Seven University of Utah Health projects have received seed grants that could promote the development and use of more scientifically based digital health applications in daily health care.
The grants, supported by the Digital Health Initiative (DHI), will focus on projects designed to produce safer and more effective digital tools than are currently available, according to Guilherme Del Fiol, M.D., Ph.D., co-director of DHI and a professor in the Department of Biomedical Informatics at U of U Health.
“There’s been a surge in the use of digital health applications in the past few years, both by patients and their doctors,” Del Fiol says. “But how many of these apps actually work as intended? Most are promoted with little or no rigorous scientific evidence.”
In fact, a 2019 analysis of studies conducted by the 25 top-funded American producers of digital health tools, including wearable biosensors and mobile health apps, found that most of these products did not have a substantial impact on health outcomes, cost, or access to care. Another study of mental health apps concluded that only 14% of the 1,400 apps evaluated were based on real-world experiences, and none mentioned a certification or accreditation process.
“We see these seed grants as a tremendous opportunity to alter that trajectory,” Del Fiol says. “They represent a starting point for taking innovative, reliable, and scientifically tested digital health applications from bench to bedside.”
The seed grant projects will receive up to $50,000 for one year. The researchers will develop, test, and evaluate digital applications that fall within one of the four main areas of interest within the DHI:
- Mobile apps and games for health
- Virtual reality and sensors
- Clinical decision support tools
- Integration with electronic health records (EHR)
If successful, the projects will progress to clinical trials designed to assess their usefulness in a larger context, says Victoria Tiase, Ph.D., R.N., director of strategic development at DHI.
“Clinicians treating patients at the bedside need better efficiency today,” Tiase says. “So, we need to get more practical and effective digital tools in the pipeline. We hope that these seed grants will be a jumping-off point for that effort.”
Recipients of the seven seed grants represent 11 U of U Health disciplines, ranging from anesthesiology to nursing to population health.
Project Titles, Summaries, & Awardees
Health Records and Community Service Integration of the Going Home Toolkit
Andrea Wallace, Ph.D. (Nursing)
Roger Altizer, Ph.D. (Entertainment Arts Engineering, Population Health Sciences)
Kensaku Kawamoto, M.D., Ph.D. (Biomedical Informatics)
Wallace and colleagues will evaluate the effectiveness of a digital resource planner to help patients self-manage their health conditions after they leave the hospital. Called the “Going Home Toolkit”, the planner includes sections on transportation, medication, errands, meals, housework, personal care, billing, and insurance. It is also designed to help patients better communicate their needs to family, friends, and health care providers.
Broadening the Impact of Symptom Care at Home Through EHR Integration and Implementation Science
Elizabeth Sloss, Ph.D., M.B.A., R.N. (Nursing, Huntsman Cancer Institute)
Kathi Mooney, Ph.D., R.N (Nursing)
Justin D. Smith, Ph.D. (Population Health Sciences)
Guilherme Del Fiol, M.D., Ph.D. (Biomedical Informatics)
Kensaku Kawamoto, M.D., Ph.D. (Biomedical Informatics)
Symptom Care at Home, a program that helps cancer patients reduce symptoms that occur during treatment for cancer, asks patients to report daily symptoms in a mobile application or by phone and receive automated coaching or follow-up from a nurse practitioner to manage their symptoms. With this DHI seed grant, the researchers will identify ways that Symptom Care at Home can incorporate patient-reported symptoms into their electronic health records.
Patient Generated Health Data for Geriatrics Patients
Jorie Butler, Ph.D. (Biomedical Informatics)
Butler will collect patient-generated health data, using mobile devices like a Fitbit, from patients aged 65 and older with chronic pain. The participating patients will review and discuss their personal data with the research team to help them understand how these data are useful to patients in managing their own health. This research can be applied to future care of pain and other health conditions.
Expanding the Capability of Intraprocedure Anesthesia Information Display and Pharmacology Forecasting
Ken B. Johnson, M.D., M.S. (Anesthesiology)
Beca Chacin (Anesthesiology Center for Patient Simulation)
Soeren Hoehne (Anesthesiology Center for Patient Simulation)
Cameron Jacobsen, M.S. (Anesthesiology)
Noah Syroid, M.S. (Anesthesiology)
Johnson and colleagues seek to combine an anesthesia forecasting system with electronic health records at U of U Health. With this system, an anesthesia provider will have visual guidance to monitor and predict the levels of sedation, analgesia (pain relief), and muscle relaxation for a patient who is undergoing general anesthesia.
Explainable AI for Equitable Risk Stratification of Atrial Fibrillation and Stroke
Mark Yandell, Ph.D. (Human Genetics, Bioinformatics)
Martin Tristani Firouzi, M.D. (Pediatrics)
Benjamin Steinberg M.D. (Cardiology)
Using artificial intelligence, Yandell and colleagues seek to produce more accurate predictions of individual stroke risk. The computational model will account for socioeconomic disparities seldom considered in previous attempts at predicting strokes. These considerations include housing, transportation, and discrimination, and accessibility to nutritious foods and exercise. These refined predictions will enable doctors to offer patients more personalized stroke prevention advice.
Remote sensing of autonomic function and mobility coupling using wearables to monitor recovery after mild traumatic brain injury.
Peter Fino, Ph.D. (Health and Kinesiology)
Melissa Cortez, D.O. (Neurology)
Leland Dibble, Ph.D., P.T. (Physical Therapy & Athletic
Fino and his colleagues seek to develop a system to assess individuals who have persistent symptoms of mild traumatic brain injury (mTBI), such as concussions. The researchers will monitor activity, heart rate, and other key indicators of mTBI using wearable at-home devices.
By combining data streams from these devices worn at home, the researchers believe they can identify individuals with persistent mTBI symptoms earlier and expedite rehabilitation when they show signs of atypical recovery.
Spanish Language Translation and Preliminary Feasibility of NeuroFlex: A Digital Cognitive Intervention for Late Life Depression
Sarah Morimoto, Psy.D. (Population Health Sciences)
Morimoto will translate and test a video game designed to relieve depression among older Spanish-speaking volunteers. The game, called Neurogrow, allows players to tend a virtual garden, performing tasks including watering, fertilizing, and eliminating pesky bugs.
In previous research, the scientists found that Neurogrow helped relieve depression and improve cognitive function among English-speaking, non-Hispanic older men and women. The researchers hope to find similar results in Spanish-speaking volunteers. If successful, they plan to use Neurogrow more broadly in Latino/Hispanic communities.