Medical Simulation 2.0: Improving value-based healthcare delivery


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  • Key FindingsThe nation is likely to experience a shortage of physicians which will grow over time.Though the supply of physicians is projected to increase modestly between now and 2025, the demand for physicians is projected to increase even more sharply.Aging of the population may drive demand sharply upward for specialties that predominantly serve the elderly (e.g., oncologists).The US Census Bureau projects that the US population will grow by more than 50 million (to 350 million) between 2006 and 2025. This alone will likely lead to a considerable increase in the demand for physician services.Growth in future demand could double if visit rates by age continue to increase at the same pace they have in recent years – with the greatest growth in utilization among those 75+ years of age.Universal health care coverage could add 4 percent to overall demand for physicians; this would increase the projected physician shortfall by 31,000 physicians (25 percent).Even a modest increase in physician productivity could do more to alleviate the projected gap between supply and demand than any other supply-side change but productivity improvements in health care have been hard to achieve as care has become more complex.Future demand for physicians would be significantly reduced if physician assistants and nurse practitioners play a larger role in patient care.Even a robust expansion of GME capacity (from 25,000 new entrants per year to 32,000) would only reduce the projected shortage in 2025 by 54,000 physicians (43 percent). Future physician workforce planningchanges in medical school capacity and the availability of GME positions as part of a broader strategymake more effective use of the limited physician supply, and to improve productivity;Recognize and respond to physician life-style concernsImprove data collection and workforce studies and expand collaboration among health professions organizations on data and workforce policies.
  • Medical Simulation 2.0: Improving value-based healthcare delivery

    1. 1. ©2011 MFMER | slide-1 Medical Simulation 2.0 Improving value-based healthcare delivery Yue Dong, M.D. Mayo Clinic Multidisciplinary Simulation Center METRIC (Multidisciplinary Epidemiology and Translational Research in Intensive Care) Mayo Clinic Center for Science of Healthcare Delivery
    2. 2. Disclosures • No financial COI
    3. 3. ©2011 MFMER | slide-3 Mayo Clinic Multidisciplinary Simulation Center
    4. 4. Fellows Anesthesiologist Medical Pulmonologist Intensivist Simulation Medicine Statistician Administration Informatics ER medicine Research Coordinator Collaborators Pediatrician METRIC (Multidisciplinary Epidemiology and Translational Research in Intensive Care)
    5. 5. Multidisciplinary Collaboration Dr. Hutian Lu Dr. Susan Lu, Sura K Ak Qudah Dr. Ashish Gupta Dr. Mark Van Oyen, Pooyan Kazemian Bjorn, Berg
    6. 6. Objectives • Challenges facing healthcare professionals to improve the healthcare delivery: Systems Thinking and Patient Safety • Summarize simulation and modeling tools for systematic analysis and optimization complex system processes and interventions • Describe common computer simulation applications for quality improvement and patient safety in ICU.
