COVID-19 Decision-Making in the Clinical Arena Relies on Ambiguity Reduction , Smart Use of Data
By Kelly M . Pyrek
The fear of the unknown relating to COVID-19 is part of the disconcerting ambiguity that healthcare professionals face . Carnegie Mellon University researchers David Rode and Paul Fischbeck acknowledge that as the COVID-19 pandemic enters its second year , notable medical advancements in the treatment of COVID-19 have been made , but there are unanswered questions around how to live with it and how to work to end it .
In a recent academic paper , “ On ambiguity reduction and the role of decision analysis during the pandemic ,” Rode and Fischbeck explore these questions , including how the decision sciences should play a major role in addressing the virus .
The sciences and medicine are not the only fields struggling with the pandemic .
“ In a very real sense , all of us , as individuals , face significant decisions about how to organize our lives right now ,” said Rode , adjunct research faculty with the Carnegie Mellon Electricity Industry Center . “ These decisions range from simple questions , such as the safety of grocery shopping in person or air travel to the complex , such as government policies dealing with mandatory quarantines and compulsory vaccination .”
In their paper , Rode and Fischbeck note that many of the key decisions facing individuals , corporations and governments all depend on two basic values : the probability a person has the virus and the probability that person transmits the virus .
“ The problem is that when we wrote the article neither of those values was known with any degree of certainty ,” says Fischbeck , professor of social and decision sciences and engineering and public policy . “ Without those probabilities , there is ambiguity , which is a difficult environment in which to work for most decision makers .”
“ In order to reduce this ambiguity ,” notes Rode , “ it ’ s important to collect the right data .”
This means collecting data on randomized samples of the population — not just people with symptoms . According to Rode and Fischbeck , this should have been done from the start .
“ When the virus first became apparent ,” said Fischbeck , “ testing resources were scarce and largely confined to people already displaying symptoms .”
The problem , according to Rode and Fischbeck , was that such testing provided no useful information to guide decision making , and rarely impacted clinical decision making , since patients displaying symptoms were treated without regard to their test results .
“ While useful , of course , for those individuals to know their status , this limited , non-randomized testing produced almost no value to policymakers trying to develop responses to the pandemic ,” said Rode . “ How do you advise people to engage , or not engage , in certain activities if you can ’ t determine how risky those activities are ?”
The healthcare sector thrives on data for its decision-making processes , and although there has been a tidal wave of data in the face of COVID-19 , ambiguity around what it all means and how it translates to patient care is profuse . Rode and Fischbeck say the decision sciences can help shed some light .
“ While data is essential to good decision-making , having too much data or incomplete data can sometimes be a problem , too ,” they say . “ One of the factors that becomes important is getting data out of silos where it is only examined from one or two perspectives . Much of the data collected by healthcare fields is
How do you advise people to engage , or not engage , in certain activities if you can ’ t determine how risky those activities are ?”
— David Rode
incredibly valuable beyond just healthcare itself . This is one of the areas our research tries to emphasize — that there are policy and personal decision-making tasks that could benefit from this information and that centering the process of decision-making can help tie different fields together . The decision sciences can also help when the interests of healthcare professionals and public policymakers ( and businesses and so forth ) may occasionally conflict or diverge . This isn ’ t just about the decision sciences having the analytical resources to evaluate the data , but also