Journal of Patient Safety最新文献

{"title":"Enhancing Patient Safety in Prehospital Environment: Analyzing Patient Perspectives on Non-Transport Decisions With Natural Language Processing and Machine Learning.","authors":"Hassan Farhat, Guillaume Alinier, Reem Tluli, Montaha Chakif, Fatma Babay Ep Rekik, Ma Cleo Alcantara, Padarath Gangaram, Kawther El Aifa, Ahmed Makhlouf, Ian Howland, Mohamed Chaker Khenissi, Sailesh Chauhan, Cyrine Abid, Nicholas Castle, Loua Al Shaikh, Moncef Khadhraoui, Imed Gargouri, James Laughton","doi":"10.1097/PTS.0000000000001228","DOIUrl":"10.1097/PTS.0000000000001228","url":null,"abstract":"<p><strong>Objective: </strong>This research explored the experiences and perspectives of patients declining hospital transportation after receiving prehospital emergency care using advanced computational techniques.</p><p><strong>Method: </strong>Between 15th June and 1st August 2023, 210 patients in Qatar, treated by Hamad Medical Corporation Ambulance Service (HMCAS) but refusing transportation to hospital, were interviewed. Key outcome variables stratified by demographics included \"reasons for refusing transport,\" \"satisfaction with HMCAS service,\" and \"postrefusal actions.\" Responses underwent sentiment analysis and topic modeling using latent Dirichlet allocation. Machine learning models, such as Naïve Bayes, K-nearest neighboring, random forest, and support vector machine, were used to predict patients' subsequent actions.</p><p><strong>Results: </strong>Participants had an average age of 38.61 ± 19.91 years. The chief complaints were primarily chest and abdominal pains (18.49%; n = 39). Sentiment Analysis revealed a generally favorable perception of HMCAS-provided service. Latent Dirichlet allocation identified two main topics pertaining to refusal reasons and service satisfaction. Naïve Bayes and support vector machine algorithms were most effective in predicting postrefusal actions with an accuracy rate of 81.58%.</p><p><strong>Conclusions: </strong>This study highlighted the utility of Natural Language Processing and ML in enhancing our understanding of patient behaviors and sentiments in prehospital settings. These advanced computational methodologies allowed for a nuanced exploration of patient demographics and sentiments, providing insights for Quality Improvement initiatives. The study also advocates for continuously integrating automated feedback mechanisms to improve patient-centered care in the prehospital context. Continuous integration of automated feedback systems is recommended to improve prehospital patient-centered care.</p>","PeriodicalId":48901,"journal":{"name":"Journal of Patient Safety","volume":" ","pages":"330-339"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140177340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Evaluation of Patient Safety Interventions: Perspectives of Operational Safety Leaders and Patient Safety Organizations.","authors":"Kylie M Gomes, Jessica Handley, Zoe M Pruitt, Seth Krevat, Allan Fong, Raj M Ratwani","doi":"10.1097/PTS.0000000000001233","DOIUrl":"10.1097/PTS.0000000000001233","url":null,"abstract":"<p><strong>Objectives: </strong>The purpose of this study is to understand how patient safety professionals from healthcare facilities and patient safety organizations develop patient safety interventions and the resources used to support intervention development.</p><p><strong>Methods: </strong>Semistructured interviews were conducted with patient safety professionals at nine healthcare facilities and nine patient safety organizations. Interview data were qualitatively analyzed, and findings were organized by the following: patient safety solutions and interventions, use of external databases, and evaluation of patient safety solutions.</p><p><strong>Results: </strong>Development of patient safety interventions across healthcare facilities and patient safety organizations was similar and included literature searches, internal brainstorming, and interviews. Nearly all patient safety professionals at healthcare facilities reported contacting colleagues at other healthcare facilities to learn about similar safety issues and potential interventions. Additionally, less than half of patient safety professionals at healthcare facilities and patient safety organizations interviewed report data to publicly available patient safety databases. Finally, most patient safety professionals at healthcare facilities and patient safety organizations stated that they evaluate the effectiveness of patient safety interventions; however, they mentioned methods that may be less rigorous including audits, self-reporting, and subjective judgment.