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korean management review - Vol. 55, No. 1

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[ Article ]
korean management review - Vol. 54, No. 6, pp. 1593-1617
Abbreviation: kmr
ISSN: 1226-1874 (Print)
Print publication date 31 Dec 2025
Received 01 Jul 2025 Revised 16 Aug 2025 Accepted 02 Sep 2025
DOI: https://doi.org/10.17287/kmr.2025.54.6.1593
Predicting Post-No-Show Visit Patterns in Health Screenings: An XAI-Based Analysis of Contributing Factors
Dongki Kwon ; Sungsu Kim
(First Author) School of Business Administration, Kyungpook National University & Korea Association of Health Promotion (777ssanai@gmail.com)

(Corresponding Author) School of Business Administration, Kyungpook National University & Research Institute for Energy, Environment and Economy (RIEEE), Kyungpook National University (sungsukim@knu.ac.kr)

건강검진 예약부도 후 방문 패턴 예측: 설명가능한 인공지능 기반 기여요인 분석
권동기 ; 김성수

(주저자) 경북대학교 경영학부, 한국건강관리협회

(교신저자) 경북대학교 경영학부 & 경북대학교 에너지환경경제연구소
Copyright 2025 THE KOREAN ACADEMIC SOCIETY OF BUSINESS ADMINISTRATION This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Patient appointment no-shows constitute a significant challenge in healthcare institutions, leading to a substantial waste of medical service resources and a deterioration of operational efficiency. Consequently, accurately predicting patient visit propensity following initial appointment absenteeism holds considerable practical significance for effective healthcare management. This study leverages longitudinal data from the Korea Association of Health Promotion, spanning 2020–2022, to predict the likelihood of patient visitation versus non-visitation subsequent to appointment defaults. Crucially, the research employs eXplainable Artificial Intelligence (XAI) methodologies to elucidate key contributory factors. A comparative analysis was conducted across multiple machine learning and ensemble modeling approaches, specifically Random Forest, Decision Trees, and LightGBM algorithms. The empirical findings demonstrate that LightGBM exhibited superior predictive performance for visit prediction, whereas Random Forest achieved optimal efficacy for non-visitation forecasting. Application of the SHapley Additive exPlanations (SHAP) methodology revealed that historical rescheduling behavior and new patient status emerged as pivotal determinants across both predictive models. Furthermore, Local Interpretable Model-agnostic Explanations (LIME) techniques were employed to provide a visual interpretation of variable-specific influence directionality and feature importance magnitudes. This investigation contributes significantly to the advancement of healthcare appointment management systems and provides actionable insights for efficient medical resource allocation and personalized patient engagement strategies through predictive modeling of post- default patient behavioral patterns.

초록

의료기관의 예약부도는 의료 서비스 자원의 낭비와 운영 효율성 저하를 초래하는 문제이며, 특히, 예약부도 이후 고객의 방문 여부를 예측하는 것은 실무적으로 중요하다. 본 연구는 한국건강관리협회의 2020~2022년 데이터를 활용하여 예약부도 이후 고객의 방문과 미방문 가능성을 예측하고, 설명 가능한 인공지능(Explainable AI, XAI) 기법을 이용해 주요 기여 요인을 분석하였다. Random Forest, Decision Trees, LightGBM을 포함한 머신러닝 및 앙상블 모형을 비교하였으며, 방문 예측에는 LightGBM 모형, 미방문 예측에는 Random Forest 모형이 가장 우수한 성능을 보였다. SHAP 기법을 적용한 결과, 과거 재예약 여부와 신규 고객 여부 변수가 공통적으로 중요한 요인으로 나타났다. 또한, LIME 기법을 통해 변수별 영향력의 방향성과 중요도를 시각적으로 해석할 수 있도록 하였다. 본 연구는 의료기관의 예약 시스템 개선에 기여하며, 예약부도 이후 고객 행동을 예측함으로써 의료 자원의 효율적 운영과 맞춤형 고객 관리 전략 수립에 통찰을 제공한다.


Keywords: No Show, Health Screening (Health Examination), Machine Learning, Operational Efficiency, eXplainable Artificial Intelligence (XAI) 키워드: 예약부도, 건강검진, 머신러닝, 운영효율성, 설명가능한 AI

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∙ 저자 권동기는 경북대학교 경영대학원에서 MBA를 받고, 경북대학교 경영학부 서비스․생산․물류 전공 박사과정에 재학하고 있다. 현재 한국건강관리협회에 재직 중이며, 주요 연구 분야는 머신러닝, 딥러닝, 설명가능한 인공지능(XAI), 자료포락분석(DEA) 등이다.

∙ 저자 김성수는 경북대학교 경영학부 서비스․생산․물류 전공 교수 및 경북대학교 에너지환경경제연구소장으로 재직 중이다. 미국 펜실베니아주립대학교에서 산업공학과 경영과학 전공으로 박사를 취득하였다. 박사 취득 이후에는 Loyola University Maryland 경영대학에서 조교수, Bank of America 본사 Global Portfolio Strategies 부서 Vice President로 근무하였다. 주요 연구분야는 AI Transformation(AX), Operations Management and Supply Chain Management, Finance 등이다.