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Purpose Length of stay (LOS) in hospital after surgery varies for each patient depending on surgeon's decision that considers criticality of the surgery, patient's conditions before and after surgery, expected time to recovery and experience of the surgeon involved. Decision on patients' LOS at hospital post-surgery affects overall healthcare performance as it affects both cost and quality of care. The purpose of this paper is to develop a model for deriving the most appropriate LOS after surgical interventions. Design/methodology/approach The study adopts an action research involving multiple stakeholders (surgeon, patients/patients' relatives, hospital management and other medics). First, a conceptual model is developed using literature and experts' opinion. Second, the model is applied in three surgical interventions in a public hospital in Malta to demonstrate the effectiveness of the model. Third, the policy alternatives developed are compared to a selection of current international standards for each surgical intervention. The proposed model analyses three LOS threshold policies for three procedures using efficiency and responsiveness criteria. The entire analysis is carried out using 325 randomly selected patient files along with structured interactions with more than 50 stakeholders (surgeon, patients/patients' relatives, hospital management and other medics). A multiple criteria decision-making method is deployed for model building and data analysis. The method involves combining the analytic hierarchy process (AHP) for verbal subjective judgements on prioritizing the four predictors of surgical LOS-medical, financial, social and risk, with pairwise comparisons of the sub-criteria under each criterion in line with the concerned interventions-the objective data of which are obtained from the patients' files. Findings The proposed model was successfully applied to decide on the best policy alternative for LOS for the three interventions. The best policy alternatives compared well to current international benchmarks. Research limitations/implications The proposed method needs to be tested for other interventions across various healthcare settings. Practical implications Multi-criteria decision-making tools enable resource optimization and overall improvement of patient care through the application of a scientific management technique that involves all relevant stakeholders while utilizing both subjective judgements as well as objective data. Originality/value Traditionally, the duration of post-surgery LOS is mainly based on the surgeons' clinical but also arbitrary decisions, with, as a result, having insufficiently explicable variations in LOS amongst peers for similar interventions. According to the authors' knowledge, this is the first attempt to derive post-surgery LOS using the AHP, a multiple criteria decision-making method.

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.

Purpose: To develop a model for the global performance measurement of intensive care units (ICUs) and to apply that model to compare the services for quality improvement., Materials and Methods: Analytic hierarchy process, a multiple-attribute decision-making technique, is used in this study to evolve such a model. The steps consisted of identifying the critical success factors for the best performance of an ICU, identifying subfactors that influence the critical factors, comparing them pairwise, deriving their relative importance and ratings, and calculating the cumulative performance according to the attributes of a given ICU. Every step in the model was derived by group discussions, brainstorming, and consensus among intensivists., Results: The model was applied to 3 ICUs, 1 each in Barbados, Trinidad, and India in tertiary care teaching hospitals of similar setting. The cumulative performance rating of the Barbados ICU was 1.17 when compared with that of Trinidad and Indian ICU, which were 0.82 and 0.75, respectively, showing that the Trinidad and Indian ICUs performed 70% and 64% with respect to Barbados ICU. The model also enabled identifying specific areas where the ICUs did not perform well, which helped to improvise those areas., Conclusions: Analytic hierarchy process is a very useful model to measure the global performance of an ICU.

There is an increasing need of a model for the process-based performance measurement of multispecialty tertiary care hospitals for quality improvement. Analytic hierarchy process (AHP) is utilized in this study to evolve such a model. Each step in the model was derived by group-discussions and brainstorming sessions among experienced clinicians and managers. This tool was applied to two tertiary care teaching hospitals in Barbados and India. The model enabled identification of specific areas where neither hospital performed very well, and helped to suggest recommendations to improve those areas. AHP is recommended as a valuable tool to measure the process-based performance of multispecialty tertiary care hospitals.