The influence of competition on quality of hospital treatment from physician behavior perspective (theoretical model verification with case presentation from Vinnitsa, Ukraine)

Abstract

Material and MethodsTo check the deduction stemming from the theoretical model of physician behavior on part of possible impairment of quality under market monopoly we exploited competing risk modeling having in mind 2 types of subsequent admissions:-the same cause as in previous admission; -different cause. It ensures us to use two types of censoring, both informative and non-informative. The non-informative censoring tells on termination of tracing before the next admission. The majority of cases demonstrated non-informative censoring. Informative censoring denotes admission due to the different cause then previous. Absence of censoring refers to subsequent admission due to the same cause as previous. Yet another, third type of censoring related to lethality. It's known as terminal event censoring. It's actually the informative censoring that precludes any following censoring or risk accumulation. If we denote Т ij as j th admission episode in і th patient with the same cause as previous Y ij -with different cause, С ij -censoring because of termination of tracing, each period between successive admissions can be classified by: 1) admission due to the same cause with frailty Z 1 with gamma distribution resulted from sum of independent gamma distributed variables Y 0 and Y 1 ;2) admission due to the different cause with gamma distributed frailty Z 2 resulted from sum of independent gamma distributed variables Y 0 and Y 2 ;3) terminal event with gamma distribution resulted from sum of independent gamma distributed variables Y 0 and Y 3 .The mutual variable Y 0 serves commonality in individual risks. Indeed, all three events induced by health problem of particular patient, the fact captured by Y 0 . To get into practicalities we used:This ensures simple first and second moments of frailties distributions:Therefore, co-variation between Z 1 and Z 2 is:With simple correlation formulae (ρ):Specification of events rendered by particular independent variables Y 1 , Y 2 , and Y 3 . Event specification also ingrained into specific linear predictors.Thereby, competing risks incorporated into mixed proportional hazard model (MPH) by:-censoring system (1), incorporating each admission due to the same cause precludes admission due to the different cause and counterwise; lethality introduces another competing risk that upon happening terminates exposure to competing risks; -expression of Z 1 and Z 2 as gamma distributed with common elements of shape and scale k 0 (2), thereby ensuring certain commonality in risks of competing events under consideration; -correlation coefficient ρ between frailties Z 1 and Z 2 , evaluating competing risks commonalities;-association between frailties Z 1 and Z 2 and frailty of terminal event Z 3 through common Y 0 . Copula.mode <-multivPenal(Surv(Between,Censored1)~ cluster(ID) + T + Dep + Exigent+ Order + Status1 + Holiday + Office + Age + Sex + Occupationc + CharlsonI + Ecallc + dStatus + Monthc + Diagnose1 + event2(Censored2) + terminal(Censored3), formula.Event2 =~ T + Exigent + Status2 +...

Analysis of the dependence of the balance sheet values of costs, quality and comfort of medical services on the individualized elasticity of patient demand

“…Ми використали 5000 ітерацій на кожному з двох незалежних ланцюгів реплікацій з адаптивними реплікаціями на перших 1000 ітерацій. Для відтворення результатів використане зерно генерацій 1234, що відображено у функції sampling: post <-sampling(sm, data =DataStan, iter = 5000, warmup=1000, chains = 2, seed=1234) [1,5,7,8].…”

“…Одним з основних економічних рушіїв оберту кривої обміну є рівень конкуренції надання медичних послуг. За зап-ропонованою теоретичною моделлю внаслідок росту конкуренції очікується зменшення кількості послуг, що надаються лікарем стаціонару, а також зменшення клінічних годин роботи [7,8,9]. Так лікар почне виконувати функції, які сприяють покращенню якості надання допомоги за рахунок зростання часу надання послуги.…”