AIMC Topic: Delivery of Health Care, Integrated

Clear Filters Showing 1 to 10 of 27 articles

Assessing Data Quality in Heterogeneous Health Care Integration: Simulation Study of the AIDAVA Framework.

JMIR medical informatics
BACKGROUND: Integrated health data are foundational for secondary use, research, and policymaking. However, data quality issues-such as missing values and inconsistencies-are common due to the heterogeneity of health data sources. Existing frameworks...

Applications and Outcomes of Telehealth and Integrated Care in Men's Health Urology.

Journal of medical Internet research
Men's health, particularly in the domain of urology, faces significant challenges in access to care, patient outcomes, and cost efficiency. Despite advances in medical treatment, conditions such as prostate cancer remain a leading cause of cancer-rel...

Data Ownership in the AI-Powered Integrative Health Care Landscape.

JMIR medical informatics
In the rapidly advancing landscape of artificial intelligence (AI) within integrative health care (IHC), the issue of data ownership has become pivotal. This study explores the intricate dynamics of data ownership in the context of IHC and the AI era...

Identifying the Severity of Heart Valve Stenosis and Regurgitation Among a Diverse Population Within an Integrated Health Care System: Natural Language Processing Approach.

JMIR cardio
BACKGROUND: Valvular heart disease (VHD) is a leading cause of cardiovascular morbidity and mortality that poses a substantial health care and economic burden on health care systems. Administrative diagnostic codes for ascertaining VHD diagnosis are ...

Identifying Elective Induction of Labor among a Diverse Pregnant Population from Electronic Health Records within a Large Integrated Health Care System.

American journal of perinatology
OBJECTIVE:  Distinguishing between medically indicated induction of labor (iIOL) and elective induction of labor (eIOL) is a daunting process for researchers. We aimed to develop a Natural Language Processing (NLP) algorithm to identify eIOLs from el...

Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

Clinical research in cardiology : official journal of the German Cardiac Society
BACKGROUND: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.

A Natural Language Processing-Based Approach for Identifying Hospitalizations for Worsening Heart Failure Within an Integrated Health Care Delivery System.

JAMA network open
IMPORTANCE: The current understanding of epidemiological mechanisms and temporal trends in hospitalizations for worsening heart failure (WHF) is based on claims and national reporting databases. However, these data sources are inherently limited by t...