Even though thousands of health outcome measures have the potential to impact the work we do every day, how well do we really understand them? In this article, we take a close look at the definitions, origins, and characteristics of health outcome measures. We break down the financial relevance of certain measures, the relationship between outcome measures and ACOs, and which measures impede, rather than enhance, a typical healthcare system. We review the role of an enterprise data warehouse and analytics, and we touch on the future of health outcome measures, all in an effort to provide deeper insight into some of the mechanics behind outcomes improvement.
The Medicare Access and CHIP Reauthorization Act (MACRA) was signed into law in 2015, with major impacts starting in 2019. MACRA attempts to prioritize quality over quantity by letting providers choose between two value-based payment tracks: MIPs and APMs. Providers won’t have to choose until 2019; until then, they will receive a .5 percent annual increase.
The industry is conflicted about MACRA. On the one hand, many believe it is part of the overall shift to value-based healthcare. On the other hand, many say the administrative burden will overwhelm providers. Another area of MACRA controversy has to do with meaningful use which, contrary to what the CMS Acting Administrator said in 2016, isn’t going away with the introduction of MACRA.
Although it seems a ways away, MACRA’s base year will likely be 2017. Armed with the seven best ways to start preparing for MACRA today, and an EDW that provides clinicians with the self-service tools to monitor their performance, health systems can be ready to tackle MACRA when it finally goes into effect.
Population health strategy can borrow a lot from public health. However, health systems haven’t had to deal with patient socioeconomic issues and need to build new skills and use new data. The skills can be adapted from the public health sphere, with hospitals developing health interventions alongside law enforcement, community-based social support, etc. The most important data are patient-reported outcomes data, social determinants of health data, and activity-based costing data. With this approach, the fundamental equation for population health would be Return on Engagement, that is the clinical outcome achieved divided by the total patient investment.
Improving patient satisfaction scores and the overall patient experience of care is a top priority for health systems. It’s a key quality domain in the CMS Hospital Value-Based Purchasing (VBP) Program (25 percent) and it’s an integral part of the IHI Triple Aim.
But, despite the fact that health systems realize the importance of improving the patient experience of care, they often use patient satisfaction as a driver for outcomes. This article challenges this notion, instead recommending that they use patient satisfaction as a balance measure; one of five key recommendations for improving the patient experience:
- Use patient satisfaction as a balance measure—not a driver for outcomes.
- Evaluate entire care teams—not individual providers.
- Use healthcare analytics to understand and act on data.
- Leverage innovative technology.
- Improve employee engagement.
This article also explains why patient experience is so closely tied to quality of care, and why it’s a prime indicator of a healthcare organization’s overall health.
A health system’s outcomes improvement program is an expensive undertaking. It’s worth the results, but there’s no need to make it even more expensive through unforeseen and unnecessary delays. We outline the three phases of managing outcomes improvement programs, from hardware and software acquisition and configuration to resource management to sustaining and scaling the gains. We also examine the nine potential pitfalls that can undermine success in each of these phases:
- Hardware and software acquisition delays
- Environment readiness
- Source system access
- Lack of resource capacity
- Lack of analytic and technical skills
- Data quality paralysis
- Lack of clinical or operational engagement
- Punitive culture: data used as a weapon
- No CEO, no go
Given the industry’s shift toward value-based, outcomes-based healthcare, organizations are working to improve outcomes. One of their top outcomes improvement priorities should be early detection and action, which can significantly improve clinical, financial, and patient experience outcomes. Through early detection and action, systems embrace a proactive approach to healthcare that aims to prevent illness; the earlier a condition is detected, the better the outcome.
But, as with most things in healthcare, improving early detection is easier said than done. This executive report provides helpful, actionable guidance about overcoming common barriers (logistical, cultural, and technical) and improving early detection and action by integrating six must-haves:
- Multidisciplinary teams
- Leadership-driven culture change
- Creative customization
- Proof-of-concept pilot projects
- Health Catalyst tools (knowledge briefs, outcomes improvement packets and worksheets, and care process improvement maps).
The report features a Thibodaux Regional Medical Center sepsis success story that demonstrates how creative customization, when paired with evidence-based standardization, can improve early detection and action efforts, as well as clinical, financial, and patient outcomes.
