Age- and sex-specific associations of frailty with mortality and healthcare utilization in community-dwelling adults from ontario, Canada.

BACKGROUND: Understanding how health trajectories are related to the likelihood of adverse outcomes and healthcare utilization is key to planning effective strategies for improving health span and the delivery of care to older adults. Frailty measures are useful tools for risk stratification in community-based and primary care settings, although their effectiveness in adults younger than 60 is not well described. METHODS: We performed a 10-year retrospective analysis of secondary data from the Ontario Health Study, which included 161,149 adults aged ≥ 18. Outcomes including all-cause mortality and hospital admissions were obtained through linkage to ICES administrative databases with a median follow-up of 7.1-years. Frailty was characterized using a 30-item frailty index. RESULTS: Frailty increased linearly with age and was higher for women at all ages. A 0.1-increase in frailty was significantly associated with mortality (HR = 1.47), the total number of outpatient (IRR = 1.35) and inpatient (IRR = 1.60) admissions over time, and length of stay (IRR = 1.12). However, with exception to length of stay, these estimates differed depending on age and sex. The hazard of death associated with frailty was greater at younger ages, particularly in women. Associations with admissions also decreased with age, similarly between sexes for outpatient visits and more so in men for inpatient. CONCLUSIONS: These findings suggest that frailty is an important health construct for both younger and older adults. Hence targeted interventions to reduce the impact of frailty before the age of 60 would likely have important economic and social implications in both the short- and long-term.

Mortality and risk factors associated with misdiagnosis of acute aortic syndrome in Ontario, Canada: a population-based study.

INTRODUCTION: Acute aortic syndrome (AAS) is a life-threatening aortic emergency. It describes three diagnoses: acute aortic dissection, acute intramural haematoma and penetrating atherosclerotic ulcer. Unfortunately, there are no accurate estimates of the miss rate for AAS, risk factors for missed diagnosis or its effect on outcomes. METHODS: A population-based retrospective cohort study of anonymously linked data for residents of Ontario, Canada, was carried out. Incident cases of AAS were identified between 2003 and 2018 using a validated algorithm based on ICD codes and death. Before multivariate modelling, all categorical variables were analysed for an association with missed AAS diagnosis using χ2 tests. These preliminary analyses were unadjusted for clustering or any covariates. Finally, we performed multilevel logistic regression analysis using a generalised linear mixed model approach to model the probability of a missed case occurring. RESULTS: There were 1299 cases of AAS (age mean (SD) 68.03±14.70, woman 500 (38.5%), rural areas (n=111, 8.55%)) over the study period. Missed cases accounted for 163 (12.5%) of the cohort. Mortality (non-missed AAS 59.7% vs missed AAS 54.6%) and surgical intervention (non-missed AAS 31% vs missed AAS 30.7%) were similar in missed and non-missed cases. However, lower acuity (Canadian triage acuity scale >2 (OR 2.45 95% CI 1.71 to 3.52) (the scale is from 1 to 5, with 1 indicating high acuity) had a higher odds of being a missed case and non-ambulatory presentation (OR 0.47 95% CI 0.33 to 0.67) and presenting to a teaching (OR 0.60 95% CI 0.40 to 0.90)) or cardiac centre (OR 0.41 95% CI 0.27 to 0.62) were associated with a lower odds of being a missed case. CONCLUSIONS: The high rate of misdiagnosis has remained stable for over a decade. Non-teaching and non-cardiac hospitals had a higher incidence of missed cases. Mortality and rates of surgery were not associated with a missed diagnosis of AAS. Educational interventions should be prioritised in non-teaching hospitals and non-cardiac centres.

Can you accurately rule out acute aortic syndrome by restricting imaging of the aorta to the area of the patient's pain?

