Impact of a goal setting and decision support telephone coaching intervention on diet, psychosocial, and decision outcomes among people with type 2 diabetes

2017 ◽  
Vol 100 (7) ◽  
pp. 1367-1373 ◽  
Author(s):  
Christine M. Swoboda ◽  
Carla K. Miller ◽  
Celia E. Wills
Keyword(s):  
Type 2 Diabetes ◽  
Decision Support ◽  
Goal Setting ◽  
Coaching Intervention ◽  
Decision Outcomes ◽  
Support Telephone ◽  
Telephone Coaching

952-P: Improving Glycemic Management of Patients with Type 2 Diabetes in Acute Geriatric Care by Using a Basal Insulin Algorithm Incorporated into a Workflow and Decision Support System

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 952-P
Author(s):  
ANGELA LIBISELLER ◽  
KATHARINA M. LICHTENEGGER ◽  
JULIA KOPANZ ◽  
ANTONELLA DE CAMPO ◽  
TATJANA WIESINGER ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Basal Insulin ◽  
Geriatric Care ◽  
Glycemic Management ◽  
Acute Geriatric Care

scholarly journals Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes

2021 ◽  
Vol 9 (1) ◽  
pp. e001557
Author(s):  
Ariana Pichardo-Lowden ◽  
Guillermo Umpierrez ◽  
Erik B Lehman ◽  
Matthew D Bolton ◽  
Christopher J DeFlitch ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Stress Hyperglycemia ◽  
Inappropriate Use ◽  
Academic Center

IntroductionInnovative approaches are needed to design robust clinical decision support (CDS) to optimize hospital glycemic management. We piloted an electronic medical record (EMR), evidence-based algorithmic CDS tool in an academic center to alert clinicians in real time about gaps in care related to inpatient glucose control and insulin utilization, and to provide management recommendations.Research design and methodsThe tool was designed to identify clinical situations in need for action: (1) severe or recurrent hyperglycemia in patients with diabetes: blood glucose (BG) ≥13.88 mmol/L (250 mg/dL) at least once or BG ≥10.0 mmol/L (180 mg/dL) at least twice, respectively; (2) recurrent hyperglycemia in patients with stress hyperglycemia: BG ≥10.0 mmol/L (180 mg/dL) at least twice; (3) impending or established hypoglycemia: BG 3.9–4.4 mmol/L (70–80 mg/dL) or ≤3.9 mmol/L (70 mg/dL); and (4) inappropriate sliding scale insulin (SSI) monotherapy in recurrent hyperglycemia, or anytime in patients with type 1 diabetes. The EMR CDS was active (ON) for 6 months for all adult hospital patients and inactive (OFF) for 6 months. We prospectively identified and compared gaps in care between ON and OFF periods.ResultsWhen active, the hospital CDS tool significantly reduced events of recurrent hyperglycemia in patients with type 1 and type 2 diabetes (3342 vs 3701, OR=0.88, p=0.050) and in patients with stress hyperglycemia (288 vs 506, OR=0.60, p<0.001). Hypoglycemia or impending hypoglycemia (1548 vs 1349, OR=1.15, p=0.050) were unrelated to the CDS tool on subsequent analysis. Inappropriate use of SSI monotherapy in type 1 diabetes (10 vs 22, OR=0.36, p=0.073), inappropriate use of SSI monotherapy in type 2 diabetes (2519 vs 2748, OR=0.97, p=0.632), and in stress hyperglycemia subjects (1617 vs 1488, OR=1.30, p<0.001) were recognized.ConclusionEMR CDS was successful in reducing hyperglycemic events among hospitalized patients with dysglycemia and diabetes, and inappropriate insulin use in patients with type 1 diabetes.

