| Abstract: |
Learning Health System (LHS) is a paradigm shift in the field of contemporary healthcare delivery, according to which clinical information created in the course of regular patient care is translated into actionable knowledge systematically and fed back into clinical practice in constant loops. The purpose of the paper is to discuss the ways LHS frameworks would help to translate data into knowledge in the hospital context, improve clinical decision-making, and improve patient outcomes due to the continuous learning cycles. The study aims to assess the essential elements and the working mechanisms of LHS in hospitals and determine the effect of the implementation of LHS on patient safety, clinical results, and the quality of healthcare. The research methodology used was a descriptive-analytical one, with the secondary data collected being published peer-reviewed articles, institutional reports, and international health databases. The hypothesis is that the hospitals implementing the LHS frameworks show statistically significant patient safety indicators, adverse events reduction, and enhanced care quality, in comparison to the traditional healthcare model. Findings indicate that hospitals with LHS facilitation saw a 3641 percent decrease in the frequency of adverse events in key clinical conditions, 96 percent of acute care hospitals had adopted the EHR, and 71 percent of predictive AI integration by 2024. The discussion confirms that the functioning of the continuous feedback mechanisms in the LHS architecture leads to quantifiable changes in healthcare provision. This paper concludes that LHS provides a scalable, evidenced-based approach to changing hospital systems into self-improving organizations, although fair application to a variety of healthcare environments is also a pressing issue. |
