Neurophobia Among Medical Students: Is Virtual Teaching the Answer?

OBJECTIVE: Neurophobia is well recognized as dissuading medical students from neurocentric specialties and limiting the success of neurology and neurosurgery teaching at medical school. Past studies have associated neurophobia with deficiencies in medical education. We performed a cross-sectional analysis of medical students' confidence and perceived level of knowledge in recognizing the following neurosurgical and neurological emergencies: ischemic stroke, hemorrhagic stroke, status epilepticus, subarachnoid hemorrhage, increased intracranial pressure, acute hydrocephalus, spinal cord injury, cauda equina syndrome, and traumatic brain injury. In addition, we assessed the usefulness of virtual seminars in neurosurgery and neurology teaching. METHODS: Medical students from King's College London were invited to a virtual teaching session. We obtained preteaching and postteaching scores for students' subjective ability to recognize specific neurologic and neurosurgical emergencies, along with their confidence in the subject. RESULTS: Ninety-seven medical students attended the teaching session. For our sample group's subjective rating on their confidence in neurology or neurosurgery as a subject, we obtained a mean score of 3.87 and a median score of 4. Across all domains, there was a significant forward shift in the distribution curve of scores after teaching. We obtained statistically significant differences for all 9 neurologic and neurosurgical emergencies evaluated in our questionnaire (asymptotic significance <0.001). Median scores for all 9 conditions improved after the teaching session, with >50% positive ranks seen within each group. Across the teaching modalities compared, placement teaching was the highest scoring, whereas online lectures received a better rating than in-person lectures. CONCLUSIONS: In neurosurgery teaching, virtual seminars may compensate for deficiencies that exist within medical education, hence limiting the effects of neurophobia.

A natural language processing approach for identifying temporal disease onset information from mental healthcare text.

Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential. In the mental health domain, an important research variable is the date of psychosis symptom onset, as longer delays in treatment are associated with worse intervention outcomes. The growing adoption of electronic health records (EHRs) within mental health services provides an invaluable opportunity to study this problem at scale retrospectively. However, disease onset information is often only available in open text fields, requiring natural language processing (NLP) techniques for automated analyses. Since this variable can be documented at different points during a patient's care, NLP methods that model clinical and temporal associations are needed. We address the identification of psychosis onset by: 1) manually annotating a corpus of mental health EHRs with disease onset mentions, 2) modelling the underlying NLP problem as a paragraph classification approach, and 3) combining multiple onset paragraphs at the patient level to generate a ranked list of likely disease onset dates. For 22/31 test patients (71%) the correct onset date was found among the top-3 NLP predictions. The proposed approach was also applied at scale, allowing an onset date to be estimated for 2483 patients.

Closed-Incision Negative Pressure Wound management Following Midline Laparotomy in Gynecological Oncology Operations: A Feasibility Pilot Study.

Introduction Surgical site infections (SSIs) are a cause of considerable morbidity and mortality in healthcare. Increasingly, closed-incision negative pressure wound therapy (ciNPWT) is being studied as a potential method of reducing incidence of SSI with conflicting results in the literature. Few studies however have looked at its use in the field of gynecological oncology. Objectives We aimed to compare the incidence of SSI when using ciNPWT dressings versus conventional dressings in gynecological oncology patients undergoing midline laparotomies. Methods This was a pilot study involving 14 patients receiving the ciNPWT dressing and 26 control patients. All patients were followed up for a period of 30 days. We used the American College of Surgeons (ACS) risk calculator to estimate each patient's risk of SSI in order to risk stratify the groups. Results The incidence of wound infection was 21% (3/14) in the ciNPWT group and 23% (6/26) in the control group (p=0.886). The ciNPWT group was found to be at significantly higher risk for SSI as calculated by the ACS tool (8.8% ciNPWT, 6% control, p=0.004). After stratifying for this difference in risk, still no significant difference in incidence of SSI was found between the two groups (27% (3/11) ciNPWT, 29% (2/7) control p=0.929). Conclusion The incidence of SSI does not appear to decrease by the prophylactic use of the closed-incision negative pressure wound dressing.

