Five percent of adults seeking healthcare (12 million adults) have an incorrect or delayed medical diagnosis. These mistakes are costly. They account for 6 to 17 percent of adverse events at hospitals and result in death more than other types of mistakes. Most people will experience at least one of these diagnostic errors in their lifetime, sometimes with fatal consequences.
The Institute of Medicine, of the National Academy of Sciences, issued a report entitled Improving Diagnosis in Health Care that addresses diagnostic errors. The report contains many suggestions for how diagnostic healthcare errors can be reduced. Check it out. The Washington Post also has a nice article on this report.
A previous Institute of Medicine 2000 report on patient safety (“To Err is Human“) addressed other types of safety issues that involve human factors issues after a diagnosis has been made. This was a landmark report that led to many safety and quality improvements in healthcare (and great research at the ISYE department at UW-Madison!). However, diagnostic errors received little attention since the publication of this report despite being a problem.
The committee for the Improving Diagnosis in Health Care report is largely composed of medical personnel with at least one notable exception: my UW-Madison ISYE colleague Pascale Carayon at UW-Madison. To a large extent, diagnosis is a medical problem: there are thousands of conditions, many of which are rare, and it’s hard to match the correct single diagnosis with a set of ambiguous outcomes and test results. I appreciate how hard this problem is, and I’m impressed that so many doctors get it right the first time. Medical expertise is a necessary first ingredient.
But medical diagnosis is also a systems problem. Earlier I blogged about the report “Operations Research – A Catalyst for Engineering Grand Challenges” that summarized ways OR can address engineering grand challenges from the National Academy of Engineering. One of the four challenge areas in this report was “OR for human health,” and treatment and diagnostic issues fell under this area. Diagnosis is increasingly a systems issue, since diagnosis is often a function of medical tests and medical imaging. OR is good at weighing the costs and benefits of diagnosis and treatment since Type II errors are often really costly.
I’ve just made a plug for OR and medical diagnosis, but to be honest, I mainly read articles for planning treatment once a positive diagnosis has been made. One important paper in the literature develops linear programming-based machine learning techniques to improve breast cancer diagnosis (more UW-Madison research!):
Mangasarian, O. L., Street, W. N., & Wolberg, W. H. (1995). Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), 570-577.
Let me know of any OR work in the area of medical diagnosis in the comments. Kudos to the committee who produced the Improving Diagnosis in Health Care report – I hope it leads to new important research in OR and industrial engineering.
September 23rd, 2015 at 10:20 am
Very interesting. I am thinking of going to grad school for OR and started following your blog this past week to learn more about the different industries OR can be applied to and what it means to be a researcher in OR.
Though not directly related to OR and medical diagnosis, I wrote a report during my undergraduate about feasibility of implementing six sigma into hospitals. The article I found most interesting that I used was “Surgical case listing accuracy: failure analysis at a high-volume academic medical center.” by Cima RR, Hale C, Kollengode A, Rogers JC, Cassivi SD, and Deschamps C.
In this case the error was not in diagnosis but in treatment specifically surgery. The thought of going to the hospital to get a procedure done and leaving having gone through the wrong procedure is extremely scary! Thanks for sharing!