AI systems in clinical practice

Decision support systems
ATHENA
Assessment and Treatment of Hypertension: Evidence-Based Automation
Decision support system for the management of hypertension in primary care

developed by clinical domains keywords
Stanford Medical Informatics, VA Palo Alto Health Care System, and Stanford Center for Primary Care and Outcomes Research Hypertension, primary care.
(Also: 2nd ATHENA DSS for Opioid Therapy, 2006)
decision support systems, clinical guidelines, safety
location commissioned status
A number of clinics in Northern California and Durham, North Carolina 2002 In clinical use
description
The ATHENA Decision Support System (DSS) implements guidelines for hypertension using Stanford Medical Informatics 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 c lasses 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.

In 2006, a second ATHENA DSS prototype application - for Opioid Therapy - was developed. The application implemented parts of the VA/DoD Clinical Practice Guideline (CPG) for the Management of Opioid Therapy for Chronic Pain (VA/DoD, 2003).

references

Chan, A., S. Martins, et al. Post-fielding Surveillance of a Guideline-Based Decision Support System. K. Henriksen, J. B. Battles, E. S. Marks and D. I. Lewin (Eds). Advances in Patient Safety: From Research to Implementation. Vol. 1. Research Findings AHRQ Pub Numb 05-0021-1. Rockville, MD 20850, AHRQ. 1: 331-339. 2005.

[]   [AHRQ]

" Quality assurance (QA) processes for new technologies are used to ensure safety. Clinical decision support systems (DSS), identified by the Institute of Medicine (IOM) as an important tool in preventing patient errors, should undergo similar predeployment testing to prevent introduction of new errors. Post-fielding surveillance, akin to post-marketing surveillance for adverse events, may detect rarely occurring problems that appear only in widespread use. To assess the quality of a guideline-based DSS for hypertension, ATHENA DSS, researchers monitored real-time clinician feedback during point-of-care use of the system. Comments (n = 835) were submitted by 44 of the 91 (48.4 percent) study clinicians (median 8.5 comments/ clinician). Twenty-three (2.8 percent) comments identified important, rarely occurring problems. Timely analysis of such feedback revealed omissions of medications, diagnoses, and adverse drug reactions due to rare events in data extraction and conversion from the electronic health record. Analysis of clinician-user feedback facilitated rapid detection and correction of such errors. Based on this experience, new technologies for improving patient safety should include mechanisms for post-fielding QA testing. "

Goldstein MK, Coleman RW, Tu SW et al. 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.

[PubMed]   [PubMed Central]    [JAMIA]

" 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. "

Steinman MA, Fischer MA, Shlipak MG et al. Clinician awareness of adherence to hypertension guidelines. Am J Med. 2004 Nov 15;117(10):747-54.

[PubMed]   []

" PURPOSE: Little is known about how well clinicians are aware of their own adherence to clinical guidelines, an important indicator of quality. We compared clinicians' beliefs about their adherence to hypertension guidelines with data on their actual performance. METHODS: We surveyed 139 primary care clinicians at three Veterans Affairs medical centers, asking them to assess their own adherence to hypertension guidelines. We then extracted data from the centers' clinical databases on guideline-concordant medication use and blood pressure control for patients cared for by these providers during a 6-month period. Data were collected for patients with hypertension and diabetes, hypertension and coronary disease, or hypertension with neither of these comorbid conditions. RESULTS: Eighty-six clinicians (62%) completed the survey. Each clinician saw a median of 94 patients with hypertension (mean age, 65 years). Patients were treated with an average of 1.6 antihypertensive medications. Overall, clinicians overestimated the proportion of their patients who were prescribed guideline-concordant medications (75% perceived vs. 67% actual, P <0.001) and who had blood pressure levels <140/90 mm Hg on their last visit (68% perceived vs. 43% actual, P <0.001). Among individual clinicians, there were no significant correlations between perceived and actual guideline adherence (r = 0.18 for medications, r = 0.14 for blood pressure control; P > or =0.10 for both). Clinicians with relatively low actual guideline performance were most likely to overestimate their adherence to medication recommendations and blood pressure targets. CONCLUSION: Clinicians appear to overestimate their adherence to hypertension guidelines, particularly with regards to the proportion of their patients with controlled blood pressure. This limited awareness may represent a barrier to successful implementation of guidelines, and could be addressed through the use of provider profiles and point-of-service feedback to clinicians. "

Lai S, Goldstein MK, Martins SB et al. Insights from Testing the Accuracy of Recommendations from an Automated Decision Support System for Primary Hypertension: ATHENA DSS. Medinfo. 2004;2004(CD):1706.

