Advancing language concordant care: a multimodal medical interpretation intervention.

BACKGROUND: Ensuring language concordant care through medical interpretation services (MIS) allows for accurate information sharing and positive healthcare experiences. The COVID-19 pandemic led to a regional halt of in-person interpreters, leaving only digital MIS options, such as phone and video. Due to longstanding institutional practices, and lack of accessibility and awareness of these options, digital MIS remained underused. A Multimodal Medical Interpretation Intervention (MMII) was developed and piloted to increase digital MIS usage by 25% over an 18-month intervention period for patients with limited English proficiency. METHODS: Applying quality improvement methodology, an intervention comprised digital MIS technology and education was trialled for 18 months. To assess intervention impact, the number of digital MIS minutes was measured monthly and compared before and after implementation. A questionnaire was developed and administered to determine healthcare providers' awareness, technology accessibility and perception of MIS integration in the clinical workflow. RESULTS: Digital MIS was used consistently from the beginning of the COVID-19 pandemic (March 2020) and over the subsequent 18 months. The total number of minutes of MIS use per month increased by 44% following implementation of our intervention. Healthcare providers indicated that digital MIS was vital in facilitating transparent communication with patients, and the MMII ensured awareness of and accessibility to the various MIS modalities. CONCLUSION: Implementation of the MMII allowed for an increase in digital MIS use in a hospital setting. Providing digital MIS access, education and training is a means to advance patient-centred and equitable care by improving accuracy of clinical assessments and communication.

Internal Medicine Virtual Specialist Assessment Program Reduces Emergency Department Transfers from Long-Term Care.

BACKGROUND: Transfers to emergency departments (EDs) from long-term care (LTC) can expose residents to care discontinuities and risks. Virtual platforms can increase the breadth of care available for residents within their facility, thus replacing transfers to EDs when safe and appropriate. The authors aimed to assess whether leveraging a virtual care platform at an LTC facility would reduce the number of transfers to EDs. METHODS: Data on the number of transfers to EDs were collected from January 2019 to October 2021 at an LTC facility. In June 2020 the home began using a virtual care platform that allowed residents to speak with specialist physicians through video and receive management plans remotely. The authors evaluated the Internal Medicine Virtual Specialist Program (IMVSP) using a pre-post study design by comparing the number of transfers to EDs and the proportion of transfers resulting in hospital admission before and after program implementation. Unstructured phone interviews were conducted with employees at the home to understand their experiences. RESULTS: The median number of transfers to EDs per month after program implementation showed a 13.0% reduction. The median proportion of these transfers resulting in hospital admission per month increased by 26.1%. Employees at the LTC home were satisfied with the program. CONCLUSION: The IMVSP reduced transfers to EDs and allowed for a higher proportion of transfers that resulted in hospital admission. Early access to specialist care via virtual platforms has important implications for improving accessibility to high-quality care for LTC residents and reducing risks associated with transfers.

Partnering to build surgical capacity in low-resource settings: a qualitative study of Canadian global surgeons.

OBJECTIVES: This qualitative study aimed to explore the perspectives of Canadian global surgeons with experience developing surgical education partnerships with low- and middle-income countries (LMICs) for the purpose of identifying factors for success. DESIGN: A purposive sample of leaders from global surgery programmes at Canadian Faculties of Medicine participated in virtual semi-structured interviews. A six-phase thematic analysis was performed using a constructivist lens on verbatim transcripts by three independent researchers. Key factors for success were thematically collated with constant comparison and inter-investigator triangulation in NVivo software until theoretical saturation was reached. PARTICIPANTS: Fifteen surgeons, representing 11 subspecialties at 6 Canadian academic institutions and a combined experience across 6 continents, were interviewed between January and June 2022. RESULTS: Four facilitators for success of global surgery training programmes were identified, with a strong undertone of relationship-building permeating all subthemes: (1) facilitative skill sets and infrastructure, (2) longitudinal engagement, (3) local ownership and (4) interpersonal humility. Participants defined facilitative skill sets to include demonstrated surgical competence and facilitative infrastructure to include pre-existing local networks, language congruency, sustainable funding and support from external organisations. They perceived longitudinal engagement as spanning multiple trips, enabled by strong personal motivation and arrangements at their home institutions. Ownership of projects by local champions, including in research output, was noted as key to preventing brain drain and catalysing a ripple effect of surgical trainees. Finally, interviewees emphasised interpersonal humility as being crucial to decolonising the institution of global surgery with cultural competence, reflexivity and sustainability. CONCLUSIONS: The interviewed surgeons perceived strong cross-cultural relationships as fundamental to all other dimensions of success when working in low-resource capacity-building. While this study presents a comprehensive Canadian perspective informed by high-profile leadership in global surgery, a parallel study highlighting LMIC-partners' perspectives will be critical to a more complete understanding of programme success.

