This document discusses information technology in healthcare, achievements to date, and future challenges. It outlines accomplishments in the last 40 years since IT was introduced, including increased information sharing, empowerment of patients, and clinical effectiveness. However, it also notes ongoing issues like medical errors and variations in care. Upcoming hurdles are described as political challenges like leadership and interoperability, as well as economic issues like evaluating new technologies. Potential solutions involve adopting national standards, focusing on population health, and using social media to improve communication between all parties in healthcare.
Enterprise systems in healthcare: leveraging what we know from other industr...CONFENIS 2012
Dr. Carol Brown - distinguished professor at Stevens Institute of Technology , The Howe School of Technology Management
enterprise systems in healthcare: leveraging what we know from other industries
Interoperability in Healthcare: Making the Most of FHIRHealth Catalyst
With the CMS and ONC March 2020 endorsement of HL7 FHIR R4, FHIR is positioned to grow from a niche application programming interface (API) standard to a common API framework. With broader adoption, FHIR promises to support expanding healthcare interoperability and prepare the industry for complex use cases by addressing significant challenges:
Engaging consumers.
Sharing data with modern standards.
Building a solid foundation for healthcare interoperability.
EHR Integration: Achieving this Digital Health ImperativeHealth Catalyst
As the digital trajectory of healthcare rises, health systems have an array of new resources available to make more effective and timely care decisions. However, to use these data analytics, machine learning, predictive analytics, and wellness applications to gain real-time, data-driven insight at the point of care, health systems must fully integrate the tools with their EHRs. Integration brings technical and administrative challenges, requiring organizations to coordinate around standards, administrative processes, regulatory principles, and functional integration, as well as develop compelling integration use cases that drive demand. When realized, full EHR integration will allow clinicians to leverage data from across the continuum of care (from health plan to patient-generated data) to improve patient diagnosis and treatment.
Interoperability in Healthcare Data: A Life-Saving AdvantageHealth Catalyst
When health system clinicians make care decisions based on their organization’s EHR data alone, they’re only using a small portion of patient health information. Additional data sources—such as health information exchanges (HIEs) and patient-generated and -reported data—round out the full picture of an individual’s health and healthcare needs. This comprehensive insight enables critical, and sometimes life-saving, treatment and health management choices.
To leverage the data from beyond the four walls of a health system and combine it with clinical, financial, and operational EHR data, organizations need an interoperable platform approach to health data. The Health Catalyst® Data Operating System (DOS™), for example, combines, manages, and leverages disparate forms of health data for a complete view of the patient and more accurate insights into the best care decisions.
Bridging the Data and Trust Gaps: Why Health Catalyst Entered the Life Scienc...Health Catalyst
Why would a healthcare data warehousing and analytics company partner with the life sciences industry? Because trust and collaboration across the industry—between life sciences, healthcare delivery systems, and insurance—is the only path to real healthcare transformation.
Health Catalyst recognizes an industrywide improvement opportunity in collaborating with life sciences to build mutual trust, integrate data, and leverage analytics insights for a common interest (i.e., patient outcomes). By aligning themselves around human health fulfillment, Health Catalyst, their provider partners, and life sciences will advance important healthcare goals:
Improving clinical trial design and execution.
Stimulating clinical innovation.
Supporting population health.
Reducing pharmaceutical costs.
Improving drug safety and pharmacovigilance.
Enterprise systems in healthcare: leveraging what we know from other industr...CONFENIS 2012
Dr. Carol Brown - distinguished professor at Stevens Institute of Technology , The Howe School of Technology Management
enterprise systems in healthcare: leveraging what we know from other industries
Interoperability in Healthcare: Making the Most of FHIRHealth Catalyst
With the CMS and ONC March 2020 endorsement of HL7 FHIR R4, FHIR is positioned to grow from a niche application programming interface (API) standard to a common API framework. With broader adoption, FHIR promises to support expanding healthcare interoperability and prepare the industry for complex use cases by addressing significant challenges:
Engaging consumers.
Sharing data with modern standards.
Building a solid foundation for healthcare interoperability.
