Content Analysis of Patient Safety Incident Reports for Older Adult Patient Transfers, Handovers, and Discharges: Do They Serve Organizations, Staff, or Patients?

Care Home Safety Incidents and Safeguarding Reports Relating to Hospital to Care Home Transitions: A Retrospective Content Analysis

OBJECTIVE

The purpose of this study was to further the understanding of reported patient safety events at the interface between hospital and care home including what active failings and latent conditions were present and how reporting helped learning.

METHODS

Two care home organizations, one in the North East and one in the South West of England, participated in the study. Reports relating to a transition and where a patient safety event had occurred were sought during the COVID-19 (SARS-CoV-2) virus prepandemic and intrapandemic periods. All reports were screened for eligibility and analyzed using content analysis.

RESULTS

Seventeen South West England care homes and 15 North East England care homes sent 114 safety incident reports and after screening 91 were eligible for review. A hospital discharge transition (n = 78, 86%) was most common. Pressure damage (n = 29, 32%), medication errors (n = 26, 29%) and premature discharge (n = 21, 23%) contributed to 84% of the total reporting. Many 'active failings' (n = 340) were identified with fewer latent conditions (failings) (n = 14, 15%) being reported. No examples of individual learning were identified. Organization and systems learning were identified in 12 reports (n = 12, 13%).

CONCLUSIONS

The findings highlight potentially high levels of underreporting. The most common safety incidents reported were pressure damage, medication errors, and premature discharge. Many active failings causing numerous staff actions were identified emphasizing the cost to patients and services. Additionally, latent conditions (failings) were not emphasized; similarly, evidence of learning from safety incidents was not addressed.

Evaluation of the Handover Training Program Given to Nursing Students Using the Kirkpatrick Model: A Quasi-Experimental Observational Study

BACKGROUND

Patient handover training given to nursing students is important to ensure patient safety. There are a variety of evaluation models that can be used to evaluate the impact of education in nursing, one of which is the Kirkpatrick model.

OBJECTIVE

This study aims to evaluate the patient handover training given to nursing students according to the Kirkpatrick model.

METHODS

A pretest and posttest design was used with a convenience sample of 33 nursing students. The training provided was evaluated according to the reaction, learning, behavior, and result levels of the Kirkpatrick model. In the study, data were collected by researchers observing students through face-to-face interactions and bedside observations using Handoff Evaluation Scale (Handoff CEX).

RESULTS

It was found that 42.1% of the nursing students practiced verbal, 19.7% taped, and 36.8% bedside handovers before the training, while all the students practiced taped, verbal, and bedside handovers after the training. While the mean (SD) scores of patient handover (2.87 [0.95]) were at an insufficient level before the training, the scores after the training (7.12 [1.06]) increased to a high level. The difference between the mean patient handover scores before and after the training was statistically significant (P < .001).

CONCLUSIONS

After the training provided in the study, it was observed that nursing students improved their patient handover levels and styles, handover participation, and information transfer. Patient handover training given to undergraduate student nurses can be evaluated using the Kirkpatrick model.

Construction of a Multi-Label Classifier for Extracting Multiple Incident Factors From Medication Incident Reports in Residential Care Facilities: Natural Language Processing Approach

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.

Development of a scoring system to quantify errors from semantic characteristics in incident reports

OBJECTIVES

Incident reporting systems are widely used to identify risks and enable organisational learning. Free-text descriptions contain important information about factors associated with incidents. This study aimed to develop error scores by extracting information about the presence of error factors in incidents using an original decision-making model that partly relies on natural language processing techniques.

METHODS

We retrospectively analysed free-text data from reports of incidents between January 2012 and December 2022 from Nagoya University Hospital, Japan. The sample data were randomly allocated to equal-sized training and validation datasets. We conducted morphological analysis on free text to segment terms from sentences in the training dataset. We calculated error scores for terms, individual reports and reports from staff groups according to report volume size and compared these with conventional classifications by patient safety experts. We also calculated accuracy, recall, precision and F-score values from the proposed 'report error score'.

RESULTS

Overall, 114 013 reports were included. We calculated 36 131 'term error scores' from the 57 006 reports in the training dataset. There was a significant difference in error scores between reports of incidents categorised by experts as arising from errors (p<0.001, d=0.73 (large)) and other incidents. The accuracy, recall, precision and F-score values were 0.8, 0.82, 0.85 and 0.84, respectively. Group error scores were positively associated with expert ratings (correlation coefficient, 0.66; 95% CI 0.54 to 0.75, p<0.001) for all departments.

CONCLUSION

Our error scoring system could provide insights to improve patient safety using aggregated incident report data.

Managing reports of trouble: designated officials' responses to reports of mistreatments initiated by service users and relatives

Mandatory reporting of elder abuse aims to detect and prevent mistreatment and improve services. Service users and their relatives can raise concerns, but only staff can file mandatory reports. This article examines how the concerns of service users and relatives were managed by designated officials in reports of mistreatments in care for older adults in Sweden. We lean on sociological theories of "interpersonal trouble" and organizational "disputes domains." The thematic analysis is based on 28 incident reports initiated by service users or their relatives. The analysis shows that the reports were managed in one of three ways: asymmetrically, by 1) dismissing or 2) supporting the complainant's position, or symmetrically, by 3) treating complainants' accounts as credible but minimizing their seriousness. There were differences between reports initiated by service users and relatives. Dismissing concerns about abuse, mainly those made by relatives, risks support for service users failing.

