History and Physical Examination

Addressing the challenges of AI-based telemedicine: Best practices and lessons learned

Telemedicine is the use of technology to provide healthcare services and information remotely, without requiring physical proximity between patients and healthcare providers. The coronavirus disease 2019 (COVID-19) pandemic has accelerated the rapid growth of telemedicine worldwide. Integrating artificial intelligence (AI) into telemedicine has the potential to enhance and expand its capabilities in addressing various healthcare needs, such as patient monitoring, healthcare information technology (IT), intelligent diagnosis, and assistance. Despite the potential benefits, implementing AI in telemedicine presents challenges that can be overcome with physician-guided implementation. AI can assist physicians in decision-making, improve healthcare delivery, and automate administrative tasks. To ensure optimal effectiveness, AI-powered telemedicine should comply with existing clinical practices and adhere to a framework adaptable to various technologies. It should also consider technical and scientific factors, including trustworthiness, reproducibility, usability, availability, and cost. Education and training are crucial for the appropriate use of new healthcare technologies such as AI-enabled telemedicine. This article examines the benefits and limitations of AI-based telemedicine in various medical domains and underscores the importance of physician-guided implementation, compliance with existing clinical practices, and appropriate education and training for healthcare providers.

Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review

OBJECTIVE

To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis.

MATERIALS AND METHODS

PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted.

RESULTS

The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies.

DISCUSSION

Studies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units.

CONCLUSIONS

Approaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.

History Taking as a Diagnostic Tool in Children With Chronic Cough

Chronic cough is a common symptom of many underlying respiratory and non-respiratory disorders and may be associated with less serious causes, such as gastroesophageal reflux and nasal diseases. Chronic cough in children differs from that in adults with respect to its etiologies and management since it can indicate a symptom of an underlying disease in children. Guidelines for managing chronic cough in children are based on recording the history, followed by physical examination, chest radiography, and spirometry. Thus, taking accurate respiratory history for coughing helps delineate the pathophysiological basis of the cause of chronic cough. Detailed history taking enhances the evaluation and treatment, and facilitates a tailored diagnostic identification of likely diagnoses. While studies have described evidence-based red flags in children with chronic cough, the value of skilled physicians regarding history taking has received less attention for the best patient care. In the present article, we outline the major questions comprising a detailed history taking for chronic cough in children.