Analytics are supposed to provide data-driven solutions, not additional healthcare analytics pitfalls and other related inefficiencies. Yet such issues are quite common. Becoming familiar with potential problems will help health systems avoid them in the future. The three common analytics pitfalls are point solutions, EHRs, and independent data marts located in many different databases. An EDW will counter all three of these problems. The two inefficiencies include report factories and flavor of the month projects. The solution that best overcomes these inefficiencies is a robust deployment system.
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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.
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) adoption or how teams and work are organized, and 3) best practice or how evidence/knowledge is gathered, evaluated, and disseminated for adoption.
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A healthcare CIO’s role can demand such an intense focus on technology that IT leaders may struggle to find natural opportunities to engage with their C-suite peers in non-technical conversations. To bridge the gap, healthcare CIOs can answer five fundamental questions to better align their programs with organizational strategic goals and guide IT services to their full potential:
- Whom do we serve?
- What services do we provide?
- How do we know we are doing a great job?
- How do we provide the services?
- How do we organize?
Academic medical centers (AMCs) are a triple threat on the healthcare court with their combined medical center, education, and research sections. With a unique set of resources, AMCs have the ability to take a comprehensive, holistic approach to patient care. However, one of the challenges they still face is utilizing healthcare analytics effectively within the patient care setting. With the Healthcare Analytics Adoption Model and other data expertise, AMCs can learn how to merge siloed data, while improving operations, and delivering the highest quality of care to each patient.
To stay in sync with healthcare’s transition to value-based care, payers today must develop the analytics capability to support alternative payment models and drive more value to their members. Payers can follow an analytics roadmap to develop a strategy that extends their data, analytics, and risk management expertise to meet growing demands. The analytics roadmap helps the payer meet these common challenges of establishing a data-driven culture:
- Recruiting and retaining high-quality providers in a competitive market.
- Managing increasing numbers of high-risk/high-cost members with limited resources.
- Efficiently reacting to federal and state legislative and payment changes.
- Controlling the rising costs of healthcare services and pharmaceuticals.
Opportunity analysis uses data to identify potential improvement initiatives and quantifies the value of these initiatives—both in terms of patient care benefits and financial impact. This process is an effective way to find unwarranted and costly clinical variation and, in turn, develop strategies to reduce it, improving outcomes and saving costs along the way. Standardizing the opportunity analysis process makes it repeatable and prioritizes actionable opportunities. Quarterly opportunity analysis should follow four steps:
- Kicking off the analysis by getting analysts together to do preliminary analysis and brainstorm.
- Engaging with clinicians to identify opportunities and, in the process, get clinician buy in.
- Digging deeper into the suggested opportunities to prioritize those that offer the greatest benefits.
- Presenting findings to the decision makers.
Bobbi Brown, MBA, and Stephen Grossbart, PhD have analyzed the biggest changes in the healthcare industry and 2018 and forecasted the trends to watch for in 2019. This report, based on their January 2019, covers the biggest 2019 healthcare trends, including the following:
- The business of healthcare including new market entrants, business models and shifting strategies to stay competitive.
- Increased consumer demand for more transparency
- Continuous quality and cost control monitoring across populations.
- CMS proposals to push ACOs into two-sided risk models.
- Fewer process measures but more quality outcomes scrutiny for providers.
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 transition to ICD-10 in 2015 saw the number of available procedure codes increase from roughly 3,000 to more than 70,000. This change gives clinicians the ability to code procedures to a much higher degree of specificity and provides health systems the ability to unlock powerful clinical insights into how inpatient procedural care is delivered. This article covers the benefits and drawback of ICD-10 PCS, as well as concrete ways health systems can use these procedure codes to provide new clinical insights. The article also walks through the anatomy of the seven-digit alphanumeric codes and provides specific clinical examples of how healthcare organizations can slice and dice this data.
A robust data analytics operation is necessary for healthcare systems’ survival. Just like any business, the analytics enterprise needs to be well managed using the principles of successful business operations. This article walks through how to run an analytics operation like a business using the following five-question framework:
- Who does the analytics team serve and what are those customers trying to do?