EHR Integration: Achieving this Digital Health ImperativeHealth Catalyst
As the digital trajectory of healthcare rises, health systems have an array of new resources available to make more effective and timely care decisions. However, to use these data analytics, machine learning, predictive analytics, and wellness applications to gain real-time, data-driven insight at the point of care, health systems must fully integrate the tools with their EHRs. Integration brings technical and administrative challenges, requiring organizations to coordinate around standards, administrative processes, regulatory principles, and functional integration, as well as develop compelling integration use cases that drive demand. When realized, full EHR integration will allow clinicians to leverage data from across the continuum of care (from health plan to patient-generated data) to improve patient diagnosis and treatment.
Interoperability in Healthcare Data: A Life-Saving AdvantageHealth Catalyst
When health system clinicians make care decisions based on their organization’s EHR data alone, they’re only using a small portion of patient health information. Additional data sources—such as health information exchanges (HIEs) and patient-generated and -reported data—round out the full picture of an individual’s health and healthcare needs. This comprehensive insight enables critical, and sometimes life-saving, treatment and health management choices.
To leverage the data from beyond the four walls of a health system and combine it with clinical, financial, and operational EHR data, organizations need an interoperable platform approach to health data. The Health Catalyst® Data Operating System (DOS™), for example, combines, manages, and leverages disparate forms of health data for a complete view of the patient and more accurate insights into the best care decisions.
Bridging the Data and Trust Gaps: Why Health Catalyst Entered the Life Scienc...Health Catalyst
Why would a healthcare data warehousing and analytics company partner with the life sciences industry? Because trust and collaboration across the industry—between life sciences, healthcare delivery systems, and insurance—is the only path to real healthcare transformation.
Health Catalyst recognizes an industrywide improvement opportunity in collaborating with life sciences to build mutual trust, integrate data, and leverage analytics insights for a common interest (i.e., patient outcomes). By aligning themselves around human health fulfillment, Health Catalyst, their provider partners, and life sciences will advance important healthcare goals:
Improving clinical trial design and execution.
Stimulating clinical innovation.
Supporting population health.
Reducing pharmaceutical costs.
Improving drug safety and pharmacovigilance.
To Safely Restart Elective Procedures, Look to the DataHealth Catalyst
Many health systems have realized they lack the data and analytics infrastructure to guide a sustainable reactivation plan and recover lost revenue from months of halted procedures due to COVID-19. However, with operational, clinical, and financial data, augmented by analytics tools, leaders have the visibility into hospital and resource capacity to guide a safe, sustainable elective surgery restart plan.
The first step on the road to recovery for health systems is access to robust analytics to understand the full impact of COVID-19 on clinical, financial, and operational outcomes. Second, organizations need data-sharing tools, like data displays and dashboards, allowing leaders to make decisions based on consistent data that support the organization’s reactivation goals. Leaders can even take the data one step further with predictive models and forecast procedure count, staff, and resources.
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.
The Top Seven Healthcare Outcome Measures and Three Measurement EssentialsHealth Catalyst
Healthcare outcomes improvement can’t happen without effective outcomes measurement. Given the healthcare industry’s administrative and regulatory complexities, and the fact that health systems measure and report on hundreds of outcomes annually, this article adds much-needed clarity by reviewing the top seven outcome measures, including definitions, important nuances, and real-life examples. The top seven categories of outcome measures are:
Mortality
Readmissions
Safety of care
Effectiveness of care
Patient experience
Timeliness of care
Efficient use of medical imaging
CMS used these seven outcome measures to calculate overall hospital quality and arrive at its 2018 hospital star ratings. This article also reiterates the importance of outcomes measurement, clarifies how outcome measures are defined and prioritized, and recommends three essentials for successful outcomes measurement.
Health Catalyst Named 2019 Healthcare IT Corporate InnovatorHealth Catalyst
Utah HIMSS (UHIMSS) recognized Health Catalyst for its innovative leadership with the 2019 UHIMSS Healthcare IT Corporate Innovator award. Dale Sanders, Health Catalyst President of Technology, accepted the honor on behalf of his organization at the UHIMSS 2019 spring conference on May 17. He shared some key insights into what makes a great environment for ongoing innovation, including these valuable sources for invention and originality:
Mischief
Humor
Depression
Pen and paper
Naivety
Pattern recognition
Walking
Six Proven Methods to Combat COVID-19 with Real-World AnalyticsHealth Catalyst
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.
