Process Mining for Disease Trajectory Analysis on the Indonesia Health Insurance Data
Angelina Prima Kurniati1, Guntur Prabawa Kusuma2, Gede Agung Ary Wisudiawan2,*
1Fakultas Informatika, Program Studi S1 Informatika, Universitas Telkom, Bandung, Indonesia
2Fakultas Ilmu Terapan, Program Studi D3 Sistem Informasi, Universitas Telkom, Bandung, Indonesia Email: 1[email protected], 2[email protected], 3[email protected]
Email Penulis Korespondensi: [email protected] Submitted 01-10-2022; Accepted 15-10-2022; Published 31-10-2022
Abstrak
Process mining telah banyak diterapkan di berbagai domain, termasuk kesehatan. Di bidang kesehatan, proyek-proyek process mining bertujuan untuk memperoleh informasi tentang pola sekuensial dari proses-proses berdasarkan eksekusi proses actual yang terekam dalam event log. Event log sebagai input utama dari process mining dapat diekstrak dari data yang otomatis terekam tentang perawatan atau diagnosis pasien. Dengan memahami pola-pola umum dari diagnosis pasien, dapat dilakukan analisis trajektori penyakit dari sekumpulan pasien dengan karakteristik tertentu. Analisis trajektori penyakit telah banyak digunakan untuk menjelaskan perkembangan penyakit, terutama penyakit kronis, yang dialami pasien sepanjang waktu. Penelitian ini menerapkan process mining sebagai metodologi utama dalam analisis trajektori penyakit, sesuai dengan tahapan dalam the process mining project methodology, untuk menganalisis data pasien dalam data sampel BPJS Kesehatan. Penelitian ini mengusulkan process mining untuk analisis trajektori penyakit serta membuka kesempatan untuk menganalisis data BPJS Kesehatan yang merepresentasikan kondisi kesehatan masyarakat Indonesia. Studi kasus dalam penelitian ini adalah trajektori penyakit dari pasien penderita kanker.Data sampel tersebut diekstraksi dan ditransformasi menjadi event log, diolah dengan algoritma process discovery untuk menghasilkan disease trajectories, serta dianalisis untuk mengetahui kesesuaiannya dengan data dalam event log. Hasil penelitian ini menunjukkan bahwa data sampel BPJS Kesehatan dapat digunakan untuk analisis trajektori penyakit serta menunjukkan model proses yang menggambarkan beberapa diagnosis sebelum dan sesudah diagnosis kanker payudara, dengan performansi tinggi yaitu trace fitness 0.9847, precision 0.735, dan generalization 0.9515.
Kata Kunci: Process Mining; Kesehatan; Trajektori Penyakit; Process Discovery Abstract
Process mining has been implemented in many domains, including healthcare. In healthcare, process mining projects aimed to inform sequential patterns of processes based on the actual process executions as they are recorded in the event log. Event log as the main input of process mining tasks can be extracted from the automatically recorded data of patient treatments or diagnoses. By understanding common patterns of patient diagnoses, we can analyse disease trajectories of a cohort of patients. Disease trajectory analysis has been used to describe the course or progression of diseases, especially chronic diseases, as experienced over time. We applied process mining as the main methodology for disease trajectory analysis, following the process mining project methodology, to analyse patient records on the Indonesia Health Insurance (BPJS Kesehatan) Data Samples. We extracted the data samples, transform them into an event log, discover the disease trajectories based on process discovery algorithm, analyse it to inform their conformance to the event log. Contributions of our research are to promote process mining for disease trajectory analysis and to open wider opportunities to analyse Indonesia Health Insurance data representing Indonesia health conditions. As a case study, we explored disease trajectory of cancer patients.
Keywords: Process Mining; Healthcare; Disease Trajectory; Process Discovery
1. INTRODUCTION
Medical data is a sensitive, complicated, having a high variability, and multidisciplinary in nature [1]. Healthcare data is abundant but difficult to comprehend. It requires structured methods to describe and visualize them. Because the data is sensitive, a real-world healthcare dataset is difficult to access. Requesting access to authentic healthcare datasets may take months. An alternative method to enhance healthcare research is by utilizing publicly accessible dataset [2], [3].