BACKGROUND: Acute aortic syndrome (AAS) is a life-threatening condition necessitating timely and accurate diagnosis for appropriate treatment. Currently, the only way to rule out the diagnosis is advanced imaging. The most accessible is computed tomography of the entire aorta. Most scans are negative, exposing patients to radiation, increased time in the emergency department (ED), and non-significant incidental findings. This study investigated whether restricting imaging to the area of aortic-related pain accurately rules out AAS. METHODS: A health records review was conducted on consecutive cases from three academic EDs between 2015 and 2020. Data were extracted and verified from multiple sources. Participants included adults diagnosed with AAS based on radiological evidence. The diagnostic performance of the restricted imaging strategy was assessed; sensitivity and likelihood ratios with 95% confidence intervals were calculated. RESULTS: Data from 149 cases of AAS were collected, with the majority presenting with chest pain (46%) or abdominal pain (24%). The restricted imaging strategy demonstrated a sensitivity of 96% (95% CI 91.4-98.5%) in ruling out AAS. In a subset of patients with systolic blood pressure > 90 mmHg and without aortic aneurysm/repair (n = 86), the sensitivity was 100% (95% CI 96-100%). CONCLUSION: Restricting imaging to the area of pain in hemodynamically stable patients without known aortic aneurysm provides a highly sensitive approach to ruling out AAS.

AEDNav: indoor navigation for locating automated external defibrillator.

BACKGROUND: In a sudden cardiac arrest, starting CPR and applying an AED immediately are the two highest resuscitation priorities. Many existing mobile applications have been developed to assist users in locating a nearby AED. However, these applications do not provide indoor navigation to the AED location. The time required to locate an AED inside a building due to a lack of indoor navigation systems will reduce the patient's chance of survival. The existing indoor navigation solutions either require special hardware, a large dataset or a significant amount of initial work. These requirements make these systems not viable for implementation on a large-scale. METHODS: The proposed system collects Wi-Fi information from the existing devices and the path's magnetic information using a smartphone to guide the user from a starting point to an AED. The information collected is processed using four techniques: turn detection method, Magnetic data pattern matching method, Wi-Fi fingerprinting method and Closest Wi-Fi location method to estimate user location. The user location estimations from all four techniques are further processed to determine the user's location on the path, which is then used to guide the user to the AED location. RESULTS: The four techniques used in the proposed system Turn detection, Magnetic data pattern matching, Closest Wi-Fi location and Wi-Fi fingerprinting can individually achieve the accuracy of 80% with the error distance ± 9.4 m, ± 2.4 m, ± 4.6 m, and ± 4.6 m respectively. These four techniques, applied individually, may not always provide stable results. Combining these techniques results in a robust system with an overall accuracy of 80% with an error distance of ± 2.74 m. In comparison, the proposed system's accuracy is higher than the existing systems that use Wi-Fi and magnetic data. CONCLUSION: This research proposes a novel approach that requires no special hardware, large scale data or significant initial work to provide indoor navigation. The proposed system AEDNav can achieve an accuracy similar to the existing indoor navigation systems. Implementing this indoor navigation system could reduce the time to locate an AED and ultimately increase patient survival during sudden cardiac arrest.

Impact of pre-operative abdominal MRI on survival for patients with resected pancreatic carcinoma: a population-based study.

Background: This study determined the impact of pre-operative abdominal MRI on all-cause mortality for patients with resected PDAC. Methods: All adult (≥18 years) PDAC patients who underwent pancreatectomy between January 2011 and December 2022 in Ontario, Canada, were identified for this population-based cohort study (ICD-O-3 codes: C250, C251, C252, C253, C257, C258). Patient demographics, comorbidities, PDAC stage, medical and surgical management, and survival data were sourced from multiple linked provincial administrative databases at ICES. All-cause mortality was compared between patients with and without a pre-operative abdominal MRI after controlling for multiple covariates. Findings: A cohort of 4579 patients consisted of 2432 men (53.1%) and 2147 women (46.9%) with a mean age of 65.2 years (standard deviation: 11.2 years); 2998 (65.5%) died while 1581 (34.5%) survived. Median follow-up duration post-resection was 22.4 months (interquartile range: 10.8-48.8 months), and median survival post-pancreatectomy was 25.9 months (95% confidence interval [95% CI]: 24.8, 27.5). Patients who underwent a pre-operative abdominal MRI had a median survival of 33.1 months (95% CI: 30.7, 37.2) compared to 21.1 months (95% CI: 19.8, 22.6) for all others. A total of 2354/4579 (51.4%) patients underwent a pre-operative abdominal MRI, which was associated with a 17.2% (95% CI: 11.0, 23.1) decrease in the rate of all-cause mortality, with an adjusted hazard ratio (aHR) of 0.828 (95% CI: 0.769, 0.890). Interpretation: Pre-operative abdominal MRI was associated with improved overall survival for PDAC patients who underwent pancreatectomy, possibly due to better detection of liver metastases than CT. Funding: Northern Ontario Academic Medicine Association (NOAMA) Clinical Innovation Fund.

