Selección de Supresores de sobrevoltaje transitorio y estándares aplicables

Depaire et al. [40] also proposed a framework for process variation analysis. This framework aimed to answer administrative questions regarding swapped activities, repeated subsequence and deviations places. This paper highlighted different categories of deviations and distin- guished between exception - a positive deviation based on deviation outcome- and anomaly- a negative deviation refers to human error or fraud.

It should be noted that, all these studies have tackled variations detection without further analysis of other critical questions, such as the possible reasons for pathway deviations or how deviation correlates to positive/negative outcomes.

Very few papers that have attempted to find the correlation between pathway deviations and outcomes. An example of this is the work of Li et al. [41] where they transformed clinical behaviours into first-order logic sequences and used a particular metric which helps in pattern recognition. This method returned promising results regarding the correlation between devia- tions and outcomes. For instance, they found a positive correlation between different congestive heart failure care-flows and the frequency of patient readmission.

2.4

Complexity in healthcare processes

Modelling the healthcare processes is a challenging task due to the inherent complexity of pa- tient care. Processes may vary considerably within the same cohort of patients as organizations and clinicians vary in response to each individual patients different physiological, psychological or social needs. Process mining techniques can play a significant role in understanding these real patterns of care through the application of machine learning algorithms to the event logs extracted from Electronic Health Record (EHR) systems [19].

EHRs log numerous events during a patients visit to a hospital including medical, administra- tive, laboratory, intensive care and billing events. An event log records each event as a tuple with identifiable attributes including event name, event time and patient ID. Many healthcare events overlap or occur in conjunction with other events which aptly reflecting the “interrelat- edness” of healthcare processes [1].

2.4.1

Complexity Definition

From healthcare point of view, the term complexity has been defined in [1] based on the interac- tion between systems components which include both people or department. These interactions are refereed to as “interrelatedness”. This definition is commonly agreed on and adopted from non-healthcare fields as [42] [43]. The complexity increases by increasing the number of system components, the interaction between them and the uniqueness relations of interaction, how of- ten an interaction happens once or repeatedly is a key consideration in determining a processs complexity.

factors alone as there might be hidden contributing factors, such as emergent events or the day when the healthcare service was needed. Yet we can think of those factors as implications or fingerprints of complexity, which then allows us to measure how complex an interaction really is.

From process mining point of view, however, complexity is defined as confounding factor that can prevent generating useful models [44]. In this thesis, we think the first definition, health- care definition for complexity, is more meaningful and should be embraced to where healthcare process model complexity is defined by how a component corresponds to an event; how an inter- action represents a link or edge between events; and how unique an interaction or relationship is based on the variation of sequence of events. Furthermore, the type of interrelatedness is significant in process modelling, and we consider it the fourth factor that increases complexity. Examples of different types of interaction in process modelling known as process structures such as; sequence, parallel and choice. These fundamental process structures are explained later in Table 2.1 in this chapter.

Therefore, we suggest to modify the range of complexity that is mentioned in [1] to include the type of interrelatedness. The complexity increases as the interaction may represent several types as illustrated in Figure 2.1. This adapted figure outlines different levels of complexity. Increasing the number of components and interaction, where this interaction represents dif- ferent types of interrelatedness between components, resulting in a more complex healthcare process. Unlike a simple healthcare process, with only a small number of components and near homogeneous interactions between components.

21 2.4. Complexity in healthcare processes

From a theoretical point of view, when the term complexity is mentioned, we should consider Occam Razor’s and its principle of favouring simple solutions over complex ones.

2.4.2

Causes of Complexity

Complexity in healthcare is a hot topic and has been discussed widely in healthcare papers. It is an expected result of several reasons that have to be taken to meet individual patients needs as discussed in [45].

A primary reason for complexity is the variation within care processes. This variation is in- evitable in the healthcare domain due to a range of causes. The causes can be categorised into; medical causes, organisational causes and implications of both medical and organisational causes.

• Medical causes include such variables as patient condition and treatment required. We as process miners have no control over these causes.

• Organisational causes involve multiple care dimensions, such as healthcare system providers, who might follow rules that are different than other systems; doctors, who diagnose patients and decide on the best treatment or intervention; and nurses, who help patients throughout their process of care, record patient notes and all three of these dimensions interact with the system.

• Implications of both medical and organisational causes that left fingerprints on event logs it could be improved using process mining techniques such as repeated events, care events that are recorded with different levels of granularity, looping over number of events. The result- ing complexity from all these factors combined will dramatically affect on the understanding, description, prediction and management of healthcare processes.

2.4.3

Complexity Measurement

Based on the complexity measurements that are discussed in [46], [47] and [48] we can classify these measurements into two types which are; measures that are generated from event log and measures that are generated from process model. It should be noted that, the measurements attained through process models are subject to the algorithm type that is used for discovering that model. Event log measurements are based on the number of cases and the number of events while process model measurements require more complex formulas for generation, including: • Size: it measures how big a model is which is sometimes depending on the number of nodes in a model or the number of nodes in addition to the control flow elements [49].

• Control-Flow Complexity (CFC): which is the number of branching edges from all nodes. • Structuredness: this metric is concerned with the structure of the control flow where every split node must have a corresponding joint node. For instance, a node with outgoing edges should have a node of incoming edges to ensure a (single-entry single-exit) block structure between the outgoing node and the incoming one. It can be calculated as one minus the

number of nodes inside a structured block divided by the number of total nodes in a model. • Understandability: an objective metric for which there is no accurate measure. This metric is, however, affected by previous complexity measurements.

According to [50], structuredness does not necessarily improve model comprehensibility; two models may have the same structuredness score, but a model of bigger size will result in low understandability, just as the more edges or branches a model has, the less understandable it will be. Therefore, the challenges of designing a process model in a structured and meaningful way are difficult to quantify.