4 keys to building patient-centric commercial data warehouses

Patients face many challenges as they seek treatment. Many struggle to be accurately diagnosed. Once they are diagnosed, navigating reimbursement, finding infusion centers, filling prescriptions, and adhering to therapy amid the many other challenges of life can be overwhelming. Life sciences companies are well-positioned to help. 

Further, we know that HCPs want brands to be patient centric above all else but that many don’t believe companies are delivering on patient centricity. While companies talk the talk of patient centricity, are they really walking the walk? To find out, look at their data warehouses. Pharma leaders know they need to make their data warehouses more patient-centric but sometimes struggle to transform traditional HCP-centric data warehouses. It’s undoubtedly a challenging undertaking but one well worth pursuing.

HCP-centric data warehouse
Patient-centric data warehouse
Data organization
Volume-focused: Data is organized around volume-focused metrics such as new patients, dispenses, referrals, new prescribers, and high-volume prescribers.
Granular: Data is organized at the most granular level possible to enable patient analytics (e.g., calculating time from referral to ship, understanding how long patients stay on therapy, uncovering challenges related to adherence).
Data volume
Less data: HCP-focused analysis requires less granular data. As a result, the overall volume of data is lower.
More data: Patient analytics requires companies to organize more data to facilitate granular tracking.
Data refreshes
Weekly: There are comparatively fewer changes to track related to HCP behavior on a day-to-day basis, so weekly data refreshes are usually sufficient.
Real-time: A patient-focused effort requires real-time data updates in order to successfully track dynamic patient journeys and leading indicators. Real-time insights help commercial leaders and the sales team take appropriate action at the right time.
Prescription volume: In an HCP-focused commercial effort, everything points toward prescription volume. The KPIs a company sets follow suit.
Patient characteristics: KPIs are patient-focused and related to characteristics such as prior therapies, treatment compliance and time on therapy.
Patient-level insights
Limited: Companies focused on HCP-related metrics often don’t prioritize uncovering patient-level insights from patient services teams.
Embedded: Insights from a company’s patient-services team are embedded in the company’s data infrastructure and therefore help inform outbound action.
Security and compliance
General data security: While companies must ensure their HCP-centric data warehouses have robust general data security to prevent unauthorized access and information loss, there are no PII concerns with HCP-level data.
Patient privacy-focused security: Patient-centric data warehouses require a much higher threshold of data security than their HCP-focused counterparts. Companies must put in place appropriate policies and controls to protect patient information and ensure patient anonymity.

Data fuels all brand activity. It helps companies set strategy, deploy promotion, measure impact and optimize. And it’s therefore crucial to helping companies become more patient-centric. It’s essential that companies acquire structured, anonymized patient-level data alongside unstructured feedback and information from patients – and use this holistic data to uncover patient-level insights. Without this data foundation, companies can’t build patient-centric workflows and operations.

The risks of the status quo

Life sciences companies often look to their data warehouses to retain, organize, and provide access to data on prescription volume from HCPs or claim counts from payers. But how can they drill a level deeper and understand the patient journey at a granular level?

When companies don’t build patient-centric data operations, they miss out on valuable insights into the patient journey that can drive commercial effectiveness. These lost insights prevent a company from understanding the nuances of the patient treatment journey (for example, why a patient falls off therapy) and limit their ability to intervene at the right time to improve patient outcomes. Without a patient-centric lens on data, companies will lack knowledge that can help them reach patients in need, especially in the specialty drug space where patient counts are smaller and treatment paths are more complex and meandering.

In an environment marked by new launch and competitive pressures, companies should strive to break out of traditional approaches and put patients at the center of their commercial efforts. Doing so will help them better support patients throughout their treatment journeys, engage more effectively with physicians, and build sustained brand loyalty.

The implications of reorienting the data warehouse around patients will extend across the brand lifecycle. For example, take sales force sizing and customer targeting. Traditionally, companies rank HCPs by volume and claim counts. But if a company understands the patients it is targeting, it can build indexes around specific patient characteristics and evaluate HCPs through this patient lens. The opportunities for granular insights and engagement expand as a company delves deeper into patient data.

The data warehouse can lead the way toward patient-centric commercial operations. But how can companies transition from building traditional HCP-centric data warehouses to patient-centric warehouses? Here are four keys:

1. Establish patient-centric goals

One fundamental requirement is a shift in thinking. Historically, those accustomed to building HCP-centric pharma data warehouses made prescription volume and account performance their primary measures of success. Making patients the centerpiece of a data warehouse calls for data collection goals, processes, evaluation, and analytics that surround patients across every facet of their care and over time.

