Today’s post begins a series on clinical intelligence. The series will consist of two posts. In part one, I will address the question, “What is clinical intelligence?” and provide you with key information on this topic.
“What is clinical intelligence (CI)?” That is a simple question that any clinician should be able to answer quickly and accurately. However, the answer is not so simple, and the definition always depends on whom you ask.
Clinicians have been trained in science to observe, measure, interpret, diagnose, plan and deliver care. CI is the knowledge derived when data of the clinical process is queried, analyzed and understood specific to the question asked. This is the new learning paradigm for care providers responding to questions of care effectiveness and quality.
Acquiring this knowledge depends on the use of skill sets, technologies, applications and practices for continuous, iterative exploration and the investigation of past clinical performance. It is used to gain insight and drive clinical planning. The process makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling and fact-based management to drive decision-making. This process is Clinical Analytics.
Four components describe this process: analytics, performance management, CI and data management. Each component provides an important, unique capability to the overall process.
Analytics is the use of modern data mining, pattern matching, data visualization and predictive modeling tools to produce analyses and algorithms that help clinicians make better decisions. With regard to topics such as patient management, costs, workflow, logistics and practice effectiveness, analytics helps IRFs answer questions such as: “Why is this happening?”, “What if trends continue?”, “What will happen next?” or “What is the best outcome?” Thus, clinical analytics can provide foresight to what events may have the most impact on the patient and the institution as a whole.
Performance management is the business term that describes the methodologies, metrics, processes and analytical applications used to monitor and manage clinical performance. Examples include budgeting, planning and forecasting, profitability modeling and optimization, scorecard applications and cost reporting, and financial consolidation. Performance management can also provide insight into the future and better understanding of current activities and events.
In part two, I will continue my discussion and focus on CI and data management. Stay tuned …