Healthcare leaders who understand data science can embrace the significant improvement potential of the industry’s vast data stores, including an estimated $300 billion in annual costs savings. Executives must know the value of data science to understand the urgency in investing and supporting the technology and data scientists to fully leverage data’s capabilities. Today’s data science-savvy executives will lead the healthcare transformation by enabling faster, more accurate diagnoses and more effective, lower-risk treatments.
Improving Sepsis Care: Three Paths to Better OutcomesHealth Catalyst
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 SuccessHealth Catalyst
Surviving on thin operating margins underscores the need for all end users at a health system to make decisions based on comprehensive data sets. This data-centered approach to decision making allows team members to take the right course of action the first time and avoid making decisions based on fragmented data that exclude key pieces of information.
To promote data-driven decision making and a data-centric culture, healthcare organizations should increase data access and availability across the institution. With easy access to complete data, end users rely on the same data to make decisions, no matter where they work within the health system.
Two strategies can help organizations integrate and deliver data to end users when they need it:
Select infrastructure that fits most people’s needs.
Ask the right questions.
Want to know the best healthcare data warehouse for your organization? You’ll need to start first by modeling the data, because the data model used to build your healthcare enterprise data warehouse (EDW) will have a significant effect on both the time-to-value and the adaptability of your system going forward. Each of the models I describe below bind data at different times in the design process, some earlier, some later. As you’ll see, we believe that binding data later is better. The three approaches are 1) the enterprise data model, 2) the independent data model, and 3) the Health Catalyst Late-Binding™ approach.
When Healthcare Data Analysts Fulfill the Data Detective RoleHealth Catalyst
There’s a new way to think about healthcare data analysts. Give them the responsibilities of a data detective. If ever there were a Sherlock Holmes of healthcare analytics, it’s the analyst who thinks like a detective. Part scientist, part bloodhound, part magician, the healthcare data detective thrives on discovery, extracting pearls of insight where others have previously returned emptyhanded. This valuable role comprises critical thinkers, story engineers, and sleuths who look at healthcare data in a different way. Three attributes define the data detective:
They are inquisitive and relentless with their questions.
They let the data inform.
They drive to the heart of what matters.
Innovative analytics leaders understand the importance of supporting the data analyst through the data detective career track, and the need to start developing this role right away in the pursuit of outcomes improvement in all healthcare domains.
Cloud-Based Open-Platform Data Solutions: The Best Way to Meet Today’s Growin...Health Catalyst
Smartphone applications, home monitoring equipment, genomic sequencing, and social determinants of health are adding significantly to the scope of healthcare data, creating new challenges for health systems in data management and storage. Traditional on-premises data warehouses, however, don’t have the capacity or capabilities to support this new era of bigger healthcare data.
Organizations must add more secure, scalable, elastic, and analytically agile cloud-based, open-platform data solutions that leverage analytics as a service (AaaS). Moving toward cloud hosting will help health systems avoid the five common challenges of on-premises data warehouses:
1. Predicting future demand is difficult.
2. Infrastructure scaling is lumpy and inelastic.
3. Security risk mitigation is a major investment.
4. Data architectures limit flexibility and are resource intensive.
5. Analytics expertise is misallocated.
The Healthcare Analytics Ecosystem: A Must-Have in Today’s TransformationHealth Catalyst
Healthcare organizations seeking to achieve the Quadruple Aim (enhancing patient experience, improving population health, reducing costs, and reducing clinician and staff burnout), will reach their goals by building a rich analytics ecosystem. This environment promotes synergy between technology and highly skilled analysts and relies on full interoperability, allowing people to derive the right knowledge to transform healthcare.
Five important parts make up the healthcare analytics ecosystem:
Must-have tools.
People and their skills.
Reactive, descriptive, and prescriptive analytics.
Matching technical skills to analytics work streams.
Interoperability.
A Health Catalyst Overview: Learn How a Data First Strategy Can Drive Increas...Health Catalyst
Without the pressure of a one-on-one demo, you can join a crowd of peers to ‘kick the tires’ if you will, as you listen to Jared Crapo—a sought after healthcare strategist—talk about what a data-first strategy is, and the strategic components to a data-first strategy employing a data operating system, a breakthrough engineering approach that combines the features of data warehousing, clinical data repositories, and health information exchanges in a single, common-sense technology platform that turns data into actionable assets used for all types of outcomes improvements.
Lest you worry about too much ‘pie in the sky’ strategy talk with few results to show, Sam Turman, Senior Solution Architect, will provide tangible solution demonstrations that are driving material results. Even if you aren’t in the market for Health Catalyst solutions and services, you will be able to:
Think with more clarity through your approach to overcoming the current market challenges.
Reconsider the strategy you are employing to build cross-organizational awareness and support to put a data-first plan at the center of your plan.
Define action you can take today to assess your gaps, understand your options, and accelerate your progress to drive outcomes improvements.
Join us and you won’t be disappointed. Jared is one of those types of thinkers that many pay big money to listen to and it is our fortune to have 60 minutes with him to think deeply about moving healthcare forward, one patient at a time. We hope you can join us.
Going Beyond Genomics in Precision Medicine: What's NextHealth Catalyst
Precision medicine processes, while involving genomics, are not confined to working with data about an individual’s genes, environment, and lifestyle. Precision medicine also means putting patients on the right path of care, taking into consideration other individual tolerances, such as participation and cost. Precision medicine processes incorporate data beyond the individual, pulling in socio-economic data, as well as relevant internal and external data, to create an entire patient data ecosystem. With reusable data modules, this information is processed within a closed-loop analytics framework to facilitate clinical decision making at the point of care. This optimizes clinical workflow, thus leading to more precise medicine.
Improving Sepsis Care: Three Paths to Better OutcomesHealth Catalyst
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 SuccessHealth Catalyst
Surviving on thin operating margins underscores the need for all end users at a health system to make decisions based on comprehensive data sets. This data-centered approach to decision making allows team members to take the right course of action the first time and avoid making decisions based on fragmented data that exclude key pieces of information.
