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Primary care workload:linking problem density to medical error
Jon Temte, MD/PhD, Mike Grasmick, PhD,
Peggy O’Halloran, Lisa Kietzer, Bentzi Karsch, PhD, Beth Potter, MD, John Beasley, MD, Paul Smith, MD, and Betsy Doherty, MS-2
AHRQ Grant #1 R03 HS016026-01
WREN
Study in a Nutshell
• This AHRQ-funded WREN study – examine 600 clinical encounters – conducted by 30 clinicians – to assess interactions of problem number, MWL and error
• Data collection completed with 31 clinician and 615 visits • Relationships between clinician MWL and patient age
and sex, continuity status, number of problems per encounter (NPPE) and perceived medical error (PME) were assessed using ANOVA and correlation analyses.
• Analysis of covariance used to assess potential differences among the 31 clinicians.
Basic Study Demographics
• Four Primary Care Clinics affiliated with WREN– 2 urban and 2 rural
• Multiple clinicians (Goal = 30)– Mix of FPs, IMs, MDs, PAs, and NPs
• Quasi-randomly selected patients – 6 random time periods per day– Age > 18, mentally competent– Current Patient Demographics
• Mean age = 54.6 +/- 17.5 years• 63.5% female
MWLDemands
Worksystemfactors
Individualfactors
Experience
Affect
Memorycapacity
Mentaldemands
Emotionaldemands
Temporaldemands
Number ofproblems
Complexity
Difficulty ofproblems
Workschedule
Socialenvironment
Supporttechnology
x
Control factors
AffectPerceived Locus
of controlCoping
strategiesSupport
technology
Provider- Disease- Burnout- Lowquality
Patient-Stress- Poorhealth- Reducedtrust
Long-term outcomes
Rest breaks
Social support
Decisionauthority
Provider- Stress- Errors- Delays
Patient- Stress- Harm- Dissatis-faction
Immediate outcomes
Baddecisions
More slips
Fatigue
PoorCommunication
Mediators
Notes1.The above components are merely examples. Clearly, others may be added and this is all amenable to modification.2.This model, despite its many components, is probably a simplification of the true nature of mental workload. However, this model (or something like it) can serve as a conceptual base camp from which studies are launched. The boxes with shaded backgrounds represent variables that can potentially be measured—albeit not all in the initial study. However, I would make the case that many of them can be measured with minimal intrusion and time demand on the docs. Some, like experience, memory capacity, social support, coping strategies, etc. can be measured only once or can be obtained without any effort from the doc (RICHARD JOHN HOLDEN, 2005; [email protected]).
Patientarrives at clinic
Patient placed in exam room by
medical assistantInformed consent
Clinician evaluates and manages
patient and problems
DE#1 Demographic data (age, sex)Patient’s anticipated number of concerns
DE#2 Clinician’s reportednumber of problems (NPPE)
DE#3 Clinician’s mental workload (NASA TLX)
DE#4 Clinician’s estimate of likelihood of error
Medical assistant exits patient
DE#7 Patient’s satisfaction, assessment of level to which concerns were addressedduring visit and estimate of error
Clinician dictates and photocopies
clinical note
DE#5 Time spent in direct patient contact
DE#6 Audit of note for quality measures
Results
• Measures of Problem Density– Number of problems per encounter
• Measures of Mental Workload– Mean– Variation– Range
• Estimates of Completeness and Error
Encounter Problem Density
• Number of Problems per Encounter– Mean = 3.30 +/- 1.96 (sd)– Range: [1 – 12]– Significant differences among clinicians
• ANOVA: P<0.001
• Number of Problems per Scheduled Time– Mean = 10.39 +/- 6.89 (sd) problems per hour– Range: [2.0 – 42.0]– Significant differences among clinicians
• ANOVA: P<0.001
Managing Multiple and Potentially Competing Problems
(current study; n = 609 visits)
0
20
40
60
80
100
120
140
160
1 2 3 4 5 6 7 8 9 10 11 12
Number of Problems per Encounter
Fre
qu
ency
Mean = 3.30Std. Dev. = 1.96
Effect of Patient Age onNumber of Problem per Encounter
0
2
4
6
8
10
12
14
0 20 40 60 80 100
Patient Age
NP
PE
r = 0.237P < 0.001
Effect of Patient Sex andContinuity Status on NPPE
0
1
2
3
4
Sex (N.S) Continuity (P<0.001)
NP
PE
female yesnomale
Mental Workload in Primary Care(n = 598; mean = 47.6 + 18.4)
0
10
20
30
40
50
60
70
80
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
NASA-TLX Composite Score
Fre
qu
ency
Relative Contributions to Effort
“NO TIME TO THINK!”
