Dataset Information

A Decision-Support Software to Improve the Standard Care in Chinese Type 2 Diabetes.

ABSTRACT: Background:To develop a decision-support software according to the Chinese Diabetes Society guideline in order to improve the standard care in type 2 diabetes. Methods:Firstly, we developed a decision-support software for healthcare professionals. It was an independent software on a tablet to record the data of patients and treatments given by their physicians. A major function of the software was to remind doctors when and how they should implement the standard care as recommended by the Chinese Diabetes Society guideline. Secondly, we compared the baseline data of standard care including statin and aspirin usage with data from a previous "3B study" to see whether there was an improvement of these standard cares. Finally, we further compared the data during four quarters of the whole year to evaluate whether there was a continuous improvement. Results:During the first quarter, 27,291 cases and 27,352 cases were collected with complete information about statin and aspirin usage, respectively. The percentage of patients treated with statins and aspirin in our study was significantly higher than that reported in the 3B study (59.6% vs. 19.9% and 59.8% vs. 18.5%, P < 0.001). There were no significant differences among the four quarters for the percentage of the patients who were taking statin or aspirin (P > 0.05). Conclusion:Our decision-support software has been shown to be effective in continuously improving the standardization of comprehensive treatment in type 2 diabetes.

SUBMITTER: Luo Y

PROVIDER: S-EPMC6881560 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Decision-Support Software to Improve the Standard Care in Chinese Type 2 Diabetes.

Luo Yingying Y Zhu Yongzan Y Chen Jing J Gao Xueying X Yang Wenjia W Zou Xiantong X Zhou Xianghai X Ji Linong L

Journal of diabetes research 20191111

<h4>Background</h4>To develop a decision-support software according to the Chinese Diabetes Society guideline in order to improve the standard care in type 2 diabetes.<h4>Methods</h4>Firstly, we developed a decision-support software for healthcare professionals. It was an independent software on a tablet to record the data of patients and treatments given by their physicians. A major function of the software was to remind doctors when and how they should implement the standard care as recommende ...[more]

PMID: 31828162

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Project description:BackgroundDiabetes mellitus is a complex disease with serious complications. Electronic decision support, providing information that is shared and discussed by both patient and physician, encourages timely interventions and may improve the management of this chronic disease. However, it has rarely been tested in community-based primary care.MethodsIn this pragmatic randomized trial, we randomly assigned adult primary care patients with type 2 diabetes to receive the intervention or usual care. The intervention involved shared access by the primary care provider and the patient to a Web-based, colour-coded diabetes tracker, which provided sequential monitoring values for 13 diabetes risk factors, their respective targets and brief, prioritized messages of advice. The primary outcome measure was a process composite score. Secondary outcomes included clinical composite scores, quality of life, continuity of care and usability. The outcome assessors were blinded to each patient's intervention status.ResultsWe recruited sequentially 46 primary care providers and then 511 of their patients (mean age 60.7 [standard deviation 12.5] years). Mean follow-up was 5.9 months. The process composite score was significantly better for patients in the intervention group than for control patients (difference 1.27, 95% confidence interval [CI] 0.79-1.75, p < 0.001); 61.7% (156/253) of patients in the intervention group, compared with 42.6% (110/258) of control patients, showed improvement (difference 19.1%, p < 0.001). The clinical composite score also had significantly more variables with improvement for the intervention group (0.59, 95% CI 0.09-1.10, p = 0.02), including significantly greater declines in blood pressure (-3.95 mm Hg systolic and -2.38 mm Hg diastolic) and glycated hemoglobin (-0.2%). Patients in the intervention group reported greater satisfaction with their diabetes care.InterpretationA shared electronic decision-support system to support the primary care of diabetes improved the process of care and some clinical markers of the quality of diabetes care. (ClinicalTrials.gov trial register no. NCT00813085.).

Project description:BACKGROUND:The EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care. METHODS:A cluster-randomized trial with before-and-after measurements was performed in Belgian PCPs over 1 year, from May 2017 to May 2018. We randomly assigned 51 practices to either the intervention group (IG), to receive the EBMeDS system, or to the control group (CG), to receive usual care. Primary and secondary outcomes were the 1-year pre- to post-implementation change in HbA1c, LDL cholesterol, and systolic and diastolic blood pressure. Composite patient and process scores were calculated. A process evaluation was added to the analysis. Results were analyzed at 6 and 12 months. Linear mixed models and logistic regression models based on generalized estimating equations were used where appropriate. RESULTS:Of the 51 PCPs that were enrolled and randomly assigned (26 PCPs in the CG and 25 in the IG), 29 practices (3815 patients) were analyzed in the study: 2464 patients in the CG and 1351 patients in the IG. No change differences existed between groups in primary or secondary outcomes. Change difference between CG and IG after 1-year follow-up was -?0.09 (95% CI -?0.18; 0.01, p-value = 0.06) for HbA1c; 1.76 (95% CI -?0.46; 3.98, p-value = 0.12) for LDL cholesterol; and 0.13 (95% CI -?0.91; 1.16, p-value = 0.81) and 0.12 (95% CI -?1.25;1.49, p-value = 0.86) for systolic and diastolic blood pressure respectively. The odds ratio of the IG versus the CG for the probability of no worsening and improvement was 1.09 (95% CI 0.73; 1.63, p-value = 0.67) for the process composite score and 0.74 (95% CI 0.49; 1.12, p-value = 0.16) for the composite patient score. All but one physician was satisfied with the EBMeDS system. CONCLUSIONS:The CCDS system EBMeDS did not improve diabetes care in Belgian primary care. The lack of improvement was mainly caused by imperfections in the organizational context of Belgian primary care for chronic disease management and shortcomings in the system requirements for the correct use of the EBMeDS system (e.g., complete structured records). These shortcomings probably caused low-use rates of the system. TRIAL REGISTRATION:ClinicalTrials.gov, NCT01830569, Registered 12 April 2013.

