Medical Subspecialty Telehealth Consults

Health care reforms are advancing new care models that emphasize population-based outcomes. Emergency clinicians are challenged to provide coordinated care with limited resources. Emerging value-based models create the incentives for emergency clinicians to collaborate with specialists to provide high-quality care while lowering cost by reducing admissions when possible. Subspecialty consultation may be difficult to obtain in regions where the population does not support a subspecialist or where there is a workforce shortage. However, obtaining a telehealth subspecialist consult from a remote pool, local network, or established patient–physician relationship can provide such a service. Several use-cases are presented to illustrate how telehealth subspecialty consultation from the emergency department can change patient care and outcomes.

Clinical Histories Animate Muybridge and Dercums Original Photographic Study of Neurologic Gait

In 1885, while working at the University of Pennsylvania, Eadweard Muybridge and Francis Dercum used nascent photographic technology to perform the first ever motion picture study of neurologic patients. To date, our clinical understanding of the Muybridge-Dercum project has been limited to those clinical details included in a handful of contemporaneous publications by Dercum and colleagues. In the present study, recently rediscovered clinical notebooks from the Hospital of the University of Pennsylvania’s Dispensary for Nervous Diseases were reviewed and found to contain the original clinical records of nine of Muybridge and Dercum’s photographic subjects. These records add new clinical insights to our understanding of this historic photographic study and revive the zeitgeist of a foundational period in the development of neurology as a medical subspecialty in the United States.

Medical student remote eConsult participation during the COVID-19 pandemic

Background Undergraduate medical education was severely impacted by the COVID-19 pandemic. As traditional clinical rotations were suspended, medical students quickly began alternative, novel educational experiences. Third-year medical students at an academic medical center were given the opportunity to join inpatient eConsult teams within the department of medicine. This study describes the development and implementation of this program as well as the experiences of student and faculty participants. Methods Student eConsult participation was rapidly developed and implemented within medical subspecialty teams in either infectious diseases (ID) or nephrology. Twelve third-year medical students and 15 subspecialty attendings participated in this program during an eight-week period from April 6 through May 29, 2020. Breadth of student clinical experience was assessed via review of clinical documentation and surveys. Participating students and attending physicians completed surveys to reflect upon their impressions of the program. Surveys were returned by nine students and eight faculty members. Survey responses were summarized with descriptive statistics. Results Over an eight-week period, student consultants wrote 126 notes on 100 patients; 74 of these patients (74%) were hospitalized with COVID-19. Student experiences were largely positive with most strongly agreeing that attendings promoted interactive and engaged learning (N = 8 of 8, 100%), that the experience helped to expand their knowledge about consultant roles (N = 6, 75%), and that they would participate in a remote eConsult program again if given the opportunity (N = 6, 75%). Faculty also were largely positive about the experience with most agreeing or strongly agreeing with the importance of teaching medical students about telehealth (N = 7 of 8, 88%) and eConsults (N = 6, 75%). In narrative responses, students and faculty agreed that teaching was a strength of the program whereas lack of in-person contact was a challenge. Conclusions Rapid development of an inpatient eConsult-based educational experience for third-year medical students was feasible and successful. Student-consultants saw a range of pathology including COVID-19 and related complications. Students were satisfied with the program. They were able to develop a strong relationship with attendings while learning about the role of a consultant. Faculty agreed with the importance of teaching students about telehealth and eConsults specifically.

Traditional Chinese herbal medicine-potential therapeutic application for the treatment of COVID-19

Traditional Chinese Medicine (TCM) is a complete medical system that has evolved over millennia to include practices and procedures such as acupuncture, herbal medicine, manual therapies, nutrition, and mind–body therapies such as qi gong. In modern-day China and other Asian countries, TCM is a medical subspecialty utilized alongside western biomedicine. During the current Coronavirus Disease 19 (COVID-19) pandemic, TCM and TCM herbal medicine is being used and a number of single herbs and combination formulas have significant bioactivity and therapeutic potential. The purpose of this paper is to highlight the use of TCM in the treatment of COVID-19. This commentary provides the reader with a concise background on COVID-19 and summarizes TCM concepts including identification, pattern diagnosis, and treatment principles commonly used for the treatment of viral influenza-like diseases. It also highlights some of the challenges and potential for using TCM in an integrated medical setting.

