Evidence map›Paper›PMID 30514693›Full record

ArticleJMIR research protocols2018

Acceptance and Resistance of New Digital Technologies in Medicine: Qualitative Study.

Sabur Safi, Thomas Thiessen, Kurt Jg Schmailzl

Abstract read
In one paragraph

Article in JMIR research protocols, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 141 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
141citing papers in PubMed, 10 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

141 citing papers in PubMed, 10 syntheses or guidelines pooled it.

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  13. Upright positioning in pediatric radiotherapy: rationale, safety considerations and a research roadmap.Technical innovations & patient support in radiation oncology · 2026
    Article
  14. Article
  15. Article
  16. Digital Transformation in Health Care: Are We on the Right Track?Journal of medical Internet research · 2026
    Article
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81 more citing papers are in PubMed but not listed here.

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

3 authors.

Sabur Safi *Medical School Brandenburg, digilog, Center for Connected Health Care UG, Neuruppin, Germany.ORCID http://orcid.org/0000-0003-3352-2676
Thomas ThiessenBSP Business School Berlin, digilog, Center for Connected Health Care UG, Berlin, Germany.ORCID http://orcid.org/0000-0001-5578-4162
Kurt Jg SchmailzlMedical School Brandenburg, digilog, Center for Connected Health Care UG, Neuruppin, Germany.ORCID http://orcid.org/0000-0002-9903-6374

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study discusses the acceptance of new medical technologies in health care settings and resistance to these technologies from hospitals, doctors' surgical centers, electronic health (eHealth) centers, and related institutions. We suggest a novel method of identifying factors that influence the acceptance of, and resistance to, new technologies by medical staff and patients.

objectiveThe objective of this study was to determine and evaluate the factors that influence acceptance and resistance to achieve a successful implementation of new technologies.

methodsThe target group was patients residing in Brandenburg and major stakeholders in the local health care structure, for instance, medical institutions and medical professionals. The process relies on 3 models: the technology acceptance model, the unified technology acceptance and use of technology model, and the theory of technical innovation diffusion. Qualitative methodology was employed in this study, and an exploratory design was adopted to gain new insights into a poorly understood phenomenon in the German context. This enabled the researcher to take a flexible approach toward exploring a wide range of secondary data and to choose a different approach when unexpected information emerged. Content analysis was used to identify and interpret the data, and the researcher assured that the meaning associated with the information has concurred with that of the original source.

resultsThis study confirmed that adoption of new technologies in health care depended on individual opinions of the factors relating to them. Some medical professionals believed that technology would interfere with their ability to make independent diagnoses and their relationships with patients. Doctors also feared that technology was a means of management control. In contrast, other medical staff welcomed technology because it provided them with more opportunities to interact with patients and their carers. Generally, patients were more enthusiastic about technology than medical professionals and health care managers because it allowed them to have greater autonomy in selecting health care options. The need for all groups to be involved in the development of the new health care approach was an important outcome, otherwise resistance to it was likely to be greater. In other words, the strategy for change management was the indicator of success or failure. Therefore, following our analysis, a number of practical precepts emerged that could facilitate user acceptance of digital solutions and innovative medical technologies.

conclusionsThe acceptance of digital solutions and innovative medical technology by patients and professionals relies on understanding their anxieties and feelings of insecurity. The process will take time because individuals accept change at different rates. Hence, the development of an extensive user community to fully and successfully implement eHealth is less likely in the short term; however, this should not prevent the push for changes in health care technology.

Indexed as

health care innovationinformation and technology communicationinnovation diffusiontelematics infrastructure

Identifiers

PMID30514693
PMCPMC6299231

What OpenQuestion holds

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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.