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.
ACOs: Four Ways Technology Contributes to SuccessHealth Catalyst
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:
Build a data repository with an analytics platform.
Bring data to the point of care.
Analyze claims data, identify outliers, including successes and failures.
Combine clinical claims, and quality data to identify opportunities for improvement.
The Four Essential Zones of a Healthcare Data LakeHealth Catalyst
The role of a data lake in healthcare analytics is essential in that it creates broad data access and usability across the enterprise. It has symbiotic relationships with an enterprise data warehouse and a data operating system.
To avoid turning the data lake into a black lagoon, it should feature four specific zones that optimize the analytics experience for multiple user groups:
1. Raw data zone.
2. Refined data zone.
3. Trusted data zone.
4. Sandbox data zone.
Each zone is defined by the level of trust in the resident data, the data structure and future purpose, and the user type.
Understanding and creating zones in a data lake behooves leadership and management responsible for maximizing the return on this considerable investment of human, technical, and financial resources.
Three Must-Haves for a Successful Healthcare Data StrategyHealth Catalyst
Healthcare is confronting rising costs, aging and growing populations, an increasing focus on population health, alternative payment models, and other challenges as the industry shifts from volume to value. These obstacles drive a growing need for more digitization, accompanied by a data-centric improvement strategy.
To establish and maintain data as a primary strategy that guides clinical, financial, and operational transformation, organizations must have three systems in place:
Best practices to identify target behaviors and practices.
Analytics to accelerate improvement and identify gaps between best practices and analytic results.
Adoption processes to outline the path to transformation.
Healthcare Relief Funding: Five Steps to Maximize COVID-19 DollarsHealth Catalyst
While federal COVID-19 relief funding for health systems sounds good in theory, many organizations have found accessing and using these monies overwhelming and frustrating. Federal guidance has been inconsistent or incomplete, and continued changes to relief packages and policies challenge organizations to develop pragmatic financial recovery strategies. Financial leaders who are confronting more questions than answers need a simple framework to move confidently into recovery.
The following five expert financial- and healthcare-based guidelines will help organizations navigate and optimize COVID-19 relief funding:
Regularly review legislative and regulatory updates and agency activity.
Make the most of what’s available.
Use required reporting as a decision-making tool.
Prepare now for the inevitable audit.
Test compliance now to eliminate headaches (or litigation) later.
Lean Healthcare: 6 Methodologies for Improvement from Dr. Brent JamesHealth Catalyst
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.
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.
Harnessing the Power of Healthcare Data: Are We There YetHealth Catalyst
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.
Machine Learning in Healthcare: What C-Suite Executives Must Know to Use it E...Health Catalyst
Machine learning (ML) is gaining in popularity throughout healthcare. ML’s far-reaching benefits, from automating routine clinical tasks to providing visibility into which appointments are likely to no-show, make it a must-have in an industry that’s hyper focused on improving patient and operational outcomes.
This executive report—co-written by Microsoft Worldwide Health and Health Catalyst—is a basic guide to training machine learning algorithms and applying machine learning models to clinical and operational use case. This report shares practical, proven techniques healthcare organizations can use to improve their performance on a range of issues.
Four Essential Ways Control Charts Guide Healthcare ImprovementHealth Catalyst
Control charts are a critical asset to any health system seeking effective, sustainable improvement. With a simple three-line format, control charts show process change over time, including the average of the data, upper control limit, and lower control limit. This insight helps improvement teams monitor projects, understand opportunities and the impact of initiatives, and sustain improved processes.
Also known as Shewhart charts or statistical process control charts, control charts drive effective improvement by addressing three fundamental questions:
1. What is the goal of the improvement project?
2. How will the organization know that a change is an improvement?
3. What change can the organization make that will result in improvement?
Population Health Success: Three Ways to Leverage DataHealth Catalyst
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:
Increase team members’ access to data.
Support widespread data utilization.
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.
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.