Long wavelength light reduces the negative consequences of dim light at night.

Many patients with autism spectrum disorders (ASD) show disturbances in their sleep/wake cycles, and they may be particularly vulnerable to the impact of circadian disruptors. We have previously shown that a 2-weeks exposure to dim light at night (DLaN) disrupts diurnal rhythms, increases repetitive behaviors and reduces social interactions in contactin-associated protein-like 2 knock out (Cntnap2 KO) mice. The deleterious effects of DLaN may be mediated by intrinsically photosensitive retinal ganglion cells (ipRGCs) expressing the photopigment melanopsin, which is maximally sensitive to blue light (480 nm). In this study, the usage of a light-emitting diode array enabled us to shift the spectral properties of the DLaN while keeping the intensity of the illumination at 10 lx. First, we confirmed that the short-wavelength enriched lighting produced strong acute suppression of locomotor activity (masking), robust light-induced phase shifts, and cFos expression in the suprachiasmatic nucleus in wild-type (WT) mice, while the long-wavelength enriched lighting evoked much weaker responses. Opn4DTA mice, lacking the melanopsin expressing ipRGCs, were resistant to DLaN effects. Importantly, shifting the DLaN stimulus to longer wavelengths mitigated the negative impact on the activity rhythms and 'autistic' behaviors (i.e. reciprocal social interactions, repetitive grooming) in the Cntnap2 KO as well as in WT mice. The short-, but not the long-wavelength enriched, DLaN triggered cFos expression in in the basolateral amygdala (BLA) as well as in the peri-habenula region raising that possibility that these cell populations may mediate the effects. Broadly, our findings are consistent with the recommendation that spectral properties of light at night should be considered to optimize health in neurotypical as well as vulnerable populations.

Deep learning using multilayer perception improves the diagnostic acumen of spirometry: a single-centre Canadian study.

RATIONALE: Spirometry and plethysmography are the gold standard pulmonary function tests (PFT) for diagnosis and management of lung disease. Due to the inaccessibility of plethysmography, spirometry is often used alone but this leads to missed or misdiagnoses as spirometry cannot identify restrictive disease without plethysmography. We aimed to develop a deep learning model to improve interpretation of spirometry alone. METHODS: We built a multilayer perceptron model using full PFTs from 748 patients, interpreted according to international guidelines. Inputs included spirometry (forced vital capacity, forced expiratory volume in 1 s, forced mid-expiratory flow25-75), plethysmography (total lung capacity, residual volume) and biometrics (sex, age, height). The model was developed with 2582 PFTs from 477 patients, randomly divided into training (80%), validation (10%) and test (10%) sets, and refined using 1245 previously unseen PFTs from 271 patients, split 50/50 as validation (136 patients) and test (135 patients) sets. Only one test per patient was used for each of 10 experiments conducted for each input combination. The final model was compared with interpretation of 82 spirometry tests by 6 trained pulmonologists and a decision tree. RESULTS: Accuracies from the first 477 patients were similar when inputs included biometrics+spirometry+plethysmography (95%±3%) vs biometrics+spirometry (90%±2%). Model refinement with the next 271 patients improved accuracies with biometrics+pirometry (95%±2%) but no change for biometrics+spirometry+plethysmography (95%±2%). The final model significantly outperformed (94.67%±2.63%, p<0.01 for both) interpretation of 82 spirometry tests by the decision tree (75.61%±0.00%) and pulmonologists (66.67%±14.63%). CONCLUSIONS: Deep learning improves the diagnostic acumen of spirometry and classifies lung physiology better than pulmonologists with accuracies comparable to full PFTs.