When it comes to Business Intelligence and Analytics, people tend to think about big data, artificial intelligence, and data mining. This “silver bullet thinking” tends to be driven by much of the hype out there. The challenge is that none of it works without two key requirements:
- The data are accurate, reliable, complete and consistent with the proper relationships.
- The definitions for the data types, groups, cohorts, metrics, and benchmarks are clear and easily available throughout the organization – ideally in context of the associated dashboards, scorecards and reports.
PwC, in their “Top health industry issues of 2018” report, calls out that “An AI tool is only as good as the data it uses for decision-making. Companies should invest in finding, acquiring and creating good data, standardizing it and checking it for errors.”
Many healthcare organizations have a master patient/member index. However, most do not have a practitioner master file or a single source or list of all of their physicians. Many a brave soul has had the experience of showing attributed data to a physician only to have it quickly picked apart. Most physicians not only have great memories, but also have over a decade of formal training as scientists. Can healthcare organizations make decisions and changes without the support of our physicians and other clinical staff?
Almost all of us had the experience of being in a meeting where people have two or more answers to the same question, based on different, or even the same data. If we want to become a data driven industry, isn’t it important that our data be correctly matched, merged, cleansed and mastered?
- Analytics beyond the EHR
- Real life illustrations that define healthcare’s unique data challenges
- Data Governance – how to start, organize and build to sustain
- Leveraging governance to prioritize clinical and business objectives
- Enabling Technology – when to buy and when to build
Watch this space to find out more.