Every hospital and health system has to juggle significant IT needs with a limited budget. In the middle of these demands and possibilities, hospital executives have to prioritize and decide which technology solutions are the most critical to the health of their organization. I call these most critical IT solutions “survival software.” Advanced clinical analytics solutions are the survival software of the near future, as they really hold the key to achieving the triple aim and survive value-based purchasing.
Learn more about Russ Staheli
Russell joined Health Catalyst as a data architect in October 2011. He started his career as an Intern and later Outcomes Analyst at Intermountain Healthcare in the Institute for Health Care Delivery Research supporting the Advanced Training Program for Executives & QI Leaders (ATP) and the Primary Care Clinical Program. Before coming to Health Catalyst he worked as a Management Engineer Programmer Analyst for the Duke University Health System in their Performance Services department supporting their Infection Control and Epidemiology efforts. While there, he also worked as an external consultant to advance the analytical work of the Duke Infection Control Outreach Network (DICON), a collaborative of over 30 community hospitals. Russ holds an Master of Public Health in Health Policy and Administration from University of North Carolina Chapel Hill and a Bachelor’s degree in Health Services Research from the University of Utah.
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The goal and responsibility of every healthcare organization and provider using a care management approach is to deliver the right care at the right time to the right patients. This standard of care management can only be achieved if five competencies are in place:
Patient Stratification and Intake
This guide to care management reviews each competency and shows how to put it all together into an effective program that gets results for organizations and patients alike.
Care management systems are defined in many ways, but the only effective system comprises three qualities:
1) It’s comprehensive and includes a suite of tools to address all five core competencies of care management.
2) It’s inclusive of all EMRs and other data sources to enable thorough communication and analysis.
3) It’s analytics-driven design facilitates clinical decision making and workflow.
Ultimately, an effective system improves outcomes and becomes an indispensable tool for managing population health.
This article describes what drives successful care management, and reveals a suite of applications that aid care team members and patients through advanced algorithms and embedded analytics. Learn how technology is helping to develop appropriate interventions and improve clinical and financial outcomes.
Analysts are most effective when they have the right tools. In healthcare, that means providing data analysts with a means of accessing and testing ALL of the available data and using it to discover more insights. To do this, analysts need guidance more than they need a detailed set of instructions. And, equally as important, they need a data warehouse and access to a testing environment and data discovery tools, so they can truly do the work they were hired to do: analyze.
As the use of data-driven Key Performance Indicators (KPIs) increases, healthcare organizations are adopting Executive Dashboards to track organizational performance. While dashboards deliver insight and identify areas for improvement, they fail to make the data actionable and the value is often offset by the unproductive fire drills and churn they create. There are three keys to create and deploy insightful and effective dashboards successfully:
Aggregation of underlying dashboards to create the executive dashboard
Establishment of clear ownership and accountability
Many healthcare organizations seem to have been in perpetual pilot stage while experimenting with value-based payment models. Healthcare organizations are focusing their efforts in two primary areas: developing the skills to successfully manage at-risk contracts and, preparing for the considerable business and care delivery transformation necessary for true population health management. But what are the foundational competencies needed to take on risk? Healthcare organizations should consider the following 5 key areas: 1) at-risk contract management, 2) network management, 3) care management, 4) performance monitoring, and 5) improvement prioritization. The value of analytics in each of these competency areas is to prioritize limited resources on the highest impact area.
The purpose of analytics in a healthcare organization is to gain insights to improve a process to address an issue such as, improving clinical quality and patient safety or improving the health of a particular patient population. Analysts are responsible for gathering disparate data from different functional areas and develop a narrative so those driving change can take the information and make it actionable.
Organizations generally build one of two analytic reporting structures. One is a centralized model, where the analytics group is its own entity, independent of any particular group. The second is a decentralized model where the analysts work directly for the different groups or departments. In this way, the group does not have to compete for the attention of the analysts and the analysts’ sole focus is to serve those “customers” well. There is a third way, as well, that optimizes the strengths of both centralized and decentralized.
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.
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.