Daily Workflow Research Articles

Readiness of Pharmacists as Providers of Social Determinants of Health and Call to Action

Social determinants of health (SDOH) are defined as the conditions in the environments where people are born, live, learn, work, play, worship, and age. SDOH has an enormous impact on achieving the goals set by Healthy People 2030. With their education and training, pharmacists are in an ideal position to provide SDOH services. Community pharmacists should take innovative approaches in collaboration with the Community Pharmacy Enhanced Services Network to develop standard protocols for SDOH and reimbursements for these services. Pharmacists not being recognized nationally as healthcare providers is the major barrier to the implementation and expansion of pharmacist-provided SDOH programs. Thus, approval of the Pharmacy and Medically Underserved Areas Enhancement Act (H.R. 2759) is crucial to overcoming this barrier. This legislation is designed to increase access to pharmacists who are primary care providers for rural and underserved populations. These practice changes align well with the American Association of Colleges of Pharmacy strategic priorities 1, 3, and 4. Moreover, one of the most important recommendations in the Pharmacy Forecast 2022 is to develop standardized SDOH protocols and integrate them into the daily workflow of pharmacists and pharmacy technicians. However, are pharmacists adequately prepared and motivated to engage in practices designed to mitigate SDOH challenges that impact patients’ health, functioning, and quality-of-life outcomes and risk? This commentary urges pharmacy education programs to implement curricular modifications and pharmacy practice professionals to advocate, develop and implement best practice models for providing SDOH services.

Tensor- and high-resolution fiber tractography for the delineation of the optic radiation and corticospinal tract in the proximity of intracerebral lesions: a reproducibility and repeatability study

PurposeFiber tracking (FT) is used in neurosurgical planning for the resection of lesions in proximity to fiber pathways, as it contributes to a substantial amelioration of postoperative neurological impairments. Currently, diffusion-tensor imaging (DTI)-based FT is the most frequently used technique; however, sophisticated techniques such as Q-ball (QBI) for high-resolution FT (HRFT) have suggested favorable results. Little is known about the reproducibility of both techniques in the clinical setting. Therefore, this study aimed to examine the intra- and interrater agreement for the depiction of white matter pathways such as the corticospinal tract (CST) and the optic radiation (OR).MethodsNineteen patients with eloquent lesions in the proximity of the OR or CST were prospectively enrolled. Two different raters independently reconstructed the fiber bundles by applying probabilistic DTI- and QBI-FT. Interrater agreement was evaluated from the comparison between results obtained by the two raters on the same data set acquired in two independent iterations at different timepoints using the Dice Similarity Coefficient (DSC) and the Jaccard Coefficient (JC). Likewise, intrarater agreement was determined for each rater comparing individual results.ResultsDSC values showed substantial intrarater agreement based on DTI-FT (rater 1: mean 0.77 (0.68–0.85); rater 2: mean 0.75 (0.64–0.81); p = 0.673); while an excellent agreement was observed after the deployment of QBI-based FT (rater 1: mean 0.86 (0.78–0.98); rater 2: mean 0.80 (0.72–0.91); p = 0.693). In contrast, fair agreement was observed between both measures for the repeatability of the OR of each rater based on DTI-FT (rater 1: mean 0.36 (0.26–0.77); rater 2: mean 0.40 (0.27–0.79), p = 0.546). A substantial agreement between the measures was noted by applying QBI-FT (rater 1: mean 0.67 (0.44–0.78); rater 2: mean 0.62 (0.32–0.70), 0.665). The interrater agreement was moderate for the reproducibility of the CST and OR for both DSC and JC based on DTI-FT (DSC and JC ≥ 0.40); while a substantial interrater agreement was noted for DSC after applying QBI-based FT for the delineation of both fiber tracts (DSC > 0.6).ConclusionsOur findings suggest that QBI-based FT might be a more robust tool for the visualization of the OR and CST adjacent to intracerebral lesions compared with the common standard DTI-FT. For neurosurgical planning during the daily workflow, QBI appears to be feasible and less operator-dependent.

Computer-aided classification of indirect immunofluorescence patterns on esophagus and split skin for the detection of autoimmune dermatoses.