[PubMed]  []

" Decision support systems for use in clinical settings require extensive testing to ensure accuracy of the recommendations generated. We tested a decision support system for hypertension, ATHENA DSS, by having an internist not previously affiliated with the project review a test set of 100 cases in comparison with ATHENA recommendations. We developed a test set of 100 cases with known correct answers that we now use as a check after updates "

Chan AS, Coleman RW, Martins SB et al. Evaluating provider adherence in a trial of a guideline-based decision support system for hypertension. Medinfo. 2004;11(Pt 1):125-9.

[PubMed]   [SMI]

" 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. "

Goldstein MK and Hoffman BB. Hypertension Record keeping and Electronic Management Systems. In Hypertension Primer: The Essential of High Blood Pressure, 3rd edition, eds J.L. Izzo, H.R. Black. Lippincott Williams & Wilkins, Dallas, TX, 2003

[]   []

" "

Goldstein MK, Hoffman BB, Coleman RW et al. Patient safety in guideline-based decision support for hypertension management: ATHENA DSS. Proc AMIA Symp. 2001;:214-8.

[PubMed] [SMI]

" 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. "
R. D. Shankar, S. B. Martins, S. W. Tu, M. K. Goldstein, & M. A. Musen. Building an Explanation Function for a Hypertension Decision-Support System. MedInfo2001, London, UK, 538-542. 2001.

SMI]   []

" ATHENA DSS is a decision-support system that provides recommendations for managing hypertension in primary care. ATHENA DSS is built on a component-based architecture called EON. User acceptance of a system like this one depends partly on how well the system explains its reasoning and justifies its conclusions. We addressed this issue by adapting WOZ, a declarative explanation framework, to build an explanation function for ATHENA DSS. ATHENA DSS is built based on a component-based architecture called EON. The explanation function obtains its information by tapping into EON's components, as well as into other relevant sources such as the guideline document and medical literature. It uses an argument model to identify the pieces of information that constitute an explanation, and employs a set of visual clients to display that explanation. By incorporating varied information sources, by mirroring naturally occurring medical arguments and by utilizing graphic visualizations, ATHENA DSS's explanation function generates rich, evidence-based explanations. "

Goldstein MK, Hoffman BB, Coleman RW et al. Implementing clinical practice guidelines while taking account of changing evidence: ATHENA DSS, an easily modifiable decision-support system for managing hypertension in primary care. Proc AMIA Symp. 2000;:300-4.

[PubMed]   [AMIA]

" 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. "

A. Advani, S. Tu, M. O'Connor, R. Coleman, M. K. Goldstein, & M. Musen. Integrating a Modern Knowledge-Based System Architecture with a Legacy VA Database: The ATHENA and EON Projects at Stanford. AMIA '99, Washington, D.C., 653-657. 1999.

[Abstract - SMI]   [Paper - SMI]

" 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 is 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 (VAPAHCS). 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 these design goals, the mediator requires two separate mapping levels and must itself involve a knowledge-based component. "
contact links

Mary Goldstein
VA Palo Alto and Stanford University School of Medicine

E: goldsteinatstanford.edu

 bullet  ATHENA (Center for Health Care Evaluation (CHCE), VA Palo Alto Health Care System & Stanford University School of Medicine)  bullet  Stanford Medical Informatics  bullet  VA Palo Alto Health Care System  bullet  Stanford Center for Primary Care and Outcomes Research  bullet  ATHENA Opioid Therapy DSS Project
acknowledgements
Samson, Tu, Stanford Medical Informatics; Mary Goldstein, VA Palo Alto and Stanford University School of Medicine, Center for Primary Care and Outcomes Research; Brian Hoffman, VA Palo Alto and Stanford University School of Medicine, Dept. of Endocrinology, Gerontology, and Metabolism; Albert S Chan, Stanford University School of Medicine.

Entry on archive: 28 November 2002
Last main updates: 28 November 2002; 12 February 2005; 25 October 2005; (19 December 2006)
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