Reinventing Clinical Decision Support

Paul Cerrato, MA, and John Halamka, MD, MS
JUNE 15, 2018

In 2011, Kathy Halamka was diagnosed with Stage III breast cancer. Although news like this is never welcome, Kathy was fortunate in that she was married to a Harvard Medical School professor (the second author of this story) and had access to some of the best oncology services in the world. By the time Kathy’s tumor was detected, a sentinel node biopsy revealed that it had already spread to a few nearby lymph nodes. The malignancy was estrogen and progesterone positive but HER-2 negative, less than 5 cm in diameter, poorly differentiated, and fast-growing. On average, the 5-year relative survival rate for women like Kathy is 72%, meaning people who have the cancer are only about 72% as likely as people who do not have it to live for at least 5 years after being diagnosed.

The standard of care for cases like this is typically chemotherapy followed by mastectomy. But having access to digital resources such as the Shared Health Research Information Network (SHRINE), Informatics for Integrating Biology and the Bedside (i2b2), and Clinical Query 2 presented new options for Kathy and an opportunity to test a personalized medicine approach to healthcare. But the fact that too few patients and physicians have access to these sophisticated resources serves to emphasize the need to reinvent clinical decision support nationwide.

Tapping Sophisticated Databases

i2b2 is an open-source software platform that gives clinicians and researchers web-based access to a hospital’s electronic health records (EHRs), a resource that has the potential to locate treatment options not yet available in the medical literature or officially endorsed practice guidelines. i2b2 can be compared to an operating system on which applications such as Clinical Query 2 sit. Clinical Query 2 (screenshot below) consists of a website and database that let clinicians search patient records at Beth Israel Deaconess Medical Center. SHRINE is a network of computer systems that are affiliated with Harvard Medical School, giving users access to the EHRs of all of its affiliated hospitals, including Massachusetts General Hospital, Brigham and Women’s Hospital, and Dana-Farber Cancer Institute. With the assistance of these resources, it was possible to perform a very specific query about Kathy, a 50-year old Asian female with Stage III breast cancer. That query asked how many patients seen in all the Harvard-affiliated hospitals fit her profile. The system found more than 17,000 and provided the medications they received, their average white blood cell counts, their prognosis, and so on.

Clinical Query2 is used by clinicians at Beth Israel Deaconess Medical Center to help find individualized diagnostic and treatment options for patients at Harvard affiliated hospitals. (Image used with the permission of Beth Israel Deaconess Medical Center, Boston, Massachusetts.)

The results of that investigation concluded that stage III breast cancer is usually managed with doxorubicin (Adriamycin), cyclophosphamide (Cytoxan), and paclitaxel (Taxol). The query also pointed out that neuropathy is a common side effect of Taxol. Because Kathy is a visual artist, that consideration was important, as neuropathy could affect her fine motor skills. Further investigation found that there was only one clinical trial looking at the use of Taxol in this context, and it used a specific number of mg/kg body weight administered in 9 doses. There were no data to indicate that this was the optimal dosage regimen or if 3 doses or 11 doses would have resulted in better outcomes, both in terms of tumor shrinkage and adverse effects. That data prompted Kathy’s physicians to personalize her treatment by administering full protocols of Adriamycin and Cytoxan but only a half protocol of Taxol, giving her 5 doses rather than 9. The individualized approach caused her tumor to shrink and eventually disappear and resulted in minimal numbness in her hands and feet.

Clinical Decision Support Is Getting Smarter

While the digital tools used to help personalize Kathy Halamka’s cancer are impressive, they join a long list of clinical decision support platforms that are available to health professionals. These new resources can transform the practice of medicine, but only if clinicians are willing to recognize the need for such tools. That need can be summed up succinctly: “The complexity of medicine now exceeds the capacity of the human mind.” That observation, made by Ziad Obermeyer, MD, and Thomas H. Lee, MD, in the New England Journal of Medicine, emphasizes the fact that physicians and nurses, despite their years of education and clinical experience, have cognitive limitations. No single clinician can retain the petabytes of medical research and patient records now available in many clinical decision support systems. Nor can they be expected to see all the correlations and patterns required to make a fully informed diagnosis. No doubt these shortcomings are partially responsible for the disturbing number of misdiagnoses reported in the scientific literature.

The Institute of Medicine estimates that about 5% of US adults who obtain care in an outpatient setting are misdiagnosed every year. Postmortem studies have also found that diagnostic errors contribute to about 10% of patient deaths. Similarly, misdiagnosis likely contributes to up to 17% of adverse events in hospitals and are a major reason why so many malpractice claims must be paid out to injured patients.

The Merck Manual outlines several reasoning errors that physicians are prone to, including affective errors, confirmation bias, and availability errors. Clinicians fall victim to an affective error when they convince themselves that what they want to be true about a patient really is true. As the singer/songwriter Paul Simon once put it: “A man hears what he wants to hear and disregards the rest.” That single-mindedness is sometimes accompanied by a confirmation bias in which clinicians cherry-pick diagnostic observations that confirm their suspicions. Availability errors occur when physicians are continuously exposed to a large number of cases of a specific disorder and then begin to assume that same disorder exists in most of their other patients with similar signs and symptoms. This particular form of blindness makes it that much harder to detect rare disorders.

Become a contributor