Autoimmune bullous dermatoses (AIBD) are rare diseases that affect human skin and mucous membranes. Clinically, they are characterized by blister formation and/or erosions. Depending on the structures involved and the depth of blister formation, they are grouped into pemphigus diseases, pemphigoid diseases, and dermatitis herpetiformis. Classification of AIBD into their sub-entities is crucial to guide treatment decisions. One of the most sensitive screening methods for initial differentiation of AIBD is the indirect immunofluorescence (IIF) microscopy on tissue sections of monkey esophagus and primate salt-split skin, which are used to detect disease-specific autoantibodies. Interpretation of IIF patterns requires a detailed examination of the image by trained professionals automating this process is a challenging task with these highly complex tissue substrates, but offers the great advantage of an objective result. Here, we present computer-aided classification of esophagus and salt-split skin IIF images. We show how deep networks can be adapted to the specifics and challenges of IIF image analysis by incorporating segmentation of relevant regions into the prediction process, and demonstrate their high accuracy. Using this semi-automatic extension can reduce the workload of professionals when reading tissue sections in IIF testing. Furthermore, these results on highly complex tissue sections show that further integration of semi-automated workflows into the daily workflow of diagnostic laboratories is promising.

Usability of the IDDEAS prototype in child and adolescent mental health services: A qualitative study for clinical decision support system development.

Child and adolescent mental health services (CAMHS) clinical decision support system (CDSS) provides clinicians with real-time support as they assess and treat patients. CDSS can integrate diverse clinical data for identifying child and adolescent mental health needs earlier and more comprehensively. Individualized Digital Decision Assist System (IDDEAS) has the potential to improve quality of care with enhanced efficiency and effectiveness. We examined IDDEAS usability and functionality in a prototype for attention deficit hyperactivity disorder (ADHD), using a user-centered design process and qualitative methods with child and adolescent psychiatrists and clinical psychologists. Participants were recruited from Norwegian CAMHS and were randomly assigned patient case vignettes for clinical evaluation, with and without IDDEAS. Semi-structured interviews were conducted as one part of testing the usability of the prototype following a five-question interview guide. All interviews were recorded, transcribed, and analyzed following qualitative content analysis. Participants were the first 20 individuals from the larger IDDEAS prototype usability study. Seven participants explicitly stated a need for integration with the patient electronic health record system. Three participants commended the step-by-step guidance as potentially helpful for novice clinicians. One participant did not like the aesthetics of the IDDEAS at this stage. All participants were pleased about the display of the patient information along with guidelines and suggested that wider guideline coverage will make IDDEAS much more useful. Overall, participants emphasized the importance of maintaining the clinician as the decision-maker in the clinical process, and the overall potential utility of IDDEAS within Norwegian CAMHS. Child and adolescent mental health services psychiatrists and psychologists expressed strong support for the IDDEAS clinical decision support system if better integrated in daily workflow. Further usability assessments and identification of additional IDDEAS requirements are necessary. A fully functioning, integrated version of IDDEAS has the potential to be an important support for clinicians in the early identification of risks for youth mental disorders and contribute to improved assessment and treatment of children and adolescents.

Computer‐Aided Molecular and Process Design

Increasing energy prices, tighter environmental regulation, and global competition urge the industry to optimize its processes for converting chemicals and energy. This optimization is far from trivial. Often, the key to major breakthrough relies not only on optimal process conditions but also on selecting suitable molecules as solvents, catalysts, or heat transfer fluids. This selection is highly coupled with the process itself: Choosing the right molecule is a crucial lever for process performance. However, the molecular search space is vast: more than 200 000 000 molecules have already been reported in the CAS registry. The search for the best molecule is thus the search for the needle in the haystack. The ultimate success of a process is only ensured if a molecule is selected based on its optimal process performance. Traditionally, however, the selection of a molecule and the design of the process have been approached sequentially and independently. Computer-aided molecular and process design (CAMPD) methods have been developed to tackle this challenge and systematically tailor molecules for a given application. Thereby, CAMPD can contribute to the transition towards a more sustainable energy and chemical industry, for example, by identifying energy-efficient carbon capture materials, sustainable working fluids for heat pumps, or green solvents for production processes. What is required for a successful application of CAMPD? The basis of a CAMPD problem is a computer-aided molecular design (CAMD) formulation that allows treating the molecular structure as a degree of freedom in an optimization problem. The CAMD formulation is linked with a property model that enables predicting all molecule properties required to evaluate the constraints and objective functions. To reflect the process performance, classical CAMD methods used simplified property-based indicators as the objective function. Recent CAMPD methods integrate detailed process models to assess process performance directly. The recent development of CAMPD/CAMD includes using advanced physically-based and data-driven property models. Moreover, the scope of predicted properties moves beyond the thermodynamic properties to environmental properties such as global warming potential. After decades of CAMPD/CAMD development, these tools have become available for industrial use by implementing CAMPD in flowsheet simulators. Thereby, the effort and required expert knowledge are greatly reduced to set up and solve a CAMPD problem. However, including such new tools in the daily workflow usually takes time. The urge to develop new sustainable processes hopefully will enhance the application of these CAMPD tools. In this special issue of Chemie Ingenieur Technik, authors from industry and academia present their latest developments and applications of CAMPD in energy and chemical engineering. The special issue cannot provide a comprehensive picture of this rapidly developing field. Thus, we aim to provide the reader with an impression of the current status and possible future directions of CAMPD. We hope readers will find inspiration in this special issue and thank all authors for their highly appreciated contributions! Norbert Asprion André Bardow Jonas Mairhofer Johannes Schilling