There’s a formula for success when putting together outcomes improvement projects and organizing the teams that make them prosper. Too often, critically strategic projects launch without the proper planning, structure, and people in place to ensure viability and long-term sustainability. They never achieve the critical mass required to realize substantial improvements, or they do, but then the project fades away and the former state returns. The formula for enduring success follows seven simple steps:
- Take an Outcomes Versus Accountability Focus
- Define Your Goal and Aim Statements Early and Stick to Them
- Assign an Owner of the Analytics (Report or Application) Up Front
- Get End Users Involved In the Process
- Design to Make Doing the Right Thing Easy
- Don’t Underestimate the Power of 1:1 Training
- Get the Champion Involved
Quality improvement in healthcare is complicated, but we’re beginning to understand what successful quality improvement programs have in common:
- Adaptive leadership, culture, and governance
- Evidence- and consensus-based best practices
- Financial alignment
Although understanding the top five essentials for quality improvement in healthcare is key, it’s equally important to understand the most useful definitions and key considerations. For example, how different service delivery models (telemedicine, ACO, etc.) impact quality improvement programs and how quality improvement starts with an organization’s underlying systems of care.
This executive report takes an in-depth look at quality improvement with the goal of providing health systems with not only the top five essentials but also a more comprehensive understanding of the topic so they’re in a better position to improve quality and, ultimately, transform healthcare.
6 Steps for Implementing Successful Performance Improvement Initiatives in Healthcare (Executive Report)
A systematic approach to performance improvement initiative includes three components: analytics, content, and deployment. Taking six steps will help an organization to effectively cover all three components of success. Step 1: Integrate performance improvement into your strategic objectives. Step 2: Use analytics to unlock data and identity areas of opportunity. Step 3: Prioritize programs using a combination of analytics and a deployment system. Step 4: Define the performance improvement program’s permanent teams. Step 5: Use a content system to define program outcomes and define interventions. Step 6: Estimate the ROI.
Why Healthcare Requires an EDW, Analytics Applications, and Visualization Tools for Quality Improvement Initiatives
Business intelligence may not sound like something that belongs in a healthcare setting. After all, what role can it possibly play in medical excellence and compassionate care? But federal mandates that require cost and care improvement and reporting on those improvement metrics, are driving the need for business intelligence tools. For healthcare, this means an enterprise data warehouse with the processing power and architecture to handle the vast volumes of data, analytics applications that will effectively unlock the data, and data visualization tools to easily illustrate areas of opportunity.
EHRs and other electronic health information has created many benefits for patients and the healthcare industry, from improved treatment to reduced duplication of services. However, electronic data also increases the risks associated with PHI. Ensuring compliance with PHI security, including auditing trails, is more important than ever. Breaches constitute a violation of HIPAA and can result in stiff financial penalties and tarnished reputations. Health Catalyst addresses security compliance using a multifaceted solution that ensures the right balance between having enough granularity of information available to the people who need it and securing that information from people who don’t need that level of detail.
There seem to be a lot of definitions for population health management and population health analytics. But all these definitions share one thing: outcomes. The goal is to provide quality care outcomes with good patient experience outcomes at a low cost outcome. So, how can organizations systematically improve their outcomes? The answer lies in three key questions: What should be done to provide optimal care? How well are those best practices being followed? And how do those best practices move into everyday care for patients? Using a systematic approach to answering these three questions will lead organizations toward becoming an outcomes improvement machine.
Precision medicine, defined as a new model of patient-powered research that will give clinicians the ability to select the best treatment for an individual patient, holds the key that will allow health IT to merge advances in genomics research with new methods for managing and analyzing large data sets. This will accelerate research and biomedical discoveries. However, clinical improvements are often designed to reduce variation. So, how do systems balance tailoring medicine to each patient with standardizing care? The answer is precise registries. For example, using registries that can account for the most accurate, specific patients and disease, clinicians can use gene variant knowledge bases to provide personalized care.
The Changing Role of Healthcare Data Analysts—How Our Most Successful Clients Are Embracing Healthcare Transformation (Executive Report)
The healthcare industry is undergoing a sea change, and healthcare data analysts will play a central role in this transformation. This report explores how the evolution to value-based care is changing the role of healthcare data analysts, how data analysts’ skills can best be applied to achieve value-based objectives and, finally, how Health Catalyst’s most successful health system clients are making this cultural transformation happen in the real world.
