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Open Access Research article

Predicting the demand of physician workforce: an international model based on "crowd behaviors"

Tsuen-Chiuan Tsai12*, Misha Eliasziw3 and Der-Fang Chen45

Author Affiliations

1 Department of Pediatrics, E-Da Hospital, No.1, Yida Road, Jiaosu Village, Yanchao District, Kaohsiung City 82445, Taiwan, Republic of China

2 Department of Chinese Medicine, I-Shou University College of Medicine, No.1, Yida Road, Jiaosu Village, Yanchao District, Kaohsiung City 82445, Taiwan, Republic of China

3 Departments of Community Health Sciences, Clinical Neurosciences, Oncology, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1 N4, Canada

4 Department of Surgery, Cathay General Hospital, No.280, Sec. 4, Ren'ai Rd., Da'an Dist, Taipei City, Taiwan, Republic of China

5 Fu-Jen Catholic University College of Medicine, No.280, Sec. 4, Ren'ai Rd., Da'an Dist, Taipei City, Taiwan, Republic of China

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BMC Health Services Research 2012, 12:79  doi:10.1186/1472-6963-12-79

Published: 26 March 2012

Abstract

Background

Appropriateness of physician workforce greatly influences the quality of healthcare. When facing the crisis of physician shortages, the correction of manpower always takes an extended time period, and both the public and health personnel suffer. To calculate an appropriate number of Physician Density (PD) for a specific country, this study was designed to create a PD prediction model, based on health-related data from many countries.

Methods

Twelve factors that could possibly impact physicians' demand were chosen, and data of these factors from 130 countries (by reviewing 195) were extracted. Multiple stepwise-linear regression was used to derive the PD prediction model, and a split-sample cross-validation procedure was performed to evaluate the generalizability of the results.

Results

Using data from 130 countries, with the consideration of the correlation between variables, and preventing multi-collinearity, seven out of the 12 predictor variables were selected for entry into the stepwise regression procedure. The final model was: PD = (5.014 - 0.128 × proportion under age 15 years + 0.034 × life expectancy)2, with R2 of 80.4%. Using the prediction equation, 70 countries had PDs with "negative discrepancy", while 58 had PDs with "positive discrepancy".

Conclusion

This study provided a regression-based PD model to calculate a "norm" number of PD for a specific country. A large PD discrepancy in a country indicates the needs to examine physician's workloads and their well-being, the effectiveness/efficiency of medical care, the promotion of population health and the team resource management.

Keywords:
Physician manpower; Medical education; Healthcare quality; Physician demand; Prediction model