Evidence map›Paper›PMID 36052095›Full record

ArticleClinicoEconomics and outcomes research : CEOR2022

Digital Disruption and Big Data in Healthcare - Opportunities and Challenges.

Mounir El Khatib, Samer Hamidi, Ishaq Al Ameeri, Hamad Al Zaabi, Rehab Al Marqab

Abstract read
In one paragraph

Article in ClinicoEconomics and outcomes research : CEOR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Navigating Teledermatology in the Post-COVID Era.International journal of dermatology · 2026
    Article
  3. Bridging Financial Inclusion and Health Equity in LMICs: Evidence from a Half-Century of Bibliometric Data.International journal of environmental research and public health · 2026
    Article
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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

5 authors.

Mounir El KhatibSchool of Business and Quality Management, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates.
Samer HamidiSchool of Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates.ORCID 0000-0002-6766-3728
Ishaq Al AmeeriSchool of Business and Quality Management, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates.
Hamad Al ZaabiSchool of Business and Quality Management, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates.
Rehab Al MarqabSchool of Business and Quality Management, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: As the amount of medical data in the electronic medical records system (EMR) is increasing tremendously, the required time to read it by health providers is growing by the exact proportionality. This means that physicians must increase the time spared for each patient again by the precise proportionality. This may lead to exposing the accuracy and quality of the course of action to be taken for the patients. Increasing the physician's required time for one patient means that the physician can see fewer patients. This will create an issue with the medical management authority as more physicians are needed, and higher expenses will be required. Purpose: The two questions that arise here are 1. Identify the potential opportunities and challenges for extensive data analysis in the healthcare sector. 2. Evaluate different ways in which big medical data can be analyzed? Methods: The authors identified the four concerned parties representing the four potential solutions dimensions to answer these two questions. These parties are 1. physicians, 2. health information systems management (HISM) departments, mainly the EMR system, and 3. Health management departments 4. Relevant Health Information Systems (HIS) parties. A literature review and 25 interviews were conducted. The interviews covered 1: Two global organizations: John Hopkins and Joint Commission International (JCI), 2: Three United Arab Emirates-based health organizations: Department of health in Abu Dhabi, SEHA in Abu Dhabi, Dubai health Authority (DHA) in Dubai, 3: 10 Physicians from different specialties, 4: Five EMR managers and 5: Five IT (Information Technology) professionals representing the HIS parties. Qualitative analysis is used as the approach for data analysis. Results: Identifying the managerial and the technical recommendations to be utilized mainly based on digital disruption technologies, tools, and processes. Conclusion: Healthcare has been slow in embracing digital disruption and transformation. In most areas, it is still in the initial stages. Recommendations are based on the UAE cases, highlighting the specific technologies and their features.

Indexed as

big datadigital disruptionelectronic medical recordsEMRhealthcarehealth information systemsHIS

Identifiers

PMID36052095
PMCPMC9426864

What OpenQuestion holds

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LicenceCC BY-NC
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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.