The digital age has set new standards for the capabilities at individuals' fingertips, and healthcare leaders are determining how this extends into patient experience.
Chris Carmody, chief technology officer at Pittsburgh-based UPMC, connected with Becker's to discuss his adjustment to the heightened expectations patients hold for managing their health.
Mr. Carmody said the commonality of using technology on a daily basis has led individuals to want to manage their health through their devices. As a way to meet these expectations, he said the UPMC team has shifted its mindset to treat patients more like consumers while maintaining focus on patient care.
He added that the goal is to close the gap between what is available and what is possible for self-managing health.
Tools such as e-visits and health management platforms can provide patients with the same level of convenience and control in managing their health as they have in other aspects of their lives, Mr. Carmody said.
He pointed to the example of an app that notifies patients of awaiting prescription refills and allows them to manage the process independently.
However, Mr. Carmody said innovative platforms on the surface are enhanced by technologies on the back end of an organization. He highlighted how leveraging machine-learning models for clinical analysis holds the potential to take patient information to the next level.
UPMC integrates predictive models to support care delivery by using algorithms that have been tailored to its patients. The algorithms are based on cumulative patient and population health data that has been digitized since the introduction of EHRs at the organization.
Natural language processing models have been used to extract data such as social determinants of health and symptoms that were not previously captured in a structured format. Feeding machine-learning tools with internal clinical analytics data drives success, as generic data is not applicable to the populations and demographics the organization serves.
Additionally, using specific patient data helps the models make more accurate predictions and uncover patterns to improve care, Mr. Carmody said.