Detection of Medication Taking Using a Wrist-Worn Commercially Available Wearable Device.

PURPOSE: Medication nonadherence is a persistent and costly problem across health care. Measures of medication adherence are ineffective. Methods such as self-report, prescription claims data, or smart pill bottles have been used to monitor medication adherence, but these are subject to recall bias, lack real-time feedback, and are often expensive. METHODS: We proposed a method for monitoring medication adherence using a commercially available wearable device. Passively collected motion data were analyzed on the basis of the Movelet algorithm, a dictionary learning framework that builds person-specific chapters of movements from short frames of elemental activities within the movements. We adapted and extended the Movelet method to construct a within-patient prediction model that identifies medication-taking behaviors. RESULTS: Using 15 activity features recorded from wrist-worn wearable devices of 10 patients with breast cancer on endocrine therapy, we demonstrated that medication-taking behavior can be predicted in a controlled clinical environment with a median accuracy of 85%. CONCLUSION: These results in a patient-specific population are exemplar of the potential to measure real-time medication adherence using a wrist-worn commercially available wearable device.

Data-driven interdisciplinary interventions to improve inpatient pain management.

Pain during hospitalization and dissatisfaction with pain management are common. This project consisted of 4 phases: identifying a pain numeric rating scale (NRS) metric associated with patient satisfaction, identifying independent predictors of maximum NRS, implementing interventions, and evaluating trends in NRS and satisfaction. Maximum NRS was inversely associated with favorable pain satisfaction for both efficacy (n = 4062, χ(2) = 66.2, P < .001) and staff efforts (n = 4067, χ(2) = 30.3, P < .001). Independent predictors of moderate-to-severe maximum NRS were younger age, female sex, longer hospital stay, admitting department, psychoactive medications, and 10 diagnostic codes. After interventions, moderate-to-severe maximum NRS declined by 3.6% per quarter in 2010 compared with 2009. Satisfaction data demonstrated improvements in nursing units meeting goals (5.3% per quarter, r (2) = 0.67) and favorable satisfaction answers (0.36% per quarter, r (2) = 0.31). Moderate-to-severe maximum NRS was an independent predictor of lower likelihood of hospital discharge (likelihood ratio = 0.62; 95% confidence interval = 0.61-0.64). Targeted interventions were associated with improved inpatient pain management.