Modern technologies like widespread internet access, artificial intelligence, machine learning, and data science have greatly expanded the possibilities for data collection and sharing in medicine. It makes sense, then, that healthcare systems are prioritizing development of comprehensive data analytics programs that use these technologies.
But despite recognition of the massive opportunities, so many healthcare organizations face challenges in moving these important initiatives forward. Why?
The truth is—there is a lot of complexity and nuance involved. Success requires a combination of new skill sets and training, infrastructure investments to support greater data access, coordination across multiple departments, and genuine enthusiasm for data-driven care at all levels of the organization. Above all, it requires the ability to bring disparate groups of people together around a common language and set of processes, as well as a shared vision for the power of data to improve care quality and patient outcomes while driving down costs.
A new model for building healthcare data programs
For any healthcare organization looking to better leverage the power of its clinical and operational data, the first step is to assess current capabilities and gaps, and then create a plan for monitoring incremental improvement in identified areas. The Phrase Health Capabilities Model is specifically designed to make this complex process easier to track and manage over time.
The model provides recognizable descriptions of the various phases of data analytics program maturity, so that healthcare systems can quickly identify their current state and delineate the step-by-step infrastructure development required for enhanced data collection, sharing, and analysis.
The model spans the three major facets of all data analytics projects: people, processes, and technology. To make it easier to consume, it is segmented into three concise volumes. The Phrase Health Capabilities Model Volume 1—introduced here—focuses on the interaction of various people and teams within a healthcare data program. Capabilities Model Volume 1 is divided into two areas related to people: data translation and data engagement.
Data translation refers to the ability that people within organizations have to transform deep insights generated through sophisticated analytics into improved decision making and real clinical impact.
When done well, data translation allows disparate groups of people to work within a single health system more easily, using shared language. Clinical staff can talk intelligently about data-related topics. Analytics staff can communicate with clinical and quality-focused teams more clearly about their work. The teams’ overlap in terminology and domain knowledge helps to effectively communicate needs and constraints between members and departments.
By using the Phrase Health Capabilities Model, Vol. 1, organizations can establish their own baseline and then steadily chart progress toward more advanced levels of data translation. To provide a snapshot of this continuum, people at health systems in Phase 1 generally lack the capacity to link analytics to clinical care objectives. Staff follow their own experience and training to deliver excellent care, but their workflows are often not substantiated with data from the organization. At the other end of the spectrum, health systems that have reached Phase 8 are able to coordinate complex analytics initiatives across large groups of quality and data-focused stakeholders. Training and constant learning about data initiatives are integral to the culture at these organizations.
For one pediatric health system in Ohio, expanding learning opportunities for staff meant establishing a leadership development curriculum that incorporated both quality improvement and analytics training. This health system now selects top talent across the organization to participate in a five-month intensive course in which leaders execute a key patient safety or quality improvement (QI) project. Individuals are able to hone their quality and analytics skills by implementing QI methodologies (like the PDSA Cycle) and liaising with technical resources to acquire and evaluate clinical data.
Volume 1 of the Phrase Health Capabilities Model is also geared to tracking the second big people-related driver of analytics program success: data engagement. This occurs when employees across the entire organization assume responsibility for applying data—not just intuition— to solving a range of problems. Clinical staff can then serve as consultants to ensure the validity and value of these efforts. Properly executed, data engagement improves care and service delivery in as many areas as possible.
One data engagement use case is optimizing an electronic health record (EHR) alert that communicates guidance to a nurse who is administering medication to a patient. In organizations that prioritize people-centric capabilities like those outlined in the model, close attention is given to the specific data incorporated into the alert, the way in which that alert is written and communicated, and evaluating how the clinician uses that information in delivering care.
Explore the model to learn more
The Phrase Health Capabilities Model provides practical guidance for making people-centered progress in the areas of data translation and data engagement—two critical components of analytics success. With recognizable descriptions and clear examples of what each stage in the journey looks like, the model is designed to help organizations at varying levels of data maturity quickly pinpoint areas for investment and growth.