To promote data-driven decision making and a data-centric culture, healthcare organizations should increase data access and availability across the institution. With easy access to complete data, end users rely on the same data to make decisions, no matter where they work within the health system.
Two strategies can help organizations integrate and deliver data to end users when they need it:
Select infrastructure that fits most people’s needs.
Ask the right questions.
Want to know the best healthcare data warehouse for your organization? You’ll need to start first by modeling the data, because the data model used to build your healthcare enterprise data warehouse (EDW) will have a significant effect on both the time-to-value and the adaptability of your system going forward. Each of the models I describe below bind data at different times in the design process, some earlier, some later. As you’ll see, we believe that binding data later is better. The three approaches are 1) the enterprise data model, 2) the independent data model, and 3) the Health Catalyst Late-Binding™ approach.
When Healthcare Data Analysts Fulfill the Data Detective RoleHealth Catalyst
There’s a new way to think about healthcare data analysts. Give them the responsibilities of a data detective. If ever there were a Sherlock Holmes of healthcare analytics, it’s the analyst who thinks like a detective. Part scientist, part bloodhound, part magician, the healthcare data detective thrives on discovery, extracting pearls of insight where others have previously returned emptyhanded. This valuable role comprises critical thinkers, story engineers, and sleuths who look at healthcare data in a different way. Three attributes define the data detective:
They are inquisitive and relentless with their questions.
They let the data inform.
They drive to the heart of what matters.
Innovative analytics leaders understand the importance of supporting the data analyst through the data detective career track, and the need to start developing this role right away in the pursuit of outcomes improvement in all healthcare domains.
Cloud-Based Open-Platform Data Solutions: The Best Way to Meet Today’s Growin...Health Catalyst
Smartphone applications, home monitoring equipment, genomic sequencing, and social determinants of health are adding significantly to the scope of healthcare data, creating new challenges for health systems in data management and storage. Traditional on-premises data warehouses, however, don’t have the capacity or capabilities to support this new era of bigger healthcare data.
Organizations must add more secure, scalable, elastic, and analytically agile cloud-based, open-platform data solutions that leverage analytics as a service (AaaS). Moving toward cloud hosting will help health systems avoid the five common challenges of on-premises data warehouses:
1. Predicting future demand is difficult.
2. Infrastructure scaling is lumpy and inelastic.
3. Security risk mitigation is a major investment.
4. Data architectures limit flexibility and are resource intensive.
5. Analytics expertise is misallocated.
The Healthcare Analytics Ecosystem: A Must-Have in Today’s TransformationHealth Catalyst
Healthcare organizations seeking to achieve the Quadruple Aim (enhancing patient experience, improving population health, reducing costs, and reducing clinician and staff burnout), will reach their goals by building a rich analytics ecosystem. This environment promotes synergy between technology and highly skilled analysts and relies on full interoperability, allowing people to derive the right knowledge to transform healthcare.
Five important parts make up the healthcare analytics ecosystem:
Must-have tools.
People and their skills.
Reactive, descriptive, and prescriptive analytics.
Matching technical skills to analytics work streams.
Interoperability.
A Health Catalyst Overview: Learn How a Data First Strategy Can Drive Increas...Health Catalyst
Without the pressure of a one-on-one demo, you can join a crowd of peers to ‘kick the tires’ if you will, as you listen to Jared Crapo—a sought after healthcare strategist—talk about what a data-first strategy is, and the strategic components to a data-first strategy employing a data operating system, a breakthrough engineering approach that combines the features of data warehousing, clinical data repositories, and health information exchanges in a single, common-sense technology platform that turns data into actionable assets used for all types of outcomes improvements.
Lest you worry about too much ‘pie in the sky’ strategy talk with few results to show, Sam Turman, Senior Solution Architect, will provide tangible solution demonstrations that are driving material results. Even if you aren’t in the market for Health Catalyst solutions and services, you will be able to:
Think with more clarity through your approach to overcoming the current market challenges.
Reconsider the strategy you are employing to build cross-organizational awareness and support to put a data-first plan at the center of your plan.
Define action you can take today to assess your gaps, understand your options, and accelerate your progress to drive outcomes improvements.
Join us and you won’t be disappointed. Jared is one of those types of thinkers that many pay big money to listen to and it is our fortune to have 60 minutes with him to think deeply about moving healthcare forward, one patient at a time. We hope you can join us.
Going Beyond Genomics in Precision Medicine: What's NextHealth Catalyst
Precision medicine processes, while involving genomics, are not confined to working with data about an individual’s genes, environment, and lifestyle. Precision medicine also means putting patients on the right path of care, taking into consideration other individual tolerances, such as participation and cost. Precision medicine processes incorporate data beyond the individual, pulling in socio-economic data, as well as relevant internal and external data, to create an entire patient data ecosystem. With reusable data modules, this information is processed within a closed-loop analytics framework to facilitate clinical decision making at the point of care. This optimizes clinical workflow, thus leading to more precise medicine.
Health Catalyst® Introduces Closed-Loop Analytics™ ServicesHealth Catalyst
Healthcare organizations face provider dissatisfaction, lack of data integration, and excessive clicks to perform basic functions within the EHR. Closed-Loop Analytics™ aggregates data, circulates that data into new or existing workflows, and then surfaces best practice alerts at the decision point for physicians, clinical providers, and financial and operational teams. With clear calls to action throughout the workflow, organizations improve the utilization and effectiveness of analytics tools, yielding simplified workflows, decreased clicks, and improved outcomes.
A Roadmap for Optimizing Clinical Decision SupportHealth Catalyst
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:
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.
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.
Healthcare NLP - Four Essentials to Make the Most of Unstructured DataHealth Catalyst
Many health systems are eager to embrace the capability of natural language processing (NLP) to access the vast patient insights recorded as unstructured text in clinical notes and records. Many healthcare data and analytics teams, however, aren’t experienced in or prepared for the unique challenges of working with text and, specifically, don’t have the knowledge to transform unstructured text into a usable format for NLP.
