Challenges and Opportunities of Big Data in Health Care: A Systematic Review
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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.062 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
BACKGROUND: Big data analytics offers promise in many business sectors, and health care is looking at big data to provide answers to many age-related issues, particularly dementia and chronic disease management. OBJECTIVE: The purpose of this review was to summarize the challenges faced by big data analytics and the opportunities that big data opens in health care. METHODS: A total of 3 searches were performed for publications between January 1, 2010 and January 1, 2016 (PubMed/MEDLINE, CINAHL, and Google Scholar), and an assessment was made on content germane to big data in health care. From the results of the searches in research databases and Google Scholar (N=28), the authors summarized content and identified 9 and 14 themes under the categories Challenges and Opportunities, respectively. We rank-ordered and analyzed the themes based on the frequency of occurrence. RESULTS: The top challenges were issues of data structure, security, data standardization, storage and transfers, and managerial skills such as data governance. The top opportunities revealed were quality improvement, population management and health, early detection of disease, data quality, structure, and accessibility, improved decision making, and cost reduction. CONCLUSIONS: Big data analytics has the potential for positive impact and global implications; however, it must overcome some legitimate obstacles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- JMIR Medical Informatics
- Topic
- Artificial Intelligence in Healthcare
- Field
- Health Professions
- Canadian institutions
- —
- Funders
- —
- Keywords
- Big dataHealth careCINAHLData scienceAnalyticsStandardizationData governanceData qualityComputer scienceData managementMEDLINEMedicineKnowledge managementBusinessPolitical scienceData miningMarketing
- Has abstract in OpenAlex
- yes