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Health Catalyst Editors

Health Catalyst Editors is a team of senior editors and writers at Health Catalyst that bring over 60+ combined years of healthcare writing experience and a broad knowledge of the industry.

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Six Tactics to Restore the Healthcare Revenue Cycle

Healthcare organizations suffered financial setbacks during the pandemic and are now looking for opportunities to recover lost revenue. Rather than focusing only on increasing profitability after months of halted elective procedures, health systems should closely examine other aspects of healthcare that impact the revenue cycle. To take a proactive approach to restore revenue cycle integrity, healthcare leaders should consider six hands-on strategies that promote near- and long-term revenue recovery:

1. Prepare for changing legislation.
2. Create positive remote work environments.
3. Manage payer policies.
4. Expand telehealth.
5. Set up prior authorization for surgical procedures.
6. Achieve price transparency.

The Healthcare Analytics Summit™ 2021 Virtual

The Healthcare Analytics Summit™ (HAS) 21 Virtual features internationally recognized speakers, national and global networking opportunities, and traditional HAS fun—including #Socks of HAS, quiz questions, daily scavenger hunts, prizes, and more. In addition, the 2021 global theme will take attendees on a virtual journey to three international destinations—Singapore, Dubai, and London—while exploring the digital trends and best practice experiences driving healthcare success in the new digital era.

The HAS 21 Virtual world-class speaker line-up includes the following:

1. Head Coach of the Golden State Warriors and two-time NBA Coach of the Year, Steve Kerr.
2. AI Rana el Kaliouby, PhD, co-founder and CEO of Affectiva and pioneer and inventor of Emotion and Human Perception.
3. Chris Chen, MD, CEO of ChenMed, and Brent James, MD, MStat, Clinical Professor, Clinical Excellence Research Center (CERC), Department of Medicine at Stanford University School of Medicine.

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:

1. Project intake and prioritization.
2. Project kickoff.
3. Model development.
4. Operationalizing the predictive model.

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:

1. Start with the data governance basics.
2. Ensure the data governance strategy supports sustainable improvement.
3.Align the data governance strategy with organizational priorities.

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:

1. Invest in one source of data truth.
2. Apply a data governance strategy.
3. Promote systemwide data literacy.
4. Implement a cybersecurity framework.

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:

1. Implement shared decision-making interventions.
2. Advance health equity.
3. Prioritize patient feedback.
4. Provide patient-centered education.

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:

1. Implement process changes.
2. Remove discharge barriers.
3. Improve care transitions.

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:
1. Maximize space utilization.
2. Improve patient scheduling.
3. Implement virtual care.

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:
1. Emergency services.
2. Operating room services.
3. Pharmacy services.
4. Supply chain and devices.
5. 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:
1. Reduce denials.
2. Increase collections with propensity-to-pay insight.
3. Improve discharged-not-final-billed efforts.

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:
1. Begin with the end in mind.
2. Focus on problem solving.
3. Master the foundational competencies.
4. 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:
1. Select infrastructure that fits most people’s needs.
2. Ask the right questions.

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 opportunities for patient engagement.

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:
1. Increase physician engagement.
2. Predict propensity to pay.
3. 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:
1. Create a patient access task force.
2. Assess barriers to patient access.
3. Turn access barriers into opportunities.
4. Implement an improved patient access plan.
5. Scale and sustain better patient access.

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:
1. Improve the patient experience.
2. Implement the Meaningful Measures Initiative.
3. Transition in-person visits to virtual.

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:

1. Understand the first ten years of value-based care and prepare for what’s next.
2. Identify essential strategies for shifting from volume to value.
3. Leverage the Medicare Shared Savings Program.
4. Use population health management as a path to value.

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.

Artificial Intelligence and Machine Learning in Healthcare: Four Real-World Improvements

As COVID-19 has strained health systems clinically, operationally, and financially, advanced data science capabilities have emerged as highly valuable pandemic resources. Organizations use artificial intelligence (AI) and machine learning (ML) to better understand COVID-19 and other health conditions, patient populations, operational and financial challenges, and more—insights that are supporting pandemic response and recovery as well as ongoing healthcare delivery. Meanwhile, improved data science adoption guidelines are making implementation of capabilities such as AI and ML more accessible and actionable, allowing organizations to achieve meaningful short-term improvements and prepare for an emergency-ready future.

Why Data-Driven Healthcare Is the Best Defense Against COVID-19

COVID-19 has given data-driven healthcare the opportunity to prove its value on the national and global stages. Health systems, researchers, and policymakers have leveraged data to drive critical decisions from short-term emergency response to long-term recovery planning.

Five areas of pandemic response and recovery stand out for their robust use of data and measurable impact on the course of the outbreak and the individuals and frontline providers at its center:
1. Scaling the hospital command center to pandemic proportions.
2. Meeting patient surge demands on hospital capacity.
3. Controlling disease spread.
4. Fueling global research.
5. Responding to financial strain.

Healthcare Process Improvement: Six Strategies for Organizationwide Transformation

Healthcare processes drive activities and outcomes across the health system, from emergency department admissions and procedures to billing and discharge. Furthermore, in the COVID-19 era’s uncertainty, process quality is an increasingly important driver in care delivery and organizational success. Given this broad scope of impact, process improvement is intrinsically linked to better outcomes and lower costs. Six strategies for healthcare process improvement illustrate the roles of strategy, skillsets, culture, and advanced analytics in healthcare’s continuing mission of transformation.

Safeguarding the Ethics of AI in Healthcare: Three Best Practices

As artificial intelligence (AI) permeates the healthcare industry, analytics leaders must ensure that AI remains ethical and beneficial to all patient populations. In absence of a formal regulatory or governing body to enforce AI standards, it’s up to healthcare professionals to safeguard ethics in healthcare AI.

The potential for AI’s use in support of the pandemic response can have enormous payoffs. However, ensuring its ethical implementation may prove challenging if healthcare professionals are not familiar with the accuracy and limitations of AI-generated recommendations. Understanding how data scientists calculate algorithms, what data they use, and how to interpret it is critical to using AI in a meaningful and ethical manner to improve care delivery. By adhering to best practices for healthcare AI, health systems can guard against bias, ensure patient privacy, and maximize efficiencies while assisting humanity.

Medical Practices’ Survival Depends on Four Analytics Strategies

With limited resources compared to large healthcare organizations and fewer personnel to shoulder burdens like COVID-19, medical practices must find ways to deliver better care with less. Delivering quality care, especially in a pandemic, is challenging, but analytics insight can guide effective care delivery methods, especially for smaller practices.

Comprehensive data combined with team members who can turn numbers into real-world information are essential for medical practices to ensure a strong financial, clinical, and operational future. Independent medical practices can rely on four analytics strategies to survive the uncertain healthcare market and plan for a sustainable future:

1. Prioritize access to up-to-date, comprehensive data sources.
2. Form a multidisciplinary approach to data governance.
3. Translate data into analytics insight.
4. Invest in analytics infrastructure to support rapid response.

Shifting to Virtual Care in the COVID-19 Era: Analytics for Financial Success and an Optimized Patient Experience

The COVID-19 era has seen a decline in visits to ambulatory care practices by 60 percent and an estimated financial loss for primary care of over $15 billion. Shutting down elective care is financially unsustainable for health systems and for patients, who continue to need non-pandemic-related care. While virtual medicine has emerged as a viable and mutually beneficial solution for patients and providers, the shift from in-person to virtual health is logistically and financially complicated.

