Identifying the Subtypes and Characteristics of Mental Workload Among Chinese Physicians in Outpatient Practice: A Latent Profile Analysis

Abstract

Objective: The purpose of this study is to investigate the mental workload level of physicians in outpatient practice since the normalization of prevention and control of the COVID-19 pandemic in China and explore the subtypes of physicians regarding their mental workload.Methods: A cross-sectional survey of 1,934 physicians primarily in 24 hospitals in 6 provinces in Eastern, Central, and Western China was conducted from November 2020 to February 2021. A latent profile analysis was performed to identify clusters based on the six subscales of the Chinese version of physician mental workload scale developed by our research team. Chi-square tests were performed to explore the differences in demographic characteristics of the subtypes among the subgroups, and multinomial logistic regression analysis was further conducted to identify the determinants of the subtypes of physicians.Results: Overall, the participating physicians reported high levels of task load but with high self-assessed performance (68.01 ± 14.25) while performing communication work tasks characterized by direct patient interaction in outpatient clinics. About 33.8% of the participating physicians were identified as “high workload and high self-assessment” subtype, compared to 49.7% “medium workload and medium self-assessment” subtype and 16.4% “low workload and low self-assessment” subtype. Physicians in “high workload and high self-assessment” subtype had the highest mean mental workload score. Physicians who were female, younger, married, worse health status, those who had lower educational level and an average monthly income of 5,001–10,000 RMB, those who worked in tertiary A hospitals, more hours per week and more than 40 h per week in outpatient clinics, and those who saw more outpatients per day, and spent more time per patient but with higher outpatient satisfaction were more likely to belong to “high workload and high self-assessment” subtype.Conclusion: Our findings can help provide a solid foundation for developing targeted interventions for individual differences across physicians regarding their mental workload. We suggest the hospital managers should pay more attention to those physicians with characteristics of the “high workload and high self-assessment” subtype and strengthen the management of the workload of this subtype of physicians to reduce the risks of their mental health, and to maintain their high work performance in outpatient clinics.

A Latent Profile Analysis of Chinese Physicians' Workload Tethered to Paperwork During Outpatient Encounters

“…These findings suggest a reliable and valid grouping for physician workload tethered to the non-physician-patient communication work tasks characterized by paperwork during outpatient encounters. However, no previous research has explored and identified the latent subgroups of physician workload tethered to paperwork during outpatient encounters, although some studies have identified the patterns or subtypes of mental workload among pandemic frontline medical workers during the COVID-19 pandemic ( 18 , 53 ), as well as physicians in outpatient practice ( 54 ). A previous study on the mental workload level of physicians in outpatient practice since the normalization of prevention and control of the COVID-19 pandemic in China revealed that the latent profile analyses identified three different subtypes of physicians in their mental workload tethered to communication work tasks characterized by direct patient interaction in outpatient clinics ( 54 ).…”

“…We performed exploratory latent profile analysis (LPA) based on the six dimension indicators of physician workload tethered to non-physician-patient work tasks characterized by paperwork during outpatient encounters in this study, where we explored homogenous subgroups in a heterogeneous group and then observed continuous variables in each subgroup. LPA, a “person-centered” statistical approach, belongs to finite mixture modeling, which can identify and describe “hidden groups” within a population ( 18 , 54 , 56 ). Data for the six dimension indicators of physician workload were input into the LPA, with one class initially and additional classes added incrementally, until a unique solution could not be determined; and therein the maximum parameter estimates with standard errors were applied.…”

“…To determine the most appropriate latent class model, the best fit model was identified using the following key model indexes: Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size Adjusted BIC (ABIC), Lo-Mendell-Rubin (LMR), adjusted likelihood ratio test and bootstrap likelihood ratio test (BLRT) and Entropy. A lower value of AIC, BIC and ABIC represents better fitness of data into the estimated model ( 18 , 54 , 56 ); LMR and BLRT compare the model fit between two neighboring models (for example, k-1class model vs. k-class model), and a significant p value indicates that the k-class model fits the data better than the k-1-class model ( 54 , 56 ). Entropy was used to assess the accuracy of classification in the estimated model, with a higher value indicating better classification, and the smallest group should have a minimum of 5% of the total sample in order to avoid over-stratification ( 56 ).…”

“…Such kind of study based on “variable-centered” methods along with human interferences on identification of physicians with high workload, although important, has obscured individual variations in the different aspects of physician workload and therefore failed to reveal the distinctive physician workload subgroups or patterns and further capture the individual characteristics associated with different physician workload groups; and thus, a “person-centered” approach may be more effective. One of the most popular and useful methods involves latent profile analysis (LPA), which provides a methodology to group individuals who share similar patterns of personal and professional characteristics, traits or behaviors into subtypes based on a set of the variables of interest ( 53 , 54 ). This statistical analysis method is rather novel in the mental workload research among medical workers, and it has been shown to be usable and valid for exploring the patterns of mental workload among pandemic frontline nurses during the COVID-19 pandemic ( 18 , 53 ), as well as the identification of the subtypes of physicians' mental workload in outpatient practice since the normalization of prevention and control of the COVID-19 pandemic in China ( 54 ).…”

“…One of the most popular and useful methods involves latent profile analysis (LPA), which provides a methodology to group individuals who share similar patterns of personal and professional characteristics, traits or behaviors into subtypes based on a set of the variables of interest ( 53 , 54 ). This statistical analysis method is rather novel in the mental workload research among medical workers, and it has been shown to be usable and valid for exploring the patterns of mental workload among pandemic frontline nurses during the COVID-19 pandemic ( 18 , 53 ), as well as the identification of the subtypes of physicians' mental workload in outpatient practice since the normalization of prevention and control of the COVID-19 pandemic in China ( 54 ).…”