Bed capacity and surgical waiting lists: a simulation analysis

Waiting time for elective surgery is a key problem in the current medical world. This paper aims to reproduce, by a Monte Carlo simulation model, the relationship between hospital capacity, inpatient activity, and surgery waiting list size in teaching hospitals. Inpatient activity is simulated by fitting a Normal distribution to real inpatient activity data, and the effect of the number of beds on inpatient activity is modelled with a linear regression model. Analysis is performed with data of the University Multi-Hospital Complex of Santiago de Compostela (Santiago de Compostela, Spain), by considering two scenarios regarding the elastiticity of demand with bed increase. If demand does not grow with an increase on bed capacity, small changes lead to drastic reductions in the waiting lists. However, if demand grows as bed capacity does, adding additional capacity merely makes waiting lists worse

Analysis of hospital costs by morbidity group for patients with severe mental illness

Objectives: The goal of this study is to analyse hospital costs and length of stay of patients admitted to psychiatric units in hospitals in a European region of the Mediterranean Arc. The aim is to identify the effects of comorbidities and other variables in order to create an explanatory cost model. Methods: In order to carry out the study, the Ministry of Health was asked to provide data on access to the mental health facilities of all hospitals in the region. Among other questions, this database identifies the most important diagnostic variables related to admission, like comorbidities, age and gender. The method used, based on the Manning-Mullahy algorithm, was linear regression. The results were measured by the statistical significance of the independent variables to determine which of them were valid to explain the cost of hospitalization. Results: Psychiatric inpatients can be divided into three main groups (psychotic, organic and neurotic), which have statistically significant differences in costs. The independent variables that were statistically significant (p <.05) and their respective beta and confidence intervals were: psychotic group (19,833.0 ± 317.3), organic group (9,878.4 ± 276.6), neurotic group (11,060.1 ± 287.6), circulatory system diseases (19,170 ± 517.6), injuries and poisoning (21,101.6 ± 738.7), substance abuse (20,580.6 ± 514, 6) and readmission (19,150.9 ± 555.4). Conclusions: Unlike most health services, access to psychiatric facilities does not correlate with comorbidities due to the specific nature of this specialization. Patients admitted to psychosis had higher costs and a higher number of average stays

Calculated Forecast for Technical Obsolescence in Computerised Tomography Equipment

Elsevier user license: Permitted: For non-commercial purposes: Read, print & download Text & data mine Translate the article Not Permitted: Reuse portions or extracts from the article in other works Redistribute or republish the final article Sell or re-use for commercial purposesTo estimate the useful life of Computerised Tomography Equipment (CT)Reyes Santias, F.; Vivas Consuelo, DJJ.; Ramos, M. (2012). Calculated Forecast for Technical Obsolescence in Computerised Tomography Equipment. Value in Health. 15(7):A318-A318. doi:10.1016/j.jval.2012.08.710SA318A31815

Budget Transparency in Local Governments: An Empirical Analysis

The aim of this paper is to shed additional light on the determinants of budget transparency in local governments. Our work is based on a Likert-type survey questionnaire specifically designed to measure budget transparency in small municipalities. The questionnaire is based on the IMF’s revised Code of Good Practices on Fiscal Transparency (2007). Results from 33 Galician municipalities are used to assess its internal consistency and to test a battery of hypotheses on the determinants of budget transparency. While several previous findings of the literature are confirmed, some new results are also obtained

¿Influye la movilidad social en el estado de salud? Una revisión sistemática

Fundamentos: En el debate sobre los determinantes de la variación de la clase social en la salud, se ha sugerido que la movilidad social y los factores asociados a ella desempeñan un papel importante en esta variación. La movilidad social describe los cambios o la estabilidad entre las posiciones de clase social. El objetivo de este trabajo fue identificar estudios sobre la asociación entre movilidad social y salud. Métodos: Las bases de datos consultadas fueron MEDLINE/PubMed, Cochrane, SciELO, CRD. Las palabras clave utilizadas (en inglés), a través de la metodología MeSH, fueron: Salud (MajorTerm), Movilidad de clase, Movilidad vertical, Posición social, Factores socioeconómicos, Clase social, Condiciones sociales, Entorno social, Pobreza y Marginación social (MeSHTerm). El periodo de búsqueda fue de enero de 2010 a diciembre de 2019. La declaración de STROBE se ha utilizado para elaborar la lista de verificación. Finalmente, la evaluación de los estudios se ha realizado mediante una revisión sistemática cualitativa. Resultados: La búsqueda identificó 1.092 estudios potencialmente relevantes. Tras el análisis, se retuvieron 376 estudios y se revisaron sus textos completos en profundidad, resultando un conjunto final de 42 estudios. De ellos, se identificaron 2 estudios sobre Movilidad de clase y Salud; también se identificaron 5 estudios sobre Pobreza y Salud, mostrando evidencia del efecto sobre la Salud por la Movilidad Social; 9 estudios sobre Clase Social y Salud, mostrando el efecto de la Movilidad Social sobre la Salud y 8 estudios que mostraron efecto de la Posición Social sobre la Salud. Conclusiones: Las medidas de movilidad social transmiten información adicional a la de los índices de pobreza. Utilizar los índices de posición social y su impacto en las desigualdades en salud podría ser empíricamente útil. Se necesitan más estudios sobre esta cuestión.Financiación: H2020 Science with and for Society, Award Number: 64357

