Abstract
As an aid to predicting future hospital admissions, we compare use of the Multinomial Logit and the Utility Maximising Nested Logit models to describe how patients choose their hospitals. The models are fitted to real data from Derbyshire, United Kingdom, which lists the postcodes of more than 200,000 admissions to six different local hospitals. Both elective and emergency admissions are analysed for this mixed urban/rural area. For characteristics that may affect a patient’s choice of hospital, we consider the distance of the patient from the hospital, the number of beds at the hospital and the number of car parking spaces available at the hospital, as well as several statistics publicly available on National Health Service (NHS) websites: an average waiting time, the patient survey score for ward cleanliness, the patient safety score and the inpatient survey score for overall care. The Multinomial Logit model is successfully fitted to the data. Results obtained with the Utility Maximising Nested Logit model show that nesting according to city or town may be invalid for these data; in other words, the choice of hospital does not appear to be preceded by choice of city. In all of the analysis carried out, distance appears to be one of the main influences on a patient’s choice of hospital rather than statistics available on the Internet.
Highlights
Choice has become more important in health care, as patients can compare hospitals and health care providers much more using the wealth of statistics that are available on the Internet
The contribution of this research is to demonstrate the potential for use of Multinomial Logit (MNL) models to describe choice of hospital for general admissions, both elective and emergency, by patients residing in mixed urban/rural regions
The Revealed preference (RP) data analysed in this case study comes from the period of time after the introduction of patient choice of hospital for elective services in 2006 in the UK
Summary
Choice has become more important in health care, as patients can compare hospitals and health care providers much more using the wealth of statistics that are available on the Internet. The introduction of choice has led to uncertainty among health care providers as to expected patient numbers and how different attributes of the hospitals will influence patients’ decisions. The county of Nottinghamshire is to the east: patients from the eastern part of Derbyshire may attend hospitals in Nottingham, which are included in our study. We consider patient choices for both elective hospital admissions (those for which the decision to admit a patient and admission itself are separated in time) and non-elective (emergency) admissions. Such a mixed urban/rural region is typical of many English counties and offers an opportunity for generalisation of the results
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