Perioperative Information Management Systems: Driving Discovery & Reliability In The Operating Room

1. Perioperative Information Management Systems: Driving Discovery & Reliability In The Operating Room Jesse M. Ehrenfeld, M.D., M.P.H. Assistant Professor of Anesthesiology Assistant Professor of Biomedical Informatics Director, Perioperative Data Systems Research Director, Center for Evidence-Based Anesthesia Medical Director, Perioperative Quality Co-Director, Vanderbilt Program for LGBTI Health Vanderbilt University School of Medicine Department of Anesthesiology jesse.ehrenfeld@vanderbilt.edu

2. Overview Part I – Perioperative Information Management Systems Overview & Functionality Reliable Processes Part II – Clinical Decision Support The Problem, The Need, Opportunities Part III – Our Research & The Future Using PIMS to Measure & Increase Reliability Predictive Modeling / Real Time Feedback Loops Case Studies: Blood Pressure Gaps & Glucose Control

3. Vanderbilt Department of Anesthesiology 60,000 adult and pediatric patient encounters 90 anesthetizing locations 20,000 patients are seen in the Vanderbilt Preoperative Evaluation Clinic (VPEC) 3,000 patients are seen annually in our Vanderbilt Interventional Pain Center 20,000 Vanderbilt adult and pediatric patients receive an anesthetic during a radiologic, gastrointestinal, or other diagnostic or therapeutic procedure Provide care in eight intensive care units, including six adult, the pediatric and neonatal intensive care units 4,000 anesthetics per year in the labor and delivery suite

4. Perioperative Data Systems Research Group • Undergraduate Students • Molly Cowan • Lindsay Lee • Shane Selig • Jacob Shiftan • Emily Wang • Graduate Students • AmlanBhattacharjee • Sean Chester • Kristen Eckstrand • AneeshGoel • Paul Hannam • Mary Marschner • Monika Jering • Ilana Stohl • Director • Jesse Ehrenfeld, MD • Project Manager • Angelo del Puerto • Data Warehouse Architect • Health Systems Database Analyst • Data Intelligence Analyst • Health Systems Database Analyst • Michealene Johnson • Dylan Snyder • Jason Denton • Chris Eldridge • Research Assistant • Research Analyst • Data Management Specialist • Rasheeda Lawson • KhensaniMarolen • TBD Last updated 7.2012

5. Overview Part I – Perioperative Information Management Systems Overview & Functionality Reliable Processes Part II – Clinical Decision Support The Problem, The Need, Opportunities Part III – Our Research & The Future Using PIMS to Measure & Increase Reliability Predictive Modeling / Real Time Feedback Loops Case Studies: Blood Pressure Gaps & Glucose Control

6. Biomedical Informatics

7. Perioperative Information Management Systems Accurate / reliable data recording Interface with hospital-wide EHR

8. PIMS Adoption in the U.S. – 2011 Small Large Stohl, Sandberg, Ehrenfeld. Assoc. of SCIP Compliance with Use of a PIMS. (submitted)

9. Areas Impacted by PIMS Ehrenfeld, J.M., Rehman, M.A. “Anesthesia Information Management Systems: Current Functionality and Limitations” (2010) Journal of Clinical Monitoring and Computing Aug 24

10. PIMS: Impact on Patients Chau, A., Ehrenfeld, J.M. “Using Real Time Clinical Decision Support to Improve Performance on Perioperative Quality and Process Measures” (2011) Anesthesiology Clinics

11. PIMS: Impact on Dept Management Chau, A., Ehrenfeld, J.M. “Using Real Time Clinical Decision Support to Improve Performance on Perioperative Quality and Process Measures” (2011) Anesthesiology Clinics

12. PIMS: Impact on Clinical Practice Chau, A., Ehrenfeld, J.M. “Using Real Time Clinical Decision Support to Improve Performance on Perioperative Quality and Process Measures” (2011) Anesthesiology Clinics

13. Mobile PIMS: VigiVUTM

14. Mobile PIMS: VigiVUTM

15. Push Notifications

16. Process Reliability • Processes are collections of systems and actions following prescribed procedures for bringing about a result. • Reliability of any processes can be determined using data when process failure criteria are established. • Results of the analysis can be graphically displayed, problems identified, categorized and identified for corrective action. • The hardest part of any reliability analysis is getting the data.

17. Process Reliability in Health Care Given our intentions, as talented providers, why are clinical processes carried out at such low levels of reliability? Don’t show up for work wanting to provide bad care! ‘‘It’s the system, not the people’’ – true, but not helpful as we aim to improve our processes Resar, RK. Making Noncatastrophic Health Care Processes Reliable. Health Serv Res. 2006.

