Own Your Value-Based Care Future with the Health Catalyst Value Optimizer™ Solution

As value-based care (VBC) becomes more common in healthcare, population health leaders need a better approach to managing risk-based contracts and optimizing VBC strategies. Unfortunately, many health systems rely on outdated population health offerings that lack data integration capabilities and only provide a fragmented view of their populations. To pinpoint high-priority groups and succeed in VBC, organizations can rely on the Health Catalyst Value Optimizer™ solution. Value Optimizer aggregates and analyzes comprehensive patient data, then instantly identifies the most valuable opportunities for improvement throughout the care continuum. With a full-service solution and increased visibility into performance, leaders can master their VBC and ensure patients receive the best care at the lowest cost.

Delivering the Right Insight to the Right Person: How Workflow Automation Optimizes EHR Decision Support

While the EHR increases the legibility and comprehensiveness of patient health data and makes vital insights more accessible, digitized records also drive longer workflows and hard-to-manage data volumes. Fortunately, the healthcare digital environment today also makes effective data curation achievable. With an automated EHR workflow, healthcare data and analytics technology mines the data platform, bringing the value of digital documentation directly to team members. Automation of routine, repeatable tasks, paired with curation of the most important information in the chart, allows providers and patients to benefit from the wealth of digitized documentation, as workflows ensure the right person accesses the right insight at the right place and time.

The Secret Behind Resilient Healthcare Organizations: High Reliability

Resilience in healthcare means that organizations are continually ready to navigate disruptions of any size without sacrificing quality of care or patient and staff safety. Health systems maintain resilience by embedding the principles of high reliability into their culture, workflows, and processes. These high-reliability organizations (HROs) don’t approach reliability as a short-term project or checklist; rather, they embed the principles into every interaction and action beginning with senior leadership. As a result of a practice, not project, approach to reliability, HROs “rarely fail even though they encounter numerous unexpected events,” as authors Karl Weick and Kathleen Sutcliffe explain in their book series, “Managing the Unexpected.”

A Five-Step Audit for Peak Charge Capture Performance

As health systems strive for financial growth and stability in a pandemic and shifting healthcare market, leaders often overlook a key opportunity to maximize profit margins for services rendered—a charge capture audit. A charge capture audit takes a deep dive into the charge capture process, exposing root causes of costly errors and suboptimal processes. These five steps derive critical insight to help health systems apply interventions and restore revenue integrity: Set the standard. Measure current practice against the standard. Compare the results at the standard to practice. Change the practice to best practice. Re-audit.

Five Practical Steps Towards Healthcare Data Governance

Health systems increasingly recognize data as one of their top strategic assets, but how many organizations have the processes and frameworks in place to protect their data? Without effective data governance, organizations risk losing trust in their data and its value in process and outcomes improvement; a 2018 survey indicated less than half of healthcare CIOs have strong trust in their data. By following five steps towards data governance, health systems can effectively steward data and grow and maintain trust in it as a critical asset: Identify the organizational priorities. Identify the data governance priorities. Identify and recruit the early adopters. Identify the scope of the opportunity appropriately. Enable early adopters to become enterprise data governance leaders and mentors.

How to Find the Best Interventions for Clinical Quality Improvement

How can health systems avoid just talking about improvement and instead achieve real progress in clinical quality performance? First, improvement teams need access to a robust data infrastructure that can provide a complete picture of performance. This analytic insight reveals process gaps and opportunity areas where the care team can target improvement efforts. After selecting an opportunity area, care teams are ready to follow the three-step process to achieve meaningful clinical improvement: The “why”: Identify the outcome goal. The “what”: Select a written, measurable, and time-sensitive process metric to evaluate the process. The “how”: Identify the best interventions that will support the desired change in a process.

Five Ways Activity-Based Costing Can Maximize Earnings

Surviving on thin operating margins means health systems must maximize every financial earning opportunity. To identify threats to the revenue stream, organizations need access to precise, accurate costing information. An activity-based costing (ABC) system leverages patient resource utilization data to reveal exactly how much it costs to deliver care. Unlike traditional costing systems that provide average cost estimates for services rendered, ABC includes five benefits that help systems understand the cost for every aspect of the care delivery process: Comprehensive costing data. Ease of use. Precision and accuracy. Near real-time analytics. A proactive cost strategy.

