Clustering-based methodology for analyzing near-miss reports and identifying risks in healthcare delivery

Patient Safety Silence and Safety Nursing Activities: Mediating Effects of Moral Sensitivity

Among the factors that threaten patient safety and quality of care due to the diversification and complication of hospital environments, nurses play a pivotal role regarding patient safety in the clinical setting. This study investigates the mediating effects of moral sensitivity on the relationship between nurses’ patient safety silence and safety nursing activities and contributes to developing strategies. Nurses (n = 120) employed for at least one year in two university hospitals in Korea between 1 September and 30 October 2020 participated in the study. Data were analyzed using t-test, Pearson’s correlation coefficients, and multiple regression using the SPSS/WIN 22.0 program. Additionally, the mediating effects were analyzed using Baron and Kenny’s method and bootstrapping. Safety nursing activities were significantly negatively correlated with patient safety silence and significantly positively correlated with moral sensitivity. Patient safety silence was significantly negatively correlated with moral sensitivity. Moral sensitivity partially mediated the relationship between patient safety silence and safety nursing activities. There is a need to develop and implement individualized ethical programs that enhance moral sensitivity in nurses to promote patient safety nursing activities.

Investigating the status of accident precursor management in East Azarbaijan Province Gas Company

Purpose. The accident precursor reporting system is a key point in preventing accidents and proper functioning of this system is critical. In the present study, the accident precursor reporting system in the Iranian East Azarbaijan Province Gas Company was investigated. Methods. This quantitative-qualitative study used field survey and document review methods to determine how the company was reporting. Qualitative content analysis approaches were used to analyze the reporting system over a 3-year period. From 1209 accident precursor reports, 2271 codes were extracted. Finally, these codes were reduced to three clusters: 996 (43.86%), 447 (19.68%) and 828 (36.46%) codes related to near misses, unsafe conditions and unsafe acts, respectively. A χ² test was used to investigate the reporting process. Results. Reporting rates varied by time (e.g., people reported more accident precursors in the first 6 solar months), by area (some areas had higher reporting rates than others) and by type of job (operational jobs had the highest reporting rate [73.16%] compared to non-operational jobs [26.84%]). Conclusion. It was suggested that policy-makers should improve the accident precursor reporting system. Changes to the current reporting system are essential to help in the prevention of future unpleasant accidents.

An unsupervised machine learning model for discovering latent infectious diseases using social media data

INTRODUCTION

The authors of this work propose an unsupervised machine learning model that has the ability to identify real-world latent infectious diseases by mining social media data. In this study, a latent infectious disease is defined as a communicable disease that has not yet been formalized by national public health institutes and explicitly communicated to the general public. Most existing approaches to modeling infectious-disease-related knowledge discovery through social media networks are top-down approaches that are based on already known information, such as the names of diseases and their symptoms. In existing top-down approaches, necessary but unknown information, such as disease names and symptoms, is mostly unidentified in social media data until national public health institutes have formalized that disease. Most of the formalizing processes for latent infectious diseases are time consuming. Therefore, this study presents a bottom-up approach for latent infectious disease discovery in a given location without prior information, such as disease names and related symptoms.

METHODS

Social media messages with user and temporal information are extracted during the data preprocessing stage. An unsupervised sentiment analysis model is then presented. Users' expressions about symptoms, body parts, and pain locations are also identified from social media data. Then, symptom weighting vectors for each individual and time period are created, based on their sentiment and social media expressions. Finally, latent-infectious-disease-related information is retrieved from individuals' symptom weighting vectors.

DATASETS AND RESULTS

Twitter data from August 2012 to May 2013 are used to validate this study. Real electronic medical records for 104 individuals, who were diagnosed with influenza in the same period, are used to serve as ground truth validation. The results are promising, with the highest precision, recall, and F₁ score values of 0.773, 0.680, and 0.724, respectively.

CONCLUSION

This work uses individuals' social media messages to identify latent infectious diseases, without prior information, quicker than when the disease(s) is formalized by national public health institutes. In particular, the unsupervised machine learning model using user, textual, and temporal information in social media data, along with sentiment analysis, identifies latent infectious diseases in a given location.

Human resource crises in German hospitals--an explorative study

BACKGROUND

The complexity of providing medical care in a high-tech environment with a highly specialized, limited labour force makes hospitals more crisis-prone than other industries. An effective defence against crises is only possible if the organizational resilience and the capacity to handle crises become part of the hospitals' organizational culture. To become more resilient to crises, a raised awareness--especially in the area of human resource (HR)--is necessary. The aim of this paper is to contribute to the process robustness against crises through the identification and evaluation of relevant HR crises and their causations in hospitals.

METHODS

Qualitative and quantitative methods were combined to identify and evaluate crises in hospitals in the HR sector. A structured workshop with experts was conducted to identify HR crises and their descriptions, as well as causes and consequences for patients and hospitals. To evaluate the findings, an online survey was carried out to rate the occurrence (past, future) and dangerousness of each crisis.

RESULTS

Six HR crises were identified in this study: staff shortages, acute loss of personnel following a pandemic, damage to reputation, insufficient communication during restructuring, bullying, and misuse of drugs. The highest occurrence probability in the future was seen in staff shortages, followed by acute loss of personnel following a pandemic. Staff shortages, damage to reputation, and acute loss of personnel following a pandemic were seen as the most dangerous crises.

CONCLUSIONS

The study concludes that coping with HR crises in hospitals is existential for hospitals and requires increased awareness. The six HR crises identified occurred regularly in German hospitals in the past, and their occurrence probability for the future was rated as high.