    7. 7. ©2011 MFMER | slide-7 © 2010 Mayo Foundation for Medical Education and Research
    8. 8. Vis-à-vis International Sepsis Campaign Institution Compliance, % Mortality, % Spain •Pre-intervention 5.3 44 •Post- intervention 10.0 39.7 International •Pre-intervention 10.9 37 •Post- intervention 41.3 30 Mayo •Baseline 10.5 31.5 •Sepsis QI 58.4 22.0
    9. 9. Time, June 22, 2010
    10. 10. Health System Safety • 33.6 million admissions to U.S. hospitals in 1997 • 44,000- 98,000 Americans die each year as a result of medical errors. • Total cost $17- $29 billion
    11. 11. *Rate of growth declining in recent years, McKinsey 2011
    12. 12. U.S. spends most, but lower life expectancy relative to developed peers Source: OECD Health Data, 2008 ~$3 Trillion (~1/5 GDP) ~ 30% may be waste USA
    13. 13. Green LW. Making research relevant: if it is an evidence-based practice, where's the practice- based evidence? Family Practice 2008; 25: i20–i24
    14. 14. “Blue Highways” on the NIH Roadmap Practice-based research Phase 3 and 4 clinical trials Observational studies Survey research Basic science research Preclinical studies Animal research Human clinical research Controlled observational studies Phase 3 clinical trials T1 Case series Phase 1 and 2 clinical trials Clinical practice Delivery of recommended care to right pt at right time Identification of new clinical questions and gaps in care T2 Translation to humans T2 Guideline development Meta-analyses Systematic reviews Translation to patients T3 Dissemination research Implementation research Translation to practice Westfall JM et al: JAMA 297:403, 2007 Bench Bedside Practice
    15. 15. The fundamental problem with the quality of American medicine is that we’ve failed to view delivery of health care as a science. • understanding disease biology • finding effective therapies • insuring those therapies are delivered effectively Peter Pronovost
    16. 16. Temporal Trends in Rates of Patient Harm Resulting from Medical Care Temporal Trends in Rates of Patient Harm Resulting from Medical Care. Landrigan, et al, N Engl J Med 2010 ; 363 : 2124 - 2134
    17. 17. Complexity in ICU
    18. 18. Critical Care at Mayo Courtesy of Dr. Vitaly Herasevich
    19. 19. Health care as a complex adaptive system W. B. Rouse. Health care as a complex adaptive system: Implications for design and management. The Bridge, 38(1), Spring 2008.
    20. 20. Complex adaptive systems • nonlinear and dynamic, system behaviors may appear to be random or chaotic. • composed of independent agents whose behavior is based on physical, psychological, or social rules rather than the demands of system dynamics. • agents’ needs or desires, their goals and behaviors are likely to conflict. In response to these conflicts or competitions, agents tend to adapt to each other’s behaviors. • agents are intelligent. As they experiment and gain experience. • adaptation and learning tend to result in self-organization. Behavior patterns emerge rather than being designed into the system. • no single point(s) of control. Rouse, 2000
    21. 21. William Worrall Mayo, MD “Left open for further thought and research”
    22. 22. The world is a complex system of systems Communication $ 3.96 Tn Transportation $ 6.95 Tn Leisure / Recreation / Clothing $ 7.80 Tn Healthcare $ 4.27 Tn Food $ 4.89 Tn Infrastructure $ 12.54 Tn Govt. & Safety $ 5.21 Tn Finance $ 4.58 Tn Electricity $ 2.94 Tn Education $ 1.36 Tn Water $ 0.13 Tn Global system-of-systems $54 Trillion (100% of WW 2008 GDP) Same Industry Business Support IT Systems Energy Resources Machinery Materials Trade Legend for system inputs IBM analysis based on OECD data. Note: Size of bubbles represents systems’ economic values. Arrows represent the strength of systems’ interaction. Source: IBM Institute for Business Value analysis of Organisation for Economic Co-operation and Development (OECD) data.
