Aggregating data from a variety of sources can provide organizations with valuable data for tracking a high number of volume-based metrics. But the real benefits of data aggregation are seen through the linkage of patient data that truly give insight into the patient’s complete journey along the healthcare continuum. Linkage of patient data introduces the opportunity to include additional patient characteristics, such as social determinants of health, as well as more clinically rich data, such as lab results, patient adherence, and other longitudinal behaviors.

With this two-part series on broadening specialty patient journey through meaningful data collection, we have discussed success factors and challenges, as well as how to apply a structured approach for aggregation and integration services. This blog will focus on the value that comes with creating integrated datasets by linking patient data with more traditional aggregated data and share a ValueTrak case study that highlights the importance of data aggregation continuity.

“Seeing” the complete patient experience

When patient data is linked appropriately, the resulting dataset expands the range of potential insights including (but not limited to), information on treatment stops or changes, exposing unmet needs and potential strategies to address them, and more clarity on the diagnostic journey. These insights can then evolve beyond the patient and into prescribers, payers, and promotional impact.

First and foremost, it is critical to find and follow the correct de-identified patients through a dataset that is easily navigable so that patients can be identified for potential treatments without identifying them. Disease characteristics for any number of therapeutic areas can also be evaluated and providers can be prioritized based on their patient mix, specialty, and treatment dynamics — and thereby, the most high-value prescribers can be targeted.

The de-identified integrated data can also be utilized to understand patient dynamics with payers and the impact of co-pay programs. Predictive analytics can be applied to create triggers that predict the next patient available for treatments, and the impact of any promotional programs that may be in flight can be measured and adapted as needed to ensure program success. 

From the perspective of the patient as the starting point in ValueTrak, we're able to get better visibility into the complete experience and treatment pathways by:

• Calculating the lifetime value of a patient
• Evaluating patient behavior pre- and post-rejects by their rejected payer claims at the pharmacy
• Examining the drivers to patient non-adherence and evaluating the patient’s reasons for abandonment

From a promotional aspect, we can analyze the impact of patient adherence and co-pay card programs. We can also evaluate the overall return on investment of co-pay card programs and other promotional programs that may be in flight. We can look at the impact of promotion on new patient share and the impact of promotion on rejection rates at the pharmacy.

When considering the prescribers, we can use IQVIA’s integrated datasets to refine the overall targeting strategy and identify high-value prescribers based on patient behaviors. We can also compare providers based on the persistency and adherence patterns of their patient populations. We can examine physician referral and prescribing networks, as well as the overall physician value to a product by predicting efficient physicians’ prescribing choices for any given indication. These patient metrics at the physician level can then be further combined and evaluated at the account and integrated delivery network level. 

Finally, for the examination of payers, we can take the integrated patient dataset to further refine payer segmentation strategies and the impact to overall contracting by evaluating payer penetration of various patient populations and looking at the payer’s net value to the product. We can then compare payer rejection reasons to the targeted and non-targeted physicians. 

Linkage case study review

Some of the benefits discussed here are represented through a recent case study conducted on the integration of an IQVIA longitudinal access and adjudication data (LAAD) with a specialty pharmacy and its digital app.

The client was launching a digital therapeutic with a companion app. While the client had visibility into the patient's activity on the app and the distribution of the product through their contracted specialty pharmacies, there was no visibility into the patients’ activity prior to initiating therapy or any visibility into the comorbidities or concomitant treatments the patients could have. In other words, there was a lack of visibility into the complete patient journey.

With IQVIA’s approach, we worked with the client to utilize IQVIA’s proprietary tokenization engine to receive specialty pharmacy shipment data, as well as the data captured by the companion app. That data was then linked to IQVIA's LAAD data via a common patient ID. We further performed a re-identification risk determination and implemented a de-identification strategy that ensured a minimum probability of re-identification, thereby allowing the client to receive the data and perform any number of analytics. 

As a result, our client was able to receive a quarterly dataset of third-party specialty pharmacy data linked to LAAD data, as well as the companion app usage data. To leverage this integrated dataset, the client was able to gain better insights into the patient journey prior to initiating therapy. In addition, the client gained incremental insight into patient outcomes post-therapy initiation. 

It is clear how properly linking and leveraging patient data can help deepen the level of insights your organization gains from the data. We dug in deeper to this topic at the recent Fusion 2021 conference; if you missed this year’s sessions, click here to watch them on demand!