This study will assess the feasibility of using patient-centered, commercial off-the-shelf (COTS) health information technology (IT) solutions to collect patient generated health data (PGHD) and patient-reported outcomes (PROs) from diverse, low-income disadvantaged populations. These data will then be mapped and reported in a way that will allow them to be made actionable and used to improve health care quality and delivery. The data mapping will be designed for data collection through technology such as mobile apps and wearables, and will be intended to support integration into interoperable electronic health records (EHRs), clinical information systems, and big data infrastructures.

Patient engagement is particularly critical to achieving good chronic disease self-management. This is especially important for disadvantaged patients, who are disproportionately affected by chronic disease. A key component of chronic disease self-management is the ability for patients to record and monitor their ongoing performance on indicator measures. While health IT solutions have been shown to improve chronic disease self-management, adoption and use of costly, specialized technologies among disadvantaged patients is lower than among higher-income populations. In contrast, COTS technologies such as mobile phones are more accessible to and widely adopted by disadvantaged patients, thus bridging the gap of the digital divide.

The central research hypothesis posits that 1) low-income, disadvantaged patients both can and will provide high quality PGHD and PROs through COTS-based health IT solutions, and 2) these data can be integrated into clinical systems and used to improve health care quality and delivery. PGHD can be collected through patient interaction with COTS health IT solutions such as mobile health apps and fitness trackers. PROs can be collected via patient response to questionnaire-based PROs measures, or PROMs. These data can be transmitted to clinical information systems, integrated into clinical workflows and used by providers to improve health care quality and delivery. Using a sequential integrated mixed-methods approach, we propose to test the central hypothesis through three specific aims, as follows:

Aim 1: To assess the needs and preferences of disadvantaged patients and safety net health care providers regarding the use of health IT for communicating PGHD and PROs.

Aim 1 Research Questions: What specific features in COTS solutions meet the needs and preferences of disadvantaged patients for communicating PGHD and PROs to their providers? What PGHD and PROs are deemed most important by providers and patients for improving health care and health outcomes?

Answering these questions will inform health IT solution selection, design, usability, and utility; assist with prioritizing PGHD and PROs collection by data element and measure type; and identify potential discrepancies between patients' and providers' perceptions of PGHD and PROs importance.

Aim 2: To demonstrate the feasibility of PGHD and PROs collection through COTS health IT solutions in a patient-centered pilot intervention for weight management among disadvantaged patients.

Aim 2 Hypothesis: Providing PGHD and PROs through COTS solutions will improve engagement among disadvantaged patients. Secondary outcomes include improving key health indicators (e.g., weight, physical activity) and PROMs (e.g., quality of life, mental health symptoms).

Weight management is important in delaying, averting, and reducing the effects of multiple chronic diseases, including diabetes, hypertension, and obesity. A weight management-related intervention also serves as an effective test of PGHD and PROMs collection, due to the existence of numerous COTS solutions which use different methods for tracking common data elements related to weight, physical activity, and fitness.

Aim 3: To create an ontology mapping and set of interoperability resources which can be used to support integration of PGHD and PRO into clinical information systems.

Aim 3 Hypothesis: PGHD and PROs can be characterized by distinct types, elements, and structures which, once described, may be modeled and mapped to existing vocabularies for health data management.

In order to make PGHD and PROs actionable, these data must be integrated into clinical information systems such as electronic health records (EHRs) where it can be used by clinicians in their practice. Creating a "translation" by matching PGHD and PROs data elements to comparable ones in existing clinical vocabularies will provide a tool to support future data integration into the EHR. Creating a resource set which can be used with multiple EHRs will improve the generalizability and broad usability of the ontology mapping tool.

Inclusion Criteria:

• BMI of 25.0-39.9,
• Has a smartphone
• English or Spanish as primary language
• assessed at "medium health risk" according a risk stratification algorithm based on clinical criteria, diagnostic scoring, and health care utilization

Exclusion Criteria:

• Does not meet inclusion criteria

An enhanced entity-relationship (EER) model will be created for the PGHD elements and PROMs used in the study. Knowledge representation techniques will be utilized to describe the model in ontological terms. Concepts from the UMLS Metathesaurus will be used to create a mapping to the SNOMED-CT clinical vocabulary. Modeled information will be structured using Fast Healthcare Interoperability Resource (FHIR) standards and packaged as a set of FHIR resources. Each FHIR resource includes: 1) common definitions and representations; 2) a common metadata set; and 3) a human-readable part to aid user interpretation. Products will include a detailed EER schema, an interface and requirements assessment, an ontology, and a list of UMLS concepts and SNOMED-CT terms used in ontology mapping.

16 weeks (duration of each participant's participation)

Standard definitions are used.