Design, Implementation and Evaluation of Scalable Decision Support for Diabetes Care
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 25,915
- 试验地点
- 1
- 主要终点
- Change in hemoglobin A1c (HbA1c) levels
研究概览
简要总结
Diabetes is a significant medical problem in the United States and across the world. Despite significant progress in understanding how to better manage diabetes, there is oftentimes still uncertainty in the optimal management strategy for a specific patient. As a result, providers and patients must often use a trial-and-error approach to identify an effective treatment regimen.
The project team has previously developed a Diabetes Dashboard that summarizes relevant patient information (e.g., medication history and recent hemoglobin A1c trend). This dashboard allows a clinician to select a target hemoglobin A1c level for the patient in 3 or 6 months, then compare and contrast different options for treatment, including weight loss and the use of different medication regimens. Included in this comparison are known benefits and side effects, as well as the likely chances of achieving the treatment target given the experience of past, similar patients. The Diabetes Dashboard is already available as an optional tab in the EHR system.
The project team has also previously developed the Disease Manager App for evidence-based chronic disease management and health maintenance. The Disease Manger Application is fully integrated with the EHR, and it provides care guidance via individual chronic disease modules as well as a unified module that encompasses all relevant modules for chronic diseases and health maintenance. The initial modules that have been developed are for chronic obstructive pulmonary disease, hypertension, diabetes mellitus, and health maintenance.
The objective of this research is to evaluate the Diabetes Dashboard integrated with the Disease Manager App. The Intervention consists of the diabetes module of the Disease Manager App, which incorporates content from the Diabetes Dashboard for pharmacotherapy prediction and provides a link to the Diabetes Dashboard.
详细描述
This study is a pragmatic pre-post trial of the Diabetes Dashboard integrated with the Disease Manager App. The Disease Manager App is available as a tab in the EHR and enables clinicians to confirm relevant patient parameters. A link to the Diabetes Dashboard will be available from the Disease Manager App diabetes module. In the Diabetes Dashboard, providers can select treatment goals and review likely outcomes from alternative treatment strategies through an interactive graphical user interface. In the review process, the Diabetes Dashboard enables providers and patients to compare up to three potential therapies side-by side including weight-loss in terms of a) personalized, predicted probability of achieving treatment goals; b) general potential risks, benefits, and medication costs; and c) relevant financial information specific to the patient's insurance. The personalized prediction is performed by a predictive model developed by analyzing data sets of patients with diabetes mellitus. The Disease Manager App and the Diabetes Dashboard are seamlessly integrated with the EHR using an interoperability standard known as SMART on FHIR (short for Substitutable Medical Apps Reusable Technologies on Fast Healthcare Interoperability Resources).
The study is being conducted at University of Utah primary care clinics. In all primary care clinics, providers will be provided with access to the Diabetes Dashboard integrated with the Disease Manager App. Iterative enhancements will be made to the tool if warranted based on the results of a formative evaluation during the 1-year pragmatic implementation study. Use of the tool and associated suggestions will be optional and up to the discretion of the clinician. Use of the tool will be regularly monitored, and a mixed-methods evaluation will be conducted of the tool and its impact.
The primary outcome measure will be hemoglobin A1c (HbA1c) levels, which are an important physiological marker of diabetes control. Secondary measures will include body mass index (BMI) and the cost of diabetes medications prescribed. Other measures will include usage of the tool and clinical users' opinions of the tool.
The primary study analyses will be limited to adult patients who were seen at least twice in the primary care clinics during the evaluation period for office visits with a visit diagnosis of diabetes mellitus, who are known to have diabetes mellitus (but not type-1 diabetes mellitus), who had at least one HbA1c of >= 7.5% during the evaluation period, and who are not already on maximal diabetes therapy (as defined by the use of short-acting insulin) at the start of the study. Secondary study analyses will be conducted on patient subsets, including a per protocol analysis of cases where the tool was used.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Treatment
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •>= 18 years old
- •are being seen at a University of Utah primary care clinic
- •has diabetes mellitus
排除标准
- •Note that the primary study analyses will be on a subset of these patients. See the Detailed Description subsection in the Study Description section for details.
研究组 & 干预措施
Diabetes Dashboard integrated with Disease Manager App
When patients are seen in clinics in this arm, the clinical providers will have access to the intervention (EHR-integrated Diabetes Dashboard that is integrated with the diabetes module of the Disease Manager App).
干预措施: Diabetes Dashboard integrated with Disease Manager App (Other)
结局指标
主要结局
Change in hemoglobin A1c (HbA1c) levels
时间窗: Through study completion, an average of 12 months for the intervention period and 12 months for the baseline period
Each patient's HbA1c level will be estimated for day 15 of each month, calculated as follows. If a value exists for that date, use that. Otherwise, estimate the value on that date based on the values immediately before and after that date.
次要结局
- Change in body mass index (BMI) levels(Through study completion, an average of 12 months for the intervention period and 12 months for the baseline period)
研究者
Kensaku Kawamoto, MD, PhD, MHS
Associate Professor of Biomedical Informatics
University of Utah