Servicios de asistencia médica en el sector privado del Reino Unido y el nuevo contrato de especialistas: estadísticas y tendencias claves

El sector de los hospitales privados en el Reino Unido es pequeño comparado con la provisión ofertada por el National Health Service (NHS). En términos per cápita, en el bienio 1997-1998, había 20 camas hospitalarias de agudos por 100.000 habitantes, comparado con las 219 camas de agudos por 100.000 habitantes ofertadas por el NHS. El monto económico por la atención médica y quirúrgica realizada por el sector privado en 1999 fue de 1.548 millones de libras. El mercado privado de la asistencia sanitaria es complejo y muy concentrado. Las tres mayores compañías de seguros médicos son Bupa, PPP y WPA. Las aseguradoras han centrado sus esfuerzos de reducción de costos en rebajar las tarifas que cobran los hospitales privados. El nivel de demanda de asistencia médica privada se ha asociado siempre con la insatisfacción provocada por la provisión pública de asistencia sanitaria del NHS. Existen aproximadamente 14.000 médicos especialistas en 15 especialidades médicas y quirúrgicas en la práctica privada de la medicina en el Reino Unido. Se estima que en el año 2000, el ingreso medio neto de un especialista del NHS con práctica privada fue de 44.000 libras. Los hospitales privados de agudos compiten entre ellos del mismo modo que las compañías privadas de seguros lo hacen por incrementar su cuota de mercado. En esta competencia, el sector privado se ha enfocado hacia estrategias de reducción de márgenes comerciales. Se considera poco probable una reacción agresiva por parte del sector privado en la competencia por el tiempo asistencial de los especialistas del NHS

Healthcare services in the U.K. P rivate sector and the new consultant Contract : key statistics and trends

The private hospital sector in the UK is small compared with the National Health Service (NHS) provisions. In per capita termsFrom a per capita perspective, there were 20 private acute beds per 100.000 population in 1997/1998 compared with 219 per 100.000 in the acute NHS. The value of private acute hospitals and clinics supply in 1999 for acute medical/surgical inpatient and outpatient labour was £1.548 millions. The private health care market is complex and quite concentrated. The three largest medical insurance companies are Bupa, PPP and WPA. Insurers have focused their cost containment efforts on reducing provider prices charged by private hospital. The demand volume for private healthcare has always been associated with dissatisfaction due to public supply provided by the NHS. There are some 14.000 private practice consultants in 15 medical and surgical specialities in the UK. The estimated average net private income per NHS consultant in 2000 was £44.000. Within the acute sector, privately-owned hospitals compete with each other for business in the same way as private insurance companies do for large, stable insured populations. In this competition, the private sector has focused on reducing margin rate strategies. We consider unlikely that an aggressive reaction of the private sector will outbid the NHS for consultant time

Healthcare services in the U.K. P rivate sector and the new consultant Contract : key statistics and trends

The private hospital sector in the UK is small compared with the National Health Service (NHS) provisions. In per capita termsFrom a per capita perspective, there were 20 private acute beds per 100.000 population in 1997/1998 compared with 219 per 100.000 in the acute NHS. The value of private acute hospitals and clinics supply in 1999 for acute medical/surgical inpatient and outpatient labour was £1.548 millions. The private health care market is complex and quite concentrated. The three largest medical insurance companies are Bupa, PPP and WPA. Insurers have focused their cost containment efforts on reducing provider prices charged by private hospital. The demand volume for private healthcare has always been associated with dissatisfaction due to public supply provided by the NHS. There are some 14.000 private practice consultants in 15 medical and surgical specialities in the UK. The estimated average net private income per NHS consultant in 2000 was £44.000. Within the acute sector, privately-owned hospitals compete with each other for business in the same way as private insurance companies do for large, stable insured populations. In this competition, the private sector has focused on reducing margin rate strategies. We consider unlikely that an aggressive reaction of the private sector will outbid the NHS for consultant time