AIMC Topic: Primary Health Care

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Mapping intellectual structure and research hotspots of cancer studies in primary health care: A machine-learning-based analysis.

Medicine
In the contemporary fight against cancer, primary health care (PHC) services hold a significant and critical position within the healthcare system. This study, as one of the most detailed investigations into cancer research in primary care, comprehen...

Empowering Health: Model for Sustainable AI Implementation.

Studies in health technology and informatics
OntarioMD (OMD), a leader in digital health, focuses on harnessing artificial intelligence (AI) technologies to reduce administrative burden and enhance patient care in primary care. Building on 20 years of digital health experience, OMD has establis...

TWINVAX: conceptual model of a digital twin for immunisation services in primary health care.

Frontiers in public health
INTRODUCTION: This paper presents a proposal for the modelling and reference architecture of a digital twin for immunisation services in primary health care centres. The system leverages Industry 4.0 concepts and technologies, such as the Internet of...

Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis.

PloS one
BACKGROUND: Post-COVID conditions (PCC) have proven difficult to diagnose. In this retrospective observational study, we aimed to characterize the level of variation in PCC diagnoses observed across clinicians from a number of methodological angles a...

Primary care research on hypertension: A bibliometric analysis using machine-learning.

Medicine
Hypertension is one of the most important chronic diseases worldwide. Hypertension is a critical condition encountered frequently in daily life, forming a significant area of service in Primary Health Care (PHC), which healthcare professionals often ...

Proactive care management of AI-identified at-risk patients decreases preventable admissions.

The American journal of managed care
OBJECTIVES: We assessed whether proactive care management for artificial intelligence (AI)-identified at-risk patients reduced preventable emergency department (ED) visits and hospital admissions (HAs).