ABSTRACT
BACKGROUND: To improve patient safety, it is important that healthcare facilities learn from critical incidents. Tools such as reporting and learning systems and team meetings structure error management and promote learning from incidents. To enhance error management in ambulatory care practices, it is important to promote a climate of safety and ensure personnel share views on safety policies and procedures. In contrast to the hospital sector, little research has been dedicated to developing feasible approaches to supporting error management and safety climate in ambulatory care. In this study, we developed, implemented, and evaluated a multicomponent intervention to address how error management and safety climate can be improved in ambulatory care practices. METHODS: In a prospective 1-group pretest-posttest implementation study, we sought to encourage teams in German ambulatory practices to use proven methods such as guidelines, workshops, e-learning, (online) meetings, and e-mail newsletters. A pretest-posttest questionnaire was used to evaluate level and strength of safety climate and psychological behavioral determinants for systematic error management. Using 3 short surveys, we also assessed the state of error management in the participating practices. In semistructured interviews, we asked participants for their views on our intervention measures. RESULTS: Overall, 184 ambulatory care practices nationwide agreed to participate. Level of safety climate and safety climate strength (rwg) improved significantly. Of psychological behavioral determinants, significant improvements could be seen in "action/coping planning" and "action control." Seventy-six percent of practices implemented a new reporting and learning system or modified their existing system. The exchange of information between practices also increased over time. Interviews showed that the introductory workshop and provided materials such as report forms or instructions for team meetings were regarded as helpful. CONCLUSIONS: A significant improvement in safety climate level and strength, as well as participants' knowledge of how to analyze critical incidents, derive preventive measures and develop concrete plans suggest that it is important to train practice teams, to provide practical tips and tools, and to facilitate the exchange of information between practices. Future randomized and controlled intervention trials should confirm the effectiveness of our multicomponent intervention.Trial registration: Retrospectively registered on 18. November 2019 in German Clinical Trials Register No. DRKS00019053.
ABSTRACT
OBJECTIVE: The aim of this study was to analyze the strength of safety measures described in incident reports in outpatient care. METHODS: An incident reporting project in German outpatient care included 184 medical practices with differing fields of specialization. The practices were invited to submit anonymous incident reports to the project team 3 times for 17 months. Using a 14-item coding scheme based on international recommendations, we deductively coded the incident reports and safety measures. Safety measures were classified as "strong" (likely to be effective and sustainable), "intermediate" (possibly effective and sustainable), or "weak" (less likely to be effective and sustainable). RESULTS: The practices submitted 245 incident reports. In 160 of them, 243 preventive measures were described, or an average of 1.5 per report. The number of documented measures varied from 1 in 67% to 4 in 5% of them. Four preventive measures (2%) were classified as strong, 37 (15%) as intermediate, and 202 (83%) as weak. The most frequently mentioned measures were "new procedure/policy" (n = 121) and "information/notification/warning" (n = 45). CONCLUSIONS: The study provides examples of critical incidents in medical practices and for the first time examines the strength of ensuing measures introduced in outpatient care. Overall, the proportion of weak measures is (too) high, indicating that practices need more support in identifying strong measures.
Subject(s)
Medical Errors , Patient Safety , Humans , Medical Errors/prevention & control , Risk ManagementABSTRACT
BACKGROUND: CIRSmedical.de is a publicly accessible, cross-institutional reporting and learning system, which is organized by the German Agency for Quality in Medicine (ÄZQ). CIRSmedical.de has existed since 2005 and has published more than 6,000 event reports. Up to now it has been common practice to analyse these reports in detail or carry out systematic evaluations focusing on specific topics. A systematic evaluation of all case reports has not yet been conducted. Natural Language Processing (NLP) is an analysis strategy from the field of Artificial Intelligence for indexing texts. The examination of case reports using NLP was carried out to describe the characteristics of event reports and comments. MATERIALS AND METHODS: For this analysis 6,480 case reports from CIRSmedical.de (as of December 10, 2019) were provided by the ÄZQ as Excel files. Several free text fields were included in the analysis as well as the feedback of the CIRS team (expert commentary). Text lengths, reporting behaviour, sentiment values and keywords were examined. The algorithms for the analysis were developed with the programming language Python and the corresponding libraries NLTK and SpaCy. RESULTS: The comparison of report lengths depending on the different subject groups presented a heterogeneous picture, in terms of both the number of reports and the number of words. There are more than 4,000 reports from the field of anaesthesiology, whereby text lengths vary particularly strongly with a right-skewed distribution. There are only a few reports from the field of psychotherapy, and these are also very short. The different professional groups (nurses, doctors, other staff) write reports of about the same length. Reports and expert commentaries also differ in terms of sentiment values. Due to the length of the comments, they are more negative in terms of sentiment. Keywords can be identified but show a high heterogeneity. DISCUSSION: Systematic analysis using NLP allows for the description of text properties in event reports and comments. It is now possible to draw a conclusion about the reporters' intention, focus and mood when they report in CIRS. The sentiment analysis is an indication of the mood which the texts convey, both as a report and as a commentary. Text length analysis draws attention to different problems and tendencies: event reports are usually much shorter. Texts that are too short, however, run the risk that the information will not be readily usable for analysis. Comments are often longer, but here one faces the opposite problem: texts that are too long may not be read. The examination of texts by means of NLP helps to rethink the reason for and the form of input, both when reporting and when commenting. It is a first step in the automatic, supportive classification of texts and an improvement of the interaction between reporters and the system.
