Patient choice modelling: how do patients choose their hospitals?

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.

Patient Views on Religious Institutional Health Care

“…8 It is unknown to what extent patients consider religious affiliation when selecting their health care facilities and whether they believe institutions have the right of conscience over their own medical desires and needs. Prior surveys [9][10][11][12][13] of factors that patients consider when choosing health care facilities have demonstrated that financial factors, travel distance and time, and hospital size were most important; none specifically inquired about religious affiliation. Given the growing religious health care sector in the United States, we set out to better understand how US patients perceive religious affiliations of health care organizations.…”

“…16 We focused survey development on understanding patient views of religious institutional health care. Our survey was informed by prior surveys, [9][10][11][12][13] and questions were added or modified to extend queries related to religious institutional health care affiliations. We used expert panel review and piloted the preliminary draft with 5 lay individuals to ensure readability and absence of ambiguity.…”

Implementing risk calculators: time for the Trojan Horse?

“…proximity, size, and teaching hospital status), and other factors unrelated to surgical performance. 19,20 On the other hand, steps have been taken to reduce the risk of surgeons avoiding difficult cases in specialties where it was feared this was an issue, 21 and recent evidence suggests that surgical behaviour is no more risk averse since publication of surgeon-specific mortality data in colorectal surgery. 22 However, and perhaps more pertinent to LMICs, there is evidence that the behaviour of staff throughout the multidisciplinary team might be affected by the perception that measurement is about performance management.…”

“…23 In lowand middle-income settings, the hierarchical structure of healthcare, with dominance of doctorsdand particularly surgeonsdover other health professionals, can exaggerate these problems, and lead to inaccurate documentation and lack of reporting of real problems. 20,24 If hospitals and healthcare professionals think they are being judged, they may deliberately or accidentally manipulate their data in order to look better, avoid reprisal, and therefore save their jobs and their reputations. This is true of all healthcare settings, but likely to be more of a problem in settings that are plagued by corruption and a 'blame' culture, both of which, regrettably, are more likely in LMICs.…”

Migration status and healthcare seeking behaviours among the Chinese labor-force: a nationally representative study

“…We use the choice of health facilities to measure it in this study. International studies compared patients’ choice of particular hospitals with adjusting characteristics of hospitals (size or capability of service),18 19 while few divided health facilities into primary healthcare facilities, private and public hospitals 20. Studies on choice of health facilities in China divided health facilities into different levels 21 22…”