Pharmacy companies can better understand and manage risk among patient populations by ‘chatting with their data’ to improve patient risk stratification as well as consultation and engagement efforts.
Centers for Medicare and Medicaid Services (CMS) STAR ratings are focused on driving improvements in care quality. New fee-for-value legislative initiatives continue, and payors shift financial risk to Pharmacy Benefit Managers (PBMs) through risk-bearing contracts and Direct and Indirect Remuneration (DIRs) with quality standards.
Also, pharmacies are looking for new ways to improve medication adherence outcomes and ratings. Therefore, pharmacies need to understand the patients more holistically. That is done through segmenting members based upon demographics, claim history, and social determinants of health.
Predicting future health risks helps pharmacies achieve the best qualitative and cost-effective outcomes in patient care. And that helps to determine which patients are at higher risk for disease and what preventive care is appropriate. Ultimately, that means pharmacies can offer more personalized patient journeys to increase care quality and associated ratings.
The challenge: “in-the-moment, iterative analysis.”
To say the challenge is simply “ad hoc analysis” understates the data challenge. The ad hoc nature of analysis is also both iterative and “in-the-moment.” Examples of when “in-the-moment” analysis is required include the time when a patient requests a prescription to be filled until pick-up, conversations during meetings, phone calls with peers, an urgent follow-up request from an executive, and an ad hoc and urgent business requirement. And it is compounded by the requirement of both in-depth business intelligence (BI) skills and pharmacy data knowledge. The requirements cannot wait days for an ad hoc analysis by a BI or data team.
BI reports often give a reasonable starting point for the investigation of an “in-the-moment” hypothesis. BI assets are developed by data analysis experts who understand the data well, and their efforts often solidly address the more obvious 80% of the reporting analysis requirements. But in many scenarios, the IT pharmacist or Informatics Pharmacist needs to go much deeper and pivot to many other related questions – all very quickly and iteratively, and in-the-moment.
Pharmacy staff such as the IT Pharmacist or Informatics Pharmacist need to be able to analyze and segment the Patient Populations with “in-the-moment iterative analysis,” based upon factors such as Claim History, Demographics, and Social Determination of Health factors.
DvSum Superior Value Proposition
With DvSUM CADDI, the staff such as the IT Pharmacist or Informatics Pharmacist can quickly and easily know which patients need additional consultation and design appropriate care support.
Complementing existing Population Health Reporting and Analytics with DvSum CADDI allows pharmacies to better allocate the right resources based on patient populations and personalize patient engagement. This can lead to improvements in medication adherence, health outcomes, patient retention, and ratings.
Example Pharmacy Use Cases
There are a variety of use cases where “in-the-moment, iterative analysis” is required, and AI-powered chat can help greatly:
1) Medication adherence
It would be ideal for pharmacy staff to be able to chat with claims and prescription history data to understand which patients:
a. Have not refilled their prescriptions, particularly if they are members of high “at risk” categories such as elderly, terminally ill, and so on.
b. Did not get critical shots this year (flu, Covid-19, shingles, and so on)
2) New clinics
When expanding to a new region, it is crucial for pharmacies to ramp up quickly to serve the patients with high-quality care. It is important to be able to determine quickly and easily, for example:
a. Patients who have not had their annual wellness visit
b. Patients with chronic disease states that need constant care like diabetes, weight management, heart disease, etc.
3) Eye clinics
Eye care requires several follow-ups to ensure proper care including:
a. Annual eye visit
b. Age related diseases like glaucoma
4) Digital app customers
It is important to reach out to patients as quickly and easily as possible. Digital app customers can be reached more easily for the use cases mentioned above as well as other situations.
5) Operational Metrics
DvSum CADDI can also be used to analyze data quickly and easily for the purpose of driving operational efficiency that can help improve patient experience and reduce cost, for example, to cut time to fill prescriptions and to cut overall wait time for patients in the store.
Conclusion
DvSum securely combines OpenAI’s GPT-4 with a powerful underlying data infrastructure so that pharmacy companies can query their data quickly, easily, and iteratively. This empowers staff to become more autonomous and productive “in-the-moment” and thus to drive patient satisfaction scores and the overall business. Contact us today to see how DvSum CADDI can help you.
