EXL supports the quest for greater “patient intelligence”

With the growing emphasis of patient centricity, life science companies are yearning for more patient intelligence. This incremental understanding of patient populations can be acquired by analyzing data such as medical and pharmacy claims, social determinates of health (SDoH), electronic health records (EHRs) and lab results. By utilizing these sources, life science companies can derive several types of patient intelligence to provide deep insights about patient adherence, tracking patient journeys, identifying and targeting undiagnosed patients, developing and leveraging risk and impact-ability scores, and quantifying total cost of care.

Gaining this deeper understanding of patience intelligence allows life sciences companies to see a robust 360-degree view of the patient, which in turn allows them to create targeted patient marketing programs, increase sales efforts, and assist with value-based contract negotiations.

Here are three types of analyses that EXL can provide to bolster patient intelligence for life sciences companies: patient adherence, the patient clinical journey, and total cost of care.

Patient Adherence

The healthcare industry is in the grips of a patient non-adherence crisis. Using advanced analytics and data visualization can help address this issue. The most prevalent adherence measures are with patients getting their prescriptions filled as written. Patient-level prescription and medical claims data can be quantified at the individual drug level or aggregated into therapeutic drug classes.

Prescription and medical claims data and its associated analytics provide valuable insights that life sciences companies need, including, identifying patients at risk for non - compliance of medications, understanding the key factors that contribute to non-adherence of medications and provide recommendations to improve medication adherence and achieve optimal health outcomes. For example, adding patient profile information such as age, gender, socioeconomic status, and behavioral data, enable marketers to stratify patient populations and develop appropriate messaging geared to enhancing patient adherence. Proportion of days covered at 80% threshold (PDC80) is the industry standard to measure adherence.

Stratified patient populations can also be aggregated and visualized using heat maps to show differences by geographic region. Based on calculated metrics, these heat maps can include scores for the top geographies, provide insights about regions that demonstrate the best sales opportunities, and display territories for targeting engagements.

The Patient Clinical Journey

Advanced analytics and data visualization can be used to follow the clinical journey of a patient population with a specific diagnosis, showing common lines of therapy and treatment regimens. The journey analysis identify patients who did not have the specific diagnosis in the base year along with underdiagnosed patients and those who subsequently were diagnosed with the stated condition. These patients are followed over several years via longitudinal patient level medical and pharmacy clams data.

Lines of therapies (LoT) are determined based on complex, clinically defined rules for the therapeutic area involved in the analysis. Once the LoTs have been defined, treatment regimens are determined. There could be multiple LoTs and regimens over the course of a patient’s treatment journey.

Further analysis of the patient clinical journey includes segmenting patients within each LoT by demographic and clinical attributes to create specific personas by LoT. Finally, individual metrics are calculated over each LoT to gain a complete picture of who, what, and when of the patient journey to help provide life sciences companies with a more complete view of their patient population.

Total Cost of Care

Data analysis and visualization also can be used to determine total cost of care (TCoC) for a patient’s complete healthcare costs, not just those associated with the treatment of a specific condition. For example, a patient diagnosed with congestive heart failure may also have other comorbidities with treatments and associated costs, including inpatient hospital stays. The sum of all these healthcare costs would define the patient’s TCoC. These costs can be then compared across multiple treatments for a diagnosed condition, providing key economic insights on a pharma’s brand.

A TCoC analysis may include the following metrics:

  • An allowed cost of care metric, per member per month (PMPM), down to the service level, by therapeutic area and product/procedure within each therapeutic area
  • A utilization of service metric, per 1,000 patients, down to the service level by therapeutic area and product/procedure within each therapeutic area
  • A patient prospective risk metric by therapeutic area and product/procedure within each therapeutic area
  • A PDC80 compliance metric by therapeutic area and product/drug within each therapeutic area
  • A geographic distribution by U.S. region, as defined by the pharmaceutical manufacturer to understand any regional variances toward the treatment of a specific diagnosis



Let EXL help you leverage your patient intelligence through advanced analytics and data visualization. Our team has extensive experience and proven expertise in healthcare data and advanced predictive and prescriptive analytics to provide insightful and productive patient-centric insights.