Inductive Miner Implementation to Improve Healthcare Efficiency on Indonesia National Health Insurance Data

Pradana, Mochammad Ivan Adhyaksa; Kurniati, Angelina Prima; Wisudiawan, Gede Agung Ary

doi:10.1109/icodsa55874.2022.9862837

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Multiple process model discovery algorithms, including Alpha Miner, Heuristic Miner, and Inductive Miner, are available for discovering process models from event logs. Similar to [ 38 ], this paper applies the Inductive Miner, since it is an improvement on the Alpha and Heuristic algorithms. The Inductive Miner is a divide and conquer type polynomial-time algorithm that returns a process tree in which each activity is seen once.…”

Section: Methodsmentioning

confidence: 99%

Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing

Aslan,

El-Raoui,

Hanson

et al. 2023

Sensors

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This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers’ actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others.

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Section: Methodsmentioning

confidence: 99%

Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing

Aslan,

El-Raoui,

Hanson

et al. 2023

Sensors

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“…Process Discovery and Performance Analysis with Process Mining: There are various process model discovery algorithms such as Alpha Miner, Heuristic Miner and Inductive Miner that can be applied to discover a process model from an event log. As in [17], here, we choose to apply the Inductive Miner, which is an improvement on the Alpha and Heuristic algorithms. We use the implementation in ProM 6.11 (https://www.promtools.org/).…”

Section: Methodsmentioning

confidence: 99%

Data-Driven Discovery of Manufacturing Processes and Performance from Worker Localisation

Aslan,

El-Raoui,

Hanson

et al. 2023

Lecture Notes in Mechanical Engineering

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In complex manufacturing industries that involve the coordination of people, equipment and materials in the completion of tasks with non-canonical sequences (such as in shipbuilding) it is important to identify deviations from the planned production schedule and locate bottlenecks in order to improve efficiency. This is not an easy task as it requires data on how workers are actually performing the manufacturing activities. Ultra-wideband (UWB) tags, which are sensors that track worker movement, can be used to collect this data. Previous research has mostly focused on using this data to detect faults and anomalies and to ensure worker safety. However, this paper presents a method for using UWB data to discover process models of manufacturing activities using process mining techniques. These models allow us to identify deviations from prescribed process steps and find bottlenecks by predicting performance metrics such as the mean duration of activities.The method involves extracting event logs based on workers' positions, defining activities based on their time spent in proximity to specific zones, and using the "Inductive Miner" algorithm to generate process models. We applied our method to a real assembly line with UWB data and were able to identify deviations from the prescribed process steps and bottlenecks in the assembly line, which indicated that the first assembly step can take twice as much time compared to other steps.

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“…In the discovery of a process model from an event log, four studies used inductive miner [1][47][53] [56], two studies used the heuristics miner algorithm [49][51], one study used both inductive miner and heuristics miner [54], and one study used the genetic algorithm [48]. Conformance checking was also conducted in the mentioned studies.…”

Section: Process Discovery Algorithms Tools and Methodologiesmentioning

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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Inductive Miner Implementation to Improve Healthcare Efficiency on Indonesia National Health Insurance Data

Cited by 7 publications

References 13 publications

Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing

Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing

Data-Driven Discovery of Manufacturing Processes and Performance from Worker Localisation

Implementing Process Mining in Indonesia Health Care: Challenges and Potentials

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