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Data Science in Healthcare: Leveraging Electronic Health Records for Predictive Analytics
Subject area: Science,Engineering and Technology · Area of research: Data Science
Abstract
Analysis of electronic health records, often known as EHR analysis, is a technique that is gaining popularity and is being used increasingly frequently to do research on patient data from the real world. When compared to other approaches to study, the use of data that is routinely obtained offers a variety of advantages, such as fewer "administrative costs, the opportunity to update studies when new patterns of behavior emerge, and larger sample sizes. EHR analysis comes with its own distinct set of methodological challenges as a result of the fact that the data in question were not collected with the goal of doing research. In this Viewpoint, we elaborate on the necessity of having an in-depth grasp of clinical procedures and outline six potential pitfalls that should be avoided while working with EHR data. Both of these topics are covered in the context of dealing with electronic health record data. In order to do this, we rely on examples from the research that has already been conducted in addition to our own personal experiences. We provide solutions that may be used to avoid or lessen the impact of each of these six concerns, which are as follows: subjective treatment allocation, sample selection bias, imprecise variable definitions, restrictions to deployment, variable measurement frequency, and model over fitting. In conclusion, we have great expectations that this Viewpoint will serve as a roadmap for researchers to follow in order to further increase the methodological rigour of EHR analysis. This optimism is based on the fact that we have high hopes that this Viewpoint will serve as a roadmap.
Keywords
Healthcare, Electronic, Data Science
References
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[48] Shashi Kant Gupta, Olena Hrybiuk, NL Sowjanya Cherukupalli, Arvind Kumar Shukla (2023). Big Data Analytics Tools, Challenges and Its Applications (1st Ed.), CRC Press. ISBN 9781032451114
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[50] Parin Somani, Shashi Kant Gupta, Chandra Kumar Dixit, Anchal Pathak (2023). AI-based Competency Model and Design in the Workforce Development System (1st Ed.), CRC Press. https://doi.org/10.1201/9781003357070
[51] Shashi Kant Gupta, Alex Khang, Parin Somani, Chandra Kumar Dixit, Anchal Pathak (2023). Data Mining Processes and Decision-Making Models in Personnel Management System (1st Ed.), CRC Press. https://doi.org/10.1201/9781003357070
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How to cite this paper
@article{1704994,
author = {Dr. Rupesh Shukla, Aaryesh Shukla},
title = {Data Science in Healthcare: Leveraging Electronic Health Records for Predictive Analytics},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {7},
number = {2},
pages = {383-393},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704994.pdf},
abstract = {Analysis of electronic health records, often known as EHR analysis, is a technique that is gaining popularity and is being used increasingly frequently to do research on patient data from the real world. When compared to other approaches to study, the use of data that is routinely obtained offers a variety of advantages, such as fewer "administrative costs, the opportunity to update studies when new patterns of behavior emerge, and larger sample sizes. EHR analysis comes with its own distinct set of methodological challenges as a result of the fact that the data in question were not collected with the goal of doing research. In this Viewpoint, we elaborate on the necessity of having an in-depth grasp of clinical procedures and outline six potential pitfalls that should be avoided while working with EHR data. Both of these topics are covered in the context of dealing with electronic health record data. In order to do this, we rely on examples from the research that has already been conducted in addition to our own personal experiences. We provide solutions that may be used to avoid or lessen the impact of each of these six concerns, which are as follows: subjective treatment allocation, sample selection bias, imprecise variable definitions, restrictions to deployment, variable measurement frequency, and model over fitting. In conclusion, we have great expectations that this Viewpoint will serve as a roadmap for researchers to follow in order to further increase the methodological rigour of EHR analysis. This optimism is based on the fact that we have high hopes that this Viewpoint will serve as a roadmap.},
keywords = {Healthcare, Electronic, Data Science},
month = {August},
}