[Show abstract][Hide abstract] ABSTRACT: To determine whether an intervention focusing clinician attention on drug choice for hypertension treatment improves concordance between drug regimens and guidelines.
Cluster-randomized controlled trial comparing an individualized intervention with a general guideline implementation in geographically diverse primary care clinics of a university-affiliated Department of Veterans Affairs healthcare system.
Participants were 36 attending physicians and nurse practitioners (16 in the general group and 20 in the individualized group), with findings based on 4500 hypertensive patients. A general guideline implementation for all clinicians, including education about guideline-based drug recommendations and goals for adequacy of blood pressure control, was compared with addition of a printed individualized advisory sent to clinicians at each patient visit, indicating whether or not the patient's antihypertensive drug regimen was guideline concordant. We measured change from baseline to end point in the proportion of clinicians' patients whose drug therapy was guideline concordant.
The individualized intervention resulted in an improvement in guideline concordance more than twice that observed for the general intervention (10.9% vs 3.8%, t = 2.796, P = .008). Bootstrap analysis showed that being in the individualized group increased the odds of concordance 1.5-fold (P = .025). The proportion of patients with adequate blood pressure control increased within each study group; however, the difference between groups was not significant.
An individualized advisory regarding drug therapy for hypertension given to the clinician at each patient visit was more effective in changing clinician prescribing behavior than implementation of a general guideline.
The American journal of managed care 12/2005; 11(11):677-85. · 2.12 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Measurement of provider adherence to a guideline-based decision support system (DSS) presents a number of important challenges. Establishing a causal relationship between the DSS and change in concordance requires consideration of both the primary intention of the guideline and different ways providers attempt to satisfy the guideline. During our work with a guideline-based decision support system for hypertension, ATHENA DSS, we document a number of subtle deviations from the strict hypertension guideline recommendations that ultimately demonstrate provider adherence. We believe that understanding these complexities is crucial to any valid evaluation of provider adherence. We also describe the development of an advisory evaluation engine that automates the interpretation of clinician adherence with the DSS on multiple levels, facilitating the high volume of complex data analysis that is created in a clinical trial of a guideline-based DSS.
Studies in health technology and informatics 02/2004; 107(Pt 1):125-9.
[Show abstract][Hide abstract] ABSTRACT: We develop a method and algorithm for deciding the optimal approach to creating quality-auditing protocols for guideline-based clinical performance measures. An important element of the audit protocol design problem is deciding which guide-line elements to audit. Specifically, the problem is how and when to aggregate individual patient case-specific guideline elements into population-based quality measures. The key statistical issue involved is the trade-off between increased reliability with more general population-based quality measures versus increased validity from individually case-adjusted but more restricted measures done at a greater audit cost. Our intelligent algorithm for auditing protocol design is based on hierarchically modeling incrementally case-adjusted quality constraints. We select quality constraints to measure using an optimization criterion based on statistical generalizability coefficients. We present results of the approach from a deployed decision support system for a hypertension guideline.
Studies in health technology and informatics 02/2004; 107(Pt 2):1003-7.
[Show abstract][Hide abstract] ABSTRACT: Information technology can support the implementation of clinical research findings in practice settings. Technology can address the quality gap in health care by providing automated decision support to clinicians that integrates guideline knowledge with electronic patient data to present real-time, patient-specific recommendations. However, technical success in implementing decision support systems may not translate directly into system use by clinicians. Successful technology integration into clinical work settings requires explicit attention to the organizational context. We describe the application of a "sociotechnical" approach to integration of ATHENA DSS, a decision support system for the treatment of hypertension, into geographically dispersed primary care clinics. We applied an iterative technical design in response to organizational input and obtained ongoing endorsements of the project by the organization's administrative and clinical leadership. Conscious attention to organizational context at the time of development, deployment, and maintenance of the system was associated with extensive clinician use of the system.
Journal of the American Medical Informatics Association 01/2004; 11(5):368-76. · 3.57 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a model representation and algorithm for deriving structured quality indicators and auditing protocols from formalized specifications of guidelines used in decision support systems. We apply the model and algorithm to the assessment of physician concordance with a guideline knowledge model for hypertension used in a decision-support system. The properties of our solution include the ability to derive automatically context-specific and case-mix-adjusted quality indicators that can model global or local levels of detail about the guideline parameterized by defining the reliability of each indicator or element of the guideline.
