20
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A study of diverse clinical decision support rule authoring environments and requirements for integration

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Efficient rule authoring tools are critical to allow clinical Knowledge Engineers (KEs), Software Engineers (SEs), and Subject Matter Experts (SMEs) to convert medical knowledge into machine executable clinical decision support rules. The goal of this analysis was to identify the critical success factors and challenges of a fully functioning Rule Authoring Environment (RAE) in order to define requirements for a scalable, comprehensive tool to manage enterprise level rules.

          Methods

          The authors evaluated RAEs in active use across Partners Healthcare, including enterprise wide, ambulatory only, and system specific tools, with a focus on rule editors for reminder and medication rules. We conducted meetings with users of these RAEs to discuss their general experience and perceived advantages and limitations of these tools.

          Results

          While the overall rule authoring process is similar across the 10 separate RAEs, the system capabilities and architecture vary widely. Most current RAEs limit the ability of the clinical decision support (CDS) interventions to be standardized, sharable, interoperable, and extensible. No existing system meets all requirements defined by knowledge management users.

          Conclusions

          A successful, scalable, integrated rule authoring environment will need to support a number of key requirements and functions in the areas of knowledge representation, metadata, terminology, authoring collaboration, user interface, integration with electronic health record (EHR) systems, testing, and reporting.

          Related collections

          Most cited references23

          • Record: found
          • Abstract: found
          • Article: not found

          Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

          Developers of health care software have attributed improvements in patient care to these applications. As with any health care intervention, such claims require confirmation in clinical trials. To review controlled trials assessing the effects of computerized clinical decision support systems (CDSSs) and to identify study characteristics predicting benefit. We updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane Library, Inspec, and ISI databases and consulting reference lists through September 2004. Authors of 64 primary studies confirmed data or provided additional information. We included randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes. Teams of 2 reviewers independently abstracted data on methods, setting, CDSS and patient characteristics, and outcomes. One hundred studies met our inclusion criteria. The number and methodologic quality of studies improved over time. The CDSS improved practitioner performance in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of 10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease management systems, and 19 (66%) of 29 drug-dosing or prescribing systems. Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%) reported improvements. Improved practitioner performance was associated with CDSSs that automatically prompted users compared with requiring users to activate the system (success in 73% of trials vs 47%; P = .02) and studies in which the authors also developed the CDSS software compared with studies in which the authors were not the developers (74% success vs 28%; respectively, P = .001). Many CDSSs improve practitioner performance. To date, the effects on patient outcomes remain understudied and, when studied, inconsistent.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Systematic review: impact of health information technology on quality, efficiency, and costs of medical care.

            Experts consider health information technology key to improving efficiency and quality of health care. To systematically review evidence on the effect of health information technology on quality, efficiency, and costs of health care. The authors systematically searched the English-language literature indexed in MEDLINE (1995 to January 2004), the Cochrane Central Register of Controlled Trials, the Cochrane Database of Abstracts of Reviews of Effects, and the Periodical Abstracts Database. We also added studies identified by experts up to April 2005. Descriptive and comparative studies and systematic reviews of health information technology. Two reviewers independently extracted information on system capabilities, design, effects on quality, system acquisition, implementation context, and costs. 257 studies met the inclusion criteria. Most studies addressed decision support systems or electronic health records. Approximately 25% of the studies were from 4 academic institutions that implemented internally developed systems; only 9 studies evaluated multifunctional, commercially developed systems. Three major benefits on quality were demonstrated: increased adherence to guideline-based care, enhanced surveillance and monitoring, and decreased medication errors. The primary domain of improvement was preventive health. The major efficiency benefit shown was decreased utilization of care. Data on another efficiency measure, time utilization, were mixed. Empirical cost data were limited. Available quantitative research was limited and was done by a small number of institutions. Systems were heterogeneous and sometimes incompletely described. Available financial and contextual data were limited. Four benchmark institutions have demonstrated the efficacy of health information technologies in improving quality and efficiency. Whether and how other institutions can achieve similar benefits, and at what costs, are unclear.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Building better guidelines with BRIDGE-Wiz: development and evaluation of a software assistant to promote clarity, transparency, and implementability.

              To demonstrate the feasibility of capturing the knowledge required to create guideline recommendations in a systematic, structured, manner using a software assistant. Practice guidelines constitute an important modality that can reduce the delivery of inappropriate care and support the introduction of new knowledge into clinical practice. However, many guideline recommendations are vague and underspecified, lack any linkage to supporting evidence or documentation of how they were developed, and prove to be difficult to transform into systems that influence the behavior of care providers. The BRIDGE-Wiz application (Building Recommendations In a Developer's Guideline Editor) uses a wizard approach to address the questions: (1) under what circumstances? (2) who? (3) ought (with what level of obligation?) (4) to do what? (5) to whom? (6) how and why? Controlled natural language was applied to create and populate a template for recommendation statements. The application was used by five national panels to develop guidelines. In general, panelists agreed that the software helped to formalize a process for authoring guideline recommendations and deemed the application usable and useful. Use of BRIDGE-Wiz promotes clarity of recommendations by limiting verb choices, building active voice recommendations, incorporating decidability and executability checks, and limiting Boolean connectors. It enhances transparency by incorporating systematic appraisal of evidence quality, benefits, and harms. BRIDGE-Wiz promotes implementability by providing a pseudocode rule, suggesting deontic modals, and limiting the use of 'consider'. Users found that BRIDGE-Wiz facilitates the development of clear, transparent, and implementable guideline recommendations.
                Bookmark

                Author and article information

                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central
                1472-6947
                2012
                12 November 2012
                : 12
                : 128
                Affiliations
                [1 ]Clinical Informatics Research and Development, Partners HealthCare, 93 Worcester Street, 2nd floor, Wellesley, MA 02481, USA
                [2 ]Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, 1620 Tremont Street, Boston, MA 02120, USA
                [3 ]Harvard Medical School, 651 Huntington Avenue, Boston, MA 02115, USA
                Article
                1472-6947-12-128
                10.1186/1472-6947-12-128
                3554596
                23145874
                29e9c68e-7b6f-4f29-bf1d-cfcd36d9557f
                Copyright ©2012 Zhou et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 June 2012
                : 1 November 2012
                Categories
                Research Article

                Bioinformatics & Computational biology
                clinical decision support,knowledge management,rule-based decision support,knowledge engineering,knowledge lifecycle,knowledge authoring tool

                Comments

                Comment on this article