    23. 23. ©2011 MFMER | slide-27 System integration
    24. 24. Human beings make mistakes because the systems, tasks and processes they work in are poorly designed. Dr. Lucian Leape Every system is perfectly designed to get the results it gets. Dr. Donald M. Berwick Systems approach to improve patient safety
    25. 25. Transforming healthcare: a safety imperative L Leape, D Berwick, C Clancy, et al. Qual Saf Health Care 2009; 18:424-428
    26. 26. Swiss Cheeses Model ©2011 MFMER | slide-30
    27. 27. Outcome + Safety + Service Value = Cost over time Leveraging for Highest Value Smoldt RK, Cortese DA. Pay-for-performance or pay for value? Mayo Clinic Proceedings 2007;82:210-3
    28. 28. Systems Approach to Improve Patient Safety Martinez, et al. Anesth Analg 2010 110: 307-311
    29. 29. “ Simply educating and training more physicians will not be enough to address these shortages. Complex changes such as improving efficiency, reconfiguring the way some services are delivered and making better use of our physicians will also be needed.” The Complexities of Physician Supply and Demand: Projections Through 2025. 2008 AAMC
    30. 30. 2011, Health IT and Patient Safety: Building Safer Systems for Better Care, Committee on Patient Safety and Health Information Technology; Institute of Medicine
    31. 31. Adjust structure and process to eliminate or minimize risks of health care-associated injury, before they have an adverse event- impact on the outcomes of care Donabedian. Evaluating of Medical Care. The Milbank Memorial Fund Quarterly, Vol. 44, No. 3, Pt. 2, 1966 (pp. 166–203)
    32. 32. System Interventions Systems Engineering Initiative for Patient Safety (SEIPS) Work system design for patient safety: the SEIPS model. Carayon P, et al . Qual Saf Health Care. 2006 Dec;15 Suppl 1:i50-8. Review.
    33. 33. WHO Global Priorities for Patient Safety Research Bates DW, et al. Global priorities for patient safety research. BMJ 2009;338:b1775
    34. 34. Structure, process or outcome: which contributes most to patients' overall assessment of healthcare quality? • Experiences regarding process aspects explained most of the variance in the global rating (16.4– 23.3%), followed by structure aspects (8.1–21.0%). Experiences regarding outcome did not explain much variance in the global rating in any of the patient groups (5.3–13.5%). • What is patient-centered care? BMJ Qual Saf doi:10.1136/bmjqs.2010.042358
    35. 35. “We can’t solve problems by using the same kind of thinking we used when we created them”
    36. 36. Delivery System
    37. 37. Order (2 lanes !) Pay Pickup System Design Thinking Service centered = Customer centered
    38. 38. Escape Fire, Berwick, 2006 Mistake Proofing/Force Functioning
    39. 39. • designing the system to prevent errors • designing procedures to make errors visible when they do occur so that they may be intercepted • designing procedures for mitigating the adverse effects of errors when they are not detected and intercepted Nolan, 2000 BMJ Department of Health and the Design Council in England 2003
    40. 40. Common patient safety improvement efforts • Culture • Crew resource management • Event reporting: close-claim; near- miss • Root cause analysis • Human factor design • Simulation • Technology • Lean, six-sigma • Etc.
    41. 41. Terminology • Model vs. Simulation (noun) Model can be used WRT conceptual, specification, or computational levels Simulation is rarely used to describe the conceptual or specification model Simulation is frequently used to refer to the computational model (program) • Model vs. Simulate (verb) To model can refer to development at any of the levels To simulate refers to computational activity Steve Park and Larry Leemis
    42. 42. Clinical Micro-system Clinical Delivery System Patient Providers Processes Complexity/SOP Bottleneck/ Waste/ no value added Education/Training Supply/Demand
    43. 43. • Simulation is the imitation or representation of one act or system by another. • Healthcare simulations can be said to have four main purposes – education, assessment, research, and health system integration to facilitate patient safety... • Simulations may also add to our understanding of human behavior in the true–to–life settings in which professionals operate.
    44. 44. Simulation based medical education
    45. 45. The 11 dimensions of simulation applications. Gaba D M Qual Saf Health Care 2004;13:i2-i10 ©2004 by BMJ Publishing Group Ltd The 11 dimensions of simulation applications
    46. 46. Medical Education • Study the effectiveness of simulation based medical education (SBME) • Developing valid outcome assessment instrument, stretch measurement endpoints from the simulation lab into clinical practice (association studies) • Provide highly reliable data for decision support and high-stakes testing.
