NEW @ THCB PRESS: Surviving Workplace Wellness. Spring 2014. Al Lewis and Vik Khanna. e-book edition. # LIGHTHOUSE Healthcare. Illuminated.

client-directed outcome informed assessment

Several email lists I am on were abuzz last week about the publication of a paper that was described in a press release from Indiana University to demonstrate that “machine learning — the same computer science discipline that helped create voice recognition systems, self-driving cars and credit card fraud detection systems — can drastically improve both the cost and quality of health care in the United States.” The press release referred to a study published by an Indiana faculty member in the journal, Artificial Intelligence in Medicine [1].

While I am a proponent of computer applications that aim to improve the quality and cost of healthcare, I also believe we must be careful about the claims being made for them, especially those derived from results from scientific research.

After reading and analyzing the paper, I am skeptical of the claims made not only by the press release but also by the authors themselves. My concern is less about their research methods, although I have some serious qualms about them I will describe below, but more so with the press release that was issued by their university public relations office. Furthermore, as always seems to happen when technology is hyped, the press release was picked up and echoed across the Internet, followed by the inevitable conflation of its findings. Sure enough, one high-profile blogger wrote, “physicians who used an AI framework to make patient care decisions had patient outcomes that were 50 percent better than physicians who did not use AI.” It is clear from the paper that physicians did not actually use such a framework, which was only applied retrospectively to clinical data.

What exactly did the study show? Basically, the researchers obtained a small data set for one clinical condition in one institution’s electronic health record and applied some complex data mining techniques to show that lower cost and better outcomes could be achieved by following the options suggested by the machine learning algorithm instead of what the clinicians actually did. The claim, therefore, is that if the data mining were followed by the clinicians instead of their own decision-making, then better and cheaper care would ensue.

Continue reading “Data Mining Systems Improve Cost and Quality of Healthcare – Or Do They?”

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