Processes and workflows from in-person care don’t directly translate to the virtual setting, and a financially successful shift requires deep understanding of the factors driving patient engagement and revenue in the new normal. As such, meeting patient needs and financial goals requires robust enterprisewide analytics that drill down to the provider level.

Healthcare Financial Transformation: Five Leading Strategies

Healthcare financial transformation—improving care delivery while lowering costs—has been an ongoing challenge for health systems in the era of value-based care and an even more prominent concern amid COVID-19.
While better care and reduced expense to organizations and consumers might seem like opposing goals, by understanding the true cost of services and other drivers of expense, organizations can successfully manage costs while maintaining, and even improving, care delivery.
To that end, health systems can use data- and analytics-driven tools and strategies to addresses financial challenges, including uncompensated care, prolonged accounts receivable days, discharged not final billed cases, inefficient resource use, and more.

Six Strategies to Navigate COVID-19 Financial Recovery for Health Systems

Research projects that 2020 healthcare industry losses due to COVID-19 will total $323 billion. As patient volumes fall and pandemic-related expenses rise, health systems need a strategy for both immediate and long-term financial recovery. An effective approach will rely on a deep, nuanced understanding of how the pandemic has altered and reshaped care delivery models. One of the COVID-19 era’s most impactful changes has been the shift from in-person office visits to virtual care (e.g., telehealth). Though patients and providers initially turned to remote delivery to free up facilities for COVID-19 care and reduce disease transmission, the benefits of virtual care (e.g., circumventing the time and resource drain of patients traveling to appointments) position telehealth as lasting model in the new healthcare landscape. As a result, healthcare financial leaders must fully understand the revenue and reimbursement implications of virtual care.

Six Proven Methods to Combat COVID-19 with Real-World Analytics

As data in healthcare becomes more available than ever before, so does the need to apply that data to the unique challenges facing health systems, especially in a pandemic. Even with massive amounts of data, health systems still struggle to move data from spreadsheets to drive change in a clinical setting.

These six methods allow health systems to transform data into real-world analytics, going beyond basic data usage and maximizing actionable insight:

1. Create effective information displays.
2. Add context to data.
3. Ensure data processes are sustainable.
4. Certify data quality.
5. Provide systemwide access to data.
6. Refine the approach to knowledge management.

Advancing data use in healthcare with real-world analytics arms health systems with effective tools to combat COVID-19 and continue delivering quality care driven by comprehensive, actionable insight.

How to Optimize the Healthcare Revenue Cycle with Improved Patient Access

Despite pandemic-driven limitations, health systems can still find ways to optimize revenue cycle and generate income. When health systems improve and prioritize patient access through a patient-centered access center, they can improve the revenue cycle performance through decreased referral leakage, better patient trust, and optimum communication across hospital departments.

Rather than relying on traditional revenue cycle improvement tactics, health systems should consider three ways a patient-centered access center can positively impact revenue cycle performance:

1. Advance access.
2. Optimize resources.
3. Engage stakeholders.

Population Health Success: Three Ways to Leverage Data

As the healthcare industry continues to focus on value, rather than volume, health systems are faced with delivering quality care to large populations with limited resources. To implement population health initiatives and deliver results, it is critical that care teams build population health strategies on actionable, up-to-date data. Health systems can better leverage data within population health and drive long-lasting change by implementing three small changes:

1. Increase team members’ access to data.
2. Support widespread data utilization.
3. Implement one source of data truth.

Access to accurate, reliable data boosts population health efforts while maintaining cost and improving outcomes. With actionable analytics providing insight and guiding decisions, population health teams can drive real change within their patient populations.

Four Strategies Drive High-Value Healthcare Analytics for COVID-19 Recovery

COVID-19 response and recovery is pushing healthcare to operate at an unprecedented level. To meet these demands and continue to improve outcomes and lower costs, healthcare analytics must perform more actionably and with broader organizational impact than ever. Health systems can follow four strategies to produce high-value analytics to withstand the pandemic and make healthcare better in the long term:

1. Minimize benchmarking.
2. Outsource regulatory reporting.
3. Grow risk-based stratification capabilities.
4. Run activity-based costing plus at-risk contracting.

The Healthcare Analytics Summit™: Top Data Discoveries and Insights from HAS 20 Virtual

The 2020 Healthcare Analytics Summit™ (HAS 20 Virtual) took place for the first time from a remote platform. But, as the 2020 HAS infographic demonstrates, the remote experience delivered on HAS event’s customary high level of engagement and meaningful healthcare insights.
The 2020 conference focused on the theme of analytics in the new normal, sharing insights around pandemic response and recovery to a record-setting audience.

Six Ways Health Systems Use Analytics to Improve Patient Safety

With preventable patient harm associated with over 400,000 deaths in the U.S. annually, improving safety is a top priority for healthcare organizations. To reduce risks for hospitalized patients, health systems are using patient safety analytics and trigger-based surveillance tools to better understand and recognize the types of harm occurring at their facilities and intervene as early as possible.

Six examples of analytics-driven patient safety success cover improvement in the following areas:

1. Wrong-patient order errors.
2. Blood management.
3. Clostridioides difficile (C. diff).
4. Opioid dependence.
5. Event reporting.
6. Sepsis.

Healthcare Analytics Summit 2020: Day Three Recap

The Healthcare Analytics Summit 20 Virtual (HAS 20 Virtual) concluded three days of online programming on Thursday, September 3, 2020. Though COVID-19 forced this year’s event to take place virtually, the geographic dispersal of attendees and presenters didn’t dampen the depth of insights or level of engagement previous summits are known for.
After two days of keynote addresses, breakout presentations, small Braindate gatherings, and project and solution showcase, HAS 20 Virtual maintained its momentum. The conference closed on a powerful note with yet more world-class speakers, groundbreaking innovations, and common theme of the power of analytics and human potential in healthcare’s new normal.

Healthcare Analytics Summit 2020: Day Two Recap

Day two of the Healthcare Analytics Summit 20 Virtual (HAS 20 Virtual) included keynote speakers followed by live Q&As, quizzes to earn points for the HAS game, the Analytics Walkabout, Machine Learning Marketplace, and Digital Innovation Showcase.
Attendees enjoyed topical keynote speakers like Amy P. Abernethy, MD, PhD, acting CIO of the U.S. Food and Drug Administration, who discussed the importance of data in addressing COVID-19; Yonatan Adiri, CEO of Healthy.io, who presented on a smartphone-enabled urine test to improve healthcare accessibility; and Sampson Davis, MD, emergency medicine physician and New York Times best-selling author, who shared how education saved his life.

Virtually Kicking Off the 2020 Healthcare Analytics Summit

For the first time from an online platform, Health Catalyst COO Paul Horstmeier welcomed attendees to the Healthcare Analytics Summit 20 Virtual (HAS 20 Virtual), promised a highly interactive online experience that would maintain the breadth and depth of expertise as well as the spirit of innovation of the conference’s in-person iterations.