A Healthcare Digitization Framework: 5 StrategiesHealth Catalyst
While most consumer-oriented industries have turned to mobile-first, cloud-based platforms for consumer interaction, healthcare lags behind in digitization, particularly when it comes to self-service consumer engagement. As digital consumer interaction increasingly drives enterprise success, healthcare must join the modern digital playing field. To get there, organizations need to establish digital investment and enablement frameworks and can then follow five strategies for stable, scalable transformation:
Formally define “digital” for the organization.
Follow 10 guiding principles to support digital.
Divide technology into appropriate portfolios.
Develop an analogy to explain the integrated portfolio approach.
Strategically select vendor partners.
A Healthcare Mergers Framework: How to Accelerate the BenefitsHealth Catalyst
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.
To Safely Restart Elective Procedures, Look to the DataHealth Catalyst
Many health systems have realized they lack the data and analytics infrastructure to guide a sustainable reactivation plan and recover lost revenue from months of halted procedures due to COVID-19. However, with operational, clinical, and financial data, augmented by analytics tools, leaders have the visibility into hospital and resource capacity to guide a safe, sustainable elective surgery restart plan.
The first step on the road to recovery for health systems is access to robust analytics to understand the full impact of COVID-19 on clinical, financial, and operational outcomes. Second, organizations need data-sharing tools, like data displays and dashboards, allowing leaders to make decisions based on consistent data that support the organization’s reactivation goals. Leaders can even take the data one step further with predictive models and forecast procedure count, staff, and resources.
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.
The Top Seven Healthcare Outcome Measures and Three Measurement EssentialsHealth Catalyst
Healthcare outcomes improvement can’t happen without effective outcomes measurement. Given the healthcare industry’s administrative and regulatory complexities, and the fact that health systems measure and report on hundreds of outcomes annually, this article adds much-needed clarity by reviewing the top seven outcome measures, including definitions, important nuances, and real-life examples. The top seven categories of outcome measures are:
Mortality
Readmissions
Safety of care
Effectiveness of care
Patient experience
Timeliness of care
Efficient use of medical imaging
CMS used these seven outcome measures to calculate overall hospital quality and arrive at its 2018 hospital star ratings. This article also reiterates the importance of outcomes measurement, clarifies how outcome measures are defined and prioritized, and recommends three essentials for successful outcomes measurement.
Health Catalyst Named 2019 Healthcare IT Corporate InnovatorHealth Catalyst
Utah HIMSS (UHIMSS) recognized Health Catalyst for its innovative leadership with the 2019 UHIMSS Healthcare IT Corporate Innovator award. Dale Sanders, Health Catalyst President of Technology, accepted the honor on behalf of his organization at the UHIMSS 2019 spring conference on May 17. He shared some key insights into what makes a great environment for ongoing innovation, including these valuable sources for invention and originality:
Mischief
Humor
Depression
Pen and paper
Naivety
Pattern recognition
Walking
Six Proven Methods to Combat COVID-19 with Real-World AnalyticsHealth Catalyst
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.
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.
ACOs: Four Ways Technology Contributes to SuccessHealth Catalyst
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:
Build a data repository with an analytics platform.
Bring data to the point of care.
Analyze claims data, identify outliers, including successes and failures.
Combine clinical claims, and quality data to identify opportunities for improvement.
The Four Essential Zones of a Healthcare Data LakeHealth Catalyst
The role of a data lake in healthcare analytics is essential in that it creates broad data access and usability across the enterprise. It has symbiotic relationships with an enterprise data warehouse and a data operating system.
To avoid turning the data lake into a black lagoon, it should feature four specific zones that optimize the analytics experience for multiple user groups:
1. Raw data zone.
2. Refined data zone.
3. Trusted data zone.
4. Sandbox data zone.
Each zone is defined by the level of trust in the resident data, the data structure and future purpose, and the user type.
Understanding and creating zones in a data lake behooves leadership and management responsible for maximizing the return on this considerable investment of human, technical, and financial resources.
Three Must-Haves for a Successful Healthcare Data StrategyHealth Catalyst
Healthcare is confronting rising costs, aging and growing populations, an increasing focus on population health, alternative payment models, and other challenges as the industry shifts from volume to value. These obstacles drive a growing need for more digitization, accompanied by a data-centric improvement strategy.
To establish and maintain data as a primary strategy that guides clinical, financial, and operational transformation, organizations must have three systems in place:
Best practices to identify target behaviors and practices.
