Surgical Workflow Analysis as a Motivating Case Study

Healthware domain is a rather known supplier of data warehousing challenges as testified by a series of re-search works: Pedersen et al. [145] used patient diagnosis data as an application scenario for their extended multidimensional data model; Golfarelli et al. [47] demonstrated the methodology of obtaining multidimen-sional schemes from existing E/R schemes at the example of hospital admission data; Song et al. [166]

use patient diagnosing and billing case study to demonstrate various strategies of handling many-to-many relationships between facts and dimensions.

Concepts and proposals presented in this work have been inspired by practical challenges encountered during the design phase of the ongoing project on developing a BI platform for the domain ofSurgical Work-flow Analysis, henceforth abbreviated as SWA. The project is hosted by the Innovation Center Computer Assisted Surgery (ICCAS)¹ and involves domestic and international collaborators from multiple scientific disciplines, such as medicine, medical engineering, databases and data warehousing, web technologies, scien-tific visualization, etc. Major directions of their projects are surgical workflow formalization [129], semantics [13], analysis [130], standardization [12], and visualization [128].

The medical informatics termSurgical Workflowsrefers to a methodology for intelligent acquisition and consolidation of process descriptions from surgical interventions for the purpose of their clinical and technical analysis [129]. This type of analysis is crucial for the development of surgical assist systems for the operating room of the future. Besides, it provides a framework for evaluating innovative devices and surgery strategies.

Process execution data is obtained manually and semi-automatically by monitoring and recording the course of a surgical intervention. Manual acquisition is carried out either in the real-time mode, i.e., by observing the surgical intervention live in the operating room, or retrospectively, typically, from a video recording.

Surgical processes clearly fall into a category of knowledge intensive processes: even though each surgery type has a pre-defined execution scheme, individual executions are highly diverse because the actual course of each intervention is largely determined by the expertise of the surgeon expressed in terms of his/her actions, reactions, instructions to other participants, etc. Therefore, one of the long-term goals of SWA is extraction and interpretation of surgical “know-how”. In order to achieve this goal, a thorough understanding of the process context needs to be gained. This is done by capturing the relevant aspects of the domain in a formal model.

Challenges of SWA as a non-conventional data warehousing application have been tackled in a series of our own works. In [114] we showed how the requirement to warehouse the original process execution data, i.e., without pre-aggregation to a set of statically defined measures of analysis, results in the necessity to extend the foundations of the multidimensional data model. Implications of propagating the extensions introduced at the conceptual level to the backend and the presentation layer of the data warehouse system are presented in [115]. The overall process of designing and implementing a data warehouse for accumulating surgical workflow data is described in [127]. In [120] we used surgical workflow modeling as an example of handling complex data in the extended multidimensional model. Finally, a book chapter on conceptual data warehouse design for Business Process Intelligence [113] summarizes and extends previously published findings putting them into a comprehensive methodological framework.

5.1.1 Requirements of Surgical Workflow Analysis

Surgeons, medical researchers, and engineers are interested in obtaining a well-defined formal recording scheme of a surgical process that would lay a foundation for a systematic accumulation of the obtained process descriptions in a centralized data warehouse to enable its comprehensive analysis and exploration.

1Innovation Center Computer Assisted Surgery (ICCAS) is located in the Hospital at the University of Leipzig, Leipzig, Germany.

Website:http://www.iccas.de

5.1 : Surgical Workflow Analysis as a Motivating Case Study 91

Whatever abstraction approach is adopted, there is a need for an unambiguous description of concepts that characterize a surgical process in a way adequate for modeling a wide range of workflow types and different surgical disciplines.

Applications of SWA are manifold: support for the preoperative planning by retrieving similar precedent cases, clinical documentation, postoperative exploration of surgical data, formalization of the surgical know-how, analysis of the optimization potential with respect to the instruments and systems involved, evaluation of the ergonomic conditions, verification of medical hypotheses, gaining input for designing surgical assist systems and workflow automation.

Obviously, such high diversity of potential applications results in the diversity of expected query types.

We distinguish the following major categories of analytical queries:

1. Quantitativequeries are concerned with performance indicators and other measurements occurrences, frequencies, duration, or availability of various events or objects.

2. Qualitative queries aim at discovering relationships, patterns, trends, and other kind of additional knowledge from the data.

3. Ergonomicqueries evaluate the design of the workspace, ergonomic limitations, positions and direc-tions of involved participants and objects.