HAS 20 Virtual will also provide some of the fun and good humor attendees have enjoyed in year’s past–from the Virtual fun run to the friendly competition for the most notable socks–HAS 20 Virtual has moved these activities online. Highlights from Day one of HAS 20 included keynotes from Eric Topol, MD and Ari Robicsek, MD, as well as two breakout session waves.

Beginning the Conversation: Health Equity

Equity impacts the fabric of society down to the type and quality of healthcare different racial and ethnic patient populations receive. COVID-19 has underscored disparities in healthcare delivery in the United States, as the pandemic has disproportionately affected the nation’s black communities.
To care for and recognize the value of all individuals, healthcare must leverage data and analytics to better understand patient populations by race and ethnicity and determine how to meet the needs of its underserved populations.

Restarting Ambulatory Care and Elective Procedures: Analytics Guide Safe, Pragmatic Decisions

As Health Catalyst continues to engage its health system partners in their COVID-19 journeys through virtual client huddles, topics are delving further into restarting ambulatory care and elective procedures. The May 21, 2020, forum explored how organizations are responding to the pandemic and planning for the next phases. Participants explored two vital topics in the COVID-19 era:

• How virtual care analytics supports rapid change in ambulatory care delivery.
• How analytic insights help drive a COVID-19 financial recovery plan.

Three Keys to Improving Hospital Patient Flow with Machine Learning

Health systems alike struggle to effectively manage hospital patient flow. With machine learning and predictive models, health systems can improve patient flow for different departments throughout the system like the emergency department. Health systems should focus on three key areas to foster successful data science that will lead to improved hospital patient flow:

Key 1. Build a data science team.
Key 2. Create a ML pipeline to aggregate all data sources.
Key 3. Form a comprehensive leadership team to govern data.

Improving hospital patient flow through predictive models results in reduced patient wait times, reduced staff overtime, improved patient outcomes, and improved patient and clinician satisfaction.

Health Systems Share COVID-19 Financial Recovery Strategies in First Client Huddle

More than 100 attendees joined the first of a series of Health Catalyst virtual client huddles designed to support client partners and aid collaboration and direct client connections in this time of unprecedented change.
According to an April 2020 survey of Health Catalyst clients, 72.6 percent said they had a strong interest in examples, guidance, and tools from other health systems. In the client-only session, insights shared included the most common COVID-19 analytic projects and one health system’s elective surgery plan.

The health system shared the challenges they faced in understanding the financial impact of halting elective surgeries as well as creating a plan for working through their backlog. They also shared the tools and strategies they are using to aid their financial recovery.

The Top Five Insights into Healthcare Operational Outcomes Improvement

Effective, sustainable healthcare transformation rests in the organizational operations that power care delivery. Operations include the administrative, financial, legal, and clinical activities that keep health systems running and caring for patients. With operations so critical to care delivery, forward-thinking organizations continuously strive to improve their operational outcomes. Health systems can follow thought leadership that addresses common industry challenges—including waste reduction, obstacles in process change, limited hospital capacity, and complex project management—to inform their operational improvement strategies.

1. Five top insights address the following aspects of healthcare operational outcomes improvement:
2. Quality improvement as a foundational business strategy.
3. Using improvement science for true change.
4. Increasing hospital capacity without construction.
5. Leveraging project management techniques.
6. Features of highly effective improvement projects.

The Healthcare Analytics Adoption Model: A Roadmap to Analytic Maturity

The focus on analytics is contributing to the “EHR problem”—doctors prioritizing the EHR over patients. The Healthcare Analytics Adoption Model (HAAM) walks healthcare organizations through nine levels that lay the framework to fully leverage analytic capabilities to improve patient outcomes:

Level 1. Enterprise Data Operating System
Level 2. Standardized Vocabulary & Patient Registries
Level 3. Automated Internal Reporting
Level 4. Automated External Reporting
Level 5. Waste and Care Variability Reduction
Level 6. Population Health Management & Suggestive Analytics
Level 7. Clinical Risk Intervention & Predictive Analytics
Level 8. Personalized Medicine & Prescriptive Analytics
Level 9. Direct-To-Patient Analytics & Artificial Intelligence

Analytics are crucial to becoming a data-driven organization, but providers and administrators can’t forget about the why behind the data—to improve outcomes. Following the HAAM enables organizations to build a sustainable, analytic platform and empower patients to become data-driven when it comes to their own care.

The Healthcare Analytics Adoption Model: A Roadmap to Analytic Maturity

The focus on analytics is contributing to the “EHR problem”—doctors prioritizing the EHR over patients. The Healthcare Analytics Adoption Model (HAAM) walks healthcare organizations through nine levels that lay the framework to fully leverage analytic capabilities to improve patient outcomes:

Level 1. Enterprise Data Operating System
Level 2. Standardized Vocabulary & Patient Registries
Level 3. Automated Internal Reporting
Level 4. Automated External Reporting
Level 5. Waste and Care Variability Reduction
Level 6. Population Health Management & Suggestive Analytics
Level 7. Clinical Risk Intervention & Predictive Analytics
Level 8. Personalized Medicine & Prescriptive Analytics
Level 9. Direct-To-Patient Analytics & Artificial Intelligence

Analytics are crucial to becoming a data-driven organization, but providers and administrators can’t forget about the why behind the data—to improve outcomes. Following the HAAM enables organizations to build a sustainable, analytic platform and empower patients to become data-driven when it comes to their own care.

Population Health Management: A Path to Value

As value-based care (VBC) definitions and goals continue to shift, organizations struggle to create a roadmap for population health management (PHM) and to track associated costs and revenue. However, health systems can move forward with PHM amid the uncertainty by following the best practices of a path to value:

• Begin with Medicare Advantage—a good growth opportunity with low barriers to entry.
• Focus on ambulatory, not acute, care as it delivers more value.
• Leverage registries based on utilization to identify the most impactable 3 to 10 percent of utilizers.
• Simplify the physician burden by focusing on reasonable measures.

A Roadmap for Optimizing Clinical Decision Support

Compared to industries such as aerospace and automotive, healthcare lags behind in decision support innovation. Following the aerospace and automotive arenas, healthcare can learn critical lessons about improving its clinical decision support capabilities to help clinicians make more efficient, data-informed decisions:

1. Achieve widespread digitization: Healthcare must digitize its assets and operations (patient registration, scheduling, encounters, diagnosis, orders, billings, and claims) for effective CDS similarly to how aerospace digitized the aircraft, air traffic control, baggage handling, ticketing, maintenance, and manufacturing.
2. Build data volume and scope: Healthcare must collect socioeconomic, genomic, patient-reported outcomes, claims data, and more to truly understand the patient at the center of the human health data ecosystem.

Four Keys to Increase Healthcare Market Share

With leadership alignment, easy access to data, and a roadmap to reach their objectives, health systems can drastically increase revenue and grow market share by applying four principles:

• Key 1. Alignment.
• Key 2. Vehicles.
• Key 3: Five tools: access to data, data acumen; finance, vision to execution, and prioritizing outcomes.
• Key 4: Education.

Access to the right data can drive changes that generate $48M in revenue, surpassing the year three market share goals in year two.

How to Design an Effective Clinical Measurement System (And Avoid Common Pitfalls)

As healthcare organizations strive to provide better care for patients, they must have an effective clinical measurement system to monitor their progress. First, there are only two potential aims when designing a clinical measurement system: measurement for selection or measurement for improvement. Understanding the difference between these two aims, as well as the connection between clinical measurement and improvement, is crucial to designing an effective system.

