The construction of a decision tool to analyse local demand and local supply for GP care using a synthetic estimation model.

BACKGROUND: This study addresses the growing academic and policy interest in the appropriate provision of local healthcare services to the healthcare needs of local populations to increase health status and decrease healthcare costs. However, for most local areas information on the demand for primary care and supply is missing. The research goal is to examine the construction of a decision tool which enables healthcare planners to analyse local supply and demand in order to arrive at a better match. METHODS: National sample-based medical record data of general practitioners (GPs) were used to predict the local demand for GP care based on local populations using a synthetic estimation technique. Next, the surplus or deficit in local GP supply were calculated using the national GP registry. Subsequently, a dynamic internet tool was built to present demand, supply and the confrontation between supply and demand regarding GP care for local areas and their surroundings in the Netherlands. RESULTS: Regression analysis showed a significant relationship between sociodemographic predictors of postcode areas and GP consultation time (F [14, 269,467] = 2,852.24; P <0.001). The statistical model could estimate GP consultation time for every postcode area with >1,000 inhabitants in the Netherlands covering 97% of the total population. Confronting these estimated demand figures with the actual GP supply resulted in the average GP workload and the number of full-time equivalent (FTE) GP too much/too few for local areas to cover the demand for GP care. An estimated shortage of one FTE GP or more was prevalent in about 19% of the postcode areas with >1,000 inhabitants if the surrounding postcode areas were taken into consideration. Underserved areas were mainly found in rural regions. CONCLUSIONS: The constructed decision tool is freely accessible on the Internet and can be used as a starting point in the discussion on primary care service provision in local communities and it can make a considerable contribution to a primary care system which provides care when and where people need it.

Do patient and practice characteristics confound age-group differences in preferences for general practice care? A quantitative study.

BACKGROUND: Previous research showed inconsistent results regarding the relationship between the age of patients and preference statements regarding GP care. This study investigates whether elderly patients have different preference scores and ranking orders concerning 58 preference statements for GP care than younger patients. Moreover, this study examines whether patient characteristics and practice location may confound the relationship between age and the categorisation of a preference score as very important. METHODS: Data of the Consumer Quality Index GP Care were used, which were collected in 32 general practices in The Netherlands. The rank order and preference score were calculated for 58 preference statements for four age groups (0-30, 31-50, 51-74, 75 years and older). Using chi-square tests and logistic regression analyses, it was investigated whether a significant relationship between age and preference score was confounded by patient characteristics and practice location. RESULTS: Elderly patients did not have a significant different ranking order for the preference statements than the other three age groups (r = 0.0193; p = 0.41). However, in 53% of the statements significant differences were found in preference score between the four age groups. Elderly patients categorized significantly less preference statements as 'very important'. In most cases, the significant relationships were not confounded by gender, education, perceived health, the number of GP contacts and location of the GP practice. CONCLUSION: The preferences of elderly patients for GP care concern the same items as younger patients. However, their preferences are less strong, which cannot be ascribed to gender, education, perceived health, the number of GP contacts and practice location.