While the world waits for a vaccine or effective treatment for COVID-19, managing disease spread is paramount. For health systems, patient and staff contact tracing is one of the top transmission-control strategies. Because the virus appears to spread mainly through respiratory droplets from person-to-person contact, knowing where infected individuals have been and with whom they’ve been in contact is an essential capability. With this insight, organizations can manage transmission with data-driven emergency planning and monitoring capabilities. The resulting appropriate and timely workflow modifications will serve disease control efforts during the 2020 pandemic and help health systems prepare for future outbreaks.
Learn more about Josh Ferguson APRN, ACNP, ANP-BC
Josh is a nurse practitioner who has over 20 years of nursing experience. Most recently, Josh joined Health Catalyst as a Clinical Outcomes Improvement Director. In this role, he helps healthcare organizations achieve their improvement goals. Prior to joining Health Catalyst, Josh worked at Intermountain Healthcare in a respiratory and medical intensive care unit as a nurse practitioner. During this time, he also worked closely with multiple clinical program leads and clinicians to develop and implement systemwide applications, protocols, and order sets to reduce variation in care. You will also, on occasion, find him lecturing to nurse practitioner students at the University of Utah.
Read articles by Josh Ferguson APRN, ACNP, ANP-BC
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.
Patients who undergo surgery frequently follow a rehabilitation program afterwards to promote recovery. However, starting this program before the procedure may help further accelerate recovery time. Prehabilitation is defined as physical or lifestyle preparation that happens before surgery and is designed to help patients regain function in less time.
Prehabilitation includes the following four main components:
Medical optimization of pre-existing medical conditions.
Providing coordinated care from the pre-surgery period to post-operative recovery helps ensure the best patient outcomes. Additionally, health systems can glean important insights about best practices when they effectively follow the patient journey and capture relevant data throughout.
Healthcare organizations know that they need to an effective clinical data analytics strategy to improve and survive in today’s challenging environment. In order to make these necessary improvements, healthcare leaders need to establish clear goals for their clinical data analytics initiatives.
Achieving these goals requires clinical teams to clearly identify problems and plan for how to achieve them. This article walks improvement teams through sometimes confusing process of identifying problems, setting clear, achievable goals, and common pitfalls along the way. Topics covered include:
Six categories of clinical data.
Three types of goals: outcome, process, and balance.
How to write an outcome goal.
Internal vs. External Benchmarks.
Getting clinical buy-in.
According to statistician W. Edwards Deming, “Uncontrolled variation is the enemy of quality.” The statement is particularly true of outcomes improvement in healthcare, where variation threatens quality across processes and outcomes. To improve outcomes, health systems must recognize where and how inconsistency impacts their outcomes and reduce unwanted variation.
There are three key steps to reducing unwanted variation:
Remove obstacles to success on a communitywide level.
Maintain open lines of communication and share lessons learned.
Decrease the magnitude of variation.
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.
To measure health outcomes that matter to everyone, it’s important to ask several questions before starting out:
How do regulatory requirements differ from outcomes improvement?
Do the measurements align with organizational goals and values?
Are the measurements worth the resources required to document them?
Will the metrics actually be applied to outcomes improvement?
Who are the beneficiaries of the outcomes improvement initiative?
The answers to these questions help save time and resources, sustain and expand the improvement effort, refine the list of measures to those that truly improve outcomes, and most of all, help avoid the outcomes measures graveyard.