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My Turn is software used to manage several Covid-19 mass vaccination campaigns in California. The objective of this article is to describe the use of My Turn at two points of dispensing in California and comment on process improvements to reduce manual tasks of six identified processes of vaccination–registration, scheduling, administration, documentation, follow-up, and digital vaccine record–and paper. We reviewed publicly available documents of My Turn and patients vaccinated at George R. Moscone Convention Center in San Francisco and Oakland Coliseum Community Vaccination Clinic. For publicly available documents of My Turn, we examined videos of My Turn on YouTube, and documentation from EZIZ, the website for the California Vaccines for Children Program. For patients, we examined publicly available vaccination record cards on Instagram and Google. At the George R. Moscone Convention Center, 329,608 vaccines doses were given. At the Oakland Coliseum Community Vaccination Clinic, more than 500,000 vaccine doses were administered. The use of My Turn can be used to reduce manual tasks and paper for mass vaccinating patients against Covid-19. [ABSTRACT FROM AUTHOR]

Precise neurosurgical guidance is critical for successful brain surgeries and plays a vital role in all phases of image-guided neurosurgery (IGN). Neuronavigation software enables real-time tracking of surgical tools, ensuring their presentation with high precision in relation to a virtual patient model. Therefore, this work focuses on the development of a novel multimodal IGN system, leveraging deep learning and explainable AI to enhance brain tumor surgery outcomes. The study establishes the clinical and technical requirements of the system for brain tumor surgeries. NeuroIGN adopts a modular architecture, including brain tumor segmentation, patient registration, and explainable output prediction, and integrates open-source packages into an interactive neuronavigational display. The NeuroIGN system components underwent validation and evaluation in both laboratory and simulated operating room (OR) settings. Experimental results demonstrated its accuracy in tumor segmentation and the success of ExplainAI in increasing the trust of medical professionals in deep learning. The proposed system was successfully assembled and set up within 11 min in a pre-clinical OR setting with a tracking accuracy of 0.5 (± 0.1) mm. NeuroIGN was also evaluated as highly useful, with a high frame rate (19 FPS) and real-time ultrasound imaging capabilities. In conclusion, this paper describes not only the development of an open-source multimodal IGN system but also demonstrates the innovative application of deep learning and explainable AI algorithms in enhancing neuronavigation for brain tumor surgeries. By seamlessly integrating pre- and intra-operative patient image data with cutting-edge interventional devices, our experiments underscore the potential for deep learning models to improve the surgical treatment of brain tumors and long-term post-operative outcomes. [ABSTRACT FROM AUTHOR]

Standards and validation practices regarding mobile health apps need to be established to ensure their proper use and integration into medical practice. This paper proposes an innovative and integrative approach to examine and compare the quality of a certified health mobile applications set. A double classification framework (verified and perceived quality) is proposed and validated to evaluate the quality of 22 verified health mobile apps, particularly to understand whether these apps provide the service demanded by its users. Evaluation of verified quality was based on a certification public program. To analysis user satisfaction, we used content analysis to examine and extract words and expressions contained in online reviews (1,574 reviews were analyzed). The R language was used to scrape data and to perform all statistical analyses. The results did not confirm a significant relationship between both classifications (p >.05). However, the requirements with the highest degree of compliance ("design and relevance" and "quality and safety") of the app accreditation program were the best rated by the users of the apps in their comments. Reflections derived from this paper represent an important contribution to the knowledge base in the expanding research area of mobile health. [ABSTRACT FROM AUTHOR]

This paper aims to highlight how to reduce medication errors through the implementation of human factors science to the design features of medication containers. Despite efforts to employ automation for increased safety and decreased workload, medication administration in hospital wards is still heavily dependent on human operators (pharmacists, nurses, physicians, etc.). Improving this multi-step process requires its being studied and designed as an interface in a complex socio-technical system. Human factors engineering, also known as ergonomics, involves designing socio-technical systems to improve overall system performance, and reduces the risk of system, and in particular, operator, failures. The incorporation of human factors principles into the design of the work environment and tools that are in use during medication administration could improve this process. During periods of high workload, the cognitive effort necessary to work through a very demanding process may overwhelm even expert operators. In such conditions, the entire system should facilitate the human operator's high level of performance. Regarding medications, clinicians should be provided with as many perceptual cues as possible to facilitate medication identification. Neglecting the shape of the container as one of the features that differentiates between classes of medications is a lost opportunity to use a helpful characteristic, and medication administration failures that happen in the absence of such intentional design arise from "designer error" rather than "user error". Guidelines that define a container's shape for each class of medication would compel pharmaceutical manufacturers to be compatible and would eliminate the confusion that arises when a hospital changes the supplier of a given medication. [ABSTRACT FROM AUTHOR]

