Enhancing EHR Systems with data from wearables: An end-to-end Solution for monitoring post-Surgical Symptoms in older adults

TL;DR

The ROAMM-EHR platform can capture data from a consumer smartwatch, send captured data to a secure server, and display information within the Epic EHR system using a user-friendly interface, thus enabling healthcare providers to monitor post-surgical symptoms effectively.

Abstract

Mobile health (mHealth) apps have gained popularity over the past decade for patient health monitoring, yet their potential for timely intervention is underutilized due to limited integration with electronic health records (EHR) systems. Current EHR systems lack real-time monitoring capabilities for symptoms, medication adherence, physical and social functions, and community integration. Existing systems typically rely on static, in-clinic measures rather than dynamic, real-time patient data. This highlights the need for automated, scalable, and human-centered platforms to integrate patient-generated health data (PGHD) within EHR. Incorporating PGHD in a user-friendly format can enhance patient symptom surveillance, ultimately improving care management and post-surgical outcomes. To address this barrier, we have developed an mHealth platform, ROAMM-EHR, to capture real-time sensor data and Patient Reported Outcomes (PROs) using a smartwatch. The ROAMM-EHR platform can capture data from a consumer smartwatch, send captured data to a secure server, and display information within the Epic EHR system using a user-friendly interface, thus enabling healthcare providers to monitor post-surgical symptoms effectively.

Figures (6)

• Figure 1: High-level architecture of ROAMM-EHR platform. It comprises three main components: the smartwatch app collects PROs and EMAs and sends data to the AWS server; the AWS server analyzes and forwards this data to the Epic app; and the Epic app displays the processed data to healthcare providers based on user ID decrypted from the Epic server.
• Figure 2: Examples of PROs in Apple watches.
• Figure 3: The server architecture involves mobile devices connecting to the AWS API Gateway via the University of Florida (UF) network and AWS Direct Connect. Data is fetched through a Lambda function, which retrieves information from the database. The processed data is then sent back to the mobile devices.
• Figure 4: Architecture of the Epic app. The healthcare provider is asked to connect to UF VPN to access the Epic app. Upon accessing the Epic app, the Epic server sends the encrypted user ID to the app. The app interacts with the AWS server via https requests, processed by the AWS API Gateway and Lambda functions for data retrieval and filtering from the database. Responses are then sent back to the app through the same secure channel and displayed on the relevant page.
• Figure 5: Overview of patients’ information. It features a pie chart displaying the percentage of responded patients, a notification box highlighting important updates and alerts, and a compliance table showing the compliance rates of all patients over the most recent week.