A data-driven support system for the efficient schedule delay management of the ultra-high-voltage projects considering subjective risk preferences

Renewable generation technologies are thriving due to sustainable transformation of the whole society, while there exists a general gap between large-scale renewable energy sources and loads in major cities which could be addressed by Ultra-high voltage (UHV) transmission projects. This paper proposes a data-driven support system for schedule delays of UHV projects. The system is composed of three modules. Firstly, general project schedule routines are interpreted by logistic curves. Classical earned values are measured by trapezoidal fuzzy numbers which are used for subjective judgement of project managers. These measures serve as the system input. Then, combined objective schedule delay and subjective severity judgement are analyzed by least square support vector regression. Enhanced particle swarm optimization is applied to determine optimal parameter combinations according to the structure of trained data. Next, delay prediction results combining subjective and objective information are divided into three severity degrees by an enhanced K-means algorithm and finally, integrated prevention measures are raised for project managers. The proposed system is validated by a real case study and results indicate good performance and reliability. The system could be integrated into the daily workflow of project managers and constantly provides references that facilitate efficient schedule management.

How to teach ERAS protocols: surgical residents' perspectives and perioperative practices for breast surgery patients.

Breast enhanced recovery after surgery (ERAS) protocols emphasize multimodal analgesia to expedite home recovery, but variable implementation remains. This study examines how residents learn and use ERAS protocols, how they conceptualize pain management, and what influences breast surgery patients' same-day discharges. Interviews were conducted with surgical residents following their breast surgery rotation using an interview guide adapted from existing pain management literature. Interviews were transcribed, de-identified, and independently inductively coded by two researchers. A codebook was developed and refined using the constant comparative method. Codes were grouped into categories and explored for thematic analysis. Twelve interviews were completed with plastic and general surgery residents. Ultimately, 365 primary codes were organized into 26 parent codes, with a Cohen's kappa of 0.93. A total of six themes were identified. Three themes described how participants learn through a mixture of templated care, formal education, and informal experiential learning. Two themes delineated how residents would teach breast surgery ERAS: by emphasizing buy-in and connecting the impetus behind ERAS with daily workflow implementation. One theme illustrated how a patient-centered culture impacts postoperative management and same-day discharges. Residents describe learning breast surgery ERAS and postoperative pain management by imitating their seniors, observing patient encounters, completing templated orders, and translating concepts from other ERAS services more so than from formal lectures. When implementing breast ERAS protocols, it is important to consider how informal learning and local culture influence pain management and discharge practices. Ultimately, residents believe in ERAS and often request further educational tools to better connect the daily how-to of breast ERAS pathways with the why behind the enhanced recovery principles.

Artificial Intelligence and Machine Learning in Dialysis: Ready for Prime Time?

Artificial Intelligence and Machine Learning in Dialysis: Ready for Prime Time?

Pros and Cons of Dual-Energy CT Systems: "One Does Not Fit All".

