Citations

This chapter provides a high-level overview of clinical informatics (CI) and artificial intelligence (AI) in healthcare. It covers foundational concepts such as electronic health records (EHRs) and data standards, current applications including Clinical Decision Support Systems (CDSS) and ambient scribing/documentation, and near-term directions for both clinicians and patients. The chapter also introduces how foundational CI systems and healthcare data are leveraged to develop AI solutions, ranging from traditional machine learning to large language models, while addressing key issues such as bias, generalizability, and regulatory considerations. Finally, the chapter examines a potential future in which decentralized AI models augment clinicians in their daily work, generate deeper clinical insights, and transform patient care through advanced predictive models and digital twins.

This chapter will explore how telemedicine can increase access to care, address the physician shortage, reduce healthcare costs, support patients in managing chronic diseases, and improve the patient experience. Today, with so much growth and reliance on technology, healthcare reform requires understanding how to incorporate technology to optimize the patient experience and outcomes. This chapter will focus on how best to harness the benefits of technology to provide optimal healthcare in a post-2020 world, from a clinician's perspective.

This chapter explores, analyzes, and discusses the challenges and potential recommendations for the unprecedented physician shortage affecting US healthcare systems. As a result, patients are waiting long periods to get an appointment, and in many cases, even longer to see a physician. Especially older patients who may need immediate attention and medical care end up waiting several weeks. Healthcare seems to be facing a crisis that is getting worse by the day. How did we get here, why, and what do we do about it are some of the questions addressed in this chapter.

Abstract Aims Monitoring pulmonary congestion in chronic heart failure (HF) reduces decompensation and hospitalization, but conventional methods such as weight and symptom tracking are often unreliable. As fluid accumulation affects the lungs and vocal tract, subtle voice alterations may serve as a non-invasive signal for early detection of worsening HF. Methods and results The Voice Analysis for Monitoring Patients with HF trial (VAMP-HF, NCT06566911) prospectively enrolled 104 patients hospitalized with acute decompensated HF (ADHF) across two academic centres in the USA and Germany. Daily voice recordings were collected from admission to discharge, with breathing features extracted from speech and acoustic features from sustained vowels. A machine-learning model was trained to classify recordings as admission-phase vs. discharge-phase using leave-one-patient-out. Patients with clinical deterioration, insufficient audio quality, or short length of stay were excluded. Seventy-nine patients were included in the final dataset. The model classified admission and discharge with an F1-score of 0.83 (95% CI: 0.77–0.90; AUC = 0.90). In patients with higher audio volume (N = 54), performance reached 0.89 (95% CI: 0.82–0.94; AUC = 0.91). When applied to intermediate hospitalization days, model-predicted scores showed progressive increases from admission towards discharge. Performance remained robust irrespective of significant weight loss during hospitalization. Conclusion In this pilot study, structured voice and breathing analysis discriminated hospitalization phase from admission through discharge in patients with ADHF. This non-invasive approach captured progressive changes during the hospital course and warrants further investigation with concurrent objective congestion markers to establish physiological specificity.

Abstract Education and allergy referral via nurse-driven online messaging to patients (n = 1,284) with penicillin allergy labels (PALs) reduced PALs by 8.6%. The percentage decrease of “unknown” (14.8%) and “rash” (7.9%) PALs were higher vs “hives” (6.1%) and “itching” (1.4%) reaction types.