Prioritize Patient Care Research Articles

Machine Learning and Causal Approaches to Predict Readmissions and Its Economic Consequences Among Canadian Patients With Heart Disease: Retrospective Study.

Unplanned patient readmissions within 30 days of discharge pose a substantial challenge in Canadian health care economics. To address this issue, risk stratification, machine learning, and linear regression paradigms have been proposed as potential predictive solutions. Ensemble machine learning methods, such as stacked ensemble models with boosted tree algorithms, have shown promise for early risk identification in specific patient groups. This study aims to implement an ensemble model with submodels for structured data, compare metrics, evaluate the impact of optimized data manipulation with principal component analysis on shorter readmissions, and quantitatively verify the causal relationship between expected length of stay (ELOS) and resource intensity weight (RIW) value for a comprehensive economic perspective. This retrospective study used Python 3.9 and streamlined libraries to analyze data obtained from the Discharge Abstract Database covering 2016 to 2021. The study used 2 sub-data sets, clinical and geographical data sets, to predict patient readmission and analyze its economic implications, respectively. A stacking classifier ensemble model was used after principal component analysis to predict patient readmission. Linear regression was performed to determine the relationship between RIW and ELOS. The ensemble model achieved precision and slightly higher recall (0.49 and 0.68), indicating a higher instance of false positives. The model was able to predict cases better than other models in the literature. Per the ensemble model, readmitted women and men aged 40 to 44 and 35 to 39 years, respectively, were more likely to use resources. The regression tables verified the causality of the model and confirmed the trend that patient readmission is much more costly than continued hospital stay without discharge for both the patient and health care system. This study validates the use of hybrid ensemble models for predicting economic cost models in health care with the goal of reducing the bureaucratic and utility costs associated with hospital readmissions. The availability of robust and efficient predictive models, as demonstrated in this study, can help hospitals focus more on patient care while maintaining low economic costs. This study predicts the relationship between ELOS and RIW, which can indirectly impact patient outcomes by reducing administrative tasks and physicians' burden, thereby reducing the cost burdens placed on patients. It is recommended that changes to the general ensemble model and linear regressions be made to analyze new numerical data for predicting hospital costs. Ultimately, the proposed work hopes to emphasize the advantages of implementing hybrid ensemble models in forecasting health care economic cost models, empowering hospitals to prioritize patient care while simultaneously decreasing administrative and bureaucratic expenses.

DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels.

The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, highlighting the need for accurate and timely risk prediction models that can prioritize patient care and allocate resources effectively. This study presents DeepCOVID-Fuse, a deep learning fusion model that predicts risk levels in patients with confirmed COVID-19 by combining chest radiographs (CXRs) and clinical variables. The study collected initial CXRs, clinical variables, and outcomes (i.e., mortality, intubation, hospital length of stay, Intensive care units (ICU) admission) from February to April 2020, with risk levels determined by the outcomes. The fusion model was trained on 1657 patients (Age: 58.30 ± 17.74; Female: 807) and validated on 428 patients (56.41 ± 17.03; 190) from the local healthcare system and tested on 439 patients (56.51 ± 17.78; 205) from a different holdout hospital. The performance of well-trained fusion models on full or partial modalities was compared using DeLong and McNemar tests. Results show that DeepCOVID-Fuse significantly (p < 0.05) outperformed models trained only on CXRs or clinical variables, with an accuracy of 0.658 and an area under the receiver operating characteristic curve (AUC) of 0.842. The fusion model achieves good outcome predictions even when only one of the modalities is used in testing, demonstrating its ability to learn better feature representations across different modalities during training.

Application of game-based online learning platform: Kahoot a formative evaluation tool to assess learning

AimThis educational innovation learning activity aims to describe the application of a game-based learning platform such as Kahoot, a formative evaluation tool to assess students’ learning. MethodsThe Kahoot game-based learning experience was implemented into a fundamentals course with 37 undergraduate nursing students. Six sessions from the Kahoot learning activity were introduced over a 15-week semester using six competencies from the Quality and Safety Education for Nurses (QSEN) framework. ResultsTwo reflective questions were given post-Kahoot sessions to assess students' perspectives about the learning activity. They attested that Kahoot helped them gain knowledge and improve their critical thinking skills to prioritize patient care. ConclusionKahoot game-based learning helps support students in achieving their learning outcomes. However, some barriers can hinder its implementation time and discourage students' efforts and achievements. The nursing faculty's roles are to assess the different learning styles of students before implementing Kahoot to see if it fits their learning needs because each student may construct their learning differently.

