Guidelines for Drug Therapy of Hypertension: Multi-Site Implementation
NCT ID: NCT00122161
Last Updated: 2015-04-07
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
PHASE3
190 participants
INTERVENTIONAL
2005-09-30
Brief Summary
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Detailed Description
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Hypertension, the most commonly reported medical problem in veterans, is a major risk factor for heart disease and stroke. Lowering blood pressure decreases the risk of these adverse clinical outcomes. Widely promoted evidence-based clinical practice guidelines set target blood pressures for adequate control, yet most hypertensives, including VA patients, do not meet the targets. Guidelines also call for use of specific drugs depending on the patient's pattern of comorbid characteristics; yet, clinicians often prescribe drugs that are not guideline-concordant.
Objectives:
The long term objective of this work is to contribute to the VA's ability to respond flexibly to rapidly evolving medical knowledge by establishing a system guidelines that can be used throughout the VA nationally for implementing multiple different clinical practice. In collaboration with Stanford Medical Informatics we developed an automated decision support system for hypertension management, known as ATHENA DSS built with EON technology for guideline-based decision support. ATHENA DSS incorporates hundreds of knowledge rules to operationalize guidelines for hypertension.
Methods:
ATHENA DSS combines patient information from VistA with an automated knowledge base of hypertension to generate patient-specific recommendations for management of hypertension that are displayed to primary care clinicians in pop-up windows in the VA�s Computerized Patient Record System (CPRS) when the record for appropriate patients is opened on the day of scheduled primary care clinic visits. The ATHENA DSS pop-up provides advice on adequacy of control of blood pressure and specific recommendations for drug therapy of hypertension, a visual display of the patient�s medication history and concurrent blood pressures, evidence supporting the main recommendations, and other information. We deployed the system at three VA medical centers--Durham, San Francisco, and Palo Alto�and conducted a clinician-randomized trial. We logged data on use of the system, monitored comments entered by clinicians, and conducted a questionnaire survey of clinicians. We planned analyses of impact on clinician prescribing and patient blood pressures. We planned preparation for dissemination of the system to additional VA medical centers.
Status:
Project work is ongoing at time of preparing this report. We recently received notice of funding for a VISN collaborative that will use the ATHENA DSS in five medical centers in VISN 1 (New England).
Conditions
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Study Design
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RANDOMIZED
PARALLEL
NONE
Study Groups
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Arm 1
ATHENA Decision Support System, An Automated Clinical Decision Support System for Health Care Providers
Profiling performance
Interventions
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ATHENA Decision Support System, An Automated Clinical Decision Support System for Health Care Providers
Profiling performance
Eligibility Criteria
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Inclusion Criteria
ALL
Yes
Sponsors
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US Department of Veterans Affairs
FED
Responsible Party
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Principal Investigators
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Mary K. Goldstein, MD MS
Role: PRINCIPAL_INVESTIGATOR
VA Palo Alto Health Care System, Palo Alto, CA
Locations
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VA Palo Alto Health Care System, Palo Alto, CA
Palo Alto, California, United States
San Francisco VA Medical Center, San Francisco, CA
San Francisco, California, United States
Durham VA Medical Center, Durham, NC
Durham, North Carolina, United States
Countries
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References
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Siegel D, Lopez J, Meier J, Goldstein MK, Lee S, Brazill BJ, Matalka MS. Academic detailing to improve antihypertensive prescribing patterns. Am J Hypertens. 2003 Jun;16(6):508-11. doi: 10.1016/s0895-7061(03)00060-8.
Bosworth HB, Dudley T, Olsen MK, Voils CI, Powers B, Goldstein MK, Oddone EZ. Racial differences in blood pressure control: potential explanatory factors. Am J Med. 2006 Jan;119(1):70.e9-15. doi: 10.1016/j.amjmed.2005.08.019.
Szeto HC, Coleman RK, Gholami P, Hoffman BB, Goldstein MK. Accuracy of computerized outpatient diagnoses in a Veterans Affairs general medicine clinic. Am J Manag Care. 2002 Jan;8(1):37-43.
Chan AS, Shankar RD, Coleman RW, Matins SB, Hoffman BB, Goldstein MK. Leveraging point-of-care clinician feedback to study barriers to guideline adherence. AMIA Annu Symp Proc. 2005;2005:915.
