Towards pervasive computing in health care – A literature review
Open accessAbstract
BACKGROUND: The evolving concepts of pervasive computing, ubiquitous computing and ambient intelligence are increasingly influencing health care and medicine. Summarizing published research, this literature review provides an overview of recent developments and implementations of pervasive computing systems in health care. It also highlights some of the experiences reported in deployment processes. METHODS: There is no clear definition of pervasive computing in the current literature. Thus specific inclusion criteria for selecting articles about relevant systems were developed. Searches were conducted in four scientific databases alongside manual journal searches for the period of 2002 to 2006. Articles included present prototypes, case studies and pilot studies, clinical trials and systems that are already in routine use. RESULTS: The searches identified 69 articles describing 67 different systems. In a quantitative analysis, these systems were categorized into project status, health care settings, user groups, improvement aims, and systems features (i.e., component types, data gathering, data transmission, systems functions). The focus is on the types of systems implemented, their frequency of occurrence and their characteristics. Qualitative analyses were performed of deployment issues, such as organizational and personnel issues, privacy and security issues, and financial issues. This paper provides a comprehensive access to the literature of the emerging field by addressing specific topics of application settings, systems features, and deployment experiences. CONCLUSION: Both an overview and an analysis of the literature on a broad and heterogeneous range of systems are provided. Most systems are described in their prototype stages. Deployment issues, such as implications on organization or personnel, privacy concerns, or financial issues are mentioned rarely, though their solution is regarded as decisive in transferring promising systems to a stage of regular operation. There is a need for further research on the deployment of pervasive computing systems, including clinical studies, economic and social analyses, user studies, etc.
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The presence of sufficient security mechanisms to support trust between providers and patients and technical measures to ensure systems compliance with data protection laws were shown to promote implementation.
“The presence of sufficient security mechanisms to support trust between providers and patients [34] and technical measures to ensure systems compliance with data protection laws [84] were shown to promote implementation.”
From Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews (2015) · cites this paper as [84]
The Results (Deployment issues; page unavailable) report that some systems used technical measures to ensure compliance with data protection laws. The paper does not show that those measures promoted implementation; its Discussion links privacy protection more generally to acceptance. The separate claim about trust between providers and patients is attached to a different citation and is not assessed here. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAdditional workload caused by the implementation of new complex interventions hindered adoption.
“Additional workload caused by the implementation of new complex interventions was also found to hinder adoption [17, 31, 44, 54, 66, 81, 84].”
From Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews (2015) · cites this paper as [84]
Physician resistance to e-health implementation is related to fear of, dissatisfaction with, and uncertainty over new roles and responsibilities created by the introduction of e-health systems.
“Physician resistance to e-health implementation is reported by several studies to be related to fear of [18], dissatisfaction with [18] and uncertainty over [43, 47] new roles and responsibilities, created by the introduction of e-health systems [18].”
From Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update) (2016) · cites this paper as [47]
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- Authors
- Orwat, C., Graefe, A., & Faulwasser, T.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Otto-von-Guericke-Universität Magdeburg (DE)
- Corresponding author
- Orwat, Carsten
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2008-06-19
- Volume
- 8
- Issue
- 1
- Pages
- 26-26
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC2467411
- OpenAlex
- W2102541915
- Cited by
- 232
- References
- 89
- Citations by year
- 2025: 2 · 2024: 7 · 2023: 3 · 2022: 3 · 2021: 6 · 2020: 9 · 2019: 11 · 2018: 15 · 2017: 16 · 2016: 14 · 2015: 16 · 2014: 26 · 2013: 31 · 2012: 22
- FWCI
- 3.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- tub.dok (Hamburg University of Technology) (submittedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (16I1546)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- ubiquitous computing, ambient intelligence, health information systems, privacy and security, financial issues, prototype system, clinical implementation
- MeSH terms
- Computer Systems, Medical Informatics Applications, Medical Informatics Computing, Qualitative Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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