Big Data And New Knowledge In Medicine: The Thinking, Training, And Tools Needed For A Learning Health System
By Harlan M. Krumholz. Health Affairs, 2014
475 citations recorded by the source corpus.
Big data in medicine--massive quantities of health care data accumulating from patients and populations and the advanced analytics that can give it meaning--hold the prospect of becoming an engine for the knowledge generation that is necessary to address the extensive unmet information needs of patients, clinicians, administrators, researchers, and health policy makers. This paper explores the ways in which big data can be harnessed to advance prediction, performance, discovery, and comparative effectiveness research to address the complexity of patients, populations, and organizations. Incorporating big data and next-generation analytics into clinical and population health research and practice will require not only new data sources but also new thinking, training, and tools. Adequately used, these reservoirs of data can be a practically inexhaustible source of knowledge to fuel a learning health care system.
Subfields: General Health Professions, Health Information Management, Organizational Behavior and Human Resource Management
View Paper: https://www.semanticscholar.org/paper/91fe52498d3f60f37220c7364ee68d8f8fbc6d79
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In General Health Professions
In Health Information Management
In Organizational Behavior and Human Resource Management
Citations in the corpus
- Scalable and accurate deep learning with electronic health records (2018, background): “It is widely suspected that use such efforts, if successful, could provide major benefits not only for patient safety and quality but also in reducing health care costs [4, 33, 26, 42].”
- Electronic health records to facilitate clinical research (2016, background): “, in a separate registryspecific eCRF), are capable of integrating large amounts of medical information accumulated throughout the patient’s life, enabling longitudinal study of diseases using the existing informatics infrastructure [18].”
- Interpretable Dynamic Treatment Regimes (2016, background): “Furthermore, technological advancements and investment in big-data infrastructure have made it possible to collect, store, and curate large amounts of patient-level data to inform the practice of precision medicine (Krumholz, 2014).”
- Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach. (2016, background): “2,10,11,14 As big data analytic methods are introduced into clinical practice, future efforts should seek to move from generalizable rules to generalizable methods that utilize the richness of local data.”
- The use of Big Data Analytics in healthcare (2022, background): “…of the complex factors and their interactions that influence health at the patient level, the health system and society, enhanced approaches to detecting safety problems with drugs and devices, as well as more effective methods of comparing prevention, diagnostic, and treatment options [40].”