Data engineers can follow four need-to-know principles to meet and overcome the challenges of making unstructured text available for advanced NLP analysis:
Text is bigger and more complex.
Text comes from different data sources.
Text is stored in multiple areas.
Text user documentation patterns matter.
Semantic Technology for Provider-Payer-Pharma Data CollaborationThomas Kelly, PMP
Semantic Technology for Provider-Payer-Pharma Cross-Industry Data Collaboration
Building Intelligent Health Data Integration
The cost to cover the typical family of four under an employer health insurance plan is expected to top
$20,000 this year. The integration of health data (including electronic health records, health insurer records, pharma research and clinical data, and real-world evidence) will increase transparency and efficiency, improve individual and population health outcomes, and expand the ability to study and improve quality of care.
Traditional approaches to data integration and analytics depend on widely understood data and well-defined use cases for analyzing that data. The integration of pharma, provider, payer, and real-world data will identify new ways in which health data can be combined and analyzed to improve quality of care. Semantic technology can speed integration of health data, while supporting an evolutionary approach to developing and leveraging expertise.
The Dangers of Commoditized Machine Learning in Healthcare: 5 Key Differentia...Health Catalyst
Many vendors deliver machine learning models with different applications in healthcare. But they don’t all deliver accurate models that are easy to implement, targeted to a specific use case, connected to actionable interventions, and surrounded by a machine learning community and support team with extensive, exclusive healthcare experience.
These machine learning qualities are possible only through a machine learning model delivered by a vendor with a unique set of capabilities. There are five differentiators behind effective machine learning models and vendors:
Vendor’s expertise and exclusive focus on healthcare.
Machine learning model’s access to extensive data sources.
Machine learning model’s ease of implementation.
Machine learning model’s interpretability and buy-in.
Machine learning model’s conformance with privacy standards.
These five factors separate the high-value vendors and models from the crowd, so healthcare systems can quickly implement machine learning and start seeing improvement results.
Charge Capture Optimization: Target Five Hotspots to Boost the Bottom LineHealth Catalyst
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.
How to Build a Healthcare Analytics Team and Solve Strategic ProblemsHealth Catalyst
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.
Artificial Intelligence and Machine Learning in Healthcare: Four Real-World I...Health Catalyst
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.
Physician Burnout and the EHR: Addressing Five Common BurdensHealth Catalyst
So far, the EHR hasn’t delivered on its original intent to improve patient care with more efficiency and personalization and lower cost. Instead, physician users blame the systems for worsening their experience and the quality of their care in significant ways:
Less time for patient interaction and worsened quality of interaction.
An extended workday.
Poor design (difficult to use).
Demands of quality measures.
Cost and maintenance.
Despite these challenges, the EHR is likely here to stay. Health systems have invested heavily in their electronic reporting systems and are now focused on making these technologies and processes work for the benefit of patients and providers. CIOs are working towards better aligning digital health goals with physician experience for an environment where EHRs enable smarter, not harder, work.
Reducing Unwanted Variation in Healthcare Clears the Way for Outcomes Improve...Health Catalyst
According to statistician W. Edwards Deming, “Uncontrolled variation is the enemy of quality.” The statement is particularly true of outcomes improvement in healthcare, where variation threatens quality across processes and outcomes. To improve outcomes, health systems must recognize where and how inconsistency impacts their outcomes and reduce unwanted variation.
There are three key steps to reducing unwanted variation:
Remove obstacles to success on a communitywide level.
Maintain open lines of communication and share lessons learned.
Decrease the magnitude of variation.
Social Determinants of Health: Tools to Leverage Today's Data ImperativeHealth Catalyst
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 OutcomesHealth Catalyst
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:
Organizations must develop measures that are more clinically relevant and better represent the care provided.
Clinician buy-in is critical. Without it, quality improvement initiatives are less likely to succeed.
Investment in tools and effort surrounding improvement work must increase. Tools should include data analytics.
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.
Employer Health Plans: Keys to Lowering Cost, Boosting BenefitsHealth Catalyst
Employers that offer robust employee health plans at affordable costs are more likely to attract and retain a great workforce. Healthcare, however, is often a top expense for organizations, making balancing attractive benefits with attractive costs a complex undertaking. Employers need a deep understanding of employee populations and opportunities to manage health plan costs without sacrificing quality.
An analytics-driven approach to employee population health management gives employers insight into two key steps to lower healthcare costs and enhance benefits:
* Manage easily fixed cost issues.
* Use healthcare cost savings to fund expanded benefits.
A 5-Step Guide for Successful Healthcare Data Warehouse OperationsHealth Catalyst
Starting and sustaining an enterprise data warehouse (EDW) for a sizeable healthcare organization might seem as challenging as, say, forming a new country. While it is an arduous undertaking, there are plenty who have gone before. In this article, one EDW operations manager shares five steps for success:
Start with a Leadership Commitment to Outcomes Improvement
Build the Right Team
Establish Effective Partnerships with IT
Develop Interest and Gain Buy-In
Pivot Toward Maintaining Success
Successfully implementing and sustaining EDW operations is about establishing and managing priorities and understanding the enterprise-wide implications.
Using Improvement Science in Healthcare to Create True ChangeHealth Catalyst
With improvement science combined with analytics, health systems can better understand how, as they implement new process changes, to use theory to guide their practice, and which improvement strategy will help increase the likelihood of success.
The 8-Step Improvement Model is a framework that health systems can follow to effectively apply improvement science:
Analyze the opportunity for improvement and define the problem.
Scope the opportunity and set SMART goals.
Explore root causes and set SMART process aims.
Design interventions and plan initial implementation.
Implement interventions and measure results.
Monitor, adjust, and continually learn.
Diffuse and sustain.
Communicate Quantitative and Qualitative Results.
With the right approach, an improvement team can measure the results and know if the changes they made will actually lead to the desired impact.
How to Design an Effective Clinical Measurement System (And Avoid Common Pitf...Health Catalyst
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.
