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Leveraging Big Data Analytics in Healthcare Enhancement: Trends, Challenges and Opportunities
Arshia Rehman, Saeeda Naz, Imran Razzak
TL;DR
Healthcare big-data analytics addresses the challenge of managing large, complex, heterogeneous health datasets that exceed traditional methods. The paper reviews analytical methods, architectures, tools, and repositories across five healthcare sub-disciplines, reporting applications in early disease detection, patient care, and related healthcare activities while noting interoperability and data-quality constraints.
Problem
Healthcare data is large, complex, heterogeneous, and increasingly difficult to manage, integrate, store, exchange, and use reliably for clinicians and patients.
Method
The paper reviews big-data methods, tools, techniques, architectures, and repositories across five healthcare sub-disciplines and presents a general analytics framework.
Results
The review identifies applications in early disease detection, disease exploration, patient care, care delivery, and community services, including early heart-attack detection and diabetes analysis.
Takeaways & Limitations
Big-data analytics is presented as supporting healthcare personnel, personalized care, lower treatment costs, reduced risks, and improved patient outcomes.
Takeaways & Limitations
Poor EHR interoperability and healthcare-data quality problems constrain the usefulness of integrated analytics.
Abstract
from arXiv · showhide
Clinicians decisions are becoming more and more evidence-based meaning in no other field the big data analytics so promising as in healthcare. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and promises us a world of opportunities. It promises us the power of early detection, prediction, prevention and helps us to improve the quality of life. Researchers and clinicians are working to inhibit big data from having a positive impact on health in the future. Different tools and techniques are being used to analyze, process, accumulate, assimilate and manage large amount of healthcare data either in structured or unstructured form. In this paper, we would like to address the need of big data analytics in healthcare: why and how can it help to improve life?. We present the emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare i.e.medical image analysis and imaging informatics, bioinformatics, clinical informatics, public health informatics and medical signal analytics. We presents different architectures, advantages and repositories of each discipline that draws an integrated depiction of how distinct healthcare activities are accomplished in the pipeline to facilitate individual patients from multiple perspectives. Finally the paper ends with the notable applications and challenges in adoption of big data analytics in healthcare.
1 Introduction
Healthcare big data analytics addresses the scale, complexity, and diversity of expanding health records by applying modern data-management and analytical approaches. The review frames five healthcare sub-disciplines and discusses how analytics can support improved healthcare.
- Need for analytics: Big data analytics is needed because healthcare datasets are large and complex enough to challenge traditional tools and methods.The data must be captured, managed, processed, and analyzed across structured and unstructured forms.
- Healthcare data landscape: Healthcare data is expanding across electronic records, clinical measurements, imaging, genetics, pharmaceutical data, and healthcare applications.Digitized systems, wearable technologies, sensors, and mobile applications contribute to this growth.
- Five characteristics: Healthcare big data includes medical records, personal and clinical data, radiology images, genetic information, and population information.Resource-intensive applications such as 3D imaging, genomics, and biological sequences also contribute to data volume.
- Five characteristics: Healthcare data variety spans structured, semi-structured, and unstructured records, including notes, prescriptions, databases, images, and medical films.These forms originate from multiple healthcare and administrative sources.
- Review scope: The review covers medical image processing and imaging informatics, bioinformatics, clinical informatics, public health informatics, and medical signal analytics.It also presents big-data theory, healthcare architectures, and advantages before reviewing these sub-disciplines.
2 Background of Big Data and Data Analytics
This section defines big data and analytics through the characteristics, processing challenges, and analytical methods used to extract information from large integrated datasets. It introduces descriptive, predictive, and prescriptive analytics alongside dimensionality reduction, optimization, clustering, and data-mining approaches.
- Big data: Big data consists of large, complex datasets that conventional information-processing techniques and databases cannot adequately manage.Its main characteristics are commonly described through volume, variety, velocity, value, and veracity.
- Big data characteristics: The 5Vs describe data quantity, format diversity, generation or processing speed, usefulness, and quality or reliability.Healthcare examples include clinical records, images, genomic data, multimedia, and real-time monitoring data.
- Data analytics: Big data analytics analyzes voluminous data from multiple sources to extract valuable or hidden patterns and support conclusions.The process includes scrutinizing, modeling, cleansing, and transforming data.
- Analytics methods: Descriptive, predictive, and prescriptive analytics respectively summarize data, forecast future outcomes, and formulate predictive recommendations.Predictive analytics uses machine learning, statistical, modeling, and data-mining techniques on recent and historical data.
- Analytical techniques: Common big-data methods include supervised, unsupervised, and hybrid machine learning, dimensionality reduction, optimization, clustering, and exploratory data analysis.Examples include PCA, SVD, KPCA, Pareto optimization, CLARA, and BIRCH.