Comparing the Three Major Approaches to Healthcare Data Warehousing: A Deep Dive Review (White Paper)
The task to improve healthcare presents a significant challenge to providers, health systems, and payers. But according to the Institute for Healthcare Improvement, if health systems focus on achieving the objectives of the Triple Aim, they will be able to meet the ongoing government mandates to improve care. A key component for meeting the Triple Aim will require the ability to overcome the current data warehouse challenges the healthcare industry faces. Because of constantly changing business rules and definitions, health systems need to choose a data warehouse that’s able to bind volatile and nonvolatile data at different stages rather than the early binding approach that’s inherent with traditional data warehouses. The best type of healthcare data warehouse should offer a late-binding approach, which will provide the following critical characteristics: data modeling flexibility, data flexibility, a record of changes saved, an iterative approach, and granular security.
Finding the perfect data governance environment is an elusive target. It’s important to govern to the least extent necessary in order to achieve the greatest common good. With the three data governance cultures, authoritarian, tribal, and democratic, the latter is best for a balanced, productive governance strategy. The Triple Aim of Data Governance is: 1) Ensuring data quality; 2) Building data literacy; and 3) Maximizing data exploitation for the organization’s benefit. The overall strategy should be guided by these three principles under the guidance of the data governance committee.
To succeed in a value-based care environment, accountable care organizations (ACO) require a solid foundation built on five competencies of population health management: infrastructure, population evaluation, provider network, quality and safety, and waste reduction. Once the foundation is built, the healthcare organization can package this “asset” to enter into truly beneficial agreements as outlined in this groundbreaking Accountable Care Transformation Framework.
Health systems are interested in population health management strategies for two reasons: to manage the escalating costs of treating chronic diseases and to survive the shift in the government’s payment model. But for health systems to survive, they’ll need to change their traditional way of accessing and analyzing only claims or clinical data because this approach omits valuable information. Overcoming the barriers to accomplish this goal won’t be easy, but by following these two strategies, health systems will be able to create a superior population health management initiative: map outpatient codes to clinical care process families and select flexible and scalable technology.
Learn how your existing data warehouse could benefit from Health Catalyst’s advanced analytics applications. Options available include:
- Custom applications
- Cloud data warehouse
- Parallel platform
- Feeder data warehouse
- New data warehouse platform
Anatomy of Healthcare Delivery Model: How a Systematic Approach Can Transform Care Delivery (white paper)
Read about this breakthrough model and framework, developed and refined by Dr. David Burton during his 25 years of executive healthcare experience. This model creates a framework that maps major healthcare processes into common patterns and process flows that can then be used to systematically examine and improve healthcare delivery. By using a systematized framework to reduce variations in clinical and operational processes, health systems can experience sustainable cost and quality gains. This framework won’t eliminate critical thinking, but it will provide a standardized, evidence-based approach to care delivery, which will bring all care up to the same, high standard.
What are the differences between a database and a data warehouse? A database is any collection of data organized for storage, accessibility, and retrieval. A data warehouse is a type of database the integrates copies of transaction data from disparate source systems and provisions them for analytical use. The important distinction is that data warehouses are designed to handle analytics required for improving quality and costs in the new healthcare environment. A transactional database, like an EHR, doesn’t lend itself to analytics.
Managing and treating patients with chronic conditions and comorbidities is difficult without coordination between the various treating physicians. To improve patient outcomes for such complex situations, an enterprise data warehouse can deliver the necessary quality improvements and coordinated care these types of patient populations require.
Population health management (PHM) is in its early stages of maturity, suffering from inconsistent definitions and understanding, overhyped by vendors and ill-defined by the industry. Healthcare IT vendors are labeling themselves with this new and popular term, quite often simply re-branding their old-school, fee-for-service, and encounter-based analytic solutions. Even the analysts —KLAS, Chilmark, IDC, and others—are also having a difficult time classifying the market. In this paper, I identify and define 12 criteria that any health system will want to consider in evaluating population health management companies. The reality of the market is that there is no single vendor that can provide a complete PHM solution today. However there are a group of vendors that provide a subset of capabilities that are certainly useful for the next three years. In this paper, I discuss the criteria and try my best to share an unbiased evaluation of sample of the PHM companies in this space.
