DATA RETRIEVAL FOR CLINICAL PROJECTS IN THE EVOLVING HEALTHCARE SYSTEM: PAST, PRESENT, AND FUTURE

Area of Science:

• Health Informatics
• Medical Data Management
• Clinical Research Methodology

Background:

• Healthcare data retrieval has evolved significantly, moving from manual chart reviews to sophisticated digital systems.
• Electronic Patient Record (EPR) systems and informatics-driven queries have improved efficiency but introduced challenges in data accuracy and coding reliability.
• The increasing complexity of healthcare data necessitates advanced methods for effective retrieval and analysis.

Purpose of the Study:

• To trace the historical evolution of data sourcing methods in healthcare, from manual to AI-assisted approaches.
• To examine the critical balance between structured and unstructured data in clinical information systems.
• To highlight the emerging roles of prompt engineering and Natural Language Processing (NLP) in enhancing clinical data retrieval.

Main Methods:

• Literature review of data sourcing methodologies in healthcare settings.
• Analysis of the transition from manual data extraction to digital workflows.
• Exploration of the impact of Artificial Intelligence (AI) on clinical data management.

Main Results:

• Each evolutionary stage of data retrieval (manual, EPR, AI) presents unique benefits and challenges regarding scale, efficiency, accuracy, and data detail.
• Structured data offers reliability but may lack clinical nuance, while unstructured data provides rich detail but poses retrieval challenges.
• Prompt engineering and NLP are emerging as key technologies to overcome current limitations in accessing clinically meaningful data.

Conclusions:

• Current data retrieval systems face limitations in accuracy, coding reliability, and accessing clinically meaningful details.
• Future innovations, particularly in prompt engineering and NLP, hold significant potential to improve the quality and accessibility of clinical data.
• A balanced approach integrating structured and unstructured data analysis is crucial for advancing healthcare informatics and research.