The healthcare industry has always been data-intensive, but the emergence of electronic health records (EHRs), wearables, and other digital health tools makes data management more complex than ever. In the rapidly evolving healthcare landscape, leveraging vast amounts of clinical and financial data is essential for healthcare organizations to solve their business challenges.
However, simply amassing data is not enough; healthcare organizations need to develop a sophisticated data strategy to unlock the full potential of their data resources.
Step 1: Organize Around a Source of Truth
Data holds immense value and presents a profound challenge within healthcare organizations. Currently, the healthcare industry is responsible for generating around 30% of the world’s data volume. It is projected that by 2025, the compound annual growth rate of healthcare data will reach 36%. This growth rate surpasses that of manufacturing by 6%, financial services by 10%, and media & entertainment by 11%.
Consolidating data acquisition, curation and provisioning to create a single source of truth is the first step in developing a modern data strategy for healthcare organizations. By consolidating data from disparate sources, organizations can eliminate data silos, reduce redundancy and establish a more comprehensive view of their operations.
Step 2: Establish Uniform Governance
To derive meaningful insights from consolidated data, healthcare organizations must establish uniform governance and glean data insights from analytic platforms. Data governance policies ensure that data is accurate, secure and accessible. By establishing uniform governance policies, healthcare organizations can ensure that data is consistent across the organization, regardless of the source.
Analytic platforms can provide healthcare organizations with a unified view of their data, allowing them to derive insights across multiple domains, including clinical, operational and financial. These platforms enable organizations to perform complex analytics on large datasets, identify trends and predict outcomes. For example, predictive analytics can identify patients at risk of hospital readmission, enabling proactive interventions to prevent readmissions and improve patient outcomes.
Step 3: Adopt Modern Data Architecture
Traditional data warehouses are not designed to handle the volume, variety and velocity of data generated by today’s healthcare organizations. To keep pace with the increasing demand for data, healthcare organizations must adopt modern data architectures with next-gen data management capabilities. This change often includes upgrading data warehouse concepts to data lakes and leveraging cloud-based computing for faster data processing, scalability and agility.
Comprehensive and centralized data architectures should support all data formats, including structured, semi-structured and unstructured data, along with data quality tools, processes and partitioning.
Step 4: Implement an Enterprise Analytics Hub
In order to convert data into meaningful analytics that can be used across the enterprise, healthcare organizations should consider centralizing or democratizing an enterprise analytics hub. This feature enables front-end analytics and visualizations that support a full spectrum of analytics use cases, from descriptive to predictive and prescriptive.
Enterprise analytics hubs enable healthcare organizations to share data across domains and derive insights that can drive clinical, operational and financial performance goals. For example, cross-domain scorecards can provide a holistic view of key performance indicators (KPIs) that help operational leaders make real-time decisions on patient care. Since the scorecard is based on structured and well-defined data (refer to the second and third steps above), there should be higher analytic quality and more time spent taking action rather than debating the data.
Step 5: Simplify and Streamline Data Access
With so much data available, it can be challenging to enable access across a healthcare organization, especially in light of regulatory compliance concerns. To overcome these challenges, healthcare organizations can democratize data through APIs or other connectors to simplify data access and provision data based on role.
Simplified data access, often known as democratized data, facilitates knowledge sharing across teams. It also enables healthcare organizations to leverage analytics by unifying and securing different data sources.
Advanced Data: Support Unstructured Data Processing
While it is preferable to have discrete data, there are many situations in healthcare where data is presented in unstructured formats, such as documents, images, audio files and videos. Processing unstructured data presents unique challenges which can be overcome by leveraging modern techniques such as OCR, imaging, genomics, NLP and AI/ML.
If the above steps are tackled appropriately, healthcare organizations should be able to unlock the full potential of unstructured data, incorporating new findings into analysis.
Take the Leap–Sooner Rather than Later
It is estimated that healthcare data in the US has exceeded 2,000 exabytes1 with growth outpacing that of many other industries. Additionally, healthcare data continues to be one of the most highly protected and regulated categories of data worldwide.
Considering the relative value and compliance requirements associated with healthcare data, it is imperative that organizations of all sizes move to modernize their data strategy and adopt these recognized standards in data architecture, processing and transformation.
How can Populi support your evolving data strategy?
As aggregators of healthcare data, we can be your one-stop shop for external claims and consumer data used across strategy, marketing, business development and strategic finance teams.
- Have a centralized data model? Populi leverages the most optimal setup for data liquidity, ensuring you have direct access to licensed data assets and can use them within your organization’s data environment.
- Want to leverage data within your internal applications? Populi has a set of developer APIs ready to connect, so you can use our data and analytics when, where and how you need them
- Looking for speed-to-value analytics? Populi offers out-of-the-box analytics to help you in your data governance and front-end analytics journey by leveraging our best practices, data dictionaries and other relevant documentation and discovery dashboards.
Coughlin S, Roberts D, O’Neill K, Brooks P. Looking to tomorrow’s healthcare today: a participatory health perspective. Intern Med J. 2018 Jan;48(1):92-96. doi: 10.1111/imj.13661. PMID: 29314515.
Lo, D., et al. “Bytes to Bucks: The Valuation of Data.” HealthCare Appraisers. Aug 1, 2019. https://healthcareappraisers.com/bytes-to-bucks-the-valuation-of-data/
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