Delivering the Right Insight to the Right Person: How Workflow Automation Opt...Health Catalyst
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.
AI in Healthcare: Finding the Right Answers FasterHealth Catalyst
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:
Predictive Analysis Before Diagnostic Analysis Leads to Correlation but Not Causation.
Change Management Isn’t Considered Part of the Process.
The Wrong Terms to Describe the Work.
Trying to Compensate for Low Data Literacy Resulting in Unclear Conclusions.
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.
Exceptions to Information Blocking Defined in Proposed Rule: Here’s What You ...Health Catalyst
Information blocking practices inhibit care coordination, interoperability, and healthcare’s forward progress. The ONC’s proposed rule ushers in the next phase of the Cures Act by defining information blocking practices and allowed exceptions. To make the final rule as strong as possible, exceptions should be narrowly defined. In proposed form these include the following:
Preventing Harm.
Promoting the Privacy of EHI.
Promoting the Security of EHI.
Recovering Costs Reasonably Incurred.
Responding to Request that are Infeasible.
Licensing of Interoperability Elements on Reasonable and Non-discriminatory Terms.
Maintaining and Improving Health IT Performance.
This article covers each of these exceptions and discusses what to watch for in the final version of the rule.
Use ACE Scores in Machine Learning to Predict Disease Earlier and Improve Out...Health Catalyst
The Adverse Childhood Experience (ACE) study conducted by the CDC and Kaiser Permanent showed a strong correlation between ACEs and negative health outcomes later in life (e.g., risky health behaviors, chronic health conditions, and early death). ACE scores help paint a more complete picture of a person’s health history—a more comprehensive data snapshot of the entire patient.
Given that ACE scores build better data sets and machine learning relies on high-quality data, health systems should incorporate these nutrient-rich data sources into their machine learning models to better predict negative health outcomes, allow for earlier interventions, and improve outcomes.
Healthcare machine learning is evolving to use ACE scores and lifestyle data (e.g., eating habits) to improve population health management.
Health Catalyst® Introduces Closed-Loop Analytics™ ServicesHealth Catalyst
Healthcare organizations face provider dissatisfaction, lack of data integration, and excessive clicks to perform basic functions within the EHR. Closed-Loop Analytics™ aggregates data, circulates that data into new or existing workflows, and then surfaces best practice alerts at the decision point for physicians, clinical providers, and financial and operational teams. With clear calls to action throughout the workflow, organizations improve the utilization and effectiveness of analytics tools, yielding simplified workflows, decreased clicks, and improved outcomes.
A Roadmap for Optimizing Clinical Decision SupportHealth Catalyst
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:
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.
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.
Healthcare NLP - Four Essentials to Make the Most of Unstructured DataHealth Catalyst
Many health systems are eager to embrace the capability of natural language processing (NLP) to access the vast patient insights recorded as unstructured text in clinical notes and records. Many healthcare data and analytics teams, however, aren’t experienced in or prepared for the unique challenges of working with text and, specifically, don’t have the knowledge to transform unstructured text into a usable format for NLP.
Data engineers can follow four need-to-know principles to meet and overcome the challenges of making unstructured text available for advanced NLP analysis:
Text is bigger and more complex.
Text comes from different data sources.
Text is stored in multiple areas.
Text user documentation patterns matter.
Semantic Technology for Provider-Payer-Pharma Data CollaborationThomas Kelly, PMP
Semantic Technology for Provider-Payer-Pharma Cross-Industry Data Collaboration
Building Intelligent Health Data Integration
The cost to cover the typical family of four under an employer health insurance plan is expected to top
$20,000 this year. The integration of health data (including electronic health records, health insurer records, pharma research and clinical data, and real-world evidence) will increase transparency and efficiency, improve individual and population health outcomes, and expand the ability to study and improve quality of care.
Traditional approaches to data integration and analytics depend on widely understood data and well-defined use cases for analyzing that data. The integration of pharma, provider, payer, and real-world data will identify new ways in which health data can be combined and analyzed to improve quality of care. Semantic technology can speed integration of health data, while supporting an evolutionary approach to developing and leveraging expertise.
The Dangers of Commoditized Machine Learning in Healthcare: 5 Key Differentia...Health Catalyst
Many vendors deliver machine learning models with different applications in healthcare. But they don’t all deliver accurate models that are easy to implement, targeted to a specific use case, connected to actionable interventions, and surrounded by a machine learning community and support team with extensive, exclusive healthcare experience.
These machine learning qualities are possible only through a machine learning model delivered by a vendor with a unique set of capabilities. There are five differentiators behind effective machine learning models and vendors:
Vendor’s expertise and exclusive focus on healthcare.
Machine learning model’s access to extensive data sources.
Machine learning model’s ease of implementation.
Machine learning model’s interpretability and buy-in.
Machine learning model’s conformance with privacy standards.
These five factors separate the high-value vendors and models from the crowd, so healthcare systems can quickly implement machine learning and start seeing improvement results.
Charge Capture Optimization: Target Five Hotspots to Boost the Bottom LineHealth Catalyst
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.
How to Build a Healthcare Analytics Team and Solve Strategic ProblemsHealth Catalyst
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.
Artificial Intelligence and Machine Learning in Healthcare: Four Real-World I...Health Catalyst
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.
Physician Burnout and the EHR: Addressing Five Common BurdensHealth Catalyst
So far, the EHR hasn’t delivered on its original intent to improve patient care with more efficiency and personalization and lower cost. Instead, physician users blame the systems for worsening their experience and the quality of their care in significant ways:
Less time for patient interaction and worsened quality of interaction.
An extended workday.
Poor design (difficult to use).
Demands of quality measures.
Cost and maintenance.
Despite these challenges, the EHR is likely here to stay. Health systems have invested heavily in their electronic reporting systems and are now focused on making these technologies and processes work for the benefit of patients and providers. CIOs are working towards better aligning digital health goals with physician experience for an environment where EHRs enable smarter, not harder, work.
