텍스트 마이닝 기법을 활용한 노인장기요양기관 부당청구 판례 분석 연구

Abstract

The increasing number of unfair claims in long-term care facilities has emerged as a risk factor for threatening the finances of the Korean national health insurance and increasing medical expenses, considering the imminent danger to super-aged society, since the implementation of the Long-term Care Insurance Act for the Elderly. Therefore, we aim to provide alternatives for unfair claim prevention and recidivism. The court's precedents and text-mining analysis were used to identify issues and factors in unfair claims to meet our research goals. Ninety-one cases (i.e., eighty-five elderly care facilities and six elderly care communal homes) were collected from January 1, 2013, to December 31, 2020. Our results revealed that fourty-four were fined, seventy-two received probation, and eighteen were sentenced to imprisonment, of the first verdicts by trial courts for senior medical welfare facilities. Among the defendants (n = 146), ninety-nine were top managers; thirteen were care workers; and five were social workers. The indictments consisted of seven violations, and of three most representative contents were violations of the Long-Term Care Insurance Act for the Elderly, of the Act on the Aggravated Punishment of Specific Economic Crimes (fraud), fraud, violations of the Elderly Welfare Act, business embezzlement and forgery of private documents. In the first trial, TF-IDF analysis indicated four keywords, 'regulations and announcements,' 'working hours,' 'additions,' 'competition,' and mandatory personnel, such as physical therapists, social workers, and nursing assistants. In the second trial, 'field investigation,' 'sanitation,' and 'resident' were the main words. Topic Modeling of the first trial showed that 'nursing care workers & sanitation center,' 'nursing care staff assignment standards,' 'social worker & physical therapist,' 'notifications and laws,' 'working hours & exploitation,' 'defendants & victim.' In the second trial, 'accused & supreme unjust (heavy),' 'placement criteria & deception (acts),' 'long-term care benefit & breach of working hours,' 'victim's claim & crime of fraud,' 'false service,' 'staff assignment standards & deceiving (acting),' and 'defendant's working rules & field investigation.' Thus, we explored causes and issues with unfair claims. Finally, we proposed several preventative solutions: ①. change in the perception of operators and works of long-term care institutions, ②. transparent management, ③. a thorough understanding of relevant regulations, such as laws and public announcements, and prevention of errors in interpretation, and lastly ④. active countermeasures focused on improving unfair claims instead of punishment.