BACKGROUND: Individuals with high risk for lung cancer may benefit from lung cancer screening, but there are associated risks as well as benefits. Shared decision-making (SDM) tools with personalized information may provide key support for patients. Understanding patient perspectives on educational tools to facilitate SDM for lung cancer screening may support tool development.
OBJECTIVE: This study aimed to explore patient perspectives related to a SDM tool for lung cancer screening using a qualitative approach.
METHODS: We elicited patient perspectives by showing a provider-facing SDM tool. Focus group interviews that ranged in duration from 1.5 to 2 h were conducted with 23 individuals with high risk for lung cancer. Data were interpreted inductively using thematic analysis to identify patients\' thoughts on and desires for a patient-facing SDM tool.
RESULTS: The findings highlight that patients would like to have educational information related to lung cancer screening. We identified several key themes to be considered in the future development of patient-facing tools: barriers to acceptance, preference against screening and seeking empowerment. One further theme illustrated effects of patient-provider relationship as a limitation to meeting lung cancer screening information needs. Participants also noted several suggestions for the design of technology decision aids.
CONCLUSIONS: These findings suggest that patients desire additional information on lung cancer screening in advance of clinical visits. However, there are several issues that must be considered in the design and development of technology to meet the information needs of patients for lung cancer screening decisions.
UNASSIGNED: Patients, service users, caregivers or members of the public were not involved in the study design, conduct, analysis or interpretation of the data. However, clinical experts in health communication provided detailed feedback on the study protocol, including the focus group approach. The study findings contribute to a better understanding of patient expectations for lung cancer screening decisions and may inform future development of tools for SDM.

UNASSIGNED: Compass Rose™, a case management tool developed by Epic®, was designed to track various patient coordination tasks, outreaches, and outcomes. This report describes the implementation of Compass Rose™ within an internal health-system specialty pharmacy (HSSP) and changes in care coordination metrics before and after implementation. To the best of our knowledge, this is the first study of its kind to discuss the implementation of Compass Rose™.
UNASSIGNED: The goals of this study were to describe the implementation process of Compass Rose™ at an internal HSSP and compare staff satisfaction before and after Compass Rose™ as the primary outcome.
UNASSIGNED: This was an Institutional Review Board exempt, retrospective cohort study conducted between June 2022 to December 2022 that assessed staff satisfaction, refill documentation time, prescription turnaround time, and patient satisfaction pre- and post- Compass Rose™ implementation through survey administration, observed time studies, and internal data reports. The process of Compass Rose™ implementation was also described and discussed.
UNASSIGNED: 24 specialty pharmacy staff members participated in the Compass Rose™ implementation survey. No statistically significant differences were observed in either staff satisfaction (3.96 ± 0.95 versus 3.70 ± 0.69, p = 0.29) or predicted versus actual challenge of implementation (3.67 ± 1.17 versus 3.09 ± 0.96, p = 0.064). There was no significant difference in refill documentation time pre- versus post- Compass Rose™ implementation (4.22 ± 3.15 minutes versus 4.10 ± 2.36 minutes, p = 0.82); however, there was a statistically significant increase in prescription turnaround time post implementation (2.59 ± 2.85 days versus 2.69 ± 2.35 days, p = 0.002).
UNASSIGNED: Compass Rose™ implementation had no significant impact on staff satisfaction, patient satisfaction, or overall refill documentation time. Prescription turnaround time increased, which could be due to significant workflow changes with Compass Rose™ or several other contributing factors such as increased prescription volume and training new staff during this period.Benefits of Compass Rose™ included standardization of workflow, ability to quantify staff performance and clinical impact, and increased transparency regarding care provided by the specialty pharmacy team.

