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Data Analytics in Healthcare: A Tertiary Study
Toni Taipalus1, Ville Isomöttönen1, Hanna Erkkilä1
1Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, FI-40014 Jyvaskyla, Finland.
SN Computer Science
|December 19, 2022
Summary
Data analytics in healthcare automates areas like medical imaging and disease recognition. This review of reviews highlights machine learning and data mining as key techniques, guiding future research.
Area of Science:
- Healthcare Data Analytics
- Medical Informatics
- Computational Health
Background:
- Healthcare has rapidly adopted data analytics over the past decades.
- Applications include medical image analysis, disease recognition, outbreak monitoring, and clinical decision support.
- Numerous secondary studies exist at the intersection of healthcare and data analytics.
Purpose of the Study:
- To provide a comprehensive overview of data analytics applications in healthcare.
- To conduct a tertiary study, specifically a systematic review of systematic reviews.
- To identify trends and popular topics in healthcare data analytics research.
Main Methods:
- Systematic review of 45 systematic secondary studies.
- Analysis of data analytics applications across various healthcare sectors.
- Identification of commonly used databases, primary study counts, and research guidelines (e.g., PRISMA).
Main Results:
- Machine learning and data mining are the most prevalent data analytics techniques in healthcare.
- A rising trend in the popularity of these techniques was observed.
- Commonly used databases, typical numbers of primary studies, and increasing adherence to guidelines like PRISMA were identified.
Conclusions:
- This review offers a high-level perspective on prominent data analytics applications in healthcare.
- It highlights popular topics and provides a broad overview of a rapidly evolving field.
- Findings can assist researchers in conducting timely literature reviews and empirical studies.
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