Project description:BackgroundEvidence-based therapy for heart failure remains underutilized at hospital discharge, particularly for patients with heart failure who are hospitalized for another cause. We developed clinical decision support (CDS) to recommend an angiotensin converting enzyme (ACE) inhibitor during hospitalization to promote its continuation at discharge. The CDS was designed to be implemented in both interruptive and non-interruptive versions.ObjectivesTo compare the effectiveness and implementation of interruptive and non-interruptive versions of a CDS to improve care for heart failure.MethodsHospitalizations of patients with reduced ejection fraction were pseudo-randomized to deliver interruptive or non-interruptive CDS alerts to providers based on even or odd medical record number. We compared discharge utilization of an ACE inhibitor or angiotensin receptor blocker (ARB) for these two implementation approaches. We also assessed adoption and implementation fidelity of the CDS.ResultsOf 958 hospitalizations, interruptive alert hospitalizations had higher rates of discharge utilization of ACE inhibitors or ARBs than non-interruptive alert hospitalizations (79.6% vs. 74.2%, p = 0.05). Utilization was higher for interruptive alert versus non-interruptive alert hospitalizations which were principally for causes other than heart failure (79.8% vs. 73.4%; p = 0.05) but no difference was observed among hospitalizations with a principal heart failure diagnosis (85.9% vs.81.7%; p = 0.49). As compared to non-interruptive hospitalizations, interruptive alert hospitalizations were more likely to have had: an alert with any response (40.6% vs. 13.1%, p < 0.001), contraindications reported (33.1% vs 11.3%, p < 0.001), and an ACE inhibitor ordered within twelve hours of the alert (17.6% vs 10.3%, p < 0.01). The response rate for the interruptive alert was 1.7%, and a median (25th, 75th percentile) of 14 (5,32) alerts were triggered per hospitalization.ConclusionsA CDS implemented as an interruptive alert was associated with improved quality of care for heart failure. Whether the potential benefits of CDS in improving cardiovascular care were worth the high burden of interruptive alerts deserves further consideration. CLINICALTRIALS.Gov identifierNCT02858674.

Project description:BackgroundInnovative interventions are needed to address gaps in preventive cancer care, especially in rural areas. This study evaluated the impact of clinical decision support (CDS) with and without shared decision making (SDM) on cancer-screening completion.MethodsIn this 3-arm, parallel-group, cluster-randomized trial conducted at a predominantly rural medical group, 34 primary care clinics were randomized to clinical decision support (CDS), CDS plus shared decision making (CDS+SDM), or usual care (UC). The CDS applied web-based clinical algorithms identifying patients overdue for United States Preventive Services Task Force-recommended preventive cancer care and presented evidence-based recommendations to patients and providers on printouts and on the electronic health record interface. Patients in the CDS+SDM clinic also received shared decision-making tools (SDMTs). The primary outcome was a composite indicator of the proportion of patients overdue for breast, cervical, or colorectal cancer screening at index who were up to date on these 1 y later.ResultsFrom August 1, 2018, to March 15, 2019, 69,405 patients aged 21 to 74 y had visits at study clinics and 25,198 were overdue for 1 or more cancer screening tests at an index visit. At 12-mo follow-up, 9,543 of these (37.9%) were up to date on the composite endpoint. The adjusted, model-derived percentage of patients up to date was 36.5% (95% confidence interval [CI]: 34.0-39.1) in the UC group, 38.1% (95% CI: 35.5-40.9) in the CDS group, and 34.4% (95% CI: 31.8-37.2) in the CDS+SDM group. For all comparisons, the screening rates were higher than UC in the CDS group and lower than UC in the CDS+SDM group, although these differences did not reach statistical significance.ConclusionThe CDS did not significantly increase cancer-screening rates. Exploratory analyses suggest a deeper understanding of how SDM and CDS interact to affect cancer prevention decisions is needed. Trial registration: ClinicalTrials.gov ID: NCT02986230, December 6, 2016.

Project description:Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.

Dataset Information

A Decision-Support Software to Improve the Standard Care in Chinese Type 2 Diabetes.

Publications

A Decision-Support Software to Improve the Standard Care in Chinese Type 2 Diabetes.

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