Six Steps Towards Meaningful, Ongoing Healthcare Performance Improvement

The long-term success of healthcare performance improvement relies on a sustainable infrastructure and strategic execution. Otherwise, improvement initiatives risk becoming one-off projects that don’t support ongoing advances in critical areas, such as critical areas, clinical outcomes, patient experience, and organizational cost. Healthcare organizations can follow six steps for a sustainable, impactful performance improvement program: 1. Integrate performance improvement into strategic objectives. 2. Use analytics to unlock data and identify areas of opportunity. 3. Prioritize programs using a combination of analytics and an adoption system. 4. Define the performance improvement program’s permanent teams. 5. Use a best-practice system to define program outcomes and interventions. 6. Estimate the ROI.

Delivering Precision Medicine: How Data Drives Individualized Healthcare

Delivering precision medicine requires healthcare to transition from a one-size-fits-all methodology to an individualized approach. This means healthcare professionals tailor treatment and prevention strategies according to each patient’s personal characteristics—their genomic makeup, environment, and lifestyle. To realize these precision care goals, researchers and clinicians must leverage vast and varied amounts of real-world data. Data access and interoperability barriers have often impeded the precision medicine transformation. However, current healthcare industry trends increase opportunities for researchers and clinicians to more comprehensively understand medical conditions and the patients in their care. These insights establish the foundation for precision medicine and support actionable pathways towards more efficient development of targeted treatments.

How Data Can Reduce Length of Stay and Keep the Revenue Stream Flowing

Many organizations face high costs and diminishing returns due to unnecessarily high length of stay (LOS) and readmission rates. Elevated LOS and readmission rates can indicate low quality care and also result in costly financial penalties. Therefore, addressing LOS and readmission rates can eliminate avoidable financial consequences, while keeping patients out of the hospital and less likely to develop hospital-acquired infections. Health systems can leverage analytic insight to reduce unnecessary patient LOS and readmission rates, resulting in lower costs for health systems and better health for patients, by applying three data-driven strategies: Implement process changes. Remove discharge barriers. Improve care transitions.

Resetting Payer-Provider Arrangements for COVID-19 and the Evolving Improvement Journey

As the healthcare industry recovers from COVID-19, providers are re-evaluating the financial arrangements that motivate them to improve their processes while benefiting payers and patients. With the pandemic driving lower provider volumes and straining hospital resources, the industry has a renewed urgency for policies that drive better outcomes while lowering cost and improving revenue. Moving forward, healthcare must reset its payer-provider performance standards to the post COVID-19 environment. Renewed approaches to the following models will consider the impact of remote care, how to reimburse telehealth services, and the need for consistent payments to providers: Pay for performance. Bundled payments. ACOs.

Three Data-Informed Ways to Drive Optimal Pediatric Care

Pediatric care has unique challenges, such as communicating with young patients through a parent or guardian and assessing pain levels with children. To overcome these challenges, organizations can rely on operational data to target pediatric improvement areas that lead to lower costs and higher profit margins. Leveraging operational data—instead of focusing solely on pediatric outcomes data—can reveal opportunities for health systems to improve pediatric patient access and, in turn, increase revenue. Organizations can deliver higher quality pediatric care while increasing profits by implementing three data-informed strategies: Maximize space utilization. Improve patient scheduling. Implement virtual care.

How Addressing Mental Health Can Improve Chronic Disease Outcomes and Cut Costs

Treating mental health is often a low priority for health systems because of its high costs and low reimbursement rate. But health systems should not underestimate the impact mental health has on one of their costliest areas—treating chronic diseases. As research links higher costs to patients with chronic disease and a mental health disorder compared to patients without a mental health disorder, organizations should consider mental health treatment a key part of chronic disease management. By following four steps, providers and care teams can address patients’ mental health, thereby improving chronic disease outcomes and lowering costs: Identify the patient population. Identify the financial impact. Develop a plan with experts. Measure the impact and show ROI.

Charge Capture Optimization: Target Five Hotspots to Boost the Bottom Line

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.

The Healthcare Revenue Cycle: How to Optimize Performance

Health systems rely on effective revenue cycle management to follow the patient journey, navigate claims, and ensure the organization collects payment for its services. In today’s complex and fluid healthcare industry, in which revenue cycle management is about much more than billing and collecting payment, traditional revenue cycle approaches can’t meet escalating demands. Additionally, with lost volume due to COVID-19, organizations can’t afford to miss an opportunity for payment. The contemporary healthcare landscape requires a comprehensive, standardized, and data-driven revenue cycle process. Health systems that leverage data to support revenue cycle management improve their financial outcomes in three significant ways: Reduce denials. Increase collections with propensity-to-pay insight. Improve discharged-not-final-billed efforts.

Predicting Denials to Improve the Healthcare Revenue Cycle and Maximize Operating Margins

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.

The Top Four Skills of an Effective Healthcare Data Analyst

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 Price Transparency: Understanding the Cost-Pricing Relationship

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.

Improving Sepsis Care: Three Paths to Better Outcomes

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.

Deliver Data to Decision Makers: Two Important Strategies for Success

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…

2021 Asia-Pacific Healthcare Trends: Growing Digitization, Universal Health Coverage, and More

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.

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

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…

Three Cost-Saving Strategies to Reduce Healthcare Spending

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.

Five Steps for Better Patient Access to Healthcare

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…

Expanding AI in Healthcare: Introducing the New Healthcare.AI™ by Health Catalyst

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.

Three Strategies to Deliver Patient-Centered Care in the Next Normal

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.

Innovative Healthcare Partnerships: Making the Most of Merging Resources and Capabilities

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.

2021 Healthcare Trends: What Leaders Need to Know from COVID-19 to New Administration Policies

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.

Shifting to Value-Based Care: Four Strategies Emphasize Agility

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…

How to Build a Healthcare Data Quality Coalition to Optimize Decision Making

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.

Data Science Reveals Patients at Risk for Adverse Outcomes Due to COVID-19 Care Disruptions

One of the biggest challenges health systems have faced since the onset of COVID-19 is the disruption to routine care. These care disruptions, such as halted routine checkups and primary care visits, place some patients at a higher risk for adverse outcomes. Health systems can rely on data science, based on past care disruption, to identify vulnerable patients and the short- and long-term effects these care disruptions could have on their health. Data science can also inform the care team which care disruptions to address first. With comprehensive information about care disruption on patients, health systems can apply the right interventions before it’s too late.

The Key to Better Healthcare Decision Making

When healthcare leaders make data-driven decisions, they often think they see the same thing in the data and assume they’re drawing the same conclusions. However, decision makers often discover later that they were looking at the data differently and didn’t derive the same insights, leading to ineffective and unsustainable choices. Healthcare leaders can manage differing data interpretations by using statistical process control (SPC) methodology to find focus, avoid divergent data interpretations, make better decisions, and monitor change for a sustainable future. By deriving concise insights, SPC separates the signal from the noise, augmenting leaders’ decision-making capabilities.