An assessment of mass casualty triage systems using the Alberta trauma registry.

OBJECTIVE: Triage is the process of identifying patients with both the greatest clinical need and the greatest likelihood of benefit in the setting of limited clinical resources. The primary objective of this study was to assess the ability of formal mass casualty incident triage tools to identify patients requiring urgent lifesaving interventions. METHODS: Data from the Alberta Trauma Registry (ATR) was used to assess seven triage tools: START, JumpSTART, SALT, RAMP, MPTT, BCD and MITT. Clinical data captured in the ATR was used to determine which triage category each of the seven tools would have applied to each patient. These categorizations were compared to a reference standard definition based on the patients' need for specific urgent lifesaving interventions. RESULTS: Of the 9448 records that were captured 8652 were included in our analysis. The most sensitive triage tool was MPTT, which demonstrated a sensitivity of 0.76 (0.75, 0.78). Four of the seven triage tools evaluated had sensitivities below 0.45. JumpSTART had the lowest sensitivity and the highest under-triage rate for pediatric patients. All the triage tools evaluated had a moderate to high positive predictive value (> 0.67) for patients who had experienced penetrating trauma. CONCLUSIONS: There was a wide range in the sensitivity of triage tools to identify patients requiring urgent lifesaving interventions. MPTT, BCD and MITT were the most sensitive triage tools assessed. All of the triage tools assessed should be employed with caution during mass casualty incidents as they may fail to identify a large proportion of patients requiring urgent lifesaving interventions.

ABSTRAIT: OBJECTIFS: Le triage est le processus qui consiste à identifier les patients qui ont à la fois les besoins cliniques les plus importants et les avantages les plus probables dans le contexte de ressources cliniques limitées. Le principal objectif de cette étude était d'évaluer la capacité des outils formels de triage des incidents impliquant des blessés de masse à identifier les patients nécessitant des interventions urgentes de sauvetage. MéTHODES: Les données du Alberta Trauma Registry (ATR) ont été utilisées pour évaluer sept outils de triage : START, JumpSTART, SALT, RAMP, MPTT, BCD et MITT. Les données cliniques saisies dans l'AR ont servi à déterminer la catégorie de triage que chacun des sept outils aurait appliquée à chaque patient. Ces catégories ont été comparées à une définition standard de référence fondée sur le besoin des patients d'interventions de sauvetage urgentes. RéSULTATS: Sur les 9448 enregistrements saisis, 8652 ont été inclus dans notre analyse. L'outil de triage le plus sensible était le TPMD, qui présentait une sensibilité de 0,76 (0,75, 0,78). Quatre des sept outils de triage évalués présentaient une sensibilité inférieure à 0,45. JumpSTART avait la sensibilité la plus faible et le taux de sous-triage le plus élevé chez les patients pédiatriques. Tous les outils de triage évalués avaient une valeur prédictive positive modérée à élevée (>0,67) pour les patients qui avaient subi un traumatisme pénétrant. CONCLUSION: La sensibilité des outils de triage pour identifier les patients nécessitant des interventions de sauvetage urgentes variait grandement. Les outils de triage les plus sensibles ont été le TCPR, le BCD et le MITT. Tous les outils de triage évalués doivent être utilisés avec prudence lors d'incidents impliquant des pertes massives, car ils peuvent ne pas identifier une grande proportion de patients nécessitant des interventions de sauvetage urgentes.

Efficacy of Fascia Iliaca Compartment Blocks in Proximal Femoral Fractures in the Prehospital Setting: A Systematic Review and Meta-Analysis.