How Regulatory Compliance Supports Optimal Patient Care and Higher Earnings

Hospitals spend over $7.5 million every year on regulatory compliance. Payers, such as CMS, rely on these quality measures to evaluate health system and provider performance and determine reimbursement rates for services rendered. As a result, regulatory performance is critical to the care process and revenue stream. However, many health systems fail to meet these care standards and maximize reimbursement rates because they lack analytic insight into regulatory performance. With a data engine that tracks and submits quality measures data, leaders understand their compliance performance, gaining insight into opportunities to improve patient-centric care and value-based performance. This data-informed approach allows organizations to increase profits through peak regulatory performance and avoid financial penalties associated with underperformance.

Three Keys to a Successful Data Governance Strategy

With data and data sources on the rise in healthcare, organizations need to more effectively organize, track, and distribute data to team members. A data governance strategy gives health systems a standardized approach to manage data, their most precious asset. Effective data governance helps leaders maximize their data, promote systemwide data-informed decision making, and drive sustainable improvement. Healthcare leaders can operationalize data governance in their organizations by considering three key elements of an effective strategy: Start with the data governance basics. Ensure the data governance strategy supports sustainable improvement. Align the data governance strategy with organizational priorities.

Understanding Population Health Management: A Diabetes Example

Diabetes is one of several chronic health conditions at the root of U.S. healthcare challenges. To improve the quality of care and costs associated with diabetes, health systems, clinicians, and patients can benefit from taking a data-centric approach to diabetes management and leveraging population health tools. Managing individual cases of diabetes require actively involving patients in their care plan, enabling each patient to monitor and understand key data, such as A1c readings, and adjust lifestyle or other factors affecting overall health. Managing diabetes across larger populations, however, is best done through the use of a data and analytics platform that can aggregate data from multiple sources and provide actionable insights. Specifically, a data platform can identify patients who aren’t up…

Four Elements that Bridge the Gap Between Using Data and Becoming Data-Driven

With mounting pressures to deliver quality care with fixed resources, data-driven healthcare is pivotal to organizations’ well-being. From operations to the front lines of clinical care, data can drive the best outcome if decision makers have relevant information when they need it. However, many organizations simply use data in one-off situations rather than integrating it into systemwide processes and workflows. To understand what it means to become data driven and take the right steps forward, organizations can apply four key elements: Invest in one source of data truth. Apply a data governance strategy. Promote systemwide data literacy. Implement a cybersecurity framework.

Five Ways Healthcare AI Gives You Superpowers

As healthcare decisions, data points, and options increase, time, resources, and margin of error decrease. To succeed in this environment, leaders and analysts must know where to focus and how to allocate resources and set accountability targets. With Healthcare.AI™, five super-powered assistive augmented intelligence capabilities help healthcare leaders and analysts determine values, understand context, and provide data-driven motivation to transform healthcare: Enhancing humans’ natural visual pattern recognition. Calculating trajectories. Accelerating the pace at which analysts produce and experiment with how to present the insights. Producing high-caliber, high-quality analytic results. Building trust by enabling immediate, visual, and transparent results.

Drive Better Outcomes with Four Data-Informed Patient Engagement Tactics

Increased patient engagement leads to better clinical outcomes, but organizations still struggle to engage patients and their families in their care. To start, patients have different levels of interest in their care and competency regarding healthcare, which adds to the challenge of treating each patient like a member of the care team. However difficult these patient engagement roadblocks are, organizations can use data to overcome them. Access to data allows healthcare leaders and providers to identify opportunities to optimize patient engagement. By implementing four data-informed tactics, systems can increase patient engagement and improve health outcomes: Implement shared decision-making interventions. Advance health equity. Prioritize patient feedback. Provide patient-centered education.

Your AI Journey Starts Here: A Four-Step Framework for Predictive Analytic Success

COVID-19 has highlighted the imperative for health systems to proactively prepare for future scenarios. One way organizations can ready themselves is by using artificial intelligence (AI), such as predictive analytics, to forecast clinical, operational, and financial needs. While many health systems have the historical and current data they need for predictive modeling, they often lack the requisite analytics foundation and knowledge to begin any AI project, let alone predictive analytics journey. Data and analytics technology lay the foundation to support a health system for a successful AI pursuit, including predictive analytics. With the right tools in place, health systems are ready to follow the four-step framework: Project intake and prioritization. Project kickoff. Model development. Operationalizing the predictive model.

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.

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.