However, due to anonymization, data curation, and representational bias, among others, the publicly available datasets were unable to depict the issues in the real datasets. Once researchers are granted data access, they must also address the issue of data quality [4]. Due to the fact that healthcare data represents records of actual activities in the health services, there are numerous instances of low-quality data, such as missing values, erroneous data, incomplete records, etc. Putting aside their quality, healthcare datasets have a complex structure that reflects their multidisciplinary nature. In Indonesia, a real-world dataset is provided by the Indonesian health insurance (BPJS Kesehatan), in the form of BPJS Kesehatan data samples [5], [6]. The dataset contains representative samples of patient data in addition to demographic and billing information, allowing for analysis from multiple angles. The dataset is also reflective of other healthcare data in which records of patient treatments and diagnoses can be located and sequenced using the timestamps contained in the information. Healthcare data analysis has been done for many purposes, including descriptive analysis, disease trajectory analysis, administrative improvement and clinical recommendations. In this paper, we analyse a real-world dataset for disease trajectory analysis, using an established method of process mining [7]. Process mining has a strong evidence- based method by analysing event log that has been extracted from the automatically recorded data in the healthcare database. Process mining can be utilized for process-oriented data science, where event data is collected and used as the primary input to find processes in health services, such as patient treatments and diagnoses [8].
Previous studies of process mining in healthcare have been done in specific domains, including primary care [9], cardiology [10], oncology [11], [12], and disease trajectory [13]. Our previous studies using the same dataset found that the dataset is potentially useful to progress research in process mining [14], [15]. In primary care, process mining has been done in many projects in relation with additional domains including dentistry, public health, and nursing homes [10].
One of the challenges is the interaction between primary care and other health facilities. In the disease specific processes, such as oncology [11], [12], process mining can go deeper into more detailed processes. For example, process mining in oncology can be used to analyse conformity to cancer treatment procedures, such as two-week-wait procedure. Whilst, process mining can also be used for disease trajectory analysis, to describe progression of diseases experienced by patients [13]. This study is limited in number, but potentially be useful to discover population-level patterns of disesases or comparison between countries. In general, process mining for healthcare can be used to: 1) discover process models of a patient's treatment and diagnosis based on event data; 2) improve the sequence of treatment and diagnosis by learning from it; and 3) suggest improved treatments for future patients based on the models they discovered.
In this study, we examined BPJS Kesehatan data samples for disease trajectory analysis, using process mining as the main approach. We followed the established process mining project methodology (PM2) and iteratively improved our findings throughout the stages. We focused on one specific diagnosis as our scope. We used one of the robust process discovery algorithms, namely heuristics miner, that has been implemented in the ProM framework. Our research aimed to help: 1) The academic community by promoting process mining for disease trajectory analysis; and 2) The healthcare field by informing disease trajectories discovered from the dataset.
2. RESEARCH METHODOLOGY
2.1 General Methodology
We analyse clinical pathways using process mining following PM2 (Process Mining Project Methodology) [16] and referring to the ClearPath method [17] and question-driven methodology [18]. The main parts of the methodology are:
process discovery, conformance checking, and enhancement. In the process discovery, we used ProM [19], [20] as an established framework for academic purposes of process mining projects. In the conformance checking, we use replay fitness, precision, generalisation, and simplicity as the conformance metrics [21]. An illustration of the general method is presented in Figure 1.
Figure 1. Methodology For Disease Trajectory Analysis Using Process Mining
Based on Figure 1, the following is an explanation regarding the research methodology of disease trajectory analysis. In the planning phase of this research, it defines the scope of coverage, namely breast cancer data on BPJS Kesehatan sample data. The research question is how the disease trajectory of breast cancer is. The teams involved are technical and clinical. In the extraction phase, this study extracts BPJS Kesehatan sample data according to the scope.
There were 1,352 cases, 15,688 events, 648 event classes, and 1,223 variants. In the data preprocessing phase, sample data that has been filtered according to the required data will be preprocessed. The preprocessing process is created view, aggregate events, filtering by attribute and merge subsequence events. This preprocessing is needed to transform BPJS Kesehatan sample data into event logs so that the mining process can be carried out. The mining and analysis phases are assisted using Prom when forming the process model using the discovery process. To assess the process of the model created, a conformance process is needed to measure the value of replay fitness, precision, generalization and simplicity [22]. The replay fitness assesses the extent to which the discovered model can accurately replay the log cases. The
generalization evaluates how well the generated model will reproduce the future behavior of the process. The simplicity dimension captures the complexity of a process model. Then the output of the mining process will be carried out by the disease trajectory analysis process. The last activity is the evaluation process using statistical evaluation.