    47. 47. ©2011 MFMER | slide-51 Simulation-based objective assessment Discern Clinical Proficiency in Central Line Placement, Dong, et. al, 2010
    48. 48. ©2011 MFMER | slide-52 Patient Outcomes Mastery n=26 Control n=24 Adjusted Analysis OR (95%CI) p-value # Patients/Repairs 48/72 38/58 Intra-op Complications* At least one of any type 5 (7) 17 (29) OR 0.15 (0.04, 0.59) 0.006 Post-op Complications* At least one of any type 4 (9) 15 (26) OR 0.17 (0.04, 0.74) 0.018 Overnight Stay* 5 (7) 12 (21) OR 0.37 (0.08, 1.67) 0.20 *N (%) Simulation-Based Mastery Learning Improves Patients Outcomes in Laparoscopic Inguinal Herniorrhaphy, Benjamin Zendejas, MD, MSc
    49. 49. Skill Acquisition Curve Impact of Zero-Risk Training CP1345275-1 Clinical competence Metricassessment (e.g.,compositescore) Time Traditional training Safety standard Simulation-based training Dong et al, Chest 2010
    50. 50. The First Research Consensus Summit of the Society for Simulation in Healthcare • Simulation for Learning and Teaching Procedural Skills: The State of the Science • Simulation-Based Team Training in Healthcare • A Path to Better Healthcare Simulation Systems: Leveraging the Integrated Systems Design Approach • The Study of Factors Affecting Human and Systems Performance in Healthcare using Simulation • Literature Review: Instructional Design and Pedagogy Science in Healthcare Simulation • Evaluating the Impact of Simulation on Translational Patient Outcomes • Research Regarding Methods of Assessing Learning Outcomes • Research Regarding Debriefing as Part of the Learning Process • Simulation-Based Assessment of the Regulation of Healthcare Professionals • Reporting Inquiry in Simulation Simul Healthc. 2011 Aug;6 Suppl:S1-9.
    52. 52. Simulation in Healthcare Simulation 1.0 • Simulation as subject • At simulation center • Education Training effectiveness Psychometric qualities Ecological validity Simulation 2.0 • Simulation as tool • Everywhere • Daily practices System integration Human factors Usability of device, process, etc.
    53. 53. Military Simulation Spectrum J G Taylor, Modeling and Simulation of Land Combat, ed L G Callahan, Georgia Institute of Technology, Atlanta, GA, 1983
    54. 54. Human factor and Usability research • Using simulation as a tool to study human performance variation under different “stress conditions” (fatigue, cognition, workload, etc.) • Investigating provider behaviors/tasks Observation “in the wild” (Ethnography) Simulation environment • Conduct usability testing of devices instrument and processes, using information driven approach for new system design • Evaluation of the impact on clinical practices
    55. 55. The effect of drug concentration expression on epinephrine dosing errors: a randomized trial Wheeler DW, Carter JJ, Murray LJ, Degnan BA, Dunling CP, Salvador R, et al.. Ann Intern Med 2008;148:11-4. (1 mg in 1 mL) (1 mL of a 1:1000 solution)
    56. 56. Ahmed, et al. Critical Care Medicine, 39(7) 1626-1634 The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance
    57. 57. Complexity of Sepsis Resuscitation in ICU Adopted from: Network medicine--from obesity to the "disease". Barabási AL., N Engl J Med. 2007 Jul 26;357(4):404-7. SHOCK DIC AKI ALI Physician RT Pharmacist Nurse Time  Baseline PatientOutcome, ProviderSatisfactions
    58. 58. Trial and error ©2011 MFMER | slide-63
    59. 59. How about the population at risk
    60. 60. Modeling & Simulation
    61. 61. Computer Simulation R. P. Science, New Series, Vol. 256, No. 5053 (Apr. 3, 1992)
    62. 62. Simulation in manufacturing and business: A review M. Jahangirian, T. Eldabi, A. Naseer, L.K. Stergioulas and T. Young, Simulation in manufacturing and business: a review, European Journal of Operational Research 203 (2010), pp. 1–13
    63. 63. Simulation-based Engineering and Science
