PreAct

Precision medicine for patients with ovarian cancer

Opportunity

New precision medicine treatments can transform once terminal, acute diseases like ovarian cancer into chronic ones. 

Historically, more than 75 percent of ovarian cancers would eventually recur, at which point they are not curable. But recent FDA approvals for a new class of oral drugs called parp inhibitors have resulted in significantly improved survival for newly diagnosed ovarian cancer patients.  

However, the effectiveness of these treatments is dependent on genetic and tumor mutations. A complex process of multiple genetic and tumor tests, each of which can take weeks to complete, must be completed within four months of diagnosis to ensure optimal treatment. When we started this work, the vast majority of Penn Medicine patients newly diagnosed with ovarian cancer were starting the genetic testing process. But, only about half were completing it in time, with drop-offs happening at numerous points along the way.

Intervention

Precision Medicine Activated (PreAct) is a model that combines elements of technology and high-touch follow-up to optimize the genetic and tumor testing pathway for providers and patients.

PreAct uses standardized inclusion criteria to identify which patients need genetic testing and subsequent reflex tumor testing. With help from Agent, all patient information is automatically aggregated into a dashboard view to allow for easy status and results tracking by care teams.

When it's time for action, automated alerts are pushed to patients and care team members. And, for patients who drop off, PreAct features a high-touch workflow that can be activated to intervene before it's too late to complete the testing pathway.

Impact

PreAct decreases the manual and mental burden of tracking genetic and tumor testing for providers, making it easier to get the right drug to the right patient at the right time. For patients, PreAct makes the process of completing genetic and tumor testing easy during a time of high stress. 
 
At scale, PreAct is projected to reduce patient drop-offs by 50 percent, resulting in approximately 60 additional ovarian cancer patients at Penn Medicine completing the genetic testing pathway each year.  This is an incredibly meaningful increase, as the selection of the appropriate parp inhibitor, based on testing, can enable patients to stay in remission up to three times longer and, in some cases, lead to a cure.
Phase 2: It does work
Collaborators

Ashley Haggerty, MD, MSCE
Danielle McKenna, LCGC
Lauren Schwartz, MD
Lainie Martin, MD
Payal Shah, MD
Susan Domchek, MD
Kate Nathanson, MD
 

Innovation leads

Ryan Schumacher
Davis Hermann, MiD
Mike Dong
Roy Rosin, MBA

Platforms
Funding

Innovation Accelerator Program

Innovation Methods

Fake back end
It is essential to validate feasibility and understand user needs before investing in the design and development of a product or service.
 
A fake back end is a temporary, usually unsustainable, structure that presents as a real service to users but is not fully developed on the back end.
 
Fake back ends can help you answer the questions, "What happens if people use this?" and "Does this move the needle?"
 
As opposed to fake front ends, fake back ends can produce a real outcome for target users on a small scale. For example, suppose you pretend to be the automated back end of a two-way texting service during a pilot. In that case, the user will receive answers from the service, just ones generated by you instead of automation.
Fake back end
We hypothesized that if we could ease the cognitive burden on physicians and reduce friction, we could increase the percentage of genetic testing orders placed on time.
 
To test this hypothesis, we manually pre-filled orders and attached them to the patient's visit in the electric health record. Next, we prompted providers to complete the order, which they could do in only a few clicks.
 
This simple fake back end pilot resulted in a 20% increase in orders placed on time, thereby validating the approach. 

Videos

Stories of Innovation