2.2 Data Sample of BPJS Kesehatan
Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS Kesehatan) is the Indonesian government's body for providing health insurance to its citizens [[23]. The number of Indonesians who have joined BPJS Kesehatan's health insurance program increases annually. Until 2018, there are 200,259,147 people are enrolled in this health insurance. The data indicates that 75.8% of Indonesian individuals have joined a health insurance plan, and the government's goal for 2019 is for all Indonesians to be covered. Currently, there are three editions of BPJS Kesehatan data samples. The first edition was released on 2019, covering data samples of BPJS Kesehatan members registered on 2015-2016 [24]. The second edition was released on 2020, covering data samples of BPJS Kesehatan members registered on 2015-2018 [6]. The third edition was released on 2021, covering data samples of BPJS Kesehatan members registered on 2015-2020 [25].
Researchers must fill out several online forms on the BPJS Health Portal in order to get sample data [26].
The BPJS Kesehatan data sample consists of several tables consisting demographic parameters (age, gender, type of membership group, and origin region), kinds of sickness (diagnosis and diagnosis group), service output (recovered, referred, died), features of health institutions, service rates, etc. The data samples have been taken considering representativeness of BPJS Kesehatan members in the country, province, and local levels for the indicators of service access and diseases of the citizens. The sample unit is family, so that the membership and service data have a high correlation in the family level [6].
There are three health facilities in the BPJS sample data, namely: the primary health facility (fasilitas kesehatan tingkat pertama/ FKTP), the advanced referral health facility (fasilitas kesehatan tingkat rujukan lanjutan/ FKRTL), and the non-capitation of the primary health facility (fasilitas kesehatan tingkat pertama non kapitasi/ FKTP non-kapitasi).
FKTP includes outpatient and inpatient treatments offered by primary care clinics. The FKTP consists of a puskesmas or its equivalent, a doctor's office, a dentist's office, a pratama clinic or its equivalent, and a hospital of class D or its equivalent. FKTRL is a health facility that provides expert or subspecialist individual health services, such as advanced outpatient, advanced level inpatient, and inpatient treatment in special care rooms. FKTP non kapitasi refers to FKTP health services not covered by capitation fees. FKTP non-kapitasi means health services are included in the contract paid by BPJS Kesehatan to FKTP based on the number of covered participants. In this paper, we examined all three health care facilities in the data samples 2015-2018.
2.3 Case Study: Cancer Patients
Cancer is the top cause of mortality worldwide in 2020, accounting for around 10 million deaths. The top five malignancies in the world, according to the World Health Organization (WHO) in 2020, were breast cancer (2.26 million cases); lung cancer (2.21 million cases); colon and rectum cancers (1.93 million cases); prostate cancer (1.41 million cases); skin (non-melanoma) (1.20 million cases); and stomach (1.09 million cases) [27]. According to the WHO, based on the following WHO data, the number of deaths caused by this cancer: lung (1.80 million deaths); colon and rectum (916,000 deaths); liver (830,000 deaths); stomach (769,000 deaths); and breast (685,000 deaths). According to the data exposed, breast cancer has the highest number of reported instances.
In Indonesia, there were 68,858 new instances of breast cancer, or 16.6% of the total 396,914 new cases of cancer.
In the meantime, the number of deaths surpassed 22,000 cases. Breast cancer is the most prevalent kind of cancer in Indonesia and one of the leading causes of cancer-related fatalities. In addition to a relatively high mortality rate, late cancer treatment results in an ever-growing financial burden. During 2019-2020, cancer treatment has used around 7.6 trillion rupiah of BPJS funding. [28]
Cancer has been coded in ICD-10 codes as malignant neoplasm. Along with the benign neoplasm, they have been classified into C00-C96 [29]. The BPJS Kesehatan data samples have also been following the ICD-10 codes. All categories of cancer diagnosis can be found in the BPJS Kesehatan data sample, except C7A (Malignant neuroendocrine tumors) and C7B (Secondary neuroendocrine tumors) that has no sample in the dataset.
3. RESULT AND DISCUSSION
We analysed disease trajectory following stages in the process mining project methodology (PM2). The results are presented in these sections following the main stages of: (1) planning and justification, (2) extraction, (3) data processing, (4) mining and analysis, and (5) evaluation.
