Process Mining on BPJS Kesehatan Data Sample for Disease Trajectory Analysis with Secondary Diagnosis
Abstract
Abstract— Disease trajectory, the course of a disease over time, and secondary diagnoses, additional medical conditions that a patient may have in addition to their primary diagnosis, can greatly impact patient outcomes, treatment, and management. This study analyzed the feasibility of disease trajectory analysis with secondary diagnoses using the Indonesia Health Insurance (BPJS Kesehatan) 2015-2018 data sample. The study followed the established Process Mining Project Methodology (PM2). We extract the data set from the BPJS Kesehatan data sample, generate an event log from them, discover the disease trajectory by doing process discovery using Heuristic Miner, assess the discovered model using conformance checking, and evaluate it. By analyzing the data sample of Acute Myocardial Infarction (AMI) patients, similar patterns were identified in the 2,100 cases with secondary diagnoses, which can be used to take proactive measures to prevent or manage these secondary diagnoses and gain a more comprehensive understanding of how patients' health changes over time.
Keyword — process mining, healthcare, disease trajectory, secondary diagnosis
References
REFERENSI
Larvin, H, Kang, J, Aggarwal, V R, Pavitt, S, and
Wu, J. 2021. Multimorbid disease trajectories for
people with periodontitis. Journal of Clinical
Periodontology, 48(12): 1587-1596.
Kusuma, G P, Kurniati, A P, Rojas, E, McInerney,
C D, Gale, C P, and Johnson, O A. 2021. Process
mining of disease trajectories: A literature review.
Studies in Health Technology and Informatics.
Munoz-Gama, J, Martin, N, Fernandez-Llatas, C,
Johnson, O A, Sepulveda, M, Helm, E, GalvezYanjari, V, Rojas, E, Martinez-Millana, A, Aloini,
D, Amantea, I A, Andrews, R, Arias, M, Beerepoot,
I, Benevento, E, Burattin, A, Capurro, D, Carmona,
J, Comuzzi, M, Dalmas, B, de la Fuente, R, Di
Francescomarino, C, Di Ciccio, C, Gatta, R,
Ghidini, C, Gonzalez-Lopez, F, Ibanez-Sanchez, G,
Klasky, H B, Kurniati, A P, Lu, X, Mannhardt, F,
Mans, R, Marcos, M, Medeiros de Carvalho, R,
Pegoraro, M, Poon, S K, Pufahl, L, Reijers, H A,
Remy, S, Rinderle-Ma, S, Sacchi, L, Seoane, F,
Song, M, Stefanini, A, Sulis, E, ter Hofstede, A H
M, Toussaint, P J, Traver, V, Valero-Ramon, Z,
Weerd, I van, van der Aalst, W M P, Vanwersch, R,
Weske, M, Wynn, M T, and Zerbato, F. 2022.
Process mining for healthcare: Characteristics and
challenges. Journal of Biomedical Informatics, 127:
2013. Sejarah Perjalanan Jaminan Sosial di
Indonesia. [Online] Available at: https://bpjskesehatan.go.id/bpjs/index.php/pages/detail/2013/4;
[Accessed 24 Jan. 2023].
Iezzoni, L I. 1992. Comorbidities, complications,
and coding bias. does the number of diagnosis codes
matter in predicting in-hospital mortality?. JAMA:
The Journal of the American Medical Association,
(16): 2197-2203.
2022. ICD-10-CM Official Guidelines
for Coding and Reporting.
[Online] Available at:
https://www.cms.gov/files/document/fy-2022-icd10-cm-coding-guidelines-updated-02012022.pdf
[Accessed 24 January 2023]
van der Aalst, W M. 2018. Process Mining Data
Science in Action. Springer.
Fernandez-Llatas, C. 2021. Interactive process
mining in Healthcare. Cham, Switzerland: Springer.
Kusuma, G, Sykes, S, McInerney, C, and Johnson,
O. 2020. Process mining of disease trajectories: A
feasibility study. Proceedings of the 13th
International Joint Conference on Biomedical
Engineering Systems and Technologies.
Kusuma, G, Kurniati, A, McInerney, C D, Hall,
M, Gale, C P, and Johnson, O. 2021. Process
mining of disease trajectories in Mimic-III: A case
study. Lecture Notes in Business Information
Processing: 305-316.
Kurniati, A P, Kusuma, G P, and Wisudiawan, G
A. 2022. Process mining for disease trajectory
analysis on the Indonesia Health Insurance Data.
JURIKOM (Jurnal Riset Komputer), 9(5): 1322.
van Eck, M L, Lu, X, Leemans, S J, and van der
Aalst, W M. 2015. PM2
: A process mining project
methodology. Advanced Information Systems
Engineering: 297-313.
Garcia, C dos, Meincheim, A, Faria Junior, E R,
Dallagassa, M R, Sato, D M, Carvalho, D R, Santos,
E A, and Scalabrin, E E. 2019. Process mining
techniques and applications - A systematic mapping
study. Expert Systems with Applications, 133: 260-
Kurniati, A P, Kusuma, G P, and Wisudiawan, G
A. 2016. Implementing heuristic miner for different
types of event logs. International Journal of Applied
Engineering Research 11.8: 5523-5529
Caesarita, Y, Sarno, R, and Sungkono, K R, 2017.
Identifying bottlenecks and fraud of business
process using Alpha ++ and heuristic miner
algorithms (case study: CV. Wicaksana Artha).
11th International Conference on Information
& Communication Technology and System
(ICTS).
Mannhardt, F, De Leoni, M, and Reijers, H A.
Heuristic Mining Revamped: An Interactive,
Data- aware, and Conformance-aware Miner. In
Bpm (demos).
Buijs, J C, van Dongen, B F, and van der Aalst, W
M. 2012. On the role of fitness, precision,
generalization and simplicity in process discovery.
On the Move to Meaningful Internet Systems: OTM
: 305-322.
Mannhardt, F, De Leoni, M, and Reijers, H A.
The multi-perspective process explorer. BPM
(Demos), 1418: 130-134.
Rolindrawan, D. 2015. The impact of BPJS health
implementation for the poor and near poor on the
use of Health Facility. Procedia - Social and
Behavioral Sciences, 211: 550-559.
Abdullah, A A. 2020. Makalah Data Sampel BPJS
Kesehatan. BPJS Kesehatan
Kurniati, A P, Wisudiawan, G A, and Kusuma, G
P. 2022. Potentials of clinical pathway analysis
using process mining on the Indonesia National
Health Insurance Data Samples: An Exploratory
Data Analysis. 2022 International Conference on
Data Science and Its Applications (ICoDSA).
Reed, G W, Rossi, J E, and Cannon, C P. 2017.
Acute myocardial infarction. The Lancet,
(10065): 197- 210.
Asaria, P, Elliott, P, Douglass, M, Obermeyer, Z,
Soljak, M, Majeed, A, and Ezzati, M. 2017. Acute
myocardial infarction hospital admissions and
deaths in England: A national follow-back and
follow-forward record-linkage study. The Lancet
Public Health, 2(4).
Jensen, A B, Moseley, P L, Oprea, T I, Ellesoe, S
G, Eriksson, R, Schmock, H, Jensen, P B, Jensen, L
J, and Brunak, S. 2014. Temporal disease
trajectories condensed from population-wide
registry data covering 6.2 million patients. Nature
Communications, 5(1).
van der Aalst, W M P. 2019. A practitioner's
guide to process mining: Limitations of the directlyfollows graph. Procedia Computer Science, 164:
-328
