To achieve improved patient outcomes, life sciences organizations, payers and pharmacies are turning to artificial intelligence (AI) tools to scale their offerings and deliver personalized support to patients and healthcare providers (HCPs). While AI is full of promise, the value of an AI solution depends on the robustness and diversity of the data provided.
Only by using complete data across the medication ecosystem (e.g., pharmacy, pharmacy benefit manager (PBM), payer, HCP and even life sciences organizations), is it possible to generate the most accurate and complete picture of individual patient behavior, consumer behavior and population demographics, then use this information to enhance experiences.
Each data set has its strengths and blind spots. By using varying claims data sources, AI can then deliver a more comprehensive analysis of patient behavior and provide insights that improve outcomes.
Claims Data Compared: The Role of Different Data Sets
To get a better idea of how using claims data impacts patient outcomes, consider what each claims data set brings to the comprehensive analysis:
Pharmacy Claims Data
Pharmacy patient claims data offers a substantial level of detail because it tracks:
- Prescription medications and directions
- Submissions for payment
- Dates medications are filled and refilled
- Dates medications are picked up
Pharmacy data offers specific, frequent information on patient adherence, gaps in therapy and patient and plan costs. But that’s not where this data set stops.
Data from pharmacies can show key patient behaviors not discernable in other claims data. For example, with pharmacy claims, we can see how many days a patient waits to pick up a prescription once it has been processed by the PBM.
When a patient changes health plans, they typically do not change their pharmacy, providing continuity in the medication filling picture. But if a patient switches pharmacies, how do you keep track? That’s where payer data comes in to fill the gaps.
Payer Claims Data
In addition to paid pharmacy claims, payers offer medical claims data and enrollment data, helping complete the healthcare picture. Medical claims data provides insights into patients’ health conditions, hospitalizations and gaps in care. Sometimes, medical claims data also shows lab work and testing results.
Using payer data, AI can determine patterns of care for individuals and patient populations. AI can even use complex payer information to develop patient communications that result in better outcomes.
Payer data also tracks enrollment in the health plan and optional programs a patient has engaged in. This provides detailed information about individual patients, including their level of engagement in these health plans and programs.
PBM Claims Data
Because PBMs manage prescription-based care, they have access to a great deal of data about drug utilization, formulary structure and benefit design. PBMs build utilization management information into the processing of a claim.
Additionally, formulary placement, how the pharmacy benefit is designed, and whether the patient’s plan will pay for the drug are all captured in PBM data. Adding these data sets to payer and pharmacy claims data and seeing how these programs impact patients result in more insightful decisions.
Healthcare Provider Data
HCP group data is often the best source of information about a patient’s specific medical condition, but it can be complicated. HCP records typically have notes that are difficult to ingest into structured systems. However, HCPs have lab and testing results and will see trends in these results over time. But many patients will see HCPs from different groups, so obtaining complete patient history data can be complex.
HCP data can help tell the whole healthcare story, but it is crucial to separate what is useful and what is not. That’s where AI comes in.
Because AI is a system that learns, it builds knowledge and then decides how to best apply it. AI can search, recognize patterns and learn from experience — meaning it can recognize which information is useful to support a specific patient’s behavior and engagement.
Life Sciences Organization Data
Life sciences organizations track prescription data to monitor sales of their products and applications of their patient and HCP-focused programs. This data is usually obtained from third parties and often incomplete because they don’t directly engage with the patient. However, life sciences organizations run programs such as copay cards and education outreaches that support patients on their therapy journeys. These programs are also an alternate source of useful information about patients and their behaviors.
Leverage the Power of Different Data Sources with AI
AllazoHealth, an AssistRx solution, uses AI to collect, analyze and learn from claims and other data to make a positive impact on an individual patient’s medication behavior. Our technology optimizes therapy initiation and adherence by targeting the right patients with the right message at the right time — at scale — maximizing the impact and efficiency of patient engagement throughout the therapy journey.