Quality and Safety

Personalized medicine is constantly evolving, and the technologies it relies on are advancing rapidly. In this environment, the associated challenges are numerous and complex. Overcoming them often requires fresh eyes and multiple perspectives.

While understanding and reconciling drugs after discharge from the hospital can be challenging, it's a necessity for greater efficacy of care delivery. HL7's Da Vinci project can help.

It's crucial we recognize that what worked well before may not work well today. And it's essential to anticipate, prepare for, respond to and adapt to incremental change.

While the 'robot' aspect of RPA gets most of the attention, successful implementation centers on the people and processes that will be impacted by the technology – and in a healthcare system where burnout is rampant, there are plenty of those.

Even when things go back to some semblance of normal and care teams return to their regular processes, burnout will still be felt. It's up to healthcare leaders to help manage workplace stress.

At Penn Medicine, integrated product teams – comprising data scientists, physicians and software engineers, among others – are helping improve AI and machine learning applications.

While traditionally deeply skeptical of artificial intelligence in clinical settings, in today's fast-changing care delivery landscape many physicians are thinking more proactively about how AI can improve quality and patient experience.

The development of PennOpen Pass, a symptom tracker and exposure alert system, offers a lesson on how challenging factors can focus the mind, enabling development of new tools that meet communities' needs.

Health systems that refuse to see themselves as engineering houses risk falling behind in their ability to properly leverage artificial intelligence and machine learning.

Data from a real-time location system, covering nearly 4.3 million square feet, offers the ability to see patients and staff who may have come into proximity with an infected person.