Last updated: October 19th, 2020
175 zettabytes in 2025 – that’s how large the global ‘datasphere’ will grow in the coming years, according to IDC, with healthcare data projected to outpace the growth of manufacturing, financial, and media data. While healthcare organizations excel at collecting ever-increasing amounts at data, many organizations have not yet perfected how to use this data to drive decision making.
Poor data quality, inefficient analytic processes, underutilized data, and a lack of interoperability each contribute to an organization’s analytic immaturity and slow progress toward data-driven patient care and operational insight. If you’re ready to make the transition, here are seven steps to move today’s healthcare analytics toward a more sophisticated data-driven, enterprise approach.
From Healthcare Data to Data-Driven Healthcare
Big data collection is not the same as data-driven healthcare. While the increase and availability of data will fuel a whole new era of fact-based innovation in healthcare, automation is required to streamline processes and clarify decision-making in a way that improves both clinical outcomes and operational agility. However, the Harvard Business Review reports: “For many companies, a strong, data-driven culture remains elusive, and data are rarely the universal basis for decision making.”
For healthcare organizations to evolve, there are seven steps toward data-driven healthcare that must be achieved:
- Eliminate disparate data silos: Healthcare organizations are still a patchwork of IT systems that lack the true interoperability necessary to fully leverage big data. Healthcare analytics is characterized by conflicting or missing data and slow manual processes. The lack of true interoperability between legacy platforms can put your patients’ health at risk. A decentralized IT network where data is captured but difficult to share makes for many moving parts that negatively affect the lives of patients, clinicians, and even the organization on the whole. For data to be truly interoperable, it must be gathered in a single repository, cleaned and democratized, and then made available at every level of decision-making in a healthcare organization.
- Assess analytics maturity: How is your organization currently leveraging the data you collect? The HIMSS Analytics Adoption Model for Analytics Maturity (AMAM) leverages a seven-stage tool that seeks to improve the business of healthcare by applying data beyond clinical decision support to the full operational improvement of an organization. From fragmented healthcare data collection at stage zero to personalized medicine and predictive healthcare analytics at stage seven, this model serves as a solid framework for IT leaders to begin to determine their analytics maturity.
- Align business goals with healthcare analytics: In the same way that a data silo fails to achieve the goals of an efficient healthcare analytics framework, collecting data and forming best use cases fails miserably if not designed with the organizational business goals in mind. While this may seem overwhelming at first, begin by supporting your business needs with a targeted solution to analyze data from one or two systems. As your use of healthcare analytics matures, you will move toward embracing data-driven decisions at all levels of the organization, from clinical, to business, to population health.
- Governance: While IT governance should be fairly locked-in, your ultimate goal is to use healthcare analytics to help both patients and your employees. Data-driven decisions can happen at all levels of your organization if you democratize the data and allow it to be used by everyone. To achieve this, ironically, you must be willing to establish standardization as a first goal to align business, clinical, and IT to become better stewards of healthcare analytics.
- Single source of truth: Collating all data into a set of reliable, standardized healthcare analytics metrics is the only way to achieve value-based care. Establishing that “single source of truth” from multiple data sources into a repository of actionable information may seem impossible. However, if you establish a baseline foundational data platform that collects, cleans, and melds disparate data into one dataset, you will have the jumping-off point for a truly data-driven healthcare organization.
- Create stakeholder-driven applications: Becker’s Hospital Review suggests an important point to consider when moving toward data-driven healthcare. “A common mistake as organizations develop data-driven processes is overfocusing on the selection of tools without corresponding investments in appropriate talent and processes to derive value from the tool.” One pitfall in the journey toward data efficiency is to be so enamored of the tool that you fail to account for the end-user. Organizations must leverage stakeholder-centric applications that can be used on the ground to inform daily executive and departmental decision-making.
- Democratize the data: The final step in the data-driven healthcare journey is to empower your employees to wrangle their own data. AI tools and data visualization can help healthcare workers leverage these tools on the ground but organizations can also reskill workers and retool processes to streamline efficiency around data capture and analysis. The goal is data democratization, where flexible, role-based, self-service analytics becomes the heart of your daily operations.
Data-driven healthcare, like the shared goal of quality care, is an evolving journey. This is not the time, however, for healthcare organizations to fall back on habit because the future alternatives look or feel risky. Becker’s Healthcare recently compiled data from other industries that have leveraged analytics into their daily operations. The research shows that treating data as the core of strategic decision-making can:
- Improve productivity
- Increase revenue
- Reduce operational costs
Healthcare data holds the key to better patient care quality, reduced readmissions, and overall improvements in outcomes. With the regulatory environment moving increasingly toward value-based care, organizations must move from collecting healthcare data to becoming data-driven healthcare organizations. The paradigm is shifting—will you be ready?
Organizations seeking to move toward true data-driven healthcare decision-making turn to ibi’s powerful analytics platform to gather, organize, and operationalize disparate data on a single unified platform. ibi provides AI-fueled data analytics to enable providers and administrators at every level in the organization.
Data-driven healthcare starts here. To learn how to solve healthcare data management challenges, get your guide.
Shawn Sutherland leads Patient and Member Outcomes at ibi. He has spent his career in leadership positions for organizations such as UT Southwestern Medical Center and Baylor Scott & White Health. He brings 20 years of experience, and a wealth of analytics, governance, and EHR knowledge, to ibi’s healthcare team. Follow him on Twitter @DataMedicine.