Transforming Healthcare Operations Through Modern DataOps

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Medicine doctor hand working with modern computer interface as medical concept

Photo credit: Depositphotos

By Jon-Michael Smith, Healthcare, Americas Practice Leader, Qlik

Health systems generate an abundance of data that has the potential to significantly improve hospital operations, resource allocation and strategic decision making—all contributing to overall better care delivery. But, if this data is siloed on different platforms and in different formats, it is virtually impossible to use for analytic purposes. During a time when it is crucial for hospitals to be continuously modernizing IT, while also staying cautious of fluctuating resources and personnel, implementing data analytics into all areas of operations should be the first stepping stone. Enter modern DataOps. 

Data operations or DataOps is an agile, process-oriented approach to improve the quality and reduce the cycle time of realizing value from data analytics. Like its better-known sister concept DevOps, it removes friction and accelerates how organizations deliver and maximize the value of key IT assets — in this case, data. DataOps is truly a strategic mindset that encompasses people, processes and technology to streamline decision-making.

Implementing a modern DataOps strategy can transform operations and provide exceptional value to many areas across the health system. Hospitals can start with the three data types below:

Patient health data

The most widespread example is an Electronic Health Record (EHR), a digitized and systematized collection of patient-centered data. With the ability to conduct analytics on patient health data, doctors are empowered to make more informed clinical decisions about specific patients, by allowing them to look at more of an individual patient’s historical data, as well as draw larger demographic insights from the collection of many patients’ data. It also enables them to make clear comparisons between separate data sets, forgoing the need to manually look through all sorts of sources to find this information. All of this equates to better patient outcomes and cost savings.

Real-time operations data

A common example of this is healthcare staffing portals. This type of data allows leadership to conduct more intelligent staffing — such as triggering more nurses only when they’re needed — and as a result, doing more with less. Gaining this visibility into whether a department is appropriately staffed (not under or overstaffed) in real time can yield tremendous cost-saving benefits. This can also indirectly help improve staff workloads, job satisfaction, reduce turnover, and prevent burnout. 

Claims data

Also referred to as administrative data, claims data encapsulates large data sets including ​​level of diagnosis, level of treatment, amount billed to insurance versus amount billed to patient. As one of the biggest datasets produced and most core to health systems operations and financial health, it should most definitely be prioritized for analysis. 

Other types of data types can include fraud prevention, clinical trials, and survey data. While each of these separate data types alone are insightful, they are most powerful when utilized together. Comparing each type of input can give health system leaders a more holistic view of their organization and empower data-driven decisions across every department.

Improving patient outcomes while minimizing costs is a complex equation. Luckily, a modern DataOps strategy could provide a solution. By providing visibility into every aspect of hospital operations, leaders can uncover new ways to reduce costs and optimize payments, while delivering better patient outcomes.