In the real world, a patient's journey through the healthcare system is incredibly complex—health is, after all, affected by a varied combination of chronic disease, comorbidities, genetics, and the dynamics of the social environment. Our understanding of this journey, however, continues to rely on narrow slivers of a patient’s record and a view of healthcare built on availability. And it is this overly generalized view that informs the development of new products, services, care protocols, and policies.

For decades, the best way to bring a patient's information together to understand their journey through the healthcare system has been by manually incorporating primary market research, one-off chart reviews, and analyses of secondary data. These costly, time-consuming and manual methods often require reconciling clinical, financial, and transactional data that are scattered digitally over several applications and database servers and physically in the file cabinets of many provider organizations. The data often lacks population stability and loses visibility of patients as they "travel" across multiple services and care settings over time. Reconciling the data's bias and fragmentation also requires intensive manual intervention and interdisciplinary collaboration of subject matter experts.

The alternative—constructing journeys from actual patient-level data at scale—is significantly more troublesome. Robust data across payers and providers is limited; lives are poorly sampled, with no guarantees that the journeys are complete; and the lag between data creation and availability can be significant—often six months or more. Data sets may also be filled with discrepancies. On the rare occasions in which a patient's healthcare record is complete, specific provider, institution, and payer information is often suppressed—and crucial contextual information along with it.

As a result, it has become impossible for innovators and decision makers to accurately contextualize patients' lives to identify the largest unmet medical needs or to target interventions. Without shifting from a few high-level "personas" to in-depth microsegments, future behaviors cannot be accurately predicted. Without real-time data availability to track how new protocols and policies are performing in the real world, patients are prevented from reaching their optimal outcomes. Our ability to understand, act on, and predict based on healthcare data needs to significantly evolve.

Healthcare data is pointing in the wrong direction.

In our white paper, Healthcare Data: It’s Time for a Revolution, learn more about the problem of the “false north” in healthcare. The paper provides a deeper dive on the following themes:

• Today’s healthcare data fails to capture the full diversity of interactions patients have with the healthcare system, leading to incomplete information that can put patients at risk.
• Systematic selection and sampling biases have long plagued healthcare’s largest commercially available data sets, and analytics built on biased data sets can deliver misleading insights.
• If healthcare is going to achieve optimal outcomes for patients, data must be representative and complete.
• Healthcare data that features complete patient journeys will be able to power the highest value applications of AI in healthcare.

Learn more about the data revolution needed in order to find “true north” in healthcare.