Journal of Patient Safety最新文献

Objectives: Patient safety is a core component of quality of hospital care and measurable through adverse event (AE) rates. A high-risk group are femoral neck fracture patients. The Dutch clinical guideline states that the treatment of choice is cemented total hip arthroplasty (THA) or hemiarthroplasty (HA). We aimed to identify the prevalence of AEs related to THA/HA in a sample of patients who died in the hospital.

Methods: We used data of a nationwide retrospective record review study. Records were systematically reviewed for AEs, preventability and contribution to the patient's death. We drew a subsample of THA/HA AEs and analyzed these cases.

Results: Of the 2998 reviewed records, 38 patients underwent THA/HA, of whom 24 patients suffered 25 AEs (prevalence = 68.1%; 95% confidence interval, 51.4-81.2), and 24 contributed to death. Patients with a THA/HA AE were of high age (median = 82.5 y) and had severe comorbidity (Charlson score ≥5). The majority of THA/HA AEs had a patient-related cause and was considered partly preventable. Examples of suggested actions that might have prevented the AEs: refraining from surgery, adhering to medication guidelines, uncemented procedures, comprehensive presurgical geriatric assessment, and better postsurgical monitoring.

Discussion: Our study shows a high prevalence of (fatal) adverse events in patients undergoing THA/HA. This seems particularly valid for cemented implants in frail old patients, indicating room for improvement of patient safety in this group. Therefore, we recommend physicians to engage in comprehensive shared decision making with these patients and decide on a treatment fitting to a patient's preexisting health status, preferences, and values.

Aims: The objectives of this study were (1) to explore the consequences of falls; (2) to find out time and place of the fall events; and (3) to explore the impact of falls on the length of hospital stays in adults' inpatient acute care.

Background: In hospitals, falls are the most common accidents that can occur to a patient during hospitalization. Injuries resulting from serious falls can cause lifelong harm to the patient due to loss of well-being and independence.

Design: A retrospective, cross-sectional, register study based on the data from electronic patient records was conducted.

Methods: The data included 114,951 inpatients, of which 743 had fallen. Data was collected between January 2014 and December 2016.

Results: One-third of falls caused injury. Most injuries were to the head area, and the most common injuries were pain or confusion. The falls usually occurred at the beginning of the treatment in the patient's room or on the way to the toilet. Falls in the hospital increased the length of stay.

Conclusions: A large proportion of falls occur at the beginning of treatment, so it is important to start fall prevention measures as soon as the patient arrives at the hospital.

Objectives: Inadequate follow-up of incidental imaging findings (IIFs) can result in poor patient outcomes, patient dissatisfaction, and provider malpractice. At our institution, radiologists flag IIFs during report dictation to trigger electronic health record (EHR) notifications to providers and patients. Nurse coordinators directly contact patients or their primary care physicians (PCPs) regarding IIFs if follow-up is not completed within the recommended time frame. Despite these interventions, many patients and their PCPs remain unaware of IIFs. In an effort to improve awareness of IIFs, we aim to investigate communication of IIFs on inpatient discharge summaries after implementation of our EHR notification system.

Methods: Inpatient records with IIFs from 2018 to 2021 were retrospectively reviewed to determine type of IIFs, follow-up recommendations, and mention of IIFs on discharge summaries. Nurse coordinators spoke to patients and providers to determine their awareness of IIFs.

Results: Incidental imaging findings were reported in 51% of discharge summaries (711/1383). When nurse coordinators called patients and PCPs regarding IIFs at the time follow-up was due, the patients and PCPs were aware of 79% of IIFs (1096/1383).

Conclusions: With implementation of EHR notifications to providers regarding IIFs, IIFs were included in 51% of discharge summaries. Lack of inclusion of IIFs on discharge summaries could be related to transitions of care within hospitalization, provider alert fatigue, and many diagnostic testing results to distill. These findings demonstrate the need to improve communication of IIFs, possibly via automating mention of IIFs on discharge summaries, and the need for care coordinators to follow up on IIFs.

Objectives: Little is known about medical students' speak-up barriers upon recognizing or becoming aware of risky or deficient actions of others. Improving our knowledge on these helps in preparing student to function in actual health care organizations. The aim was to examine medical students' perceived reasons for silence in respect to different speak-up situations (i.e., vignette content) and to test if vignette difficulty had an effect on reasons indicated.

Methods: This study was a randomized, controlled, single-blind trial, with text-based vignettes to investigate speak-up barriers. Vignette contents described speak-up situations that varied systematically with respect to speak-up barrier (i.e., environmental norm, uncertainty, hierarchy) and difficulty (i.e., easy, difficult). For each vignette, participants indicated which speak-up barriers they regarded as important.Descriptive analysis was performed for the study population, the numbers of barriers perceived and rating of vignette difficulty. Logistic regression analysis was used to examine the association between barriers perceived and vignette contents, designed vignette difficulty and subjectively rated vignette difficulty.

Results: A total of 265 students were included. The response rate was 100%. Different barriers were relevant for the different vignettes and varied in a consistent way with the theme of the vignette. Significantly more speak-up barriers were indicated for participants with the difficult version for vignette 1 (not an environmental norm) and vignette 3 (hierarchy) with odds ratio (OR) = 1.52 and 95% confidence interval (95% CI: 1.33-1.73) and OR = 1.25 (95% CI: 1.09-1.44). For (OR) estimates, confidence intervals were rather large.

Conclusions: Perceived barriers for speak-up vary consistently with the characteristics of the situation and more barriers preventing speak-up were related to the difficult versions of the vignettes.

Objective: This research explored the experiences and perspectives of patients declining hospital transportation after receiving prehospital emergency care using advanced computational techniques.

Method: 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.

Results: 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%.

Conclusions: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Abstract: 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?

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: Future quality improvement projects could benefit from integrating models and tools from BI to guide and support behavior change.

Objective: 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.

Methods: 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.

Results: 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).

Conclusions: 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.