Can you model growth of trust? A study of the sustainability of a rural community health centre in North India

Smith, Honora; Harper, Paul Robert

doi:10.1057/jos.2014.31

Cited by 11 publications

(4 citation statements)

References 53 publications

(50 reference statements)

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“…Moreover, we have not considered levels of deprivation in the different areas, which could reasonably affect the distances that patients are prepared to travel and car ownership. A further limitation is that distance of travel may have a non-linear effect on patient attendance at healthcare facilities; exponential drop-off of demand with distance has been modelled in the literature [34].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…They showed a nonlinear decay in attendance as distance increased but there was considerable heterogeneity depending on age, gender and ailment, and it was not possible to come up with precise numerical estimates. In a more recent paper that is tangentially related, Smith and Harper [18] describe a possible approach (using Monte Carlo simulation) to track the spatio-temporal spread of the usage of services at a community health center in northern India. Given the general difficulty in accurately estimating coverage levels, we suggest running our models under different assumptions of coverage.…”

Section: Discussion and Summarymentioning

confidence: 99%

Coverage models to determine outreach vaccination center locations in low and middle income countries

Lim

Claypool

Norman

et al. 2016

Operations Research for Health Care

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The Expanded Programme on Immunization (EPI) was established in 1974 to ensure that children all around the world benefit from life-saving vaccines. However, in many low and middle income countries, it is extremely difficult to vaccinate the entire population with the standard regimen of vaccines. One important reason for this is geographically dispersed or nomadic populations. To improve vaccination rates, these countries typically use outreach, where health workers take vaccines to remote locations. Outreach is the last, critical link in the vaccine supply chain, and the locations selected to offer outreach directly impact the number of additional children that can be vaccinated. This research presents four quantitative models that can be used to optimize the selection of outreach locations, in order to maximize the number of residents that can be reached; each model addresses a different type of coverage possibility. The models are analyzed and contrasted using an example with inputs generated from a subset of data from the state of Bihar in India that was made available to the authors.

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“…The model can be used to explore alternative scenarios to ensure the system is aligned with patients' needs and the health-care system remains sustainable. Smith and Harper (2015) focus on the importance of trust for ensuring sustainable operations of health facilities. They consider the build-up of trust as the societal component of sustainability and present a practical application of trust modelling.…”

Section: The Next Paper By Van Huele and Vanhouckementioning

confidence: 99%