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Three Cost-Saving Strategies to Reduce Healthcare Spending

March 23, 2021

Article Summary


Health systems continue to face fiscal challenges and burdens due to changing reimbursement rates, COVID-19, and managing the aftermath of care disruptions from the pandemic. Operating on thin margins with limited resources means health systems need to adopt alternative cost-saving measures to maximize limited resources.

Comprehensive, reliable data increases visibility into expenses across the care continuum so that leaders can leverage new methods to save money, generate income, and accelerate cashflow to keep patients healthy and hospital doors open. With access to recent data, health systems can focus on three cost-saving strategies:
1. Increase physician engagement.
2. Predict propensity to pay.
3. Implement evidence-based standards of care.

Healthcare Revenue Cycle

The healthcare market is subject to constant change for many reasons, including fluctuating reimbursement rates, impending changes to healthcare legislation (e.g., the Affordable Care Act), and Medicaid expansions that differ by federal and state governments. Additionally, in 2020, COVID-19 has added acute financial stress to healthcare organizations. After a temporary ban on lucrative elective procedures, health systems could restart surgeries as of mid-April 2020 if they met CMS state-specific criteria. However, with the fall 2020 spike in COVID-19 numbers, and many states reaching record-breaking highs of new cases, hospitals have had to slow elective procedures again to expand their ICUs and reserve space for patients with COVID-19.

Healthcare’s stressful fiscal landscape combined with unpredictable changes from COVID-19 make effective healthcare cost-saving strategies critical for health systems to survive. Patient visits have continued during the pandemic (mostly through telehealth), but many health systems have failed to effectively scale virtual care in a short timeframe and prepare for unclear reimbursement rates compared to in-person care. Even still, these measures fall short of creating a reliable cost-saving strategy—essential if hospitals want to outlive COVID-19 and other future crises. While most healthcare organizations already use data, they can rethink its value in managing costs and driving down expenditures. A renewed focus on healthcare cost-saving measures, based on comprehensive cost data, can foster survival in a tumultuous time for health systems.

Three Healthcare Cost-Saving Strategies

Although many health systems’ fiscal hurdles are ever changing, organizations can rely on up-to-date data to stay apprised of the latest developments and respond accordingly. Access to reliable, recent data reveal three healthcare cost-saving strategies organizations can use:

#1: Increase Physician Engagement

Physician engagement is key to improving quality and safety processes, often associated with high health systems costs. Because physicians are at the frontlines of healthcare delivery and interact directly with patients, process improvements aimed at decreasing costs only work if physicians support the changes and then implement them in clinical practice.

Data-driven insights provide unbiased information about provider performance related to high-cost areas, such as complication rates and length of stay (LOS). With the latest data readily available, physicians can see firsthand how their provider performance scores impact the health system’s financial state and identify realistic areas to improve performance, resulting in lower costs.

One health system, Thibodaux Regional Health System, formed a steering committee and used data to increase physician engagement and saved millions without compromising quality of care. The increase in physician engagement led to more than $6.6 million in financial improvements and a 17.6 percent relative reduction in LOS for patients with pneumonia, resulting in patients spending 100 more days at home.

#2: Predict Propensity to Pay

Along with health systems, patients are sharing the burden of expensive healthcare costs as out-of-pocket spending increases. When patients are responsible for paying more for their care, there is a risk that the patient might not pay his portion of the bill. When patients fail to pay their bills, the hospital has to cover the cost, incurring bad debt—an unrecoverable expense that negatively impacts a hospital’s revenue stream. As the out-of-pocket spending trend continues, health systems inevitably increase bad debt exposure because a larger pool of patients financially responsible for their care means a higher possibility that patients will not, or cannot, pay.

Even though health systems face unavoidable healthcare costs (e.g., incurring bad debt), they can generate income by identifying bad debt vulnerabilities early with propensity to pay (P2P) predictive models. With P2P technology, health systems use data-informed predictive models to forecast the likelihood that a patient will pay his bill.

With many patients uninsured, on high deductible plans, or facing expensive co-payments, it is not uncommon for patients to not pay their bills. Reasons for failure to pay may include an inability to pay the lump sum all at once or a lack of awareness about alternative payment options. When coding and billing departments can use a P2P strategy to identify which patients are less likely to pay for a procedure before the procedure occurs, they can offer different payment options to best fit the patient’s needs. The patient feels more comfortable with the financial plan, and the hospital decreases bad debt exposure, resulting in increased profits. For example, Allina Health implemented a P2P strategy to eliminate/minimize bad debt and generated revenue through overall patient collections by $2 million in one year.

#3: Implement Evidence-Based Standards of Care

Variation in care processes can lead to a variation in patient outcomes, costing health systems millions of dollars for readmissions and longer LOS. Health systems can overcome these care variations with access to granular-level data that identifies opportunities to improve care delivery and decrease the care’s associated costs. With analytics insight from nuanced data, health systems can see variations in care processes and then implement evidence-based workflows to improve care consistency and coordination.

A lack of standardization and data-driven processes that leads to costly care usually stems from a lack of actionable data. Variation in care processes is normal, but without specific data, leaders don’t have a way to flag, what they consider out of range, for typical care variation. With early identification of variance, health systems can rely on results-based processes to avoid wasting money on the wrong intervention. For example, Billings Clinic implemented standardized, evidence-based protocols to improve care coordination for patients with heart failure, decreased variation in outcomes, and saved $544,000.

Sustainable Healthcare Cost-Saving Measures Start with Comprehensive Data

Fraught with overwhelming financial burdens—from the spread of COVID-19 and demand to manage complex, aging populations on thin margins—health systems must know they are expending precious healthcare dollars in the right place. Meanwhile, as organizations financially recover from COVID-19 and navigate its aftermath, it is critical for healthcare leaders to be certain that they are targeting improvement areas that will lead to healthcare cost savings. By leveraging comprehensive data, healthcare leaders can reduce costs and improve healthcare revenue cycle performance through increased physician engagement, P2P strategies, and evidence-based workflows.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

  1. Healthcare Financial Transformation: Five Leading Strategies
  2. Reduce Bad Debt: Four Tactics to Limit Exposure During COVID-19
  3. Healthcare Price Transparency: Three Opportunities for Transformation
  4. Six Strategies to Navigate COVID-19 Financial Recovery for Health Systems
  5. Six Proven Methods to Combat COVID-19 with Real-World Analytics

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The Right Way to Build Predictive Models for the Most Vulnerable Patient Populations