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Completed UNCLASSIFIED Swedish Research Council

What is the effect of decision fatigue on healthcare workers’ medical judgment? Analysis and assessment of telephone triage (1177), antibiotics prescribing (primary care), and diagnosis of skin cancer

56.5M kr SEK

Funder Forte
Recipient Organization Linköping University
Country Sweden
Start Date Jan 01, 2021
End Date Dec 31, 2024
Duration 1,460 days
Number of Grantees 3
Roles Principal Investigator; Co-Investigator
Data Source Swedish Research Council
Grant ID 2020-00864_Forte
Grant Description

Everyday healthcare workers make repeated decisions about patient care. Many of these decisions require careful deliberation and hard thinking.

According to behavioral theory, repeated decision-making is mentally demanding and makes people tired, and when they are tired, they start using mental shortcuts (heuristics) that allow them to make decisions based on simple rules of thumb without engaging in cognitively demanding reasoning. Which can lead to mistakes and bad judgment.

This depleting effect of repeated decision-making is called decision fatigue. It is a cause of concern in healthcare, where long and demanding sessions are common. Previous results indicate that the effect of decision fatigue is substantial.

However, the existing literature has a number of limitations, including methodological, which means that substantial uncertainty remains regarding the effect of decision fatigue. Our project fills this knowledge gap.

The aim of our project is to estimate the effect of decision fatigue on healthcare workers’ medical judgment, and find ways to reduce this effect. The project is conducted in close collaboration with the profession.

It is centered on an evaluation of telephone triage at the Swedish national medical-advisory service (1177), where we use a novel approach to study decision fatigue, and we make a comparative assessment across different units of 1177.

We complement with two large-scale register-based studies on decision fatigue in decisions to prescribe antibiotics (primary care) and in diagnosis of skin cancer.

We also develop a quality benchmark based on tailored algorithmic predictions (using machine learning) that enables detailed analysis of how the quality of decision-making deteriorates over the course of a workday, as doctors make more and more decisions.

Our project makes several important contributions to the literature and it meets the need for research on how organization and work environment in healthcare affects the quality of care.

All Grantees

Linköping University

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