Multi-Source Data Interpretation For Field Scale Precision Management In Healthcare Industry
Zhiying Huang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences ，Urumqi ，China， 830011
Shaoxiong Hu
Yanqing Liang
DOI:https://doi.org/10.5912/jcb1316
Abstract:
Precision management is a critical component of healthcare, where access to accurate and timely data is essential for effective decision-making. In the healthcare industry, precision management involves the use of multiple data sources to understand patient needs and improve health outcomes. Multi-source data interpretation, specifically at the field scale, is becoming increasingly popular as a means to obtain a comprehensive view of patient care. Field scale precision management in healthcare involves the integration of data from various sources, including electronic health records, patient-generated data, and social determinants of health. Through the use of advanced analytics and machine learning algorithms, this data can be processed and interpreted to identify patterns, trends, and insights that can help healthcare providers make more informed decisions. The application of multi-source data interpretation in healthcare is particularly important because it allows for a more holistic view of patient health, taking into account factors beyond traditional medical metrics. For example, social determinants of health, such as access to healthy food and safe housing, can have a significant impact on patient health outcomes, and these factors can be identified through the analysis of non-medical data sources. Overall, the use of multi-source data interpretation for field scale precision management in healthcare has the potential to significantly improve patient outcomes by providing healthcare providers with a more complete understanding of patient needs and circumstances. With advances in technology and analytics, healthcare providers can better harness the power of data to optimize patient care and improve health outcomes at scale.