Mays Impacts

Medical errors account for 98,000 deaths each year in the U.S., according to a 1999 report published by The Institute of Medicine (IOM). In a more recent report, the IOM claims medical errors harm 1.5 million people and cost $3.5 billion every year. Interestingly, the report claims that medical errors are not due to incompetent people, but to bad systems that include the processes and methods used to carry out various functions.

Janakiraman

These staggering numbers and facts have caught the attention of many researchers, including Ram Janakiraman, assistant professor of marketing at Mays Business School, Shelley and Joe Tortorice ’70 Faculty Research Fellow and Mays Teaching Fellow.

Janakiraman says he has always been interested in several aspects of healthcare. “As a marketing researcher, the context of doctor and patient relationships greatly interests me,” he says.

This interest led him and a group of other researchers from around the nation to explore and analyze the impact of system automation on medical errors.

Janakiraman explains that medical errors are most commonly traced back to the manual transmission of information across different functional units of the hospital, manual calculations of doses and unmonitored clinical interventions. The big question surrounding the research, he says, was, “Can automation really reduce the rate of errors in various hospital wards?”

Janakiraman’s co-researchers on the article in Information Systems Research were Ravi Aron, an assistant professor at the Johns Hopkins Carey Business School; Shantanu Dutta, vice dean for graduate programs and professor of marketing at USC Marshall School of Business; and Praveen Pathak, American economics institutions professor at University of Florida’s Warrington College of Business Administration.

The researchers hypothesized that the “Automation of information capture and transmission between agents and across the different functional units of the hospital can reduce the rate of medical errors, because they enable the automation of the checks and procedures, thereby removing the “human touch.'”

Janakiraman drew insight from the Joint Commission on Accreditation of Healthcare Organization (JCAHO), as well as the Joint Commission International (JCI). The Joint Commission recommends that hospitals adopt three procedural norms:

1. Observe and record actions of agents (Sensing Function)
2. Recommend context-specific procedural controls (Control Function)
3. Undertake periodic managerial review of the extent to which agents are in compliance with norms (Monitoring Function)

The researchers recognized these three functions as having potential for automation. One example that could be automated is logging the time an item is removed from storage. Rather than recording it in a logbook, a technician could swipe a digital card to record the time.

Janakiraman says this research is important for a number of reasons: No study has empirically analyzed the relationship between automation and medical errors using actual hospital data and no study has looked at the differential impact of automating these three functions on the incidence of two types of medical errors (procedural and interpretative errors) in hospitals. Also, no other study has examined the effect of quality training programs and their complementary effect on automation of error prevention functions using actual data.

“Collecting this data was a humongous feat,” Janakiraman says. The researchers used panel data of incremental automation over time of the error prevention functions and actual rates of medical errors at several wards of two large, top-notch hospitals.

With this data, Janakiraman describes the two categories of medical errors the researchers found: procedural errors (deviations from norms irrespective of what the context and circumstances are) and interpretative errors (deviations from norms that are classified as errors based on the underlying circumstances and the context).

Results from the study confirmed Janakiraman’s hypothesis: automation of the three core error prevention functions (sensing, control and monitoring) helps reduce both kinds of medical errors (procedural and interpretative).

In addition, the researchers found evidence of a significant complimentarity between automation of certain functions and the training of clinical and nonclinical workers in quality management.

“The research demonstrates that there are hidden benefits to the automation of manual functions that are often not captured in a cost benefit analysis,” he says.

Janakiraman plans to continue with his research on healthcare — this time focusing on hospitals’ decision to adopt various technologies, rather than just the impact of technology.