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UT MSK Clinic: Patient Generated Health Data
Project Type
Service Design
Date
February 2024
Location
Austin, Texas
Problem/ Goal
How might we advance user engagement and visualization of active and passive forms of PGHD for individual level functions including decision support and shared decision making, and aggregate level functions, including care redesign and population health measurement within learning health systems?
Customer/ User
Musculoskeletal Institute UT Austin
Approach
Engage patients and educate them about patient generated health data to increase trust, strengthen understanding of their personal importance and improve response rate to surveys.
Solution
Based on our research, observation within the clinic, and interviews with patients/clinicians our team found that patients were not aware of the importance of the Patient Reported Outcome Measures (PROMs) surveys or how they were utilized by their care team to improve their care, as well as, the care of future patients. Therefore, patients did not see the significance of participating in the surveys over months after they received care from the clinic.
Our team suggested adding educational information (in the form of posters/pamphlets) in multiple contact points within the clinic and integrating a visual representation of PROMs information during the consultation between clinician and patient to reinforce their importance and use within the clinical setting.
My Role
In a team of four other students in the Masters in Design focused on Health I researched, interviewed clinicians and patients, synthesized research and developed a proposal for the Musculoskeletal Institute.
Working with clinicians and staff from the Musculoskeletal Institute at the University of Texas in Austin, I researched how patient generated health data was captured and utilized by clinicians to improve patient outcomes.
Our mission (from the client):
"How might we advance user engagement and visualization of active and passive forms of PGHD for individual level functions including decision support and shared decision making, and aggregate level functions, including care redesign and population health measurement within learning health systems?
Project Context:
There has been considerable hype surrounding person generated health data (PGHD). The development and proliferation of active measures of health status (or Patient Reported Outcome (PRO) measures) and digital platforms (EHRs, registries, outcome measurement platforms) that capture PROs, serve as strong 'innovation triggers'. However, despite increased access and availability of such tools and technologies, many stakeholders in health care have experienced a 'peak of inflated expectations' and universally failed to utilize PGHD at the point of care. Finding themselves in a 'trough of disillusionment', they have struggled to shift the application of PGHD from research bench to clinical practice while facing human, technical, and system-level barriers to adoption. There is a critical need for stakeholders in healthcare to emerge from the trough toward 'slopes of enlightenment' and ‘plateaus of productivity’. Clinical teams need to visualize and engage users with precise, efficient PGHD at the point-of-care for decision support and shared decision making; health systems desperately seek to implement these data within clinical workflows to improve care path design and population health; and technologists continually attempt to advance the nexus between AI technologies and both active and passive forms of PGHD using sensors and digital phenotyping."