Predictors of Mortality among Patients Hospitalized with COVID-19 during the First Wave in India: A Multisite Case-Control Study.

Severe acute respiratory syndrome coronavirus 2 disease (COVID-19) has caused more than 6 million deaths globally. Understanding predictors of mortality will help in prioritizing patient care and preventive approaches. This was a multicentric, unmatched, hospital-based case-control study conducted in nine teaching hospitals in India. Cases were microbiologically confirmed COVID-19 patients who died in the hospital during the period of study and controls were microbiologically confirmed COVID-19 patients who were discharged from the same hospital after recovery. Cases were recruited sequentially from March 2020 until December-March 2021. All information regarding cases and controls was extracted retrospectively from the medical records of patients by trained physicians. Univariable and multivariable logistic regression was done to assess the association between various predictor variables and deaths due to COVID-19. A total of 2,431 patients (1,137 cases and 1,294 controls) were included in the study. The mean age of patients was 52.8 years (SD: 16.5 years), and 32.1% were females. Breathlessness was the most common symptom at the time of admission (53.2%). Increasing age (adjusted odds ratio [aOR]: 46-59 years, 3.4 [95% CI: 1.5-7.7]; 60-74 years, 4.1 [95% CI: 1.7-9.5]; and ≥ 75 years, 11.0 [95% CI: 4.0-30.6]); preexisting diabetes mellitus (aOR: 1.9 [95% CI: 1.2-2.9]); malignancy (aOR: 3.1 [95% CI: 1.3-7.8]); pulmonary tuberculosis (aOR: 3.3 [95% CI: 1.2-8.8]); breathlessness at the time of admission (aOR: 2.2 [95% CI: 1.4-3.5]); high quick Sequential Organ Failure Assessment score at the time of admission (aOR: 5.6 [95% CI: 2.7-11.4]); and oxygen saturation < 94% at the time of admission (aOR: 2.5 [95% CI: 1.6-3.9]) were associated with mortality due to COVID-19. These results can be used to prioritize patients who are at increased risk of death and to rationalize therapy to reduce mortality due to COVID-19.

Predictors of Mortality Among Post-COVID-19 Discharged Patients in Northern India: A Case-Control Study.

Introduction The post-discharge all-cause mortality of COVID-19 disease is known, but predictors for the same have not been studied as much. The objective of this study was to develop an understanding of predictors of mortality to guide in prioritizing patient care and preventive approaches. Methods This current research is a single-center unmatched case-control study conducted at a tertiary care center in northern India, between April and September 2022. The data were extracted retrospectively from the hospital's electronic medical records of patients with the assistance of trained physicians using a standardized data extraction sheet. Results A total of 184 patients were enrolled and were segregated into two groups, cases and control, with 92 in each. The mean age of patients was 49.3 ± 17.53 years. The mortality group had a higher mean age (53.24 ± 18.53 yrs) as compared to the control group (45.37 ± 15.58 yrs, p=0.002). Bivariate analysis revealed a significant difference in the two groups with respect to O2 saturation at the time of admission (case - 91.12 ± 12.49 %, control - 95.46 ± 5.01 %, p=0.003); maximum O2 flow rate (L/min) (case - 11.01 ± 22.2, control - 6.41 ± 13.31, p=0.04); ICU need (p=0.005), cancer (p=0.001), O2 requirement at discharge (p=0.001) and acute kidney injury (AKI; p=0.007). On multiple regression analysis, cancer (adjusted odds ratio (aOR) - 2.469; 95% CI1.183-5.150, p=0.016), ICU admission (aOR - 2.446; 95% CI1.212-4.938, p=0.013), oxygen at discharge (aOR - 2.340; 95% CI0.971-5.640, p=0.0586) and AKI (aOR - 5.6; 95% CI2.351- 13.370, p=0.00) only found to be significant. Conclusion Among the patients released from the hospital post-COVID-19 treatment, the following aspects oxygen requirement (2.3 times), malignancy (2.4 times), ICU admission (2.4 times), and AKI (5.6 times) are risk factors of mortality. The presence of these variables would warrant a close follow-up for these patients in order to decrease post-COVID mortality.

Development and validation of a pharmaceutical assessment screening tool to prioritise patient care in a tertiary care hospital.