Rising disease prevalence early during the COVID-19 pandemic in the State of Qatar led to stoppage of all non-emergency health care services. To maintain continuity of care and information exchanges for non-emergency patients, a physician-operated telephone hotline was set up that involved triage followed by immediate consultation with a specialized physician. We describe the initiation and evaluate the operations of the Urgent Consultation Centre (UCC) hotline manned by 150 physicians and aimed at urgent non-life-threatening consultations at Hamad Medical Corporation, the public health provider in Qatar. UCC established a hotline to triage inbound patient calls related to 15 medical and surgical specialties. For calls between April-August 2020, we describe call volume, distribution by specialty, outcomes, performance of UCC team, as well as demographics of callers. During the study period, UCC received 60229 calls (average 394 calls/day) from Qatari nationals (38%) and expatriates (62%). Maximum total daily calls peaked at 1670 calls on June 14, 2020. Call volumes were the highest from 9 AM to 2 PM. Response rate varied from 89% to 100%. After an initial telephone triage, calls were most often related to and thus directed to internal medicine (24.61%) and geriatrics (11.97%), while the least percentage of calls were for pain management and oncology/hematology (around 2% for each). By outcome of consultation, repeat prescriptions were provided for 60% of calls, new prescriptions (15%), while referrals were to outpatient department (17%), emergency department/pediatric emergency center (5%), and primary health care centres (3%). We conclude that during a pandemic, physician-staffed telephone hotline is feasible and can be employed in innovative ways to conserve medical resources, maintain continuity of care, and serve patients requiring urgent care. [ABSTRACT FROM AUTHOR]

Literature proposes numerous initiatives for optimization of the Operating Room (OR). Despite multiple suggested strategies for the optimization of workflow on the OR, its patients and (medical) staff, no uniform description of 'optimization' has been adopted. This makes it difficult to evaluate the proposed optimization strategies. In particular, the metrics used to quantify OR performance are diverse so that assessing the impact of suggested approaches is complex or even impossible. To secure a higher implementation success rate of optimisation strategies in practice we believe OR optimisation and its quantification should be further investigated. We aim to provide an inventory of the metrics and methods used to optimise the OR by the means of a structured literature study. We observe that several aspects of OR performance are unaddressed in literature, and no studies account for possible interactions between metrics of quality and efficiency. We conclude that a systems approach is needed to align metrics across different elements of OR performance, and that the wellbeing of healthcare professionals is underrepresented in current optimisation approaches. [ABSTRACT FROM AUTHOR]

Electronic recording of newborn health information contributes to improving the quality of care. Nonetheless, there is limited evidence on the implementation of perinatal electronic medical records models. We describe the development and implementation of an electronic recording model that includes data on the health care provided to both the mother and the newborn, standardised for six hospitals of a regional health care system. The implementation process was developed in 2 stages. During stage 1, the tool was introduced in hospitals to stablish first contact with the healthcare staff. The second stage consisted in designing a new strategy to stabilise the model. Technical issues were fixed, and a new version was drawn up based on multidisciplinary agreement. Indicators to monitor implementation were measured in both stages and compared using the chi-squared test. During stage 1, nearly every newborn got its electronic medical record with an appropriate connection to the mother's data. However, certain forms that were meant to be filled in by staff were frequently neglected (completion rates: 36.7%-55.3%). In stage 2, there was a statistically significant increase in the completion rates of all these forms. As a result, a standardised discharge report was provided to every newborn at the end of stage 2. The PCR model implemented in the Region of Murcia is an innovative example of how the digitalisation and standardisation of data related to the care of healthy newborns at maternity wards is feasible across an entire network of hospitals. [ABSTRACT FROM AUTHOR]

Petabytes of health data are collected annually across the globe in electronic health records (EHR), including significant information stored as unstructured free text. However, the lack of effective mechanisms to securely share clinical text has inhibited its full utilization. We propose a new method, DataSifterText, to generate partially synthetic clinical free-text that can be safely shared between stakeholders (e.g., clinicians, STEM researchers, engineers, analysts, and healthcare providers), limiting the re-identification risk while providing significantly better utility preservation than suppressing or generalizing sensitive tokens. The method creates partially synthetic free-text data, which inherits the joint population distribution of the original data, and disguises the location of true and obfuscated words. Under certain obfuscation levels, the resulting synthetic text was sufficiently altered with different choices, orders, and frequencies of words compared to the original records. The differences were comparable to machine-generated (fully synthetic) text reported in previous studies. We applied DataSifterText to two medical case studies. In the CDC work injury application, using privacy protection, 60.9-86.5% of the synthetic descriptions belong to the same cluster as the original descriptions, demonstrating better utility preservation than the naïve content suppressing method (45.8-85.7%). In the MIMIC III application, the generated synthetic data maintained over 80% of the original information regarding patients' overall health conditions. The reported DataSifterText statistical obfuscation results indicate that the technique provides sufficient privacy protection (low identification risk) while preserving population-level information (high utility). [ABSTRACT FROM AUTHOR]