Patient centricity starts at the top. If a company’s C-suite is asking about account-based performance before patient outcomes, the company’s brand teams will orient their focus toward HCPs and accounts. Companies therefore need to take a hard look at the metrics on which they are focused. Start implementing patient-focused KPIs at the onset of data warehouse planning. Insights from a patient-centric data warehouse should shed light on things like:

  • Is commercial success defined by positive patient outcomes?
  • Is the treatment reaching the intended patient segments?
  • What hurdles do patients face related to starting therapy?
  • When are patients dropping off therapy?

Putting in place KPIs that measure successful treatment outcomes can help organizations move toward patient-led commercial strategy development. When leadership sets the tone on patient centricity, the focus on patients will permeate the rest of the organization and speed a company’s advancement toward patient-centric commercial operations.

2. Collect a robust set of patient-level data

Understanding your patient population is the obvious prerequisite to setting up a patient-centric data warehouse. To better understand patients, companies must first get patients to agree to share their data and information. Without this consent process, companies risk noncompliance with privacy regulations and limit their ability to engage with and help patients.

Then, a company needs to collect data that covers as much of the patient journey as possible. A comprehensive set of anonymized, tokenized data covering the patient journey will give the company insight into a patient’s path to treatment, treatment sequences, treating physicians and more. Companies should marry anonymized EHR- and claims-based patient-level data with insights into the patient experience (based on surveys and observational studies). This mix of structured and unstructured data will help companies gain a holistic view of the patient experience and thereby enable improved support.

Collecting this data should enable the company to generate insights that help it better serve patients and their care teams.

Examples of

patient-level data types

Structured Data

Specialty pharmacy (dispense, status, referral forms)


Lab results

Copay card enrollment and claims


Social determinants of health

Clinical trial outcomes

Patient-reported outcomes

Patient registries

Unstructured Data

Social media

Online forums

Patient support case notes


EHR/patient charts

Patient market research

Video/audio recordings

Faxed forms

There are many angles to think through on the data front. It’s important that companies put in the time early to wrap their arms around their use cases and the variety of data sets available. It’s also important to develop a strong point of view on what insights they need and how they plan to access those insights (via data acquisition, hub engagement with patients, technology investments, and more).

One note is that companies can struggle to collect patient-level insights when their products flow through distributors. There are ways to get around these challenges and uncover patient-level insights. But it requires a bit of extra planning and effort that companies in this situation should consider.

3. Explore novel ways of managing and sharing data

When a company breaks out of the bounds of an HCP- and account-focused data warehouse, the potential for innovation increases. For example, companies can explore tokenization to facilitate the compliant use of end-to-end patient data (potentially starting as early as the clinical trial stage).

Data clean rooms can unlock data sharing – with other life sciences companies and across third-party vendors managing patient service or outbound campaigns — and allow various teams to access patient-level insights in a compliant way. To fully capitalize on the use of data clean rooms, a company should put in place scalable and open data analytics technology as the backbone of its data operation.

4. Sync up with patient-facing teams

Augment data collection by equipping those interfacing directly with patients to collect information that sheds light on treatment journeys. For example, when a patient is starting therapy (or refilling), a patient services team can ask them a handful of questions related to prior therapies, experiences with those therapies, reasons for discontinuation of past therapies, and more. It’s important to ensure these teams collect information from patients in a consistent manner and home in on the most essential pieces of information.

From there, companies should put in place feedback loops so that insights into patients’ treatment experience and disease-related support needs influence the company’s patient-centric initiatives.

Focus on patient-centric data operations early in the product lifecycle

The reality is that once a company sets up a data warehouse, it is difficult and painful to rebuild it. Therefore, it’s crucial that a company puts patients at the heart of its data strategy from the start, well before launch. Companies should think about data comprehensively and start early in the brand lifecycle to construct a data analytics foundation that ties together (in a compliant way that includes thorough patient consent processes) patient-level medical, clinical and commercial data – and the resulting analytics. This early planning is especially critical for resource-constrained emerging pharma companies.

The data is there. The technology is there. The onus is on life sciences company leaders to push their organizations out of their HCP-centric comfort zones and tap into granular patient-level insights that bring to life the many dimensions of unmet need for serving patients. These insights can empower companies to better serve patients and improve outcomes, which is their ultimate mission.