This article walks through the distinct difference between these two aims as well as how to avoid the common pitfalls that come with clinical measurement. It also discusses how to identify and track the right data elements using a seven-step process.

A Healthcare Mergers Framework: How to Accelerate the Benefits

Health system mergers can promise significant savings for participating organizations. Research, however, indicates as much as a tenfold gap between expectation and reality, with systems looking for a savings of 15 percent but more likely to realize savings around 1.5 percent.

Driving the merger expectation-reality disparity is a complex process that, without diligent preparation and strategy, makes it difficult for organizations to fully leverage cost synergies. With the right framework, however, health systems can achieve the process management, data sharing, and governance structure to align leadership, clinicians, and all stakeholders around merger goals.

The Top Three 2020 Healthcare Trends and How to Prepare

After a tumultuous 2019, healthcare organizations are pivoting to make sense of the latest changes and prepare to face the top 2020 healthcare trends:

• Consumerism—Can health systems respond to the consumer demands of better access and price transparency?
• Financial Performance—With mergers, acquisitions, and private sector companies entering the healthcare arena, how will traditional hospitals and clinics compete?
• Social Issues—How will health organizations respond to the opioid crisis and consider social determinants of health as part of the care process to provide comprehensive treatment?

As health systems struggle to survive amidst constant change, they must look forward and proactively prepare for what’s to come in 2020.

Putting Patients Back at the Center of Healthcare: How CMS Measures Prioritize Patient-Centered Outcomes

Today’s healthcare encounters are too often marked by more clinician screen time than patient-clinician engagement. Increasing regulatory reporting burdens are diverting clinician attention from their true priority—the patient. To put patients back at the center of care, CMS introduced its Meaningful Measures framework in 2017. The initiative identifies the highest priorities for quality measurement and improvement, with the goal of aligning measures with CMS strategic goals, including the following:

1. Empowering patients and clinicians to make decisions about their healthcare.
2. Supporting innovative approaches to improve quality, safety, accessibility, and affordability.

AI in Healthcare: Finding the Right Answers Faster

Health systems rely on data to make informed decisions—but only if that data leads to the right conclusion. Health systems often use common analytic methods to draw the wrong conclusions that lead to wasted resources and worse outcomes for patients. It is crucial for data leaders to lay the right data foundation before applying AI, select the best data visualization tool, and prepare to overcome five common roadblocks with AI in healthcare:

1. Predictive Analysis Before Diagnostic Analysis Leads to Correlation but Not Causation.
2. Change Management Isn’t Considered Part of the Process.
3. The Wrong Terms to Describe the Work.
4. Trying to Compensate for Low Data Literacy Resulting in Unclear Conclusions.
5. Lack of Agreement on Definitions Causes Confusion.

As AI provides more efficiency and power in healthcare, organizations still need a collaborative approach, deep understanding of data processes, and strong leadership to effect real change.

AI-Assisted Decision Making: Healthcare’s Next Frontier

While many healthcare organizations have implemented Artificial Intelligence (AI) and Machine Learning (ML) tools at the point of care, few have successfully applied them to high-level decision making. A new frontier is expanding AI from artificial intelligence to augmented intelligence; traditional AI focuses on improving analytics efficiency while augmented intelligence is about improving the decision-making ability of healthcare leaders.

This article addresses the capabilities health systems should embrace and provides two examples of how AI can assist with leaders with their most important decisions. Healthcare leaders’ biggest needs of from AI are the ability to separate signal from noise and make decisions that impact the future.

Achieving Stakeholder Engagement: A Population Health Management Imperative

To succeed in population health management (PHM), organizations must overcome barriers including information silos and limited resources. Due to the systemwide nature of these challenges, widespread stakeholder engagement is an imperative in population-based improvement.

An effective PHM stakeholder engagement strategy incorporates the following:

1. Includes as many stakeholders as possible at the beginning of the journey.
2. Meets the unique analytics and reporting needs of the organization.
3. Enables users to measure, and therefore manage, PHM outcomes.
4. Provides the real-time analytics value-based care requires.

Removing Barriers to Clinician Engagement: Partnerships in Improvement Work

With clinicians driving many of the decisions that affect health system quality and cost, they’re an essential part of successful improvement efforts. Clinicians are, however, notoriously overburdened in today’s healthcare setting, and getting their buy-in for additional projects is often a big challenge. To successfully partner with these professionals in improvement work, health systems must develop engagement strategies that prioritize clinician needs and concerns and leverage data that’s meaningful to clinicians.

Improvement leaders can approach clinician engagement on three levels:

1. Clinician-led local programs.
2. Department- or division-level programs.
3. Leadership-level growth and improvement programs.

Artificial Intelligence in Healthcare: A Change Management Problem

The key to successfully leveraging artificial intelligence (AI) in healthcare rests not wholly in the technical aspects of predictive and prescriptive machines but also in change management within healthcare organizations. Better adoption and results with AI rely on a commitment to the challenge of change, the right tools, and a human-centered perspective.

To succeed in change management and get optimal value from predictive and prescriptive models, clinical and operational leaders must use three perspectives:

1. Functional: Does the model make sense?
2. Contextual: Does the model fit into the workflow?
3. Operational: What benefits and risks are traded?

Three Key Strategies for Healthcare Financial Transformation

To succeed in today’s rapidly evolving business environment, healthcare organizations must have accurate financial data. Approximately 50 percent of CMS payments are now tied to a value component; hospital operating margins are at an all-time low; and consumer demands are rising with their costs.
In order to meet these new challenges, health systems must shift their strategy or risk being left behind. This article details the operational, organizational, and financial strategies that drive financial transformation, as well as examples of how to obtain and utilize financial data, find waste reduction opportunities, and much more.

Machine Learning Tools Unlock the Most Critical Insights from Unstructured Health Data

Patient comments such as “I feel dizzy” or “my stomach hurts” can tell clinicians a lot about an individual’s health, as can additional background, including zip code, employment status, access to transportation, and more. This critical information, however, is captured as free text, or unstructured data, making it impossible for traditional analytics to leverage.

Machine learning tools (e.g., NLP and text mining) help health systems better understand the patient and their circumstances by unlocking valuable insights residing unstructured data:

1. NLP analyzes large amounts of natural language data for human users.
2. Text mining derives value through the analysis of mass amounts of text (e.g., word frequency, length of words, etc.).

Healthcare Quality Improvement: A Foundational Business Strategy

Waste is a $3 trillion problem in the U.S. Fortunately, quality improvement theory (per W. Edwards Deming) intrinsically links high-quality care with financial performance and waste reduction. According to Deming, better outcomes eliminate waste, thereby reducing costs.

To improve quality and process and ultimately financial performance, an industry must first determine where it falls short of its theoretic potential. Healthcare fails in five critical areas:

1. Massive variation in clinical practices.
2. High rates of inappropriate care.
3. Unacceptable rates of preventable care-associated patient injury and death.
4. A striking inability to “do what we know works.”
5. Huge amounts of waste.

The DOS™ E-Book: A Launchpad for the Healthcare Cloud Journey

While over 90 percent of organizations in industries worldwide now use cloud computing in their operations, healthcare still lags behind.
As health systems grow their ability to capture data, they still have only a fraction of the data they need to achieve today’s population health and precision medicine goals.

