How Dataops Can Facilitates Healthcare Operations
DataOps focuses on automated processes, continuous data flow, and self-service portals for modern data analytics at a macro level. It's a significant change from the traditional DevOps culture. DataOps uses processing tools to track and continually learn from data patterns and identify changes, rather than relying on data infrastructure to provide descriptive analytics. This allows for more advanced analytics (predictive and prescriptive), giving companies the knowledge they need to make real-time business decisions.
Many diverse technologies are used by today's healthcare institutions, not the least of which are the traditional dynamic corporate health records networks. Electronic Medical Record systems are used in clinics and physician practices, while behavioral treatment health systems are used in mental health facilities.
Here is how DataOps can help:
Self-Sustaining single source of data: The DataOps product can automatically identify and respond to data changes from the integrated systems once data is consolidated into a single location. New integrations would be quickly onboarded, and data processing within the health system would be simplified, allowing data to be accessed through the entire enterprise.
Improving clinical staffing optimization: DataOps can use predictive modeling to forecast future staffing needs against anticipated future demand by evaluating past clinical staffing data and comparing past patient demand. This modeling can be achieved by:
Obtaining a historical perspective from the data marketplace that contrasts how demand and capability are matched
Using real-time data to build future predictive models
Develop future predictive patient volumes according to historical data volumes estimated over time, taking into account seasonal demand and procedure type variability.
Make future staffing allocation models to demonstrate capacity based on patient demand in the future.
The hospital will ensure that regular staffing levels are optimized by using these predictive models. For cases where clinical areas are severely understaffed, this optimization will reduce costs from overstaffing and improve patient satisfaction.
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