Data has the potential to revolutionize healthcare, driving smarter decisions and better outcomes. But even as healthcare has access to more and more data — from EHR and claims data to demographic and consumer data — many organizations still lack the capability to effectively leverage data for decision-making.
“While it may seem like there’s an abundance of data, lots of data available to health systems isn’t necessarily usable. It could be unstructured, incomplete or be too unreliable for analysis,” explains Kristine Hartley, principal, digital at Huron.
That means many healthcare organizations struggle to generate retrospective reports (using data to perform analysis of trends that have already happened). And very few currently have the capability for forward-looking predictive analytics, she explains.
Yet predictive data analytics play a critical role in helping health systems build financial resilience. They help leaders predict how new challenges may impact the organization and scenario-map potential responses to find the next best step. Effectively using data analytics to strengthen performance and fuel growth requires a strong data foundation and resolving key challenges that impede forward progress.
The high cost of unreliable data
Although many healthcare leaders understand that data should drive decision-making, they may still underestimate how limited access to analytics undermines growth.
For example, data analytics can help pinpoint where organizations have the opportunity to optimize throughput while maintaining quality of care. “We frequently see missed potential to fuel revenue, from losing appointment slots to no-shows to missing out on untapped referral opportunities due to leakage to increased readmissions leading to revenue loss,” Hartley explains. “Whereas if organizations were able to collect and analyze the relevant data, they’d have more visibility and opportunities to address these problems.”
Limited insights can prove particularly challenging in dynamic economic and regulatory environments, where leaders need to adapt to new and even unprecedented challenges on a near-daily basis, she explains. “Without predictive analytics, you just need to take your best guess.”
Leaders face challenges in curating data sets for sound analysis
The technology to power data analytics is advancing rapidly, and healthcare organizations are struggling to keep pace with the speed of innovation. “Lots of health systems are struggling with master data management and data governance, so leaders are tasked with making decisions using data they aren’t sure if they can trust,” Hartley explains.
Lack of integration also limits health systems’ ability to perform analytics, since data may be siloed in multiple systems across the organization. Plus, there are few industry-wide standards for how data should be formatted, which means healthcare organizations must convert data into a usable format before analysis.
How AI-powered technologies can help
Despite these challenges, healthcare leaders have an opportunity to structure and analyze data with help from AI. As a baseline, AI-powered technologies excel at data entry, allowing health systems to collect complete and accurate data sets that can inform decision-making.
Analytics can also identify unmet needs across the health system, creating opportunities to improve patient outcomes and build resilience as more and more payers shift to value-based care. “Machine learning can recognize patterns across claims, EMR, SDOH and demographic information, allowing CFOs to increase the accuracy of medical spend forecasting,” says Hartley. “This enables financial leaders to pinpoint shared savings opportunities and design targeted clinical interventions to reduce medical expenses and more effectively control costs."
Health systems can use predictive analytics to anticipate patient deterioration in the ICU, creating opportunities for clinicians to intervene early to reduce mortality rates and length of stay. They can also enhance operational efficiencies, such as optimizing hospital operations to reduce wait times and boost patient satisfaction.
To fuel growth, finance leaders must get additional stakeholders on board
Each health system has its own unique needs — and its own path to establishing and advancing predictive analytics capabilities. However, these three steps can help financial leaders move their organizations forward.
1. Cultivate a data-driven culture
When it comes to influencing change within a health system, Hartley recommends a top-down approach focused on getting leadership on board. “Focus on getting the entire C-suite involved, demonstrating how analytics fit into the organization’s larger strategic initiatives, such as boosting efficiencies or improving patient satisfaction” she advises.
Working across teams allows leadership to identify their highest-priority pain points and rally around a common cause, creating a sense of shared ownership and accountability. From there, leaders can set short- and long-term goals and start evaluating potential solutions to find the right fit.
2. Measure the optimal KPIs to demonstrate value
Even organizations with the goal of robust analytics capabilities usually start with leveraging data for one or two use cases. It’s essential to have a plan to measure success on these early objectives, Hartley says. “The results from one use case can become the rationale for another, and selecting the appropriate KPIs helps you make a compelling argument for further investment.”
A health system using analytics to reduce patient deterioration in the ICU may track a range of KPIs, including average length of stay, readmission rate and the percentage of high-risk interventions. However, leaders should also look for early indicators. For example, the number of early interventions based on predictive analytics or the percentage of correct early warnings to quickly demonstrate value.
3. Center the employee experience for a smoother rollout
While a data-driven culture starts from the top, it should enhance the user experience throughout the organization. “Stressed clinical staff aren’t necessarily excited by analytics, but they are interested in automating data entry to spend more time with patients,” Hartley says. “It’s important to pair investment in innovation with educational initiatives that highlight how data analytics can improve employees’ day-to-day lives.”
Doing this well requires a deep understanding of team members’ pain points, and a high level of coordination between leadership, middle management and staff before, during and after rollout. But, when done well, the results can be transformative. “The more effectively you communicate across the organization, the more likely you are to make each employee an individual stakeholder throughout the process.”
The bottom line
As healthcare leaders face mounting pressure to do more with less, data analytics play a key role in identifying trends, optimizing efficiencies, and strategically positioning their organizations for long-term growth. The question is no longer about access to data, but rather how effectively it can be harnessed to meet the financial and operational demands of today’s healthcare landscape while preparing for what’s ahead.
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Huron helps healthcare systems drive dual transformation, working closely with CFOs and other executives to reimagine what’s possible for their organization, then turn the vision into reality with the right people, processes, and technology. Visit us here to learn more.
