Potentials of Clinical Pathway Analysis Using Process Mining on the Indonesia National Health Insurance Data Samples: an Exploratory Data Analysis

Kurniati, Angelina Prima; Wisudiawan, Gede Agung Ary; Kusuma, Guntur Prabawa

doi:10.1109/icodsa55874.2022.9862851

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…The number of variables for reports is limited, but as the system evolves, the number of variables will be increased to reach the optimal numbers. The potential of using the BPJS health data samples for clinical pathway analysis has also been explored in previous research [16].…”

Section: Health Data Resource In Indonesiamentioning

confidence: 99%

Implementing Process Mining in Indonesia Health Care: Challenges and Potentials

Kusuma,

Kurniati,

Hafidz

et al. 2023

IJAIT

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Indonesia has become one of the countries having the biggest single-payer national insurance with a population-based membership. The insurance had helped up to 217 million patients since its inception in 2014. Capturing insights from a large-scale electronic health record has the potential to give a valuable improvement to the health care processes quality. Process mining is an emerging approach to “bridge” between the domain of data science and process science. It has also been recognized to contribute to the domain of health care where complex and multi-discipline processes are happening. The BPJS Kesehatan data containing routinely collected medical records becomes a valuable source of knowledge to improve the quality of health care. In contrast to its benefits, exploiting and managing population-based health care data for research brings challenges and potential. This paper presents the challenges of conducting process mining projects in the perspective of diversity, data quality, ethics, and security. Process mining in health care also brings potentials in care process comparisons, precision medicine, audit and compliance, and the opportunities of using virtual research environment to conduct research using a population-based data set based on unique characteristics of human biology.

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Section: Health Data Resource In Indonesiamentioning

confidence: 99%

Implementing Process Mining in Indonesia Health Care: Challenges and Potentials

Kusuma,

Kurniati,

Hafidz

et al. 2023

IJAIT

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“…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.…”

Section: Planning and Justificationmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 97%

Process Mining for Disease Trajectory Analysis on the Indonesia Health Insurance Data

Kurniati

Kusuma²,

Wisudiawan³

2022

Jur. Ris. Kom.

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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

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