A Data Analysis Methodology for

Thus, a comprehensive data analytics framework is still needed for process diagnosis and redesign in healthcare. Based on these problem statements, this dissertation focuses on a comprehensive data analysis methodology for process diagnosis and redesign in healthcare.

Research Background

This dissertation not only addresses process mining in healthcare, but also focuses on comprehensive data analysis for process diagnosis and redesign. Data-driven process analysis using process mining is essential for both concepts; Therefore, this study proposes a data analysis methodology for process diagnosis and redesign in healthcare.

Figure 1. Process mining in healthcare [1]

Research Motivation and Problem Statement

While some previous methods have built a simulation model based on collected data, no concrete method has been established for the healthcare environment. In summary, a sound method for building a simulation model for redesign options based on clinical data analysis in healthcare is still required.

Goals and Scope of Research

The second research method is the development of a redesign methodology for clinical processes, based on process mining and discrete event simulation (DES). Clinical event logs are then constructed for process mining based on the collected healthcare data using the ETL process.

Figure 2. The goal and research methods of this dissertation

Structure of This Dissertation

This chapter provides a framework for clinical process redesign with discrete event simulation and process mining. This chapter develops a framework for evaluating the effects of process redesign in health care with a quantitative, data-driven approach.

Business Process Management

The life cycle of business process management becomes a solution and there are numerous literatures to conceptualize it. Among these three levels, process mining is actively implemented in the BPM lifecycle for process model management, in particular, the analysis phase.

Figure 4. A simple example of a clinical business process

Process Mining

First and foremost, process model discovery, one of the most challenging tasks of process mining, deals with the automatic construction of a process model using event logs without prior information [3, 40–45]. Orthogonal to the three types of analysis, process mining defines four perspectives, viz. control flow perspective, case perspective, organization and time perspective [3].

Table 1. A partial example of event logs

Data Science in Healthcare

Due to the complexity of medical data and increasing demands for data analysis with a clinical process level, there have been many attempts at process mining in healthcare. In the following, we provide a detailed explanation of the relevant research according to the process mining types.

Figure 7 depicts a latest conceptual model for CDM [4]. It consists of six different data groups (e.g., standardized vocabularies, standardized meta-data, standardized clinical data, stan-dardized health system data, standardized health economics, and sta

Data Preparation

Scope: Users must determine which clinical activities should be included in the event log. Process Type The clinical event log is specified with one of the following .

Figure 10. The patient-related data in common data model

Data Preprocessing

In the figure, while most cases have a complete sequence consisting of multiple events, it is identified that some of the cases below have an incomplete sequence;. As presented earlier, it is indispensable to perform data preprocessing that applies effective techniques or thoroughly verifies data with a heuristic approach.

Data Analysis

As such, dot graph [51], one of the process mining techniques, can be used to efficiently explore the data. In other words, it is required to have a comprehensive understanding of the clinical processes based on the multiple techniques involved in the types of mining processes.

Figure 13. The main factors for process mining analysis in healthcare

Post-hoc Analysis

Compliance is associated with the evaluation of the discovered or reference process with the fitness with log replay or corresponding rate analysis. As for the process mining types, users do not need to take a single type; so, if necessary, users can take multiple types.

Summary

Therefore, it focuses on reporting and diagnosis, and we use process model discovery, process pattern analysis, performance analysis and others (see Chapter 4.2 for details). In the four types of data analysis, this is related to prediction as well as reporting and diagnosis, and we use transmission pattern analysis, LOS performance analysis to identify the factors that influence LOS, and also predict the LOS of inpatients (see chapter 4.3 for details). As such, it covers all types of data analysis, from reporting to recommendation (see Chapter 4.4 for details).

A Data Analysis Framework for Outpatient Processes

Standard activity ratios are defined as causal activity ratios defined in the reference model. Performance analysis using process mining can be used to measure various process performance indicators (PPIs) of a clinical process for outpatients. For each type of entity t ∈ T Vt denotes the set of possible values, i.e. a set of entity identifiers of type t.

Figure 15. The detailed data analysis framework for outpatients

A Data Analysis Framework for Inpatient Processes

Long-term care is one of the process types according to the definition of the common data model. Long-term hospital patients can therefore be analyzed separately as one of the analysis categories. For this reason, analysis for long-term hospital patients is included as one of the analysis methods for inpatients.

Figure 20. The detailed data analysis framework for inpatients Table 9. A partial example of inpatient event logs

A Data Analysis Framework for Clinical Pathways

Based on the four numerical matching results defined in the previous chapter, we specify the rate of application of orders to the CP (ARcp) and the matched ratio of events in the event log (M Rlog). ARcp means the percentage of CP orders used in the event log, where NcpandMcp uses. TheM Rlog refers to the percentage of events in the event log that are included in the CP.