BACKGROUND: Recording and coding of ageing syndromes in hospital records is known to be suboptimal. Natural Language Processing algorithms may be useful to identify diagnoses in electronic healthcare records to improve the recording and coding of these ageing syndromes, but the feasibility and diagnostic accuracy of such algorithms are unclear.
METHODS: We conducted a systematic review according to a predefined protocol and in line with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Searches were run from the inception of each database to the end of September 2023 in PubMed, Medline, Embase, CINAHL, ACM digital library, IEEE Xplore and Scopus. Eligible studies were identified via independent review of search results by two coauthors and data extracted from each study to identify the computational method, source of text, testing strategy and performance metrics. Data were synthesised narratively by ageing syndrome and computational method in line with the Studies Without Meta-analysis guidelines.
RESULTS: From 1030 titles screened, 22 studies were eligible for inclusion. One study focussed on identifying sarcopenia, one frailty, twelve falls, five delirium, five dementia and four incontinence. Sensitivity (57.1%-100%) of algorithms compared with a reference standard was reported in 20 studies, and specificity (84.0%-100%) was reported in only 12 studies. Study design quality was variable with results relevant to diagnostic accuracy not always reported, and few studies undertaking external validation of algorithms.
CONCLUSIONS: Current evidence suggests that Natural Language Processing algorithms can identify ageing syndromes in electronic health records. However, algorithms require testing in rigorously designed diagnostic accuracy studies with appropriate metrics reported.

Children with pulmonary hypertension (PH) often demonstrate limited exercise capacity. Data support exercise as an effective nonpharmacologic intervention among adults with PH. However, data on exercise training in children and adolescents are limited, and characteristics of the optimal exercise program in pediatric PH have not been identified. Exercise programs may have multiple targets, including muscle deficits which are associated with exercise limitations in both adult and pediatric PH. Wearable accelerometer sensors measure physical activity volume and intensity in the naturalistic setting and can facilitate near continuous data transfer and bidirectional communication between patients and the study team when paired with informatics tools during exercise interventions. To address the knowledge gaps in exercise training in pediatric PH, we designed a prospective, single arm, nonrandomized pilot study to determine feasibility and preliminary estimates of efficacy of a 16-week home exercise intervention, targeting lower extremity muscle mass and enriched by wearable mobile health technology. The exercIse Training in pulmONary hypertEnsion (iTONE) trial includes (1) semistructured exercise prescriptions tailored to the participant\'s baseline level of activity and access to resources; (2) interval goal setting fostering self-efficacy; (3) real time monitoring of activity via wearable devices; (4) a digital platform enabling communication and feedback between participant and study team; (5) multiple avenues to assess participant safety. This pilot intervention will provide information on the digital infrastructure needed to conduct home-based exercise interventions in PH and will generate important preliminary data on the effect of exercise interventions in youth with chronic cardiorespiratory conditions to power larger studies in the future.

Despite the importance of communication, radiology departments often depend on communication tools that were not created for the unique needs of imaging workflows, leading to frequent radiologist interruptions. The objective of this study was test the hypothesis that a novel asynchronous communication tool for the imaging workflow (RadConnect) reduces the daily average number of synchronous (in-person, telephone) communication requests for radiologists. We conducted a before-after study. Before adoption of RadConnect, technologists used three conventional communication methods to consult radiologists (in-person, telephone, general-purpose enterprise chat (GPEC)). After adoption, participants used RadConnect as a fourth method. Technologists manually recorded every radiologist consult request related to neuro and thorax CT scans in the 40 days before and 40 days after RadConnect adoption. Telephone traffic volume to section beepers was obtained from the hospital telephone system for the same period. The value and usability experiences were collected through an electronic survey and structured interviews. RadConnect adoption resulted in 53% reduction of synchronous (in-person, telephone) consult requests: from 6.1 ± 4.2 per day to 2.9 ± 2.9 (P < 0.001). There was 77% decrease (P < 0.001) in telephone volume to the neuro and thorax beepers, while no significant volume change was noted to the abdomen beeper (control group). Survey responses (46% response rate) and interviews confirmed the positive impact of RadConnect on interruptions. RadConnect significantly reduced radiologists\' telephone interruptions. Study participants valued the role-based interaction and prioritized worklist overview in the survey and interviews. Findings from this study will contribute to a more focused work environment.

BACKGROUND: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis, combined with clinical, microbiological, and immunological data, by machine learning (ML), can help achieve it.
METHODS: Six rhesus macaques were experimentally inoculated with pathogenic Mycobacterium tuberculosis in the lung. Data, including Computed Tomography (CT), were collected at 0, 2, 4, 8, 12, 16, and 20 weeks.
RESULTS: Our ML-based CT analysis (TB-Net) efficiently and accurately analyzed disease progression, performing better than standard deep learning model (LLM OpenAI\'s CLIP Vi4). TB-Net based results were more consistent than, and confirmed independently by, blinded manual disease scoring by two radiologists and exhibited strong correlations with blood biomarkers, TB-lesion volumes, and disease-signs during disease pathogenesis.
CONCLUSIONS: The proposed approach is valuable in early disease detection, monitoring efficacy of therapy, and clinical decision making.

