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For several years, health-care teams at St. Michael’s Hospital in Toronto focused on speeding up turnaround times in operating rooms to avoid having to postpone surgeries at the end of a shift.

But there were still days that the staff had to break the news to patients who had prepared emotionally that they’d have to come back another day.

Thanks to the findings of a computer analysis of patient care, hospital teams discovered an effective solution: Be sure to start the day on time.

“What we thought was that our people were spending too much time between cases,” says Catherine Hogan, program director of perioperative services. “But that wasn’t actually happening.”

A computer analysis of the results of the 31,000 surgeries the hospital does each year showed that turnaround times had already been made very efficient. The biggest saving would come from ensuring that the first patient was in the operating room before 8 a.m. and everyone was ready to start to perform surgery on time.

”If you’re not started by 8 a.m., you’re already behind time for the rest of the day and forever playing catch-up,” Hogan says.

It’s one of many examples of how increasingly sophisticated methods of computer analysis are uncovering ways to reduce health-care wait times, provide more effective care and cut costs.

“As computers have become more capable of crunching and comparing large sets of data, patterns emerge that we couldn’t see before,” says Michael Carter, a professor in the Department of Mechanical and Industrial Engineering at the University of Toronto.

Such insights weren’t possible before the implementation of electronic medical record-keeping across Canada over the past decade. Before that, it was difficult to collate enough information because records were kept in paper files or isolated electronic files not easily accessible for analysis.

Prof. Carter became a pioneer in computer modelling of health care when he realized that there are many analogies to computer analysis of bottlenecks in manufacturing processes, as well as some challenging differences.

An early success was software he developed to run simulations of how to most effectively schedule elective surgeries. In 2011 Prof. Carter was approached by the Saskatchewan Surgical Initiative, which had a goal of slashing wait times for all surgeries to three months.

At the time, one particular hospital reported 18-month wait times for hip and knee replacements and was asking for additional resources.

The analysis looked at electronic patient data and analyzed different scenarios that could result in more surgeries in less time by changing scheduling and turnaround times between surgeries.

By 2014 the three-month wait-time goals were met after making the changes and targeting additional funding to specific areas identified by the analysis. 

Part of the success came down to marketing new ways of providing health care.

“There were a lot of skeptics,” Prof. Carter recalls. “Nobody likes change. There are always health-care providers and administrators who are really keen and others who dig in their heels and say, ‘We don’t want to be told how to do our jobs.’ ”

Computer analysis, however, gives real evidence of where there are inefficiencies and gaps. “Eventually, even the most tenacious clinicians went along once they saw the improvement in patient treatment,” Prof. Carter says.

He remembers how hospital administrators used to tell him they were too busy to listen.

“Now my phone rings regularly asking for help with software to find efficiency in scheduling and managing patients.”

Focusing on fundamentals of patient flow and what might improve wait times and hospital efficiency has become a primary concern of health-care systems across the country, reports Jason Garay, vice-president of analytics and informatics at Cancer Care Ontario.

The provincial organization is using computer analysis of patient outcomes to find gaps or areas where intervention priorities can be improved. “We know that in the case of cancer surgery, there is obviously a direct linkage between longer waits for surgery and potential complications,” Garay says. “We never want a patient to have to wait longer than the time appropriate for their conditions.

“The quality of electronic data in the past few years has been good enough to enable us to see very significant decreases in the amount of wait times for every type of cancer surgery.” 

This can result in better clinical outcomes for patients, as well as lower anxiety and less absenteeism from work. For example, improvements identified by computer analysis to ensure that magnetic resonance scanners are used to their full capacity throughout the day have significantly cut wait times, Garay notes. Adjusting scheduling has seen full use of outpatient MRI facilities increase from 84 per cent in September 2014 to 91 per cent this past July, he says.

“The bottom line is that for the first time, we have easy-to-understand explanations of the complex things that happen in our own hospitals,” Hogan says. “In this day and age, when funds are tight, these give us models of how we can be better at using the resources we have.

“And that’s gold.”