Table 10. An partial example of clinical pathways

Evaluation

We found that there were no significant changes in the most frequent ambulatory care processes before and after the construction of the new building. The total time required for ambulatory care did not increase significantly considering the rate of increase in the number of patients. 3.36 minutes) in the neuroscience clinical center compared to consultation wait times before the new building opened. Finally, Group C was characterized by a significantly higher median and IQR of length of hospital stay.

Figure 24. The discovered clinical process models using different discovery algorithms was measured as 89.01%

Summary and Discussion

After the improvement, ARcp and M Rlog increased by 0.1 and 0.02, respectively, resulting in a 0.05 increase in the standard matching rate. Ncp Mcp Nlog Rlog ARcp M Rlog SCM R .. hospital stay performance analysis, and CP matching rate) were defined with a formal approach. This chapter is organized as follows: Chapter 5.1 presents the background of this research, including the illustration of the problem.

Background

In this chapter, we propose a new approach to perform clinical process reengineering using discrete event simulation based on process mining. The main advantages of this approach are that it is performed automatically without any qualitative methods and is very accurate due to the data-driven approach. To determine the research object of this chapter, we focus on the long waiting time, which is considered a key challenge in the outpatient clinical process [158, 159].

A Discrete Event Simulation Approach based on Process Mining

Evaluation

Based on the results of the three process mining analyses, we can now easily derive a simulation model. In the upper left example in Figure 38, we provide a graphical explanation of the first scenario. In the upper right example in Figure 38, we give a graphical explanation of the second scenario.

Figure 30. The results of the analysis according to the average and IQR of LOS per department the related etiology, anatomic site, or severity, and the seventh character for expansion

Summary and Discussion

As we explained earlier, we performed the simulation analyzes based on four scenarios that reduce the consultation waiting time. Through the simulation analysis of scenario 1, it becomes clear that the number of patients has a significant impact on the consultation waiting time. In particular, techniques that automatically reflect improvements based on redesign best practices in the simulation model can maximize the effectiveness of simulation analysis.

Background

This chapter proposes a business process evaluation framework focused on process redesign and closely related to process mining as an operational framework for calculating indicators. The two main concepts of this framework, i.e. best practice implementation indicators and process performance indicators, are introduced in Chapter 6.3 and 6.4 respectively. From a practical point of view, the proposed framework gives process analysts and decision makers actionable tools to evaluate the results of their choices in BPR initiatives.

An Overview of Indicators for Assessing The Effects of Redesigns

Flexible allocation Number of events performed by each resource for activities (BPI7)→Allocated resources for each time frame (BPI8). Specialist-generalist Number of events performed by each resource for activities (BPI7) → Specialist-generalist ratio (BPI9). External trusted party Whether there are activities related to obtaining information from outside (BPI17).

Table 21. Summary of BPIs and PPIs

BP Implementation Indicators (BPIs)

Therefore, control-flow mining algorithms [36] can be used to verify the implementation of this best practice. To monitor the implementation of this best practice, it is necessary to measure the number of resources involved per case. To verify the application of this best practice, an analysis of existing and future social networks is required (BPI10).

Process Performance Indicators (PPIs)

As we discussed earlier, we derive time for instances in the log based on the consecutive time points and the state mapping algorithm. Output records of time for instances in the log {CT, OT, W T}, where CT is the list of cycle time for instances in the log;. Function PPIF1 The total number of variants in the log Number of elements − PPIF2 The total number of relations in the.

Table 22. Process Performance Indicators in Time Perspective

Evaluation

BP2: One of the problems in the hospital was the long delay in the payment process. In the flexibility perspective, the number of variants in the logbook and the number of relationships in the social network increased after BPR. However, there was no noticeable difference in the number of relationships in the process model, as the new KIOSK did not change the control flow of the process.

Table 26. Summary of event logs

Summary and Discussion

In the quality perspective, PPIs showed both positive and negative effects resulting from the application of the best practice. Thus, we could not conclude whether the implementation of best practice had a positive or negative effect on the process. The proposed indicators were based on the literature review and the authors' experiences.

Summary and Implications

Thus, it is believed that this research can act as a motivation for others to expand the use of process mining in healthcare. The fundamental contributions of this research explained above (eg, creating event logs with CDM, providing mining process functions, and developing systematic frameworks) are evidence of this. Therefore, it is believed that this research will improve the financial condition of healthcare organizations by reducing length of stay and increasing patient turnover, as well as increasing customer satisfaction by minimizing waiting time.

Future Research

Amyot, “Process mining in healthcare: a systematic literature review,” International Journal of Electronic Healthcare, vol. Yoo, "A Systematic Methodology for Ambulatory Process Analysis Based on Mining Processes". International Journal of Industrial Engineering: Theory, Applications and Practice. Yoo, "A Systematic Methodology for Ambulatory Process Analysis Based on Mining Processes." In Asia Pacific Business Process Management, Vol.