4. Cognitivequeries attempt to assess such “fuzzy” issues as usefulness, relevance, satisfaction, etc.

Considering the expected kinds of queries, the multidimensional database technology seems a promising solution as it allows analysts to view the data from different perspectives, to define various metrics, and to aggregate the latter to a desired level of detail. A simple example should convey the idea of benefiting from the OLAP approach in SWA context. Figure 5.1 shows a fragment of a 3-dimensional data cube, storing instrument usage statistics (number of instrumentsas the cube’s measure) determined by dimensionsSurgeon, Treated Structure, andDate. Besides the original cube storing the data at the finest granularity, Figure 5.1 also displays the results of two roll-up operations, which summarize the measure across all treated structures and, subsequently, across all dates, thus providing different abstractions of instrument usage numbers.

Surgeon

eye heart lung nose

16

Figure 5.1: A sample 3-dimensional cube (fragment) storing surgical instrument usage statistics (left) and its aggregated views (right)

92 Chapter 5 : Measures, Facts, and Galaxies in the Multidimensional Data Model

Discipline

Diagnosis

Surgery

Phase

Activity

Work Step

Movement

Figure 5.2: Vertical (de-)composition of a surgical process

5.1.2 Structuring Surgical Workflows

Surgical workflow is an abstraction of a surgical intervention obtained by capturing the characteristics of the original process that are relevant for the analysis. A common approach to structuring a process is to decompose it vertically, i.e., along the timeline, into logical units, such as subprocesses, stages, and work steps. Figure 5.2 shows a possible vertical decomposition scheme of a surgery: a surgical process consists of phases, which, in their turn, consist of activities, the latter being a series of work steps, each performing a certain action. Technically, an action may be executed by multiple participants and/or using multiple in-struments. To account for this observation, we refine the granularity to the instrument usage level, denoted movement. Each movement refers to a part of the work step performed by a single actuator (i.e., a body part of a participant) on a single treated structure of a patient using a single surgical instrument. In the upward direction, surgical instances can be grouped into classes by diagnosis or therapy, which, in their turn, are associated with particular surgical disciplines. Discipline and diagnosis are the determining factors in the classification of surgery types. The above decomposition is calledlogical, ortask-based, as it relies on the reasoning of a human expert in recognizing process elements.

An alternative decomposition practice is astate-based one, aimed at automated data acquisition. This approach uses the conceptssystem,state, andeventto capture the state evolution of involved systems and the events that trigger state transitions. The concept of asystemis very generic and may refer to a participant or his/her body part, a patient or a treated structure, an instrument or a device, etc. For instance, if surgeon’s eyes are considered a system, then their gaze direction can be then modeled as states, while surgeon’s directives to other participants may be captured as events.

The two data acquisition practices can be used as complementary ones to benefit from both the human and the systemic perspective. We introduce a superordinate concept ofcomponent, which is synonymous to the termflow objectin the Business Process Modeling Notation [137], to enable uniform treatment of logical (activities and work steps) and technical (states and events) units of a process with regard to their common properties. Thereby, the analyst is able to retrieve a unified timeline for the whole course of a surgery.

In the vertical decomposition, we identify two major granularity levels of the acquired data:

Workflow levelrefers to the characteristics of a surgical intervention as a whole, such as patient, loca-tion, date, and surgeon. This data is normally imported from clinical information systems. Workflow-level data is useful for high-Workflow-level analysis, such as hospital utilisation or patient history.

Intra-workflow levelrefers to the properties of process components (events, activities), such as instru-ments used or structures treated. This fine-grained data is acquired from running surgical interventions and is used for analyzing workflow execution within as well as across surgery instances.

5.1 : Surgical Workflow Analysis as a Motivating Case Study 93

Figure 5.3: Recording scheme of a surgical process model as a UML class diagram

SURGERY

Figure 5.4: Recording scheme of a surgical process model as an E/R diagram

Figure 5.3 presents the initial approximation of the surgical workflow recording scheme, originally pro-posed by the collaborators from the ICCAS in [129] and refined in our joined follow-up works [114, 115].

Notice how the graphical presentation reveals the two-level structure of the recording scheme.

94 Chapter 5 : Measures, Facts, and Galaxies in the Multidimensional Data Model

Figure 5.4 shows a revised model of the same application, however, expressed in the E/R (Entity/Relation-ship) modeling notation. This E/R diagram represents a more recent model that evolved as a result of multiple refinements. For example, this model distinguishes between a surgery itself and a surgical workflow as its ab-straction, thus accounting for the possibility to produce multiple recordings of the same instance. Conceptual models shown in Figures 5.3 and 5.4 will be refined in the upcoming sections.