Steinman MA, Fischer MA, Shlipak MG, Bosworth HB, Oddone EZ, Hoffman BB, Goldstein MK. Clinician awareness of adherence to hypertension guidelines. Am J Med. 2004 Nov 15;117(10):747-54. doi: 10.1016/j.amjmed.2004.03.035.
Tu SW, Musen MA, Shankar R, Campbell J, Hrabak K, McClay J, Huff SM, McClure R, Parker C, Rocha R, Abarbanel R, Beard N, Glasgow J, Mansfield G, Ram P, Ye Q, Mays E, Weida T, Chute CG, McDonald K, Molu D, Nyman MA, Scheitel S, Solbrig H, Zill DA, Goldstein MK. Modeling guidelines for integration into clinical workflow. Stud Health Technol Inform. 2004;107(Pt 1):174-8.
Advani A, Jones N, Shahar Y, Goldstein MK, Musen MA. An intelligent case-adjustment algorithm for the automated design of population-based quality auditing protocols. Stud Health Technol Inform. 2004;107(Pt 2):1003-7.
Shankar RD, Tu SW, Martins SB, Fagan LM, Goldstein MK, Musen MA. Integration of textual guideline documents with formal guideline knowledge bases. Proc AMIA Symp. 2001:617-21.
Chan AS, Coleman RW, Martins SB, Advani A, Musen MA, Bosworth HB, Oddone EZ, Shlipak MG, Hoffman BB, Goldstein MK. Evaluating provider adherence in a trial of a guideline-based decision support system for hypertension. Stud Health Technol Inform. 2004;107(Pt 1):125-9.
Goldstein MK, Coleman RW, Tu SW, Shankar RD, O'Connor MJ, Musen MA, Martins SB, Lavori PW, Shlipak MG, Oddone E, Advani AA, Gholami P, Hoffman BB. Translating research into practice: organizational issues in implementing automated decision support for hypertension in three medical centers. J Am Med Inform Assoc. 2004 Sep-Oct;11(5):368-76. doi: 10.1197/jamia.M1534. Epub 2004 Jun 7.
Shankar RD, Martins SB, Tu SW, Goldstein MK, Musen MA. Building an explanation function for a hypertension decision-support system. Stud Health Technol Inform. 2001;84(Pt 1):538-42.
Lin ND, Martins SB, Chan AS, Coleman RW, Bosworth HB, Oddone EZ, Shankar RD, Musen MA, Hoffman BB, Goldstein MK. Identifying barriers to hypertension guideline adherence using clinician feedback at the point of care. AMIA Annu Symp Proc. 2006;2006:494-8.
Bosworth HB, Olsen MK, Goldstein MK, Orr M, Dudley T, McCant F, Gentry P, Oddone EZ. The veterans' study to improve the control of hypertension (V-STITCH): design and methodology. Contemp Clin Trials. 2005 Apr;26(2):155-68. doi: 10.1016/j.cct.2004.12.006.
Tu SW, Hrabak KM, Campbell JR, Glasgow J, Nyman MA, McClure R, McClay J, Abarbanel R, Mansfield JG, Martins SM, Goldstein MK, Musen MA. Use of declarative statements in creating and maintaining computer-interpretable knowledge bases for guideline-based care. AMIA Annu Symp Proc. 2006;2006:784-8.
Peralta CA, Shlipak MG, Wassel-Fyr C, Bosworth H, Hoffman B, Martins S, Oddone E, Goldstein MK. Association of antihypertensive therapy and diastolic hypotension in chronic kidney disease. Hypertension. 2007 Sep;50(3):474-80. doi: 10.1161/HYPERTENSIONAHA.107.088088. Epub 2007 Jul 30.
Martins SB, Lai S, Tu S, Shankar R, Hastings SN, Hoffman BB, Dipilla N, Goldstein MK. Offline testing of the ATHENA Hypertension decision support system knowledge base to improve the accuracy of recommendations. AMIA Annu Symp Proc. 2006;2006:539-43.
Related Links
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Click here for more information about this study: Guidelines for Drug Therapy of Hypertension: Multi-Site Implementation
Other Identifiers
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CPI 99-275
Identifier Type: -
Identifier Source: org_study_id
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