Analytics to accelerate improvement and identify gaps between best practices and analytic results.
Adoption processes to outline the path to transformation.
Healthcare Relief Funding: Five Steps to Maximize COVID-19 DollarsHealth Catalyst
While federal COVID-19 relief funding for health systems sounds good in theory, many organizations have found accessing and using these monies overwhelming and frustrating. Federal guidance has been inconsistent or incomplete, and continued changes to relief packages and policies challenge organizations to develop pragmatic financial recovery strategies. Financial leaders who are confronting more questions than answers need a simple framework to move confidently into recovery.
The following five expert financial- and healthcare-based guidelines will help organizations navigate and optimize COVID-19 relief funding:
Regularly review legislative and regulatory updates and agency activity.
Make the most of what’s available.
Use required reporting as a decision-making tool.
Prepare now for the inevitable audit.
Test compliance now to eliminate headaches (or litigation) later.
Lean Healthcare: 6 Methodologies for Improvement from Dr. Brent JamesHealth Catalyst
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.
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.
Harnessing the Power of Healthcare Data: Are We There YetHealth Catalyst
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.
Machine Learning in Healthcare: What C-Suite Executives Must Know to Use it E...Health Catalyst
Machine learning (ML) is gaining in popularity throughout healthcare. ML’s far-reaching benefits, from automating routine clinical tasks to providing visibility into which appointments are likely to no-show, make it a must-have in an industry that’s hyper focused on improving patient and operational outcomes.
This executive report—co-written by Microsoft Worldwide Health and Health Catalyst—is a basic guide to training machine learning algorithms and applying machine learning models to clinical and operational use case. This report shares practical, proven techniques healthcare organizations can use to improve their performance on a range of issues.
Four Essential Ways Control Charts Guide Healthcare ImprovementHealth Catalyst
Control charts are a critical asset to any health system seeking effective, sustainable improvement. With a simple three-line format, control charts show process change over time, including the average of the data, upper control limit, and lower control limit. This insight helps improvement teams monitor projects, understand opportunities and the impact of initiatives, and sustain improved processes.
Also known as Shewhart charts or statistical process control charts, control charts drive effective improvement by addressing three fundamental questions:
1. What is the goal of the improvement project?
2. How will the organization know that a change is an improvement?
3. What change can the organization make that will result in improvement?
Population Health Success: Three Ways to Leverage DataHealth Catalyst
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:
Increase team members’ access to data.
Support widespread data utilization.
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.
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.
A Healthcare Digitization Framework: 5 StrategiesHealth Catalyst
While most consumer-oriented industries have turned to mobile-first, cloud-based platforms for consumer interaction, healthcare lags behind in digitization, particularly when it comes to self-service consumer engagement. As digital consumer interaction increasingly drives enterprise success, healthcare must join the modern digital playing field. To get there, organizations need to establish digital investment and enablement frameworks and can then follow five strategies for stable, scalable transformation:
Formally define “digital” for the organization.
Follow 10 guiding principles to support digital.
Divide technology into appropriate portfolios.
Develop an analogy to explain the integrated portfolio approach.
Strategically select vendor partners.
A Healthcare Mergers Framework: How to Accelerate the BenefitsHealth Catalyst
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.
LinkedIn Lunch and Learn Presented by Ryan Swindall (@swinrs) from AccellionRyan Swindall
A short presentation on LinkedIn and how to get started, a few thoughts on being effective with this digital tool, and a few thoughts on the costs and benefits of the service.
The version history of the Android mobile operating system began with the release of the Android beta in November 2007. The first commercial version, Android 1.0, was released in September 2008. Android is under ongoing development by Google and the Open Handset Alliance (OHA), and has seen a number of updates to its base operating system since its initial release.
HIC2012 The Future of Healthcare: Innovation at the EdgeRajiv Mehta
This was an invited keynote delivered in Sydney, at Australia's annual health informatics conference HIC2012. I was asked to speak about the Quantified Self, and the self-tracking movement in general, and its potential impact on healthcare.