Reducing Unwanted Variation in Healthcare Clears the Way for Outcomes Improve...Health Catalyst
According to statistician W. Edwards Deming, “Uncontrolled variation is the enemy of quality.” The statement is particularly true of outcomes improvement in healthcare, where variation threatens quality across processes and outcomes. To improve outcomes, health systems must recognize where and how inconsistency impacts their outcomes and reduce unwanted variation.
There are three key steps to reducing unwanted variation:
Remove obstacles to success on a communitywide level.
Maintain open lines of communication and share lessons learned.
Decrease the magnitude of variation.
Social Determinants of Health: Tools to Leverage Today's Data ImperativeHealth Catalyst
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 OutcomesHealth Catalyst
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:
Organizations must develop measures that are more clinically relevant and better represent the care provided.
Clinician buy-in is critical. Without it, quality improvement initiatives are less likely to succeed.
Investment in tools and effort surrounding improvement work must increase. Tools should include data analytics.
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.
Employer Health Plans: Keys to Lowering Cost, Boosting BenefitsHealth Catalyst
Employers that offer robust employee health plans at affordable costs are more likely to attract and retain a great workforce. Healthcare, however, is often a top expense for organizations, making balancing attractive benefits with attractive costs a complex undertaking. Employers need a deep understanding of employee populations and opportunities to manage health plan costs without sacrificing quality.
An analytics-driven approach to employee population health management gives employers insight into two key steps to lower healthcare costs and enhance benefits:
* Manage easily fixed cost issues.
* Use healthcare cost savings to fund expanded benefits.
A 5-Step Guide for Successful Healthcare Data Warehouse OperationsHealth Catalyst
Starting and sustaining an enterprise data warehouse (EDW) for a sizeable healthcare organization might seem as challenging as, say, forming a new country. While it is an arduous undertaking, there are plenty who have gone before. In this article, one EDW operations manager shares five steps for success:
Start with a Leadership Commitment to Outcomes Improvement
Build the Right Team
Establish Effective Partnerships with IT
Develop Interest and Gain Buy-In
Pivot Toward Maintaining Success
Successfully implementing and sustaining EDW operations is about establishing and managing priorities and understanding the enterprise-wide implications.
Using Improvement Science in Healthcare to Create True ChangeHealth Catalyst
With improvement science combined with analytics, health systems can better understand how, as they implement new process changes, to use theory to guide their practice, and which improvement strategy will help increase the likelihood of success.
The 8-Step Improvement Model is a framework that health systems can follow to effectively apply improvement science:
Analyze the opportunity for improvement and define the problem.
Scope the opportunity and set SMART goals.
Explore root causes and set SMART process aims.
Design interventions and plan initial implementation.
Implement interventions and measure results.
Monitor, adjust, and continually learn.
Diffuse and sustain.
Communicate Quantitative and Qualitative Results.
With the right approach, an improvement team can measure the results and know if the changes they made will actually lead to the desired impact.
How to Design an Effective Clinical Measurement System (And Avoid Common Pitf...Health Catalyst
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.
Delivering the Right Insight to the Right Person: How Workflow Automation Opt...Health Catalyst
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.
AI in Healthcare: Finding the Right Answers FasterHealth Catalyst
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:
Predictive Analysis Before Diagnostic Analysis Leads to Correlation but Not Causation.
Change Management Isn’t Considered Part of the Process.
The Wrong Terms to Describe the Work.
Trying to Compensate for Low Data Literacy Resulting in Unclear Conclusions.
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.
Exceptions to Information Blocking Defined in Proposed Rule: Here’s What You ...Health Catalyst
Information blocking practices inhibit care coordination, interoperability, and healthcare’s forward progress. The ONC’s proposed rule ushers in the next phase of the Cures Act by defining information blocking practices and allowed exceptions. To make the final rule as strong as possible, exceptions should be narrowly defined. In proposed form these include the following:
Preventing Harm.
Promoting the Privacy of EHI.
Promoting the Security of EHI.
Recovering Costs Reasonably Incurred.
Responding to Request that are Infeasible.
Licensing of Interoperability Elements on Reasonable and Non-discriminatory Terms.
Maintaining and Improving Health IT Performance.
This article covers each of these exceptions and discusses what to watch for in the final version of the rule.
Use ACE Scores in Machine Learning to Predict Disease Earlier and Improve Out...Health Catalyst
The Adverse Childhood Experience (ACE) study conducted by the CDC and Kaiser Permanent showed a strong correlation between ACEs and negative health outcomes later in life (e.g., risky health behaviors, chronic health conditions, and early death). ACE scores help paint a more complete picture of a person’s health history—a more comprehensive data snapshot of the entire patient.
Given that ACE scores build better data sets and machine learning relies on high-quality data, health systems should incorporate these nutrient-rich data sources into their machine learning models to better predict negative health outcomes, allow for earlier interventions, and improve outcomes.
Healthcare machine learning is evolving to use ACE scores and lifestyle data (e.g., eating habits) to improve population health management.
4 Essential Lessons for Adopting Predictive Analytics in HealthcareHealth Catalyst
Predictive analytics is quite a popular current topic. Unfortunately, there are many potential side tracks or pit falls for those that do not approach this carefully. Fortunately for healthcare, there are numerous existing models from other industries that are very efficient at risk stratification in the realm of population management. David Crocket, PhD shares 4 key pitfalls to avoid for those beginning predictive analytics. These include
1) confusing data with insight
2) confusing insight with value
3) overestimating the ability to interpret the data
4) underestimating the challenge of implementation.
Introducing catalyst.ai and MACRA Measures & InsightsHealth Catalyst
Join Eric Just, Senior Vice President of Product Development, as he will discuss:
How machine learning is now included into our analytics platform and being built into all our applications.
The toolsets we have developed to automate and democratize machine learning tasks both within Health Catalyst clients and to the broader healthcare industry.
Processes to gain clinician buy-in, and engage the best machine learning engine in the world.
Demonstrations and examples of this life-saving technology.
