medication  Medication  Dispensing Administration  Records  (e‐MAR) Barcoded Medication  Barcoded Dispensing Medication  Administration25
  • Clinical Decision Support Systems (CDSSs) Some risks  Alert fatigue26
  • Workarounds27
  • 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
  • Unintended Consequences of Health ITAsh et al. (2004)29
  • 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  OvercompletenessAsh et al. (2004)30
  • 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 errorsAsh et al. (2004)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 errorsAsh et al. (2004)32
  • Unintended Consequences of Health ITCampbell et al. (2006)33
  • Unintended Consequences of Health ITCampbell et al. (2006)34
  • Unintended Consequences of Health ITKoppel et al. (2005)35
  • Unintended Consequences of Health ITKoppel et al. (2005)36
  • 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 innovativenessTheera‐Ampornpunt (2011)37
  • Health IT Successes & Failures38 Kaplan & Harris‐Salamone (2009)
  • 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 workflow39 Kaplan & Harris‐Salamone (2009)
  • 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 others40 Kaplan & Harris‐Salamone (2009)
  • Health IT Change Management41 Lorenzi & Riley (2000)
  • Health IT Change Management42 Lorenzi & Riley (2000)
  • Health IT Change Management43 Lorenzi & Riley (2000)
  • Health IT Change Management44 Lorenzi & Riley (2000)
  • 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/Improvement45 Ash et al. (2003)
  • 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 & support46 Ash et al. (2003)
  • 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 systems47 Ives & Olson (1984)
  • Gartner Hype Cycle Image source: Jeremy Kemp via http://en.wikipedia.org/wiki/Hype_cycle48 http://www.gartner.com/technology/research/methodologies/hype‐cycle.jsp
  • Rogers’ Diffusion of Innovations: Adoption Curve Rogers (2003)49
  • Balanced Focus of Informatics People Techno‐ Process logy50
  • A Physician’s Story...51
  • 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
  • Q & A... Download Slides SlideShare.net/Nawanan Contacts nawanan.the@mahidol.ac.th www.tc.umn.edu/~theer002 groups.google.com/group/ThaiHealthIT53
  • 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
  • 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
  • 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