ArticleJournal of diabetes science and technology2011
Design of a decision support system to help clinicians manage glycemia in patients with type 2 diabetes mellitus.
Article in Journal of diabetes science and technology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- A multi-site randomized trial of a clinical decision support intervention to improve problem list completeness.Journal of the American Medical Informatics Association : JAMIA · 2023Trial
- Effectiveness of shared goal setting and decision making to achieve treatment targets in type 2 diabetes patients: A cluster-randomized trial (OPTIMAL).Health expectations : an international journal of public participation in health care and health policy · 2017Trial
- Translating What Works: A New Approach to Improve Diabetes Management.Journal of diabetes science and technology · 2015Trial
- Shared decision making in type 2 diabetes with a support decision tool that takes into account clinical factors, the intensity of treatment and patient preferences: design of a cluster randomised (OPTIMAL) trial.BMC family practice · 2015Trial
- Rapid improvement of glycemic control in type 2 diabetes using weekly intensive multifactorial interventions: structured glucose monitoring, patient education, and adjustment of therapy-a randomized controlled trial.Diabetes technology & therapeutics · 2011Trial
- A dashboard-based system for supporting diabetes care.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Person centered prediction of survival in population based screening program by an intelligent clinical decision support system.Gastroenterology and hepatology from bed to bench · 2017Article
- Designing Decision-Support Technologies for Patient-Generated Data in Type 1 Diabetes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Article
- A decision support tool for appropriate glucose-lowering therapy in patients with type 2 diabetes.Diabetes technology & therapeutics · 2015Article
- Personalized therapy algorithms for type 2 diabetes: a phenotype-based approach.Pharmacogenomics and personalized medicine · 2014Review
- Role of health information technologies in the Patient-centered Medical Home.Journal of diabetes science and technology · 2013Review
- Personalized decision support in type 2 diabetes mellitus: current evidence and future directions.Current diabetes reports · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe sought to develop a computerized clinical decision support for clinicians treating patients with type 2 diabetes mellitus (T2DM).
methodsWe designed, developed, and tested a computer-assisted decision support (CADS) system using statistical analyses of self-monitoring of blood glucose data, laboratory data, medical and medication history, and individualized hemoglobin A1c goals. A rule-based expert system generated recommendations for changes in therapy and accompanying explanations.
resultsA clinical decision support system (CADS) was developed that considers 9 classes of medications and 69 regimens with combinations of up to 4 therapeutic agents. The preferred sequences of regimens can be customized. The program is integrated with a "comprehensive diabetes management system," electronic medical record systems, and a method for uploading data from memory glucose meters via telephone without use of a computer or the Internet. The software provides a report to the clinician regarding the overall quality of glycemic control and identifies problems, e.g., hypoglycemia, hyperglycemia, glycemic variability, and insufficient data. The program can recommend continuation of current therapy, adjustment of dosages of current medications, or change of regimen and can provide explanations for its recommendations. If the user rejects the recommendations, the program will recommend alternative approaches. The CADS also provides access to Food and Drug Administration-approved prescribing information, guidelines from professional organizations, and selections from the general medical literature. The system has been extensively tested with real and synthetic data and is ready for evaluation in multicenter clinical trials.
conclusionA clinical decision support system to assist with the management of patients with T2DM was designed, developed, tested, and found to perform well.
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Registered trials
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