Recommendations for Insulin Dose Calculator Risk Management

Abstract

Several studies have shown the usefulness of an automated insulin dose bolus advisor (BA) in achieving improved glycemic control for insulin-using diabetes patients. Although regulatory agencies have approved several BAs over the past decades, these devices are not standardized in their approach to dosage calculation and include many features that may introduce risk to patients. Moreover, there is no single standard of care for diabetes worldwide and no guidance documents for BAs, specifically. Given the emerging and more stringent regulations on software used in medical devices, the approval process is becoming more difficult for manufacturers to navigate, with some manufacturers opting to remove BAs from their products altogether. A comprehensive literature search was performed, including publications discussing: diabetes BA use and benefit, infusion pump safety and regulation, regulatory submissions, novel BAs, and recommendations for regulation and risk management of BAs. Also included were country-specific and international guidance documents for medical device, infusion pump, medical software, and mobile medical application risk management and regulation. No definitive worldwide guidance exists regarding risk management requirements for BAs, specifically. However, local and international guidance documents for medical devices, infusion pumps, and medical device software offer guidance that can be applied to this technology. In addition, risk management exercises that are algorithm-specific can help prepare manufacturers for regulatory submissions. This article discusses key issues relevant to BA use and safety, and recommends risk management activities incorporating current research and guidance.

Smartphone apps for calculating insulin dose: a systematic assessment

“…Issues identified within apps were aggregated for analysis. The schema used for grouping (Table 1 ) was informed by previous work [ 19 , 29 ] and refined through discussion and divided issues into two broad categories depending on whether they concerned the process of data entry (input issues) or the results that were generated (output issues, Table 1 ). This partitioning accounted for both the differing potential for error, in our view, and the availability of potentially mitigating strategies.…”

Improved A1C Levels in Type 1 Diabetes with Smartphone App Use

“…However, the majority of people with type 1 diabetes do not review their records formally (6,18). Current smartphone apps like ours and others (8,22,26) help in this task, but there are risk management issues, such as those that occur if the user makes the wrong modifications (10,27,28). Machine learning has the potential of producing an insulin adjustment app that is based on a large dataset of earlier patients and can use that evidence to advise particular persons with diabetes about how best to adjust the insulin regimen parameters on an ongoing basis; we and others are working toward this goal (29)(30)(31).…”

“…In the meantime, apps such as the one we have developed will assist people with diabetes and their teams in managing individuals' diabetes while at the same time accumulate the data needed to train an effective machine learning app. In time, such machine learning apps need to have proven benefit, a risk-management strategy, algorithm documentation and a global data base and must stimulate innovation (28). Such an app could have widespread appeal and minimal cost and could deliver advice on an ongoing basis, a program of continuous insulin-regimen adjustment.…”

Bolus Advisors: Sources of Error, Targets for Improvement

“…15 Bolus when the current BG is above the BG target, that is, they do not follow the logic of equation 3 (similar to equation 3 above) and thereby the patients run the risk of insulin overdosing." 16 Aggressive and nonsequential BA logic can lead to illogical insulin doses at shown in Table 2.…”