INTRODUCTION: Proximal femoral fractures are characterized as one of the most common and most painful injuries sustained by patients of all ages and are associated with high rates of oligoanalgesia in the prehospital setting. Current treatments include oral and parenteral opiates and sedative agents, however regional anesthesia techniques for pain relief may provide superior analgesia with lower risk of side effects during patient transportation. The fascia iliaca compartment block (FICB) is an inexpensive treatment which is performed with minimal additional equipment, ultimately making it suitable in prehospital settings. PROBLEM: In adult patients sustaining proximal femoral fractures in the prehospital setting, what is the effect of the FICB on non-verbal pain scores (NVPS), patient satisfaction, success rate, and adverse events compared to traditional analgesic techniques? METHODS: A librarian-assisted literature search was conducted of the Cochrane Database, Ovid MEDLINE, PubMed, Ovid EMBASE, Scopus, and Web of Science indexes. Additionally, reference lists for potential review articles from the British Journal of Anesthesia, the College of Anesthetists of Ireland, the Journal of Prehospital Emergency Care, Annales Francaises d'Anesthesie et Réanimation, and the Scandinavian Journal of Trauma, Resuscitation, and Emergency Medicine were reviewed. Databases and journals were searched during the period from January 1, 1980 through July 1, 2022. Each study was scrutinized for quality and validity and was assigned a level of evidence as per Oxford Center for Evidence-Based Medicine guidelines. RESULTS: Five studies involving 340 patients were included (ie, two randomized control trials [RCTs], two observational studies, and one prospective observational study). Pain scores decreased after prehospital FICB across all included studies by a mean of 6.65 points (5.25 - 7.5) on the NVPS. Out of the total 257 FICBs conducted, there was a success rate of 230 (89.3%). Of these, only two serious adverse events were recorded, both of which related to local analgesia toxicity. Neither resulted in long-term sequelae and only one required treatment. CONCLUSION: Use of FICBs results in a significant decrease in NVPS in the prehospital setting, and they are ultimately suitable as regional analgesic techniques for proximal femur fractures. It carries a low risk of adverse events and may be performed by health care practitioners of various backgrounds with suitable training. The results suggest that FICBs are more effective for pain management than parenteral or oral opiates and sedative agents alone and can be used as an appropriate adjunct to pain management.

Epidemiology, mortality and miss rate of acute aortic syndrome in Ontario, Canada: a population-based study.

INTRODUCTION: Acute aortic syndrome (AAS) is a life-threatening emergency. It describes three distinct diagnoses: acute aortic dissection, acute intramural hematoma and penetrating atherosclerotic ulcer. There are currently no accurate estimates for incidence, mortality or misdiagnosis. Our objectives were to determine the incidence, mortality and miss rate of acute aortic syndrome in the emergency department (ED). METHODS: A population-based retrospective cohort study of anonymously linked data for residents of Ontario, Canada, was carried out. Incident cases of acute aortic syndrome were identified between 2003 and 2018 using a validated algorithm based on ICD-10 codes and death. Incidence (number of cases/population of Ontario), mortality, and miss rate were calculated. Miss rate was defined as when a patient was seen in the ED within 14 days prior to an acute aortic syndrome diagnosis with a presenting complaint consistent with acute aortic syndrome. RESULTS: There were 1299 cases of acute aortic syndrome over the study period [age mean (SD) 68.03 ± 14.70; female (n = 500, 38.5%); rural areas (n = 111, 8.6%)]. The overall annual incidence for acute aortic syndrome was 0.61 per 100,000. One year mortality decreased from 47.4 to 29.1%. ED mortality was 14.9%. In the 14 days prior to diagnosis 12.5% of patients were seen in the ED with a presentation consistent with acute aortic syndrome. CONCLUSIONS: Annual incidence of acute aortic syndrome was found to be lower than other population-based studies. Also, the burden of mortality is seen in the ED. Education initiatives should focus on the identification of acute aortic syndrome in the ED to address mortality and miss rate.

