Process mining project methodology in healthcare: a case study in a tertiary hospital

Pereira, Gustavo Bernardi; Santos, Eduardo Alves Portela; Maceno, Marcell Mariano Corrêa

doi:10.1007/s13721-020-00227-w

Cited by 8 publications

(7 citation statements)

References 43 publications

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“…Process mining has been successfully used to map health care processes, clarifying how patients are served as a net effect of activities performed by health care providers. So far, most examples of its use in health care are related to an intrahospital context [17][18][19][20][21][22][23][24][25][26][27]. By automatically generating process maps using factual observations retrieved from data, process mining has important advantages over other modeling techniques that rely on manual observations of the actual system or inspection of documents [17,34].…”

Section: Process Miningmentioning

confidence: 99%

“…Process mining has an advantage over other mapping techniques as it uses factual observations retrieved from data rather than man-made process models. Process mining has been successfully used to analyze health care processes, usually in an intrahospital context [17][18][19][20][21][22][23][24][25][26][27]. However, its application to regional health care networks is new.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Frequent Health Care Users and Care Consumption Patterns: Process Mining of Emergency Medical Services Data

2021

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Background Tracing frequent users of health care services is highly relevant to policymakers and clinicians, enabling them to avoid wasting scarce resources. Data collection on frequent users from all possible health care providers may be cumbersome due to patient privacy, competition, incompatible information systems, and the efforts involved. Objective This study explored the use of a single key source, emergency medical services (EMS) records, to trace and reveal frequent users’ health care consumption patterns. Methods A retrospective study was performed analyzing EMS calls from the province of Drenthe in the Netherlands between 2012 and 2017. Process mining was applied to identify the structure of patient routings (ie, their consecutive visits to hospitals, nursing homes, and EMS). Routings are used to identify and quantify frequent users, recognizing frail elderly users as a focal group. The structure of these routes was analyzed at the patient and group levels, aiming to gain insight into regional coordination issues and workload distributions among health care providers. Results Frail elderly users aged 70 years or more represented over 50% of frequent users, making 4 or more calls per year. Over the period of observation, their annual number and the number of calls increased from 395 to 628 and 2607 to 3615, respectively. Structural analysis based on process mining revealed two categories of frail elderly users: low-complexity patients who need dialysis, radiation therapy, or hyperbaric medicine, involving a few health care providers, and high-complexity patients for whom routings appear chaotic. Conclusions This efficient approach exploits the role of EMS as the unique regional “ferryman,” while the combined use of EMS data and process mining allows for the effective and efficient tracing of frequent users’ utilization of health care services. The approach informs regional policymakers and clinicians by quantifying and detailing frequent user consumption patterns to support subsequent policy adaptations.

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Section: Process Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying Frequent Health Care Users and Care Consumption Patterns: Process Mining of Emergency Medical Services Data

2021

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“…In this study, these shortcomings were tried to be eliminated, although partially [4]. In other study, a methodology for the application of process mining in healthcare entitled "Process Mining Project Methodology in Healthcare" was developed (PM2 HC) [5]. Moreover, it was developed over a series of steps involving bibliographical reviews on the methodologies of application of process mining in the general and on applications of process mining in health case studies.…”

Section: Related Workmentioning

confidence: 99%

Creating a Data Generator and Implementing Algorithms in Process Analysis

Bakir

Yuzkat

Karabiber

2022

ELEKTRON ELEKTROTECH

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Process mining is a new field of work that aims to meet the need of the business world to improve efficiency and productivity. This field focuses on analysing, discovering, managing, and improving business processes. Process mining uses event logs as a resource and works on this resource. Hence, the system is developed by analysing the event logs, including each step in the process model. Our study is made up of two significant stages: a data generator for processes and algorithms applied for discovering the created processes. In the first stage, the aim was to develop a simulator with the ability to generate data that could help process modelling and development. Within the framework of this study, a system was created that could work with various process models and extract meaningful information from these models. More productive and efficient processes can be developed as a result of his system. The simulator consists of three modules. The first module is the part where users create a process model. In this module, the user can create his own business process model in the system’s interface or select from other registered models. In the second module, team-based data are simulated through these process models. These generated data are used in the third module, called “analysis”, and meaningful information is extracted. In conclusion, the process can be improved considering the information about time, resource, and cost in the generated data. At the second stage, processes were discovered using alpha, heuristic, and genetic algorithms, which are process mining discovery algorithms and synthetic and real event logs. The discovered processes were demonstrated with Petri nets, and the algorithms’ performances were compared using the fitness function, accuracy rates, and running times. In our study, the heuristic algorithm is more successful because it improves the noise in the data and incomplete processes, which are the disadvantages of the alpha algorithm. However, the genetic algorithm yielded more successful results than the alpha and heuristic algorithms due to its genetic operators.

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“…Because of the abundance of the data and the complexity of the healthcare processes, new methodologies have recently surged in applications of PM to healthcare; for example, Pereira's work in [50] presents a case study in a tertiary hospital of PM project methodology in healthcare. In addition, recommendations have been published about the use, application, challenges and lessons learned; see for example [51].…”

Section: Related Workmentioning

confidence: 99%

Comparative Assessment of Data-driven Process Models in Health Information Technology

Klasky

Özmen

Omitaomu

et al. 2021

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Background and PurposeProcess mining for conformance analysis consists of comparing a reference process model against a datadriven process model generated via log files from information technology systems. However, in the absence of a complete reference process model, we found no suggested approaches in the literature to address the need for evaluating process conformance among different healthcare facilities to assess standardization of care. Our goal is to find similarities and dissimilarities in data-driven process models among US Veterans Health Administration (VHA) facilities that can be indicative of patient safety issues. Our hypothesis was that the analysis would not produce statistically significant differences in outcome. MethodsWe present a unique implementation of conformance analysis in process mining that consists of combining process mining, process mapping and statistical metrics. We illustrate our approach by applying it to the analysis of two clinical radiology order process models generated from healthcare data provided by two similar facilities in the VHA. ResultsThe comparative assessment showed that about 70% of the orders completed successfully and 30% were not completed due to policy and duplications. Our analysis found a good statistical correlation between both facilities, as the Spearman's correlation coefficient between facilities for the frequency of cases per total hours was 0.87879, for the frequency of cases by state transition was 0.79702 and for the throughput time per state transition was 0.63582. Additional statistical analyses using the Mann-Whitney U test and the root mean square error both produced values that were not significant. ConclusionsThe foregoing approach validated our hypothesis by demonstrating a good statistical correlation of data describing the flow of clinical radiology orders absent a credible reference model. Finding good agreement between both facilities was important in confirming that the clinical orders flow in a similar manner, suggesting standardization of care.

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Process mining project methodology in healthcare: a case study in a tertiary hospital

Cited by 8 publications

References 43 publications

Identifying Frequent Health Care Users and Care Consumption Patterns: Process Mining of Emergency Medical Services Data

Identifying Frequent Health Care Users and Care Consumption Patterns: Process Mining of Emergency Medical Services Data

Creating a Data Generator and Implementing Algorithms in Process Analysis

Comparative Assessment of Data-driven Process Models in Health Information Technology

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