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.

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Presentation transcript:

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 u Tattersall: No Disclosures u Khunlertkit: No Disclosures u Hoonakker: No Disclosures u Keevil: Founder/Owner HealthDecision, LLC – a zero revenue company building decision support tools.

Background u 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” u The level of cardiovascular risk determines corresponding lipid goals. u The level of current lipid goals determines the need for pharmacotherapy. AHA Guidelines for Primary Prevention of Cardiovascular Disease and Stroke: Circulation 2002;106; ATP III JAMA. May ;285(19):

Background u 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 u Methods used to calculate risk: Pad and Paper Hand-Held Calculators Online Calculators u Overall risk calculation is not being performed. McBride et. al.: Only 17% of primary care physicians routinely calculate cardiovascular risk.

Clinician Barriers u Time consuming u Where to find a calculator to calculate risk? u Which risk model to use? u Multi-staged, dynamic guidelines with changing lipid goals u Which evidence-based pharmacotherapy should be used?

Computer Decision Support Tools (CDST) CDST Disease Diagnosis Preventive Medicine Chronic Disease Management Drug Dosing

CDST Barriers u While CDST’s improve: Diagnosis Prevention Management of chronic diseases u 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 u 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. ( )

HeartDecision Pilot u 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 u 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 u 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 On an average, the physicians spent 13 minutes using the HD tool Time Study of the HeartDecision CDST

Facilitators u “The tool is intuitive” u “The tool presents patient assessment in logical sequence” u “Data is automatically populated” u “The risk level (low, moderate, and high) is clear to the patient” u “The graphical display helps with communication with patient”  “Hand out provides good information for patient”

Barriers u Clinician Work Flow Time pressure: “Patients with multiple conditions” u Work Environment “Cannot print educational PDF files and graphs” “Cannot open PDF files on Winterms” u Program Interface “20 second delay upon opening the program” u Program “No pharmacotherapy recommendations” “Nice to have patient peer comparisons”

Web-Based Survey u 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 u 73 respondents (50%) from Department of Family Medicine, 71 respondents (49%) from Department of Medicine.

Web-Based Survey BarrierSurvey 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 u 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 u Hypothesis #2: Since implementation of the HeartDecision CDST into the UW electronic medical record the frequency of cardiovascular risk documentation has increased.

Measuring Impact u Retrospective Pre-Post Chart Review u 6 WREN Physicians at 5 different clinics u Patients identified by CDST use u Compared two time periods: versus u 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 u Inclusions: No high risk conditions (CVD, PVD, DM2) Must have at least one visit in each time period with provider u 62 patients met inclusion criteria 27 male (44%) 35 Female (56%)

Descriptive Statistics CharacteristicMean (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 %) 50% (95% CI 37-63%) Post HD rate not dependent on Physician p=0.42 (Fishers Exact) P<0.0001

Impact Assessment Conclusions u The rates of CV risk documentation improved in this small selective physician cohort. u Hypothesis generating

Conclusions u Workflow barriers exist with use of HD CDST Time constraints Need for more treatment recommendations Printer friendly graphs More patient education tools u Since incorporation of HD into Epic In a small, selective group of physicians CV risk documentation rates have improved since HD CDST incoporation. u 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