Dual-energy computed tomography (DECT) uses different energy spectrum x-ray beams for differentiating materials with similar attenuation at a certain energy. Compared with single-energy CT, it provides images with better diagnostic performance and a potential reduction of contrast agent and radiation doses. There are different commercially available DECT technologies, with machines that may display two x-ray sources and two detectors, a single source capable of fast switching between two energy levels, a specialized detector capable of acquiring high- and low-energy data sets, and a filter splitting the beam into high- and low-energy beams at the output. Sequential acquisition at different tube voltages is an alternative approach. This narrative review describes the DECT technique using a Q&A format and visual representations. Physical concepts, parameters influencing image quality, postprocessing methods, applicability in daily routine workflow, and radiation considerations are discussed. Differences between scanners are described, regarding design, image quality variabilities, and their advantages and limitations. Additionally, current clinical applications are listed, and future perspectives for spectral CT imaging are addressed. Acknowledging the strengths and weaknesses of different DECT scanners is important, as these could be adapted to each patient, clinical scenario, and financial capability. This technology is undoubtedly valuable and will certainly keep improving.

Reducing unnecessary diagnostic phlebotomy in intensive care: a prospective quality improvement intervention

BackgroundCritically ill patients receive frequent routine and recurring blood tests, some of which are unnecessary.AimTo reduce unnecessary routine phlebotomy in a 30-bed tertiary medical-surgical intensive care unit (ICU) in Toronto,...

Evaluation of a convolution neural network for baseline total tumor metabolic volume on [18F]FDG PET in diffuse large B cell lymphoma.

New PET data-processing tools allow for automatic lesion selection and segmentation by a convolution neural network using artificial intelligence (AI) to obtain total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) routinely at the clinical workstation. Our objective was to evaluate an AI implemented in a new version of commercial software to verify reproducibility of results and time savings in a daily workflow. Using the software to obtain TMTV and TLG, two nuclear physicians applied five methods to retrospectively analyze data for 51 patients. Methods 1 and 2 were fully automated with exclusion of lesions ≤ 0.5 mL and ≤ 0.1 mL, respectively. Methods 3 and 4 were fully automated with physician review. Method 5 was semi-automated and used as reference. Time and number of clicks to complete the measurement were recorded for each method. Inter-instrument and inter-observer variation was assessed by the intra-class coefficient (ICC) and Bland-Altman plots. Between methods 3 and 5, for the main user, the ICC was 0.99 for TMTV and 1.0 for TLG. Between the two users applying method 3, ICC was 0.97 for TMTV and 0.99 for TLG. Mean processing time (± standard deviation) was 20 s ± 9.0 for method 1, 178 s ± 125.7 for method 3, and 326 s ± 188.6 for method 5 (p < 0.05). AI-enabled lesion detection software offers an automated, fast, reliable, and consistently performing tool for obtaining TMTV and TLG in a daily workflow. • Our study shows that artificial intelligence lesion detection software is an automated, fast, reliable, and consistently performing tool for obtaining total metabolic tumor volume and total lesion glycolysis in a daily workflow.

Nurse-Administered Auricular Point Acupressure for Cancer-Related Pain.

The study aimed to (1) examine the feasibility of providing a training course on auricular point acupressure (APA) for clinical oncology nurses to integrate APA into real-world nursing care settings, and (2) examine the effectiveness of APA on cancer-related pain (CRP) under usual inpatient oncology ward conditions. This was a 2-phase feasibility study. Phase 1, an in-person, 8 hour training program was provided to oncology nurses. Phase 2, a prospective and feasibility study was conducted to evaluate the integration of APA into nursing care activities to manage CRP. Oncology patients were included if their pain was rated at ≥4 on a 0 to 10 numeric rating scale in the past 24 hours. Patients received 1 APA treatment administered by the nurses and were instructed to stimulate the points for 3 days. Study outcomes (pain intensity, fatigue, and sleep disturbance), pain medication use, and APA practice were measured by a phone survey daily. Ten oncology nurses received APA training in phase 1. APA had been added to the hospital's electronic health records (EHRs) as a pain treatment. In phase 2, 33 oncology patients received APA treatment with a 100% adherence rate (pressing the seeds 3 times per day, 3 minutes per time based on the suggestion). The side effects of APA were minimal (~8%-12% felt tenderness on the ear). After 3 days of APA, patients reported 38% pain relief, 39% less fatigue, and 45% improvement in sleep disturbance; 24% reduced any type of pain medication use and 19% reduced opioid use (10 mg opioids using milligram morphine equivalent). The major barrier to integrating APA into routine nursing practice was time management (how to include APA in a daily workflow). It is feasible to provide 8-hour training to oncology nurses for mastering APA skill and then integrating APA into their daily nursing care for patients with CRP. Based on the promising findings (decreased pain, improved fatigue and sleep disturbance, and less opioid use), the next step is to conduct a randomized clinical trial with a larger sample to confirm the efficacy of APA for oncology nurses to treat CRP in real-world practice.ClinicalTrial.gov identifier number: NCT04040140.