Clinical pharmacy plays an integral role in optimizing inpatient care. Nevertheless, prioritising patient care remains a critical challenge for pharmacists in a hectic medical ward. In Malaysia, clinical pharmacy practice has a paucity of standardized tools to prioritise patient care. Our aim is to develop and validate a pharmaceutical assessment screening tool (PAST) to guide medical ward pharmacists in our local hospitals to effectively prioritise patient care. This study involved 2 major phases; (1) development of PAST through literature review and group discussion, (2) validation of PAST using a three-round Delphi survey. Twenty-four experts were invited by email to participate in the Delphi survey. In each round, experts were required to rate the relevance and completeness of PAST criteria and were given chance for open feedback. The 75% consensus benchmark was set and criteria with achieved consensus were retained in PAST. Experts' suggestions were considered and added into PAST for rating. After each round, experts were provided with anonymised feedback and results from the previous round. Three Delphi rounds resulted in the final tool (rearranged as mnemonic 'STORIMAP'). STORIMAP consists of 8 main criteria with 29 subcomponents. Marks are allocated for each criteria in STORIMAP which can be combined to a total of 15 marks. Patient acuity level is determined based on the final score and clerking priority is assigned accordingly. STORIMAP potentially serves as a useful tool to guide medical ward pharmacists to prioritise patients effectively, hence establishing acuity-based pharmaceutical care.

Organizational support and Nurse-Physician collaboration during SARS-CoV-2 pandemic: A qualitative study.

Health care professionals experienced multiple uncertainties during the pandemic. Exploring health care professionals' views about collaboration and organizational support can offer insights into organizational processes and issues during the pandemic. This research explored the perspectives of nurses and physicians about organizational support and nurse-physician collaboration during the SARS-CoV-2pandemic. Using a qualitative descriptive design, interviews were conducted with nurses and physicians working in hospital settings. The interviews lasted for 24-61 min. Reflexive thematic analysis was used for data analysis. Nurses and physicians were disappointed with the organizational support, but they were satisfied with nurse-physician collaboration. The theme "Management Abusing Authority and Blaming the Victimized Workforce" included organizational nepotism, unethical managerial actions, and neglecting frontline workforce. Nurses and physicians supported each other in tackling the intensive and complex demands of the pandemic. The theme "Demonstrating Professional Humility and Overcoming Patient Care Issues at Hand" entailed subthemes -negotiating conflicts and prioritizing patient care, practicing kindness, and jointly managing conflicts with patients' families. Nurses and physicians reported frustrations with limited organizational support and abusive practices of managers. Still, they prioritized patient care needs and family-related conflicts over interprofessional tensions.

Post-Pandemic Telehealth: An Unhealthy Privacy Prescription

The COVID-19 pandemic catapulted the use of telehealth services across the United States (US). In response to the public health emergency, the Office for Civil Rights (OCR) at the US Department for Health and Human Services (HHS) issued a notice relaxing privacy and security safeguards outlined in the Health Insurance Portability and Accountability Act (HIPAA). During the past two years, several novel legal issues have arisen due to these lenient standards. The US government has not adequately considered standardizing rules for remote telehealth consultations and regulating the flow of sensitive patient data in a post-COVID world. This article addresses key legal issues associated with telehealth services while recommending national standards to mitigate physician liability while prioritizing patient care.

Medical Professionalism and Ethics

Frequent media reports of disputes between medical teachers, resident doctors, and patients have been disturbing thus indicating an increasing gap and decline in the values of the profession. The foundation of medical professionalism and ethics is trust, truth, human values, and strong societal commitment. Our professional responsibilities as medical educators have immediate and long-term impact on the quality of training, patient care, and overall image of the professional group in the society. Formal teaching and training in professionalism and medical ethics that emphasize humanistic aspects and standard of conduct, respectively, are equally essential as biomedical aspects. Interestingly, the principles of medical ethics and attributes of professionalism overlap. However, real-life experiences and complex clinical scenarios place medical educators at the crossroads that contradict their professional commitments resulting in ethical dilemmas. COVID-19 pandemic has further challenged the principles of medical professionalism, especially patient welfare, autonomy, and social justice to prioritize patient care and make tricky decisions based on insufficient resources including withholding and withdrawing potentially lifesaving treatments. Unfortunately, until now the teaching and training in medical professionalism and ethics have remained subtle and medical students learn by chance. The recent introduction of the AETCOM module and Code of Medical Ethics in the curriculum is a ray of hope to brighten the image of medical professionals.

