ABSTRACT
Current tools for objectively measuring young children's observed behaviors are expensive, time-consuming, and require extensive training and professional administration. The lack of scalable, reliable, and validated tools impacts access to evidence-based knowledge and limits our capacity to collect population-level data in non-clinical settings. To address this gap, we developed mobile technology to collect videos of young children while they watched movies designed to elicit autism-related behaviors and then used automatic behavioral coding of these videos to quantify children's emotions and behaviors. We present results from our iPhone study Autism & Beyond, built on ResearchKit's open-source platform. The entire study-from an e-Consent process to stimuli presentation and data collection-was conducted within an iPhone-based app available in the Apple Store. Over 1 year, 1756 families with children aged 12-72 months old participated in the study, completing 5618 caregiver-reported surveys and uploading 4441 videos recorded in the child's natural settings. Usable data were collected on 87.6% of the uploaded videos. Automatic coding identified significant differences in emotion and attention by age, sex, and autism risk status. This study demonstrates the acceptability of an app-based tool to caregivers, their willingness to upload videos of their children, the feasibility of caregiver-collected data in the home, and the application of automatic behavioral encoding to quantify emotions and attention variables that are clinically meaningful and may be refined to screen children for autism and developmental disorders outside of clinical settings. This technology has the potential to transform how we screen and monitor children's development.
ABSTRACT
OBJECTIVE: We present the design and feasibility testing for the "Digital Drag and Drop Pillbox" (D-3 Pillbox), a skill-based educational approach that engages patients and providers, measures performance, and generates reports of medication management skills. METHODS: A single-cohort convenience sample of patients hospitalized with heart failure was taught pill management skills using a tablet-based D-3 Pillbox. Medication reconciliation was conducted, and aptitude, performance (% completed), accuracy (% correct), and feasibility were measured. RESULTS: The mean age of the sample (n = 25) was 59 (36-89) years, 50% were women, 62% were black, 46% were uninsured, 46% had seventh-grade education or lower, and 31% scored very low for health literacy. However, most reported that the D-3 Pillbox was easy to read (78%), easy to repeat-demonstrate (78%), and comfortable to use (tablet weight) (75%). Accurate medication recognition was achieved by discharge in 98%, but only 25% reported having a "good understanding of my responsibilities." CONCLUSIONS: The D-3 Pillbox is a feasible approach for teaching medication management skills and can be used across clinical settings to reinforce skills and medication list accuracy.
Subject(s)
Heart Failure/drug therapy , Medication Adherence/statistics & numerical data , Patient Compliance/statistics & numerical data , Patient Education as Topic/methods , Telemedicine/methods , Adult , Aged , Aged, 80 and over , Feasibility Studies , Female , Health Literacy/statistics & numerical data , Humans , Male , Middle Aged , Patient Outcome AssessmentABSTRACT
OBJECTIVE: Recognizing a need for our EHR to be highly interoperable, our team at Duke Health enabled our Epic-based electronic health record to be compatible with the Boston Children's project called Substitutable Medical Apps and Reusable Technologies (SMART), which employed Health Level Seven International's (HL7) Fast Healthcare Interoperability Resources (FHIR), commonly known as SMART on FHIR. METHODS: We created a custom SMART on FHIR-compatible server infrastructure written in Node.js that served two primary functions. First, it handled API management activities such rate-limiting, authorization, auditing, logging, and analytics. Second, it retrieved the EHR data and made it available in a FHIR-compatible format. Finally, we made required changes to the EHR user interface to allow us to integrate several compatible apps into the provider- and patient-facing EHR workflows. RESULTS: After integrating SMART on FHIR into our Epic-based EHR, we demonstrated several types of apps running on the infrastructure. This included both provider- and patient-facing apps as well as apps that are closed source, open source and internally-developed. We integrated the apps into the testing environment of our desktop EHR as well as our patient portal. We also demonstrated the integration of a native iOS app. CONCLUSION: In this paper, we demonstrate the successful implementation of the SMART and FHIR technologies on our Epic-based EHR and subsequent integration of several compatible provider- and patient-facing apps.
