peak patient flow and patient safety in hospitals
DESCRIPTION
Peak Patient Flow and Patient Safety in Hospitals. Joel S. Weissman, Ph.D. MGH/Harvard Institute for Health Policy. AcademyHealth Annual Meeting Boston, Massachusetts June 26, 2005. Study Personnel. MGH Joel S. Weissman, Ph.D. (PI) Eran Bendavid, MD - PowerPoint PPT PresentationTRANSCRIPT
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Peak Patient Flow and Patient Safety in Hospitals
AcademyHealth Annual Meeting
Boston, Massachusetts
June 26, 2005
Joel S. Weissman, Ph.D.
MGH/Harvard Institute for Health Policy
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Study Personnel
MGH Joel S. Weissman, Ph.D. (PI) Eran Bendavid, MD Joann David-Kasdan, RN (Central
Study RN) Jenya Kaganovich, Ph.D Peter Sprivulis, MD
BWH Jeffrey Rothschild, MD (Co-PI) Fran Cook, Sc.D. David Bates, MD
LDS – Dept of Informatics Scott Evans, Ph.D. (PI-Aim2) Peter Haug, Ph.D. Jim Lloyd
Vanderbilt Univ Harvey Murff, MD
NWH Les Selbovitz, MD
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Study Aims
• The project had two major aims:• Determine relationship between peak hospital
crowding, aka, workload, and the rate of adverse events (AEs)
• Develop new methods to monitor and track adverse events using electronic medical records
Usual Patient
Workload/Activity
Usual Processes of
Care
Usual or Desirable Outcomes
Conceptual Model -- Uncrowded State
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What Happens Under Crowded Conditions?
System Constraints
/Capacity
Limits
Inadequate Responses by Staff &
Other Systems
Increases in Patient Workload/
Activity
Increase in Undesirabl
e outcomes?
?
Over-Crowdin
g
Process of Care
Crowded State
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Sample and Study Question
4 hospitals 2 major teaching hospitals 2 community hospitals
~10,000 chart reviews of pre-screened cases Med-Surg Patients hospitalized during 2000-2001 Collected data on workload and staffing for each
calendar day
Study Questions: How does the daily rate of adverse events vary with workload? Does
control for patient or admission characteristics, and nurse staffing matter?
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Data Collection Goals
• Three data collection goals, each with a different source:• Discharge abstracts used to screen cases to
“enrich” the sample• Medical Charts RN abstraction to identify
presence and date of AEs, and MD review to describe severity and preventability
• Hospital administrative data Collection of workload and staffing information
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Primary Measures of Crowding/ Workload & Patient Complexity
Census/Occupancy rates Throughput (admissions/discharges) Weighted Census (Sum of DRG weights) Diversion Average nursing acuity (Hospital A, only)
Each of the following may vary from day to day, and can be measured at various levels of aggregation,
i.e., for various work units:
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Primary Measures of Staffing
Total RN staff Total non-RN staff Ratio of RNs / non-RNs Variance between actual and “planned” Patients per nurse
Each of the following may vary from day to day, and can be measured at various levels of aggregation ,
i.e., for various work units:
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Primary Control Variables: Patient-Level and Admission Characteristics
Patient age Patient DRG (adjacent DRGs) Nurse assigned acuity (Hospital A) Day of the week Emergent admission via ED “Superunit” – ICU vs. Non-ICU
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Analysis Basic Model:Prob (AE) = f (Patient vars, Day vars, Workload data) Day analysis (N = 365 days)
Dep Var = Rate of AEs Aggregated patient-level characteristics Workload measures divided into quartiles
Patient-Day analysis (N = # patients X ALOS) Dep Var = = 0, 1, 2, or 3 AEs Poisson regression; patient-day is unit of analysis Control for clustering within admission
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Daily Occupancy Rate Fluctuations Hospital A
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Hospitals Get More Crowded Toward the End of the Work Week
0%
20%
40%
