A Pilot Study of Use of a Software Platform for the Collection, Integration, and Visualization of Diabetes Device Data by Health Care Providers in a Multidisciplinary Pediatric Setting

Abstract

Background: Diabetes devices provide data for health care providers (HCPs) and people with type 1 diabetes to make management decisions. Extracting and viewing the data require separate, proprietary software applications for each device. In this pilot study, we examined the feasibility of using a single software platform (Tidepool) that integrates data from multiple devices. Materials and Methods: Participating HCPs (n = 15) used the software with compatible devices in all patient visits for 6 months. Samples of registration desk activity and office visits were observed before and after introducing the software, and HCPs provided feedback by survey and focus groups. Results: The time required to upload data and the length of the office visit did not change. However, the number of times the HCP referred to the device data with patients increased from a mean of 2.8 (-1.2) to 6.1 (-3.1) times per visit (P = 0.0002). A significantly larger proportion of children looked at the device data with the new application (baseline: 61% vs. study end: 94%, P = 0.015). HCPs liked the web-based user interface, integration of the data from multiple devices, the ability to remotely access data, and use of the application to initiate patient education. Challenges included the need for automated data upload and integration with electronic medical records. Conclusions: The software did not add to the time needed to upload data or the length of clinic visits and promoted discussions with patients about data. Future studies of HCP use of the application will evaluate clinical outcomes and effects on patient engagement and self-management.

Clinical Decision Support and Implications for the Clinician Burnout Crisis

“…The most common evaluation was via interviews (n=29) 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 , followed by surveys or questionnaires (n=16) 27 29 50 53 55 56 57 58 59 60 61 62 63 64 65 66 . Other qualitative feedback responses were obtained by focus groups or workshops (n=5) 43 57 61 67 68 . The alert firing rate (n=12) 43 55 62 69 70 71 72 73 74 75 76 77 and alert acceptance rate (n=15) 27 29 43 ...…”

“…The 75 non-review, non-opinion articles used a variety of metrics to evaluate the usability and acceptability of their CDS tools, with many using more than one metric. The most common evaluation was via interviews (n=29) , followed by surveys or questionnaires (n=16) [27,29,50,53,[55][56][57][58][59][60][61][62][63][64][65][66]. Other qualitative feedback responses were obtained by focus groups or workshops (n=5) [43,57,61,67,68].…”

“…The most common evaluation was via interviews (n=29) , followed by surveys or questionnaires (n=16) [27,29,50,53,[55][56][57][58][59][60][61][62][63][64][65][66]. Other qualitative feedback responses were obtained by focus groups or workshops (n=5) [43,57,61,67,68]. The alert firing rate (n=12) [43,55,62,[69][70][71][72][73][74][75][76][77] and alert acceptance rate (n=15) [27,29,43,58,61,62,69,74,[78][79][80][81][82][83][84] were also common metrics used to understand alarm fatigue and acceptability.…”

“…Other qualitative feedback responses were obtained by focus groups or workshops (n=5) [43,57,61,67,68]. The alert firing rate (n=12) [43,55,62,[69][70][71][72][73][74][75][76][77] and alert acceptance rate (n=15) [27,29,43,58,61,62,69,74,[78][79][80][81][82][83][84] were also common metrics used to understand alarm fatigue and acceptability. Although metrics of relevance such as sensitivity, specificity, and positive predictive value (PPV) were referenced several times [77,[84][85][86][87][88], only one study evaluated these metrics as pre-specified outcomes [49].…”

Artificial Pancreas Control Strategies Used for Type 1 Diabetes Control and Treatment: A Comprehensive Analysis

“…Hjerde et al [200] implemented and evaluated several hybrid closed-loop deep Q learning (HCL-DQL) algorithms for the task of regulating BG in T1DM patients. Low-cost tools such as tidepool [201] and glucose meters [202], are used to collect, integrate, and visualize the diabetes device data in a pediatric clinic setting. American Diabetes Association (ADA) made standards of the medical care for the diabetes.…”