Interest in predictive modeling is part of a larger trend to employ business and clinical intelligence applications in healthcare. Until recently, organizations that had the ability to mine and analyze data were mostly conducting retrospective analyses. Using tools available today, organizations with the right technical infrastructure, including a data warehouse, can link predictions to specific clinical priorities, set up new workflows, apply analytics to emergency departments and to slowly changing clinical situations and more.
Keys to a Successful Health Catalyst Data Warehouse Platform and Analytics Implementation (Executive Report)
During the process of learning about the Health Catalyst Late-Binding ™ data warehouse platform and analytics solutions, we have found that many customers ask similar questions about how the process really works. So, we thought it would be useful to produce a document that we hope will answer the majority of these and other common questions. The keys for a successful Health Catalyst data warehouse platform and analytics implementation are outlined step-by-step format.
Pre-step (most important): Identify key personnel resources needed on the health system side
Step 1: Implementation Planning
Step 2: Deploy Hardware
Step 3: Technical Kickoff Meeting with the Client and Health Catalyst Deployment Teams
Step 4: Access Source Data
Step 5: Install Platform
Step 6: Load Data
Step 7: Install Foundational Applications
Step 8: Install Discovery Applications
Step 9: Install Advanced Applications
At the beginning of the project, Health Catalyst will begin a collaborative implementation planning process resulting in a timeline tailored to each project. Some projects can be accelerated, with the initial phase completed in 90 days.
Your health system will have questions specific to your organization and your circumstances. We are happy to answer those in person.
BI tool and visualization solution vendors recognize the importance of a healthcare enterprise data warehouse, so they sometimes market themselves as cloud data warehouses. Here are 5 things a healthcare BI tool cannot do:
i. Optimize the healthcare data.
ii. Handle large amounts of healthcare data.
iii. Work with healthcare data at different levels of granularity.
iv. Optimize healthcare data for multiple user types.
v. Provide for modularity, understandability, and code reuse.
In today’s reality, healthcare is more dependent than ever upon business intelligence to survive and ultimately thrive. Get a review of the available solutions, a summary of what each does as well as the pros and cons. Also discover how a Late-Binding™ data warehouse stacks up against other solutions in its ability to aggregate data and make it accessible and foster a truly data-driven culture.
Based on 25 years of healthcare IT experience, Dale outlines a detailed set of criteria for evaluating clinical analytic vendors. These criteria include 1) completeness of vision, 2) culture and values of senior leadership, 3) ability to execute, 4) technology adaptability and supportability, 5) total cost of ownership, 6) company viability, and 7) nine elements of technical specificity including data modeling, master data management, metadata, white space data, visualization, security, ETL, performance and utilization metrics, hardware and software infrastructure.the field of vendors and focus on the best solution available for your organization today and for the future.
Chances are, if you are reading this blog, you have heard some flavor of the “build vs. buy” question in the context of data warehousing. For example, here are two conflicting ways that I’ve personally heard this question posed:
“Do we need to buy [a data warehouse], or can we build it?”
“Are there any vendors we can buy this from, or will we have to build this?”
As you can imagine, both approaches resonate differently with different people, cultures, and strategies, and the same basic questions sound very different depending on who is asking it.
Outlined for practical use in the healthcare industry, this eight-level, industry-specific roadmap acts as a guide for organizations striving to be truly data driven. Developed by an independent, cross-industry group, this white paper explains the Analytics Adoption Model, its history and purpose. Organizations can then use the model to determine direction, highlight goals and gauge progress, as well as see the next steps needed to achieve goals associated using data to drive and assess clinical improvements.
Hospitals and healthcare systems need a systematic approach and tools to demonstrate ROI from their healthcare improvement projects. Bobbi Brown, VP of Financial Engagement, shares a four-step process for demonstrating ROI: 1) define the project and business need, 2) begin to quantify ROI, 3) recruit, train and plan, and 4) evaluate costs, revenue and direct benefits. Download the Health Catalyst Clinical Improvement Financial and Executive Communications tools for estimating, calculating and communicating your ROI results.
You have options when it comes to data warehouses – but which one is right for your healthcare organization? Discover the difference of the Late-Binding (TM) data warehouse architecture. And see why this unique system offers quick time-to-value and the agility necessary to meet the changing demands of the healthcare industry.
Population health management will require healthcare providers to care more effectively, efficiently, and safely for more people—despite shrinking reimbursements and rising costs. This white paper outlines the strategies you can adopt to help to turn the reality of population health into a solid, marketable asset for your health system.
