Background Participant recruitment, especially for frail, elderly, hospitalized patients, remains one of the greatest challenges for many research groups. Traditional recruitment methods such as chart reviews are often inefficient, low-yielding, time consuming, and expensive. Best Practice Alert (BPA) systems have previously been used to improve clinical care and inform provider decision making, but the system has not been widely used in the setting of clinical research. Objective The primary objective of this quality-improvement initiative was to develop, implement, and refine a silent Best Practice Alert (sBPA) system that could maximize recruitment efficiency. Methods The captured duration of the screening sessions for both methods combined with the allotted research coordinator hours in the Emerald-COPD (chronic obstructive pulmonary disease) study budget enabled research coordinators to estimate the cost-efficiency. Results Prior to implementation, the sBPA system underwent three primary stages of development. Ultimately, the final iteration produced a system that provided similar results as the manual Epic Reporting Workbench method of screening. A total of 559 potential participants who met the basic prescreen criteria were identified through the two screening methods. Of those, 418 potential participants were identified by both methods simultaneously, 99 were identified only by the Epic Reporting Workbench Method, and 42 were identified only by the sBPA method. Of those identified by the Epic Reporting Workbench, only 12 (of 99, 12.12%) were considered eligible. Of those identified by the sBPA method, 30 (of 42, 71.43%) were considered eligible. Using a side-by-side comparison of the sBPA and the traditional Epic Reporting Workbench method of screening, the sBPA screening method was shown to be approximately four times faster than our previous screening method and estimated a projected 442.5 hours saved over the duration of the study. Additionally, since implementation, the sBPA system identified the equivalent of three additional potential participants per week. Conclusions Automation of the recruitment process allowed us to identify potential participants in real time and find more potential participants who meet basic eligibility criteria. sBPA screening is a considerably faster method that allows for more efficient use of resources. This innovative and instrumental functionality can be modified to the needs of other research studies aiming to use the electronic medical records system for participant recruitment.

Clinical polysomnography (PSG) databases are a rich resource in the era of “big data” analytics. We explore the uses and potential pitfalls of clinical data mining of PSG using statistical principles and analysis of clinical data from our sleep center. We performed retrospective analysis of self-reported and objective PSG data from adults who underwent overnight PSG (diagnostic tests, n=1835). Self-reported symptoms overlapped markedly between the two most common categories, insomnia and sleep apnea, with the majority reporting symptoms of both disorders. Standard clinical metrics routinely reported on objective data were analyzed for basic properties (missing values, distributions), pairwise correlations, and descriptive phenotyping. Of 41 continuous variables, including clinical and PSG derived, none passed testing for normality. Objective findings of sleep apnea and periodic limb movements were common, with 51% having an apnea–hypopnea index (AHI) >5 per hour and 25% having a leg movement index >15 per hour. Different visualization methods are shown for common variables to explore population distributions. Phenotyping methods based on clinical databases are discussed for sleep architecture, sleep apnea, and insomnia. Inferential pitfalls are discussed using the current dataset and case examples from the literature. The increasing availability of clinical databases for large-scale analytics holds important promise in sleep medicine, especially as it becomes increasingly important to demonstrate the utility of clinical testing methods in management of sleep disorders. Awareness of the strengths, as well as caution regarding the limitations, will maximize the productive use of big data analytics in sleep medicine.

Background Type 2 diabetes (T2D) is the seventh leading cause of death (2017) in the United States, and by 2030 it is estimated that it will affect 439 million globally. Effective glycemic control can be challenging for patients. A tool to guide patients’ in their self-management behaviors and share this data with their physician may improve insulin adherence leading to lower HbA1c. We examined an integrated diabetes management (IDM) system that utilizes a Bluetooth-enabled insulin event capture device, a Bluetooth-enabled glucometer, and an Android smartphone app. IDM data can be viewed by clinicians in the electronic medical record (EMR). Objective The primary aim of this study is to describe how app use is related to insulin adherence, blood glucose measurements, meal snapshots, and step count. Secondarily, we assessed the impact on HbA1c levels over a 3- and 6-month period. Methods Thirty-five participants were enrolled from Boston-area hospitals in this single-arm pilot study. Use of the IDM system was defined as the number of days per week participants logged into the app and moved past the home screen. Three app use groups were created: low app use (0.33-2.46 days per week), medium app use (2.54-5.08 days per week), and high app use (>5.4 days per week). Adherence to insulin, blood glucose measurements, and meal snapshots were defined as a ratio of actual weekly events recorded by participants’ app use divided by their physician’s recommendation. Step count was defined as the total weekly steps for each participant. Daily app-generated data on app use and indicators of diabetes management were collected. HbA1c levels were assessed via blood test at enrollment, 3-months, and 6-months. Using a hierarchical linear mixed model, we examined changes in outcome measures while accounting for random intercepts and slopes to control for variation in individual outcomes over the study. Results Overall app use (average unique days using the app per week) declined from 6.19 days to 3.00 days (at 1 and 24 weeks, respectively). Participants with high app use had significant improvement in bolus and basal insulin adherence per week (0.009 P=.041 [95% CI 0.0004 to 0.018] and 0.016 P<.001 [95% CI 0.0079 to 0.023], respectively), but participants had no significant improvements in blood glucose and meal snapshot adherence or absolute step count. HbA1c significantly decreased per week (coefficient –0.025 [95% CI –0.044 to –0.007], P=.007) with an overall change of 0.6. Participants with high app use significantly improved their HbA1c per week (–0.037 P=.016 [–0.066 to –0.0067]) compared to participants with medium and low app use, yielding a total improvement of 0.88 over 24 weeks. Conclusions Results show that bolus and basal insulin may have increased with higher app use. HbA1c significantly improved over the course of the study, along with significantly greater improvement in HbA1c among participants with higher app use compared to participants in the middle or low app use groups. This study is not designed or intended to evaluate efficacy but provides results to guide the future design and development of this prototype IDM system.

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