1 / 26

The HeartDecision Computer Decision Support Pilot Study

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

taber
Download Presentation

The HeartDecision Computer Decision Support Pilot Study

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The HeartDecision Computer Decision Support Pilot Study Matthew C. Tattersall D.O. AdjhapornKhunlertkit Ph.D. Peter Hoonakker Ph.D. Jon G. Keevil M.D.

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

  3. 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

  4. 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.

  5. 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.

  6. 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?

  7. Computer Decision Support Tools (CDST)

  8. 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.

  9. 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)

  10. 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

  11. 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.

  12. 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.

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

  14. 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”

  15. 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”

  16. 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.

  17. Web-Based Survey

  18. 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.

  19. 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.

  20. 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.

  21. 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%)

  22. Descriptive Statistics

  23. CV Risk Documentation Post HD rate not dependent on Physician p=0.42 (Fishers Exact) 50% (95% CI 37-63%) P<0.0001 3.2% (95% CI 0.4-11.2%)

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

  25. Conclusions • Workflow barriers exist with use of HD CDST • Time constraints • Need for more treatment recommendations • Printer friendly graphs • More patient education tools • Sinceincorporation 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.

  26. Thank You

More Related