Analyze in Depth Health Care Business Process and Key Performance Indicators using Process Mining

Elhadjamor, Emna Ammar; Ghannouchi, Sonia Ayachi

doi:10.1016/j.procs.2019.12.227

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…Multiple studies have examined the application of AHP in assessing process mining software for healthcare systems, covering a broad range of perspectives and methodologies. Elhadjamor and Ghannouchi [35] and Dallagassa et al, [11] highlight the potential of process mining in healthcare, emphasizing the need for data integration and compliance evaluation. The use of AHP in health technology assessment was discussed in Improta et al, [36], that presented a dynamic AHP framework.…”

Section: Ahp Analysis In Evaluating Process Mining Software For Healt...mentioning

confidence: 99%

“…Martinez-Millana et al, [37] and Batra et al, [38] applied AHP to evaluate healthcare systems, with the former focusing on the features of a process mining dashboard and the latter using a fuzzy AHP strategy [39]. Elhadjamor and Ghannouchi [35] and Mesabbah et al, [40] proposed models for evaluating operational process variables and automated simulation modeling in healthcare, respectively, with the former incorporating data visualization techniques. Pereira et al, [19] developed a methodology for applying process mining in healthcare, emphasizing stakeholder involvement and KPI evaluation.…”

Section: Ahp Analysis In Evaluating Process Mining Software For Healt...mentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Healthcare Business Processes with Process Mining Software: A Comparative Analysis

Matonya,

Pusztai,

Budai

2024

Decis. Mak. Appl. Manag. Eng.

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This study focuses on the application of process mining in the healthcare sector. Despite its potential to enhance efficiency, reduce costs, and improve patient satisfaction, the selection of process-mining software poses significant challenges due to the diverse nature of healthcare processes and the lack of comprehensive evaluation methods. To bridge this gap, this study employed a hybrid Multi-Criteria Decision-Making (MCDM) approach, integrating the Neural Network-Augmented Analytical Hierarchy Process (NNA-AHP) and Grey Relational Analysis—a technique for Order Preference by Similarity to Ideal Solution (GRA-TOPSIS). The study evaluated process mining software on functionalities, ease of use, cost, technical support, scalability, and security with their respective sub-criteria. The principal results indicate that Disco is the top-performing alternative, followed by Celonis and ProM. Sensitivity analysis was conducted to understand the influence of variations in criteria weights on evaluating alternatives. In the NNA-AHP, Celonis consistently scored the highest. The GRA-TOPSIS method provided performance scores, indicating that higher scores yield better performance. The new hybrid method consolidates evaluations from all methods and offers the most comprehensive and dependable alternative assessment. Disco and its alternatives, Celonis and ProM, are recommended for optimizing healthcare processes. Further research is needed to investigate the integration of NNA-AHP and GRA-TOPSIS in healthcare management, especially in areas beyond business process analysis. This study provides valuable insights for professionals and researchers in the field and contributes to understanding the effectiveness of process mining.

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Section: Ahp Analysis In Evaluating Process Mining Software For Healt...mentioning

confidence: 99%

Section: Ahp Analysis In Evaluating Process Mining Software For Healt...mentioning

confidence: 99%

Optimizing Healthcare Business Processes with Process Mining Software: A Comparative Analysis

Matonya,

Pusztai,

Budai

2024

Decis. Mak. Appl. Manag. Eng.

View full text Add to dashboard Cite

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“…Authors projected the health indication parameters which is the non-controversy of the face mask and it created the repository disease problem also increased acidity, toxify, short of breathiness, chronic stress all are the important parameters of the health extraction features of any communal disease. Emna Ammar et al [ 9 ] presented the extracting the event logs related to health records for mining the health indicators content associated with instant data. Authors defined Business Process Management (BPM) as the key factor of health indicator continuous process improvement.…”

Section: Literature Surveymentioning

confidence: 99%

Artificial intelligence based health indicator extraction and disease symptoms identification using medical hypothesis models

