We need to chat about artificial intelligence With the arrival of large language models such as ChatGPT, AI is reshaping how we work and interact Long foretold and often dismissed, artificial intelligence (AI) is now reshaping how we work and interact as a society.1 For every claim that AI is overhyped and underperforming, only weeks or
“Note Bloat” impacts deep learning-based NLP models for clinical prediction tasks One unintended consequence of the Electronic Health Records (EHR) implementation is the overuse of content-importing technology, such as copy-and-paste, that creates “bloated” notes containing large amounts of textual redundancy. Despite the rising interest in applying machine learning models to learn from real-patient data, it
An evaluation of existing text de-identification tools for use with patient progress notes from Australian general practice Digitized patient progress notes from general practice represent a significant resource for clinical and public health research but cannot feasibly and ethically be used for these purposes without automated de-identification. Internationally, several open-source natural language processing tools have
Visit Types in Primary Care With Telehealth Use During the COVID-19 Pandemic: Systematic Review Background:Telehealth was rapidly incorporated into primary care during the COVID-19 pandemic. However, there is limited evidence on which primary care visits used telehealth. Objective:The objective of this study was to conduct a systematic review to assess what visit types in primary
Early prediction of diagnostic-related groups and estimation of hospital cost by processing clinical notes As healthcare providers receive fixed amounts of reimbursement for given services under DRG (Diagnosis-Related Groups) payment, DRG codes are valuable for cost monitoring and resource allocation. However, coding is typically performed retrospectively post-discharge. We seek to predict DRGs and DRG-based case
Consumer workarounds during the COVID-19 pandemic: analysis and technology implications using the SAMR framework Objective To understand the nature of health consumer self-management workarounds during the COVID-19 pandemic; to classify these workarounds using the Substitution, Augmentation, Modification, and Redefinition (SAMR) framework; and to see how digital tools had assisted these workarounds. Read More.
Using automated methods to detect safety problems with health information technology: a scoping review Health information technology (HIT) can play an important role in supporting care delivery and improving patient safety.1–6 Problems with HIT however can introduce new, often unforeseen,7 modes of failure that reduce the safety and quality of clinical care and may lead to patient
Family informatics While families have a central role in shaping individual choices and behaviors, healthcare largely focuses on treating individuals or supporting self-care. However, a family is also a health unit. We argue that family informatics is a necessary evolution in scope of health informatics. To deal with the needs of individuals, we must ensure technologies account
How general practitioners and patients discuss type 2 diabetes mellitus and cardiovascular diseases concerns during consultations: Implications for digital health Increasingly, patients are expected to take care of their health outside of medical settings (i.e. self-management).1 Self-management includes the actions taken by individuals to lead a healthy lifestyle, manage their long-term condition and prevent further illness,
Tasks as needs: reframing the paradigm of clinical natural language processing research for real-world decision support. Electronic medical records are increasingly used to store patient information in hospitals and other clinical settings. There has been a corresponding proliferation of clinical natural language processing (cNLP) systems aimed at using text data in these records to improve clinical
Integrating microbial sequencing data into electronic health records, while presenting privacy concerns, will improve patient care and population health and will expand the secondary uses of such data. The growing availability of microbial genomes sequenced for health care rather than research raises the question of whether such data should be included in an individual’s electronic
Summary: Healthcare has well-known challenges with safety, quality, and effectiveness, and many see artificial intelligence (AI) as essential to any solution. Emerging applications include the automated synthesis of best-practice research evidence including systematic reviews, which would ultimately see all clinical trial data published in a computational form for immediate synthesis. Digital scribes embed themselves in the
Healthcare 4.0 (H4.0) adapts principles and applications from the Industry 4.0 movement to healthcare, enabling real-time customization of care to patients and professionals. As such, H4.0 can potentially support resilient performance in healthcare systems, which refers to their adaptive capacity to cope with complexity. This paper explores the impact of ten H4.0 digital technologies on
Objective: To examine how and to what extent medical devices using machine learning (ML) support clinician decision making. Methods: We searched for medical devices that were (1) approved by the US Food and Drug Administration (FDA) up till February 2020; (2) intended for use by clinicians; (3) in clinical tasks or decisions and (4) used
Background: As a major public health crisis, the novel coronavirus disease 2019 (COVID-19) pandemic demonstrates the urgent need for safe, effective, and evidence-based implementations of digital health. The urgency stems from the frequent tendency to focus attention on seemingly high promising digital health interventions despite being poorly validated in times of crisis. Aim: In this