The knowledge of triage system in disaster among emergency medical service personnel at Saudi Red Crescent Authority in Riyadh city stations

PurposeSimple Triage and Rapid Treatment (START) is a unique triage system used by prehospital providers during disasters to quickly categorize and prioritize patient care according to severity. This study aims at evaluating knowledge about the START triage system among field emergency medical service (EMS) personnel working at the Saudi Red Crescent Authority (SRCA) in the stations of the city of Riyadh.Design/methodology/approachThis is a cross-sectional study that examined data collected from August 2019 to January 2020. The statistical population is from all field EMS personnel working in the SRCA located in Riyadh, Saudi Arabia. Using simple random sampling, 239 field EMS personnel were assessed, and 235 completed the study (98.3% response rate). Data were collected electronically using demographics and 15 multiple choice emergency scenarios based on the START protocol.FindingsThe mean correct score is 8.21 ± 3.36 out of 15 questions of triage knowledge (score of 0–15 points), indicating that those respondents have moderate knowledge levels on the START triage tool. Physicians and paramedics have higher mean scores (10.13 ± 3.42 and 9.07 ± 3.22, respectively), which are significantly higher than emergency medical technicians and nurses (7.25 ± 3.15 and 5.63 ± 2.72, respectively; p &lt; 0.05). The providers who attended the training course had higher mean scores (p &lt; 0.05).Originality/valueBased on the results of the study, field EMS personnel did not reflect full knowledge of START triage tool. An interdisciplinary approach that adopts reinforcement education and periodical training courses is highly recommended to improve the respondents' performance and productivity. Moreover, there was a noticeable correlation between performance of respondents on the one hand and their education levels and prior training on the other hand.

Attitude of nursing students following the implementation of comprehensive computer-based nursing process in medical surgical internship: a quasi-experimental study

BackgroundThe nursing process is the core and the standard of practice in nursing profession. Nowadays, the use of information technology in the field of nursing processes, education and practice has been emphasized. Since nurse’s attitudes towards clinical information systems are considered as an indicator of the success rate of information systems, and nurse’s attitudes about the nursing process can affect their execution of the process. So the purpose of this study was to evaluate nursing students’ attitudes towards the nursing process software.MethodsIn this quasi-experimental study, 160 undergraduate nursing students (terms 4–8) in Tabriz University of Medical Sciences were selected by convenience sampling. To evaluate the effectiveness of nursing process software in this study, Mazlom and Rajabpoor (IJME 14(4):312–322, 2014) a questionnaire consisting of 21 components based on a five-point Likert scale was completed by students after using the software. Data were then analyzed by SPSS 19 software.ResultsThe mean score of students’ attitude toward nursing process software was high (80.70 ± 5.58). The nursing students’ highest scoring attitudes were respectively related to “Effectiveness of software in prioritizing patient care and problems”, “Completeness of patient’s electronic information compared to handwritten mode” and “Software’s effectiveness in saving your time”. The lowest scoring attitudes towards the software was respectively related to the “feeling of fairness in labor division”, “the effectiveness of the software in determining your workload” and “the feeling of satisfaction in labor division”. There was a statistically significant relationship between gender and age, and student’s attitude toward nursing process software.ConclusionsAccording to the results and analysis of nursing student’s attitudes toward nursing process software, the use of such software would be welcomed by students. It seems that changing policies in the educational and clinical substructure of nursing in order to develop, adapt and use the nursing process software is an important responsibility for nursing authorities to consider. Providing educational and clinical technology equipment, periodic evaluation of software by stakeholders and promoting the use of this software, can be fundamental steps in operationalizing the findings of this research.

Health Disparities in Pulmonary Arterial Hypertension and the Impact of the COVID-19 Pandemic

Health Disparities in Pulmonary Arterial Hypertension and the Impact of the COVID-19 Pandemic

Residents\u2019 identification of learning moments and subsequent reflection: impact of peers, supervisors, and patients