    64. 64. ©2011 MFMER | slide-69
    65. 65. Simulation and Healthcare Delivery
    66. 66. System Engineering Tools for Healthcare Delivery Proctor P. Reid, W. Dale Compton, Jerome H. Grossman, and Gary Fanjiang, Editors, Committee on Engineering and the Health Care System, Institute of Medicine and National Academy of Engineering, 2005
    67. 67. ©2011 MFMER | slide-73 Systems Engineering: Modeling and Simulation • Using system engineering/operation research approach and readily available software(discreet event simulation, etc.) build a “test and learn” capacity to study system performance and identify the bottleneck, • provide re-designed alternatives to improve safety and efficiency of healthcare delivery system. • conduct a valid test of quality improvement innovations before clinical implementation
    68. 68. ©2011 MFMER | slide-74 Project 1: Sepsis Workflow Redesign
    69. 69. Sepsis Care Optimization by Discrete Event Simulation (S-CODES) Place Central Line Central Line Approval Etc, etc, etc Dong Y, Lu H, Rotz J, et al. Simulation Modeling of Healthcare Delivery During Sepsis Resuscitation. Critical Care Medicine 2009;37:A334
    70. 70. Project 2: Scheduling for Critical Care Fellows using Modeling and Simulation: The Trade Off Between Duty Hours and Hand-offs Fellow A Fellow B Fellow C 7 am 7 pm Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Handoffs 0 2 1 0 1 4 Provider Transfer Patient Handoff
    71. 71. Comparison of Provider Scheduling ©2011 MFMER | slide-77 Provider Transfers (H/L) per month Patient Handoffs (avg./mo) ICU Coverage (hrs/wk) Average Duty Hours (hrs/wk) Old Schedule 84 (84/0) 650 ± 4 294 73.5 New Schedule 112 (67/45) (+25%) 860 ± 5 (+33%) 312 (+6%) 62.4 (-15%) Janish, Dong, SCCM, 2011
    72. 72. Project 3: Time-motion observational study of multidisciplinary ICU rounding in a teaching hospital • To describe the current practice, and structure of the morning multidisciplinary round in the ICU practices (MICU, SICU) • Prospective field observation of ICU provides task (consultant, fellow, resident/intern, nurse, pharmacist) based on systems engineering approach • Task categories defined based on provider survey • Purpose strategies (work-flow redesign, new EMR interface) to improve the efficiency of ICU round, reduce MEOW patient outcome provider satisfaction
    73. 73. ©2011 MFMER | slide-79 Project 4: Education Game: The Friday Night at ER ™
    74. 74. Professional Society
    75. 75. Challenges and opportunities • Fragmentation of care delivery • Access information from various sources • Clinical implementation • System integration • Health IT (mobile, cloud, social networking, big data) • Provider education and change culture
    76. 76. • 1920’: BME, Biophysics, Medical Physics • 1943: German Biophysical Society • 1948: Annual Conference of Engineering in Medicine and Biology/Radiation Research Society • 1961: International Federation of Medical and Biological Engineering • 1968:Biomedical Engineering Society
    77. 77. Road map for better healthcare delivery
    78. 78. Road map for better healthcare delivery Dong Y, et al. ICU Operational Modeling and Analysis. In: Kolker A, Story P, eds. Management Engineering for Effective Healthcare Delivery: Principles and Applications. Hershey, Pennsylvania, USA: IGI Global; 2011.
    79. 79. Key Messages • The complexity of healthcare delivery systems contributes to preventable medical error and insufficient quality • Computer modeling/simulatio coupled with realistic patient simulation represents a potent catalyst in adapting systems engineering principles to healthcare • The medical community needs partnership with the systems engineering community to best deliver high value care
    80. 80. ©2011 MFMER | slide-89 Medicine: Human interactions
    81. 81. • email: • Blog: • Twitter: dongyue • LinkedIn: • CiteUlike: simdoc