Ying-Chih (Inga) Wang, PhD, OTR/L, associate professor in the Department of Occupational Science and Technology, is working to streamline the way occupational therapists (OTs) in the field assess cognitive, emotional, sensory and motor function in patients.
Building from the research she began during her doctoral and post-doctoral work, Wang and her research partners have developed computer-adaptive outcomes measurements, which are superior to methods that require clinicians and OTs to survey each patient at each appointment.
Using statistics to serve patients
Early in her career, Wang identified the need to shift assessment methods away from the typical survey toward a computer-based statistical model that could streamline the process.
“When assessing patients’ abilities across the spectrum of movement, the more information you can get, the better,” Wang said. “But what we’ve found is that most patients don’t have the time to respond to the amount of questions it would take for clinicians to get the information they need, and elderly persons or persons with disabilities aren’t always able to respond as fully.”
Developing statistical models of individual survey results allows clinicians to abbreviate the surveys they administer to each patient, thereby streamlining the time it takes to assess patients and prescribe a treatment plan.
In this era of big data and electronic health records, Wang noted the advantages of shifting from a traditional survey toward computer-based methods. “We are using statistical models and surveys in tandem to create a measure that is more specific in the information it captures and that can track a patient’s progress over time, as the data is processed and stored,” she said.
Looking to the future
The work Wang and her fellow researchers are doing responds to the need for more concrete, efficient and specific measurements when assessing patient outcomes. As next steps in her research, Wang hopes to pursue deeper statistics about rehabilitation outcomes and develop practical applications based on her models, specifically related to the clinical interpretation of assessment tools that use the functional staging method.
Besides statistical modeling, Wang’s research interests focus on upper extremity recovery, using the ArmeoSpring exoskeletal device (pictured above). Her work has also incorporated a portable motion capture system, used in house for motion analysis. “In the long run, I’d like to develop technology that can be used by clinicians to improve patient care and for patients to use in daily tasks in their home environments,” said Wang.
Dr. Wang currently directs the Sensory Motor Performance and Rehabilitation Outcomes Research labs in the College of Health Sciences.