BACKGROUND: Digital healthcare aims to deliver on the quadruple aim: enhance patient experiences, improve population health, reduce costs and improve provider experiences. Despite large investments, it is unclear how advancing digital health enables these healthcare aims.
OBJECTIVE: Our objectives were to: 1) measure the correlation between digital capability and health system outcomes mapped to the quadruple aim, and 2) measure the longitudinal impact of electronic medical record implementations upon health system outcomes.
METHODS: We undertook two studies: 1) Digital health correlational study investigating the association among healthcare system capability and healthcare aims, and 2) Digital hospital longitudinal study investigating outcomes pre and post electronic medical record implementation.
RESULTS: Digital health capability was associated with lower staff turnover. Digitising healthcare services was associated with decreased medication errors, decreased nosocomial infections, increased hospital activity, and a transient increase in staff leave.
CONCLUSIONS: These results suggest positive impacts on the population health and healthcare costs aim, minimal impacts on the provider experience aim and no observed impacts to the patient experience aim.
CONCLUSIONS: These findings should provide confidence to healthcare decision-makers investing in digital health.

JMIR Bioinformatics and Biotechnology supports interdisciplinary research and welcomes contributions that push the boundaries of bioinformatics, genomics, artificial intelligence, and pathology informatics.

UNASSIGNED: Precise segmentation of Odontogenic Cystic Lesions (OCLs) from dental Cone-Beam Computed Tomography (CBCT) is critical for effective dental diagnosis. Although supervised learning methods have shown practical diagnostic results in segmenting various diseases, their ability to segment OCLs covering different sub-class varieties has not been extensively investigated.
UNASSIGNED: In this study, we propose a new supervised learning method termed OCL-Net that combines a Multi-Scaled U-Net model, along with an Auto-Adapting mechanism trained with a combined supervised loss. Anonymous CBCT images were collected retrospectively from one hospital. To assess the ability of our model to improve the diagnostic efficiency of maxillofacial surgeons, we conducted a diagnostic assessment where 7 clinicians were included to perform the diagnostic process with and without the assistance of auto-segmentation masks.
UNASSIGNED: We collected 300 anonymous CBCT images which were manually annotated for segmentation masks. Extensive experiments demonstrate the effectiveness of our OCL-Net for CBCT OCLs segmentation, achieving an overall Dice score of 88.84%, an IoU score of 81.23%, and an AUC score of 92.37%. Through our diagnostic assessment, we found that when clinicians were assisted with segmentation labels from OCL-Net, their average diagnostic accuracy increased from 53.21% to 55.71%, while the average time spent significantly decreased from 101s to 47s (P<0.05).
UNASSIGNED: The findings demonstrate the potential of our approach as a robust auto-segmentation system on OCLs in CBCT images, while the segmented masks can be used to further improve OCLs dental diagnostic efficiency.

UNASSIGNED: Iatrogenic blood loss is an important cause of neonatal anemia. In this study, a spreadsheet tool was developed to reduce blood collection, providing a new idea for the prevention of iatrogenic blood loss in newborns.
UNASSIGNED: Based on hematocrit, minimum test volume and dead volume, a new tool was to calculate the minimum blood collection volume and the number of containers required for the test portfolio. We collected data from October 2022 to October 2023 from Xiamen Maternal and Child Health Hospital for analysis and validation.
UNASSIGNED: During this year, there were 16,434 patients and 13,696 plasma/serological samples in the neonatology department. Among them, there were 8 test combinations of greater than 1%, and 9490 samples in total. According to the hospital manual, the recommended amount of blood collection is 27,534 ml and 9490 containers. Through the analysis of this tool, total blood collection was 8864.77 ml, marked qnantity of upward containers (closest level to the calculated blood collection volume) was 10301 ml, and the amount of containers was 8835, which decreased by 67.8%, 62.58% and 6.9% respectively. Besides, if the hematocrit information cannot be obtained in advance and the high hematocrit is calculated as 0.8, the recommended amount of blood collection is 14334.3 ml, and the marked amount of the upward container markering is 17340 ml, decreasing by 47.9% and 37.02% respectively.
UNASSIGNED: We have developed an auxiliary tool that can manage neonatal blood specimen collection in a fine and personalized way and can be applied among different laboratory instruments by parameters modification.