Information technology and home glucose clamping

Patient generated health data use in clinical practice: A systematic review

BACKGROUND

Precision health calls for collecting and analyzing large amounts of data to capture an individual's unique behavior, lifestyle, genetics, and environmental context. The diffusion of digital tools has led to a significant growth of patient generated health data (PGHD), defined as health-related data created, gathered or inferred by or from patients and for which the patient controls data collection and data sharing.

PURPOSE

We assessed the current evidence of the impact of PGHD use in clinical practice and provide recommendations for the formal integration of PGHD in clinical care.

METHODS

We searched PubMed, Ovid, Embase, CINAHL, Web of Science, and Scopus up to May 2018. Inclusion criteria were applied and four reviewers screened titles and abstracts and consequently full articles.

FINDINGS

Our systematic literature review identified 21 studies that examined the use of PGHD in clinical settings. Integration of PGHD into electronic records was extremely limited, and decision support capabilities were for the most part basic.

DISCUSSION

PGHD and other types of patient-reported data will be part of the health care system narrative and we must continue efforts to understand its impact on health outcomes, costs, and patient satisfaction. Nursing scientists need to lead the process of defining the role of PGHD in the era of precision health.

A systematic review of telemedicine interventions to support blood glucose self-monitoring in diabetes

AIMS

To evaluate evidence for feasibility, acceptability and cost-effectiveness of diabetes telemedicine applications.

METHODS

MEDLINE, EMBASE, PSYCHINFO, CINAHL, Cochrane, and INSPEC were searched using the terms diabetes and telemedicine for clinical studies using electronic transfer of blood glucose results in people with diabetes. The technology used, trial design and clinical outcome measures used were extracted for trials and prospective cohort studies. Randomized controlled trials with HbA(1c) as an outcome were pooled using standard meta-analytical methods.

RESULTS

We identified 539 papers among which 32 papers described 10 prospective cohort studies, 12 parallel group randomized controlled trials (RCT), three crossover trials, and one non-parallel group trial. Only two studies described full details of randomization, blinding of outcomes and dropouts and withdrawals. Electronic transfer of glucose results appears feasible in a clinical setting. Only two of the RCTs included more than 100 patients, and only three extended to 1 year. Only one study was designed to show that telemedicine interventions might replace clinic interventions without deterioration in HbA(1c). Results pooled from the nine RCTs with reported data did not provide evidence that the interventions were effective in reducing HbA(1c) (-0.1%, 95% CI -0.4% to 0.04%).

CONCLUSIONS

Telemedicine solutions for diabetes care are feasible and acceptable, but evidence for their effectiveness in improving HbA(1c) or reducing costs while maintaining HbA(1c) levels, or improving other aspects of diabetes management is not strong. Further research should seek to understand how telemedicine might enhance educational and self-management interventions and RCTs are required to examine cost-effectiveness.

Computerised decision-support tools in diabetes care: hurdles to implementation

In this Diabetes Information Technology & WebWatch column hurdles to the use of computerised decision-support tools in clinical diabetes care will be considered. The clinical background with respect to insulin-dependent (type 1) diabetes mellitus and the Diabetes Control and Complications Trial is reviewed, and an overview is given of various computer applications. The use of decision-support tools is discussed, and the importance of identifying the proposed user, e.g., health-care professional, student, or patient, is highlighted. Validation/evaluation issues are considered as important topics that remain to be properly addressed for many decision-support prototypes. The column concludes by highlighting that in this era of evidence-based medicine well-conducted, rigorous evaluation and validation studies are required to inform decisions about whether or not to make use of current computerised decision-support prototypes.

A bolus calculator is an effective means of controlling postprandial glycemia in patients on insulin pump therapy

Accurate bolus insulin doses require calculations based on (1) current blood glucose, (2) target blood glucose, (3) carbohydrate-to-insulin ratios, (4) total grams of carbohydrate in meals, and (5) insulin sensitivity factors. Patients may often forego these calculations for insulin doses based on empirical estimates. A bolus calculator (Medtronic MiniMed) uses these five parameters to generate a recommended bolus insulin dose. This study provides the evidence that a hand-held bolus calculator is effective in controlling postprandial blood glucose. Subjects (n = 49) with Type 1 diabetes and experienced with continuous subcutaneous insulin infusion therapy were randomized to begin one of two bolus dosing methods. After 7 days, subjects crossed over to the alternate method. Prior to entering the Bolus Calculator period, physicians established patients' bolus parameters and programmed them into a personal digital assistant (PDA). Subjects used the PDA to obtain recommended pre-meal insulin bolus doses. During the Standard Bolus period, significantly more correction boluses were administered to curtail postprandial hyperglycemia (p = 0.008) and more supplemental carbohydrate was consumed to raise low blood glucose (p = 0.046). Similar values were observed between the two bolus dosing methods in average deviation of 2-h postprandial blood glucose. Subjects reported that the bolus calculator was easy to use and that they were confident in the bolus doses suggested by the device. These results confirm that bolus insulin doses computed by a bolus calculator, compared with standard bolus techniques, achieve target postprandial blood glucose but with fewer correction boluses and supplemental carbohydrate. A bolus calculator, which can be integrated into insulin pump software, may help patients to more accurately meet prandial insulin dosage requirements, improve postprandial glycemic excursions, and achieve optimal glycemic control.