Nearly 40 years ago in Silicon Valley, a group of pioneers leveraged technological advances and new ways of thinking to make computing personal. Computing went from being dismissed as a tool of bureaucratic control to being embraced as a symbol of individual expression and liberation. The creativity of millions of individuals was unleashed. Their experimentation has changed the world, often exceeding the innovation from traditional institutions. Today another generation is leveraging technological advances and new ways of thinking to make healthcare personal. They are developing and using tools, technologies, ideas and communities to enable and empower individuals to understand and manage their own health. They are encouraging and supporting crowd-sourced scientific advancements. What are these people doing? What tools are they using? What have they learnt? And how is all this activity going to impact traditional healthcare institutions, the nature of care services, and the pace of health technology innovation?
Pharma and Social Media: What's the New Normal?Steve Woodruff
When considering the role of social media in the pharma/healthcare industry, it is best to step back and grasp the overall trends shaping the way we now communicate. What is the New Normal?
Mobile devices and applications in healthcare: Security and Compliance Risksdata brackets
Recent HHS analysis of reported breaches indicates that almost 40% of large breaches involve lost or stolen devices.” Majority of these devices are laptops, smart phones, etc., This 50-minute webinar will focus on how to effectively comply and secure mobile devices in healthcare industry.
Relinquishing Control: Creating Space for Open Innovationfrog
frog Creative Director Thomas Sutton spoke on the main stage at the Lift conference in Geneva, Switzerland on February 2. His presentation is about cultivating empty spaces for open innovation to understand what people need and want from their products.
On the future of healthcare - it’s less about being sick, more about staying well & healthy - the ages of Genomic medicine and Self monitoring will lead to healthcare which becomes consumer-driven, engaging, addictive, fun and social – in short: Precise, Participatory, Predictive & Preventive
The purpose of this project is to use foresight practices to describe the process for creating a potential future of personalized healthcare for the year 2045 and explain how strategic innovation can be applied to a personalized healthcare company in 2019.
In search of a digital health compass: My data, my decision, our powerchronaki
Knowledge is power. Despite extensive investments in digital health technology, navigating the health system online is challenging for most citizens. Also for eHealth, the “Inverse Care Law” proposed by Hart in 1971, seems to apply. Availability of good medical or social care services and tools online, varies inversely with the need of the population. The low adoption of eHealth services, and persistent disparities in health triggers a call for multidisciplinary action.
Barriers and challenges are not to be underestimated. Culture, education, skills, costs, perceptions of power and role, are essential for multidisciplinary action. This comes together in digital health literacy, which ought to become an integral part to navigate any health system. Patients living with an implanted device or coping with persistent, chronic disease such as diabetes, as well as citizens engaged in self-care, caring for an elderly relative, a neighbor, or their child with illness or deteriorating health, need a digital health compass.
The panel will engage the audience to elaborate on a vision for this personal, digital health compass and drive advancement in health informatics and digital health standards. The transformative power of health data fueled by targeted digital health literacy interventions can be leveraged by open, massive, and individualized delivery. This way, digital health literate, confident patients and citizens join health professionals, researchers and policy makers to address age-related health and wellness changes to shape the emerging precision medicine and population health initiatives.
From a panel in the eHealthweek 2016. http://www.ehealthweek.org/ehome/128630/hl7-efmi-sessions/
Leveraging the Internet of Things to Improve Patient OutcomesAlex Taser
This public thought leader dialogue reinforced that we are in midst of a technology-enabled revolution in healthcare. A world of IoT sensors and the Big Data it enables has the power to personalize and improve care, predict conditions, and enable access and affordable service to previously under-reached communities.
Rather than a sci-fi fantasty, the future of IoT healthcare is already here. While fractured, the technology exists and its capabilities are growing exponentially. The success in ensuring patient health and empowerment hinges on our ability to shift the culture of care, rethink incentives, collaborate across systems, and put the patient voice at the center of it all.
Big data in the real world opportunities and challenges facing healthcare -...Leo Barella
The Healthcare system will be target of major disruption more than any other industry in the next 10 years.
