Impact and Feasibility of Personalized Decision Support for Older Patients with Diabetes: A Pilot Randomized Trial

Abstract

Background Diabetes guidelines recommend individualizing glycemic goals (A1C) for older patients. We assess a personalized web-based decision support tool. Design We randomized physicians and their patients (≥65 years of age) with type 2 diabetes to support tool or educational pamphlet (75:25 patients). Prior to a visit, intervention patients interacted with the tool, which provided personalized risk predictions and elicited treatment preferences. Main outcomes included 1) patient-doctor communication, 2) decisional conflict, 3) changes in goals, 4) intervention acceptability. Results We did not find significant differences in proportions of patients that had an A1C discussion (91% intervention vs. 76% control, p=0.19). Intervention patients had larger declines in the Informed Subscale of Decisional Conflict (-20.0 vs. 0, p=0.04). There were no significant differences in proportions of patients with changes in goals (49% vs. 28%, p=0.08). Most intervention patients reported that the tool was easy to use (91%) and helped them to communicate (84%). Limitations Pilot trial at one academic institution. Conclusions Web-based decision support tools may be a practical approach to facilitating personalization of goals for chronic conditions. Trial Registration ClinicalTrials.govNCT02169999, https://clinicaltrials.gov/show/NCT02169999

Management of diabetes mellitus in older people with comorbidities

“…82 An example of integration of categories A and B decision support involved a web based decision support tool designed to encourage goal setting based on patients' prognosis and treatment preferences. Unique features of this tool included a geriatric diabetes simulation model, to calculate life expectancy and complication rates, and formal elicitation of patients' preferences.…”

Decision aids for people with Type 2 diabetes mellitus: an effectiveness rapid review and meta‐analysis

“…In one study, risk of bias assessment was based only on data presented in a conference abstract without contacting the corresponding authors [23]. Overall, eight studies were considered low risk [22,24,28,29,31,[34][35][36], four raised some concerns [23,30,33,37] and three studies were deemed at high risk of bias [25,27,38] due to concerns about the randomization process [25], high attrition rate [38] and inclusion of a participating practice after randomization [27].…”

“…Eleven studies [22,25,[27][28][29][30][31]33,[36][37][38] evaluating seven PtDAs assessed decision quality using the Decisional Conflict Scale (DCS), a validated instrument measuring uncertainty in health-related decision-making [45]. Eleven studies [22,25,[27][28][29][30][31]33,[36][37][38] evaluating seven PtDAs assessed decision quality using the Decisional Conflict Scale (DCS), a validated instrument measuring uncertainty in health-related decision-making [45].…”

“…We developed a taxonomy of a variety of outcomes that are commonly assessed in trials with PtDAs [6], allowing for a comprehensive assessment of the effect of PtDAs, while helping with better translation of our findings in clinical practice. Moreover, we focused on the highest quality of evidence derived exclusively from RCTs and, in comparison with a previous systematic review on shared decision-making and outcomes in Type 2 diabetes [8], we included additional data from seven publications [23][24][25][26][27]36,38] that assessed different types of PtDAs. Moreover, we focused on the highest quality of evidence derived exclusively from RCTs and, in comparison with a previous systematic review on shared decision-making and outcomes in Type 2 diabetes [8], we included additional data from seven publications [23][24][25][26][27]36,38] that assessed different types of PtDAs.…”

“…In particular, when applied prior to screening abstracts, the RCT model accurately excluded approximately half of search records as non-RCTs. Furthermore, sample size was generally small in most trials and some RCTs reported failure to achieve planned sample size [22,25,27,28]; Nevertheless, despite some individual trials being underpowered, we increased the statistical power to detect an effect and improved precision of estimates for many outcomes by synthesizing data in meta-analyses. Of note, a recent study comparing systematic reviews and sametopic rapid reviews found that median duration of completion was 9.5 months for systematic reviews and 3 months for rapid reviews, although findings were generally consistent between the two types of reviews [54].…”