3.1 Planning and Justification
The BPJS Kesehatan data sample has been analysed to be potentially useful for process mining projects [14]. One of the potential uses is disease trajectory analysis. We analyse disease trajectory of patients diagnosed with cancer/ malignant neoplasms. The diagnosis of cancer is indicated by the ICD-10 code of C00-C96. We checked on the FKRTL table of the BPJS Kesehatan data sample and found that there are 17,833 patients diagnosed with cancer. The proportions of cancer types are presented in Table 1.
Table 1. Proportions of Cancer Types in the FKRTL Data ICD-10
code Cancer type #
patients %
C00-C14 Malignant neoplasms of lip, oral cavity and pharynx 627 12.4%
C15-C26 Malignant neoplasms of digestive organs 543 10.7%
C30-C39 Malignant neoplasms of respiratory and intrathoracic organs 349 6.9%
C40-C41 Malignant neoplasms of bone and articular cartilage 102 2.0%
C43-C44 Melanoma and other malignant neoplasms of skin 198 3.9%
C45-C49 Malignant neoplasms of mesothelial and soft tissue 366 7.2%
C50-C50 Malignant neoplasms of breast 1,352 26.7%
C51-C58 Malignant neoplasms of female genital organs 748 14.8%
C60-C63 Malignant neoplasms of male genital organs 118 2.3%
C64-C68 Malignant neoplasms of urinary tract 288 5.7%
C69-C72 Malignant neoplasms of eye, brain and other parts of central nervous system 144 2.8%
C73-C75 Malignant neoplasms of thyroid and other endocrine glands 187 3.7%
C76-C80 Malignant neoplasms of ill-defined, other secondary and unspecified sites 312 6.2%
C81-C96 Malignant neoplasms of lymphoid, hematopoietic and related tissue 604 11.9%
C00-C96 Neoplasms 5,064 100%
Table 1 shows that the most populated diagnosis of cancer is C50 (Malignant neoplasms of breast), or breast cancer. There are 1,352 patients (26.7%) diagnosed with breast cancer in the FKRTL data. This is representative of the condition of Indonesia healthcare [28], where in 2020, the number of new cases of breast cancer was 68,858 cases (16.6%) of the total of 396,914 new cases of cancer in Indonesia. Meanwhile, there were more than 22 thousand patients died of breast cancer. For those reasons, we continued with breast cancer patients selected to be analysed in this study.
3.2 Extraction
We extracted data of patients diagnosed with cancer from three tables in the BPJS Kesehatan data sample, which are:
fktpkapitasi, fktpnonkapitasi, and fkrtl. For this study, we also limit our extraction to include only the primary diagnoses.
We followed the following the logical steps to conduct the data selection and event log generation.
1. patient <- From fkrtl, select all patient_id where diagnosis = ‘C50’
2. Table eventLog <- From {fktp, fktpnon, fkrtl}, select all rows where patient_id in patient 3. From table eventLog, select row ( patient_id, diagnosis, facility, timestamp)
4. Rename row patient_id from eventLog table into CaseID 5. Rename row diagnosis from eventLog table into Activity 6. Rename row timestamp from eventLog table into TimeStamp 7. Rename row facility from eventLog table into Facility
Algorithm 1. Logical steps of data extraction and event log generation
As presented in Algorithm 1, in this stage, we also combined patient records from FKTP, FKRTL, and FKTP non- capitation tables of all 1,352 patients identified in the previous stage. Table 2 summarized the number of patients and records from each table.
Table 2. Summary of the selected data of cancer patients in three tables Table name # patients # records
FKTP 1,237 10,114
FKTP non-capitation 43 200
FKRTL 1,352 27,699
Total 1,352 32,196
3.2 Data processing
The extracted data were transformed into an event log, consisting of the minimum components for process mining analysis: patient_id, diagnosis, facility, and timestamp. We load the event log into ProM and analyse the event log with basic characteristics presented in Table 3.
Table 3. Characteristics of the event log
Characteristic Before preprocessing After preprocessing
Cases 1,352 1,352
Events 32,196 15,688
Event classes 648 648
Events per case Min: 1; Mean: 24; Max: 189 Min: 1; Mean: 12; Max: 112 Event classes per case Min: 1; Mean: 6; Max: 27 Min: 1; Mean: 6; Max: 27
Variants 1,285 1,223
Events per trace 23.814 11.604
Table 3 shows that before preprocessing, among all 1,352 cases, there are 1,285 variants, which means that this case study has a very high variability so that almost all cases are unique. Another thing to note is that there are 32,196 events in total from all 1,352 cases, with a range of 1 up to 189 events per case, which represents a high complexity of this case study. Our further investigation shows that the top 8 trace variants are showing occurrences of C50 diagnoses from a single occurrence up to 26 occurrences, as presented in Figure 2.
