Healthcare financial leaders are constantly brainstorming ways to increase operating margins through better revenue cycle performance. These efforts often lead revenue cycle leaders to denied claims—when a payer doesn’t reimburse a health system for a service rendered. Although denials are a common reason for lost revenue, experts deem nearly 90 percent avoidable. Effective denials management starts with prevention. Organizations can use revenue cycle performance data, combined with artificial intelligence, to predict areas within each claim’s lifecycle that are likely to result in a denial. With denial insight, health systems can optimize revenue cycle processes to prevent denials and increase operating margins.
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As health systems experience more pressure to deliver quality care with limited resources during a pandemic, data analysts play a vital role in helping organizations overcome new COVID-19-induced challenges. Data analysts provide direction about the best way to dissect data, identify areas for improvement, and solve complex problems that stand in the way of better healthcare delivery. However, by developing four specific skills, data analysts can optimize their work and help leaders make sound operational, clinical, and financial decisions:
- Begin with the end in mind.
- Focus on problem solving.
- Master the foundational competencies.
- Play the data detective.
Healthcare consumers are demanding the same level of price transparency for medical care they have in other transactions—particularly as healthcare moves away from a fee-for-service model and patients are responsible for larger portions of their medical bills. Meanwhile, as of January 2021, federal regulation requires health systems to make their service charges publicly available. The healthcare industry, however, hasn’t historically succeeded with consumer-grade price transparency. Organizations must now figure out how to bridge the gap between their costs and patient charges. Doing so requires comprehensive understanding of all the costs behind a service and consumer-friendly explanation of how these expenses translate into prices.
Sepsis affects at least 1.7 million U.S. adults per year, making it a pivotal improvement opportunity for healthcare organizations. The condition, however, has proven problematic for health systems. Common challenges including differentiating between sepsis and a patient’s acute illness and data access. In response, organizations must have comprehensive, timely data and advanced analytics capabilities to understand sepsis within their populations and monitor care programs. These tools can help organizations identify sepsis, intervene early, save lives, and sustain improvements over time.
Surviving on thin operating margins underscores the need for all end users at a health system to make decisions based on comprehensive data sets. This data-centered approach to decision making allows team members to take the right course of action the first time and avoid making decisions based on fragmented data that exclude key pieces of information. To promote data-driven decision making and a data-centric culture, healthcare organizations should increase data access and availability across the institution. With easy access to complete data, end users rely on the same data to make decisions, no matter where they work within the health system. Two strategies can help organizations integrate and deliver data to end users when they need it:
- Select infrastructure that fits most people’s needs.
- Ask the right questions.
Along with the rest of the globe, 2021 healthcare trends across Asia-Pacific (APAC) countries will center on COVID-19 recovery and resuming the healthcare improvement journey. In the APAC region, however, a mix of developed and developing countries poses unique challenges, as healthcare access and basic infrastructure vary widely between urban and rural populations and economic levels. To shepherd healthcare out of the pandemic and enhance delivery overall in 2021, APAC nations will focus on increasing investment in digital health (including virtual care, machine learning, and EMR adoption), achieving universal health coverage, shifting more towards value, and improving payer-provider relationships.
Predictive artificial intelligence (AI) models can help health systems manage population health initiatives by identifying the organization’s most vulnerable patient populations. With these patients identified, organizations can perform outreach and interventions to maximize the quality of patient care and further enhance the AI model's effectiveness. The most successful models leverage a mix of technology, data, and human intervention. However, assembling the appropriate resources can be challenging. Barriers include multiple technology solutions that don’t share information, hundreds of possible, often disparate, data points, and the need to appropriately allocate resources and plan the correct interventions. When it comes to predictive AI for population health, simple models may harness the most predictive power, which allows for more informed risk stratification and identifies opportunities for patient engagement.
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:
- Increase physician engagement.
- Predict propensity to pay.
- Implement evidence-based standards of care.
As health systems continue to adapt to the pandemic healthcare landscape, certain challenges remain—including generating revenue on thin operating margins. Poor charge capture is a common reason behind lost revenue that healthcare leaders often fail to address. Because charge capture is the process of getting paid for services rendered at a hospital, poor charge capture processes mean the hospital does not get paid in full for a service, resulting in lost revenue that is typically unrecoverable.
Health systems can avoid financial leakage and increase profits by focusing on five problem areas within charge capture practice:
- Emergency services.
- Operating room services.
