The article intends to present a discussion of recent accident cases which occurred in health service units in Brazil. The costs related to failures or accidents in hospital environment are associated to large financial and social harm. The adverse events were analyzed at three different hospitals. The analyzed cases demonstrate the disappointment in appropriate maintenance policies. The global conclusion of the article indicates the need of professionalizing the maintenance management in hospital environment.

O equipamento de imagem por ressonância magnetica (RM) e o mais confiavel na area de diagnosticos. Falhas no sistema geram serias consequencias: perdas financeiras diretas, atrasos no diagnostico de pacientes, aumento do custo do contrato de manutencao, entre outros. Este trabalho verificou o impacto na disponibilidade do equipamento devido a fatores adversos (infraestrutura e humanos). Ao termino do periodo de analise, algumas sugestoes sao feitas para reduzir a indisponibilidade gerada por eventos adversos ao equipamento.

The equipment for magnetic resonance imaging (MRI) is the most reliable in the diagnosis area. System failures generate a series of problems: direct financial cost, delay in diagnosis of the patient, increased cost of maintenance contract, among others. This study examined the impact of infrastructure needed for the proper functioning of the MRI equipment availability. After a period of analysis, some suggestions were implemented able to reduce equipment downtime caused by failures in infrastructure.

OBJECTIVE:To develop a computer model to analyse the performance of a standard physiotherapy clinic in the city of Rio de Janeiro, Brazil.DESIGN AND SETTING:The clinic receives an average of 80 patients/day and offers 10 treatment modalities. Details of patient procedures and treatment routines were obtained from direct interviews with clinic staff. Additional data (e.g. arrival time, treatment duration, length of stay) were obtained for 2000 patients from the clinic's computerised records from November 2005 to February 2006.METHODS AND MAIN OUTCOME MEASURES:A discrete-event model was used to simulate the clinic's operational routine. The initial model was built to reproduce the actual configuration of the clinic, and five simulation strategies were subsequently implemented, representing changes in the number of patients, human resources of the clinic and the scheduling of patient arrivals.RESULTS:Findings indicated that the actual clinic configuration could accept up to 89 patients/day, with an average length of stay of 119minutes and an average patient waiting time of 3minutes. When the scheduling of patient arrivals was increased to an interval of 6.5minutes, maximum attendance increased to 114 patients/day. For the actual clinic configuration, optimal staffing consisted of three physiotherapists and 12 students. According to the simulation, the same 89 patients could be attended when the infrastructure was decreased to five kinesiotherapy rooms, two cardiotherapy rooms and three global postural reeducation rooms.CONCLUSIONS:The model was able to evaluate the capacity of the actual clinic configuration, and additional simulation strategies indicated how the operation of the clinic depended on the main study variables.

ObjectiveThis work develops a cost analysis estimation for a mammography clinic, taking into account resource utilization and equipment failure rates.Materials and methodsTwo standard clinic models were simulated, the first with one mammography equipment, two technicians and one doctor, and the second (based on an actually functioning clinic) with two equipments, three technicians and one doctor. Cost data and model parameters were obtained by direct measurements, literature reviews and other hospital data. A discrete-event simulation model was developed, in order to estimate the unit cost (total costs/number of examinations in a defined period) of mammography examinations at those clinics. The cost analysis considered simulated changes in resource utilization rates and in examination failure probabilities (failures on the image acquisition system). In addition, a sensitivity analysis was performed, taking into account changes in the probabilities of equipment failure types.ResultsFor the two clinic configurations, the estimated mammography unit costs were, respectively, US$ 41.31 and US$ 53.46 in the absence of examination failures. As the examination failures increased up to 10% of total examinations, unit costs approached US$ 54.53 and US$ 53.95, respectively. The sensitivity analysis showed that type 3 (the most serious) failure increases had a very large impact on the patient attendance, up to the point of actually making attendance unfeasible.ConclusionsDiscrete-event simulation allowed for the definition of the more efficient clinic, contingent on the expected prevalence of resource utilization and equipment failures.

OBJECTIVE This study used the discrete-events computer simulation methodology to model a large hospital surgical centre (SC), in order to analyse the impact of increases in the number of post-anaesthetic beds (PABs), of changes in surgical room scheduling strategies and of increases in surgery numbers. METHODS The used inputs were: number of surgeries per day, type of surgical room scheduling, anaesthesia and surgery duration, surgical teams' specialty and number of PABs, and the main outputs were: number of surgeries per day, surgical rooms' use rate and blocking rate, surgical teams' use rate, patients' blocking rate, surgery delays (minutes) and the occurrence of postponed surgeries. Two basic strategies were implemented: in the first strategy, the number of PABs was increased under two assumptions: (a) following the scheduling plan actually used by the hospital (the 'rigid' scheduling - surgical rooms were previously assigned and assignments could not be changed) and (b) following a 'flexible' scheduling (surgical rooms, when available, could be freely used by any surgical team). In the second, the same analysis was performed, increasing the number of patients (up to the system 'feasible maximum') but fixing the number of PABs, in order to evaluate the impact of the number of patients over surgery delays. CONCLUSION It was observed that the introduction of a flexible scheduling/increase in PABs would lead to a significant improvement in the SC productivity.

Objective: This work develops a discrete-event computer simulation model for the analysis of a mammography clinic performance. Material and methods: Two mammography clinic computer simulation models were developed, based on an existing public sector clinic of the Brazilian Cancer Institute, located in Rio de Janeiro city, Brazil. Two clinics in a total of seven configurations (number of equipment units and working personnel) were studied. Models tried to simulate changes in patient arrival rates, number of equipment units, available personnel (technicians and physicians), equipment maintenance scheduling schemes and exam repeat rates. Model parameters were obtained by direct measurements and literature reviews. A commercially-available simulation software was used for model building. Results: The best patient scheduling (patient arrival rate) for the studied configurations had an average of 29min for Clinic 1 (consisting of one mammography equipment, one to three technicians and one physician) and 21min for Clinic 2 (two mammography equipment units, one to four technicians and one physician). The exam repeat rates and equipment maintenance scheduling simulations indicated that a large impact over patient waiting time would appear in the smaller capacity configurations. Conclusions: Discrete-event simulation was a useful tool for defining optimal operating conditions for the studied clinics, indicating the most adequate capacity configurations and equipment maintenance schedules.

Discrete-event simulation allows for a fast and effective evaluation of alternative scenarios in hospital settings. This work presents a discrete-event simulation analysis of a mammographic clinic model, with the objective of assessing the impact of equipment failures over exam costs, taking into account the clinic installed capacity and patient scheduling strategies. A survey of mammography exam costs was also performed. It was found that exam costs were in the range R$ 102.92 (no equipment failures) to R$ 107.90 (rate of failures approaching 10%). The discrete-event simulation allowed for the definition of the more efficient clinic configuration, contingent on the expected prevalence of resource utilization and equipment failures. Palavras-chave: custos, simulação, mamografia. Introdução