Evidence map›Paper›PMID 35891056›Full record

SynthesisSensors (Basel, Switzerland)2022

IoT Adoption and Application for Smart Healthcare: A Systematic Review.

Manal Al-Rawashdeh, Pantea Keikhosrokiani, Bahari Belaton, Moatsum Alawida, Abdalwhab Zwiri

Abstract readSystematic Review
In one paragraph

Synthesis in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 5 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

44 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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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.

Manal Al-RawashdehSchool of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia.ORCID 0000-0002-6386-6149
Pantea KeikhosrokianiSchool of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia.ORCID 0000-0003-4705-2732
Bahari BelatonSchool of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia.ORCID 0000-0002-9099-1498
Moatsum AlawidaSchool of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia.ORCID 0000-0001-8146-5843
Abdalwhab ZwiriSchool of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kelantan 16150, Malaysia.ORCID 0000-0002-6951-5163

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In general, the adoption of IoT applications among end users in healthcare is very low. Healthcare professionals present major challenges to the successful implementation of IoT for providing healthcare services. Many studies have offered important insights into IoT adoption in healthcare. Nevertheless, there is still a need to thoroughly review the effective factors of IoT adoption in a systematic manner. The purpose of this study is to accumulate existing knowledge about the factors that influence medical professionals to adopt IoT applications in the healthcare sector. This study reviews, compiles, analyzes, and systematically synthesizes the relevant data. This review employs both automatic and manual search methods to collect relevant studies from 2015 to 2021. A systematic search of the articles was carried out on nine major scientific databases: Google Scholar, Science Direct, Emerald, Wiley, PubMed, Springer, MDPI, IEEE, and Scopus. A total of 22 articles were selected as per the inclusion criteria. The findings show that TAM, TPB, TRA, and UTAUT theories are the most widely used adoption theories in these studies. Furthermore, the main perceived adoption factors of IoT applications in healthcare at the individual level are: social influence, attitude, and personal inattentiveness. The IoT adoption factors at the technology level are perceived usefulness, perceived ease of use, performance expectancy, and effort expectations. In addition, the main factor at the security level is perceived privacy risk. Furthermore, at the health level, the main factors are perceived severity and perceived health risk, respectively. Moreover, financial cost, and facilitating conditions are considered as the main factors at the environmental level. Physicians, patients, and health workers were among the participants who were involved in the included publications. Various types of IoT applications in existing studies are as follows: a wearable device, monitoring devices, rehabilitation devices, telehealth, behavior modification, smart city, and smart home. Most of the studies about IoT adoption were conducted in France and Pakistan in the year 2020. This systematic review identifies the essential factors that enable an understanding of the barriers and possibilities for healthcare providers to implement IoT applications. Finally, the expected influence of COVID-19 on IoT adoption in healthcare was evaluated in this study.

Indexed as

COVID-19TelemedicineWearable Electronic DevicesDelivery of Health CareHealth PersonnelHumansadoption factorsadoption theoriesdeep learning (DL)Internet of Things in healthcareIoMTIoTIoT adoptionmachine learning (ML)systematic review

Identifiers

PMID35891056
PMCPMC9316993

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.