Organizations looking to migrate to more agile cloud-based platforms and leverage data for measurable improvements can learn the fundamentals of this critical transformation in an e-book about the Health Catalyst Data Operating System (DOS™).

Harnessing the Power of Healthcare Data: Are We There Yet?

What can healthcare learn from Formula One racing?
According to Dr. Sadiqa Mahmood, SVP of medical affairs and life sciences for Health Catalyst, race support teams leverage about 30TB of baseline data to create a digital twin of the car, track, and racer for simulation models that drive decisions at each race.
Applied in the healthcare setting, a digital twin can help clinicians better understand each patient and their health conditions and circumstances in real time and make comprehensive, informed care decisions.
But for the healthcare digital twin to happen, the industry must move away from data silos and towards a digital learning healthcare ecosystem.

The DOS™ eBook: A Launchpad for the Healthcare Cloud Journey

Learn how the Health Catalyst data platform brings healthcare organizations the benefits of a more flexible computing infrastructure in the cloud.

A New Era of Personalized Medicine: The Power of Analytics and AI

Healthcare is looking towards an era of personalized medicine in which providers customize treatments for the individual patient. Realizing this tailored level of care s a new level of data volume and analytics and AI capabilities that, while novel to healthcare, other industries are thriving in. Choosing the right role models as healthcare works towards the analytics- and AI-driven territory of personalized medicine will guide informed strategies and establish best practices.

With experience and expertise in these key areas, the military, aerospace, and automotive industries can serve as healthcare’s best examples:

1. The human cognitive processes of complex decision making.
2. The digitization of their industries, with the “health” of their assets as key drivers.
3. Operating in a “big data” ecosystem.

Activity-Based Costing: Healthcare’s Secret to Doing More with Less

Delivering high-quality, cost-efficient care to specific patient populations within a service line is nearly impossible without a sophisticated costing methodology. Activity-based costing (ABC) provides a nuanced, comprehensive view of cost throughout a patient’s journey and reveals the “true cost” of care—the real cost for each product and service based on its actual consumption—which traditional costing systems don’t provide.

With the true cost of care at their fingertips, healthcare leaders can identify at-risk populations earlier—such as pregnant women diagnosed with gestational diabetes mellitus—and more quickly implement effective interventions (e.g., more scrupulous monitoring and earlier screenings). Health systems that leverage the actionable insight from ABC further benefit by implementing the same, or similar, process/clinical improvement measures across other service lines.

The Healthcare Analytics Summit™: 2019’s Top Data Discoveries and Insights

The 2019 Healthcare Analytics Summit™ (HAS) was packed full of insightful discussions about data democratization, delivering healthcare in a digital age, and the future of analytics and AI.
The 2019 HAS infographic reveals 1,600 industry leaders attended, with 60 percent of attendees from the IT/analyst industry, discussing trending data topics, interacting with presenters through polling mechanisms, and utilizing networking opportunities to share solutions and problem-solving methods.

How Artificial Intelligence Can Overcome Healthcare Data Security Challenges and Improve Patient Trust

As healthcare organizations today face more security threats than ever, artificial intelligence (AI) combined with human judgment is emerging as the perfect pair to improve healthcare data security.
Together, they power a highly accurate privacy analytics model that allows organizations to review access points to patient data and detect when a system’s EHR is potentially exposed to a privacy violation, attack, or breach.
With specific techniques, including supervised and unsupervised machine learning and transparent AI methods, health systems can advance toward more predictive, analytics-based, collaborative privacy analytics infrastructures that safeguard patient privacy.

The 2019 Healthcare Analytics Summit: Thursday Recap

HAS attendees are accustomed to innovation and projections for the future of digital health. But on the final day of HAS 19, they met the next generation of transformation in person: teenager Justin Aronson presented a keynote on how data democratization will empower him and his peers to solve the challenges of coming decades.
Other keynotes—Google’s Marianne Slight, former Bayer CDO Jessica Federer, and Beth Israel Deaconess System CIO Dr. John Halamka—contributed their visions for healthcare’s next era, and presenters in 20 breakout sessions shared the experiences, processes, and technologies that will carry digital transformation forward.

The 2019 Healthcare Analytics Summit: Wednesday Recap


The first full day of the 2019 Healthcare Analytics Summit (HAS 19 - Healthcare Analytics Summit 19) featured keynotes from Thomas Jefferson University CEO Dr. Steve Klesko, best-selling author Daniel Pink, former New Jersey Attorney General Anne Milgram, and President of MDLIVE Medical Group Dr. Lyle Berkowitz. Two waves of breakout sessions covered success stories from organizations around the country and their journeys to transformation through further digitization.

Justin Aronson: A High School Student and HAS 19 Keynote Who’s Transforming the Understanding of Genetic Variants

According to the next generation of healthcare transformation leaders, data democratization is mission critical for the future of improvement.
High school student Justin Aronson explains how he leverages open-source health laboratory data to build a tool that improves the clinical interpretation of sequenced genetic variants.
Aronson’s cloud-based data integration and visualization system, Variant Explorer, runs on genomic and phenotype data that’s feely accessible on the public archive ClinVar. He says that large-scale data democratization is the key to current and future healthcare problem solving.

Healthcare’s Next Revolution: Finding Success in the Medicare Shared Savings Program

A series of revolutions has driven the development of the U.S. healthcare system, enabling dramatic improvements in all aspects of healthcare quality and outcomes over the past century. Although healthcare organizations have focused on moving towards value-based care for decades, the data shows that the shift is indeed taking place and fee-for-service models are declining.

New changes to the Medicare Shared Savings Program (MSSP) will help drive this change as revisions to MSSP require ACOs to take on more financial risk earlier. This article covers the following topics:

1. Important moments in history that led to today’s current challenges.
2. Why financial imperatives drive cultural change in our economic model.3. Ways MSSP can help healthcare organizations achieve financial success.
4. How to utilize data to develop better healthcare delivery systems.

Healthcare’s Next Revolution: Finding Success in the Medicare Shared Savings Program

A series of revolutions has driven the development of the U.S. healthcare system, enabling dramatic improvements in all aspects of healthcare quality and outcomes over the past century. Although healthcare organizations have focused on moving towards value-based care for decades, the data shows that the shift is indeed taking place and fee-for-service models are declining.

New changes to the Medicare Shared Savings Program (MSSP) will help drive this change as revisions to MSSP require ACOs to take on more financial risk earlier. This article covers the following topics:
1. Important moments in history that led to today’s current challenges.
2. Why financial imperatives drive cultural change in our economic model.
3. Ways MSSP can help healthcare organizations achieve financial success.
4. How to utilize data to develop better healthcare delivery systems.

Introducing Population Builder™: Stratification Module

The Health Catalyst Population Builder: Stratification Module allows healthcare organizations to identify the right patient populations in order to deliver the right care at the right time.
The solution provides a seamless process for stratifying populations from multiple sources (EMR, claims, and clinical), using pre-defined, easily customized populations as building blocks.
With a comprehensive view of the patients they manage, organizations can map populations along their continuum of care and confidently transition appropriate populations to population health interventions.

