BACKGROUND: Medication safety in residential care facilities is a critical concern, particularly when nonmedical staff provide medication assistance. The complex nature of medication-related incidents in these settings, coupled with the psychological impact on health care providers, underscores the need for effective incident analysis and preventive strategies. A thorough understanding of the root causes, typically through incident-report analysis, is essential for mitigating medication-related incidents.
OBJECTIVE: We aimed to develop and evaluate a multilabel classifier using natural language processing to identify factors contributing to medication-related incidents using incident report descriptions from residential care facilities, with a focus on incidents involving nonmedical staff.
METHODS: We analyzed 2143 incident reports, comprising 7121 sentences, from residential care facilities in Japan between April 1, 2015, and March 31, 2016. The incident factors were annotated using sentences based on an established organizational factor model and previous research findings. The following 9 factors were defined: procedure adherence, medicine, resident, resident family, nonmedical staff, medical staff, team, environment, and organizational management. To assess the label criteria, 2 researchers with relevant medical knowledge annotated a subset of 50 reports; the interannotator agreement was measured using Cohen κ. The entire data set was subsequently annotated by 1 researcher. Multiple labels were assigned to each sentence. A multilabel classifier was developed using deep learning models, including 2 Bidirectional Encoder Representations From Transformers (BERT)-type models (Tohoku-BERT and a University of Tokyo Hospital BERT pretrained with Japanese clinical text: UTH-BERT) and an Efficiently Learning Encoder That Classifies Token Replacements Accurately (ELECTRA), pretrained on Japanese text. Both sentence- and report-level training were performed; the performance was evaluated by the F1-score and exact match accuracy through 5-fold cross-validation.
RESULTS: Among all 7121 sentences, 1167, 694, 2455, 23, 1905, 46, 195, 1104, and 195 included \"procedure adherence,\" \"medicine,\" \"resident,\" \"resident family,\" \"nonmedical staff,\" \"medical staff,\" \"team,\" \"environment,\" and \"organizational management,\" respectively. Owing to limited labels, \"resident family\" and \"medical staff\" were omitted from the model development process. The interannotator agreement values were higher than 0.6 for each label. A total of 10, 278, and 1855 reports contained no, 1, and multiple labels, respectively. The models trained using the report data outperformed those trained using sentences, with macro F1-scores of 0.744, 0.675, and 0.735 for Tohoku-BERT, UTH-BERT, and ELECTRA, respectively. The report-trained models also demonstrated better exact match accuracy, with 0.411, 0.389, and 0.399 for Tohoku-BERT, UTH-BERT, and ELECTRA, respectively. Notably, the accuracy was consistent even when the analysis was confined to reports containing multiple labels.
CONCLUSIONS: The multilabel classifier developed in our study demonstrated potential for identifying various factors associated with medication-related incidents using incident reports from residential care facilities. Thus, this classifier can facilitate prompt analysis of incident factors, thereby contributing to risk management and the development of preventive strategies.

BACKGROUND: Task shifting and task sharing in health care are rapidly becoming more common as the shortage of physicians increases. However, research has not yet examined the changing roles of hospital administrative staff. This study clarified: (1) the adverse incidents caused by hospital administrative staff, and the direct and indirect impact of these incidents on patient care; and (2) the incidents that directly involved hospital administrative staff.
METHODS: This study used case report data from the Japan Council for Quality Health care collected from April 1, 2010 to March 31, 2019, including a total of 30,823 reports. In April 2020, only the 88 self-reported incidents by hospital administrative staff were downloaded, excluding incidents reported by those in medical and co-medical occupations. Data from three reports implicating pharmacists were rejected and the quantitative and textual data from the remaining 85 case reports were analyzed in terms of whether they impacted patient care directly or indirectly.
RESULTS: Thirty-nine reports (45.9%) involved direct impact on patient care, while 46 (54.1%) involved indirect impact on patient care. Most incidents that directly impacted patient care involved administrative staff writing prescriptions on behalf of a doctor (n = 24, 61.5%); followed by errors related to system administration, information, and documentation (n = 7, 17.9%). Most reported errors that indirectly affected patient care were related to system administration, information, and documentation used by administrative staff (n = 22, 47.8%), or to reception (n = 9, 19.6%). Almost all errors occurred during weekdays. Most frequent incidents involved outpatients (n = 23, 27.1%), or occurred next to examination/operation rooms (n = 12, 14.1%). Further, a total of 14 cases (16.5%) involved patient misidentification.
CONCLUSIONS: Incidents involving hospital administrative staff, the most common of which are medication errors from incorrect prescriptions, can lead to severe consequences for patients. Given that administrative staff now form a part of medical treatment teams, improvements in patient care may require further submission and review of incident reports involving administrative staff.