Patient Input for Design of a Type 1 Diabetes (T1D) Decision Support Smartphone Application

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 991-P ◽  
Author(s):  
LEAH M. WILSON ◽  
VIRGINIA GABO ◽  
NICHOLE S. TYLER ◽  
RAVI REDDY ◽  
PETER G. JACOBS ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Decision Support ◽  
Smartphone Application

scholarly journals Patient Input for Design of a Decision Support Smartphone Application for Type 1 Diabetes

2019 ◽  
Vol 14 (6) ◽  
pp. 1081-1087
Author(s):  
Leah M. Wilson ◽  
Nichole Tyler ◽  
Peter G. Jacobs ◽  
Virginia Gabo ◽  
Brian Senf ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Decision Support ◽  
Decision Support Tool ◽  
Smartphone Application ◽  
Valuable Insight ◽  
Support Tool ◽  
Health Community ◽  
Continuous Glucose Monitors ◽  
Survey Responses

Background: Decision support smartphone applications integrated with continuous glucose monitors may improve glycemic control in type 1 diabetes (T1D). We conducted a survey to understand trends and needs of potential users to inform the design of decision support technology. Methods: A 70-question survey was distributed October 2017 through May 2018 to adults aged 18-80 with T1D from a specialty clinic and T1D Exchange online health community ( myglu.org ). The survey responses were used to evaluate potential features of a diabetes decision support tool by Likert scale and open responses. Results: There were 1542 responses (mean age 46.1 years [SD 15.2], mean duration of diabetes 26.5 years [SD 15.8]). The majority (84.2%) have never used an app to manage diabetes; however, a large majority (77.8%) expressed interest in using a decision support app. The ability to predict and avoid hypoglycemia was the most important feature identified by a majority of the respondents, with 91% of respondents indicating the highest level of interest in these features. The task that respondents find most difficult was management of glucose during exercise (only 47% of participants were confident in glucose management during exercise). The respondents also highly desired features that help manage glucose during exercise (85% of respondents were interested). The responses identified integration and interoperability with peripheral devices/apps and customization of alerts as important. Responses from participants were generally consistent across stratified categories. Conclusions: These results provide valuable insight into patient needs in decision support applications for management of T1D.

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.

Outsourcing glucose management to AI

2020 ◽  
Vol 12 (564) ◽  
pp. eabe8120
Author(s):  
James Ankrum
Keyword(s):  
Type 1 Diabetes ◽  
Decision Support ◽  
Insulin Therapy ◽  
Intensive Insulin Therapy ◽  
Decision Support Tool ◽  
Support Tool ◽  
Glucose Management

An automated decision support tool could expand access to intensive insulin therapy for patients with type 1 diabetes.

Factors Affecting Type One Diabetes Self-Management According to Youth and Parents (Preprint)

2020 ◽  
Author(s):  
Janet Panoch ◽  
Lisa Yazel ◽  
Courtney Moore ◽  
Sarah Wiehe ◽  
Tamara Hannon
Keyword(s):  
Type 1 Diabetes ◽  
Decision Support ◽  
Decision Support Tool ◽  
Self Management ◽  
Adult Care ◽  
Patient Centered ◽  
Diabetes Clinic ◽  
Human Centered Design ◽  
Support Tool

BACKGROUND Adolescents with type 1 diabetes differ from their parents and physicians about what they need from healthcare. Therefore, it is important to implement patient-centered diabetes care for adolescents. OBJECTIVE This study used human-centered design to reveal diabetes self-management challenges faced by youth with type 1 diabetes and their parents. This was a pre-study design phase of a larger study to develop a patient-centered automated decision support tool for diabetes clinic. METHODS Data were collected from youth and parents in two settings 1) a diabetes summer camp to capture challenges faced by youth and parents, 2) youth and parents participating in human-centered design sessions to further explore challenges. RESULTS Fifty-six people completed the camp worksheet, identifying 15 unique themes. The sessions further verified three problematic themes each for youth and parents. Youth generated 23 questions and parents identified 33 questions for potential use for the decision support tool development. CONCLUSIONS Including patient and parent self-management needs is vital. Providers should understand the psychosocial factors associated with barriers to self-management. The incorporation of patient and parent questions, ideas, and subsequent patient-provider communication in the support tool may improve trust in the provider and youth self-efficacy as they navigate the transition to independent adult care. CLINICALTRIAL Trial Registration: ClinicalTrials.gov NCT03084900

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