Figure 2. The top eight trace variants consist of single and multiple occurences of C50 diagnoses
The finding presented in Figure 2 is representative of the real conditions where patients having breast cancer might need to visit doctors several times with the same diagnosis due to the recurrences of breast cancer conditions. To handle these conditions, we use two basic preprocessing plugins, which are ‘Merge subsequent events’ to consider only the first occurrence of a repeating events and ‘Remove unique traces’ to filter out traces followed by only one case.
3.2 Mining and analysis
This stage is the main part of the study. Process mining activity consists of process discovery and conformance checking.
We used the heuristics miner for process discovery, because of its ability to work with large data and handle noise. For this step, we included only multiple traces by filtering out unique traces. The final event log consists of 147 cases with 236 events of 11 event classes, with 18 variants. We used the Interactive Data-aware Heuristics Miner (IDHM) that can handle noises and infrequent events. The discovered process model is presented as a causal net in Figure 3.
Figure 3. The discovered process model using IDHM
Figure 3 shows that there are five diagnoses commonly come before the diagnosis of breast cancer, namely the Carcinoma in situ of breast; Other and unspecified soft tissue disorders, nec.; Acute tonsillitis; Benign mammary dysplasia; and Benign neoplasm of breast. Meanwhile, there are four diagnoses commonly come after the diagnosis of breast cancer, namely the Other benign neoplasms of connective and other soft tissue; Encounter for other aftercare and medical care; Encounter for follow-up examination after completed treatment for conditions other than malignant neoplasm; and Encounter for other postprocedural aftercare.
In the conformance checking step, we used trace fitness, precision, and generalization to check for the conformance of the discovered process model with the records in the event log. The discovered process model has been checked for its conformance to the records in the event log, resulted in trace fitness 0.9847, precision 0.735, and generalization 0.9515.
The numbers show that the discovered process model has been representative to the records in the event log.
3.2 Evaluation
Our experiment has shown the potential of using process mining in the disease trajectory analysis. We followed PM2 as the general methodology and used heuristics miner as the process discovery algorithm. PM2 has been used in many previous process mining projects [30]–[32], and this paper can be added as another supporting success story to use this methodology. We reflected on our way of selecting the cohort of patients, where we chose a specific diagnosis code to begin with the selection. Breast cancer is the most populated data in the BPJS Kesehatan dataset, consisting of 1,352 patients (26% of all cancer patients in the dataset). This approach to select a cohort of patients has been successfully found a number of patients having similar diagnoses in their disease trajectories. We used the interactive data-aware heuristics miner (IDHM) plugin in ProM and was helped by the functionalities provided in the plugin, especially where we can choose among different options of process model notations, namely directly-follows graph, causal net, dependency graph, and Petri net. We chose the causal net to be presented in this paper due to its compact presentation that is suitable to support understanding of the model. We used three metrics of conformance checking, namely the trace fitness, precision, and generalization, that are suitable to reflect on the representativeness of the discovered process model.
4. CONCLUSION
Our study has been showing a potential use of BPJS Kesehatan data sample to be used for disease trajectory analysis of a specific diagnosis of cancer. Our experiment shows the applicability of process mining as the main approach in disease trajectory analysis in this dataset. The BPJS Kesehatan data sample is potentially useful to support many purposes of healthcare research projects, and process mining for disease trajectory analysis is one of those purposes. We explored the dataset and found that the richness of details provided in the dataset would be useful for further analysis, including comparison of disease trajectories among different cohort of patients, among different locations, among different age groups, etc. We used IDHM as our main algorithm in process discovery, but there are plenty of other algorithms that can be explored to find the most suitable algorithm in disease trajectory analysis. Future work of this study can be in four folds. First, the BPJS Kesehatan data sample can be explored to find other research designs to work with. Second, the BPJS Kesehatan data sample of 2015-2018 also including another table of secondary diagnosis that is out of scope of this study, but can be considered to increase complexity and accuracy of disease trajectory analysis. Third, the process discovery algorithms can be explored and enhanced to suit the need of disease trajectory analysis. Four, future research can use the BPJS Kesehatan is currently provided the third version of the data sample, which is the 2015-2020 data sample, that can be expected to have a more detailed and more populated dataset.