The Digital economics and increasing demand by consumers for more real time information in order to make better decisions on who they want to "hire" to perform services for them or in their behalf will be the driver of this disruption. Analytics, Big Data and Machine Learning will lay the foundation for the next generation of healthcare yet there are still many challenges to truly revolutionize the healthcare system end to end (Providers, Pharma, Payers)
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
The future of Health and Pharma An emerging view 03 05 16Future Agenda
Drawing from the 2015 Future Agenda expert discussions around the world, this is a view of emerging trends that will impact the future of health and the pharmaceutical sector over the next decade. Used as both a keynote and stimulus for workshops, this material is shared under Creative Commons Non Commercial license. For more information on the Future Agenda programme please see www.futureagenda.org
Future of patient data global summary - 29 may 2018Future Agenda
We are witnessing a growing revolution around the provision of healthcare. Much is being driven by the proliferation of medical data and the technology that supports this. As the pressures on healthcare providers continue to escalate, the better collection, management and use of more patient-specific information provides a significant opportunity for innovation and change. The Future Agenda team made this, the Future of Patient Data, the focus of our major Open Foresight project for 2017/18 – 12 discussions across 11 countries, gathering views from over 300 experts.
This report shares the findings from the Future of Patient Data research project. It highlights several important emerging issues that are the source of major differences of opinion around the world. These include how to best accommodate rising data sovereignty concerns, the privatisation of health information and the growing value of health data. Some of the challenges and opportunities are technical in nature, but many are concerned with different ethical, philosophical and cultural approaches to health and how we treat the sick in society.
To access the full report please see https://www.futureofpatientdata.org
با گسترش فناوری اطلاعات و سرویس های مختلفی امروزه در زندگی انسان ها ارائه می شود حوزه سلامت و درمان هم بی بهره از این گسترش فناوری نبوده و در صورتی که سیاستمداران و برنامه ریزان کشور بتوانند از ظرفیت های ترکیب دانش پزشکی و فناوری اطلاعات بهره ببرند شاید با وجود افزایش جمعیت کهنسال و نیاز به رسیدگی های خاصی که در این قشر احساس می شود بتوان در کاهش هزینه های درمان گامی برداشت
Although there have been enormous strides made in the area of health information technology, most developers and users feel frustrated by the pace of change. This new institute will drive Strategy, Innovation and Design for Health ICT
Andy Bleaden - ECO 18: How digital innovation can support workforce strategiesInnovation Agency
Presentation by Andy Bleaden, International Projects Manager, Andy Bleaden, International Projects Manager, ECHAlliance at ECO 18: How digital innovation can support workforce strategies on Wednesday 27 March at Haydock Park Racecourse.
In this digital era, the healthcare sector is going through a remarkable revolution enabled by sophisticated technologies. Amongst these advancements, web development has been particularly instrumental in enhancing the industry's capabilities.
7 Reasons why Companies & Government should invest in Digital TransformationIsmail Sayeed
Early adoption of digital solutions to provide services, whether health related or not, allows organisations to be ready for future user demands. The large pool of data on patterns of service/product consumption, feedback and possible future behaviour (extracted from data analytics) can guide strategic decisions on what to invest in and for whom.
Digital healthcare innovation was needed decades ago, with or without a global health emergency. Other industries with complex systems have rapidly adopted digital transformation; such as logistics networks, taxation, commerce and others
- except healthcare.
A company that is already accustomed to some form of digital-based communication and operations (as much as possible) are the ones most able to survive and thrive in these circumstances.
A government body that can still function and serve remotely and digitally is the most ideal form of democracy. An organisation with remote workers, paperless reporting, established telecommunications through all chains of command are really agile in its truest form.
I had predicted 2 years ago that digital healthcare solutions would be the dominant narrative for the emerging middle class of many developing countries in Asia.
it is time for the global industry to transform itself to the new reality.
Now.
Similar to Information Technology In Healthcare Past, Present, Future, Achievents, Hurdles And Solutions (20)
7 Reasons why Companies & Government should invest in Digital Transformation
Information Technology In Healthcare Past, Present, Future, Achievents, Hurdles And Solutions
1. Information Technology in
Healthcare: Achievements to
Date and Challenges Ahead
DR. DONALD W. M. JUZWISHIN
HINF 580
UNIVERSITY OF VICTORIA
OCTOBER 15, 2009
www.ideastoaction.ca
2. Professor Protti‟s Assignment
What has Health Informatics accomplished over the
last 40 years since information technology was first
introduced into health care delivery in the late „60‟s;
What policy, organizational, economic,
technological, political and social hurdles are going
to be faced in the next 5-10 years; and
What are some political, policy, social, organization
and economic solutions.