RéSUMé: INTRODUCTION: Le syndrome aortique aigu (SAA) est une urgence qui met la vie en danger. Il décrit trois diagnostics distincts: dissection aortique aiguë, hématome intramural aigu et ulcère athéroscléreux pénétrant. Il n'existe actuellement aucune estimation précise de l'incidence, de la mortalité ou des diagnostics erronés. Nos objectifs étaient de déterminer l'incidence, la mortalité et le taux d'échec du syndrome aortique aigu dans le service des urgences (SU). MéTHODES: Une étude de cohorte rétrospective basée sur la population a été réalisée à partir de données liées anonymement pour les résidents de l'Ontario, Canada. Les cas incidents de syndrome aortique aigu ont été identifiés entre 2003-2018 à l'aide d'un algorithme validé basé sur les codes CIM-10 et les décès. L'incidence (nombre de cas/population de l'Ontario), la mortalité et le taux d'absence ont été calculés. Le taux d'omission a été défini comme le cas où un patient a été vu à l'urgence dans les 14 jours précédant un diagnostic de syndrome aortique aigu et que la plainte était conforme au syndrome aortique aigu. RéSULTATS: Il y a eu 1 299 cas de syndrome aortique aigu pendant la période d'étude (âge moyen (ET) 68,03 ±14,70 ; femmes (n = 500, 38,5 %) ; zones rurales (n = 111, 8,6%)). L'incidence annuelle globale du syndrome aortique aigu était de 0,61 pour 100 000. La mortalité à un an a diminué de 47,4 % à 29,1 %. La mortalité aux urgences était de 14,9 %. Au cours des 14 jours précédant le diagnostic, 12,5 % des patients ont été vus aux urgences avec une présentation compatible avec le syndrome aortique aigu. CONCLUSIONS: L'incidence annuelle de syndrome aortique aigu s'est avérée inférieure à celle d'autres études basées sur la population. En outre, le poids de la mortalité est observé aux urgences. Les initiatives de formation devraient se concentrer sur l'identification des syndrome aortique aigu aux urgences afin de réduire la mortalité et le taux d'échec.

Evaluation of the Canadian Clinical Practice Guidelines Risk Prediction Tool for Acute Aortic Syndrome: The RIPP Score.

Introduction: Acute aortic syndrome (AAS) is a rare clinical syndrome with a high mortality rate. The Canadian clinical practice guideline for the diagnosis of AAS was developed in order to reduce the frequency of misdiagnoses. As part of the guideline, a clinical decision aid was developed to facilitate clinician decision-making (RIPP score). The aim of this study is to validate the diagnostic accuracy of this tool and assess its performance in comparison to other risk prediction tools that have been developed. Methods: This was a historical case-control study. Consecutive cases and controls were recruited from three academic emergency departments from 2002-2020. Cases were identified through an admission, discharge, or death certificated diagnosis of acute aortic syndrome. Controls were identified through presenting complaint of chest, abdominal, flank, back pain, and/or perfusion deficit. We compared the clinical decision tools' C statistic and used the DeLong method to test for the significance of these differences and report sensitivity and specificity with 95% confidence intervals. Results: We collected data on 379 cases of acute aortic syndrome and 1340 potential eligible controls; 379 patients were randomly selected from the final population. The RIPP score had a sensitivity of 99.7% (98.54-99.99). This higher sensitivity resulted in a lower specificity (53%) compared to the other clinical decision aids (63-86%). The DeLong comparison of the C statistics found that the RIPP score had a higher C statistic than the ADDRS (-0.0423 (95% confidence interval -0.07-0.02); P < 0.0009) and the AORTAs score (-0.05 (-0.07 to -0.02); P = 0.0002), no difference compared to the Lovy decision tool (0.02 (95% CI -0.01-0.05 P < 0.25)) and decreased compared to the Von Kodolitsch decision tool (0.04 (95% CI 0.01-0.07 P < 0.008)). Conclusion: The Canadian clinical practice guideline's AAS clinical decision aid is a highly sensitive tool that uses readily available clinical information. It has the potential to improve diagnosis of AAS in the emergency department.

Staffing rural emergency departments in Ontario: The who, what and where.

