In This Story
Like virtually every other industry, health care is increasingly prioritizing digital transformation. The sector is unique, however, in that its results are measured not only in business terms but also tangible outcomes for people—often, literal life and death. So are newly acquired technologies actually paying off for patients?
Nirup Menon, a professor of information systems at the Donald G. Costello College of Business at George Mason University, says that the answer is “not always.”
His recently published paper in Decision Support Systems tackles the so-called “HIT paradox,” or the widespread perception that health information technologies (HIT) have not yet moved the needle on important outcomes such as productivity, quality of care, and patient safety.
Menon co-authored the paper with Costello colleagues Amitava Dutta and Sidhartha Das.
Based on comprehensive survey data from approximately 6,000 U.S. hospitals, the research team looked into whether those that adopted Clinical Decision Support Systems (CDSS) saw lower mortality rates for cardiac patients.
“CDSS is not only for cardiologists,” Menon explains. “It is hospital-based—a system that helps with clinical decision-making. But we know that many cardiac patients may not necessarily have cardiac as their only problem. There are probably decisions being made about them using all kinds of ailments and medications, and so on.”
The basic idea behind CDSS is to use technology to mine actionable insights from a wealth of patient data, giving clinicians key tools to make informed decisions at the point of care. Theoretically, a hospital with CDSS solutions should be much better equipped to handle complex cases—such as a heart-attack sufferer with diabetes or another comorbidity—in real time than one without.
However, Menon and his co-authors discovered that when it came to preventing deaths from cardiac emergencies, the impact of CDSS was context-specific. Their paper finds a number of complementary effects suggesting that health care technologies need help from their environment in order to be most effective. For example, the presence of cardiac medical services (CMS), e.g. diagnostic catheterization and electrophysiology, was unsurprisingly associated with lower mortality rates—but CMS combined with CDSS was more impactful than either on its own.
“The labor force—by which I mean the physician and the entire team of nurses and technicians—should be trained to use this technology appropriately,” Menon summarizes. “You also need real-time integration between CDSS and other IT systems, because if it’s not well-integrated, the provider will not have all the data at their fingertips. If you don’t provide the right inputs into a CDSS, it’s not going to give you the right outputs.”
Menon points out that the “HIT paradox” isn’t limited to CDSS or any single technology. President Obama’s 2009 economic stimulus package, after all, included tens of billions in financial incentives for health care providers to digitize their patient records. By 2017, 95 percent of U.S. hospitals had adopted electronic patient records. Yet, as Menon tells it, “hospitals are just chugging along. The quality remains the same and the costs are just increasing. Or you might see improvements in one small department. So we are trying to find the variables that create complementarities within large samples.”
Menon knows, however, that the applications of health care tech can be closely targeted to relatively tiny patient populations, too. Another recent paper of his, published in JMIR Medical Informatics, uses causal survival forests, a machine-learning algorithmic technique, to determine which of two chemotherapy drugs promoted the most longevity for terminal prostate cancer patients. Taking into account age, race and comorbidity symptoms, their analysis produced an easy-to-use prescription policy tree that, by itself, could extend patients’ lives by almost two months—if the test sample, comprised of 2,886 veterans treated at VA health centers, was representative of the wider patient population.
“If you go down every branch of the policy tree, the numbers become very small,” Menon says. “It almost becomes like personalized medicine, because you can factor in age, race, gender—although gender didn’t matter in our study—PSA numbers, bilirubin numbers, etc.”
Menon has ongoing research projects aimed at improving health care through technology, at both the patient level (a la the prostate cancer study) and the ecosystem level (a la the CDSS study). One paper in progress focuses on Covid-19 and how the data-sets research scientists selected for their studies influenced their findings. Another looks at telemedicine’s effects on quality of care.
“My foray into health care began with my PhD dissertation, which was on IT in hospitals,” Menon says. “At that time, I was working primarily from a hospital administration point of view. As a business school researcher, it seemed logical to stay there. But as you come across more problems, and you read more, you realize that the patient is the center of everything, not the hospital.”