ACKNOWLEDGMENT
The authors thank Telkom University for sponsoring this research project and BPJS Kesehatan for granting access to the 2015-2018 data sample.
REFERENCES
[1] J. Munoz-Gama et al., “Process mining for healthcare: Characteristics and challenges,” J Biomed Inform, vol. 127, Mar. 2022, doi: 10.1016/j.jbi.2022.103994.
[2] A. P. Kurniati, G. Hall, D. Hogg, and O. Johnson, “Process Mining in Oncology using the MIMIC-III Dataset,” IOP Journal of Physics: Conference Series 971, vol. 971, no. 012008, pp. 1–10, 2018.
[3] G. Kusuma, A. Kurniati, C. D. McInerney, M. Hall, C. P. Gale, and O. Johnson, “Process Mining of Disease Trajectories in MIMIC-III: A Case Study,” pp. 305–316, 2021, doi: 10.1007/978-3-030-72693-5_23.
[4] A. P. Kurniati, E. Rojas, D. Hogg, G. Hall, and O. A. Johnson, “The assessment of data quality issues for process mining in healthcare using Medical Information Mart for Intensive Care III, a freely available e-health record database,” Health Informatics J, vol. 25, no. 4, pp. 1878–1893, Dec. 2019, doi: 10.1177/1460458218810760.
[5] P. Mundiharno et al., “Makalah Data Sampel BPJS Kesehatan 2015-2016,” 2019. Accessed: Feb. 13, 2022. [Online]. Available:
http://www.kpmak-ugm.org/id/assets/public/Makalah%20Data%20Sampel%20BPJS%20Kesehatan.pdf [6] Andi Afdal Abdullah et al., “Makalah Data Sampel BPJS Kesehatan 2015-2018,” Jakarta, 2020.
[7] W. M. P. van der Aalst and J. Carmona, Eds., Process Mining Handbook, vol. 448. Cham: Springer International Publishing, 2022. doi: 10.1007/978-3-031-08848-3.
[8] M. S. Sundari and R. K. Nayak, “Process Mining in Healthcare Systems: A Critical Review and its Future,” International Journal of Emerging Trends in Engineering Research, vol. 8, no. 9, pp. 5197–5208, Sep. 2020, doi: 10.30534/ijeter/2020/50892020.
[9] R. Williams, E. Rojas, N. Peek, and O. A. Johnson, “Process Mining in Primary Care: A Literature Review,” Stud Health Technol Inform, vol. 247, pp. 376–380, 2018.
[10] G. Kusuma, M. Hall, C. P. Gale, and O. Johnson, “Process Mining in Cardiology: A Literature Review,” Int J Biosci Biochem Bioinforma, vol. 8, no. 4, p. (in press), 2018.
[11] A. P. Kurniati, G. Hall, D. Hogg, and O. Johnson, “Process mining to explore variation in chemotherapy pathways for breast cancer patients,” British Journal of Cancer Supplement (Abstract 42), vol. 119, no. S1, p. 16, 2018, doi: 10.1038/s41416-018- 0299-z.
[12] A. P. Kurniati, C. McInerney, K. Zucker, G. Hall, D. Hogg, and O. Johnson, “A Multi-level Approach for Identifying Process Change in Cancer Pathways,” in Lecture Notes in Business Information Processing, 2019, vol. 362 LNBIP, pp. 595–607. doi:
10.1007/978-3-030-37453-2_48.
[13] G. P. Kusuma, A. P. Kurniati, E. Rojas, C. D. Mcinerney, C. P. Gale, and O. A. Johnson, “Process mining of disease trajectories:
A literature review,” in Public Health and Informatics: Proceedings of MIE 2021, IOS Press, 2021, pp. 457–461. doi:
10.3233/SHTI210200.
[14] A. P. Kurniati, G. A. A. Wisudiawan, and G. P. Kusuma, “Potentials of Clinical Pathway Analysis Using Process Mining on the Indonesia National Health Insurance Data Samples: an Exploratory Data Analysis,” 2022.
