1. 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

2. 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.

3. 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

4. MWL Demands Work system factors Individual factors Experience Affect Memory capacity Mental demands Emotional demands Temporal demands Number of problems Complexity Difficulty of problems Work schedule Social environment Support technology x Control factors Affect Perceived Locus of control Coping strategies Support technology Provider - Disease - Burnout - Low quality Patient -Stress - Poor health - Reduced trust Long-term outcomes Rest breaks Social support Decision authority Provider - Stress - Errors - Delays Patient - Stress - Harm - Dissatis- faction Immediate outcomes Bad decisions More slips Fatigue Poor Communication Mediators Notes 1.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).

5. Patient arrives at clinic Patient placed in exam room by medical assistant Informed consent Clinician evaluates and manages patient and problems DE#1 Demographic data (age, sex) Patient’s anticipated number of concerns DE#2 Clinician’s reported number 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 addressed during 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

6. Results Measures of Problem Density –Number of problems per encounter Measures of Mental Workload –Mean –Variation –Range Estimates of Completeness and Error

7. 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

8. Managing Multiple and Potentially Competing Problems (current study; n = 609 visits) Mean = 3.30 Std. Dev. = 1.96

9. Effect of Patient Age on Number of Problem per Encounter r = 0.237 P < 0.001

10. Effect of Patient Sex and Continuity Status on NPPE

11. Mental Workload in Primary Care (n = 598; mean = 47.6 + 18.4)

12. Relative Contributions to Effort “NO TIME TO THINK!”

13. Distribution of Subscores 20 highest visits

14. Distribution of Subscores 20 lowest visits

15. 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 Clinician Average

16. Effect of Patient Age on Workload r = 0.152 P < 0.001

17. Effect of Patient Sex, Continuity Status, and Presenting Problem on Workload

18. Workload Rises over the Week (ANOVA; P=0.002)

19. MWL is Related to Complexity (TLX = 36.3 + 3.45*NPPE; r 2 = 0.134)

20. Workload Increases with Additional Medical Problems

21. Emergent Themes for Outlier Analysis of Clinical Visits with Lower and Higher than Expected Work Load Lower than ExpectedHigher 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

22. 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

23. Medical Error is related MWL (PME = 5.64 + 0.026*TLX; r 2 = 0.044)

24. 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