Enabling Data-Driven Decision Making for Personalized Care in Type 2 Diabetes with Clinical and Lifestyle Journey Data Using a Digital Therapeutic

Abstract Background We investigate whether an educational intervention of GPs increases patient-centeredness and perceived shared decision making in the treatment of patients with poorly controlled type 2 diabetes mellitus? Methods We performed a cluster-randomized controlled trial in German primary care. Patients with type 2 diabetes mellitus defined as HbA1c levels ≥ 8.0% (64 mmol/mol) at the time of recruitment (n = 833) from general practitioners (n = 108) were included. Outcome measures included subjective shared decision making (SDM-Q-9; scale from 0 to 45 (high)) and patient-centeredness (PACIC-D; scale from 1 to 5 (high)) as secondary outcomes. Data collection was performed before intervention (baseline, T0), at 6 months (T1), at 12 months (T2), at 18 months (T3), and at 24 months (T4) after baseline. Results Subjective shared decision making decreased in both groups during the course of the study (intervention group: -3.17 between T0 and T4 (95% CI: -4.66, -1.69; p < 0.0001) control group: -2.80 (95% CI: -4.30, -1.30; p = 0.0003)). There were no significant differences between the two groups (-0.37; 95% CI: -2.20, 1.45; p = 0.6847). The intervention's impact on patient-centeredness was minor. Values increased in both groups, but the increase was not statistically significant, nor was the difference between the groups. Conclusions The intervention did not increase patient perceived subjective shared decision making and patient-centeredness in the intervention group as compared to the control group. Effects in both groups might be partially attributed to the Hawthorne-effect. Future trials should focus on patient-based intervention elements to investigate effects on shared decision making and patient-centeredness. Trial registration The trial was registered on March 10th, 2011 at ISRCTN registry under the reference ISRCTN70713571.

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A Data-driven Decision-making Approach for Complex Product Design Based on Deep Learning

2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) ◽

10.1109/cscwd49262.2021.9437761 ◽

2021 ◽

Author(s):

Zou Lai ◽

Siqin Fu ◽

Hang Yu ◽

Shulin Lan ◽

Chen Yang

Keyword(s):

Decision Making ◽

Deep Learning ◽

Product Design ◽

Data Driven ◽

Data Driven Decision Making ◽

Complex Product

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Improving the management of type 2 diabetes through large-scale general practice: the role of a data-driven and technology-enabled education programme

BMJ Open Quality ◽

10.1136/bmjoq-2020-001087 ◽

2021 ◽

Vol 10 (1) ◽

pp. e001087

Author(s):

Tarek F Radwan ◽

Yvette Agyako ◽

Alireza Ettefaghian ◽

Tahira Kamran ◽

Omar Din ◽

...

Keyword(s):

Type 2 Diabetes ◽

Primary Care ◽

Large Scale ◽

Education Programme ◽

Educational Programme ◽

Data Driven ◽

Treatment Targets ◽

Care Processes ◽

Data Driven Approach

A quality improvement (QI) scheme was launched in 2017, covering a large group of 25 general practices working with a deprived registered population. The aim was to improve the measurable quality of care in a population where type 2 diabetes (T2D) care had previously proved challenging. A complex set of QI interventions were co-designed by a team of primary care clinicians and educationalists and managers. These interventions included organisation-wide goal setting, using a data-driven approach, ensuring staff engagement, implementing an educational programme for pharmacists, facilitating web-based QI learning at-scale and using methods which ensured sustainability. This programme was used to optimise the management of T2D through improving the eight care processes and three treatment targets which form part of the annual national diabetes audit for patients with T2D. With the implemented improvement interventions, there was significant improvement in all care processes and all treatment targets for patients with diabetes. Achievement of all the eight care processes improved by 46.0% (p<0.001) while achievement of all three treatment targets improved by 13.5% (p<0.001). The QI programme provides an example of a data-driven large-scale multicomponent intervention delivered in primary care in ethnically diverse and socially deprived areas.

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Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

npj Digital Medicine ◽

10.1038/s41746-021-00465-w ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Brinnae Bent ◽

Peter J. Cho ◽

Maria Henriquez ◽

April Wittmann ◽

Connie Thacker ◽

...

Keyword(s):

Type 2 Diabetes ◽

Data Driven ◽

Self Management ◽

Noninvasive Method ◽

Medical Interventions ◽

Interstitial Glucose ◽

Using Data ◽

Glucose Prediction ◽

Digital Biomarkers

AbstractPrediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.

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