The European journal of general practice · 2025
Variation in the efficiency of English general practices and associated factors: A cross-sectional study of 5069 general practices.
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Onwunduba Augustine, Wu Jianhua, Painter Helena, Pearce Helen, Ford John
Аннотация
BACKGROUND: Healthcare demand in English general practice exceeds supply, necessitating practice efficiency. To our knowledge, no study has explored factors associated with practice efficiency in England using a quality-adjusted output.
OBJECTIVES: To determine practice-level efficiency in England and identify associated factors using a quality-adjusted output.
METHODS: We conducted a cross-sectional study using NHS datasets from 2023. Practice efficiency was determined by comparing input (measured using funding and workforce) with output (measured using clinical quality, patient experience, and service volume). Practices were classified as efficient (low input, high output), neutral (same input and output levels), or inefficient (high input, low output) using K-medoids clustering, a machine learning technique. Multivariable logistic regression was used to identify factors associated with practice efficiency (i.e. efficient or inefficient, excluding neutral).
RESULTS: Of 5069 practices, 1117 were classified as efficient, 2773 as neutral, and 1179 as inefficient. Efficiency was lower in practices with a larger patient list (adjusted odds ratio 0.23, 95% CI 0.19-0.28), those with a higher percentage of patients with a chronic condition (0.47, 0.38-0.58) or patients ≥ 65 years (0.63, 0.49-0.81), those in a higher deprivation area (0.25, 0.20-0.32), those that dispense medications (0.52, 0.37-0.73), and those with an alternative provider medical services (vs. general medical services) contract (0.15, 0.07-0.33). Efficiency was higher in urban practices (1.38, 1.00-1.90) and practices with a higher percentage of mixed (1.66, 1.24-2.21) or other ethnicity patients (1.78, 1.22-2.60).
CONCLUSION: Smaller practices were more efficient. Therefore, policies that encourage practice mergers may not deliver the efficiency gains expected.
OBJECTIVES: To determine practice-level efficiency in England and identify associated factors using a quality-adjusted output.
METHODS: We conducted a cross-sectional study using NHS datasets from 2023. Practice efficiency was determined by comparing input (measured using funding and workforce) with output (measured using clinical quality, patient experience, and service volume). Practices were classified as efficient (low input, high output), neutral (same input and output levels), or inefficient (high input, low output) using K-medoids clustering, a machine learning technique. Multivariable logistic regression was used to identify factors associated with practice efficiency (i.e. efficient or inefficient, excluding neutral).
RESULTS: Of 5069 practices, 1117 were classified as efficient, 2773 as neutral, and 1179 as inefficient. Efficiency was lower in practices with a larger patient list (adjusted odds ratio 0.23, 95% CI 0.19-0.28), those with a higher percentage of patients with a chronic condition (0.47, 0.38-0.58) or patients ≥ 65 years (0.63, 0.49-0.81), those in a higher deprivation area (0.25, 0.20-0.32), those that dispense medications (0.52, 0.37-0.73), and those with an alternative provider medical services (vs. general medical services) contract (0.15, 0.07-0.33). Efficiency was higher in urban practices (1.38, 1.00-1.90) and practices with a higher percentage of mixed (1.66, 1.24-2.21) or other ethnicity patients (1.78, 1.22-2.60).
CONCLUSION: Smaller practices were more efficient. Therefore, policies that encourage practice mergers may not deliver the efficiency gains expected.