the heartdecision computer decision support pilot study
DESCRIPTION
The HeartDecision Computer Decision Support Pilot Study. Matthew C. Tattersall D.O. Adjhaporn Khunlertkit Ph.D. Peter Hoonakker Ph.D. Jon G. Keevil M.D. Disclosures. Tattersall: No Disclosures Khunlertkit: No Disclosures Hoonakker: No Disclosures - PowerPoint PPT PresentationTRANSCRIPT
The HeartDecision Computer Decision Support
Pilot Study
Matthew C. Tattersall D.O.Adjhaporn Khunlertkit Ph.D.
Peter Hoonakker Ph.D.Jon G. Keevil M.D.
Disclosures
Tattersall: No Disclosures Khunlertkit: No Disclosures Hoonakker: No Disclosures Keevil: Founder/Owner HealthDecision, LLC – a zero
revenue company building decision support tools.
Background
Current 2010 AHA/ACC guidelines recommend calculation of absolute cardiovascular risk. (Class I LOE: B) • “All adults ≥ 40 y/o should know their absolute
risk of developing coronary heart disease” The level of cardiovascular risk
determines corresponding lipid goals. The level of current lipid goals determines
the need for pharmacotherapy.
AHA Guidelines for Primary Prevention of Cardiovascular Disease and Stroke: Circulation 2002;106;388-391
ATP III JAMA. May 16 2001;285(19):2486-2497
Background
Importance of Cardiovascular Risk Assessment:• Clinicians over and under-estimate risk (as
high as 76% of patients)
• Initial errors in risk assessment lead to inappropriate use of pharmacotherapy
• A recent meta-analysis displayed CHD risk assessment improves patient outcomes with no harm.
Friedmann PD, et al. Differences in generalists’and cardiologists’ perceptions of cardiovascular risk and the outcomes of preventive therapy in cardiovascular disease. Ann Intern Med. 1996;124:414 –21.
Grover SA, et al. Do doctors accurately assess coronary risk in their patients? Preliminary results of the coronary health assessment study. BMJ. 1995;310:975– 8.
Sheridan SL et al., Does the routine use of global coronary heart disease risk scores translates into clinical benefits or harms? A systemic review of the literature. BMC Health Serv Res. 2008;8:60.
Background
Methods used to calculate risk:• Pad and Paper• Hand-Held Calculators• Online Calculators
Overall risk calculation is not being performed.• McBride et. al.: Only 17% of primary care
physicians routinely calculate cardiovascular risk.
Clinician Barriers
Time consuming Where to find a calculator to calculate
risk? Which risk model to use? Multi-staged, dynamic guidelines with
changing lipid goals Which evidence-based pharmacotherapy
should be used?
Computer Decision Support Tools (CDST)
CDST Barriers
While CDST’s improve:• Diagnosis• Prevention • Management of chronic diseases
Many CDST’s Fail:• Poor integration into clinician workflow:
– AHRQ GLIDES study: Clinician workflow integration significant barrier.
– Very little field testing of CDST’s. – Previous studies focus solely on performance.
HeartDecision CDST Pilot Study
Multi-disciplinary collaborative pilot study with two aims:• To address usability, integration into work flow
and field testing.• To assess impact of the CDST since launch
date. (2-1-2010)
HeartDecision Pilot Systems Engineering Initiative for Patient
Safety (SEIPS) part of the UW College of Engineering.• Previously developed a work system design
model integrating– Human factors engineering– Healthcare quality models
HeartDecision Pilot
Hypothesis #1: Application of the SEIPS model will help identify and characterize the enablers and barriers to the integration of the HeartDecision CDST into primary care clinician workflow.
HeartDecision Pilot: Methods
Human Factors Engineering Field Testing:• 8 Physicians from 5 WREN DFM clinics.• Clinic encounter with standardized patient
from UW School of Medicine with mock EMR.• Data collected/analyzed via SEIPS qualitative
methods using time study, observation and post-encounter interviews.