Introducing the Health Catalyst Population Health Foundations Solution: A Data- and Analytics-first Approach to PHM

Introducing the Health Catalyst Population Health Foundations solution, which draws on integrated claims and clinical data, and provides essential, extensible tools and machine-learning capabilities for optimizing results in value-based risk arrangements.
Accompanying solution services ensure that the strategic value of data is maximized to improve performance in risk contracts—and provide side-by-side subject matter expert partnership for establishing short- and long-term goals for population health management.

ACOs and CINs: Past, Present, and Future

Accountable Care Organizations (ACOs) and clinically integrated networks (CINs) are two types of organizations working to address the problem of rising costs. As ACOs and CINs continue to evolve, organizations moving into value-based care (VBC) face an ever-changing landscape.
This article looks at the evolution of the ACO and CIN models, what new tools ACOs employ today to promote success, and lessons learned from organizations that have succeeded in alternative payment models. It also explores what healthcare experts believe the future of alternative payment models will look like and competencies to develop to meet those changing demands.

The Top Six Examples of Quality Improvement in Healthcare

In order to thrive in an increasingly challenging healthcare environment, undertaking quality improvement projects is more important than ever for healthcare systems’ continued survival. However, health systems need to tackle the right projects at the right time to maximize the impact to their organization.

This article shares both clinical and financial and operational examples of quality improvement in healthcare that may help others as they tackle improvement projects. Some examples shared include:

• Pharmacist-led Medication Therapy Management (MTM) reduces total cost of care.
• Optimizing sepsis care improves early recognition and outcomes.
• Boosting readiness and change competencies successfully reduces clinical variation.
• New generation Activity-Based Costing (ABC) accelerates timeliness of decision support.
• Systematic, data-driven approach lowers length of stay (LOS) and improves care coordination.
• Clinical and financial partnership reduces denials and write-offs by more than $3 million.

How to Increase Cash Flow Using Data and Analytics

In today’s challenging environment, healthcare leaders must seek opportunities to boost revenue through improved financial performance and reimbursement. Some common strategies include reducing the number of outstanding bill hold accounts, reducing A/R days, and managing discharged not final billed (DNFB) cases.

This article tackles, the following topics:

• Common reasons accounts remain unbilled.
• Identifying opportunities for improvement.
• Using data analytics and process improvement to achieve financial goals.
• Creating lasting improvements.

Five Action Items to Improve HCC Coding Accuracy and Risk Adjustment With Analytics

A hot topic in healthcare right now, especially in the medical coding world is the Hierarchical Condition Category (HCC) risk adjustment model and how accurate coding affects healthcare organizations’ reimbursement.

With almost one third of Medicare beneficiaries enrolled in Medicare Advantage plans, it’s more important than ever for healthcare organizations to pay attention to this model and make sure physicians are coding diagnoses appropriately to ensure fair compensation. This article walks through basics of the risk adjustment model, why coding accuracy is so important, and five action items for interdisciplinary work groups to take. They include:

1. Having an accurate problem list.
2. Ensuring patients are seen in each calendar year.
3. Improving decision support and EMR optimization.
4. Widespread education and communication.
5. Tracking performance and identifying opportunities.

Healthcare NLP: The Secret to Unstructured Data’s Full Potential

While healthcare data is an ever-growing resource, thanks to broader EHR adoption and new sources (e.g., patient-generated data), many health systems aren’t currently leveraging this information cache to its full potential. Analysts can’t extract and analyze a significant portion of healthcare data (e.g., follow-up appointments, vitals, charges, orders, encounters, and symptoms) because it’s in an unstructured, or text, form, which is bigger and more complex than structured data.

Natural language processing (NLP) taps into the potential of unstructured data by using artificial intelligence (AI) to extract and analyze meaningful insights from the estimated 80 percent of health data that exists in text form. Though still an evolving capability, NLP is showing promise in helping organizations get more from their data.

Four Steps to Effective Opportunity Analysis

Opportunity analysis uses data to identify potential improvement initiatives and quantifies the value of these initiatives—both in terms of patient care benefits and financial impact. This process is an effective way to find unwarranted and costly clinical variation and, in turn, develop strategies to reduce it, improving outcomes and saving costs along the way. Standardizing the opportunity analysis process makes it repeatable and prioritizes actionable opportunities.

Quarterly opportunity analysis should follow four steps:

• Kicking off the analysis by getting analysts together to do preliminary analysis and brainstorm.
• Engaging with clinicians to identify opportunities and, in the process, get clinician buy in.
• Digging deeper into the suggested opportunities to prioritize those that offer the greatest benefits.
• Presenting findings to the decision makers.

The Top Five 2019 Healthcare Trends

Bobbi Brown, MBA, and Stephen Grossbart, PhD have analyzed the biggest changes in the healthcare industry and 2018 and forecasted the trends to watch for in 2019. This report, based on their January 2019, covers the biggest 2019 healthcare trends, including the following:

• The business of healthcare including new market entrants, business models and shifting strategies to stay competitive.
• Increased consumer demand for more transparency
• Continuous quality and cost control monitoring across populations.
• CMS proposals to push ACOs into two-sided risk models.
• Fewer process measures but more quality outcomes scrutiny for providers.

Customer Journey Analytics: Cracking the Patient Engagement Challenge for Payers

Customer journey analytics uses machine learning and big data to track and analyze when and through what channels customers interact with an organization, with an aim to influence behavior (e.g., buying behaviors among retail customers). Similarly, healthcare organizations want to influence health-related behaviors, such a taking medication as prescribed and not smoking, to improve outcomes and lower the cost of care. In a partnership with an analytics services provider, a payer organization is leveraging customer journey analytics among healthcare consumers to identify the best opportunities and channels for patient outreach. With this analytics-driven engagement strategy, the payer has found an opportunity to significantly improve patient engagement—a predicted overall increase from 18 percent to 31 percent.

How to Build a Healthcare Analytics Team and Solve Strategic Problems

Health systems have vast amounts of data, but frequently struggle to use that data to solve strategic problems in a timely fashion. A healthcare analytics team, made up of the right people with the right tools and skillsets, can help address these challenges. This article walks through the steps organizations need to take to put an effective analytics team in place.

These include the following:

• Recognizing the need for change.
• Demonstrating the value of an analytics team.
• Conducting a current state assessment.
• Identifying solutions.
• Implementing a phased approach.
• Building a roadmap.
• Making the pitch.
• Putting the roadmap into action.

The article also includes the foundation skills to look for when putting together the team and tips on how best to organize.

Leveraging Technology to Increase Patient Satisfaction and Employee Engagement

Health systems are challenged by the need to keep patients and employees satisfied and engaged. This can be especially difficult for organizations in flux, growing, merging, and changing. And as leaders of these organizations know, poor patient satisfaction ratings lead to reduced reimbursements, which affect the bottom line.

To meet this challenge and improve patient satisfaction, health system leaders are taking advantage of technology, such as rounding software, that supports effective communication and drives the type of culture change that boosts both caregiver and patient satisfaction and encourages engagement.
Embedding rounding technology into current processes makes rounding better and easier. The correlation between effective, efficient rounding and high patient satisfaction scores is clear. Rounding can and does increase engagement and satisfaction, which in turn leads to higher reimbursement potential. Learn how health system leaders can move from talking about rounding technology to incorporating it into daily workflow.