Generation and evaluation of artificial mental health records for Natural Language Processing.

A serious obstacle to the development of Natural Language Processing (NLP) methods in the clinical domain is the accessibility of textual data. The mental health domain is particularly challenging, partly because clinical documentation relies heavily on free text that is difficult to de-identify completely. This problem could be tackled by using artificial medical data. In this work, we present an approach to generate artificial clinical documents. We apply this approach to discharge summaries from a large mental healthcare provider and discharge summaries from an intensive care unit. We perform an extensive intrinsic evaluation where we (1) apply several measures of text preservation; (2) measure how much the model memorises training data; and (3) estimate clinical validity of the generated text based on a human evaluation task. Furthermore, we perform an extrinsic evaluation by studying the impact of using artificial text in a downstream NLP text classification task. We found that using this artificial data as training data can lead to classification results that are comparable to the original results. Additionally, using only a small amount of information from the original data to condition the generation of the artificial data is successful, which holds promise for reducing the risk of these artificial data retaining rare information from the original data. This is an important finding for our long-term goal of being able to generate artificial clinical data that can be released to the wider research community and accelerate advances in developing computational methods that use healthcare data.

Temporal information extraction from mental health records to identify duration of untreated psychosis.

BACKGROUND: Duration of untreated psychosis (DUP) is an important clinical construct in the field of mental health, as longer DUP can be associated with worse intervention outcomes. DUP estimation requires knowledge about when psychosis symptoms first started (symptom onset), and when psychosis treatment was initiated. Electronic health records (EHRs) represent a useful resource for retrospective clinical studies on DUP, but the core information underlying this construct is most likely to lie in free text, meaning it is not readily available for clinical research. Natural Language Processing (NLP) is a means to addressing this problem by automatically extracting relevant information in a structured form. As a first step, it is important to identify appropriate documents, i.e., those that are likely to include the information of interest. Next, temporal information extraction methods are needed to identify time references for early psychosis symptoms. This NLP challenge requires solving three different tasks: time expression extraction, symptom extraction, and temporal "linking". In this study, we focus on the first step, using two relevant EHR datasets. RESULTS: We applied a rule-based NLP system for time expression extraction that we had previously adapted to a corpus of mental health EHRs from patients with a diagnosis of schizophrenia (first referrals). We extended this work by applying this NLP system to a larger set of documents and patients, to identify additional texts that would be relevant for our long-term goal, and developed a new corpus from a subset of these new texts (early intervention services). Furthermore, we added normalized value annotations ("2011-05") to the annotated time expressions ("May 2011") in both corpora. The finalized corpora were used for further NLP development and evaluation, with promising results (normalization accuracy 71-86%). To highlight the specificities of our annotation task, we also applied the final adapted NLP system to a different temporally annotated clinical corpus. CONCLUSIONS: Developing domain-specific methods is crucial to address complex NLP tasks such as symptom onset extraction and retrospective calculation of duration of a preclinical syndrome. To the best of our knowledge, this is the first clinical text resource annotated for temporal entities in the mental health domain.

Annotating Temporal Relations to Determine the Onset of Psychosis Symptoms.

For patients with a diagnosis of schizophrenia, determining symptom onset is crucial for timely and successful intervention. In mental health records, information about early symptoms is often documented only in free text, and thus needs to be extracted to support clinical research. To achieve this, natural language processing (NLP) methods can be used. Development and evaluation of NLP systems requires manually annotated corpora. We present a corpus of mental health records annotated with temporal relations for psychosis symptoms. We propose a methodology for document selection and manual annotation to detect symptom onset information, and develop an annotated corpus. To assess the utility of the created corpus, we propose a pilot NLP system. To the best of our knowledge, this is the first temporally-annotated corpus tailored to a specific clinical use-case.