BackgroundThe clinical workplace offers residents many opportunities for learning. Reflection on workplace experiences drives learning and development because experiences potentially make residents reconsider existing knowledge, action repertoires and beliefs. As reflective learning in the workplace cannot be taken for granted, we aimed to gain a better insight into the process of why residents identify experiences as learning moments, and how residents reflect on these moments.MethodsThis study draws on semi-structured interviews with 33 medical residents. Interviews explored how residents identified learning moments and how they reflected on such moments, both in-action and on-action. Aiming for extensive explanations on the process of reflection, open-ended questions were used that built on and deepened residents’ answers. After interviews were transcribed verbatim, a within-case and cross-case analysis was conducted to build a general pattern of explanation.ResultsThe data analysis yielded understanding of the crucial role of the social context. Interactions with peers, supervisors, and patients drive reflection, because residents want to measure up to their peers, meet supervisors’ standards, and offer the best patient care. Conversely, quality and depth of reflection sometimes suffer, because residents prioritize patient care over learning. This urges them to seek immediate solutions or ask their peers or supervisor for advice, rather than reflectively deal with a learning moment themselves. Peer discussions potentially enhance deep reflection, while own supervisor involvement sometimes feels unsafe.DiscussionOur results adds to our understanding of the social-constructivist nature of reflection. We suggest that feelings of self-preservation during interactions with peers and supervisors in a highly demanding work environment shape reflection. Support from peers or supervisors helps residents to instantly deal with learning moments more easily, but it also makes them more dependent on others for learning. Since residents’ devotion to patient care obscures the reflection process, residents need more dedicated time to reflect. Moreover, to elaborate deeply on learning moments, a supportive and safe learning climate with peers and supervisors is recommended.

Racionalización de los servicios de salud parte 1

Presentación de un caso de toma de decisiones de asignación de recursos de salud en una situación de crisis, donde la oferta de camas y recursos de la unidad de cuidados intensivos (UCI) no alcanzan para atender la de­manda desbordada por una pandemia. Se presentan y analizan los diferentes criterios propuestos por los miem­bros del equipo de salud y se cuestiona la decisión finalmente tomada por el director de la institución de fijar como criterio de priorización para asignar camas, ventiladores y demás recursos de UCI por estricto orden de llegada de los pacientes, independientemente de su situación de salud y de sus posibilidades de recuperación. Se hace un análisis desde la ética de cuál sería el criterio más adecuado para priorizar la atención de los pacientes cuando hay un desfase entre recursos y necesidades o solicitudes.

I-PASS Illness Severity Identifies Patients at Risk for Overnight Clinical Deterioration.

The I-PASS framework is increasingly being adopted for patient handoffs after a recent study reported a decrease in medical errors and preventable adverse events. A key component of the I-PASS handoff included assignment of illness severity. We evaluated whether illness severity categories can identify patients at higher risk of overnight clinical deterioration as defined by activation of the rapid response team (RRT). The I-PASS handoff documentation created by internal medicine residents and patient charts with overnight RRT activations from April 2016 through March 2017 were reviewed retrospectively. The RRT activations, illness severity categories, vital signs prior to resident handoff, and patient outcomes were evaluated. Of the 28 235 written patient handoffs reviewed, 1.3% were categorized as star (sickest patients at risk for higher level of care), 18.8% as watcher (unsure of illness trajectory), and 79.9% as stable (improving clinical status). Of the 98 RRT activations meeting the inclusion criteria, 5.1% were labeled as star, 35.7% as watcher, and 59.2% as stable. Patients listed as watcher had an odds ratio of 2.6 (95% confidence interval 1.7-3.9), and patients listed as star had an odds ratio of 5.2 (95% confidence interval 2.1-13.1) of an overnight RRT activation compared with patients listed as stable. The overall in-hospital mortality of patients with an overnight RRT was 29.6%. The illness severity component of the I-PASS handoff can identify patients at higher risk of overnight clinical deterioration and has the potential to help the overnight residents prioritize patient care.

Nursing Shift Handoff Process: Using an Electronic Health Record Tool to Improve Quality.

Nursing shift handoffs can be frustrating for nursing staff when information received about a patient is inaccurate or inadequate. The safety of the patient may be compromised, and missed opportunities for care interventions may occur. The electronic health record (EHR) provides healthcare professionals with useful information that can highlight the most important items the nurse needs to prioritize patient care. A unit leadership team at a large academic hospital developed a peer-to-peer training plan to provide evidence-based nursing education using the nursing handoff feature in the EHR.

Management of hepatitis C in children and adolescents during COVID-19 pandemic.