Unlocking the Power of Patient-Reported Outcome Measures (PROMs)

Health systems attempt to measure an ever-increasing amount of clinical measures, these often miss the mark of what matters to patients. Patient-Reported Outcomes (PROs) are the missing link in empowering patients and helping to define good outcomes.  This article walks through how patient-reported outcome measures (PROMs) can help identify best practices and drive system-wide quality improvement. PROMs can help health systems do the following:

• Serve as a guide for appropriateness and efficiency.
• Lead to better shared decision-making.
• Demonstrate value and transparency

This article also discusses the effect of PROMs on providers in a culture of “one more thing,” and tips for effective implementation.

Patient Safety Best Practices E-Book: The Intersection of Patient Care and Technology

Patient safety is a top concern for healthcare organizations. Fortunately, health IT assists leadership and frontline clinicians in the ongoing effort to improve patient care. This e-book comprises ten articles outlining the intersection of technology and patient care, highlighting how organizations can implement patient safety best practices.

ACOs: Four Ways Technology Contributes to Success

With an increasing emphasis on value-based care, Accountable Care Organizations (ACOs) are here to stay. In an ACO, healthcare providers and hospitals come together with the shared goals of reducing costs and increasing patient satisfaction by providing high-quality coordinated healthcare to Medicare patients.

However, many ACOs lack direction and experience difficulty understanding how to use data to improve care. Implementing a robust data analytics system to automate the process of data gathering and analysis as well as aligning data with ACO quality reporting measures.

The article walks through four keys to effectively implementing technology for ACO success:

1. Build a data repository with an analytics platform.
2. Bring data to the point of care.
3. Analyze claims data, identify outliers, including successes and failures.
4. Combine clinical claims, and quality data to identify opportunities for improvement.

The Four Keys to Increasing Hospital Capacity Without Construction

Many health systems have a hospital capacity problem as demand for patient beds rises. When the supply of usable patient beds can’t meet demand, the negative impact on patients and staff can be significant.

Hospitals can solve capacity problems with four key concepts:

1. Using data, start with the problem and the ideal solution.
2. Be sure the analytics team works with teams throughout the organization—including leadership.
3. Have leaders spend time with the operations team to understand workflow.
4. Focus on the impact, not the tool.

Why Clinical Quality Should Drive Healthcare Business Strategy

Healthcare today is in the midst of a massive transformation. The opportunities for improvement are great if healthcare systems can do the following:

• Reduce clinical variation.
• Reduce rates of inappropriate care and care-associated patient injury and death.
• Follow accepted best care practices.
• Eliminate waste.

This article covers the different types of waste in healthcare systems, ways to reduce them, financial alignment around waste reduction opportunities, and the importance of reducing clinical variation. The core driver of healthcare systems must be improving clinical quality. Almost always, with proper clinical management, better care is cheaper care through waste management.

How to Evaluate Emerging Healthcare Technology With Innovative Analytics

As healthcare systems are pressured to cut costs and still provide high-quality care, they will need to look across the care continuum for answers, reduce variation in care, and look to emerging technologies. This article walks through how to evaluate the safety and effectiveness and of emerging healthcare technology and prioritize high-impact improvement projects using a robust data analytics platform. Topics covered include:

• The importance of identifying variation in innovation.
• Ways to improve outcomes and decrease costs.
• The value of an analytics platform.
• The reliable information that produce sparks for innovation.
• Identifying and evaluating emerging healthcare technology.
• Knowing what data to use.
• The difference between efficacy and effectiveness in evaluation of emerging healthcare technology.

Reducing Hospital Readmissions: A Case for Integrated Analytics

Health systems continue to prioritize reducing hospital readmissions as part of their value-based payment and population health strategies. But organizations that aren’t fully integrating analytics into their readmission reduction workflows struggle to meet improvement goals. By embedding predictive models across the continuum of care, versus isolated them in episodes of care, health systems can leverage analytics for meaningful improvement.

Organizations that integrate predictive models into readmissions reduction workflows have achieved as much as a 40 percent reduction in risk-adjusted readmissions indexes. Effective analytics integration strategies use a multidisciplinary development approach to meet the needs of a patient’s entire care team and deliver common tools for all involved in the patient’s healthcare journey.

Emergency Department Quality Improvement: Transforming the Delivery of Care

Overcrowding in the emergency department has been associated with increased inpatient mortality, increased length of stay, and increased costs for admitted patients. ED wait times and patients who leave without seeing a qualified medical provider are indicators of overcrowding. A data-driven system approach is needed to address these problems and redesign the delivery of emergency care.

This article explores common problems in emergency care and insights into embarking on a successful quality improvement journey to transform care delivery in the ED, including an exploration of the following topics:

• A four-step approach to redesigning the delivery of emergency care.
• Understanding ED performance.
• Revising High-Impact Workflows.
• Revising Staffing Patterns.
• Setting Leadership Expectations.
• Improving the Patient Experience.

Social Determinants of Health: Tools to Leverage Today’s Data Imperative

Social determinants of health (SDOH) data captures impacts on patient health beyond the healthcare delivery system. Traditional health data (e.g., from healthcare encounters) only tells a portion of the patient and population health story. To understand the full spectrum of health impacts (e.g., from environment to relationship and employment status), organizations need data from their patient’s daily lives. The urgency for SDOH data is particularly strong today, as value-based payment increasingly presses health systems to raise quality and lower cost. Without fuller insight into patient health (what happens beyond healthcare encounters) organizations can’t align with community services to help patients meet needs of daily living—prerequisites for maintaining good health.

Standardizing SDOH data into healthcare workflows, however, requires an informed strategy. Health systems will benefit by following a standardization protocol that includes relevant and comprehensive domains, engages patients, enables broader understanding of patient health, integrates with organizational EHRs, and is easy for clinicians to follow.

Improving Quality Measures Can Lead to Better Outcomes

Current quality measures are expensive and time consuming to report, and they don’t necessarily improve care. Many health systems are looking for better ways to measure the quality of their care, and they are using data analytics to achieve this goal. Data analytics can be helpful with quality improvement. There are four key considerations to evaluate quality measures:

1. Organizations must develop measures that are more clinically relevant and better represent the care provided.
2. Clinician buy-in is critical. Without it, quality improvement initiatives are less likely to succeed.
3. Investment in tools and effort surrounding improvement work must increase. Tools should include data analytics.
4. Measure improvement must translate to improvement in the care being measured.

When the right measures are in place to drive healthcare improvement, patient care and outcomes can and do improve.

The Digitization of Healthcare: Why the Right Approach Matters and Five Steps to Get There

While many industries are leveraging digital transformation to accelerate their productivity and quality, healthcare ranks among the least digitized sectors. Healthcare data is largely incomplete when it comes to fully representing a patient’s health and doesn’t adequately support diagnoses and treatment, risk prediction, and long-term health care plans. But even with the obvious urgency for increased healthcare digitization, the industry must raise this trajectory with sensitivity to the impacts on clinicians and patients. The right digital strategy will not only aim for more comprehensive information on patient health, but also leverage data to empower and engage the people involved.

Health systems can follow five guidelines to digitize in a sustainable, impactful way:

1. Achieve and maintain clinician and patient engagement.
2. Adopt a modern commercial digital platform.
3. Digitize the assets (the patients) and the processes.
4. Understand the importance of data to drive AI insights.
5. Prioritize data volume.