Lightweight deep learning methods for panoramic dental X-ray image segmentation

Dental X-ray image segmentation is helpful for assisting clinicians to examine tooth conditions and identify dental diseases. Fast and lightweight segmentation algorithms without using cloud computing may be required to be implemented in X-ray imaging systems. This paper aims to investigate lightweight deep learning methods for dental X-ray image segmentation for the purpose of deployment on edge devices, such as dental X-ray imaging systems. A novel lightweight neural network scheme using knowledge distillation is proposed in this paper. The proposed lightweight method and a number of existing lightweight deep learning methods were trained on a panoramic dental X-ray image data set. These lightweight methods were evaluated and compared by using several accuracy metrics. The proposed lightweight method only requires 0.33 million parameters (sim 7.5 megabytes) for the trained model, while it achieved the best performance in terms of IoU (0.804) and Dice (0.89) comparing to other lightweight methods. This work shows that the proposed method for dental X-ray image segmentation requires small memory storage, while it achieved comparative performance. The method could be deployed on edge devices and could potentially assist clinicians to alleviate their daily workflow and improve the quality of their analysis.

573: FAMILY COMMUNICATION IN THE ICU: CHALLENGES AND SOLUTIONS

Introduction: The COVID-19 pandemic created major barriers to communication with patient families, impacting patient care and staff satisfaction. We surveyed an interprofessional group of medical intensive care unit (MICU) stakeholders to identify their concerns surrounding family communication and solicited recommendations for performance improvement. Methods: An anonymous survey was distributed electronically to 260 MICU physicians, advanced practice providers (APPs), nurses, and physical, occupational, and respiratory therapists, with a 1 week reminder. The results were analyzed using thematic analysis. Results: Thirty-nine participants (response rate 15%) completed the survey, providing 95 unique comments. Major themes included challenges created by visitor policy restrictions (providers unable to connect well with families by phone; families not understanding how to work video platforms; frequent misunderstandings); medical system distrust (antivaccine and overall hostility, unrealistic expectations, challenging treatment decisions); trainee communication concerns (lack of communication training, not seeing the “big picture”, nurse not knowing whether families were contacted/what was discussed), and A2F bundle failures (F component not organized, family updates too infrequent, distorted messages). This feedback prompted a quality improvement initiative to strengthen our family communication process. The responsible resident or APP is now encouraged to use a “Get to Know Me Board” to structure the collection of relevant social history with patient and family on admission, establishing an initial relationship and line of communication. Our rounding template was modified to discuss the family communication plan each day using a tiered structure (brief summary/introduction on admission; update; serious update/acute event; goals of care; transition to comfort) and clarify which team members will be present for the conversation. Afternoon rounds were also modified to routinely confirm successful family contact and address any concerns. Conclusions: We describe the results of our interprofessional survey to define the barriers to family communication during the COVID-19 pandemic, and process improvements in our MICU to make daily family involvement and contact feasible and efficient within our daily workflow.

916. Increased efficiency and impact of implementing ILUM insight within an antimicrobial stewardship program (ASP) at an academic medical center

