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; rholden@students.wisc.edu).

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