Pediatric Immunization Practices in Nephrotic Syndrome: An Assessment of Provider and Parental Knowledge

Background: Children with nephrotic syndrome (NS) are at high risk for vaccine-preventable infections due to the immunological effects from the disease and concurrent treatment with immunosuppressive medications. Immunizations in these patients may be deferred due to their immunosuppressive treatment which may increase the risk for vaccine-preventable infections. Immunization practices in children with NS continue to vary among pediatric nephrologists. This raises the question of whether children with NS are receiving the recommended vaccinations at appropriate times. Therefore, it is critical to understand the practices and patient education provided by physicians to patients on the topic of vaccinations.Methods: After informed consent, parents/guardians of 153 pediatric patients (<18 years old) diagnosed with NS from 2005 to 2018 and 50 pediatric nephrologists from 11 participating centers completed anonymous surveys to evaluate immunization practices among pediatric nephrologists, assess the vaccine education provided to families of children with NS, assess the parental knowledge of immunization recommendations, and assess predictors of polysaccharide pneumococcal vaccine adherence. The Advisory Committee on Immunization Practices (ACIP) Immunization 2019 Guideline for those with altered immunocompetence was used to determine accuracy of vaccine knowledge and practices.Results: Forty-four percent of providers self-reported adherence to the ACIP guidelines for inactive vaccines and 22% to the guidelines for live vaccines. Thirty-two percent of parents/guardians reported knowledge that aligned with the ACIP guidelines for inactive vaccines and 1% for live vaccines. Subjects residing in the Midwest and provider recommendations for vaccines were positive predictors of vaccine adherence (p < 0.001 and p 0.02, respectively).Conclusions: Vaccine recommendation by medical providers is paramount in vaccine adherence among pediatric patients with NS. This study identifies potential educational opportunities for medical subspecialty providers and family caregivers about immunization recommendations for immunosuppressed patients.

Deep Learning CT Image Reconstruction in Clinical Practice

Background Computed tomography (CT) is a central modality in modern radiology contributing to diagnostic medicine in almost every medical subspecialty, but particularly in emergency services. To solve the inverse problem of reconstructing anatomical slice images from the raw output the scanner measures, several methods have been developed, with filtered back projection (FBP) and iterative reconstruction (IR) subsequently providing criterion standards. Currently there are new approaches to reconstruction in the field of artificial intelligence utilizing the upcoming possibilities of machine learning (ML), or more specifically, deep learning (DL). Method This review covers the principles of present CT image reconstruction as well as the basic concepts of DL and its implementation in reconstruction. Subsequently commercially available algorithms and current limitations are being discussed. Results and Conclusion DL is an ML method that utilizes a trained artificial neural network to solve specific problems. Currently two vendors are providing DL image reconstruction algorithms for the clinical routine. For these algorithms, a decrease in image noise and an increase in overall image quality that could potentially facilitate the diagnostic confidence in lesion conspicuity or may translate to dose reduction for given clinical tasks have been shown. One study showed equal diagnostic accuracy in the detection of coronary artery stenosis for DL reconstructed images compared to IR at higher image quality levels. Consequently, a lot more research is necessary and should aim at diagnostic superiority in the clinical context covering a broadness of pathologies to demonstrate the reliability of such DL approaches. Key Points: Citation Format

Policy Statement on Clinical Informatics Fellowships and the Future of Informatics-Driven Medicine

Board certified clinical informaticians provide expertise in leveraging health IT (HIT) and health data for patient care and quality improvement. Clinical Informatics experts possess the requisite skills and competencies to make systems-level improvements in care delivery using HIT, workflow and data analytics, knowledge acquisition, clinical decision support, data visualization, and related informatics tools. However, these physicians lack structured and sustained funding because they have no billing codes. The sustainability and growth of this new and promising medical subspecialty is threatened by outdated and inconsistent funding models that fail to support the education and professional growth of clinical informaticians. The Clinical Informatics Program Directors' Community is calling upon the Centers for Medicare and Medicaid Services to consider novel funding structures and programs through its Innovation Center for Clinical Informatics Fellowship training. Only through structural and sustained funding for Clinical Informatics fellows will be able to fully develop the potential of electronic health records to improve the quality, safety, and cost of clinical care.

In Silico Method for Sentiment and Emotion Analysis in Medicine Using Social Media (Preprint)

BACKGROUND Clinical data present in social media is an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes and preferences. OBJECTIVE We describe a novel and broadly applicable method for sentiment analysis and emotion detection to free text from online medical health forums and the factors to consider during its application. METHODS We mined the full discussion and user information of all posts containing search terms related to a specific medical subspecialty (oculoplastics) from MedHelp, the largest online platform for patient health forums. We employed a variety of data cleaning and processing to define the relevant subset of results and prepare those results for sentiment analysis. We executed sentiment and emotion analysis through IBM Watson Natural Language Understanding service to generate sentiment and emotion scores for the posts and their associated keywords. Keywords were aggregated using natural language processing tools. RESULTS 39 oculoplastics-related search terms resulted in 46,381 eligible posts within 14,329 threads, written by 18,319 users (117 doctors; 18,202 patients) and 201,611 associated keywords. Keywords that occurred ≥500 times in the corpus were used to identify most prominent topics, including specific symptoms, medication and complications. The sentiment and emotion scores of these keywords and eligible posts were further analyzed to provide concrete examples of the methodology’s potential to allow better understanding of patients’ attitudes. CONCLUSIONS This comprehensive report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients’ perspectives and promote patient-centric care. Important factors to be considered during application include evaluating the scope of the search, selecting search terms and understanding their different linguistic usages, and establishing robust selection, filtering and processing criteria for posts and keywords tailored to the results.