The demand for high-value analytics has outpaced the ability of many health systems to deliver valuable insights. Healthcare providers must create a business case for integrated data to reduce excessive waste, outdated efficiency measures, and unpredictable outcomes in patient care.
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This article is based on a healthcare conference presentation by Chris Harper, CHCIO, MBAi, MPM, Joint Chief Information Officer, The University of Kansas Health System and KU Medical Center titled, “Creating Your Business Case for Integrated Data and Analytics: Learnings From our Multi-Year Journey” at the 2022 Healthcare Analytics Summit.
The beleaguered healthcare industry continues to battle an economic downturn. From the rising costs of medical services and infrastructure improvements to a shortage of hospital staff and caring for underinsured patients, hospital leaders are overwhelmed as they shoulder the steep responsibility of enhancing financial performance without compromising patient care. What’s more, the shift towards telehealth appointments on a large-scale prompted by the COVID-19 pandemic highlighted inadequacies in healthcare providers’ access to and exchange of high-quality data analytics, particularly when caring for patients with complex or chronic diseases. Patients are also frustrated with inadequate access to their health records despite advancements in digital health technology.
Moreover, hospital analysts require more complex integrated data and analytics tools to support decision-making and to institute improvements that can directly impact patient care quality, operational efficiencies, and financial metrics.
In a rush to take control of their data, health systems make themselves vulnerable to purchasing expensive point solutions that fail to meet the needs of all stakeholders. As a result, the downstream impact of siloed, disorganized systems that lack integration with other applications perpetuates data adoption and utilization challenges, creating bottlenecks that can lead to workforce reductions, poor patient outcomes, and financial woes. As such, the demand for high-value data and analytics represents a critical juncture for hospitals and health systems and necessitates a thoughtful yet earnest approach.
What roadblocks are healthcare organizations facing today that impede access to high-value data and analytics?
It is worth mentioning data privacy and security as well. Legacy technologies and outdated reports put organizations at risk for cyber-attacks and costly remediation. In fact, the average cost of a data breach has risen to an all-time high of $4.35 million – a 13 percent increase over the past two years, according to IBM Security's annual Cost of a Data Breach Report. The obstacles to minimizing security breaches include the following:
The current economic and regulatory environment makes the best case for migrating to a high-value data analytics platform if hospitals and health systems are to achieve tangible and sustainable clinical, operational, and financial improvement.
The first step in adopting integrated data and analytics software is to identify a partner who can ensure decision-makers across the organization, from administrators to clinicians, have immediate access to actionable data that can move the needle on their population health management goals. For instance, how can providers improve care quality and enter alternative reimbursement arrangements without knowing which patients to target and how to track their progress, clinical outcomes, and financial impact? The Health Catalyst Data Platform and a robust suite of analytics applications can serve as the cornerstone for solving such clinical problems. These real-time analytics tools improve healthcare data quality and patient profiling by curating enterprise-wide data into a unified data model.
Providers can make care pathway decisions that are appropriate and unique to the patient if they have access to the following during and after clinical integration:
Data and analytics tools make clinical and financial data apparent by eliminating the need to hunt for information, which can delay care decisions and hamper outcomes. Pre-built, normalized data collections simplify requirements for enterprise analytics and increase time-to-value. Indeed, an analytics platform can contribute to millions in annual savings for the organization if labor and resource hours spent producing reports can be reallocated to value-add work and free up storage space previously required by access databases.
Knowing the best path to high-value analytics starts with assessing or validating the various business options available for healthcare leaders to manage their data.
While EHR vendors excel at some things, they cannot claim analytics as a core competency. With their lack of flexibility in ways to ingest and model data and limited visualization capabilities to tailor storytelling, relying on an EHR for analytics will leave users frustrated and lagging in their journey to achieving high-value data analytics. Often with EHRs, the advanced analytics roadmap could be clearer, more efficient, and more reliable to achieve the computation and capabilities desired.
When considering building an internal data and analytics platform, healthcare leaders underestimate the lifetime total cost of ownership (TCO) for this decision. The real costs are in the “data as a service layer” over 10 to 20 years, something the organization must continually invest in and monitor. Not to mention the ability to scale this type of internal system to support increases in locations, service lines, or patient volume. The investment could amount to tens of millions over the TCO. What’s more, staffing this solution becomes costly and cumbersome as finding and retaining readily available resources and talent for the entire lifecycle isn’t sustainable. To that end, analytics is a specialized field with stiff competition for valuable talent who may need additional certifications and training to optimally maintain the platform. Putting off the apparent risk of building an internal analytics platform lies in ensuring that the custom-made solution remains current with technological advancements, upgrades, and enhanced functionalities. However, as healthcare providers who chose to build their own EHRs and enterprise resource planning systems can attest, going that route with analytics platforms is an uphill battle without a clear path to victory.
