Updating Training in the Medical Field

This chapter briefly reviews the literature that explores the training technique of deliberate practice and the related constructs, training outcomes of achievement goal orientation, self-efficacy, perceived instrumentality, and reflective practice. This work explains how educators can use and measure these variables to enhance current training methodologies. As part of creating more effective training, the TARGET model, developed by Ames, will be utilized to discuss potential ways to enhance training outcomes in a post-COVID-19 world. Specifically, suggestions are offered for enhancing online training using deliberate practice combined with the TARGET model within a medical setting where there are limited resources.

Visibility of Chinese ad hoc medical interpreters through text ownership: A case study

Professional interpreters’ visibility in the European context has been widely discussed in the field of community interpreting, but the visibility of untrained ad hoc interpreters in non-European contexts such as China has received little academic attention. By adopting the concept of “text ownership” proposed by Angelelli (2004a), this study examines Chinese ad hoc interpreters’ manifestations of visibility in an authentic medical setting. Based on field observations, audio recordings and interviews, the study reports on four types of visibility demonstrated by ad hoc interpreters: (a) replacing the interlocutor; (b) expressing affect towards a patient; (c) exploring answers; and (d) brokering comprehension. Other forms of visibility are also identified, such as omissions of doctors’ or patients’ remarks and small talk between doctor and interpreter. Interpreters’ deeply held views on social factors as well as the institutional and social norms they have been exposed to are believed to influence their manipulation of medical discourses. This study concludes that in a context where professional medical interpreting services are unavailable, ad hoc interpreters may act as linguistic facilitators by taking on various roles that go beyond mere interpreting. However, their excessive visibility may give rise to potential clinical risks, especially when direct doctor–patient communication is compromised. Attention is drawn to the importance of proper training as well as to the need for the professionalization of medical interpreting in China.

Are Opioids Overprescribed to Pediatric Neurosurgery Patients? A Prospective Study on Prescribing Practices and Patient Use

Background and Hypothesis: The opioid crisis continues to worsen in the United States with opioid overdose deaths reaching record highs in 2020. While a large body of literature exists surrounding the risks of opioids in adults, opioids also pose unique risks to pediatric patients, including accidental ingestion, nonmedical use, and acute cerebellitis causing death. Opioid medications prescribed in the medical setting are often an unwitting source of excess opioids, with half of pediatric overdoses in those under 2 years of age. Although legislative efforts have significantly limited opioid prescribing, recent studies suggest these medications may still be overprescribed. We hypothesized opioid medications are overprescribed to pediatric neurosurgery patients upon hospital discharge. Methods: Pediatric patients undergoing neurosurgical procedures at Riley Hospital for Children were identified prospectively. Surgery type, length of stay, and inpatient use of opioid medications were collected. Patients prescribed an opioid medication upon hospital discharge were contacted 7 days after discharge and asked to report the number of doses of opioid medication used. Results: Thirty patients were successfully contacted 7 days after hospital discharge. Patients underwent a variety of cranial and spinal procedures and the mean length of hospital stay was 3.9 days. An average of 24.9 doses of opioid medication were prescribed at hospital discharge, while an average of 3.8 doses were used by patients in the 7 days following hospital discharge. Twelve patients (40%) had used zero doses of the prescribed opioid medication at 7-day follow-up. Conclusions: Pediatric neurosurgery patients used only 15.3% of prescribed opioids in 7 days after hospital discharge. This creates an excess of leftover opioid medication that may increase the risk of accidental ingestion and misuse. The present study highlights the need for educational initiatives for providers to minimize excess opioids prescribed and for parents to safely dispose of leftover opioid medication.

