New insights into measurement variability in glaucomatous visual fields from computer modelling.

To develop a model to simulate visual fields (VFs) in glaucoma patients, and to characterize variability of the Mean Deviation (MD) VF summary measurement using real VFs and simulations

Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: The impact of a local calibration

<p>Abstract</p> <p>Background</p> <p>In the financing of a national health system, where pharmaceutical spending is one of the main cost containment targets, predicting pharmacy costs for individuals and populations is essential for budget planning and care management. Although most efforts have focused on risk adjustment applying diagnostic data, the reliability of this information source has been questioned in the primary care setting. We sought to assess the usefulness of incorporating pharmacy data into claims-based predictive models (PMs). Developed primarily for the U.S. health care setting, a secondary objective was to evaluate the benefit of a local calibration in order to adapt the PMs to the Spanish health care system.</p> <p>Methods</p> <p>The population was drawn from patients within the primary care setting of Aragon, Spain (n = 84,152). Diagnostic, medication and prior cost data were used to develop PMs based on the Johns Hopkins ACG methodology. Model performance was assessed through r-squared statistics and predictive ratios. The capacity to identify future high-cost patients was examined through c-statistic, sensitivity and specificity parameters.</p> <p>Results</p> <p>The PMs based on pharmacy data had a higher capacity to predict future pharmacy expenses and to identify potential high-cost patients than the models based on diagnostic data alone and a capacity almost as high as that of the combined diagnosis-pharmacy-based PM. PMs provided considerably better predictions when calibrated to Spanish data.</p> <p>Conclusion</p> <p>Understandably, pharmacy spending is more predictable using pharmacy-based risk markers compared with diagnosis-based risk markers. Pharmacy-based PMs can assist plan administrators and medical directors in planning the health budget and identifying high-cost-risk patients amenable to care management programs.</p

Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages

EMERGENCY DEPARTMENT OCCUPANCY ASSESSMENT IN LITHUANIA

Emergency department (ED) occupancy can cause many negative consequences for the quality of patient care. The purpose was to find out the reasons for the increased occupancy of the ED, to determine the appropriate criteria for the assessment of ED occupancy and the limits of wait-ing queues or waiting time. The heads and managers of Lithuanian in-patient health care institutions and ambulance services, in-patient reanimation and intensive care units and emergency departments were interviewed. The reasons for the increased waiting time of the ED and the appropriate criteria for the assessment of ED occupancy were determined: "the number of patients waiting in the queue" and “the estimated waiting time before doctor examination”