In the 1990s, largely driven by The Joint Commission’s (TJC) then-Agenda for Change, total quality management and continuous quality improvement became the most omnipresent trend in health care management. Training courses, boot camps, and certification programs on Total Quality Management/Continuous Quality Improvement (TQM/CQI), Lean, Six Sigma, Juran, Crosby, etc., were softly mandated by TJC as a means to succeed with their newly recast standards, survey process, and scoring algorithms. The intent was to introduce new thinking, processes, metrics, and tools that would help leaders, managers, physicians and clinicians drive the quality agenda through the accreditation process.
THE CHALLENGE OF EMBEDDING QUALITY
So, what happened? Well, quality management was an unstoppable force, so it proceeded, morphed, evolved and has ultimately taken a form that works for health care. But it wasn’t easy for physicians and clinicians. The idea, let alone the mandate, for physicians and clinicians to become proficient with the tools and techniques of quality management was more than they bargained for. Physicians saw it as a ham-fisted intrusion on their intensifying demand to see more patients and reduce the margin of error for avoidable harm, sub-optimal outcomes, unnecessary costs, or bad patient experiences.
In response, quality leaders and influencers started to integrate quality management principles and practices into the work physicians already did, such as care pathways and protocols, learning through CME, clinical metrics, even the content and interface with electronic health records. To the physician, learning about quality became invisible, almost subliminal; just a different lens on their work. Today, quality management has seeped into the clinical world like fluoride in the water supply.
Fast forward 20 years. Health care is awash in data, nearly drowning it. Some good and useful data, much of it not. And health care is becoming equally overwhelmed with analytical models and products and services that endeavor to clean, organize, analyze and make sense of all that data. Not surprisingly, among the fastest-growing new professions in health care are analysts and data scientists, professionals deeply trained and experienced with complex analytical tools and models.
THE CHALLENGE OF EMBEDDING DATA
As the pressure to use data and analytics has increased, health care executives and clinical leaders are saying, “I don’t want more data, I want answers.” This means that either the few analysts and data scientists working in health care need to provide all the answers; or the many physicians, clinicians, managers and professionals who contribute, touch or consume data need to become data literate. It seems we find ourselves in a similar predicament to the quality movement in the 1990s. How do you make millions of busy people comfortable using data in their work?
Lessons in change management tell us that forcing people with one-size-fits-all training on data analytics will not work. Those in the industry who operate large data sets, like CRICO Strategies, are approaching it differently. The traditional way to address this challenge is to ask, who needs to know what about data and analytics, and what”’s the best way to teach them? Is it conferences, in-house training programs, certification, online courses, etc.?
One way to encode new things into someone’s neurons is to not even try. Rather, teach health care providers how to use data by making the use of it require very little training or new knowledge. Make data so easy to consume and use that you don’t have to “sell” its value to providers, they already believe it.
CODING MPL CASES TO GAIN INSIGHTS
At Candello, we are addressing this challenge by looking at how our data can inform the work people do and the practical needs they have. In other words, who in a hospital or malpractice company could benefit from our medical professional liability (MPL) data? What processes are someone responsible for that could be usefully informed by our data? What pressures are they feeling to have more and better data to inform their decisions? How can we make accessing data easy, fast and reliable for them?
When we code cases for inclusion in our national database, the coding process for every case produces a single document called a Loss Abstract. The Loss Abstract contains all the clinical, financial, and insurance information gathered by coders from source material (legal files and medical records). Information is drawn from the Loss Abstract by our analysts, customers and members to glean insights from the data and to make comparisons to peer groups.
One way in which the data is easily accessible is through our new Clinical Event Search Engine. This tool was designed specifically so its users’ frontline risk and patient safety managers’ can easily access the data insights without needing the support of a data analyst. Like their leaders, those on the front lines of care donâ€™t want more data, they too want answers. And they simply don’t have the time to learn a new skill or wait for a capable analyst to answer their questions.
Screenshot from the Clinical Event Search Engine tool showing a query about diagnosis-related medical malpractice claims.
We heard from risk and safety managers that they have limited resources that can quickly or easily tell them whether their peers around the country have dealt with a major claim, serious reportable event (SRE), or near miss like the one they are dealing with and how those cases resolved. Boards, leaders and physicians often need immediate perspective and assurance that they are not alone, and that their organization is learning from others. More practically, risk and safety managers are required by TJC and state departments of health to conduct research on the SREs they experience, which can be a time-consuming and imprecise process. We developed the Clinical Event Search Engine so that risk and safety managers could quickly and easily reach inside our national claims database and extract aggregate data about cases like theirs, so they can instantly inform and assure their stakeholders and help meet their regulatory requirements.
The net effect of this approach to data is that frontline practitioners are accessing and using data in a practical way without the need to understand predictive modeling, regression analysis, or random forests. We believe that the broad use of data will happen infinitely faster if we focus on the work data can benefit and making access to that data easy and fast instead of trying to teach everyone about data science, data literacy, and analytics. The former does not replace the latter, but it ensures that data finds its way into the hands and heads of people who will use it.