The impact of patient choice of provider on equity: Analysis of a patient survey

Robertson, Ruth; Burge, Peter

doi:10.1258/jhsrp.2010.010084

Cited by 39 publications

(54 citation statements)

References 16 publications

(19 reference statements)

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“…Research shows that few patients actively choose their healthcare provider [16,30,41,47-49,64]. For example, Schwartz (2005) found that only ten per cent of patients seriously considered an alternative to their local hospital when undergoing surgery [49].…”

Section: Resultsmentioning

confidence: 99%

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Determinants of patient choice of healthcare providers: a scoping review

Victoor

Delnoij

Friele

et al. 2012

BMC Health Serv Res

389

393

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BackgroundIn several northwest European countries, a demand-driven healthcare system has been implemented that stresses the importance of patient healthcare provider choice. In this study, we are conducting a scoping review aiming to map out what is known about the determinants of patient choice of a wide range of healthcare providers. As far as we know, not many studies are currently available that attempt to draw a general picture of how patients choose a healthcare provider and of the status of research on this subject. This study is therefore a valuable contribution to the growing amount of literature about patient choice.MethodsWe carried out a specific type of literature review known as a scoping review. Scoping reviews try to examine the breadth of knowledge that is available about a particular topic and therefore do not make selections or apply quality constraints. Firstly, we defined our research questions and searched the literature in Embase, Medline and PubMed. Secondly, we selected the literature, and finally we analysed and summarized the information.ResultsOur review shows that patients’ choices are determined by a complex interplay between patient and provider characteristics. A variety of patient characteristics determines whether patients make choices, are willing and able to choose, and how they choose. Patients take account of a variety of structural, process and outcome characteristics of providers, differing in the relative importance they attach to these characteristics.ConclusionsThere is no such thing as the typical patient: different patients make different choices in different situations. Comparative information seems to have a relatively limited influence on the choices made by many patients and patients base their decisions on a variety of provider characteristics instead of solely on outcome characteristics. The assumptions made in health policy about patient choice may therefore be an oversimplification of reality. Several knowledge gaps were identified that need follow-up research.

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Section: Resultsmentioning

confidence: 99%

“…Generally, patients rely on their GP to choose for them [2,41,49,67,70,79,80] or go to the nearest provider [27,59,81]. Furthermore, patients rely on their previous healthcare experiences when deciding where to receive care [25,46,47,49]. This seems to apply to both Europe and the USA (for those patients who can choose).…”

Section: Resultsmentioning

confidence: 99%

Determinants of patient choice of healthcare providers: a scoping review

Victoor

Delnoij

Friele

et al. 2012

BMC Health Serv Res

389

393

View full text Add to dashboard Cite

show abstract

“…Their justification for conducting a binary choice experiment rather than a full DCE was that the choice being made is much simpler for participants to understand for a binary choice experiment, suggesting that it might be possible to see more evidence of tradeoffs between different characteristics. [29] use a full DCE administered as a postal survey to determine how patient characteristics might affect the decisions made; in particular a patient's education and social status are considered. This followed on from a large DCE carried out in the UK and described in [9].…”

Section: Background: Discrete Choice Modellingmentioning

confidence: 99%

Patient choice modelling: how do patients choose their hospitals?

Smith

Currie

Chaiwuttisak

et al. 2017

Health Care Manag Sci

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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.

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“…Utilisation is a function of access to health services and is affected by geographical accessibility, alongside many other factors [3-9]. In low income countries, such as those of the African continent where the burden of ill health is greatest [10-14], adequate information on the location of populations, health services, facility workload, patient addresses and socio-demographic characteristics are rarely available to develop high resolution utilisation models nationally [15,16].…”

Section: Introductionmentioning

confidence: 99%

Spatial modelling of healthcare utilisation for treatment of fever in Namibia

et al. 2012

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BackgroundHealth care utilization is affected by several factors including geographic accessibility. Empirical data on utilization of health facilities is important to understanding geographic accessibility and defining health facility catchments at a national level. Accurately defining catchment population improves the analysis of gaps in access, commodity needs and interpretation of disease incidence. Here, empirical household survey data on treatment seeking for fever were used to model the utilisation of public health facilities and define their catchment areas and populations in northern Namibia.MethodThis study uses data from the Malaria Indicator Survey (MIS) of 2009 on treatment seeking for fever among children under the age of five years to characterize facility utilisation. Probability of attendance of public health facilities for fever treatment was modelled against a theoretical surface of travel times using a three parameter logistic model. The fitted model was then applied to a population surface to predict the number of children likely to use a public health facility during an episode of fever in northern Namibia.ResultsOverall, from the MIS survey, the prevalence of fever among children was 17.6% CI [16.0-19.1] (401 of 2,283 children) while public health facility attendance for fever was 51.1%, [95%CI: 46.2-56.0]. The coefficients of the logistic model of travel time against fever treatment at public health facilities were all significant (p < 0.001). From this model, probability of facility attendance remained relatively high up to 180 minutes (3 hours) and thereafter decreased steadily. Total public health facility catchment population of children under the age five was estimated to be 162,286 in northern Namibia with an estimated fever burden of 24,830 children. Of the estimated fevers, 8,021 (32.3%) were within 30 minutes of travel time to the nearest health facility while 14,902 (60.0%) were within 1 hour.ConclusionThis study demonstrates the potential of routine household surveys to empirically model health care utilisation for the treatment of childhood fever and define catchment populations enhancing the possibilities of accurate commodity needs assessment and calculation of disease incidence. These methods could be extended to other African countries where detailed mapping of health facilities exists.

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The impact of patient choice of provider on equity: Analysis of a patient survey

Cited by 39 publications

References 16 publications

Determinants of patient choice of healthcare providers: a scoping review

Determinants of patient choice of healthcare providers: a scoping review

Patient choice modelling: how do patients choose their hospitals?

Spatial modelling of healthcare utilisation for treatment of fever in Namibia

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