Addressing Systemic Racism by Creating a Diversity, Equity, and Inclusion Department Initiative

Background Catalyzed by social injustice and worsening racial inequities highlighted by the COVID-19 pandemic, a diverse academic geriatrics and palliative medicine department in NYC launched a DEI initiative. This report presents key program components and lessons learned in launching this initiative in the interprofessional academic medicine setting. Methods First, DEI core and departmental administration met 2-4 times/month to plan and review program activities, vision, and mission. The team conducted confidential roundtable discussions about DEI issues and 1:1 interviews to assess needs. A monthly Humanities, Arts, and Books (HAB) Initiative provided a safe space for discussion and learning. The HAB platform supported a longitudinal curriculum emphasizing (1) group discussion and self-reflection on DEI concerns, (2) knowledge dissemination including a “Learning Pathway” series, and (3) skill-based workshops. With each event, we collected anonymous feedback. Comments were systematically recorded and an engagement evaluation was conducted to iteratively shape future sessions. Departmental administration was engaged to track DEI-focused measures of recruitment, career advancement, and retention. Finally, we centralized DEI activities on a departmental website, including an anonymous online feedback box. Results Quantitative and qualitative assessment of DEI initiatives are forthcoming. Metrics include DEI and professional development surveys, departmental demographic and diversity measures, increase in DEI-related projects and grants, and individual participation in DEI programs. Conclusions Creating a strong and sustainable DEI initiative within an academic medical setting requires a passionate and diverse core team, deliberate backing by administration, and thoughtful dissemination of sensitive content in the midst of a highly charged social justice landscape.

Detecting Bone Lesions in The Emergency Room With Medical Infrared Thermography

Introduction: Low to high-energy impact trauma may cause from small fissures up to extended bone losses, which can be classified as closed or opened injuries (when they are visible at a naked eye). Objective: The aim of this study was to investigate the feasibility of clinical diagnosis of bone trauma through medical infrared thermography, in a hospital emergency room. Methods: Forty-five patients with suspected diagnosis of bone fracture were evaluated by means of medical infrared images, and the data correlated to the gold standard radiographic images, in the anteroposterior, lateral, and oblique views, at the orthopedic emergency department. The control group consisted of thermal images of the contralateral reference limb of the volunteers themselves. Data were acquired with a medical grade infrared camera in the regions of interest (ROIs) of leg, hand, forearm, clavicle, foot, and ankle. Results: In all patients evaluated with a diagnosis of bone fracture, the mean temperature of the affected limb showed a positive difference greater than 0.9 °C (towards the contralateral), indicating the exact location of the bone trauma according, while the areas diagnosed with reduced blood supply, showed a mean temperature with a negative variation. Conclusion: Clinical evaluation using infrared imaging indicates a high applicability potential as a tool to support quick diagnosis of bone fractures in patients with acute orthopedic trauma in an emergency medical setting. The thermal results showed important physiological data related to vascularization of the bone fracture and areas adjacent to the trauma well correlated to radiographic examinations.

Repair of Soft Tissue Defect with Intraoperative Tissue Expansion after Surgical Resection of Dermatofibrosarcoma Protuberans: A Case Report

While it is the most frequently observed subtype of all cutaneous soft tissue sarcomas, dermatofibrosarcoma protuberans is still uncommon, with a high local recurrence rate. Although surgical resection could be a simple curative procedure, surgeons can encounter difficulties when dissecting or closing the wound, depending on the location and the size of the tumor. We present a case report of direct repair of soft tissue defect with intraoperative tissue expansion after sarcoma resection. A 62-year-old male visited our clinic with an approximately 1.5×1.5 cm-sized mass on his lower leg. We excised the mass, which a pathologic study confirmed as dermatofibrosarcoma protuberans. The authors planned a wide excision of the lesion as soon as possible. The excised lesion was not large; however, the soft tissue defect could not be closed by direct approximation. We eventually closed the tumor resection wound using a Foley catheter as a tissue expander. The patient’s wound healed well, albeit leaving a minimal scar. This case suggests that a Foley catheter could be an effective and versatile tool that is readily available in any medical setting, including after a small tumor resection in outpatient surgery.

Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study

Background Recently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML. Objective This study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems. Methods To gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care–specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process. Results With the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process. Conclusions Many clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.