Introduction: The emergency department (ED) in rural communities is essential for providing care to patients with urgent medical issues and those unable to access primary care. Recent physician staffing shortages have put many EDs at risk of temporary closure. Our goal was to describe the demographics and practices of the rural physicians providing emergency medicine services across Ontario in order to inform health human resource planning. Methods: The ICES Physician database (IPDB) and Ontario Health Insurance Plan (OHIP) billing database from 2017 were used in this retrospective cohort study. Rural physician data were analysed for demographic, practice region and certification information. Sentinel billing codes (i.e., a billing code unique to a particular clinical service) were used to define 18 unique physician services. Results: A total of 1192 physicians from the IPDB met inclusion as rural generalist physicians out of a total of 14,443 family physicians in Ontario. From this physician population, a total of 620 physicians practised emergency medicine which accounted for 33% of their days worked on average. The majority of physicians practising emergency medicine were between the ages of 30 and 49 and in their first decade of practice. The most common services in addition to emergency medicine were clinic, hospital medicine, palliative care and mental health. Conclusion: This study provides insight into the practice patterns of rural physicians and the basis for better targeted physician workforce-forecasting models. A new approach to education and training pathways, recruitment and retention initiatives and rural health service delivery models is needed to ensure better health outcomes for our rural population.

Résumé Introduction: Le service d'urgence des communautés rurales est essentiel pour la prise en charge des patients présentant des problèmes médicaux urgents et de ceux qui ne peuvent accéder aux soins primaires. En raison de la récente pénurie de médecins, de nombreux services d'urgence risquent de fermer temporairement. Notre objectif était de décrire les caractéristiques démographiques et les pratiques des médecins ruraux qui fournissent des services de médecine d'urgence en Ontario afin d'éclairer la planification des ressources humaines en santé. Méthodes: La base de données des médecins de l'ICES (IPDB) et la base de données de facturation de l'assurance-santé de l'Ontario (OHIP) de 2017 ont été utilisées dans cette étude de cohorte rétrospective. Les données sur les médecins ruraux ont été analysées pour obtenir des renseignements sur la démographie, la région de pratique et la certification. Les codes de facturation sentinelle (c'est-à-dire un code de facturation unique pour un service clinique particulier) ont été utilisés pour définir 18 services médicaux uniques. Résultats: Sur un total de 14 443 médecins de famille en Ontario, 1 192 médecins de l'IPDB ont été inclus en tant que médecins généralistes ruraux. Parmi cette population de médecins, 620 pratiquaient la médecine d'urgence, ce qui représentait 33% de leurs jours de travail en moyenne. La majorité des médecins qui pratiquaient la médecine d'urgence étaient âgés de 30 à 49 ans et en étaient à leur première décennie de pratique. Les services les plus courants en plus de la médecine d'urgence étaient la clinique, la médecine hospitalière, les soins palliatifs et la santé mentale. Conclusion: Cette étude permet de mieux comprendre les modes de pratique des médecins ruraux et de jeter les bases de modèles de prévision des effectifs médicaux mieux ciblés. Une nouvelle approche des parcours d'éducation et de formation, des initiatives de recrutement et de rétention et des modèles de prestation de services de santé en milieu rural est nécessaire pour garantir de meilleurs résultats en matière de santé pour notre population rurale. Mots-clés: Médecine d'urgence, médecins ruraux, planification des ressources humaines en santé.

Medical Text Simplification Using Reinforcement Learning (TESLEA): Deep Learning-Based Text Simplification Approach.

BACKGROUND: In most cases, the abstracts of articles in the medical domain are publicly available. Although these are accessible by everyone, they are hard to comprehend for a wider audience due to the complex medical vocabulary. Thus, simplifying these complex abstracts is essential to make medical research accessible to the general public. OBJECTIVE: This study aims to develop a deep learning-based text simplification (TS) approach that converts complex medical text into a simpler version while maintaining the quality of the generated text. METHODS: A TS approach using reinforcement learning and transformer-based language models was developed. Relevance reward, Flesch-Kincaid reward, and lexical simplicity reward were optimized to help simplify jargon-dense complex medical paragraphs to their simpler versions while retaining the quality of the text. The model was trained using 3568 complex-simple medical paragraphs and evaluated on 480 paragraphs via the help of automated metrics and human annotation. RESULTS: The proposed method outperformed previous baselines on Flesch-Kincaid scores (11.84) and achieved comparable performance with other baselines when measured using ROUGE-1 (0.39), ROUGE-2 (0.11), and SARI scores (0.40). Manual evaluation showed that percentage agreement between human annotators was more than 70% when factors such as fluency, coherence, and adequacy were considered. CONCLUSIONS: A unique medical TS approach is successfully developed that leverages reinforcement learning and accurately simplifies complex medical paragraphs, thereby increasing their readability. The proposed TS approach can be applied to automatically generate simplified text for complex medical text data, which would enhance the accessibility of biomedical research to a wider audience.

