Abstract
Maintenance decisions by medical staff play an essential role in achieving availability, quality and safety in care services provided. This has, in turn, an effect on the quality of care perceived by patients. Nonetheless, despite its importance, there is a serious deficiency in models facilitating optimization of maintenance decisions in critical care equipment. This chapter shows a decision support system (DSS) for choosing the best combination of maintenance policies, together with other actions for improvement, such as the increase in the number of back-up devices used in the assisted breathing unit in the Neonatology Service of a hospital. This DSS is combined with an innovative form of continuous time Markov chains, and the multicriteria Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH). The result is a ranking of the various maintenance alternatives to be applied. Finally, the real implications for availability and quality of care of applying the best solution are described.
TopBackground
Although there are many mathematical models and optimization techniques applied to maintenance in the literature, they mostly optimize a single maintenance policy, and are mathematically so complex that to put them into practice in organizations is extremely difficult.
Choosing a maintenance policy in an organization is a complex decision, as it is necessary to analyse technical aspects, related to machinery and facilities, at the same time as organizational and strategic matters. This decision affects availability of machinery and facilities, plant and staff safety, quality of the product or service, and maintenance costs.
It is therefore necessary to take into account a variety of criteria, both quantitative and qualitative, when taking this decision, which is why the use of Multi-Criteria Decision Analysis (MCDA) techniques is justified (Carnero, 2014). In fact, Almeida and Bohoris (1995) and Martorell et al. (2005) underlined the benefits of using multi-criteria techniques in the field of maintenance, especially when reliability, maintainability, availability and safety are involved in the decision.
Key Terms in this Chapter
Neonatal Ventilator: This is a medical device which mixes air and oxygen and provides the resulting warm, wet gas to the new born baby. In turn, this generates positive pressure in the breathing tubes which replaces the active stage of the breathing cycle.
Markov Chains: A stochastic process in which, if the current state and the previous states are known, the probability of a future state depends only on the current state and not on the previous states.
Corrective Maintenance: A maintenance policy consisting of carrying out unplanned maintenance activity to return a machine or facility to the desired state, after a fault has occurred and the machine has ceased to perform its function.
MACBETH: Multicriteria decision-making approach created by Carlos Antonio Bana e Costa, Jean-Marie de Corte and Jean-Claude Vansnick in 1997. From a structuring of the problem in value trees, the construction of descriptors associated to each criterion, the weighting of criteria and the construction of value functions from the qualitative judgements of a group or individual decision maker, an additive model provides a complete ranking of alternatives. This method can be applied with the help of M-MACBETH software.
Preventive Maintenance: Maintenance policy consisting of carrying out, in advance of the appearance of faults, periodic revisions and repairs of machinery and facilities with the aim of anticipating these faults and increasing the life cycle of assets.
Neonatology: Branch of paediatrics dealing with diagnosis and treatment of illness during the first 28 days of life of newborns who are ill or require special care because they are of low weight, are premature or suffer from severe complications.