Dorian DiNardo, Vice President, will share how the Health Catalyst® MACRA Measures & Insights product can help you:
Integrate hundreds of measures across financial, regulatory, and quality departments.
Monitor the behavior, activities, and other changing information needed to influence, manage, or change outcomes.
Tactically and strategically identify measures to take on risk in multi-year value-based care contracts.
Optimize physician workflow and you’ll contribute to optimizing patient care. But what is it physicians look for to improve diagnoses, decision-making, patient care, and ultimately, outcomes? To answer this, consider what constitutes ideal working conditions in any industry: the right tools, training, and information to maximize productivity and deliver results. Physicians need analytics integrated into the EHR to maximize their efficiency, a common quest among the chronically overworked. And by flowing the universe of global, local, and individual data back into an enterprise data warehouse, a healthcare system can close the analytics loop, and begin to realize true precision medicine.
As per the Market Data Forecast report, the global healthcare prescriptive analytics market is likely to grow at a CAGR of 17.4% from 2022-2027. Organizations use prescriptive analytics to predict outcomes and to identify the logical course of action.
Prescriptive Analytics of user-generated data in the healthcare domain indicates what is likely to occur and suggests the best actions to avoid and mitigate risks. To know more about how healthcare is optimizing its operations with prescriptive analytics
Data Mining in Healthcare: How Health Systems Can Improve Quality and Reduce...Health Catalyst
This is the complete 4-part series demonstrating real-world examples of the power of data mining in healthcare. Effective data mining requires a three-system approach: the analytics system (including an EDW), the content system (and systematically applying evidence-based best practices to care delivery), and the deployment system (driving change management throughout the organization and implementing a dedicated team structure). Here, we also show organizations with successful data-mining-application in critical areas such as: tracking fee-for-service and value-based payer contracts, population health management initiatives involving primary care reporting, and reducing hospital readmissions. Having the data and tools to use data mining and predict trends is giving these health systems a big advantage.
The Top Seven Quick Wins You Get with a Healthcare Data WarehouseHealth Catalyst
In an industry known for its complex challenges that can take years to overcome, health systems can leverage healthcare data warehouses to generate seven quick wins—reporting and analytics efficiencies that empower healthcare organizations to thrive in a value-based world:
Provides significantly faster access to data.
Improves data-driven decision making.
Enables a data-driven culture.
Provides world class report automation.
Significantly improves data quality and accuracy.
Provides significantly faster product implementation.
Improves data categorization and organization.
Health systems that leverage healthcare data warehouses position themselves to do more than just survive the transition to value-based care; they empower themselves to achieve and sustain long-term outcomes improvement by enabling data-driven decision making based on high quality data.
5 Things to Know About the Clinical Analytics Data Management Challenge - Ext...Michael Dykstra
5 Things to Know About the Clinical Analytics Data Management Challenge - Extracting Real Benefit From Your EHR Data
The EHR revolution has created immense promise for improved patient outcomes and reduced costs but most healthcare organizations are struggling to experience significant benefits. The power of Applied Clinical Analytics lies in a simple but powerful concept: the importance of focusing on the accuracy and availability of the underlying data, first and foremost.
How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...Health Catalyst
There’s a new trend in the healthcare industry to adopt analytics software solutions to help organizations achieve clinical and financial success. Because of the high demand for analytics, there are many players touting their ability to delivery comprehensive solutions. With so many options available, health systems need to be able to cut through the marketing hype to find tools that provide the best value for their needs. Key solutions include an enterprise data warehouse and analytics software applications (from foundational to discovery to advanced). Other considerations include the organization’s readiness for cultural change, the total cost of ownership required, and the viability of the company providing the technology.
Healthcare Interoperability: New Tactics and TechnologyHealth Catalyst
Every provider agrees on the need for healthcare interoperability to achieve clinical data insights at the point of care. The question is how to get there from the myriad technologies and the volumes of data that comprise electronic medical records. It’s been difficult to organize among participants that have had little incentive to cooperate. And standards for sending and receiving data have been slow to develop. This is changing, but the key components that are still vital to realizing insights are closed-loop analytics and its accompanying tools, an enterprise data warehouse and analytics applications. This article defines the problems and explores the solutions to optimizing clinical decision making where it’s needed most.
Three Keys to Improving Hospital Patient Flow with Machine LearningHealth Catalyst
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.
The Digitization of Healthcare: Why the Right Approach Matters and Five Steps...Health Catalyst
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:
Achieve and maintain clinician and patient engagement.
Adopt a modern commercial digital platform.
Digitize the assets (the patients) and the processes.
Understand the importance of data to drive AI insights.
Prioritize data volume.
Similar to Data Science for Healthcare: What Today’s Leaders Must Know (20)
Empowering ACOs: Leveraging Quality Management Tools for MIPS and BeyondHealth Catalyst
Join us as we delve into the crucial realm of quality reporting for MSSP (Medicare Shared Savings Program) Accountable Care Organizations (ACOs).
In this session, we will explore how a robust quality management solution can empower your organization to meet regulatory requirements and improve processes for MIPS reporting and internal quality programs. Learn how our MeasureAble application enables compliance and fosters continuous improvement.
Unlock the Secrets to Optimizing Ambulatory Operations Efficiency and Change ...Health Catalyst
Today’s healthcare leaders are seeking technology solutions to optimize efficiencies and improve patient care. However, without effective change management and strategies in place, healthcare leaders struggle to strategically improve patient flow, space, to strategically improve patient flow, space, and schedule management, and implement daily huddles. The role of technology in supporting operational efficiency and change management initiatives is inevitable.
During this webinar, attendees will learn how to optimize Ambulatory Operational Efficiencies and Change Management. Attendees will also learn about the importance of visual management boards in enhancing clinic performance and insights into effective change management approaches.
Patient expectations are rising, and organizations are continuously being asked to do more with less.
Additionally, the convergence of several significant emerging market and policy trends, economic uncertainty, labor force shortages, and the end of the COVID-19 public health emergency has created a unique set of challenges for healthcare organizations.
