Population health has become one of the key priorities across healthcare. The question, therefore, is: How do you enable open access to data across entire healthcare systems to drive better insights and outcomes?
By Pritam Potnis, Senior Product Advisor, Healthcare, CSC
From a data supply chain point of view, this has presented a massive challenge. Healthcare organizations use various models to help each group discover patterns of behavior or trends. For example, one group might have an initiative to identify patients at risk of readmission for sepsis, while another group might be assessing the correlation between prostate cancer and smoking habits.
These are useful ways to look at data, find patterns and conduct analyses. The problem is each group creates its own models from scratch, manages its own operations and maintenance, and builds its own security model. The result is that data from multiple systems is duplicated in each of these models and can’t be shared easily across the enterprise.
These duplicative activities result in 70 percent of the time being spent on creating and securing data, leaving just 30 percent of time for analysts to actually work with the data and develop valuable population health insights.
If we add to that the enormous number of applications and IT systems that exist across a healthcare enterprise and which typically don’t communicate well with each other, the scale of data involved and the complexity of reaching across those different systems become abundantly clear.
Let’s take one aspect of healthcare: scheduling. A large hospital system might have many different scheduling systems, each handling different aspects of a patient’s care. If these applications aren’t seamlessly interconnected, it’s all too easy for conflicting appointments to be made. Now let’s add other applications, such as for orders and results, or care management notes. Without a way to connect those systems, it’s almost impossible for healthcare providers to gain a consistent picture of what is happening with that patient or for a data scientist to collect important insights on disease trends.
Driving Data Insight
The way to overcome these challenges is through mass homogenization of data across the care delivery network. This is made possible through an open data approach that enables collaboration and data sharing across the extended health enterprise. Such an approach needs to ensure that the data is complete so it can drive insight; that it is ubiquitous so it can be trusted; that it is secure so it can’t be tainted or abused; and that it is easy to access and simple for those needing the data for analysis, reports or dealing with patients to use.
So, for example, if a scientist wanted an app to analyze patient demographics, it would access the same data store as another app accessing scheduling information. The data all comes from the same source and is accessed through the same API gateway.
This approach has enabled one large healthcare network to strengthen interoperability, exchange healthcare information electronically, and produce insights that can be distributed and used across the enterprise.
With the adoption of an open data approach, provider systems can anticipate improved performance, productivity, greater flexibility and an openness that allows advanced functionality to be added in the future. We call it Open Health Connect.
Open Health Connect is a game changer as the industry shifts from episodic treatment to population health with a focus on improved care coordination. In such an environment, data transparency and easy access to clean and complete health information across the enterprise is critical.