How Has Quantitative Analysis Changed Health Care?

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In health care, groundbreaking solutions often follow a new capacity for measurement and pattern finding. For example, developing the ability to measure blood glucose levels led to better treatments of diabetes. Florence Nightingale changed nursing forever with her careful measurements of hospital care outcomes. Today, we’re in the midst of an even more significant change in the health care industry: Troves of data are mixing with technologies newly powerful enough to adequately analyze them. As a result, the unprecedented pattern-finding power of quantitative analysis is remaking the health care industry.

Quantitative analysis refers to the process of using complex mathematical or statistical modeling to make sense of data and potentially to predict behavior. Though quantitative analysis is well-established in the fields of economics and finance, cutting-edge quantitative analysis has only recently become possible in health care. Some experts insist that the unfurling of QA in health care will radically change the industry—and how all of us maintain our health and are treated when we’re sick.

It will be up to professionals in the transforming field of health care information technology to make the most of the opportunities borne from these expanding data sets. But what are a few of the specific ways in which quantitative analysis could improve health care?

Stronger Research

Dr. Richard Biehl, former education coordinator of the online Master of Science in Health Care Systems Engineering program at the University of Central Florida, explains that QA stands to change the face of research in the health care field, because, suddenly, it may become very easy to test the strength of correlations between thousands of variables with the touch of a button. In other words, no researcher will need to make the concerted decision to build a study around a question such as “Is this particular allele driving lipid metabolism?” Powerful analytical tools driven by QA will be able to point researchers in the direction of promising correlations between variables they might not have realized were linked.

“We used to get the data to support our research; now we’re getting the data to suggest our research,” Dr. Biehl says. “That’s very, very different.”

The upshot? The field of health care research will become a much more targeted and efficient space—and more likely to regularly uncover lifesaving treatments.

Saving Time, Money, and Lives Through Efficiency and Safety

New QA tools will decrease wait times and call patients into doctors’ offices only when a visit is necessary. As more and more data is crunched to determine, for example, what bodily indicators tend to precede a heart attack, the provider (who will be monitoring the patient’s vital signs via wearable devices) will be able to alert the patient when his or her indicators are trending in a worrisome direction. That means paying for fewer checkup appointments when one is healthy.

Even more importantly, QA tools will allow health care professionals to decrease the impact of human error in prescribing medication and invasive health care procedures. More data can save lives by uncovering complicated patterns (in physiology, DNA, diet, or lifestyle) that help explain why certain medications can prove dangerous for some.

Making Sure Supply Meets Demand

Certain geographic locations and clinical specialties are already facing doctor shortages as mergers and acquisitions reform the health care landscape and financial difficulties force providers to close their doors. But by filling in the picture of oversupply and undersupply around the country, QA can help providers plug holes where they need to.

“Making sure there’s an adequate supply of health care in the right places, in the right specialties, and at the right times, is a health care systems engineering challenge,” Dr. Biehl says.

Amid all the exciting possibilities, QA’s application to health care is still newer than other industries and faces challenges. This type of analysis requires that variables be recorded as numerical data so that they can be analyzed with statistical tools—a format that health care has struggled to conform with, as much of its outcome data is recorded as “positive” or “negative.”

Additionally, QA statistical tools work best when fed with huge amounts of data, as more data makes for clearer patterns and stronger conclusions. Big data analysts at corporations such as Amazon and Google—where every click is tracked and measured—have been collecting unprecedented amounts of data to feed into complex statistical tools for years. Health care has yet to catch up, but it likely will once more wearable technology options, such as expanded versions of Fitbit devices and trackers embedded in up-and-coming “internet of things” appliances, track more and more users’ every move, bite, and night of sleep.

“Once we start collecting all this personal, wearable data from people, health care will start to look more like the Googles and Amazons of the world,” Dr. Biehl says. “We’ll have hundreds of millions of people collecting tens of thousands of data points a day. We’ll finally have big data; we’re heading in that direction.”

Additional Resources

https://www.worldcat.org/wcpa/servlet/DCARead?standardNo=0787971642&standardNoType=1&excerpt=true
https://bizfluent.com/info-8168865-benefits-quantitative-research-health-care.html
https://www.ruralhealthinfo.org/community-health/rural-toolkit/4/quantitative-qualitative
https://www.dotmed.com/news/story/37262

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