- What services does the analytics team provide to help customers accomplish their goals?
- How does the analytics team know they’re doing a great job and how do they communicate that effectively to the leadership team?
- What is the most efficient way to provide analytics services?
- What is the most effective way to organize?
Health systems feel mounting pressure to demonstrate ROI from analytics investments but are faced with inefficacies and delays. Fortunately, the Rapid Response Analytics Solution delivers a 10x increase in analytics productivity and a 90 percent decrease in the time required to develop new analytic insights. The Rapid Response Analytics Solution solves these tough analytics problems through two primary elements: curated, modular data kits called DOS Marts; and Population Builder, a powerful self-service tools that lets any time of user, from physician executive to frontline nurse, explore data and quality build cohorts of patients without relying on IT staff and with no need for sophisticated and customized SQL and data science coding.
As healthcare systems are pressured to cut costs and still provide high-quality care, they will need to look across the care continuum for answers, reduce variation in care, and look to emerging technologies. This article walks through how to evaluate the safety and effectiveness and of emerging healthcare technology and prioritize high-impact improvement projects using a robust data analytics platform. Topics covered include:
- The importance of identifying variation in innovation.
- Ways to improve outcomes and decrease costs.
- The value of an analytics platform.
- The reliable information that produce sparks for innovation.
- Identifying and evaluating emerging healthcare technology.
- Knowing what data to use.
- The difference between efficacy and effectiveness in evaluation of emerging healthcare technology.
Health systems continue to prioritize reducing hospital readmissions as part of their value-based payment and population health strategies. But organizations that aren’t fully integrating analytics into their readmission reduction workflows struggle to meet improvement goals. By embedding predictive models across the continuum of care, versus isolated them in episodes of care, health systems can leverage analytics for meaningful improvement. Organizations that integrate predictive models into readmissions reduction workflows have achieved as much as a 40 percent reduction in risk-adjusted readmissions indexes. Effective analytics integration strategies use a multidisciplinary development approach to meet the needs of a patient’s entire care team and deliver common tools for all involved in the patient’s healthcare journey.
In today’s high-pressured world of healthcare, health systems don’t need report writers. They need highly valuable healthcare data analysts. A top healthcare data analyst becomes a partner for clinical and operational improvement by using a five-step method for solving complex problems. This article walks through this step-by-step approach and demonstrates its application using the real-world example of building a diabetes registry. In addition to this specialized approach to solving problems, the article discusses the five essential skills for data analysts needed in the diabetes registry example:
- Data query
- Data movement
- Data modeling
- Data analysis
- Data visualization
Healthcare organizations are turning to the enterprise data warehouse (EDW) as the foundation of their analytics strategy. But simply implementing an EDW doesn’t guarantee an organization’s success. One obstacle organizations come up against is that their analytics team members don’t have the right skills to maximize the effectiveness of the EDW. The following six skills are essential for analytics team members: structured query language (SQL); the ability to perform export, transform, and load (ETL) processes; data modeling; data analysis; business intelligence (BI) reporting; and the ability to tell a story with data.
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.
Despite the complexity of healthcare analytics, one key strategy for effective, sustainable analytics stands out: designating an executive sponsor to oversee the program. This sponsor is a C-suite level leader who’s committed to championing analytics throughout the organization and has the influence and relationships to drive widespread outcomes improvement. Healthcare executives can use four criteria to identify a great executive sponsor for their analytics programs:
- Have a single accountable leader.
- Find a sponsor with passion for and knowledge about data.
- Choose organizational clout and a vision for analytics over a specific title.
- Build a partnership with the CIO.
Healthcare data scientists are in high demand. This shortage limits the ability of healthcare organizations to leverage the power of artificial intelligence (AI). Health systems must better utilize their data analysts, and, where possible, turn some data analysts into data scientists. This report covers the following:
- Healthcare use cases and which ones data analysts can take the lead on.
- Specific steps for turning data analysts into data scientists.
- How to identify the best candidates among your data analysts.
- Recommended resources to get started on an AI journey.