HeartDecision Pilot: Results
Time (in minutes) spent in HD
0.08
4.03
5.45
7.068.34
9.56 9.55
12.52
0
5
10
15
20
25
30
Start HD running Risk Page Goals page Ideal page Handoutspage
Summarypage
End
Observation 1
Observation 2
Observation 3
Observation 4
Observation 5
Observation 6
Observation 7
Observation 8
Average
On an average, the physicians spent 13 minutes using the HD tool
Time Study of the HeartDecision CDST
Facilitators
“The tool is intuitive” “The tool presents patient assessment in logical
sequence” “Data is automatically populated” “The risk level (low, moderate, and high) is clear to
the patient” “The graphical display helps with communication
with patient” “Hand out provides good information for patient”
Barriers
Clinician Work Flow• Time pressure: “Patients with multiple
conditions” Work Environment
• “Cannot print educational PDF files and graphs”
• “Cannot open PDF files on Winterms” Program Interface
• “20 second delay upon opening the program” Program
• “No pharmacotherapy recommendations”
• “Nice to have patient peer comparisons”
Web-Based Survey
To further delineate barriers and enablers a web-based survey was sent to clinicians within the Department of Family Medicine and the Department of Medicine
73 respondents (50%) from Department of Family Medicine, 71 respondents (49%) from Department of Medicine.
Web-Based Survey
Barrier Survey Results Means: (N=66)(1=strongly disagree, 5=strongly agree)
Time: “I have too little time to use the tool.”
2.3
Work Environment:“I cannot use some functions of the HeartDecision tool because of lack of support from the computer-, Winterms-, or Health Link-
system.”
2.9
This tool fits well in my workflow. 3.8
Program Interface:“When I open up the tool, the tool is occasionally delayed.”
3.1
Program:“It would be helpful to add medication recommendations to treat
cholesterol in patients with certain risk in the tool.”
4.0
“It would be helpful if the (Ideal) Graph could be printed.” 4.1
Field Testing Conclusions
Work flow barriers exist with the HD CDST.• Time• Work Environment improvements: (Printing,
speed).• Post encounter patient handouts/chart
documentation.• Need for specific treatment recommendations.
Assessing Early Impact
Hypothesis #2: Since implementation of the HeartDecision CDST into the UW electronic medical record the frequency of cardiovascular risk documentation has increased.
Measuring Impact Retrospective Pre-Post Chart Review 6 WREN Physicians at 5 different clinics Patients identified by CDST use Compared two time periods:
• 1-1-2009-1-31-2010 versus 2-1-2010 -3-11-2011 Assessed rate of cardiovascular risk documentation
pre-HD and post-HD• Compared rates using an exact McNemar’s Chi Squared• Compared Physician rate changes using Fisher’s Exact
test.
Inclusions/Exclusions
Inclusions: • No high risk conditions (CVD, PVD, DM2)• Must have at least one visit in each time
period with provider 62 patients met inclusion criteria
• 27 male (44%)• 35 Female (56%)
Descriptive Statistics
Characteristic Mean (SD)
Age (years) 54.2 (10.7)
Total Visits (N) 4.1 (2.2)
LDL-C (mg/dL) 139.0 (40.5)
Systolic Blood Pressure (mmHg) 124.3 (15.8)
Total Cholesterol (mg/dL) 220.2 (40.9)
Framingham Risk Score (%) 5.3 (5.8)
HDL (mg/dL) 47 (13.3)
CV Risk Documentation
3.2%(95% CI 0.4-11.2%)
50% (95% CI 37-63%)
Post HD rate not dependent on Physician p=0.42 (Fishers Exact)
P<0.0001
Impact Assessment Conclusions
The rates of CV risk documentation improved in this small selective physician cohort.
Hypothesis generating
Conclusions
Workflow barriers exist with use of HD CDST• Time constraints• Need for more treatment recommendations• Printer friendly graphs• More patient education tools
Since incorporation of HD into Epic• In a small, selective group of physicians CV risk
documentation rates have improved since HD CDST incoporation.
Overall, hypothesis generating • Will the use of a CDST that is well integrated into clinician
workflow improve CV measures of performance in a large cohort of physicians.
Thank You