Developing a machine learning model to predict patient need for computed tomography imaging in the emergency department.

Overcrowding is a well-known problem in hospitals and emergency departments (ED) that can negatively impact patients and staff. This study aims to present a machine learning model to detect a patient's need for a Computed Tomography (CT) exam in the emergency department at the earliest possible time. The data for this work was collected from ED at Thunder Bay Regional Health Sciences Centre over one year (05/2016-05/2017) and contained administrative triage information. The target outcome was whether or not a patient required a CT exam. Multiple combinations of text embedding methods, machine learning algorithms, and data resampling methods were experimented with to find the optimal model for this task. The final model was trained with 81, 118 visits and tested on a hold-out test set with a size of 9, 013 visits. The best model achieved a ROC AUC score of 0.86 and had a sensitivity of 87.3% and specificity of 70.9%. The most important factors that led to a CT scan order were found to be chief complaint, treatment area, and triage acuity. The proposed model was able to successfully identify patients needing a CT using administrative triage data that is available at the initial stage of a patient's arrival. By determining that a CT scan is needed early in the patient's visit, the ED can allocate resources to ensure these investigations are completed quickly and patient flow is maintained to reduce overcrowding.

Starting, building and sustaining a program of research in emergency medicine in Canada.

OBJECTIVE: To develop pragmatic recommendations for starting, building and sustaining a program of research in emergency medicine (EM) in Canada at sites with limited infrastructure and/or prior research experience. METHODS: At the direction of the Canadian Association of Emergency Physicians (CAEP) academic section, we assembled an expert panel of 10 EM researchers with experience building programs of research. Using a modified Delphi approach, our panel developed initial recommendations for (1) starting, (2) building, and (3) sustaining a program of research in EM. These recommendations were peer-reviewed by emergency physicians and researchers from each of the panelist's home institutions and tested for face and construct validity, as well as ease of comprehension. The recommendations were then iteratively revised based on feedback and suggestions from peer review and amended again after being presented at the 2020 CAEP academic symposium. RESULTS: Our panel created 15 pragmatic recommendations for those intending to start (formal research training, find mentors, local support, develop a niche, start small), build (funding, build a team, collaborate, publish, expect failure) and sustain (become a mentor, obtain leadership roles, lead national studies, gain influence, prioritize wellness) a program of EM research in centers without an established research culture. Additionally, we suggest four recommendations for department leads aiming to foster a program of research within their departments. CONCLUSION: These recommendations serve as guidance for centres wanting to establish a program of research in EM.

RéSUMé: OBJECTIF: Développer des recommandations pragmatiques pour lancer, établir et soutenir un programme de recherche en médecine d'urgence (MU) au Canada dans des sites avec une infrastructure et / ou une expérience de recherche antérieure limitée. MéTHODES: Sous la direction de la section académique de l'Association canadienne des médecins d'urgence (ACMU), nous avons réuni un comité d'experts de 10 chercheurs en MU possédant de l'expérience dans le développement des programmes de recherche. En utilisant une approche Delphi modifiée, notre comité a mis en place des recommandations initiales pour 1) lancer, 2) établir et 3) soutenir un programme de recherche en MU. Ces recommandations ont été examinées par des médecins d'urgence et des chercheurs appartenant aux établissements d'origine des chacun des membres de comité et ont été testées pour leur validité apparente et conceptuelle, ainsi que leur facilité de compréhension. Les recommandations ont ensuite été fréquemment révisées en fonction des commentaires et suggestions de l'examen des pairs et modifiées à nouveau après avoir été présentées au symposium académique 2020 de l'ACMU. RéSULTATS: Notre comité a créé 15 recommandations pragmatiques pour ceux qui ont l'intention de lancer (formation formelle en recherche, trouver des mentors, soutien local, développer un créneau, débuter à petite échelle), d'établir (financer, constituer une équipe, collaborer, publier, s'attendre à l'échec) et de soutenir (devenir un mentor, obtenir des rôles de leadership, diriger des études nationales, gagner en influence, prioriser le bien-être) un programme de recherche en MU dans des centres sans culture de la recherche établie. De plus, nous suggérons 4 recommandations aux responsables de département visant à promouvoir un programme de recherche au sein de leur département. CONCLUSION: Ces recommandations servent de guide aux centres qui souhaitent établir un programme de recherche en MU.

