Around the world, healthcare is coming under increasing pressure as expectations on health systems continue to rise and the cost of care continues to approach unsustainable levels.
In the US, there are mounting efforts to move from the traditional fee-for-service model to value-based reimbursement, where providers assume risk for the overall cost of care. Globally, health systems are striving to improve the delivery of care but are inevitably hamstrung by their inability to measure and track outcomes, ever-changing quality measures, and the processes they’re responsible for. At the heart of these issues and initiatives is the inability for healthcare organizations to effectively enable system-wide quality improvements and cost-reduction efforts.
Measuring care processes and outcomes across a health network is complicated by the fact that patients, especially those with significant health challenges, receive care from a variety of services within, or outside of, that network. Meaningful analytics is therefore impossible unless information from those various care settings can be aggregated into a single repository in which the analysis and measurement can be performed. While the digitization of health information is progressing at an impressive rate due to the increased adoption of EMR systems and the growing prevalence of devices that capture and measure health information, healthcare systems have failed to truly harness the value of that data, as it continues to exist in silos, which makes sharing difficult. And even when it is shared, it’s usually unaccompanied by any other meaningful information except that which must be supplied in order to comply with existing regulations and avoid financial penalties.
As a result, the majority of measurement done within healthcare systems today tends to be focused solely on problems that can be adequately analyzed from data from either a single or small number of source systems, and even those quality measures are flawed due to a lack of near-real-time clinical data. This typically restricts analysis to either (a) activity that occurs within a single care setting or (b) areas that can be adequately measured through the use of claims data which, despite providing limited insight into the nature of the care delivered, does provide a single source of information that covers a broad range of healthcare activity across a population. As healthcare organizations are well aware, this current approach leaves significant gaps in the insights they have available about their populations—care gap identification is plagued by false positives and lateness, predictive models fail to reach the levels of accuracy required to implement meaningful response programs, and siloed analysis fails to adequately identify the true causes of inefficiency in an overall health system.
These challenges are not insurmountable, and immense benefits will be realized once the challenges have been addressed, but it will require a new breed of technology to resolve it effectively. Specifically, tomorrow’s healthcare analytics platforms will need to:
- Address the evolving integration challenges presented by the healthcare environment
- Scale to effectively handle the increasing volumes of data being collected about a patient’s healthcare and status
- Be flexible enough to keep pace with the evolving quality-measurement needs of health systems
- Integrate directly into care delivery processes and workflows
- Leverage advanced analytics and machine-learning techniques to more accurately understand and predict outcomes in a healthcare environment and beyond
Orion Health made it a priority to approach each of these challenges in the design and build of Amadeus, its next-generation population health management platform. We’ll be telling you more about the different things identified above that an organization should consider in a series of blogs over the next few weeks.
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