In this 6 A's blog series, we have been outlining our proven process for organizations to successfully re-architect their care delivery infrastructure to adopt new, value-based payment and care delivery models.
The first four A's entail: the acquisition of data, aggregation of data, access to data and adoption of system components. We now move on to the 5th A – a fundamental component for a successful ACO – analytics.
There are many issues that drive the need for analytics and make it a key component of a successful ACO: mining data for views into population health, finding the actionable insights that can drive improvements of quality and efficiency, predicting outcomes, keeping up with the ongoing and ever-increasing regulatory reporting requirements – just to name a few. Leveraging data-driven intelligence to improve care delivery is also something that well-constructed ACO IT infrastructure is uniquely able to do. With solid data acquisition and aggregation, comes the ability to learn from and act on this data in a meaningful way.
The essential IT infrastructure requirements for ACO data analytics include:
- All of the foundational data gathering, normalization and repository creation components we have discussed so far
- Solid business intelligence tools that not only allow analysts and non-technical users to mine data for useful insights, but also automatically generate the core reports and dashboards that deliver data to the point of impact
One of the key advantages in taking the “6 A's” sequenced approach as opposed to implementing a stand-alone analytics solution is the ability to analyze real-time clinical data. Traditionally, analytics tools, especially those used by payers or across multiple entities, have relied on claims data. While this data is broad-based, it lacks the richness of clinical data available by acquiring data from EMRs and patient devices, and it is not timely enough to do much useful predictive analysis.
Conversely, when the acquired data comes from multiple clinical data sources in addition to claims and is aggregated in a structured clinical data warehouse, the analytics are much more powerful. As an example, one of Orion Health's clients, a cross-community organization acquiring clinical data from the large majority of healthcare organizations in its jurisdiction, uses analytics to predict whether a new inpatient admission is likely to be readmitted within thirty days and which patients are likely to become high cost patients in the next six months. Action, the focus of our next blog, can then be taken in order to prevent such undesired consequences.
For regulatory reporting purposes, ACOs need analysis and reporting tools that automate the process of staying up-to-date on the latest requirements and meeting them in a timely fashion. By working on this second to last step, we advance to our ultimate A: action – the step that makes your ACO flourish. Stay tuned!