Attend this timely webinar to learn about new trends and their impact on key healthcare issues, such as patient engagement, migration to value-based care, analytics adoption, the use of alternative care sites, and data governance and management challenges.
During this webinar, we will discuss the complexities of AI, trends, and platforms in the industry. Dive deep into understanding the true essence of AI, exploring its potential, real-world use cases, and common misconceptions. Gain valuable insights into the latest technology trends impacting healthcare and discover strategies for maximizing ROI in your technology investments.
Explore the profound impact of data literacy on healthcare organizations and how it shapes the utilization of data and technology for transformative outcomes. Understand the top technology priorities for healthcare organizations and learn how to navigate the digital landscape effectively. Furthermore, simplify industry jargon by defining common data elements, fostering clearer communication and collaboration across stakeholders.
Finally, uncover the transformative potentials of platforms in healthcare and how they can revolutionize scalability, interoperability, and innovation within your organization. Don't miss this opportunity to gain invaluable insights from industry experts and stay ahead in the ever-evolving healthcare landscape. Reserve your spot now for an enlightening journey into the future of healthcare technology!
Three Keys to a Successful Margin: Charges, Costs, and LaborHealth Catalyst
How can cost management and complete charge capture protect and enhance the margin?
In this webinar, we will look at 2024 margin pressures likely to impact your organization’s financial resiliency. This presentation will also share how organizations can move from Fee-for-Service to Value; bringing Cost to the forefront.
2024 CPT® Updates (Professional Services Focused) - Part 3Health Catalyst
Each year the CPT code set undergoes significant changes. Physicians and their office staff need to be aware of the changes in order to ensure a smooth transition into 2024. Join us for a discussion of the new, deleted and revised CPT codes and associated guidelines for 2024. This presentation will focus on the changes to the CPT dataset and the associated work RVU value changes that impact professional service reporting.
During this complimentary webinar, we will empower you to correctly apply the new and revised codes and discuss the rationale behind this year’s changes. You will leave with an understanding of the financial implications of the changes on your practice.
2024 CPT® Code Updates (HIM Focused) - Part 2Health Catalyst
Each year the CPT code set and the HCPCS code set undergo significant changes, and your coding staff needs to be aware of the changes in order to ensure a smooth transition into 2024. Join us for a discussion of the new, deleted and revised CPT codes and associated guidelines for 2024. This is part two in a three-part series.
During these complimentary webinars, we will empower you to correctly apply the new and revised codes and discuss the rationale behind this year’s changes. This presentation will be geared towards hospital staff with a focus on the surgical section of the CPT book in addition to surgical Category III codes.
2024 CPT® Code Updates (CDM Focused) - Part 1Health Catalyst
Each year the CPT and the HCPCS code sets undergo significant changes, and your staff needs to be aware of the changes in order to ensure a smooth transition into 2024. Join us for a discussion of the new, deleted, and revised CPT codes and associated guidelines for 2024. This is part one in a three-part series, with a CDM focus.
During these complimentary webinars, we will empower you to correctly apply the new and revised codes and discuss the rationale behind this year’s changes. This presentation will be geared towards hospital staff with a focus on the non-surgical sections of the CPT book.
What’s Next for Hospital Price Transparency in 2024 and BeyondHealth Catalyst
The Centers for Medicare & Medicaid Services (CMS) published updates to the hospital price transparency requirements in the CY 2024 Outpatient Prospective Payment System (OPPS) Final Rule. The updates will be phased in over the next 14 months and include several significant changes including the use of a CMS-mandated template, a requirement for an affirmation statement from the hospital, and several new data elements. Join us to discover what changes are scheduled for implementation in 2024 and 2025 and how they’ll impact your facility.
During this complimentary 60-minute webinar, we’ll analyze the key provisions of the Price Transparency regulations and provide insights to help you prepare for the upcoming changes.
Automated Patient Reported Outcomes (PROs) for Hip & Knee ReplacementHealth Catalyst
What was once voluntary reporting will soon be made mandatory with penalties.
On July 1, 2024, all health systems will be required to collect Patient Reported Outcome Measures (PROM) as part of the Centers for Medicare & Medicaid Services (CMS) regulation for the following measures:
Hospital-Level, Risk Standardized Patient-Reported Outcomes Performance Measure (PRO-PM) Following Elective Primary Total Hip Arthroplasty (THA) and/or Total Knee Arthroplasty (TKA)
Hospital-Level Risk-Standardized Complication Rate (RSCR) Following Elective Primary THA/TKA
Are you equipped to handle these new requirements?
Mandatory data collection begins April 1, 2024, and failure to submit timely data can result in a 25 percent reduction in payments by Medicare.
Attend this webinar to learn how mobile engagement can empower your organization to meet this requirement.
2024 Medicare Physician Fee Schedule (MPFS) Final Rule UpdatesHealth Catalyst
According to the Centers for Medicare & Medicaid Services (CMS), the calendar year (CY) 2024 MPFS final rule was created to advance health equity and improve access to affordable healthcare. This webinar will cover the major policy updates of the MPFS final rule including updates to the telehealth services policy and remote monitoring services and enrollment of MFTs and MHCs as Medicare providers. The conversation will also cover policy changes on split (or shared) evaluation and management (E/M) visits, and the Appropriate Use Criteria (AUC) for Advanced Diagnostic Imaging.
What's Next for OPPS: A Look at the 2024 Final RuleHealth Catalyst
During this webinar, we’ll analyze the key provisions of the OPPS final rule and identify the significant changes for the coming year to help prepare your staff for compliance with the 2024 Medicare outpatient billing guidelines.
Insight into the 2024 ICD-10 PCS Updates - Part 2Health Catalyst
Prepare for mandatory ICD-10 PCS diagnosis code updates, which take effect on October 1, 2023. By attending this 60-minute educational session, medical coders and healthcare professionals will gain a comprehensive understanding of the changes to the 2024 ICD-10 procedure codes and their guidelines, enabling accurate and compliant coding for optimal billing and reimbursement.