Abstract Background An ASP is mandated for all hospitals and requires extensive resources with multidisciplinary collaboration. We measured the impact of implementing real-time decision support software (ILUM Insight) within our ASP. Methods Our ASP has relied on prior authorization since 2002 and focused audit and feedback since 2015. In August 2021 we implemented to bring actionable data to front-line stewards. ILUM provides real-time notifications, organizes communications, and tracks patient-and provider-level data. We hypothesized that ILUM would increase the efficiency of ASP workflow and result in decreased antimicrobial utilization. We compared data 6 months before (8/20 – 1/21) and after (8/21 – 1/22) implementation. There were no significant staffing changes during either period. Results Existing notifications within ILUM were tailored to local practices, including alerts with intervention for positive blood cultures, antibiotic de-escalation, and bug-drug mismatches. New notifications were built for restricted antimicrobials, antibiotic timeouts, and MRSA screening. ASP pharmacists and physicians received training in July and November, respectively. A breakdown of all notifications received during the post-implementation period is provided in Fig 1. With increased ILUM usage, the number of interventions made by our ASP increased while missed opportunities decreased (Fig 2.). During the same time period, ASP communications rose from 205 to 1200 per month. Comparing pre- and post-implementation periods, antimicrobial days of therapy (DOT) per 1,000 patient days (PD) decreased by 14.5% from a median of 969 to 846 per month (Fig 3;P=0.002). Antimicrobial expenditures were decreased by a median 21% per month during the post-intervention period compared to baseline. Among patients prescribed antimicrobials during an index admission, 30-day re-admissions decreased from 330 to 262 and re-admissions associated with re-ordering of antimicrobials decreased from 235 to 182 (Fig 4). Conclusion Custom-designed, task-specific software improves the efficiency of daily ASP workflow and significantly decreased antimicrobial utilization without the need for additional ASP team members. Disclosures Ryan K. Shields, PharmD, MS, Infectious Disease Connect: Advisor/Consultant|Merck: Advisor/Consultant|Merck: Grant/Research Support|Roche: Grant/Research Support J Ryan Bariola, MD, Infectious Disease Connect: Salary support|Merck: Grant/Research Support Caley Yakemowicz, n/a, Infectious Disease Connect: Employee Courtney Simonick, n/a, Infectious Disease Connect: Stocks/Bonds Riaan Erwee, na, Infectious Disease Connect: Employee Erin K. McCreary, PharmD, Infectious Disease Connect: Advisor/Consultant Rima Abdel-Massih, MD, Infectious Disease Connect: Co founder and Chief Medical Officer|Infectious Disease Connect: Ownership Interest.

Increasing Language Interpreter Services Use and Documentation: A Quality Improvement Project.

Based on the Civil Rights Act of 1964, hospitals receiving funding from the Department of Human and Health Services must ensure adequate language assistance via a qualified interpreter for Limited English Proficiency (LEP) patients. Despite availability at our institution, official interpreter services were underutilized with inconsistent documentation of use. Baseline data over a 6-week period revealed a median of 43% of LEP parents reported use of official interpreter services, with a median of 0% documentation of use. We aimed to improve both over a 3-month period. This quality improvement project was conducted at a children's hospital between April and August 2020. Surveys were provided to inpatient LEP families. Four intervention periods were implemented with ongoing data collection for 12 weeks. Interventions included developing best practices, incorporating language identification into daily workflow, empowering families, and standardizing documentation. Data were analyzed by run chart and descriptive statistics. After interventions and several plan-do-study-act cycles, a median of 73% reported use of official interpreter services, and a median of 59% were documented. After multiple interventions, specifically noted after intervention period #2, improvement of official interpreter service use and documentation were identified in all inpatient units.

Risk analysis of the Unity 1.5 T MR-Linac adapt-to-position workflow.

Newer technologies allow for daily treatment adaptation, providing the ability to account for setup variations and organ motion but comes at the cost of increasing the treatment workflow complexity. One such technology is the adapt-to-position (ATP) workflow on the Unity MR-Linac. Prospective risk assessment of a new workflow allows clinics to catch errors before they occur, especially for processes that include novel and unfamiliar steps. As part of a quality management program, failure modes and effects analysis was performed on the ATP treatment workflow following the recommendations of AAPM's Task Group 100. A multidisciplinary team was formed to identify and evaluate failure modes for all the steps taken during a daily treatment workflow. Failure modes of high severity and overall score were isolated and addressed. Mitigations were determined for high-ranking failure modes and implemented into the clinic. High-ranking failure modes existed in all steps of the workflow. Failure modes were then rescored to evaluate the effectiveness of the mitigations. Failure modes and effects analysis on the Unity MR-Linac highlighted areas in the ATP workflow that could be prone to failures and allowed our clinic to change the process to be more robust.

Development, implementation, and evaluation of Teach Back curriculum for community health workers.

Teach Back is a commonly used communication method to improve patient understanding and retention of health information. The method has been shown to be effective in improving patient and healthcare system outcomes, including patient health literacy and hospital readmissions. Community health workers (CHWs) are frontline healthcare workers who can help address patient health and social needs associated with hospital readmissions. However, a gap exists in Teach Back curricula and training methods reflecting the scope of work for CHWs. The objective of this training was to provide CHWs with didactic information and skill building practice curriculum focused on the integration of Teach Back into clinical patient interactions, care coordination, and follow-up support. A multidisciplinary team of academic and clinical partners at a large academic health university developed, implemented, and evaluated a 3-week pilot Teach Back training with CHWs through a quality improvement approach. The CHWs reported overall satisfaction with the training and instructors. The academic clinical partnership allowed the training to be tailored to the daily clinical workflow as reflected in the CHWs agreement that the training was relevant and practical. With the repeated exposure to Teach Back each week, the CHWs also reported an increase in confidence and conviction in using Teach Back. Additional implementation and evaluation of the training curriculum for CHWs is needed to gain further insights into Teach Back and training best practices and translation into practice.