Epidemiologic features of medical emergencies in remote First Nations in northern Ontario: a cross-sectional descriptive study using air ambulance transport data.

BACKGROUND: For about 25 000 Ontarians living in remote northern First Nations communities, seeing a doctor in an emergency department requires flying in an airplane or helicopter. This study describes the demographic and epidemiologic characteristics of patients transported from these communities to access hospital-based emergency medical care. METHODS: In this cross-sectional descriptive study, we examined primary medical data on patient transportation from Ornge, the provincial medical air ambulance service provider, for 26 remote Nishnawbe Aski Nation communities in northern Ontario from 2012 to 2016. We described these transports using univariate descriptive statistics. RESULTS: Over the 5-year study period, 10 538 patients (mean 2107.6 per year) were transported by Ornge from the 26 communities. Transport incidence ranged from 9.2 to 9.5 per 100 on-reserve population per year. Women aged 65 years or more had the highest transport incidence (25.9 per 100). Girls aged 5-9 years had the lowest mean incidence (2.1 per 100). Gastrointestinal issues accounted for 13.3% of transfers. Neurologic issues, respiratory issues and trauma each accounted for about 11% of transfers, and cardiac issues for 9.6%. Patients with obstetric issues accounted for 7.6% of transfers per year, and toxicologic emergencies for 7.5%. INTERPRETATION: This study provides the epidemiologic foundation to improve emergency care and emergency transport from remote First Nation communities in Ontario.

Assessing Canadians Health Activity and Nutritional Habits Through Social Media.

When conducting data analysis in the twenty-first century, social media is crucial to the analysis due to the ability to provide information on a variety of topics such as health, food, feedback on products, and many others. Presently, users utilize social media to share their daily lifestyles. For example, travel locations, exercises, and food are common subjects of social media posts. By analyzing such information collected from users, health of the general population can be gauged. This analysis can become an integral part of federal efforts to study the health of a nation's people on a large scale. In this paper, we focus on such efforts from a Canadian lens. Public health is becoming a primary concern for many governments around the world. It is believed that it is necessary to analyze the current scenario within a given population before creating any new policies. Traditionally, governments use a variety of ways to gauge the flavor for any new policy including door to door surveys, a national level census, or hospital information to decide health policies. This information is limited and sometimes takes a long time to collect and analyze sufficiently enough to aid in decision making. In this paper, our approach is to solve such problems through the advancement of natural language processing algorithms and large scale data analysis. Our in-depth results show that the proposed method provides a viable solution in less time with the same accuracy when compared to traditional methods.

Developing emergency department physician shift schedules optimized to meet patient demand.

OBJECTIVES: 1) To assess temporal patterns in historical patient arrival rates in an emergency department (ED) to determine the appropriate number of shift schedules in an acute care area and a fast-track clinic and 2) to determine whether physician scheduling can be improved by aligning physician productivity with patient arrivals using an optimization planning model. METHODS: Historical data were statistically analyzed to determine whether the number of patients arriving at the ED varied by weekday, weekend, or holiday weekend. Poisson-based generalized additive models were used to develop models of patient arrival rate throughout the day. A mathematical programming model was used to produce an optimal ED shift schedule for the estimated patient arrival rates. We compared the current physician schedule to three other scheduling scenarios: 1) a revised schedule produced by the planning model, 2) the revised schedule with an additional acute care physician, and 3) the revised schedule with an additional fast-track clinic physician. RESULTS: Statistical modelling found that patient arrival rates were different for acute care versus fast-track clinics; the patterns in arrivals followed essentially the same daily pattern in the acute care area; and arrival patterns differed on weekdays versus weekends in the fast-track clinic. The planning model reduced the unmet patient demand (i.e., the average number of patients arriving at the ED beyond the average physician productivity) by 19%, 39%, and 69% for the three scenarios examined. CONCLUSIONS: The planning model improved the shift schedules by aligning physician productivity with patient arrivals at the ED.