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
1
Study Personnel

MGH
S. Weissman, Ph.D. (PI)
 Eran Bendavid, MD
 Joann David-Kasdan, RN
(Central Study RN)
 Jenya Kaganovich, Ph.D
 Peter Sprivulis, MD

 Joel

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

NWH
 Les

Selbovitz, MD
Vanderbilt Univ
 Harvey
Murff, MD
2
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
3
Conceptual Model -- Uncrowded
State
Usual
Patient
Workload/
Activity
Usual
Processes of
Care
Usual or
Desirable
Outcomes
What Happens Under Crowded
Conditions?
5
Crowded State
System
Constraints
/
Capacity
Limits
Increases
in Patient
Workload/
Activity
Process
of Care
Inadequate
Responses
by Staff &
Other
Systems
OverCrowding
Increase in
Undesirable
outcomes??
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?

7
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
8
Primary Measures of Crowding/
Workload & Patient Complexity
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:
 Census/Occupancy
rates
 Throughput (admissions/discharges)
 Weighted Census (Sum of DRG weights)
 Diversion
 Average nursing acuity (Hospital A, only)
9
Primary Measures of Staffing
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:
 Total
RN staff
 Total non-RN staff
 Ratio of RNs / non-RNs
 Variance between actual and “planned”
 Patients per nurse
10
Primary Control Variables: PatientLevel 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
11
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
12
Daily Occupancy Rate
Fluctuations Hospital A
13
Hospitals Get More Crowded
Toward the End of the Work Week
100%
80%
60%
A
B
C
D
40%
20%
0%
Sun
Mon
Tue
Wed
Thu
Fri
Sat
14
The Rate of AEs per Patient in the Hospital
is Higher on Certain Days of the Week
All Hospitals
1.3%
1.1%
0.9%
0.7%
0.5%
0.3%
Sun
Mon
Tue
Wed
Thu
Fri
Sat
P <.05
15
Percent Increase in Adverse Event
Rate (Relative to Lowest Quartile) by
Occupancy Rate – Non-ICU
Hospital A - Adult Med-Surg
50%
40%
% Incr in
30%
AE Rate
20%
10%
0%
16%
0%
0%
1st Qrtile
2nd Qrtile
3rd Qrtile
11%
4th Qrtile
Quartiles of Hospital Occupancy Rates
16
Percent Increase in Adverse Event
Rate (Relative to Lowest Quartile) by
Admissions to Unit – Non-ICU
Hospital A - Adult Med-Surg
50%
40%
% Incr in 30%
AE Rate
20%
10%
0%
27%
27%
3rd Qrtile
4th Qrtile
12%
0%
1st Qrtile
2nd Qrtile
Quartiles of Predictor
17
Percent Increase in Adverse Event
Rate (Relative to Lowest Quartile) by
RN staff variance - ICU
Hospital A - Adult Med-Surg
100%
80%
% Incr in
60%
AE Rate
40%
20%
0%
59%
33%
36%
2nd Qrtile
3rd Qrtile
0%
1st Qrtile
4th Qrtile
Quartiles of Predictor
18
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”
19
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.
20
End of presentation
21
Sample – Oct 2000 – Sep 2001
Hospital
Admissions
Excluded
A
65,158
B
%
Screened
%
36,910
56.6%
28,248
43.4%
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%
22
Percent Increase in Adverse Event
Rate (Relative to Lowest Quartile) by
RN Staff Variance – Non-ICU
Hospital A - Adult Med-Surg
50%
40%
% Incr in 30%
AE Rate
20%
10%
0%
0%
0%
1st Qrtile
2nd Qrtile
5%
3rd Qrtile
10%
4th Qrtile
Quartiles of Predictor
23
Percent Increase in Adverse Event
Rate (Relative to Lowest Quartile) by
Occupancy Rate - ICU
Hospital A - Adult Med-Surg
100%
80%
% Incr in
60%
AE Rate
40%
20%
0%
72%
58%
33%
0%
1st Qrtile
2nd Qrtile
3rd Qrtile
4th Qrtile
Quartiles of Predictor
24
What I will Cover
 Study Aims
 Conceptual
Model
 Data Collection Goals
 Preliminary Results
 Conclusions and Next Steps
25
Why Adverse Events and not
Errors?
“The cause is hidden. The effect is visible to all.” Ovid
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

26
Errors versus Adverse Events
Errors
&
Near Misses
Non-preventable
Adverse
Events
Preventable
Increased Risk During Early Days
of Hospital Stay
0.4
Risk of A.E. per patient
0.35
0.3
0.25
Upper 95th CI
AEs per Patient
Lower 95th CI
0.2
0.15
0.1
0.05
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21
Day of admission
28
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
Harv Practice Study
Screens, e.g., return to
OR, death, readmission
Total screened for
review
186
27
-
161
374
3,778
880
1,264 1,835 7,757
4,313
960
1,264 2,285 8,822
29