CCS Medical Unveils PropheSee for Personalized Chronic Care

The initiative marks a shift from the traditional one-size-fits-all approach to a more customized method of patient outreach.

CCS Medical has introduced a new AI tool, PropheSee, to enhance adherence to chronic care treatments through personalized messaging. Developed in collaboration with Accenture, PropheSee uses patient data to send tailored notifications to individuals managing diabetes. 

The initiative marks a shift from the traditional one-size-fits-all approach to a more customized method of patient outreach.

CCS Medical stated that personalized notifications, delivered at the optimal time, ensure patients stay on track with their treatments more effectively. 

"A standard outreach model doesn't suit all patients. Effective notifications are customized and timely," a representative from CCS reported.

Adapting Models from Other Industries

The PropheSee model employs an attrition model commonly used in telecommunications, financial services, and entertainment industries. Chinmoy Barua, MD at Accenture, highlighted the novelty of applying this model to chronic care. 

“Though this model is not new, it’s the first time it’s being used in the chronic care sector, where there’s a continuous need for medical equipment resupply,” said Richard Mackey, CTO, CCS.

PropheSee aims to enhance the patient care experience by using patient data to send messages via the patient’s preferred communication method at the most effective times. 

"This approach is meaningful and differentiated. We're using proven methods from other industries and applying them innovatively in chronic care," Mackey added

Data-Driven Personalization

To build comprehensive profiles of their customers, CCS and Accenture utilized extensive data sets, including claims, socioeconomic, and outcomes data. This data integration helps predict the likelihood of patients discontinuing their treatment. 

"We merge first-party and third-party data to create a holistic view of our customers," Barua explained.

The model is reportedly 90% accurate in predicting a patient’s likelihood of treatment non-adherence over a 90-day period. It considers various factors, such as economic hardships, to send personalized messages reminding patients of the importance of maintaining their therapy. 

"As patients journey through care, their interaction with us can change. Our framework adapts to these changes, sending relevant messages as needed," Mackey noted.

Moreover, CCS collects social determinants of health data through alternative means, supporting patients who might not feel comfortable disclosing their health information directly to a provider. 

Further, Barua expressed hope that the accuracy of the predictions would improve by including more data sources.

Testing & Implementation

Before sending personalized messages, Accenture’s Learn, Test, and Validate procedure tests them on a control group. This ensures the efficacy of the communications and prevents patients from receiving unnecessary or unhelpful messages. Patients have the option to opt out of these communications if they choose.

CCS provided examples of the messaging system in action. One example showed a text message focusing on wellness sent to a caregiver with diabetes. Another depicted an email reminding a patient to reorder diabetes supplies.

In addition to PropheSee, CCS, and Accenture are working on a generative AI framework to guide CCS’s future AI initiatives.

CCS Medical and Accenture partnership represents a significant step towards personalized chronic care management, leveraging advanced data analytics to improve patient outcomes.