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3. Accomplishments against what measure?
Improve democratization of society
Empower and engage informed citizens
Increase accountability and transparency of
governments
Understand population health and social
determinants of health (SDOH)
Improve the welfare and wellbeing of Canadians
Contribute to a high performing health care system
“Out” the truth
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4. Accomplishments 1970 -2010
Social
Information symmetry Organization
Technological Capacity building
Informational Ubiquity
Differentiation Best practice
Data, info, knowledge, truth Clinical effectiveness
Empowerment Technological
Private vs. public Molecules to genome
Political Rapidity
Democratization of data Relevance - customization
Monitoring & reporting Comprehensiveness
Transparency Causality
Accountability Machine/machine interface
Public & private surveillance Economic
Rights & responsibilities Opportunity cost
Commoditization of information
Scenario building
Cost effectiveness
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5. Muir Gray‟s vexatious problems
The problems are:
a persistence of errors;
poor quality care delivery;
poor experience of patients;
waste;
unknowing variations in policy and practice;
failure to introduce high value interventions;
uncritical adoption of low value interventions; and
failure to recognize uncertainty and ignorance
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6. Hurdles 2010 - 2020
Political Policy
Canadian federalism Leadership
Leadership Management
Governance Incremental tampering
Structural & process Population health and
interoperability SDOH approach
Public confidence Privacy and confidentiality
Legislation & regulations Incentives/disincentives
Access, quality and
sustainability
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7. Hurdles 2010 - 2020
Economic Technological
One solution vs. many Parochial thinking
Public confidence National harmonization
Societal perspective in Standards
cost effectiveness studies Definitions
Comparative effectiveness Global convergence
analysis Interoperability
Macro resource allocation Protecting the public
decisions vs. technical interest
allocation decisions
HIT evaluation &
assessment
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8. Hurdles 2010 - 2020
Social Organization
Professional boundaries Ontario vs. Alberta
Paternalism Disincentives to
Who owns it? interoperability
Hierarchical People centered health
Privacy/confidentiality
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9. Solutions
Political Policy
National consensus on Population health and
standards and definitions SDOH
commensurate with global All government approach
developments Health system structure
Being explicit with private and process
and public split in funding interoperability
and delivery One patient – one record
Benefits coverage
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10. Solutions
Economic Social
Improved quality saves Web 2.0
lives Medicine 2.0
Improved quality saves Health 2.0
money Apomediation
Disinvestment Social networking
Clinical and cost
ineffectiveness
Team work
Ubiquity of cost and price Self care
data Remote sensing
Link interventions to
outcomes
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11. Web 2.0
informed choice
collaboration
openness
provider commitment to excellence of practice
(peering)
researcher autonomy
fair and egalitarian state direction based on the
principle of social solidarity
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12. Web 2.0 & 3.0 Potentialities
Improving citizen knowledge, access and choice
regarding effective health care interventions to benefit
their personal health care status;
Improving provider autonomy and practices to best serve
the interests and health outcomes of patients and the
health of the population;
Improving researchers‟ capability and capacity to bridge
between the creation of new knowledge and contributing
to its application; and
Improving the state‟s direction of the health care system
through better data, information and knowledge thereby
improving health care policy making.
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13. Using Web 2.0 to improve understanding, access,
trust, discourse, practice and behavior in the
health care system
Dimensions
for Citizens Providers Researchers Policy
Improvement makers
Understanding
What mechanisms exist or are emerging?
Access
How can the mechanisms be improved?
Trust
What are the issues and problems?
Discourse
What are the opportunities?
Behavior/practice
What research is necessary?
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14. Concluding Remarks
What a wonderful clash of values!
Coiera‟s rules
Technical systems have social consequences;
Social systems have technical consequences;
We don‟t design technology, we design sociotechnical systems; and
To design sociotechnical systems, we must understand how people
and technologies interact (Coiera, pp. 1198-1199).
Citizens
Politicians, policy makers, researchers
Ubiquitous knowledge
Information technology IS our future!
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