Telemedicine during the COVID-19 pandemic

2021 ◽  
Vol 103-B (6 Supple A) ◽  
pp. 196-204
Author(s):  
Jeffrey Shi Chen ◽  
Daniel B. Buchalter ◽  
Chelsea S. Sicat ◽  
Vinay K. Aggarwal ◽  
Matthew S. Hepinstall ◽  
...  

Aims The COVID-19 pandemic led to a swift adoption of telehealth in orthopaedic surgery. This study aimed to analyze the satisfaction of patients and surgeons with the rapid expansion of telehealth at this time within the division of adult reconstructive surgery at a major urban academic tertiary hospital. Methods A total of 334 patients underging arthroplasty of the hip or knee who completed a telemedicine visit between 30 March and 30 April 2020 were sent a 14-question survey, scored on a five-point Likert scale. Eight adult reconstructive surgeons who used telemedicine during this time were sent a separate 14-question survey at the end of the study period. Factors influencing patient satisfaction were determined using univariate and multivariate ordinal logistic regression modelling. Results A total of 68 patients (20.4%) and 100% of the surgeons completed the surveys. Patients were “Satisfied” with their telemedicine visits (4.10/5.00 (SD 0.98)) and 19 (27.9%) would prefer telemedicine to in-person visits in the absence of COVID-19. Multivariate ordinal logistic regression modelling revealed that patients were more likely to be satisfied if their surgeon effectively responded to their questions or concerns (odds ratio (OR) 3.977; 95% confidence interval (CI) 1.260 to 13.190; p = 0.019) and if their visit had a high audiovisual quality (OR 2.46; 95% CI 1.052 to 6.219; p = 0.042). Surgeons were “Satisfied” with their telemedicine experience (3.63/5.00 (SD 0.92)) and were “Fairly Confident” (4.00/5.00 (SD 0.53)) in their diagnostic accuracy despite finding the physical examinations to be only “Slightly Effective” (1.88/5.00 (SD 0.99)). Most adult reconstructive surgeons, seven of eight (87.5%) would continue to use telemedicine in the future. Conclusion Telemedicine emerged as a valuable tool during the COVID-19 pandemic. Patients undergoing arthroplasty and their surgeons were satisfied with telemedicine and see a role for its use after the pandemic. The audiovisual quality and the responsiveness of physicians to the concerns of patients determine their satisfaction. Future investigations should focus on improving the physical examination of patients through telemedicine and strategies for its widespread implementation. Cite this article: Bone Joint J 2021;103-B(6 Supple A):196–204.

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1602
Author(s):  
Katja E. Isaksen ◽  
Lori Linney ◽  
Helen Williamson ◽  
Elizabeth J. Norman ◽  
Nick J. Cave ◽  
...  

Working farm dogs are essential to many livestock farmers. Little is known about factors that influence dogs’ risk of being lost from work. This paper explores risk factors for farm dogs being lost through death, euthanasia and retirement. All enrolled dogs were working and a minimum of 18 months old. Five data collection rounds were performed over four years. Data about dogs were collected from owners and dogs were given physical examinations by veterinarians. Dogs that were lost from work were counted and owner-reported reasons for loss were recorded. Multivariable logistic regression modelling was used to investigate risk factors for loss. Of 589 dogs, 81 were lost from work. Of these, 59 dogs died or were euthanized and 22 were retired. Farm dogs tended to reach advanced ages, with 38% being 10 years or older when last examined. Acute injury or illness was the most commonly owner-reported reason for loss. Age group (p < 0.0001) and lameness (p = 0.04, OR = 1.8) significantly affected dogs’ risk of being lost. These results expand our knowledge about factors that affect health, welfare and work in farm dogs. Further investigation into reasons for lameness may help improve health and welfare in working farm dogs.


QJM ◽  
2009 ◽  
Vol 103 (1) ◽  
pp. 23-32 ◽  
Author(s):  
B. Silke ◽  
J. Kellett ◽  
T. Rooney ◽  
K. Bennett ◽  
D. O’Riordan

2021 ◽  
Vol 8 ◽  
Author(s):  
Robert A. Reed ◽  
Andrei S. Morgan ◽  
Jennifer Zeitlin ◽  
Pierre-Henri Jarreau ◽  
Héloïse Torchin ◽  
...  

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.


2019 ◽  
Vol 146 ◽  
pp. 962-976 ◽  
Author(s):  
Helios Chiri ◽  
Ana Julia Abascal ◽  
Sonia Castanedo ◽  
Raul Medina

2018 ◽  
Vol 52 ◽  
pp. 29-30
Author(s):  
Christian Rønn Hansen ◽  
Anders Bertelsen ◽  
Ruta Zukauskaite ◽  
Lars Johnsen ◽  
Uffe Bernchou ◽  
...  

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