Applying an equity lens to hospital safety monitoring: a critical interpretive synthesis protocol

INTRODUCTION

Hospital safety monitoring systems are foundational to how adverse events are identified and addressed. They are well positioned to bring equity-related safety issues to the forefront for action. However, there is uncertainty about how they have been, and can be, used to achieve this goal. We will undertake a critical interpretive synthesis (CIS) to examine how equity is integrated into hospital safety monitoring systems.

METHODS AND ANALYSIS

This review will follow CIS principles. Our initial compass question is: How is equity integrated into safety monitoring systems? We will begin with a structured search strategy of hospital safety monitoring systems in CINAHL, EMBASE, MEDLINE and PsycINFO for up to May 2023 to identify papers on safety monitoring systems generally and those linked to equity (eg, racism, social determinants of health). We will also review reference lists of selected papers, contact experts and draw on team expertise. For subsequent literature searching stages, we will use team expertise and expert contacts to purposively search the social science, humanities and health services research literature to support the development of a theoretical understanding of our topic. Following data extraction, we will use interpretive processes to develop themes and a critique of the literature. The above processes of question formulation, article search and selection, data extraction, and critique and synthesis will be iterative and interactive with the goal to develop a theoretical understanding of equity in hospital monitoring systems that will have practice-based implications.

ETHICS AND DISSEMINATION

This review does not require ethical approval because we are reviewing published literature. We aim to publish findings in a peer-reviewed journal and present at conferences.

Analysis of the nature and contributory factors of medication safety incidents following hospital discharge using National Reporting and Learning System (NRLS) data from England and Wales: a multi-method study

Introduction

Improving medication safety during transition of care is an international healthcare priority. While existing research reveals that medication-related incidents and associated harms may be common following hospital discharge, there is limited information about their nature and contributory factors at a national level which is crucial to inform improvement strategy.

Aim

To characterise the nature and contributory factors of medication-related incidents during transition of care from secondary to primary care.

Method

A retrospective analysis of medication incidents reported to the National Reporting and Learning System (NRLS) in England and Wales between 2015 and 2019. Descriptive analysis identified the frequency and nature of incidents and content analysis of free text data, coded using the Patient Safety Research Group (PISA) classification, examined the contributory factors and outcome of incidents.

Results

A total of 1121 medication-related incident reports underwent analysis. Most incidents involved patients over 65 years old (55%, n = 626/1121). More than one in 10 (12.6%, n = 142/1121) incidents were associated with patient harm. The drug monitoring (17%) and administration stages (15%) were associated with a higher proportion of harmful incidents than any other drug use stages. Common medication classes associated with incidents were the cardiovascular (n = 734) and central nervous (n = 273) systems. Among 408 incidents reporting 467 contributory factors, the most common contributory factors were organisation factors (82%, n = 383/467) (mostly related to continuity of care which is the delivery of a seamless service through integration, co-ordination, and the sharing of information between different providers), followed by staff factors (16%, n = 75/467).

Conclusion

Medication incidents after hospital discharge are associated with patient harm. Several targets were identified for future research that could support the development of remedial interventions, including commonly observed medication classes, older adults, increase patient engagement, and improve shared care agreement for medication monitoring post hospital discharge.

Plain language summary

Study using reports about unsafe or substandard care mainly written by healthcare professionals to better understand the type and causes of medication safety problems following hospital discharge Why was the study done? The safe use of medicines after hospital discharge has been highlighted by the World Health Organization as an important target for improvement in patient care. Yet, the type of medication problems which occur, and their causes are poorly understood across England and Wales, which may hamper our efforts to create ways to improve care as they may not be based on what we know causes the problem in the first place.What did the researchers do? The research team studied medication safety incident reports collected across England and Wales over a 5-year period to better understand what kind of medication safety problems occur after hospital discharge and why they happen, so we can find ways to prevent them from happening in future.What did the researchers find? The total number of incident reports studied was 1121, and the majority (n = 626) involved older people. More than one in ten of these incidents caused harm to patients. The most common medications involved in the medication safety incidents were for cardiovascular diseases such as high blood pressure, conditions such as mental illness, pain and neurological conditions (e.g., epilepsy) and other illnesses such as diabetes. The most common causes of these incidents were because of the organisation rules, such as information sharing, followed by staff issues, such as not following protocols, individual mistakes and not having the right skills for the task.What do the findings mean? This study has identified some important targets that can be a focus of future efforts to improve the safe use of medicines after hospital discharge. These include concentrating attention on medication for the cardiovascular and central nervous systems (e.g., via incorporating them in prescribing safety indicators and pharmaceutical prioritisation tools), staff skill mix (e.g., embedding clinical pharmacist roles at key parts of the care pathway where greatest risk is suspected), and implementation of electronic interventions to improve timely communication of medication and other information between healthcare providers.

Qualitative content analysis: A framework for the substantive review of hospital incident reports

For decades, incident reports have been utilized as a part of comprehensive healthcare risk management and patient safety programs. As the roles of healthcare risk managers and patient safety professionals become more complex, it is essential that standard tools and strategies used by these professionals, like incident report analysis, be standardized to improve efficiency and effectiveness. Qualitative content analysis provides a structured framework that can be successfully used to describe the categories and themes of incidents, so that, they can be used to develop individual and organizational learning.