As the proliferation of data collection and utilization continues throughout the healthcare industry, common problems related to poor data quality are also frequently exposed. The more manufacturers rely upon data to make critical business decisions, especially those that affect patients, the more carefully that data needs to be collected, stored, managed, and analyzed; the data must tell the patient’s “story” analytically.

It’s becoming increasingly evident that the best way for manufacturers to avoid the misuse of data, while maximizing the effectiveness of the data, is to consult and contract with a data partner who possesses the appropriate expertise.

In part one of this series  on data contracts, we discussed how developing a data strategy serves as the foundation to forming partnerships. Here, we’ll address the key mistakes that occur with misguided data, the components to beneficial contracts (including contract language), and provide an overview of how specialty consulting services can assist data analytics when the right partner is chosen.

Mistakes to avoid

Based on consultant experience at IQVIA, one of the more common data-related errors is misstated patient count. This could be due to misunderstanding how patients travel through the network or the patient data not being tokenized or leveraged longitudinally using a HIPAA-certified, de-identification engine; in other words, the manufacturer loses visibility into patients as they move through channels. Another example is a lack of insight by way of status code utilization when there isn’t enough granular information, or when the patient’s experience doesn’t make sense depending on the supplier’s reports. Related to these examples is also a lack of timely, actionable insights required by any field reimbursement manager. If the patient journey and timing of data reporting is not well understood, you may experience gaps in data. These gaps will cause the end user to struggle to piece together the disparate information into a concise, accurate story.

These issues could have significant consequences to your business and brand, most notably, in the form of an inability to recognize market conditions or, even, to lose patients and associated revenue. An inability to accurately forecast demand or to effectively enforce a performance-based contract with a vendor could also result in loss of confidence in data.

Based on years of experience, IQVIA has helped many clients navigate these potential data issues and have developed some key best practices to avoid them.

Benefits of well-established partnerships

A valuable data aggregation partner offers support to data strategy in multiple ways:

• A data aggregation service provider should become a true extension of the organization. A key consideration when selecting a partner is to not underestimate the complexity of integrating multiple technologies to fit business needs and the added cost that could be associated if multiple types of providers are chosen. Many different types of valuable data sources can be fed into an aggregation system, and once those datasets are cleansed, validated, and aggregated, they can be transformed to provide important insights.
• The recommendations that an aggregator delivers on your behalf should instill confidence in the insights for such areas as brand and sales operations. Many who aggregate data and offer solutions to manufacturers are similar in nature. Confidence in the data received and validation is crucial to gaining visibility into the patients. Contemplate what an aggregator can do for the organization beyond its platform capabilities.
• Experienced aggregators should also offer recommendations for all areas of a data model, data strategy, and program needs. Given that the aggregator has gained experience while working on several launches, the aggregator should be able to assist the organization by influencing collaboration with its specialty providers.

Negotiating and writing the data partner contract

Specific contract language sets expectations for the manufacturer and details how the aggregator will measure those expectations. During contract negotiations between data suppliers and the manufacturer, IQVIA consultants ensure that requirement changes made by the specialty data provider will not negatively affect the reporting of important data points. We also dive into status code mapping, an activity during the advisory phase that creates a standard list of master status codes that are expected to be reported to drive more valuable insights, as well as the capabilities and limitations of each data provider. Our teams educate program participants and business stakeholders on matters such as patient tokenization and HIPAA consent management and help to draw connections between data availability and business use. Contract negotiations can be long-winded but are especially important to ensure that the “data lab,” or all critical data elements, requirements, essential values, and any unique business roles, are correctly captured.

Data contract language should include aggregator-specific requirements around system implementation and file transmission, as well as recommendations for error resolution, such as metrics for key performance indicators (KPIs) and service level agreements (SLAs), otherwise known as scorecarding. Scorecarding typically measures three core elements of data quality: timeliness, accuracy, and completeness — the measurement of critical or required data elements being reported timely and accurately. Requirements must be measurable by structuring SLAs to reflect compensation expectations and payment calculations. An example of a KPI is “time-to-fill,” or how long it takes for patients to receive products or services after a hub or provider has received a prescription or enrollment form. Understanding this metric helps manufacturers investigate, optimize, and improve efficiencies.

Once a data aggregator is confirmed, it is also crucial to capture specific platform requirements, such as secure file transfer protocol, file type and frequency, and legalities needed to support a de-identification engine process — all of which are necessary to support data delivery.

Depending on the nuances of your patient journey or data flow model, there could be data points that are necessary to capture at a certain time from a certain entity to support your business. Strong contracts result in strong data quality, which equates to strong insights.

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!