Without visibility into each individual patient access journey, your specialty brand cannot consistently intervene to help patients overcome barriers to therapy initiation and adherence.

A recent analysis of specialty patient sub-status data led by our Data Science team found that poor data quality is preventing specialty brands from accurately calculating baseline KPI metrics and acting on diagnostic patient access insights.

One notable finding in our analysis showed that leading Immunology and Oncology brands are receiving only 50-60% of the specialty pharmacy (SP) patient status updates they are contractually obligated to receive.

This blog post will examine three critical patient status data quality challenges and recommend solutions your specialty brand can implement to overcome these challenges and ultimately help patients start therapy faster and stay on therapy longer.

Missing Specialty Patient Status Updates

From a data standpoint, many patients progress through steps to initiate on therapy with no record of the obstacles they face or the length of time it takes to overcome these obstacles. When IntegriChain analyzed patient sub-status data across multiple Immunology and Oncology brands sharing four specialty pharmacies in common, missing updates was a widespread data quality issue.

Table 1 shows the percentage of updates specialty pharmacies actually reported for active patients and patients yet to initiate compared with the number of updates expected. Our analysis is predicated on a best practice that an SP should provide an update when a patient status changes or, at minimum, every seven calendar days.

Filling in the gaps that result from missing patient status updates is a considerable challenge to overcome. However, there are also issues with data that is reported.

Incorrect Use and Mismapping of Sub-Statuses

The patient status and sub-statuses being reported by specialty pharmacies are often used incorrectly in the context of patient access. Incorrect use of patient sub-statuses leaves specialty brands with too few details to determine where patients are encountering barriers as they initiate and try to remain adherent to therapy.

One data quality issue in this area worth calling out is poor utilization or “lazy” utilization of patient sub-statuses. When the majority of updates land in catch-all sub-statuses like Pending ⎯ New or Pending ⎯ Other, there is little a brand can learn about specialty pharmacy patient access.

A large specialty pharmacy in our analysis reported that new patients starting three Immunology therapies spent at least 88% of their time as Pending ⎯ New (see Table 2 above). Patients starting those same Immunology therapies through a medium-size SP included in our analysis spent 30-40% of their time in Pending ⎯ Other.

Patient sub-statuses are meant to provide a more granular level of detail for patient access. Routinely reporting catch all sub-statuses just serves to hide critical patient access details.

Modeling Patient Access

Patient access modeling is the process of defining each specialty brand’s unique patient access, informing business decisions to model unique brand patient access pathways. Brand-specific modeling drives all data-driven KPI metrics and insights reflecting a brand’s true patient access.

As an example, a specialty brand would want to define when patient access starts and stops. That decision will have implications for calculating critical metrics such as Time for First Fill. Figure 1 below illustrates a decision specialty brands face when a Cancelled status is reported in the middle of an individual patient access journey that eventually is reported as Active. The decision to treat this as 1 continuous patient access journey or 2 distinct patient access journeys can significantly change the Time to First Fill (TTFF) calculation.

A brand should consider mapping all patient access scenarios where data anomalies can impact the accuracy of metrics and calculations. When these anomalies are identified in the data, one business rule can uniformly correct for them.

Overcoming Data Quality Challenges

Unless specialty pharmacies report close to 100% of patient statuses completely and correctly or the specialty data aggregators shift their business model to something that resembles genuine data stewardship, specialty brands need solutions to overcome the data quality challenges described in this post.

IntegriChain delivers business process, analytics, and data modeling solutions to combat the problem of poor patient status data quality.

Monitor and Triage Status Update Completeness at an Individual Patient Level

IntegriChain runs real-time data quality analytics at the individual patient level. With our business rules in place to model patient access, not only are missing status updates identified upon receipt, misordered and incorrect patient access records are automatically corrected to enable accurate metric calculations and diagnostic insights.

We’ve also developed metrics like Status Completeness depicted in Table 1 above as tools specialty brands can use in business reviews to hold their specialty pharmacy partners accountable. Of course this type of metric is only effective when data quality and reporting requirements are spelled out in contract language.

Assessing data quality in real time, automatically correcting for erroneous patient sub-status records, and calculating data quality metrics all serve to improve the actionability of the data and facilitate collaboration with SPs.

Analyze Trends in Sub-Status Utilization with Specialty Pharmacy Operations

As we showed in the previous section, reporting sub-statuses alone without any oversight won’t accomplish the goal of actionable patient access insights. Specialty brands need a way to monitor how SPs are using Pending sub-statuses and whether or not a sufficient level of detail is being reported for Cancellation and Discontinuation reasons.

Monthly or quarterly analysis of sub-status utilization and reasons is a great data point to include in SP account reviews and serve to highlight coaching opportunities.

KPI Metrics & Analytics Configured to Reflect a Brand’s Unique patient access

The preferred patient access is unique to each specialty brand and largely depends on factors like therapeutic category, indication, route of administration, and side effect profile. Analytics and metrics must reflect a specialty brand’s unique circumstances and barriers to initiation/adherence.

Starting well before launch, and ideally prior to signing data agreements with SPs, a brand should write business rules that answer critical questions like when does patient access/stop start. Answers to these questions can then be used internally or by analytics partners to process data as it arrives from SPs and configure KPI metrics. IntegriChain has organized workshops with a number of specialty brands to review specialty pharmacy data contract strategies and develop data modeling for patient access barriers.  

Conclusion

Poor quality patient status data is a problem that’s likely to persist and all but cancel out a specialty brand’s ability to accurately calculate baseline KPI metrics, identify barriers in patient access insights, and take action to improve patient therapy initiation and adherence.

As specialty pharmacies and brands collaborate to improve data quality, an effective solution is one that combines real-time data quality analytics and data modeling to define unique aspects of patient access.  

For more information on specialty patient status data quality issues and solutions, watch the IntegriChain webinar The Current State of Specialty Patient Status Data and How Specialty Brands Can Improve Data Quality. 

Tags: patient journey, Patient Journey Analytics, Patient Journey Modeling, Specialty Data Aggregation, Specialty Patient Status Data