Surviving Value-Based Purchasing in Healthcare: Connecting Your Clinical and Financial Data for the Best ROI
Reducing healthcare costs is a major driving force in bundled payments, home-centered medical care, and accountable care organizations. But each new delivery model is built on the premise of reducing revenue per patient. So how can a health system win? Find out what you can do financially survive in today’s environment.
About 40% of U.S. healthcare providers are participating in some type of shared accountability arrangement.. That means that the majority is on the sideline in a “wait and see” mode. Yet this trend will move forward, albeit in fits and starts, as purchasers and providers understand that they have oversimplified and undervalued what the other participants in the equation contribute. As the industry moves through three phases of vertical integration, fair-sighted accountable care initiatives will need to focus on at least three critical elements in designing their system for success in the steady state, including: 1) constructing the vertically integrated healthcare delivery system, 2) stratifying and allocating the risk, and 3) managing the financial risk.
Anyone who has looked into implementing “analytics” for their organization knows that a multitude of options for healthcare analytics are available—and each vendor touts its approach to analytics as the best.I’d like to take a moment here to summarize four primary analytics options available to healthcare organizations today.
More and more, healthcare is molded and critically impacted by the software and information technology that surrounds and supports the industry. As a consequence, the C-level suite beyond the CIO must actively participate in the evolution of their organization’s IT strategy, particularly at the layer of technology where software directly supports workflows and business processes. There are five information systems indispensable to the success of an ACO.
Analytics packages offered by their EHR vendor and their existing business intelligence/analytics tools are not up to the task of supporting the transformation currently underway. Adaptive data warehouses and the analytical tools now available provide crucial, actionable intelligence that health system clinicians can use to identify opportunities to improve clinical effectiveness, cost effectiveness and safety.
Data is now one of the most valuable assets in any organization, especially healthcare as we transition into a more analytically driven industry. If we accept the assertion that healthcare is a knowledge delivery industry—that is, the application of specialized skills and knowledge, along with specialized tools—it is our obligation to exploit the data assets in our environment to augment and optimize that knowledge and those skills.
Although population health management appears to be a recent trend, it really is an extension and improvement on past care management models. Get the details of population health management including its evolution, data needs, business models and vendor solutions, along with insight from Health Catalyst President, Brent Dover.
It’s no secret that the failure rate of data warehouses across all industries is high – Gartner once estimated as many as 50 percent of data warehouse projects would have only limited acceptance or fail entirely. So what makes the difference between a healthcare data warehouse project that fails and one that succeeds? As a former co-founder of HDWA, Steve details six common reasons: 1) a solid business imperative is missing, 2) executive sponsorship and engagement is weak or non-existent., 3) frontline healthcare information users are not involved from start to finish, 4) boil-the-ocean syndrome takes over, 5) the ideal trumps reality, and 6) worrying about getting governance “perfect” immobilizes the project.
Healthcare has remained entrenched in its cottage industry-style of operation, even within huge medical centers and significant medical innovation. The result, as documented by Dr. John Wennberg’s Dartmouth Atlas of Health Care project , is unwarranted variation in the practice of medicine and in the use of medical resources including underuse of effective care, misuse of care, and overuse of care provided to specific patient populations. The root of the problem, Wennberg concludes, is that there is no healthcare “system.” At Health Catalyst, we agree. Healthcare needs to be systematized and standardized in three key areas: 1) healthcare analytics or measurement, 2) deployment or how teams and work are organized, and 3) content or how evidence/knowledge is gathered, evaluated, and disseminated for adoption.
Finding a sustainable approach to healthcare analytics can be a challenge and requires a meaningful comparison of some of the more prevalent methods out there. Let’s start by looking at those that seem to fail time and again. These include 1) “the report factory” — this approach uses an analytics platform alone and assumes that “if you build it, they will come.”, 2) the “flavor of the month” — which is usually driven by the “squeaky wheel” or management’s favorite pet project, or 3) point solutions, which have “sub-optimization” and “technology-spaghetti-bowl” challenges.
At no time in the history of U.S. healthcare has a flexible, scalable platform for delivering data-driven insights been more important than it is today. But EHRs alone don’t provide the intelligence that physicians, group practices, and hospitals need to significantly improve both the effectiveness and efficiency of care delivery. Learn what you can do to harness all of the data you’re collecting to make real change.