DataOps can help heathcare organizations use modern data analytics practices and drive sound business practices that effectively reduce costs and increase revenues.
Healthcare organizations are grappling with data-related issues. The inability to handle large volumes of data and derive real-time insights is preventing them from operating at the highest levels of efficiencies. With data residing in both internal and external systems, extracting, integrating, and standardizing the data is an ongoing challenge. Budgetary constraints and staffing issues add to the complexity, as it calls for resources to monitor and manage the integrations. Healthcare organizations are bearing the brunt of such mismanaged systems. A use case in point, a version change in a source system that doesn’t integrate in real-time can cause critical billing data to go missing. This could cost the hospital significant revenue leakage in the form of missing reimbursements from late filing or, at the very least, delay in cash flows. All of these issues can be addressed with the adoption of DataOps.
DataOps is an innovative breakthrough in data management. Organizations manage and operate data for healthcare organizations rather than just engineering and monitoring the data. This allows them to utilize modern data analytics practices and drive sound business practices that effectively reduce costs and increase revenues.
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DataOps
At a macro level, DataOps focuses on automated processes, continuous data flow, and self-service portals for modern data analytics. It is a paradigm shift from the traditional world of DevOps. Rather than relying on data infrastructure to supply descriptive analytics, DataOps uses processing tools to monitor and continuously learn from data patterns and detect changes, to self-correct. This enables enhanced analytics (predictive and prescriptive), which equips businesses with the right information to make real-time business decisions.
How does one implement DataOps?
The core of building a DataOps program relies on three key ingredients: continuous development, continuous operations, and continuous data flow.
1) Continuous development: This looks for repeating patterns to identify data changes and make course corrections necessary to protect the integrity of data and the processes.
This is a marked shift from the traditional programs, which consist of static integration engines that are set up for each instance and require manual intervention to respond to version and data schema changes. The new technological advancement of DataOps has allowed greater freedom from these manual processes and increased data quality. Data integrations are built to automate and reuse data processes that adjust to variations to keep the data pipeline operating at the highest quality and efficiency levels.
2) Continuous operations: This consists of continuous monitoring, data drift identification, and the application of machine learning to identify and respond to operational data problems.
- Continuous monitoring provides tools that allow the use of metrics that can be used to monitor the performance of the data operations processes. These exposures can perform a health assessment and automate tasks to make necessary course corrections.
- Data drift identification allows operations to respond to schema and version changes without manual intervention.
- Data operations utilizing machine learning include training the data to provide insight on patterns and allow prescriptive and predictive analytics along with real-time data processing to provide modern business intelligence to drive sound business decisions.
3) Continuous data flow: This is the infrastructure needed to handle large amounts of data. Traditional methods utilizing multiple technology stacks are costly and difficult to maintain. A data marketplace solves those problems by streamlining data processing, alerting end-users when new data is available, and creating metadata management operations. Immediate benefits of these processes include automation of manual processes, ensuring business transparency, and enabling metadata for wider usage among business partners.
How DataOps can play a significant role: Day in the life of a Provider
Today’s healthcare organizations typically operate multiple disparate systems, not the least of which includes the typical complex enterprise health records platforms. Clinics and Physician practices utilize Electronic Medical Record systems, while mental health systems use behavioral management health systems.
How DataOps can help:
Self-Sustaining single source of data: Once data is centralized into a single location, the DataOps product would automatically detect and respond to data changes from the integrated systems. Onboarding new integrations would be easily automated and would streamline data management within the health system allowing for data to be viewed across the entire organization.
Improving clinical staffing optimization: By analyzing past clinical staffing data and comparing past patient demand, DataOps can use predictive modeling to project future staffing needs against anticipated future demand. This modeling can be accomplished by:
- Obtaining a past view from the data marketplace that compares how demand matches capacity
- Creating future predictive models based on
real-time data
- Create future predictive patient volumes based on historical data volumes measured over time while accounting for fluctuations such as those caused by seasonal demand and type of procedure.
- Create future staffing allocation models to show availability based on future patient demand.
Providing these predictive models allows the hospital to ensure that daily staffing levels are optimized. This optimization can decrease cost from overstaffing and increase patient satisfaction in those cases where clinical areas are typically chronically understaffed.
Conclusion
In summary, as many healthcare organizations are on the path of data transformation programs, they will need to include DataOps as an integral component of the overall digital strategy. It is a transformative solution, which, when implemented right, can respond to ever-changing requirements needed to run organizations most effectively.