Nurses' assessment of subsyndromal delirium and barriers to assessment: A cross-sectional survey in the intensive care unit.

The aims of the study are to investigate the current status of nurses' assessment of subsyndromal delirium (SSD) in the intensive care unit (ICU) and explore possible barriers to assessment. SSD is a dynamic, recognizable disorder commonly seen in the ICU that can lead to poor patient outcomes. Timely recognition and management can prevent its progression. A cross-sectional survey design was used to collect data from ICU registered nurses in southwest China. The online survey containing an analysis of the current status of SSD assessment and barriers was completed by 237 nurses. A total of 51.5% of nurses chose to assess SSD using an assessment tool, the most commonly used being the Confusion Assessment Method for the Intensive Care Unit; the frequency of assessment was mostly once a day (66, 41.0%) and often at shift change (178, 87.3%). There were statistically significant differences in the barrier factor scores by assessment frequency, assessment method, status of training in SSD, ability of SSD-related knowledge to meet clinical needs and willingness to receive SSD training. Our study confirms that the current state of assessment of SSD in the ICU is unsatisfactory, with nurses' lack of assessment knowledge and skills, poor organization and management, and the complexity of patients' conditions being barriers. Nursing managers should systematically conduct training programmes on effective SSD assessment knowledge and skills, incorporate SSD assessment into the daily workflow, have standardized assessment tools, develop standardized processes and assign dedicated staff to monitor, audit and provide feedback on SSD assessments.

Patient-Specific Daily Updated Deep Learning Auto-Segmentation for MRI-Guided Adaptive Radiotherapy

<h3>Purpose/Objective(s)</h3> MR-guided adaptive radiotherapy (MRgART) requires accurate and efficient daily image segmentation for plan adaption. Deep learning (DL) technique has shown great potential on medical image segmentation, but still has limited success on MRIs, partially due to the large variations for MRIs from different scanners, protocols, and patients. The longitudinal MRIs and contours during MRgART contain the anatomy variation for the specific patient. However, it has been ignored in current auto-segmentation solutions. This study proposed a patient-specific DL auto-segmentation strategy using patient's previous images and contours to update a prototype model, to improve segmentation accuracy and efficiency for MRgART. <h3>Materials/Methods</h3> For each patient, a prototype model was trained based on SegResNet using the first set of MRI scan and the corresponding contours as inputs. The patient-specific prototype model was updated with each newly added fractional MRI/contours and then used to predict contours for next MRI of the specific patient. During model training, a mutant of the prototype tried to fit the patient's previous data under consistency constraints, which is to limit the difference between the predictions for the latest MRI (volume, length, centroid) within a reasonable range. Prototype weights were partially inherited from the mutant using stochastic weight averaging to get the final model. Six abdominal or pelvic patients (each with 8 longitudinal MRIs) underwent MRgART on a 1.5T MRI-Linac were utilized. A total of 16 abdominal and pelvic organ-at-risks (OARs) and the tumors were manually contoured by three senior radiation oncologists and used as ground truth. The model performance was compared with deformable image registration (DIR) and frozen DL model using Dice Similarity Coefficient (DSC). The contouring time including model inference and contour reviewing and editing time until physician's approval was recorded and compared with fully manual contouring. <h3>Results</h3> The proposed daily updated model achieved superior performance with average DSC of 0.907±0.117, as compared to DIR (0.874±0.109) and frozen DL models (0.889±0.121). As for tumors, the proposed method achieved an average DSC of 0.922±0.097, which is significantly higher than those from DIR (0.723±0.183) and frozen DL (0.591±0.232). The DSC increased by average 3.1% with one additional prior images/contours compared with the prototype model. The contouring time is significant shorter using proposed method (average 6 minutes) than the manual process (20∼40 minutes). <h3>Conclusion</h3> The proposed patient-specific DL auto segmentation method can significantly improve the efficiency and accuracy of contour generation on longitudinal MRIs. It can be integrated into the daily adaption workflow, accelerating the contouring process, thereby facilitating routine practice of MRgART.