Use of a mobile app to capture supplemental health information during pregnancy: Implications for clinical research.

PURPOSE: Mobile applications ("apps") may be efficient tools for improving the quality of clinical research among pregnant women, but evidence is sparse. We assess the feasibility and generalizability of a mobile app for capturing supplemental data during pregnancy. METHODS: In 2017, we conducted a pilot study of the FDA MyStudies mobile app within a pregnant population identified through Kaiser Permanente Washington (KPWA), an integrated healthcare delivery system. We ascertained health conditions, medications, and substance use through app-based questionnaires. In a post-hoc analysis, we utilized electronic health records (EHR) to summarize sociodemographic and health characteristics of pilot participants and, for comparison, a pregnant population identified using similar methods. RESULTS: Six percent (64/1070) of contacted women enrolled in the pilot study. Nearly half (23/53) reported taking medication for headaches and one-fourth for constipation (13/53) and nausea (12/53) each. Few instances (2/92) of over-the-counter medication use were identified in electronic dispensing records. One-quarter to one-third of participants with depression and anxiety/panic, respectively, reported recently discontinuing medications for these conditions. Eighty-eight percent of pilot participants reported White race (95%CI: 81-95%), versus 67% of the comparison population (N = 2065). More pilot participants filled ≥1 prescription for antianxiety medication (22% [95%CI: 13-35%]) and antidepressants (19% [95%CI 10-31%]) pre-pregnancy than the comparison population (10 and 9%, respectively). CONCLUSIONS: Mobile apps may be a feasible tool for capturing health data not routinely available in EHR. Pregnant women willing to use a mobile app for research may differ from the general pregnant population, but confirmation is needed.

The FDA MyStudies app: a reusable platform for distributed clinical trials and real-world evidence studies.

We developed a mobile application and secure patient data storage platform, FDA MyStudies, to address privacy, engagement, and extensibility challenges in mobile clinical research. The system extends the capabilities of the mobile frameworks Apple ResearchKit and ResearchStack through an intuitive front-end application and secure storage environment that can support health research studies. The platform supports single or multisite studies via role-based access and can be implemented within highly secure data environments. As a proof-of-concept, pregnant women participated in a descriptive study via the app in which data not routinely captured in electronic health records (EHR) were collected and linked with existing patient data to provide a more wholistic view of the patient and illustrate how patient data combined with EHR data could be used to support public health research.

Cross-Network Directory Service: Infrastructure to enable collaborations across distributed research networks.

INTRODUCTION: Existing large-scale distributed health data networks are disconnected even as they address related questions of healthcare research and public policy. This paper describes the design and implementation of a fully functional prototype open-source tool, the Cross-Network Directory Service (CNDS), which addresses much of what keeps distributed networks disconnected from each other. METHODS: The set of services needed to implement a Cross-Directory Service was identified through engagement with stakeholders and workgroup members. CNDS was implemented using PCORnet and Sentinel network instances and tested by participating data partners. RESULTS: Web services that enable the four major functional features of the service (registration, discovery, communication, and governance) were developed and placed into an open-source repository. The services include a robust metadata model that is extensible to accommodate a virtually unlimited inventory of metadata fields, without requiring any further software development. The user interfaces are programmatically generated based on the contents of the metadata model. CONCLUSION: The CNDS pilot project gathered functional requirements from stakeholders and collaborating partners to build a software application to enable cross-network data and resource sharing. The two partners-one from Sentinel and one from PCORnet-tested the software. They successfully entered metadata about their organizations and data sources and then used the Discovery and Communication functionality to find data sources of interest and send a cross-network query. The CNDS software can help integrate disparate health data networks by providing a mechanism for data partners to participate in multiple networks, share resources, and seamlessly send queries across those networks.