60%
80%
100%
Sun Mon Tue Wed Thu Fri Sat
A
B
C
D
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The Rate of AEs per Patient in the Hospital is Higher on Certain Days of the Week
All Hospitals
0.3%
0.5%
0.7%
0.9%
1.1%
1.3%
Sun Mon Tue Wed Thu Fri SatP <.05
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Percent Increase in Adverse Event Rate (Relative to Lowest Quartile) by
Occupancy Rate – Non-ICU
Hospital A - Adult Med- Surg
0% 0%
16%11%
0%
10%
20%
30%
40%
50%
1st Qrtile 2nd Qrtile 3rd Qrtile 4th Qrtile
Quartiles of Hospital Occupancy Rates
% I ncr in
AE Rate
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Percent Increase in Adverse Event Rate (Relative to Lowest Quartile) by
Admissions to Unit – Non-ICU
Hospital A - Adult Med- Surg
0%
12%
27% 27%
0%
10%
20%
30%
40%
50%
1st Qrtile 2nd Qrtile 3rd Qrtile 4th Qrtile
Quartiles of Predictor
% I ncr in
AE Rate
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Percent Increase in Adverse Event Rate (Relative to Lowest Quartile) by RN staff
variance - ICU
Hospital A - Adult Med- Surg
0%
33% 36%
59%
0%
20%
40%
60%
80%
100%
1st Qrtile 2nd Qrtile 3rd Qrtile 4th Qrtile
Quartiles of Predictor
% I ncr in
AE Rate
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What Do We Do About It?
• Too soon given to say until patient-day level analyses are complete, but if results hold, may have to think “outside the box” of usual approaches to patient care
“Never, ever, think outside the box”
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Why is the Study Important?
We focus on system explanations, NOT individual fault or blame
There is concern that hospitals are becoming over-crowded and under-staffed, but we can NOT determine optimum nurse staffing levels from this particular study
In Aim 2: We will be able to identify new, inexpensive methods for tracking AEs.
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End of presentation
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Sample – Oct 2000 – Sep 2001
HospitalAdmis-sions
Exclu-ded %
Screen-ed %
A 65,158 36,910 56.6% 28,248 43.4%
B 13,150 6,694 50.9% 6,456 49.1%
C 18,510 9,764 52.7% 8,746 47.3%
D 30,710 16,017 52.2% 14,693 47.8%
Total 127,528 69,385 54.4% 58,143 45.6%
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Percent Increase in Adverse Event Rate (Relative to Lowest Quartile) by RN Staff
Variance – Non-ICU
Hospital A - Adult Med- Surg
0% 0%5%
10%
0%
10%
20%
30%
40%
50%
1st Qrtile 2nd Qrtile 3rd Qrtile 4th Qrtile
Quartiles of Predictor
% I ncr in
AE Rate
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Percent Increase in Adverse Event Rate (Relative to Lowest Quartile) by
Occupancy Rate - ICU
Hospital A - Adult Med- Surg
0%
58%
33%
72%
0%
20%
40%
60%
80%
100%
1st Qrtile 2nd Qrtile 3rd Qrtile 4th Qrtile
Quartiles of Predictor
% I ncr in
AE Rate
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What I will Cover
Study Aims Conceptual Model Data Collection Goals Preliminary Results Conclusions and Next Steps
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Why Adverse Events and not Errors?
Most errors are not reported in charts Many deviations from procedure are not viewed as errors by
staff Many errors are not known without a “root cause analysis” Many adverse events, while not errors, are still cause for
review since they are poor outcomes that therefore have implications for overall quality of care
“The cause is hidden. The effect is visible to all.” -Ovid
Errors&
Near MissesAdverseEvents
Preventable
Non-preventable
Errors versus Adverse Events
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Increased Risk During Early Days of Hospital Stay
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21Day of admission
Ris
k o
f A
.E. p
er p
atie
nt
Upper 95th CI
AEs per Patient
Lower 95th CI
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Chart Review Sample: Screened Positive for Possible AEs
Hospital A B C D Total
Patient Safety Indicators 349 53 - 289 691
Complication Screening Program, not Patient Safety Indicators
186 27 - 161 374
Harv Practice Study Screens, e.g., return to OR, death, readmission
3,778 880 1,264 1,835 7,757
Total screened for review
4,313 960 1,264 2,285 8,822