Claims Data Compared: Pharmacy, Payer, PBM, Provider, Pharma

Artificial Intelligence

To achieve improved patient outcomes, pharmaceutical companies, payers, and pharmacies are turning to artificial intelligence (AI) engines. However, the value of an artificial intelligence engine depends on the robustness and diversity of the data it is provided.

Without complete data across the medication ecosystem (i.e., pharmacy, pharmacy benefit manager, payer, provider, and even pharmaceutical company), it’s impossible to generate the most accurate and complete picture of individual patient behavior, consumer behavior, and population demographics.

Each data set has its strengths and blind spots. When an AI engine uses different data sets, it can provide insights that impact patient behavior and outcomes. To give a specific example, let’s look at claims data and see how using varying claims data sources enables a more comprehensive analysis of patient behavior.

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 complete 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 like this offers specific, frequent information on patient adherence, gaps in care, 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 script once it has been processed by the pharmacy benefit manager (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.

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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 AI, payer data can determine patterns of care for individuals and patient populations. AI can even use complex payer information to develop patient interventions 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 pharmacy benefit managers (PBMs) manage prescription-based care, they have access to a great deal of data about drug utilization, formulary structure, and benefit design. Utilization management—including utilization review—is crucial, ensuring that the right drugs are prescribed to the right patient for the right condition at the right time. 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 results in more insightful decisions.

Provider Data

Provider group data is often the best source of information about a patient’s specific medical condition, but it can be complicated. Provider records typically have notes (some even handwritten) that are difficult to ingest into structured systems. However, providers have lab and testing results and will see trends in these results over time. But many patients will see providers from different groups, so obtaining complete patient history data can be complex.

Provider 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.

Pharmaceutical Company Data

Pharmaceutical companies track prescription data to monitor sales of their products and applications of their patient and provider-focused programs. This data is usually obtained from third parties and often incomplete because they don’t directly engage with the patient. However, pharmaceutical companies run programs like copay cards and medical education outreaches that attempt to support patients on their medication journeys. These programs are also an alternate source of useful information about patients and their behaviors.

Use AI to Leverage the Power of Different Data Sources

AllazoHealth uses AI to collect and analyze claims and other data to make a positive impact on individual patient behavior. We optimize medication adherence outcomes for pharmaceuticals, payers, and pharmacies. Our AI engine targets the right patients with the right interventions at the right time.

Find out how AllazoHealth can optimize medication adherence, therapy initiation, and quality outcomes for your organization. Request a demo now.

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