As we covered in our first blog in this series on health care data, leaders across the industry see immense opportunity in data and are investing in efforts and companies to support their goals. At the same time, health care data is notoriously difficult to work with, preventing progress on many shared and organizational goals (and perhaps part of why the data vendor market is so large and diverse).
In this blog, we will outline the key problems with health care data and demonstrate the challenges these problems cause for cross-industry leaders.
Health care's 3 key data challenges
1. Clinical data is unstructured, inconsistent, and often outdated.
Health care organizations increasingly seek to conduct analytics on their clinical data to improve clinical protocols and business development. But clinical data is first and foremost a record of patients' health and care that supports clinicians' decision-making at the point of care. The language that clinicians need—detailed and context-specific—is often misaligned with the data tracking needed to support large-scale analysis—structured and uniform.
At the same time, the process of aggregating and cleaning clinical data can often take months, precluding real-time decision making based on the data for efforts like clinical standardization and risk stratification. This generates a cost: our inability to adapt clinical protocols in real time worsens outcomes and increases cost of care for the patients that could have benefitted from improved protocols and processes.