A Framework for High-Reliability Organizations in Healthcare

Drs. Allen Frankel and Michael Leonard have developed a framework for creating high-reliability organizations in healthcare. This report, based on their 2018 webinar, covers the components and factors of this frame work, including:

• Leadership
• Transparency
• Reliability
• Improvement and Measurement
• Continuous Learning
• Negotiation
• Teamwork and Communication
• Accountability
• Psychological Safety

The Healthcare Data Warehouse: Lessons from the First 20 Years

Twenty years after Intermountain Healthcare launched its enterprise data warehouse in 1998, industry leaders are looking at what they did right, what they’d do differently, and what the future holds for healthcare data and analytics.
While early successes (such as a hiring framework of social, domain, and technical skills; lightweight data governance; and late-binding architecture) continue to hold their value, advanced analytics and technology and innovation in diagnosis and treatment are reshaping the capabilities of and demands on the healthcare data warehouse.
Present-day and future healthcare IT leaders will have to revisit approaches to data warehousing people, processes, and technology to understand how they can improve, continue to adapt, and fully leverage emerging opportunities.

Three Principles for Making Healthcare Data Analytics Actionable

Data is everywhere. But without a plan to extract meaning from data and turn insights into action, data can’t impact outcomes. Generating value from data takes work, but it can be done. To create compelling data insights that promote action, health systems can follow three guiding principles for actionable healthcare data analytics as well as hire analysts with seven important skills.

Three principles form the foundation for actionable healthcare data analytics:

1. Balance investments.
2. Hire generalists over specialists.
3. Develop a team that’s highly aligned and loosely coupled.

Four Critical Phases for Effective Healthcare Data Governance

Based on a 2018 Healthcare Analytics Summit presentation, this report details the four phases necessary for successful healthcare data governance:

1. Elevate a vision and agenda that align with organizational priorities.
2. Establish an organizational structure to fulfill the data governance mandate.
3. Execute with prioritized data governance projects, people and resource assignment, and disciplined focus on the work.
4. Extend data governance investments and efforts through established practices.

Each step must follow the core principles of stakeholder engagement, shared understanding, alignment, and focus. Effective healthcare data governance is not a one-time event and requires ongoing and iterative efforts.

The Secret to Patient Compliance: An Application of The Four Tendencies Framework

Every day, healthcare professionals face the challenge of determining how to get patients to make good healthcare decisions and follow recommendations. The Four Tendencies framework, developed by The New York Times bestselling author Gretchen Rubin, can make this task easier and improve patient compliance by revealing how each person responds to expectations. By asking this question, healthcare practitioners can gain exciting insights into how patients respond to expectations to in order to help them achieve their goals.

This report covers the following:

1. An overview of each of the Four Tendencies.
2. An understanding of how these tendencies can affect behavior in a healthcare setting.
3. Practical tips for working with patients and colleagues that fall into different tendencies.

Lean Healthcare: 6 Methodologies for Improvement from Dr. Brent James

The survival of healthcare organizations depends on applying lean principles. Organizations that adopt lean principles can reduce waste while improving the quality of care. By applying stringent clinical data measurement approaches to routine care delivery, healthcare systems identify best practice protocols and incorporate those into the clinical workflow. Data from these best practices are applied through continuous-learning loop that enables teams across the organization to update and improve protocols–ultimately reducing waste, lowering costs, and improving access to care.

This executive report based on a presentation by Dr. Brent James at a regional medical center, covers the following:

1. How lean healthcare principles can help improve the quality of care.
2. The steps healthcare organizations need to take to create a continuous-learning loop.
3. How a lean approach creates financial leverage by eliminating waste and improving net operating margins and ROI.

Infographic: Statistics from the 2018 Healthcare Analytics Summit

The 2018 Healthcare Analytics Summit statistics are on display in this fun infographic. A few of those statistics show our commitment to put on an educational, valuable summit:

• 1300 attendees from 419 organizations
• 97% overall satisfaction rating
• 98% likely to recommend to a friend

The 2018 Healthcare Analytics Summit: Thursday Recap

In the final day of the 2018 Healthcare Analytics Summit in Salt Lake City, we were treated a continuation of the highest-rated keynote lineup in the event’s 5-year history.
Dr. Penny Wheeler shared some important tips about improvement.
Three digital innovators showed mind-blowing technology and approaches that will forever change healthcare, and Kim Goodsell showed us all why she’s the only the first of her kind—the data-empowered, genomified patient of the future.

The 2018 Healthcare Analytics Summit: Wednesday Recap

The first full-day of the 2018 Healthcare Analytics Summit (HAS 18) featured keynotes from Marc Randolph (Co-Founder, Netflix), Dr. Brent James, Dr. Daniel Kraft, Dr. Toby Cosgrove, Dr. Jill Hoggard Green, and Dr. Robert Wachter. Two waves of breakout sessions covered success stories from organizations all over the nations, complete with countless lessons learned.

Survey Shows the Role of Technology in the Progress of Patient Safety

A lack of effective technology is impeding the progress of patient safety, according to a 2018 survey of healthcare professionals. Even though most healthcare organizations claim safety as a priority, serious challenges remain to making a significant impact on patient safety outcomes.

Survey respondents said ineffective information technology and the related lack of real-time warnings for possible harm events were the top barriers to improving patient safety. They cited a number of key obstacles:

1. Lack of resources.
2. Organization structure.
3. Lack of reimbursement for safety measures.
4. Changes in patient population.

This survey of more than 400 healthcare professionals tackles a big question many hospital leaders are asking: Why aren’t we seeing improvements in patient safety despite our efforts?

Five Reasons Why Health Catalyst Acquired Medicity and What It Means for Interoperability, as Explained by Dale Sanders, President of Technology

Why did Health Catalyst acquire Medicity? Dale Sanders, President of Technology, shares five reasons and what it means for interoperability:

1. Medicity has several petabytes of valuable data content.
2. Medicity’s data governance expertise.
3. Medicity’s 7 x 24 real-time cloud operations expertise.
4. Medicity’s expertise in real-time EHR integration.
5. Medicity’s presence and expertise in the loosely affiliated, community ambulatory care management space.

Data Warehousing in Healthcare: A Guide to Success

Looking for a way to share his extensive experience with data warehousing in healthcare, in 2002 Dale Sanders wrote what many consider to be the “EDW Bible.”
It’s a document with guidance that, if followed, will drive value and utilization from a data warehouse. We’ve made that report available now.

The Future of Healthcare AI: An Honest, Straightforward Q&A

Health Catalyst President of Technology, Dale Sanders, gives straightforward answers to tough questions about the future of AI in healthcare.
He starts by debunking a common belief: We are awash in valuable data in healthcare as a consequence of EHR adoption. The truth involves a need for deeper data about a patient.

The Key to Healthcare Mergers and Acquisitions Success: Don’t Rip and Replace Your IT

Healthcare mergers and acquisitions can involve a lot of EMRs and other IT systems. Sometimes leaders feel like they have to rip and replace these systems to fully integrate organizations. However, this is not the answer, according to Dale Sanders.
This report, based upon his July 2017 webinar, outlines the importance of a data-first strategy and introduces the Health Catalyst® Data Operating System (DOS™) platform. DOS can play a critical role in facilitating IT strategy for the growing healthcare M&A landscape.