The ideal solution for hospitals, health systems, and healthcare provider organizations starts with an integrated data and analytics strategic partner who offers a comprehensive technology stack versus a componentized value solution with variable costs. A collaborative and partnering approach also confers the benefit of more effective data governance. An open data architecture supports common ways to access data with open API resulting in interoperability and data flow that meets the organization’s needs. Real-time engines, integrated AI, and domain-specific applications are ready for implementation and use.
Healthcare providers should choose a platform and partner best positioned for cross-industry, vendor-agnostic methodology with proven historical expertise and a track record for quantifiable results. With experts in data and analytics, healthcare organizations can expect more ROI from the analytics technology investment designed to provide expertise and relevant information to clients on their analytics adoption journey.
After building a business case, a roadmap to high-value data and analytics applications to inform decision-making related to patient satisfaction, quality improvements, or financial stewardship may look similar to the list below.
Data Sources
Foundational Application Capabilities
Data Sources
Foundational Application
Advance Applications for Consideration
Advance Application Options
Healthcare leaders and stakeholders involved in data and analytics need proper governance to flex and adjust to the organization's evolving needs. A lack of leadership alignment or varying levels of support for improvement work across clinical departments can slow improvement efforts.
Hiring new resources isn’t practical as hiring and onboarding processes can take months before the new team members are able to contribute to immediate implementation needs. Growing team members from within the organization into the desired skill set incorporate the human side of analytics, requires cross-team collaboration, and opens communication channels to build lasting relationships. Healthcare leaders are often challenged with data literacy, and expecting the literacy levels to fluctuate will help establish a baseline of knowledge to tap into and share across the enterprise. A bimodal delivery process–where one maintains focus, stability, and predictability while the other advances exploration and agility–is beneficial as it delivers solutions at different speeds. Opportunities that give the ability to analyze and pivot quickly, when necessary, will prove invaluable in achieving success through high-value analytics.
Stakeholders in the data and analytics journey should know and adhere to the following guiding principles for using data as a tool for continuous improvement.
Develop standard definitions of important measures and metrics. Standard definitions for key measures and metrics are consistent and well communicated across the organization. Where multiple definitions exist for a measure, ensure the differences and when to use each definition are clearly understood.
Constantly increase the data literacy of the organization. Teams across the organization continually increase their understanding and use of data and analytics in daily operations and decision-making.
Develop products and solutions which support timely decision-making. Self-service tools, dashboards, scorecards, and analytics support are all impactful and well-utilized across the organization in support of operational excellence and prompt decision-making.
Share information openly and easily. The information generated from data is easily available to all in the organization, and definitions, sources, and measurements are transparent.
Develop teams that constantly advance the ability to create a competitive advantage from data. Nurture analytics, business intelligence, and management teams to create unique insights to support a competitive advantage.
Manage ad hoc requests. Ad hoc requests are assessed and supported in a timely manner and answer the requestor’s question. Requests for data available using a self-service tool initiate training for the requestor.
Transforming data to drive improvements will significantly increase the value that can be measured through a benefit-cost ratio (BCR) analysis. Improvement examples that can be calculated and contribute to the BCR include:
A continuous improvement mindset with data and analytics will advance the organization’s clinical and financial outcomes. Future goals can include real-time data dissemination to know what is happening as soon as it happens. Creating symbiotic relationships between content developers and clinical business units is advantageous for cross-department collaboration as content developers can become problem solvers, not just content developers. The analytics platform can then become an integrated model to facilitate research and discovery for other areas of the healthcare provider organization or its affiliates.
Organizations can achieve impressive results by adopting data and analytics and an adaptive problem-solving approach to drive improvement. The University of Kansas Health System’s efforts to build a strategic performance improvement system is delivering their desired results. All 21 clinical departments have annual growth and improvement plans in place, and the organization has generated $4.7M in cost savings. Organizations can also leverage augmented intelligence (AI) to improve organizational performance and patient outcomes, a result of implementing an analytics platform that is flexible and innovative in its capabilities.
In summary, here are key takeaways for healthcare stakeholders in creating a business case for integrated data and analytics:
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