- Pharmacy services.
- Supply chain and devices.
- CDM mapping.
While patient access challenges have been ongoing in healthcare, COVID-19 further stressed access infrastructure. Stay-at-home orders, temporary halts on in-person primary visits, transportation challenges, and more resulted in deferred or missed care. Meanwhile, pandemic-era workarounds, such as a shift to virtual care, have pushed a more digitized patient experience. As healthcare consumers and providers increasingly relying on touchless and asynchronous processes, health systems are discovering opportunities to improve patient access and the overall experience. With the following five steps in a patient access improvement framework, organizations can scale and sustain innovations and lessons learned during the pandemic:
- Create a patient access task force.
- Assess barriers to patient access.
- Turn access barriers into opportunities.
- Implement an improved patient access plan.
- Scale and sustain better patient access.
As healthcare leaders continue to face unprecedented decisions around revenue, cost, and quality, they turn to augmented intelligence (AI) to maximize their analytics. However, leaders struggle to implement AI into existing business intelligence workflows, demonstrate ROI, and move AI efforts beyond predictive models. Health systems can overcome AI’s implementation challenges with the New Healthcare.AI™ offering by Health Catalyst. As a suite of AI products and expert services, Heatlhcare.AI integrates transparent, cutting-edge technology into existing workflows, allowing analysts to produce high-quality insights in minutes. The AI offering dramatically broadens the use and use cases of AI for any healthcare organization with a mix of self-service products and expert services:
- Analytics integration.
- Choosing/building predictive models.
- Optimizing predictive models.
- Retrospective comparisons.
- Prescriptive optimization.
Juggling financial demands, uncertain healthcare legislation, and COVID-19 can distract healthcare leaders from the most important aspect of care—patients. Delivering patient-centered care in this volatile market can be challenging, especially when traditional healthcare methods (e.g., in-person visits) are on hold. These sudden disruptions to routine care have highlighted the importance of keeping patients at the center of care, whether care delivery is in-person or virtual. Health systems can manage competing priorities, adjust to pandemic-induced changes, and deliver patient-centered care by focusing on three strategies:
- Improve the patient experience.
- Implement the Meaningful Measures Initiative.
- Transition in-person visits to virtual.
Healthcare mergers and acquisitions performed solidly in 2020, despite the downturn in the U.S. economy and healthcare in general. Organizations responded to new challenges by partnering with each other to build core business strengths, address gaps in care delivery the pandemic exposed, and enhance their resources to navigate current and future crises. Realizing the potential of emerging healthcare partnerships requires an open and scalable analytics infrastructure plus a cultural and contractual openness to allow innovation to flourish. Organizations that have adopted an open analytics platform have the data operating advantage to form partnerships, efficiently and smoothly bring best-of-breed solutions to market, and enable the innovative potential of collaborations.
While much of the healthcare industry was eager to put 2020 behind it, the new year brings its own challenges, concerns, and promises. Trends in the three main categories of new Biden administration policy, care delivery, and healthcare technology will shape 2021, with key issues including the long-term effects of COVID-19, future emergency preparedness, and the outlook for the Affordable Care Act (ACA). Healthcare leaders can prepare for this pivotal year by understanding critical areas to watch within these categories and how events, activities, and political appointments will affect the healthcare ecosystem.
As the healthcare payment shift from fee-for-service (FFS) to value-based reimbursement takes longer than expected, health systems must balance existing volume-based models with a growing emphasis on value. Organizations are in different phases of the journey from volume to value, and policies continue to evolve. In response, the industry’s best stance is to sustain FFS revenue while following guidelines and strategies to be increasingly ready for value. Organizations can use four methods to remain agile as they navigate the limbo between volume and value:
- Understand the first ten years of value-based care and prepare for what’s next.
- Identify essential strategies for shifting from volume to value.
- Leverage the Medicare Shared Savings Program.
- Use population health management as a path to value.
Healthcare data-informed decision making’s complexity and consequences demand the highest-quality data—a relationship that COVID-19 has amplified. Decision-making challenges associated with pandemic-driven urgency, variety of data, and a lack of resources have made it more critical than ever that organization’s build a data quality coalition and strategy to ensure systemwide data is fit for purpose. Having the people, processes, and technology necessary to define, evaluate, and monitor data quality allows for a quick, effective, and sustained response at an organizational scale. The coalition keeps all resources working together on the task at hand within a well-defined structure.