Vitalware Insight Into the 2024 ICD10 CM Updates.pdfHealth Catalyst
Prepare for mandatory ICD-10 CM diagnosis code updates, which take effect on October 1, 2023. By attending this 60-minute educational session, medical coders and healthcare professionals will gain a comprehensive understanding of the changes to the 2024 ICD-10 diagnosis codes and their guidelines, along with major complication or comorbidity (MCC), complication or comorbidity (CC), and Medicare Severity Diagnosis Related Groups (MS-DRGs) classification changes. With this information, professionals can ensure accurate and compliant diagnosis coding for optimal billing and reimbursement.
Driving Value: Boosting Clinical Registry Value Using ARMUS SolutionsHealth Catalyst
Many hospitals today face a perfect storm of operational and financial challenges. With increasing competition from outpatient facilities and rising care costs negatively impacting budgets, now is the time to boost your clinical registry’s value. However, collecting and analyzing data can be time-consuming and costly without the right tools. During this webinar, we will share insights and best practices for increasing the value of registry participation and how it’s possible to reduce costs while improving outcomes using the ARMUS Product Suite.
Tech-Enabled Managed Services: Not Your Average OutsourcingHealth Catalyst
During this webinar you'll learn the following:
The importance of optimizing performance, reducing labor costs and sourcing talent given current market challenges.
Highlighting the need for a balanced approach to cost reduction.
How to reap the benefits of outsourcing (cost cutting, expertise, etc) while protecting yourself from the collateral damage that often comes with them.
This webinar will provide an in-depth review of the CPT/HCPCS code set changes that will be effective on July 1, 2023. The review will include additions and deletions to the CPT/HCPCS code set, revisions of code descriptors, payment changes, and rationale behind the changes.
How Managing Chronic Conditions Is Streamlined with Digital TechnologyHealth Catalyst
Chronic conditions across the United States are prevalent and continue to rise. Managing one or more chronic diseases can be very challenging for patients who may be overwhelmed or confused about their care plan and may not have access to the resources they need. At the same time, care teams are overburdened, making it difficult to provide the support these patients require to stay as healthy as possible. A new approach to chronic condition management leverages technology to enable organizations to scale high-quality care, identify gaps in care, provide personalized support, and monitor patients on an ongoing basis. Such streamlined management will result in better outcomes, reduced costs, and more satisfied patients.
COVID-19: After the Public Health Emergency EndsHealth Catalyst
In this fast-paced webinar, we will discuss the impact of the end of the public health emergency (PHE), including upcoming changes to the different flexibilities allowed during the PHE and the timeline for when these flexibilities will end. We’ll also cover coding changes and reimbursement updates.
Automated Medication Compliance Tools for the Provider and PatientHealth Catalyst
When it comes to sustaining patient health outcomes, compliance and adherence to medication regimens are critically important, especially as providers manage patients with complex care needs and multiple medications. But, with provider burnout and staffing shortages at an all-time high, an efficient solution is critical. The use of automated medication management workflows to decrease provider burnout, while improving both medication compliance and patient engagement, is the way forward.
We understand the unique challenges pickleball players face and are committed to helping you stay healthy and active. In this presentation, we’ll explore the three most common pickleball injuries and provide strategies for prevention and treatment.
Navigating Challenges: Mental Health, Legislation, and the Prison System in B...Guillermo Rivera
This conference will delve into the intricate intersections between mental health, legal frameworks, and the prison system in Bolivia. It aims to provide a comprehensive overview of the current challenges faced by mental health professionals working within the legislative and correctional landscapes. Topics of discussion will include the prevalence and impact of mental health issues among the incarcerated population, the effectiveness of existing mental health policies and legislation, and potential reforms to enhance the mental health support system within prisons.
Global launch of the Healthy Ageing and Prevention Index 2nd wave – alongside...ILC- UK
The Healthy Ageing and Prevention Index is an online tool created by ILC that ranks countries on six metrics including, life span, health span, work span, income, environmental performance, and happiness. The Index helps us understand how well countries have adapted to longevity and inform decision makers on what must be done to maximise the economic benefits that comes with living well for longer.
Alongside the 77th World Health Assembly in Geneva on 28 May 2024, we launched the second version of our Index, allowing us to track progress and give new insights into what needs to be done to keep populations healthier for longer.
The speakers included:
Professor Orazio Schillaci, Minister of Health, Italy
Dr Hans Groth, Chairman of the Board, World Demographic & Ageing Forum
Professor Ilona Kickbusch, Founder and Chair, Global Health Centre, Geneva Graduate Institute and co-chair, World Health Summit Council
Dr Natasha Azzopardi Muscat, Director, Country Health Policies and Systems Division, World Health Organisation EURO
Dr Marta Lomazzi, Executive Manager, World Federation of Public Health Associations
Dr Shyam Bishen, Head, Centre for Health and Healthcare and Member of the Executive Committee, World Economic Forum
Dr Karin Tegmark Wisell, Director General, Public Health Agency of Sweden
India Clinical Trials Market: Industry Size and Growth Trends [2030] Analyzed...Kumar Satyam
According to TechSci Research report, "India Clinical Trials Market- By Region, Competition, Forecast & Opportunities, 2030F," the India Clinical Trials Market was valued at USD 2.05 billion in 2024 and is projected to grow at a compound annual growth rate (CAGR) of 8.64% through 2030. The market is driven by a variety of factors, making India an attractive destination for pharmaceutical companies and researchers. India's vast and diverse patient population, cost-effective operational environment, and a large pool of skilled medical professionals contribute significantly to the market's growth. Additionally, increasing government support in streamlining regulations and the growing prevalence of lifestyle diseases further propel the clinical trials market.
Growing Prevalence of Lifestyle Diseases
The rising incidence of lifestyle diseases such as diabetes, cardiovascular diseases, and cancer is a major trend driving the clinical trials market in India. These conditions necessitate the development and testing of new treatment methods, creating a robust demand for clinical trials. The increasing burden of these diseases highlights the need for innovative therapies and underscores the importance of India as a key player in global clinical research.