5 Fundamentals of Data-Driven Healthcare Delivery

by | Sep 26, 2022 | Healthcare, Life Sciences, Master Data Management

Data-driven healthcare in practice.

While digital transformation and a shift towards data-driven decision-making surged in many industries during the last two years as organizations adapted to pandemic challenges, the healthcare industry remains far behind in adopting digital technologies that enable effective data use. Nevertheless, if the industry closed this gap, it would substantially improve patient outcomes, efficiency, and overall profitability. Experts agree that data-driven healthcare is the way forward for organizations large and small throughout the industry. 

Healthcare data management and interoperability products had a global market value of $2.5 billion in 2019 and, by current estimates, will reach $4.9 billion by 2026, demonstrating a sustained compound annual growth rate (CAGR) of 11.2% for the duration of the forecast period. As more data-focused products and services become available to healthcare organizations, leaders and stakeholders must develop a keen understanding of what data-driven healthcare means.

In this guide, you’ll learn the fundamentals of data-driven healthcare and how organizations can leverage data to deliver better patient care and reduce costs.  

Key Takeaways:
  • The healthcare industry has lagged behind others in the implementation of data-driven processes.
  • Growing investment in data management solutions for healthcare is bringing better technologies to the market, enabling more organizations to adopt data-driven practices.
  • Delivering data-driven healthcare requires coordinating people, processes, and technology to implement sequential steps, culminating in better patient outcomes and reduced costs. 

    What Does it Mean to Be Data-Driven?

    In the modern digital world, data has become a resource with a total market value exceeding that of oil. Yet like oil, data requires processing – a transformation from its raw form – to become useful and valuable. Data-driven organizations use analyzed data from various sources to guide their decision-making processes. 

    Organizations can employ data-driven processes at all levels with proper data visibility, from corporate strategies and goals to daily individual tasks. In practice, data-driven processes rely on four kinds of analytics. 


    Four types of data analytics.
    Image Source: https://publichealth.tulane.edu/blog/data-driven-decision-making/
    • Descriptive Analytics:  Describes behaviors and patterns of activity in an organization’s historical data about their customers or clients
    • Diagnostic Analytics: Identifies causes for observed phenomena such as sudden increases or decreases in categories like sales, memberships, or satisfaction
    • Predictive Analytics: Assesses probabilities of future behaviors based on past trends
    • Prescriptive Analytics: Applies analytical tools such as AI and machine learning to data to model future outcomes

      5 Fundamentals of Data-Driven Healthcare

      Becoming data-driven is a complex challenge for organizations in any industry, and only a small minority of decision-makers consistently rely on data-backed processes rather than hunches and traditions. As healthcare is a unique industry with ethical and regulatory obligations that don’t apply in most business contexts, the transition to delivering data-driven healthcare can prove to be a steep curve. Nevertheless, organizations that commit to the process can begin with five fundamental steps.

      1. Remove Partitions Between Data Sets in Disparate Systems

      Healthcare organizations often rely on patchwork, legacy IT systems that store data on a mix of local and cloud servers and lack system-wide visibility. Heavily partitioned systems lead to poor data quality as inconsistencies and redundancies accumulate over time. Across industries, data management problems related to unintegrated systems have become endemic, with 91% of IT leaders reporting serious data quality concerns. 

      The solution for most organizations is to adopt a single platform and repository from which authorized users can access data in all other systems. Additionally, IT staff need to assess overall data quality and apply data management tools to reduce inconsistencies between systems. 

      2. Assess Analytics Maturity

      An analytics maturity assessment is a combined process of creating an inventory of all data handling methods in an organization and submitting that inventory to an analytical program – typically a proprietary software-as-a-service (SaaS) provider – to identify areas where automated data processes can replace manual labor. In healthcare, redundant manual data entry consumes valuable time and causes errors and duplicates, as much as 20% of the time in the case of electronic health records. (EHRs). Studies show that organizations in the top 5% of analytics maturity ratings outperform the bottom 5% by 60%. 

      3. Adopt a Data Governance Plan

      Data governance is creating data standards in an organization and ensuring that high-quality data is available to all authorized users. Data governance relies on the coordination of people, processes, and technology. Typical necessary roles include:

      • Chief Data Officer: An executive-level position responsible for the performance of the data governance program
      • Data Governance Committee: Sets policies and determines data authorizations
      • Data Stewards: Responsible for data quality and availability in individual data sets or systems

      4. Establish a Single Source of Truth for the Whole of Your Organization

      The primary purpose of adopting data-driven processes is to deliver better care and improve patient outcomes. To achieve that goal at the point of care – when individual healthcare professionals make decisions in the treatment of individual patients – organizations must create a consistent unified view of patient data and deliver that view to everyone responsible for care decisions. Comprehensive patient data will include:

      • Patient Health Records
      • State Health Records
      • Data from Medical Devices
      • Laboratory Data
      • Data from Medical Imaging Services
      • Any Data Gathered from Smart Phone Apps and Internet of Things (IoT) Devices

      In practice, developing a single source of truth – a master data set that takes priority over others – is the culmination of the previous three steps– integrating systems, establishing better data automation processes, and implementing data governance. Regarding technology, this requires the adoption of an integration platform with sufficient strategic data management capabilities. 

      5. Democratize Your Data

      Types of data visualization.
      Image Source: https://boostlabs.com/blog/10-types-of-data-visualization-tools/

      Data democratization has become a popular way to describe how effectively an organization distributes data to the typical authorized end user. It also refers to the quality of tools and user-friendly interfaces available to non-specialists to use data effectively in their work. Platforms with a wide variety of data visualization tools excel in this task, communicating meaningful data-backed insights to non-specialists in a simplified visual medium.

      Achieve Data-Driven Healthcare Delivery with Coperor by Gaine

      Coperor is an industry-first scalable, ecosystem-wide master data management platform that enables healthcare organizations of any size and technical sophistication to create a single unified source of truth, encompassing internal and contracted partner data.

      To learn more and schedule a live demo, visit Gaine today.


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