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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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

  • 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:
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

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

  • 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.


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.