Kumar¹,

Routray

Prabu

et al. 2022

Cluster Comput

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Patient health record analysis models assist the medical field to understand the current stands and medical needs. Similarly, collecting and analyzing the disease features are the best practice for encouraging medical researchers to understand the research problems. Various research works evolve the way of medical data analysis schemes to know the actual challenges against the diseases. The computer-based diagnosis models and medical data analysis models are widely applied to have a better understanding of different diseases. Particularly, the field of medical electronics needs appropriate health indicator extraction models in near future. The existing medical schemes support baseline solutions but lack optimal hypothesis-based solutions. This work describes the optimal hypothesis model and Akin procedures for health record users, to aid health sectors in clinical decision-making on health indications. This work proposes Medical Hypothesis and Health Indicators Extraction from Electronic Medical Records (EMR) and International Classification of Diseases (ICD-10) patient examination database using the Akin Method and Friendship method. In this Health Indicators and Disease Symptoms Extraction (HIDSE), the evidence checking procedures find and collect all possible medical evidence from the existing patient examination report. Akin Method is making the hypothesis decision from count-based evidence principles. The health indicators extraction scheme extracts all relevant information based on the health indicators query and partial input. Similarly, the friendship method is used for making information associations between medical data attributes. This Akin-Friendship model helps to build hypothesis structures and trait-based feature extraction principles. This is called as Composite Akin Friendship Model (CAFM). This proposed model consists of various test cases for developing the medical hypothesis systems. On the other hand, it provides limited accuracy in disease classification. In this regard, the proposed HIDSE implements Deep Learning (DL) based Akin Friendship Method (DLAFM) for improving the accuracy of this medical hypothesis model. The proposed DLAFM, Convolutional Neural Networks (CNN) associated Legacy Prediction Model for Health Indicator (LPHI) is developed to tune the CAFM principles. The results show the proposed health indicator extraction scheme has 8–10% of better system performance than other existing techniques.

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“…The main limitation of this study is the complexity and heterogeneity of healthcare processes. In [ 14 ], a healthcare case was presented to improve the business process by enhancing the KPIs associated with process instances data by using the process mining tools to extract knowledge from event logs related to the health care process, most of the patients were satisfied to the emergency department. The authors in [ 15 ] proposed a process redesign lifecycle phase coupled with process mining as an operational framework.…”

Section: Introductionmentioning

confidence: 99%

Analysis the patients’ careflows using process mining

et al. 2023

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Recently, The Egyptian health sector whether it is public or private; utilizes emerging technologies such as data mining, business intelligence, Internet of Things (IoT), among many others to enhance the service and to deal with increasing costs and growing pressures. However, process mining has not yet been used in the Egyptian organizations, whereas the process mining can enable the domain experts in many fields to achieve a realistic view of the problems that are currently happening in the undertaken field, and thus solve it. This paper presents application of the process mining techniques in the healthcare field to obtain meaningful insights about its careflows, e.g., to discover typical paths followed by certain patient groups. Also, to analyze careflows that have a high degree of dynamic and complexity. The proposed methodology starts by the preprocess step on the event logs to eliminate outliers and clean the event log. And then apply a set of the popular discovery miner algorithms to discover the process model. Then careflows processes are analyzed from three main perspectives: the control-flow perspective, the performance perspective and, the organizational perspective. That contributes with many insights for the domain experts to improve the existing careflows. Through evaluating the simplicity metric of extracted models; the paper suggested a method to quantify the simplicity metric. The paper used a dataset from a cardiac surgery unit in an Egyptian hospital. The results of the applied process mining techniques provide the hospital managers a real analysis and insights to make the patient journey easier.

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Analyze in Depth Health Care Business Process and Key Performance Indicators using Process Mining

Cited by 8 publications

References 5 publications

Optimizing Healthcare Business Processes with Process Mining Software: A Comparative Analysis

Optimizing Healthcare Business Processes with Process Mining Software: A Comparative Analysis

Artificial intelligence based health indicator extraction and disease symptoms identification using medical hypothesis models

Analysis the patients’ careflows using process mining

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