</p><p><strong>Conclusions: </strong>Patient safety professionals often utilize similar methods and resources to develop and evaluate patient safety interventions; however, many of these efforts are not coordinated across healthcare organizations and could benefit from working collectively in a systematic fashion. Additionally, healthcare facilities and patient safety organizations face similar challenges and there are several opportunities for optimization on a national level that may improve patient safety.</p>","PeriodicalId":48901,"journal":{"name":"Journal of Patient Safety","volume":" ","pages":"345-351"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140912189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Healthcare Violence and the Potential Promises and Harms of Artificial Intelligence.","authors":"Kevin T Kavanagh, Christine Pontus, Lindsay E Cormier","doi":"10.1097/PTS.0000000000001245","DOIUrl":"10.1097/PTS.0000000000001245","url":null,"abstract":"<p><strong>Abstract: </strong>Currently, the healthcare workplace is one of the most dangerous in the United States. Over a 3-month period in 2022, two nurses were assaulted every hour. Artificial intelligence (AI) has the potential to prevent workplace violence by developing unique patient insights through accessing almost instantly a patient's medical history, past institutional encounters, and possibly even their social media posts. De-escalating dialog can then be formulated, and hot-button topics avoided. AIs can also monitor patients in waiting areas for potential confrontational behavior.Many have concerns implementing AIs in healthcare. AIs are not expected to be 100% accurate, their performance is not compared with a computer but instead measured against humans. However, AIs are outperforming humans in many tasks. They are especially adept at taking standardized examinations, such as Board Exams, the Uniform Bar Exam, and the SAT and Graduate Record Exam. AIs are also performing diagnosis. Initial reports found that newer models have been observed to equal or outperform physicians in diagnostic accuracy and in the conveyance of empathy.In the area of interdiction, AI robots can both navigate and monitor for confrontational and illegal behavior. A human security agent would then be notified to resolve the situation. Our military is fielding autonomous AI robots to counter potential adversaries. For many, this new arms race has grave implications because of the potential of fielding this same security technology in healthcare and other civil settings.The healthcare delivery sector must determine the future roles of AI in relationship to human workers. AIs should only be used to support a human employee. AIs should not be the primary caregiver and a single human should not be monitoring multiple AIs simultaneously. Similar to not being copyrightable, disinformation produced by AIs should not be afforded 'free speech' protections. Any increase in productivity of an AI will equate with a loss of jobs. We need to ask, If all business sectors utilize AIs, will there be enough paid workers for the purchasing of services and products to keep our economy and society a float?</p>","PeriodicalId":48901,"journal":{"name":"Journal of Patient Safety","volume":" ","pages":"307-313"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141301937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to \"Taking Up the Challenge to Improve Name and Role Recognition in the Operating Room\".","authors":"Becky J Wong, Aussama K Nassar, Sara N Goldhaber-Fiebert","doi":"10.1097/PTS.0000000000001247","DOIUrl":"10.1097/PTS.0000000000001247","url":null,"abstract":"","PeriodicalId":48901,"journal":{"name":"Journal of Patient Safety","volume":" ","pages":"e85-e86"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Behavioral Insights to Strengthen Strategies for Change. Practical Applications for Quality Improvement in Healthcare.","authors":"Rie Laurine Rosenthal Johansen, Simon Tulloch","doi":"10.1097/PTS.0000000000001242","DOIUrl":"10.1097/PTS.0000000000001242","url":null,"abstract":"<p><strong>Objectives: </strong>For over 30 years, quality improvement (QI) methods have been used as a means of increasing the quality and safety of healthcare services, but with mixed success. One explanation highlighted in the literature for this outcome is the overemphasis on technical elements of change, and a failure to fully appreciate the human side of change. Behavioral insights (BI) is an approach that utilizes knowledge and tools from a broad range of scientific disciplines, such as neuroscience and behavioral psychology, to support behavior change. The aim of this paper is to explore the possibility of supplementing QI methods with tools and understanding from BI.