In recent years, significant progress in the antiviral treatment of chronic hepatitis C (CHC) has been made due to the development of interferon-free therapies. Three different highly effective, oral direct-acting antiviral (DAA) regimens have been approved for use in adolescents with CHC between the ages of 12-years-old and 17-years-old in Europe. According to the current recommendations, all treatment-naïve and treatment-experienced children with CHC virus infection should be considered for DAA therapy to prevent the possible progression of hepatitis C virus-related liver disease and its complications. However, the novel coronavirus disease 2019 outbreak, which was classified as a pandemic in March 2020, is currently spreading throughout the world, resulting in a disruption of the healthcare system. This disruption is having a negative impact on the care of patients with chronic diseases, including children with CHC. Thus, several efforts have to be made by pediatric hepatologists to prioritize patient care in children with CHC. These efforts include promoting telemedicine in the outpatient setting, using local laboratory testing for follow-up visits, and engaging in the home delivery of DAAs for patients under antiviral therapy whenever possible.

The University of Kansas School of Medicine.

The University of Kansas School of Medicine.

Prioritizing patient care during the COVID-19 pandemic: A data-driven approach.

Journal of Dental EducationVolume 85, Issue S1 p. 1088-1089 ADVANCING THROUGH INNOVATION Prioritizing patient care during the COVID-19 pandemic: A data-driven approach Aaron Glick DDS, Corresponding Author Aaron Glick DDS Aaron.R.Glick@uth.tmc.edu Department of General Practice and Dental Public Health, UTHealth School of Dentistry at Houston, Houston, Texas, USA Correspondence Aaron Glick, DDS, Clinical Assistant Professor, Department of General Practice and Dental Public Health, UTHealth School of Dentistry at Houston, Houston, TX, USA 77054. Email: Aaron.R.Glick@uth.tmc.eduSearch for more papers by this authorKrishna Kumar Kookal MS, Krishna Kumar Kookal MS Technology Services and Informatics, UTHealth School of Dentistry at Houston, Houston, Texas, USASearch for more papers by this authorMuhammad F. Walji MS, PhD, Muhammad F. Walji MS, PhD Technology Services and Informatics, UTHealth School of Dentistry at Houston, Houston, Texas, USA Department of Diagnostic and Biomedical Sciences, UTHealth School of Dentistry at Houston, Houston, Texas, USASearch for more papers by this authorSophia G. Saeed DMD, Sophia G. Saeed DMD Department of General Practice and Dental Public Health, UTHealth School of Dentistry at Houston, Houston, Texas, USASearch for more papers by this author Aaron Glick DDS, Corresponding Author Aaron Glick DDS Aaron.R.Glick@uth.tmc.edu Department of General Practice and Dental Public Health, UTHealth School of Dentistry at Houston, Houston, Texas, USA Correspondence Aaron Glick, DDS, Clinical Assistant Professor, Department of General Practice and Dental Public Health, UTHealth School of Dentistry at Houston, Houston, TX, USA 77054. Email: Aaron.R.Glick@uth.tmc.eduSearch for more papers by this authorKrishna Kumar Kookal MS, Krishna Kumar Kookal MS Technology Services and Informatics, UTHealth School of Dentistry at Houston, Houston, Texas, USASearch for more papers by this authorMuhammad F. Walji MS, PhD, Muhammad F. Walji MS, PhD Technology Services and Informatics, UTHealth School of Dentistry at Houston, Houston, Texas, USA Department of Diagnostic and Biomedical Sciences, UTHealth School of Dentistry at Houston, Houston, Texas, USASearch for more papers by this authorSophia G. Saeed DMD, Sophia G. Saeed DMD Department of General Practice and Dental Public Health, UTHealth School of Dentistry at Houston, Houston, Texas, USASearch for more papers by this author First published: 04 August 2020 https://doi.org/10.1002/jdd.12361Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume85, IssueS1Supplement: Advancing Through Innovation in a Challenging TimeJune 2021Pages 1088-1089 This article also appears in:Advancing Through Innovation in a Challenging Time RelatedInformation

Educación quirúrgica en Colombia en la era del COVID–19

La enfermedad por Coronavirus-2 en poco tiempo ha logrado encender las alarmas de los sistemas de salud a nivel mundial. Además de las implicaciones económicas, sanitarias, políticas, culturales y sociales, está claro que ha cambiado la vida de los seres humanos incluyendo como se desarrollan los programas de postgrado en medicina.&#x0D; Múltiples comunidades científicas alrededor del mundo han manifestado la necesidad de diferir los procedimientos quirúrgicos electivos y priorizar la atención de los pacientes por encima de la academia, lo que podría disminuir la exposición de residentes de cirugía general a actos quirúrgicos esenciales para su aprendizaje.&#x0D; Como respuesta a ello, los autores presentan este artículo en donde se discute el papel de la educación virtual y la simulación como posibles respuestas a la dificultad educativa que representa la pandemia del COVID-19.