Smartphone data during the COVID-19 pandemic can quantify behavioral changes in people with ALS.

INTRODUCTION: Passive data from smartphone sensors may be useful for health-care research. Our aim was to use the coronavirus disease-2019 (COVID-19) pandemic as a positive control to assess the ability to quantify behavioral changes in people with amyotrophic lateral sclerosis (ALS) from smartphone data. METHODS: Eight participants used the Beiwe smartphone application, which passively measured their location during the COVID-19 outbreak. We used an interrupted time series to quantify the effect of the US state of emergency declaration on daily home time and daily distance traveled. RESULTS: After the state of emergency declaration, median daily home time increased from 19.4 (interquartile range [IQR], 15.4-22.0) hours to 23.7 (IQR, 22.2-24.0) hours and median distance traveled decreased from 42 (IQR, 13-83) km to 3.7 (IQR, 1.5-10.3) km. The participant with the lowest functional ability changed behavior earlier. This participant stayed at home more and traveled less than the participant with highest functional ability, both before and after the state of emergency. DISCUSSION: We provide evidence that smartphone-based digital phenotyping can quantify the behavior of people with ALS. Although participants spent large amounts of time at home at baseline, the COVID-19 state of emergency declaration reduced their mobility further. Given participants' high level of daily home time, it is possible that their exposure to COVID-19 could be less than that of the general population.

Design and implementation of an application and associated services to support interdisciplinary medication reconciliation efforts at an integrated healthcare delivery network.

Confusion about patients' medication regimens during the hospital admission and discharge process accounts for many preventable and serious medication errors. Many organizations have begun to redesign their clinical processes to address this patient safety concern. Partners HealthCare, an integrated delivery network in Boston, Massachusetts, has answered this interdisciplinary challenge by leveraging its multiple outpatient electronic medical records (EMR) and inpatient computerized provider order entry (CPOE) systems to facilitate the process of medication reconciliation. This manuscript describes the design of a novel application and the associated services that aggregate medication data from EMR and CPOE systems so that clinicians can efficiently generate an accurate pre-admission medication list. Information collected with the use of this application subsequently supports the writing of admission and discharge orders by physicians, performance of admission assessment by nurses, and reconciliation of inpatient orders by pharmacists. Results from early pilot testing suggest that this new medication reconciliation process is well accepted by clinicians and has significant potential to prevent medication errors during transitions of care.

Prioritizing strategies for preventing medication errors and adverse drug events in pediatric inpatients.

OBJECTIVES: Medication errors in pediatric inpatients occur at similar rates as in adults but have 3 times the potential to cause harm. Error prevention strategies in this setting remain largely untested. The objective of this study was to classify the major types of medication errors in pediatric inpatients and to determine which strategies might most effectively prevent them. METHODS: A prospective cohort study was conducted of 1020 patients who were admitted to 2 academic medical centers during a 6-week period in April and May 1999. Medication errors were characterized by subtype. Physician raters evaluated error prevention strategies and identified those that might be most effective in preventing errors. RESULTS: Of 10 778 medication orders reviewed, 616 contained errors. Of these, 120 (19.5%) were classified as potentially harmful, including 115 potential adverse drug events (18.7%) and 5 preventable adverse drug events (0.8%). Most errors occurred at the ordering stage (74%) and involved errors in dosing (28%), route (18%), or frequency (9%). Three interventions might have prevented most potentially harmful errors: 1) computerized physician order entry with clinical decision support systems (76%); 2) ward-based clinical pharmacists (81%); and 3) improved communication among physicians, nurses, and pharmacists (86%). Interrater reliability of error prevention strategy assignment was good (agreement: 0.92; kappa: 0.82). CONCLUSIONS: Of the assessed interventions, computerized physician order entry with clinical decision support systems; ward-based clinical pharmacists; and improved communication among physicians, nurses, and pharmacists had the greatest potential to reduce medication errors in pediatric inpatients. Development, implementation, and assessment of such interventions in the pediatric inpatient setting are needed.