</p><p><strong>Methods: </strong>We outline a practical case that involved applying aspects BI methods into a QI program aimed at reducing the use of intravenous antibiotics in patients accessing services at a busy university hospital in Copenhagen, Denmark. We exemplify how to use BI tools to guide the analysis of staff behaviors during standard clinical processes and develop targeted interventions aimed at increasing actions and behaviors more aligned to best clinical practice.</p><p><strong>Results: </strong>Outcomes suggest that it is possible to combine the models and methods from BI and QI in a way that is helpful in focusing attention on the human side of change when developing strategies for change. Potential psychological barriers identified from the analysis included the following: 'default inertia,' 'decision complexity,' 'risk aversion,' and biases related to confidence, confirmation, and omission.</p><p><strong>Conclusions: </strong>Future quality improvement projects could benefit from integrating models and tools from BI to guide and support behavior change.</p>","PeriodicalId":48901,"journal":{"name":"Journal of Patient Safety","volume":" ","pages":"e78-e84"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140922708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Reproducibility of Research Based on the Food and Drug Administration Manufacturer and User Facility Device Experience Data.","authors":"Xinyu Li, Yubo Feng, Yang Gong, You Chen","doi":"10.1097/PTS.0000000000001220","DOIUrl":"10.1097/PTS.0000000000001220","url":null,"abstract":"<p><strong>Objective: </strong>This article aims to assess the reproducibility of Manufacturer and User Facility Device Experience (MAUDE) data-driven studies by analyzing the data queries used in their research processes.</p><p><strong>Methods: </strong>Studies using MAUDE data were sourced from PubMed by searching for \"MAUDE\" or \"Manufacturer and User Facility Device Experience\" in titles or abstracts. We manually chose articles with executable queries. The reproducibility of each query was assessed by replicating it in the MAUDE Application Programming Interface. The reproducibility of a query is determined by a reproducibility coefficient that ranges from 0.95 to 1.05. This coefficient is calculated by comparing the number of medical device reports (MDRs) returned by the reproduced queries to the number of reported MDRs in the original studies. We also computed the reproducibility ratio, which is the fraction of reproducible queries in subgroups divided by the query complexity, the device category, and the presence of a data processing flow.</p><p><strong>Results: </strong>As of August 8, 2022, we identified 523 articles from which 336 contained queries, and 60 of these were executable. Among these, 14 queries were reproducible. Queries using a single field like product code, product class, or brand name showed higher reproducibility (50%, 33.3%, 31.3%) compared with other fields (8.3%, P = 0.037). Single-category device queries exhibited a higher reproducibility ratio than multicategory ones, but without statistical significance (27.1% versus 8.3%, P = 0.321). Studies including a data processing flow had a higher reproducibility ratio than those without, although this difference was not statistically significant (42.9% versus 17.4%, P = 0.107).</p><p><strong>Conclusions: </strong>Our findings indicate that the reproducibility of queries in MAUDE data-driven studies is limited. Enhancing this requires the development of more effective MAUDE data query strategies and improved application programming interfaces.</p>","PeriodicalId":48901,"journal":{"name":"Journal of Patient Safety","volume":" ","pages":"e45-e58"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11636620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140111863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Reliability in a Safety Net Hospital Leading to Operational Excellence.","authors":"Lisa Didion, Candice Whitfield, Phyllis Bishop, Alan E Jones, J Michael Henderson","doi":"10.1097/PTS.0000000000001236","DOIUrl":"https://doi.org/10.1097/PTS.0000000000001236","url":null,"abstract":"<p><strong>Objective: </strong>The objective of this work was to establish sustainable systems for quality improvement in an Academic Medical Center and Safety Net Hospital.</p><p><strong>Method: </strong>High reliability principles of leadership engagement, a culture of safety, and sustainable performance improvement were used. Target areas for improvement were clinical outcomes for patients, public reputation scores, and lower cost of care. The system was based on annual focused goals with specific targets, improvement teams, transparent scorecards, and data driven work. Program visibility was championed by leaders. Consistent education on quality, safety, efficiency, and effectiveness for all employees created buy-in. Data review and accountability tracked progress, helped resource allocation, and defined next steps.