Post COVID-19 Effect on Medical Staff and Doctors' Productivity - Analysed by Machine Learning

Aug 5, 2026

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The COVID-19 pandemic significantly disrupted healthcare delivery and reduced the productivity of medical staff and doctors. This study applies machine learning to evaluate post-COVID-19 productivity effects across multiple specialties using cross-sectional data from 960 participants collected between June 1, 2022, and April 5, 2023. Results show an average productivity decline of 23%, with larger reductions among older, female, and lower socioeconomic status participants, and among those with fewer sleep hours. Predictors include age, specialty, COVID-19 complications, socioeconomic status, and sleep time, informing mitigation strategies for Iraq.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Patients and Methods

Study Design and Participants

| 2023, 20 (Special Issue): 1507-1519
[https://dx.doi.org/10.21123/bsj.2023.8875](https://dx.doi.org/10.21123/bsj.2023.8875)
[P-ISSN: 2078-8665-E-ISSN: 2411-7986](P-ISSN: 2078-8665-E-ISSN: 2411-7986)

Baghdad Science Journal | | --- | | Post COVID-19 Effect on Medical Staff and Doctors' Productivity Analysed by Machine Learning
Maitham G. Yousif *1, Khalid Hashim 2 , Salman Rawaf 3
1Department of Biology, College of Science, University of Al-Qadisiyah, Iraq, Visiting Professor in Liverpool John Moors University, Liverpool, UK.
2Department of Civil Engineering, School of Civil Engineering and Built Environment, Liverpool John Moors University, Liverpool, UK.
3Professor of Public Health Director, WHO Collaboration Center, Imperial College, London, UK.
*Corresponding Author.
Received 07/04/2023, Revised 11/08/2023, Accepted 13/08/2023, Published 30/08/2023
This work is licensed under a Creative Commons Attribution 4.0 International License. | Abstract The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. These results reflect the overall impact observed following the entire course of the COVID-19 pandemic and are not specific to a particular wave. The analysis revealed that older participants experienced a more pronounced decline in productivity, with a mean decrease of 35% compared to younger participants. Female participants, on average, had a 28% decrease in productivity compared to their male counterparts. Moreover, individuals with lower socioeconomic status exhibited a substantial decline in productivity, experiencing an average decrease of 40% compared to those with higher socioeconomic status. Similarly, participants who slept for fewer hours per night had a significant decline in productivity, with an average decrease of 33% compared to those who had sufficient sleep. The machine learning analysis identified age, specialty, COVID-19 complications, socioeconomic status, and sleeping time as crucial predictors of productivity score. The study highlights the significant impact of post-COVID-19 on the productivity of medical staff and doctors in Iraq. The findings can aid healthcare organizations in devising strategies to mitigate the negative consequences of COVID-19 on medical staff and doctors'productivity. Keywords: Analysis, COVID-19, Medical Staff, Machine Learning, Productivity. Introduction The COVID-19 pandemic has had a profound impact on the healthcare industry worldwide, affecting not only patients but also medical staff and doctors. The pandemic has caused significant changes in the work environment, resulting in increased workload, stress, and burnout for medical staff and doctors 1,2 . Moreover, the pandemic has led to a shortage of medical supplies, equipment, 2023, 20 (Special Issue): 1507-1519 [https://dx.doi.org/10.21123/bsj.2023.8875](https://dx.doi.org/10.21123/bsj.2023.8875) [P-ISSN: 2078-8665-E-ISSN: 2411-7986](P-ISSN: 2078-8665-E-ISSN: 2411-7986) Baghdad Science Journal and staff, exacerbating the already strained healthcare systems 3. The impact of COVID-19 on medical staff and doctors' productivity has been a topic of growing concern 4. Productivity is a critical indicator of the efficiency of healthcare systems and the quality of care provided to patients. A declin

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Which factors were identified as key predictors of productivity score?

Age, specialty, COVID-19 complications, socioeconomic status, and sleeping time were identified as crucial predictors by the machine learning analysis.

Aug 5, 2026

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