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“Working Conditions and Patient Safety"

“Working Conditions and Patient Safety". Joel S. Weissman, Ph.D. MGH/Harvard Institute for Health Policy. AHRQ National Meeting October 27, 2007. Background - Hospitals are under pressure…. To increase revenue and lower costs To cope with dramatic variation in clinical demand

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“Working Conditions and Patient Safety"

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  1. “Working Conditions and Patient Safety" Joel S. Weissman, Ph.D. MGH/Harvard Institute for Health Policy AHRQ National Meeting October 27, 2007

  2. Background - Hospitals are under pressure… • To increase revenue and lower costs • To cope with dramatic variation in clinical demand • To provide more complex care, to more patients, in busier units, with fewer or less experienced staff, and reduced resident workloads

  3. The IOM’s messages: 1. Errors are serious. 2. The system is at fault not people. 3. Improvements in both safety and efficiency are recommended Although both goals are laudable, the IOM did not address the possibility that achieving one goal might be at odds with the other.

  4. Study #1: A Pre/Post Study of Resident Experiences and Perceptions About Medical Errors Study Question: “Did the ACGME Limit on Residents’ Work Hours Affect Patient Safety? – Evidence as Reported by Residents” From Jagsi et al, Archives 2005; Jagsi, et al, Acad Med 2006; And, Jagsi R, Weinstein D, Shapiro J, Kitch BT, Dorer D, Weissman JS, in press

  5. Background • ACGME mandated limits on resident work hours, effective July 2003 • 80-hour average limit • 30-hour limit on continuous shifts • Concerns that reduced continuity of care and/or increases in patient load might offset benefit from reducing resident fatigue

  6. Study Design • Survey of all residents/fellows in the 76 accredited training programs at MGH & BWH • 2003 (pre-implementation) and 2004 (post-implementation) • Goal of Primary Study - to assess self-reported workhours of residents • Secondary goal – to report on patient safety events • All residents were classified post-hoc based upon mean self-reported workhours for their program into: • Reduced hours programs • All other programs • Pre-post analysis -- Difference in Differences

  7. Questionnaire Content – 1Perceptions of Fatigue and Causes of Mistakes • Fatigue • “On how many days did you experience significant fatigue while on-duty over the past four weeks?” • “How often has fatigue had a negative impact on the safety of patients you care for?” • Causes of Mistakes • “…to what extent do the following contribute to mistakes in patient care?” • residents working too many hours • poor handoffs by residents • carrying or admitting too many patients • inadequate supervision • cross covering too many patients

  8. Questionnaire Content – 2Safety Events • Adverse Events • “About how many adverse events affecting patient(s) under your care occurred in the last week?” • “…about how many were due to a mistake for which you felt at least partly responsible?” • “About how many times in the last week was there a near miss or close call for which you felt at least partly responsible?” • Medical Error = Near miss or AE due to mistake

  9. Study Sample Response Rate – 60%

  10. Workhours in the “reduced hours” programs declined substantially Significant reductions between 2003-4 in: • Mean weekly duty hours (76.6 to 68.0, p<0.001) • Percent working over 80 hours per week (44.0% to 16.6%, p<0.001) • But - little change in reported patient load, or volume of call-night activities

  11. Residents in “reduced hours” programs experienced reductions in fatigue and fatigue-related safety problems relative to other programs **A 25% reduction in mean days of self-reported “significant fatigue” in last month among the reduced hours programs vs 8% reduction in other programs (p=.03) **A decrease from 7% to 3% in reduced hours group saying that fatigue “frequently” or “always” impacted the safety of patients they cared for, compared with an increase from 3.8% to 3.9% among other programs (p=.03)

  12. Over time, residents in the “Reduced Hours” programs said that workhour pressures causing mistakes were reduced ** ** * * * * * - Year-Year difference p<.05 ** - Difference in difference p<.05

  13. Over time, residents in the “Reduced Hours” programs reported reductions of safety events relative to other programs * - Year-Year difference p<.05 ** - Difference in difference p<.05

  14. Conclusions • There was a significant and substantial reduction in the proportion of respondents in reduced hours programs who felt that working too many hours contributed to mistakes in patient care on the clinical services of their program. • Reducing workhours may reduce the extent to which fatigue affects patient safety as perceived by residents • There was a trend suggesting that residents in the reduced hours programs experienced a greater reduction in medical errors than residents in other programs

  15. Study #2: The Relation of Hospital Occupancy to Adverse Events Study Questions: How does the daily rate of adverse events vary with workload as measured by hospital occupancy? Weissman JS, Rothschild JM, Bendavid E, Sprivulis P, Cook EF, Evans RS, Kaganova Y, Bender M, David-Kasdan J, Haug P, Lloyd J, Selbovitz LG, Murff HJ, Bates DW. Hospital Workload and Adverse Events. Medical Care 2007; 45(5): 448–455. NIH/AHRQ Grant

  16. Conceptual Model -- Uncrowded State Usual or Desirable Outcomes Usual Patient Workload/ Activity Usual Processes of Care

  17. What Happens Under Crowded Conditions?

  18. Conceptual Model -- Crowded State System Constraints/ Capacity Limits Increase in Undesirable outcomes?? Increases in Patient Workload/ Activity Over-Crowding Process of Care Inadequate Responses by Staff & Other Systems

  19. Methods • 4 hospitals • 2 major teaching hospitals • 2 community hospitals • ~10,000 chart reviews using computer entry tool • Med-Surg Patients hospitalized during 2000-2001 • Collected data on workload and staffing for each calendar day

  20. Primary Measures of Crowding/ Workload & Patient Complexity • Census/Occupancy rates • Throughput (admissions/discharges) • Patients per nurse • Weighted Census (Sum of DRG weights) Each of the following vary day to day, and were measured at various levels of aggregation, i.e., for various work units:

  21. “Enhanced” Occupancy Rates – All Med-Surg

  22. Poisson Regression Results for Hospital A Controlling for: Patient’s age, sex, DRG, day of week, emergent admission, ICU/non-ICU

  23. Why is the Study Important? • Hospitals that operate at or near capacity on a regular basis should examine their safety data • The ratio of patients per nurse is correlated with adverse events, but we can’t say which ratio is “safe”. • Interventions and safety measures may have to be designed for periods of high volume as well as average activity levels

  24. The IOM Report: 7 Years Later • The questions on everyone’s minds: • “Are we any safer today than we were 7 years ago?” • “What can we do to be safer?”

  25. THE CONCEPT OF SLACK • Tight coupling and high complexity (versus loose coupling and low complexity) are more accident prone. Perrow, 1984, Normal Accidents • Also known as “slack”, i.e., usual procedures work less well when there is less slack in the system, because the system loses its resilience to additional interruptions. Rudolph and Repenning, 2002

  26. There is No Easy Way to Achieve System-Wide Patient Safety

  27. End of presentation

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