A recent article in Healthcare IT News described an industry trend to take clinical data captured at the point of care and use it to determine how providers and hospitals can best meet requirements for meaningful use, and decisions regarding participation in ACOs. In technology circles, this trend is described as "Business Intelligence (BI)" or "Knowledge Management (KM)."
I prefer to describe uses of BI or KM software as "Clinical Intelligence" that can be used to analyze patient trends using data collected at the bedside and data contained in the Patient's EMR. Reported trends in the HIT News article describe reasons to collect and analyze data including:
- "Pay for Performance" or bundled payment models that require efficient delivery of high quality healthcare services that result in improved health outcomes, and
- An exciting move into the world of "Predictive Analytics" (within reasonable levels of confidence).
In 2008 I read a post by Tom Davenport, Harvard Business Review, about 10 Principles of the New Business Intelligence that included ideas that are relevant not only to Business Intelligence but can also form the basis for Principles of Healthcare Clinical Intelligence.. I loosely paraphrase:
- Healthcare decisions are made based upon objective and subjective reports by patients.
- Just like other industries, access to data isn't enough if the goal is improved health outcomes.
- Provide the right data in a clinically intuitive manner that supports the user's need for the right information, at the right time, for the right patient
Tom speaks to the "loosely coupled" relationship of information and decisions that are efficient enough to provide information for more than one question. He says that the provision of information supports decision making, but doesn't always "lead to better decisions."
I read this and considered that the most important factor in achieving clinical intelligence is the "human factor" ... Technology in and of itself does not change the way healthcare is delivered. Physicians, Clinicians, Nurses, Therapists, et. al. make decisions based on the data they - or computers - collect from and about the patient. He continued by saying that business intelligence results will increasingly be achieved by IT solutions that are specific to particular industries and decisions within them."
Interestingly, Tom commented about my post and recommended another post from his HBR blog, What Medicine Can Teach Us About Decision-Making. In this post Davenport mentioned Jerome Groopman, a distinguished doctor in Boston and a writer for The New Yorker (and the author of several books, including the recent How Doctors Think). "... many medical errors result from "poor thinking"--incorrect diagnoses, unreliable heuristics, and so forth... CEO's and other senior executives make similar errors. The difference is that "hospitals convene regular meetings where all faculty and trainees--from the chief to the beginning medical student--revisit cases that had poor outcomes." Needless to say, such self-examination is rare in the corporate boardroom.
Consider that business performance management is a "close cousin" to clinical outcomes management, healthcare analytics, predictive analytics, or any other name du jour. Three simple concepts can form the basis of an intelligent data platform upon which sound clinical decisions can be made.
- Capture: Is data captured in a format that is flexible enough to be used for decision making? With timely updates, accuracy, and availability with appropriate security from any mobile device?
- Analyze: Does the software tool provide an easy to use capability that searches, categorizes, slices, and dices data? Does it also save reports and recommend similar searches?
- Predict: Am I able to discern .. of the X number of patients admitted to the ICU each month over the past year, how many exhibited clinical factors indicating risk of Sepsis? What interventions were used to prevent progression?
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