[Show abstract][Hide abstract] ABSTRACT: Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a unified model representation and algorithm for evidence-adaptive quality assessment scoring that can: (1) use both complex case-specific guidelines and single-step population-wide performance-indicators as quality measures; (2) score adherence consistently with quantitative population-based medical utilities of the quality measures where available; and (3) give worst-case and best-case scores for variations based on (a) uncertain knowledge of the best practice, (b) guideline customization to an individual patient or particular population, (c) physician practice style variation, or (d) imperfect reliability of the quality measure. Our solution uses fuzzy measure-theoretic scoring to handle the uncertain knowledge about best-practices and the ambiguity from practice variation. We show results of applying our method to retrospective data from a guideline project to improve the quality of hypertension care.
[Show abstract][Hide abstract] ABSTRACT: Quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe an approach for evaluating and consistently scoring clinician adherence to medical guidelines using the intentions of guideline authors. We present the Quality Indicator Language (QUIL) that may be used to formally specify quality constraints on physician behavior and patient outcomes derived from medical guidelines. We present a modeling and scoring methodology for consistently evaluating multi-step and multi-choice guideline plans based on guideline intentions and their revisions.
[Show abstract][Hide abstract] ABSTRACT: The Institute of Medicine recently issued a landmark report on medical error.1 In the penumbra of this report, every aspect of health care is subject to new scrutiny regarding patient safety. Informatics technology can support patient safety by correcting problems inherent in older technology; however, new information technology can also contribute to new sources of error. We report here a categorization of possible errors that may arise in deploying a system designed to give guideline-based advice on prescribing drugs, an approach to anticipating these errors in an automated guideline system, and design features to minimize errors and thereby maximize patient safety. Our guideline implementation system, based on the EON architecture, provides a framework for a knowledge base that is sufficiently comprehensive to incorporate safety information, and that is easily reviewed and updated by clinician-experts.
[Show abstract][Hide abstract] ABSTRACT: This paper describes the ATHENA Decision Support System (DSS), which operationalizes guidelines for hypertension using the EON architecture. ATHENA DSS encourages blood pressure control and recommends guideline-concordant choice of drug therapy in relation to comorbid diseases. ATHENA DSS has an easily modifiable knowledge base that specifies eligibility criteria, risk stratification, blood pressure targets, relevant comorbid diseases, guideline-recommended drug classes for patients with comorbid disease, preferred drugs within each drug class, and clinical messages. Because evidence for best management of hypertension evolves continually, ATHENA DSS is designed to allow clinical experts to customize the knowledge base to incorporate new evidence or to reflect local interpretations of guideline ambiguities. Together with its database mediator Athenaeum, ATHENA DSS has physical and logical data independence from the legacy Computerized Patient Record System (CPRS) supplying the patient data, so it can be integrated into a variety of electronic medical record systems.
[Show abstract][Hide abstract] ABSTRACT: We present a methodology and database mediator tool for integrating modern knowledge-based systems, such as the Stanford EON architecture for automated guideline-based decision-support, with legacy databases, such as the Veterans Health Information Systems & Technology Architecture (VISTA) systems, which are used nation-wide. Specifically, we discuss designs for database integration in ATHENA, a system for hypertension care based on EON, at the VA Palo Alto Health Care System. We describe a new database mediator that affords the EON system both physical and logical data independence from the legacy VA database. We found that to achieve our design goals, the mediator requires two separate mapping levels and must itself involve a knowledge-based component.
[Show abstract][Hide abstract] ABSTRACT: We present a methodology and tool for providing retrospective review and critiquing of guideline-based medical care given to patients. We show how our guideline representation language, ASBRU, which supports the use of physicians intentions in addition to physician's actions, allows us to compare the care given to a patient at the level of the intention to treat in addition to the more detailed plan carried out. This approach allows us to take the physician's and institution's preferences and policies into account when assessing the quality of care given to patients in accordance with a medical guideline and in explaining or justifying physician deviations from the recommendations of a guideline. We have developed an algorithm which uses this more robust representation of guidelines to implement a critiquing module for retrospective quality assessment of guideline-based care. Text I.
[Show abstract][Hide abstract] ABSTRACT: We present a methodology and tool for providing retrospective review and critiquing of guideline-based medical care given to patients. We show how our guideline representation language, Asbru, which supports the use of physicians intentions in addition to physician's actions, allows us to compare the care given to a patient at the level of the intention to treat in addition to the more detailed plan carried out. We have developed an algorithm based on this representation for retrospective quality assessment of guideline-based care. Our method takes the physician's and institution's preferences and policies into account in explaining or justifying physician deviations from the recommendations of a guideline.