Business Intelligence ≠ Healthcare Intelligence

Dr. G makes her rounds on the medical ward looking for clues. She is worried. There has been an increase in the number of urinary infections related to catheters over the past month and she is not sure why.  Right now, her nurses are going from room to room, taking a manual count of all the patients with catheters in place. They do this every day and tabulate the results onto a spreadsheet. An infection control expert will then review all the positive urine cultures and figure out which came from patients with catheters. That expert will then review the medical records of those patients and decide if each has a true catheter-related urinary tract infection, or CAUTI. It is a tedious and time consuming process.

These issues have important ramifications for Dr. G as well as the hospital administration. The CAUTI rates get reported on a publicly available website so prospective patients, administrators, and third party payors can compare “quality.”  Given a propensity for manual error, some ambiguity in definitions leaving the final call open to subjectivity, and political and financial pressure to have low rates, data validity and reliability becomes suspect.  This scenario plays out in hospitals across the country daily, frustrating doctors and compromising patient care.

The greatest obstacle to measuring and improving the quality of care is the lack of access to quantifiable data describing pathways of care and their outcomes.  Put simply, those responsible for improving healthcare rarely have access to the three most basic and fundamental questions of quality improvement: what has been done, to whom was it done, and did it work? This problem is only likely to get worse as U.S. healthcare reimbursement shifts from pay for service to pay for performance or global payment models in which the ability to measure and improve care has a direct effect on the bottom line.

To remedy this situation, healthcare is turning to “business intelligence” or BI for short.  This class of technologies has helped other industries realize tremendous efficiencies through the use of data warehouses, reporting packages, dashboards of metrics, and analytics.  The premise of learning from the past to improve the future is obviously correct and there is little doubt of the importance of these technologies in helping healthcare address and improve its quality.

Unfortunately, what has been absent in the rush to implement BI solutions is a recognition of the fundamental differences between the nature of business versus healthcare information.  These differences have a significant effect on the designs and approaches needed to deliver meaningful “intelligence.”  For example:

● Large amounts of useful information is stored as unstructured free text – Whereas most business intelligence data is quantifiable (e.g., sales of a department last quarter), studies have found that up to 70% of the information useful in making care decisions is formatted as narrative free text.  Any healthcare intelligence solution that doesn’t provide for access to this information in quantitative form is therefore working with, at best, 30% of the available information.

● The heterogeneous nature of medical data – Business intelligence applications are, for the most part, designed to handle continuous or discrete variables.  While some of healthcare data is so straightforward, much of it is formatted as any one of several modalities of imaging data, signal data (e.g. EKG, EEG, etc.), as well as biological information such presence or absence of a protein or gene.  While such data can be discretized or converted to formats amenable to analysis, little is currently offered in the way of tools to facilitate such conversions.

● Temporal resolution of events - Whereas much of the temporal data generated in business intelligence applications adheres to a consistent calendar (e.g., quarters or fiscal year), most healthcare data is relative to events. This requires the ability to consider time relative to events such as the number of days a Foley catheter has been inserted in a patient or the length of a stay in a hospital.

● Information where it’s needed most – A reasonable expectation is that healthcare intelligence be delivered at the point of care.  In other words, a goal is the facilitation of clinical decision support, not just “off line” analytics.  This implies an infrastructure capable of fast access to data and high performance computing.

● Context is key – Anyone that’s been involved in measuring quality has heard the question “where did that data come from?”  Yet, one would be hard pressed to identify a BI product that offers data provenance to the end user.  This matters because very little if any of the data stored in the course of caring for patients is intended for secondary uses such as quality measurement.  As a result, understanding the context surrounding information is critical to interpreting its value.

There has been considerable progress in the development and use of technologies capable of addressing these healthcare-specific needs.  Natural language processing technologies are finally becoming available outside of the labs of their creators. Databases can store increasing amounts and formats of data and novel NoSQL data models.  High performance computing clusters have proven capable of serving up data at the speeds required for real time clinical decision support.  Machine learning algorithms can quickly sort cohorts by disease type or risk categories.  There’s nothing preventing database designers and programmers from recording where the data came from.  However, the application of technology without careful consideration of healthcare’s specific needs will lead to little more than a temporary spike in hospital IT investments and BI vendor stock prices.

If, on the other hand, we build solutions based on the needs of healthcare intelligence, Dr. G and her colleagues may one day have access to a button that can automatically calculate CAUTI rates.  Better yet, it will point her toward the antibiotics, wards, providers, and practices most likely to have swayed the rates in either direction. If all hospitals were to use a similar button, patients and clinicians could have confidence in quality measures and comparison between hospitals becomes possible.  Dr. G, in turn, can get back to what she does best - taking care of patients rather than spending time in front of her computer.

Leonard D’Avolio, PhD is faculty at Harvard Medical School and Director of Informatics for the Massachusetts Veterans Epidemiology Research and Information Center, a clinical research organization within the Department of Veterans Affairs. He can be reached at [email protected] or @ldavolio. Read more of his writings at Medicine’s 4th Paradigm.