[15] M. Ivan, A. Pradana, A. P. Kurniati, G. Agung, and A. Wisudiawan, “Inductive Miner Implementation to Improve Healthcare Efficiency (Case Study: BPJS Kesehatan 2015-2016 Dataset),” in 2022 International Conference on Data Science and Its Applications (ICoDSA), Jul. 2022, pp. 239–244. doi: 10.1109/ICoDSA55874.2022.9862837.
[16] E. Zuidema-Tempel, Robin Effing, and Jos van Hillegersberg, “Bridging the Gap between Process Mining Methodologies and Process Mining Practices,” in International Conference on Business Process Management, 2022, pp. 70–86.
[17] O. A. Johnson, T. Ba Dhafari, A. Kurniati, F. Fox, and E. Rojas, The ClearPath Method for Care Pathway Process Mining and Simulation, vol. 342. 2019. doi: 10.1007/978-3-030-11641-5_19.
[18] C. Alvarez et al., “Discovering role interaction models in the Emergency Room using Process Mining,” J Biomed Inform, vol.
78, pp. 60–77, Feb. 2018, doi: 10.1016/j.jbi.2017.12.015.
[19] F. Almira Yasmin, R. Bemthuis, M. Elhagaly, F. Wijnhoven, and F. Allah Bukhsh, “A Process Mining Starting Guideline for Process Analysts and Process Owners: A Practical Process Analytics Guide using ProM,” 2020, [Online]. Available:
www.processmining.org.
[20] ProM, “Start | ProM Tools,” 2022. https://www.promtools.org/doku.php (accessed Aug. 02, 2022).
[21] J. C. A. M. Buijs, B. F. van Dongen, and W. M. P. van der Aalst, “On the role of fitness, precision, generalization and simplicity in process discovery,” in OTM Confederated International Conferences, 2012, pp. 305–322. doi: 10.1007/978-3-642-33606- 5_19.
[22] S. Dunzer, M. Stierle, M. Matzner, and S. Baier, “Conformance checking: A state-of-the-art literature review,” Jun. 2019. doi:
10.1145/3329007.3329014.
[23] Humas, “Tindak Lanjuti Inpres 1/2022, BPJS Kesehatan - Kementerian ATR/BPN Perkuat Kolaborasi,” BPJS KEsehatan, Mar.
23, 2022. https://www.bpjs-kesehatan.go.id/bpjs/post/read/2022/2235/Tindak-Lanjuti-Inpres-12022-BPJS-Kesehatan- Kementerian-ATRBPN-Perkuat-Kolaborasi (accessed Jul. 25, 2022).
[24] Ariawan I, Sartono B, and Jaya C, “Data Sampel BPJS Kesehatan Tahun 2015–2016,” 2019.
[25] I. Ariawan et al., Buku Data Sampel BPJS Kesehatan 2015-2020. 2021.
[26] BPJS Kesehatan, “Website PPID BPJS Kesehatan.” https://data.bpjs-kesehatan.go.id (accessed Feb. 12, 2022).
[27] World Health Organization, “Cancer Fact Sheet,” 2018. http://www.who.int/mediacentre/factsheets/fs297/en/ (accessed Jan. 07, 2019).
[28] MF, “Kanker Payudara Paling Banyak di Indonesia, Kemenkes Targetkan Pemerataan Layanan Kesehatan,” Feb. 04, 2022.
https://www.kemkes.go.id/article/view/22020400002/kanker-payudaya-paling-banyak-di-indonesia-kemenkes-targetkan- pemerataan-layanan-kesehatan.html (accessed Sep. 30, 2022).
[29] A. G. Fritz, International classification of diseases for oncology : ICD-O, 3rd ed. 2013.
[30] A. Augusto, T. Deitz, N. Faux, J. A. Manski-Nankervis, and D. Capurro, “Process mining-driven analysis of COVID-19’s impact on vaccination patterns,” Journal of Biomedical Informatics, vol. 130. Academic Press Inc., Jun. 01, 2022. doi:
10.1016/j.jbi.2022.104081.
[31] C. Capitan-Agudo, M. Salas-Urbano, C. Cabanillas, and M. Resinas, “Analyzing How Process Mining Reports Answer Time Performance Questions,” in International Conference on Business Process Management, 2022, pp. 234–250.
[32] G. B. , S. E. A. P. , M. M. M. C. Pereira, “Process mining project methodology in healthcare_a case study in a tertiary hospital,”
Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 9, no. 1, 2020.