3 Architectures For Big Data Analytics
The proposed healthcare architecture moves data from collection through processing, transformation, and analysis to reports and queries. The section situates this pipeline within Hadoop-based components and related distributed storage, querying, encoding, and machine-learning tools.
- Big Data Analytics Architecture: The general framework collects healthcare data, processes and transforms it, analyzes it with big-data platforms, and produces reports or queries.Inputs include EHRs, clinical images, and monitoring-device logs; outputs use data-mining and OLAP tools.
- Big Data Analytics Architecture: The framework transforms data into warehouses, middleware, CSV or tables, Hadoop, or HDFS before analytical processing.The analytical phase uses Hadoop, MapReduce, Hive, HBase, Jaql, Avro, and other platforms.
- Hadoop: Hadoop is an open-source collection of utilities for distributed computation, processing, and storage of huge datasets.Its two core components are HDFS and MapReduce.
- HDFS: HDFS uses a master-slave architecture in which the NameNode manages the file-system structure and DataNodes store and handle data across servers or nodes.It partitions large data collections for distributed storage and provides high-throughput access.
- MapReduce: MapReduce divides large-data analysis into Map and Reduce phases and executes subtasks in parallel through JobTracker and TaskTracker components.The client submits jobs to JobTracker, which assigns them to TaskTrackers.
- Related Hadoop tools: Hive converts SQL queries into MapReduce jobs, while HBase provides column-oriented, non-relational key/value operations on HDFS.Presto supports distributed querying from gigabytes to petabytes, Avro supports serialization and versioning, and Mahout provides distributed machine-learning applications.
4 Advantages of Big Data to Healthcare
Big data analytics can help healthcare stakeholders derive insights from diverse patient data, supporting quality care, prevention, efficiency, disease discovery, and cost reduction.
- Analyzing EHR data can reveal associations and patterns that help practitioners provide quality care, save lives, and lower costs.
- Early disease detection and prevention can reduce deaths, healthcare costs, and resource pressures.
- Big data supports efficiency by examining historical patient admissions and staff performance when traditional tools cannot manage healthcare data diversity and volume.
- Genomic analytics can extract hidden patterns and unknown correlations from large datasets to support efforts to discover cancer cures.
- Predictive analytics can detect disease early, reduce medication errors and readmissions, and compare treatment regimens to save resources and money.
- Machine learning applied to human genomes can help uncover correlations and identify drugs or treatments for cancer.
5 Review Methodology
The review uses a systematic methodology to identify and select English-language articles and reviews on big data analytics in healthcare, published from 2000 to 2019.
- The review methodology systematically finds relevant literature from different sources.
- The review aims to define big data concepts, examine five healthcare sub-disciplines, and describe their repositories and complex datasets.
- It also identifies healthcare analytical architectures and techniques, potential advantages and applications, and open challenges with strategies for addressing them.
- Relevant articles were collected through Google Scholar and reference scanning, followed by selection using stated inclusion and exclusion criteria.
- Eligible studies were articles or reviews in English, related to big data analytics in healthcare, and published from 2000 to 2019.
- Searches used keywords covering big data, healthcare, biomedical analytics, and the five targeted sub-disciplines.
6 Key Application in Healthcare
The review surveys big data applications across five healthcare sub-disciplines, spanning imaging, bioinformatics, clinical and public health informatics, and medical signal analytics.
- Healthcare professionals use large datasets to reduce treatment costs, predict epidemics, prevent diseases, and improve quality of life.
- The review covers five sub-disciplines and evaluates healthcare analytics literature published from 2000 to 2019.
- Medical Image Processing and Imaging Informatics: Medical imaging uses modalities including MRI, CT, photo-acoustic, ultrasound, 3D ultrasound, fMRI, and PET for healthcare data acquisition.
- Medical Image Processing and Imaging Informatics: Integrated data mining, artificial intelligence, and parallel computing reduced a cardiac-imaging case rate from 10% to 5% across 55 participating sites.
- Bioinformatics: Bioinformatics analyzes genomic, RNA, DNA, proteomic, pathway, interaction, and disease-network data using distributed tools such as Hydra, SeqWare, CloVR, and CloudBioLinux.
- Clinical Informatics: Clinical informatics organizes largely unstructured clinical and laboratory data into computerized forms to improve retrieval, reporting, diagnostic efficiency, and patient outcomes.
- Public Health Informatics: Public health informatics uses systems such as SEER to help planners and epidemiologists analyze cancer distribution, while monitoring coincided with declining mortality rates.
- Medical Signal Analytics: Medical signal analytics applies machine learning to physiological signals, achieving 78.13% accuracy for lung-cancer recognition and 91.66% for tremor classification.
7 Key Findings
The review integrates evidence on big data techniques, sources, applications, and usability across five healthcare sub-disciplines, while identifying uneven adoption across fields.