</p><p><strong>Results: </strong>In the first 5 years, all patient quality and safety metrics improved between 10% and 60%. This improvement resulted in higher CMS Star Ranking and Leapfrog patient safety grade. The next phase included maximizing value by expanding into hospital operations and finance with a focus on improved clinical documentation and reduced length of stay and cost of care. Clinical documentation improvement led to a 15% increase in comorbidity capture. This positively impacted reported outcomes and hospital payment by appropriate risk adjustment. Length of stay was addressed with a new care coordination program and physician-driven utilization review.</p><p><strong>Conclusions: </strong>High reliability principles are applicable in a resource limited healthcare system. Improved clinical and operational results were achieved through goal setting, improvement teams, and data driven projects leading to creation of an office of operational excellence.</p>","PeriodicalId":48901,"journal":{"name":"Journal of Patient Safety","volume":"20 5","pages":"375-380"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141724836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supporting Error Management and Safety Climate in Ambulatory Care Practices: The CIRSforte Study.","authors":"Beate S Müller, Dagmar Lüttel, Dania Schütze, Tatjana Blazejewski, Marina Pommée, Hardy Müller, Katharina Rubin, Christian Thomeczek, Romy Schadewitz, Reiner Heuzeroth, David Schwappach, Corina Güthlin, Michael Paulitsch, Ferdinand M Gerlach","doi":"10.1097/PTS.0000000000001225","DOIUrl":"10.1097/PTS.0000000000001225","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":48901,"journal":{"name":"Journal of Patient Safety","volume":" ","pages":"314-322"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140137381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient Outcomes Compared Between Admissions Coordinated by the Transfer Center and Emergency Department at a U.S. Tertiary Care Hospital.","authors":"Sandeep R Pagali, Alexander J Ryu, Karen M Fischer, Riddhi S Parikh, James S Newman, M Caroline Burton","doi":"10.1097/PTS.0000000000001232","DOIUrl":"10.1097/PTS.0000000000001232","url":null,"abstract":"<p><strong>Background: </strong>Patient admissions at a U.S. tertiary care hospital occur via the emergency department (ED), or transfer center. We aim to compare the clinical outcomes of patients admitted from the ED to admissions coordinated by the transfer center.</p><p><strong>Methods: </strong>Admissions to Mayo Clinic Hospital, Rochester, MN, between July 2019 to June 2021 were identified in this retrospective study and categorized into two cohorts-transfer center and ED. The two cohorts were then matched for age, sex, admitting service, and Charlson Comorbidity Index. Univariate and multivariate analyses were performed to compare hospital length of stay (LOS), mortality, 30-day mortality, and 30-day readmissions between the two cohorts.</p><p><strong>Results: </strong>73,685 admissions were identified, of which 24,262 (33%) were transfer center admissions. In the matched cohorts (n = 19,093, each), in-hospital mortality (2.4% versus 1.9%), 30-day mortality (5.4% versus 3.9%), 30-day readmission (12.7% versus 7.2%), and LOS (6.4 days versus 5.1 days) were significantly higher ( P < 0.001) among the admissions coordinated by transfer center. A higher palliative care consultation rate (9.4% versus 6.2%, P < 0.001), and a lower proportion of home discharges home (76.2% versus 82.5%, P < 0.001) among transfer center admissions was observed. Similar findings were noted in multivariate analysis, even when adjusting for LOS.</p><p><strong>Conclusions: </strong>Transfer center admissions had higher in-hospital mortality, LOS, 30-day mortality, and 30-day readmission compared to ED admissions. This study also highlights new considerations for palliative care consultation before transfer acceptance, especially to avoid futile transfers. Additional studies analyzing factors behind the outcomes of transfer center admissions are required.</p>","PeriodicalId":48901,"journal":{"name":"Journal of Patient Safety","volume":" ","pages":"352-357"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141072163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Telehealth Safety Framework: Addressing a New Frontier in Patient Safety.","authors":"Kylie M Gomes, Nate Apathy, Seth Krevat, Ethan Booker, Raj M Ratwani","doi":"10.1097/PTS.0000000000001243","DOIUrl":"10.1097/PTS.0000000000001243","url":null,"abstract":"","PeriodicalId":48901,"journal":{"name":"Journal of Patient Safety","volume":" ","pages":"358-359"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141285130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}