Patient Journey Analytics: KPIs, Dashboards and How to Build It

Patient journey analytics is the practice of linking longitudinal patient-level data (claims, prescriptions, specialty pharmacy, hub and EHR records) to measure how patients move from symptoms and diagnosis through access, first fill, adherence, switching and discontinuation, and where and why they drop out.

For a pharma brand it turns a slide of arrows into numbers a team can manage: how many referred patients actually start therapy, how many days access takes, which payer or specialty pharmacy slows patients down, how long patients stay on treatment and what they switch to when they stop.

This guide covers the stages of the journey, the KPIs that measure each one (with formulas), four dashboards every brand and patient services team should have, the data sources behind them, and a practical method to build the capability. It is written by a team that came up through pharma commercial operations consulting and now builds these systems for specialty pharma brands. If you want the platforms and AI tools that support this work, see our patient journey analytics platforms guide.

The first dashboard every brand needs shows the whole journey on one screen: how many patients enter at referral and how many remain at each stage, with the losses broken out. A Sankey view makes the largest leak obvious. In the example below, prior authorization loses more patients than any other access step, but the biggest single loss is discontinuation in the first six months.

Decisions it drives: where to put hub, field reimbursement and nurse resources; which stage to fix first; how to forecast patients on therapy from referral volume.

Adherence and persistence are different questions. Adherence (PDC or MPR) asks whether patients take the medicine as prescribed while they are on it; persistence asks how long they stay on it at all. A Kaplan-Meier persistence curve, split by a programme such as hub support, shows whether the programme is worth its cost. The PDC distribution shows how many patients sit below the 80 percent threshold, and hub-reported reasons explain the stops.

Decisions it drives: nurse and adherence programme targeting, copay assistance design, refill reminder timing, and the persistence assumption in the brand forecast.

No single dataset covers the whole journey. Each source sees part of it, with its own lag and gaps, which is why linking them is most of the work.

The sources are joined at patient level with privacy-preserving tokens, so the same patient can be followed across claims, specialty pharmacy and hub data without exposing identity. Prescribers and accounts are joined through a mastered HCP and HCO record. We describe the specialty pharmacy side in detail in our specialty pharmacy data analytics guide.

The two are often confused. Journey mapping is qualitative and describes the experience; journey analytics is quantitative and measures it. Most brands need both, mapping first and analytics to keep it current.

We are a senior-led team that builds the data layer and the dashboards: ingestion and validation of specialty pharmacy and hub feeds, patient linking, HCP and HCO mastering, the event table, the KPI dictionary and the dashboards your teams use every week. Our founder came up through pharma commercial operations consulting at ZS, so we start from the questions brand and access teams already ask.

In one engagement, a specialty therapy provider was rebuilding its patient access reporting by hand every cycle. We built clean identifiers and an MDM structure across sources, automated case pipelines and standardised reporting, and the team cut manual work by 60 to 80 percent with a clear view of the patient journey for leadership.

We work in the platforms you already run, including Snowflake, Databricks, Veeva, Power BI and Tableau. See also our pharma commercial analytics service and the wider pharma practice.