18. Process Reliability in Health Care • Reasons for reliability gap: • Health care improvement methods excessively dependent on vigilance and hard work • We benchmarking to mediocre outcomes in health care – leads to false sense of process reliability • Allow clinical autonomy  creates wide, unjustifiable, performance variation • Processes not designed to meet specific, articulated reliability goals. Resar, RK. Making Noncatastrophic Health Care Processes Reliable. Health Serv Res. 2006.

19. Overview Part I – Perioperative Information Management Systems Overview & Functionality Reliable Processes Part II – Clinical Decision Support The Problem, The Need, Opportunities Part III – Our Research & The Future Using PIMS to Measure & Increase Reliability Predictive Modeling / Real Time Feedback Loops Case Studies: Blood Pressure Gaps & Glucose Control

20. Clinical Decision Support

21. Problem/Need • Why do we need clinical decision support? • Mistakes happen • You own a calculator don’t you? • Knowledge evolves • Pubmed / Medline

22. Problem/Need

23. General Solution: Decision Support “Clinical consultation systems that use population statistics and expert knowledge to offer real-time advice to clinicians…they provide for patient specific information management and consultation.” - EH Shortliffe, JAMA 1987;258:61-6 Clinical Decision Support Objective: assist clinicians in • (1) making the best clinical decision and • (2) following recommended practices Wide range of tools: • very simple data field checks • complex calculations performed in the background Potential to changes approaches to patient safety • Reactive  Proactive

24. General Solution: Decision Support • Goals in the Operating Room: • Optimize outcomes by enabling physicians • Reduce errors by providing reminders • Increase skill by sharing information Data Data Data Data

25. OR Decision Support Hierarchy

26. OR Decision Support Hierarchy

27. OR Decision Support Hierarchy

28. OR Decision Support Hierarchy

29. Clinical Decision Support I’m not convinced. Does it really make a difference? Perioperative Information Management Systems (PIMS) Mediate Improved SCIP Compliance Compared to Hospitals Without PIMS Stohl, Sandberg, Ehrenfeld. Assoc. of SCIP Compliance with Use of an PIMS. (submitted)

30. Decision Support Version 1.0 • Outside the Operating Room • Web-based tools • Computerized Physician Order Entry • PDA, iPhone applications • Inside the Operating Room • Anesthesia Information Management Systems

31. Clinical Decision Support 2.0 Machine Learning Techniques Contextual Information Processing Advanced Algorithms Real-Time Data Artificial Intelligence Previous Cases Clinical Guidlines

32. Clinical Decision Support 2.0 SURGICAL EVENT (blood loss, allergy, etc) or EXTERNAL EVENT (lab values, new info, etc) IDEAL RESPONSE SUGGESTIONS / GUIDELINES / STATISTICS DATA FROM ALL PREVIOUS CASES

33. Clinical Decision Support 2.0 Envelop of Care Case Progression Over Time

34. Clinical Decision Support 2.0 Envelop of Care Case Progression Over Time

35. Clinical Decision Support 2.0 Envelop of Care Case Progression Over Time

36. Clinical Decision Support 2.0 Envelop of Care Alert Case Progression Over Time

37. Clinical Decision Support 2.0 Envelop of Care Alert Case Progression Over Time

38. Clinical Decision Support 2.0 Envelop of Care Alert Case Progression Over Time

39. Alerting • Once you generate knowledge/ information, how do you disseminate it? • Alerting modalities: Who and How? • Identify appropriate provider • Get their attention: • On-screen pop-ups • Pager messages • Emails

41. Limitations/Factors • Usability: • Ability to provide a useful function. • Does it do anything of value?

42. Limitations/Factors • Ergonomics: • The study of how people interact with their environment. • Can physicians use it?

43. Limitations/Factors • Latency: • Delays in usage and availability. • Will it work in a time-sensitive scenario?

44. Limitations/Factors • Interconnectivity / Interoperability: • Ability to connect to other sources of information and share information effectively. • Does it network well with existing infrastructure?

45. Limitations/Factors • Ability to Adapt: • If we don’t have the knowledge, can the system be used to generate missing info? • Can it develop a hypothesis?

46. Summary: Process Monitoring & Control • Goal: right info right time right person • Keys to electronic process monitoring • Process models • Process exceptions • Alert Generation

47. Overview Part I – Perioperative Information Management Systems Overview & Functionality Reliable Processes Part II – Clinical Decision Support The Problem, The Need, Opportunities Part III – Our Research & The Future Using PIMS to Measure & Increase Reliability Predictive Modeling / Real Time Feedback Loops Case Studies: Blood Pressure Gaps & Glucose Control

48. Required Components Measure Outcomes Alerting Mechanism Real-Time Data Capture Define Norms of Practice / Baseline

49. Required Components Increasing Difficulty Measure Outcomes Alerting Mechanism Real-Time Data Capture Define Norms of Practice / Baseline

50. Required Components Decision Support Engine Increasing Difficulty Measure Outcomes Alerting Mechanism Real-Time Data Capture Define Norms of Practice / Baseline