- The survey maps big data techniques and analytical practices across five healthcare sub-disciplines into an integrated patient-focused pipeline.
- Healthcare analytics draws on data from providers, laboratories, diagnostic companies, insurers, pharmaceutical firms, and web-health portals.
- Common techniques include machine learning, data mining, clustering, pattern recognition, neural networks, deep learning, and spatial analysis.
- Hadoop dominates batch processing, while Spark, Storm, and GraphLab support real-time and streaming data processing.
- Reported applications include personalized medicine, clinical decision support, operational optimization, cost effectiveness, quality improvement, and early patient identification.
- Bioinformatics has developed substantial big data tools and platforms, whereas applications appear less developed in imaging, clinical, public health, and signal informatics.
8 Big Data Analystics Applications
Big data analytics is applied across clinical, operational, genomic, imaging, cardiovascular, and remote-care domains to support prediction, diagnosis, treatment, and resource decisions. These applications aim to improve care while reducing costs, waiting times, fraud, and avoidable readmissions.
- Strategic Planning: Machine learning and data analytics predict patient flow, helping hospitals reduce waiting periods and provide timely treatment.Patient Flow Manager and Q-nomy’s provide graphical views of inpatient, elective, emergency, and outpatient flow information.
- Fraud Detection: Analytics is used to detect healthcare fraud, waste, and abuse and to streamline insurance-claims processing.The Centers for Medicare and Medicaid Services saved over $210.7 million in fraud-related costs.
- Resource Management: Risk prediction models identify patients likely to experience 30-day readmission or emergency-department return visits, supporting disease-management programs.The intended benefits include reducing readmissions and healthcare costs.
- Medical Signal Analytics: Big data analytics supports early heart-attack detection using medical biosensors and healthcare information systems built with IoT and Hadoop.The cited systems are designed to detect heart attacks at an early stage and provide guidance about heart disease.
- Clinical informatics: Data mining and analytics are applied to Parkinson’s disease prediction and diabetes analysis, including descriptive datasets and predictive models.Hive and R are cited among the tools used for diabetes analysis.
- Healthcare applications: Healthcare data analytics supports applications including patient diagnosis, remote consultation, medicine ordering, and accurate patient identification.Examples include AmWell, Practo, Portea, and Isabel.
9 Challenges and Open Research Issues
Healthcare analytics faces limitations in data quality, interoperability, privacy, security, standardization, and the management of complex data from multiple sources. Addressing these issues requires improved infrastructure, governance, preparation, and analytical methods.
- Open research issues: Successful healthcare analytics depends on how data is stored, prepared, and mined, while challenges include complexity, access, compliance, security, interoperability, and method maturity.The paper also identifies manageability, development, and re-usability as challenges.
- Multiple Source Information Management: Poor interoperability across EHRs makes healthcare data difficult to manage, store, exchange, and integrate across providers.Integration requires infrastructure that enables data providers to collaborate and share information.
- Multiple Source Information Management: Hospitals have yet to achieve interoperability, limiting improvements in patient care; standards such as HL7, HIPAA, and HITECH are identified as part of the response.Stakeholders associate interoperability with improved care, fewer medical errors, and lower costs.
- Security and Privacy and Confidentiality: Privacy requirements restrict data sharing and access, while excessive access limitation can remove information important for analytics.HIPAA establishes patient rights and provider responsibilities concerning personally identifiable information.
- Data Quality: Healthcare data is increasingly large, unstructured, nonstandard, and multimedia, with 80% of electronic health data reported as unstructured.Quality problems include incompleteness, inconsistency, inaccuracy, fragmentation, duplication, outliers, and stale records.
- Data Standardization: Clinical notes remain difficult to use analytically because they are natural-language data rather than discrete fields.NLP using ICD or SNOMED CT is identified as the route for converting this unstructured data into discrete data.
10 Conclusion
Big data analytics is presented as a means to collect, analyze, manage, and store complex healthcare data while supporting disease identification, care delivery, and patient services. The paper surveys methods, tools, techniques, architectures, and repositories across five healthcare sub-disciplines, while emphasizing challenges and stakeholder coordination for implementation.
- Big data analytics supports healthcare personnel, care delivery, early disease detection, disease exploration, patient care, and community services.
- The paper reviews analytics methods, tools, techniques, architectures, and repositories across medical imaging, bioinformatics, clinical informatics, public health informatics, and medical signal analytics.
- Multiple-source data management, privacy, security, standards, governance, advanced analysis, and data quality remain notable healthcare analytics challenges.
- Future big-data trends may enhance communication among clinicians, executives, logistics managers, and analysts while diminishing costs, reducing risks, and improving personalized care.
- Government agencies, healthcare professionals, technology companies, pharmaceutical industries, researchers, data scientists, people, and vendors must participate in developing the healthcare big-data framework.