But with increased reliance on these comes increased complexity and costs, both in creation and management of all this data.*
(*Do not confuse “all this data” with “big data.” As told in the story of an online bookseller’s success, a big data approach involves storing everything first and interpreting later. The KPI model, in traditional data management style, identifies desired data first and stores that data in context.)
Because of the sheer volume of KPIs in use, as well as the complexity of calculating even one KPI, the intricacy of the KPI world should not be underestimated.
Consider the many factors to be addressed in determining “ER waiting time”: information about each patient, symptoms they present with, severity of the condition, real-time calculations about number of patients in various stages of treatment, and the list goes on.
Herein lies the potential for creating a monster.
A KPI is very far downstream in the business objectives pipeline, and the work involved in calculating any KPI runs the gamut from validating the integrity of the data, to executing the data product the KPI and its underlying data live in, to managing systems for analyzing and reporting on that KPI. This lifecycle includes:
Managing KPIs, particularly in healthcare, is extremely complex. Healthcare has more stakeholders than in typical corporate KPI’s, and each of those groups may be interested in different metadata about those KPIs. For instance, “over 75 years of age” and “super senior” might be compatible in spoken language, but have a completely different taxonomy when expressed in metadata.
Often, however, the IT systems responsible for KPI management are already overly complex and serve too many disparate functions, as they’ve usually evolved over a long period of time by many, many people.
In turn, the IT department’s management of these systems is hyper-focused on routine execution of processes that could and should be automated, and ends up expending its resources on troubleshooting and supporting. Statisticians, who ideally evaluate the trends and spot opportunities for improvement, may spend more time manually calculating standard statistics than doing the value-added analysis they were hired to do.
The prospect of adding new KPIs means the exponential addition of – potentially – thousands of data points, and incorporation into an IT system that is already overburdened. And with an exponential increase in complexity comes an exponential increase in management costs.
The underlying goal in a KPI model is to create a structured, common language, understandable to all of your stakeholders, to communicate what’s supposed to happen and then build execution engines to translate that into actual activity. Welcome to Master Data Management (MDM).
Creating a common language means developing an XML (Extensible Markup Language) – whose words and letters are always pronounced (defined) exactly the same – and there is no guesswork, unlike, say the English language, which is full of pronunciation exceptions. So “super senior” or “redhead” would ALWAYS mean exactly the same thing to all stakeholders.
Now that all parties, even IT, understand and speak the same language, life for all stakeholders becomes less about tedious maintenance and troubleshooting, and more about value-add and better outcomes:
The beauty of a properly built and executed KPI strategy is that you can leverage the talent and skills you already have using well-proven technology. There’s no compelling reason to invest in risky, cutting edge technology because you already know exactly what you need. (Again – highlighting the separation of this discussion from “big data.”)
Are you ready for the future by creating the KPI-MDM strategy that can be ported into the future? This readiness starts with the understanding that an investment into IT infrastructure has proven ROI. The KPI model underscores the value IT brings to healthcare – and any industry whose outcomes can be objectively measured.
Between government mandated healthcare improvements and internal, mission-based commitments to providing quality health care, healthcare providers are recognizing both the need, and the opportunity to deliver better results through IT.