The Determinants of Healthcare Cost for Glaucoma Patients in Cicendo Eye Hospital, Bandung, Indonesia

Glaucoma is the second foremost cause of impaired vision. People whosuffered from Glaucoma face the independent expenditure for thetreatment as blind people with Glaucoma could not be cured perfectly.This study intends to analyze the effect of age, types of patient care, typesof Glaucoma, and types of payment on the total cost of care of glaucomapatients at Cicendo Eye Hospital, Bandung, Indonesia. This study uses3,358 patient medical records of Cicendo Eye Hospital, Bandung,Indonesia, in 2018. The 3,358 samples were selected from the patient’smedical record based on patients’ categories indicated or convicted ofhaving Glaucoma. Robust Linear Regression is applied in this study tomeasure the additional cost for Glaucoma treatment. The results showedthat the total cost of patient care was positively and significantly affectedby hospitalization status (p=0.000), age (p=0.000), times of treatment(p=0.000), having primary glaucoma (p=0.000), having congenitalglaucoma (p=0.000), and presence of intraocular (p=0.000). Conversely,patient care’s total cost was negatively and significantly affected usinginsurance (p=0.082). This result would be a precautionary measure forthe medical institution to consider better financial planning, servicedelivery improvement, and the patient’s payment scheme effectiveness

A semiparametric nonlinear mixed model approach to phase I profile monitoring

When process data follow a particular curve in quality control, profile monitoring is suitable and appropriate for assessing process stability. Previous research in profile monitoring focusing on nonlinear parametric (P) modeling, involving both fixed and random-effects, was made under the assumption of an accurate nonlinear model specification. Lately, nonparametric (NP) methods have been used in the profile monitoring context in the absence of an obvious linear P model. This study introduces a novel technique in profile monitoring for any nonlinear and auto-correlated data. Referred to as the nonlinear mixed robust profile monitoring (NMRPM) method, it proposes a semiparametric (SP) approach that combines nonlinear P and NP profile fits for scenarios in which a nonlinear P model is adequate over part of the data but inadequate of the rest. These three methods (P, NP, and NMRPM) account for the auto-correlation within profiles and treats the collection of profiles as a random sample with a common population. During Phase I analysis, a version of Hotelling’s T2 statistic is proposed for each approach to identify abnormal profiles based on the estimated random effects and obtain the corresponding control limits. The performance of the NMRPM method is then evaluated using a real data set. Results reveal that the NMRPM method is robust to model misspecification and performs adequately against a correctly specified nonlinear P model. Control charts with the NMRPM method have excellent capability of detecting changes in Phase I data with control limits that are easily computable.Scopu