Evidence map›Paper›PMID 41586204›Full record

ReviewFrontiers in digital health2025

Enhancing healthcare outcome with scalable processing and predictive analytics via cloud healthcare API.

Seyede Sana Salehi, Hamid Saadatfar, Solomon Sunday Oyelere, Sadiq Hussain, Javad Hassannataj Joloudari, Mohammad Taheri Ledari, Emrah Arslan, Behnam Barzegar

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Seyede Sana SalehiDepartment of Computer Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran.
Hamid SaadatfarDepartment of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
Solomon Sunday OyelereDepartment of Computer Science, Faculty of Environment, Science and Economy, University of Exeter, Exeter, United Kingdom.
Sadiq HussainCentre for Computer Science and Applications, Dibrugarh University, Dibrugarh, India.
Javad Hassannataj JoloudariDepartment of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
Mohammad Taheri LedariDepartment of Computer Engineering, Bab.C., Islamic Azad University, Babol, Iran.
Emrah ArslanDepartment of Computer Engineering, Faculty of Engineering, KTO Karatay University, Konya, Türkiye.
Behnam BarzegarDepartment of Computer Engineering, Bab.C., Islamic Azad University, Babol, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This systematic literature review investigates the Google Cloud Healthcare API's role in transforming healthcare delivery through advanced analytics, machine learning, and cloud-based solutions. The study examines current features of cloud-based healthcare platforms in managing heterogeneous healthcare data formats, analyzes the effectiveness of cloud solutions in enhancing clinical outcomes, and compares Google Cloud Healthcare API with alternative platforms. The findings reveal that Google Cloud Healthcare API demonstrates notable advantages through its fully managed, serverless architecture, native support for healthcare standards (e.g., FHIR, HL7v2, DICOM), and seamless integration with advanced AI/ML services. Cloud-based predictive analytics platforms have proven effective in reducing hospital readmissions, addressing physician burnout, and enabling scalable telemedicine solutions. However, significant challenges persist including data privacy concerns, regulatory compliance complexities, infrastructure dependencies, and potential vendor lock-in risks. The research demonstrates that healthcare organizations implementing comprehensive cloud-based solutions achieve measurable improvements in patient outcomes, operational efficiency, and care delivery models. While technical challenges around latency in medical imaging and interoperability remain, the evidence strongly supports cloud adoption for healthcare transformation, provided organizations address security, compliance, and implementation challenges through strategic planning and comprehensive change management approaches.

Indexed as

cloud healthcare APIdata privacymachine learningpredictive analyticsscalable processing

Identifiers

PMID41586204
PMCPMC12827720

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.