Automated dispensing cabinet (ADC) overrides are used to access emergent or urgent medications when time delay from computerized provider order entry may result in patient harm. [1] Although necessary, ADC overrides bypass the safety features of order entry and verification which increase the risk of an error occurring and potential patient harm. To protect patient safety, national organizations such as The Joint Commission and Institute for Safe Medication Practices have called for hospitals to review overriding trends and available medications on override. AdventHealth Central Florida Division – South (CFD-S) met the recommendations to track overrides but there was limited understanding of the data. A quality improvement project was necessary to investigate the division's risk of error and identify interventions to proactively limit patient risk. The initial task of the quality improvement project was to create a standardized ADC override report that could be shared with pharmacy and nursing leaders within AdventHealth CFD-S monthly. As the project progressed, multiple interventions were identified such as standardizing the information reflected in the report, improving education about ADC overrides across multi-disciplinary departments, and critically reviewing the data to identify needed changes within the division. The efforts to share the ADC override metrics across all levels has improved understanding of ADC override goals and intentions of monitoring ADC overrides. This has paved the path to improving ADC override unit culture and identify gaps within the system that allows overrides to occur. [ABSTRACT FROM AUTHOR]

The classification of esophageal disease based on gastroscopic images is important in the clinical treatment, and is also helpful in providing patients with follow-up treatment plans and preventing lesion deterioration. In recent years, deep learning has achieved many satisfactory results in gastroscopic image classification tasks. However, most of them need a training set that consists of large numbers of images labeled by experienced experts. To reduce the image annotation burdens and improve the classification ability on small labeled gastroscopic image datasets, this study proposed a novel semi-supervised efficient contrastive learning (SSECL) classification method for esophageal disease. First, an efficient contrastive pair generation (ECPG) module was proposed to generate efficient contrastive pairs (ECPs), which took advantage of the high similarity features of images from the same lesion. Then, an unsupervised visual feature representation containing the general feature of esophageal gastroscopic images is learned by unsupervised efficient contrastive learning (UECL). At last, the feature representation will be transferred to the down-stream esophageal disease classification task. The experimental results have demonstrated that the classification accuracy of SSECL is 92.57%, which is better than that of the other state-of-the-art semi-supervised methods and is also higher than the classification method based on transfer learning (TL) by 2.28%. Thus, SSECL has solved the challenging problem of improving the classification result on small gastroscopic image dataset by fully utilizing the unlabeled gastroscopic images and the high similarity information among images from the same lesion. It also brings new insights into medical image classification tasks. [ABSTRACT FROM AUTHOR]

Patient wait time can negatively impact treatment quality in a proton therapy center, where multiple treatment rooms share one proton beam. Wait time increases patient discomfort that can lead to patient motion, dissatisfaction, and longer treatment delay. This study was to develop a patient call-back model that reduced patient wait while efficiently utilizing the proton beam. A "Gatekeeper" logic allowing therapists to adjust the time of a patient's call-back to the treatment room was developed. It uses a two-pronged approach to minimize overlap of long treatment and the possibility of excessive wait in the queue to receive the proton beam. The goal was to reduce the maximum wait time to less than eight minutes per field for a four-room facility. The effectiveness of this logic was evaluated through simulation, and five scenarios were compared. Four scenarios implementing various levels of gatekeeper logic were compared with the original scenario without the logic. The best performing model provided a reduction of the maximum field wait by 26% and met the predefined goal. Adjusting call-back extended the treatment day length by an average of 6 min and a maximum of 12 min in total. The use of this gatekeeper logic significantly reduces patient field wait with minimal impact on treatment day length for a four-room proton facility. A sample interface that adopts this logic for therapists to make informed decision on patient call-back time is demonstrated. [ABSTRACT FROM AUTHOR]

Surgical trays contain unused instruments which generate wasted resources from unnecessary reprocessing/replacement costs. We implemented a quality improvement initiative to optimize surgical trays for common otolaryngology procedures, and examined the impact on costs, operating room (OR) efficiency, and patient safety. We studied five common otolaryngology procedures over a 10-month period at a single community hospital. We compared pre- and post-intervention outcome measures including instrument utilization, tray set up time, tray rebuilding time, and balancing measures (operative time, instrument recall, patient safety). We estimated cost-savings from an institutional perspective over 1- and 10-year time horizons. Costs were expressed in 2017 Canadian dollars and modeled as a function of surgical volume, labor costs, instrument depreciation, and indirect costs. A total of 238 procedures by six surgeons were observed. At baseline, only 35% of instruments were utilized. We achieved an average instrument reduction of 26%, yielding 1-year cost savings of $9,010 CDN and 10-year cost savings of $69,576 CDN. Tray optimization reduced average OR tray setup time by 2.5 ± 0.4 min (p = 0.03) and average tray rebuilding time by 1.4 ± 0.2 min (p = 0.06). There was minimal impact on balancing measures such as OR time, stakeholder perception of patient safety and trainee education, and only a single case of instrument recall. Surgical tray optimization is a simple, effective, and scalable strategy for reducing costs and improving OR efficiency without compromising patient safety. [ABSTRACT FROM AUTHOR]