Information and decision support needs in patients with type 2 diabetes

2014 ◽  
Vol 22 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Nina Weymann ◽  
Martin Härter ◽  
Jörg Dirmaier
Keyword(s):  
Type 2 Diabetes ◽  
Decision Support ◽  
Support Needs

scholarly journals Personalizing Second-Line Type 2 Diabetes Treatment Selection: Combining Network Meta-analysis, Individualized Risk, and Patient Preferences for Unified Decision Support

2019 ◽  
Vol 39 (3) ◽  
pp. 239-252 ◽  
Author(s):  
Sung Eun Choi ◽  
Seth A. Berkowitz ◽  
John S. Yudkin ◽  
Huseyin Naci ◽  
Sanjay Basu
Keyword(s):  
Type 2 Diabetes ◽  
Decision Support ◽  
Patient Preferences ◽  
Meta Analysis ◽  
Second Line ◽  
Quality Adjusted Life Years ◽  
Glucose Testing ◽  
Life Years ◽  
Quality Adjusted Life

Background. Personalizing medical treatment often requires practitioners to compare multiple treatment options, assess a patient’s unique risk and benefit from each option, and elicit a patient’s preferences around treatment. We integrated these 3 considerations into a decision-modeling framework for the selection of second-line glycemic therapy for type 2 diabetes. Methods. Based on multicriteria decision analysis, we developed a unified treatment decision support tool accounting for 3 factors: patient preferences, disease outcomes, and medication efficacy and safety profiles. By standardizing and multiplying these 3 factors, we calculated the ranking score for each medication. This approach was applied to determining second-line glycemic therapy by integrating 1) treatment efficacy and side-effect data from a network meta-analysis of 301 randomized trials ( N = 219,277), 2) validated risk equations for type 2 diabetes complications, and 3) patient preferences around treatment (e.g., to avoid daily glucose testing). Data from participants with type 2 diabetes in the U.S. National Health and Nutrition Examination Survey (NHANES 2003–2014, N = 1107) were used to explore variations in treatment recommendations and associated quality-adjusted life-years given different patient features. Results. Patients at the highest microvascular disease risk had glucagon-like peptide 1 agonists or basal insulin recommended as top choices, whereas those wanting to avoid an injected medication or daily glucose testing had sodium-glucose linked transporter 2 or dipeptidyl peptidase 4 inhibitors commonly recommended, and those with major cost concerns had sulfonylureas commonly recommended. By converting from the most common sulfonylurea treatment to the model-recommended treatment, NHANES participants were expected to save an average of 0.036 quality-adjusted life-years per person (about a half month) from 10 years of treatment. Conclusions. Models can help integrate meta-analytic treatment effect estimates with individualized risk calculations and preferences, to aid personalized treatment selection.

scholarly journals A Mobile Computerized Decision Support System to Prevent Hypoglycemia in Hospitalized Patients With Type 2 Diabetes Mellitus

2016 ◽  
Vol 11 (1) ◽  
pp. 20-28 ◽  
Author(s):  
Stephan Spat ◽  
Klaus Donsa ◽  
Peter Beck ◽  
Bernhard Höll ◽  
Julia K. Mader ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Decision Support ◽  
Feasibility Study ◽  
Health Care Professionals ◽  
Diabetes Management ◽  
Insulin Dose ◽  
User Acceptance

Background: Diabetes management requires complex and interdisciplinary cooperation of health care professionals (HCPs). To support this complex process, IT-support is recommended by clinical guidelines. The aim of this article is to report on results from a clinical feasibility study testing the prototype of a mobile, tablet-based client-server system for computerized decision and workflow support (GlucoTab®) and to discuss its impact on hypoglycemia prevention. Methods: The system was tested in a monocentric, open, noncontrolled intervention study in 30 patients with type 2 diabetes mellitus (T2DM). The system supports HCPs in performing a basal-bolus insulin therapy. Diabetes therapy, adverse events, software errors and user feedback were documented. Safety, efficacy and user acceptance of the system were investigated. Results: Only 1.3% of blood glucose (BG) measurements were <70 mg/dl and only 2.6% were >300 mg/dl. The availability of the system (97.3%) and the rate of treatment activities documented with the system (>93.5%) were high. Only few suggestions from the system were overruled by the users (>95.7% adherence). Evaluation of the 3 anonymous questionnaires showed that confidence in the system increased over time. The majority of users believed that treatment errors could be prevented by using this system. Conclusions: Data from our feasibility study show a significant reduction of hypoglycemia by implementing a computerized system for workflow and decision support for diabetes management, compared to a paper-based process. The system was well accepted by HCPs, which is shown in the user acceptance analysis and that users adhered to the insulin dose suggestions made by the system.

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