0 5 10 15 20 25 30
mental
time
effort
performance
frustration
physical
Percent of Overall Effort
Distribution of Subscores20 highest visits
0%
20%
40%
60%
80%
100%95
.3
88.3
85.3
85.3
82.3
82.3
81.7
81.7
80.7
80.7
80.3
79.7
79.7 79 79 79 79
78.7
78.7
77.7
AV
E
Composite TLX
Fre
qu
ency
Distribution of Subscores20 lowest visits
0%
20%
40%
60%
80%
100%13
12.7
12.3
12.3
11.7
11.3
11.3
10.3 10 10 10
9.67
9.67
9.67
9.67
8.67
8
6.67
5 5
AV
E
Composite TLX
Fre
qu
ency
Mental Workload in Primary Care
Composite NASA-TLX• n = 598• Range: [5.00 to 95.3]• Mean = 47.6• Std dev = 18.4
Individual Variation• N = 31 clinicians• ANOVA: P<0.001 0 20 40 60 80
Cli
nic
ian
Composite NASA-TLX
Clinician Average
Effect of Patient Age on Workload
0
20
40
60
80
100
120
0 20 40 60 80 100
Patient Age
Co
mp
osi
te T
LX
r = 0.152P < 0.001
Effect of Patient Sex, Continuity Status, and Presenting Problem on Workload
30
35
40
45
50
55
60
Sex (N.S) Continuity (P=0.023) Problem (P<0.001)
Mea
n C
om
po
site
TL
X
female yesnomale acute chronic
Workload Rises over the Week(ANOVA; P=0.002)
40
42
44
46
48
50
52
54
56
MON TUE WED THU FRI
Co
mp
osi
te T
LX
MWL is Related to Complexity (TLX = 36.3 + 3.45*NPPE; r2 = 0.134)
0
20
40
60
80
100
120
0 5 10 15
Number of Problems per Encounter
NA
SA
-TL
X
Workload Increases with Additional Medical Problems
30
35
40
45
50
55
60
65
70
1 2 3 4 5 6 7 8
Number of Problems
Mea
n N
AS
A-T
LX
Emergent Themes for Outlier Analysis of Clinical Visits with Lower and Higher
than Expected Work Load
Lower than Expected Higher than Expected
• straightforward problem• adequate time• clinician knows patient well• encounter had good outcome• patient satisfied• lack of major problems• management clear (standard
care plan)• patient not-demanding
• unexpected problems and needs
• being behind, insufficient time• patient is not known to clinician• unhappiness or conflict in
encounter• discordant relationship• unclear decision making,
unclear what to do• demanding, questioning,
worked-up, high maintenance, non-responsive patient
Distribution of Perceived Medical Error
• Mean = 6.9 +/- 2.2 (sd) → relatively low• Range: [3 – 16] → moderate variation
– Significant differences among clinicians • ANOVA: P<0.001
0
20
40
60
80
100
120
140
160
4 5 6 7 8 9 10 11 12 13 14 15 16 14 15 16
NASA TLX
Fre
qu
ency
0
2
4
6
8
10
12
14
16
18
0 20 40 60 80 100
Composite NASA-TLX
Per
ceiv
ed M
edic
al E
rro
rMedical Error is related MWL
(PME = 5.64 + 0.026*TLX; r2 = 0.044)
Conclusion
• Primary care encounters are complex– Mean of 3.3 problems per visit
• Visits are associated with moderately high workloads with a tremendous range– Workload is associated with
• Complexity and type of visit• Patient, clinician and workplace factors• Relationships
• Errors is associated with level of workload– Some components are not modifiable– Time factors and frustration can be modified