- Research article
- Open Access
- Published: 20 June 2019
BMC Health Services Research volume 19, Article number: 400 [2019] Cite this article
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Abstract
Background
The existence, usage and benefits of digital technologies in nursing care are relevant topics in the light of the current discussion on technologies as possible solutions to problems such as the shortage of skilled workers and the increasing demand for long-term care. A lack of good empirical overviews of existing technologies in the present literature prompted us to conduct this review. Its purpose was to map the field of digital technologies for informal and formal care that have already been explored in terms of acceptance, effectiveness and efficiency [AEE], and to show the scope of the used methods, target settings, target groups and fields of support.
Methods
A systematic literature search was conducted using Medline, Scopus, CINAHL, Cochrane Library, ACM Digital Library, IEEE Xplore, the Collection of Computer Science Bibliographies, GeroLit and CareLit. In addition, project websites were manually screened for relevant publications.
Results
Seven hundred fifteen papers were included in the review. Effectiveness studies have been most frequently performed for ICT, robots and sensors. Acceptance studies often focussed on ICT, robots and EHR/EMR. Efficiency studies were generally rare. Many studies were found to have a low level of evidence. Experimental designs with small numbers and without control groups were the most common methods used to evaluate acceptance and effectiveness. Study designs with high evidence levels were most commonly found for ICT, robots and e-learning. Technologies evaluated for informal caregivers and children or indicated for formal care at home or in cross-sectoral care were rare.
Conclusion
We recommend producing high-quality evaluations on existing digital technologies for AEE in real-life settings rather than systematic reviews with low-quality studies. More focus should be placed on research into efficiency. Future research should be devoted to a closer examination of the applied AEE evaluation methods. Policymakers should provide funding to enable large-scale, long-term evaluations of technologies in the practice of care, filling the research gaps for technologies, target settings and target groups identified in this review.
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Background
Digital technologies promise great opportunities to overcome existing problems and challenges in the care sector. Many health care systems face challenges such as a shortage of skilled workers and, simultaneously, an increasing demand for long-term care owing to demographic change [1]. Research activities on digital technologies and care are flourishing, nurtured by the expectation that information technologies can help people in need of care to maintain their independence and improve their quality of life and health [2], and also support formal and informal caregivers. Initial studies emphasize positive effects of electronic systems on, for example, patient safety and improvements in the care process [3], which could help to make the best possible use of the available resources.
The German cooperative research project “Pflegeinnovationszentrum” [Nursing Care Innovation Centre], funded by the Federal Ministry of Education and Research [BMBF], aims at establishing a competence centre for innovations in nursing care. Its intention is to collate and produce evidence on the acceptance, effectiveness, and efficiency [AEE] of digital technologies in nursing care and translate these findings into practice. This includes the translation of competencies on these technologies into nursing education A first, essential step of the project is to assess the broad range of technologies developed to support nursing care and nursing education and to provide an overview on existing evidence relating to the AEE of these technologies by conducting this review. We are interested in these outcome dimensions because they can indicate whether a technology has a realistic chance to be transferred into nursing practice. The scope of the existing literature on technology in nursing care and nursing education is very broad. In the present scoping review, we aim to provide insight into the full scope of studies containing information on AEE for informal and formal care.
There is a large number of small-scale studies that explore individual technologies for informal and formal care in the present literature. For example, electronic point-of-care wound documentation for residential long-term care [4], noise-sensor light alarms for the intensive care unit [5], companion robots for elderly care [6] or multi-municipal support networks for informal carers [7]. Virtual reality technology is tested in nursing education [8] and nursing homes use electronic medical records to organize their patient data and thereby optimize their performance [9]. Existing overview articles usually focus on individual technologies [10,11,12,13,14] or on specific target groups like stroke survivors [15], often in combination with single outcome dimensions, such as effectiveness [11], acceptance [16] or efficiency [17]. Still, many systematic reviews in the field of technology and nursing care resume that solid evidence with respect to effectiveness and efficiency of the investigated technologies is still missing or scarce [11, 18,19,20,21,22,23]. To the best of our knowledge, there is no review article that outlines the broad range of technologies developed to support formal and informal care, and no research findings are available that outline the existing evidence with respect to AEE for this broad field of technologies. This study thus makes a significant contribution to the overview of the entire study scope on the subject of digital technology and nursing care covering all areas of informal and formal care, including nursing education. The study contributes to reveal for which areas of technology there may be evidence that qualifies to be justifiably analysed in detail and for which areas solid research on AEE needs to be intensified.
Objective and research question
The ultimate objective of this scoping review is to identify technology areas that are promising for further research, to identify current research gaps and to examine how research is conducted [24]. We therefore aim to map the field of digital technologies for informal and formal care that have already been explored in terms of AEE and to show the scope of the used methods, target settings, fields of support and target groups of these technologies. This scoping review should enable researchers to identify the areas of technologies for which it is necessary to systematically analyse the existing evidence and for which areas of technologies further research is necessary. Since our aim is therefore not only to summarize well-researched technologies, but also to identify less-researched technologies that have so far been studied at a low level of evidence, a scoping review is the appropriate method.
This review is thus guided by the following main research questions:
[i] Which areas of digital technologies aiming to support informal or formal care are most frequently researched with respect to all outcome dimensions [AEE]? [ii] Which target settings, fields of support and target groups are addressed in these studies? [iii] Which study designs have been used to analyse the outcome dimensions?
Methods
Methodological basis
Our scoping review was conducted on the basis of Arksey and O’Malley’s scoping review framework [25]. Additional processual advice by Levac, Colquhoun et al. [26] was taken into consideration to enhance the scientific process. The processual advices were particularly used for the identification of relevant studies by balancing comprehensiveness with the feasibility of resources and the iterativity of the team process to select, extract and chart the data.
Data sources
The database search included the following nine electronic databases: Medline, Scopus, CINAHL, Cochrane Library, ACM Digital Library, IEEE Xplore, the Collection of Computer Science Bibliographies, GeroLit and CareLit. An additional hand-search of relevant projects from German-speaking countries was carried out to supplement the results. The literature search was carried out in March 2018. Due to the large number of studies found, the reference lists of the included studies were not scrutinized.
Eligibility criteria
We included scientific papers that were published between 2011 and 2018, contained empirical studies [abstract available] in German or English language. All Databases have been searched in March 2018, which limits the included time period from January 2011 to March 2018. The considered time period was limited to 7 years, to make the scope manageable and to focus on the most innovative developments.
Included papers had to report study results relating to acceptance, effectiveness [including efficacy] or efficiency [including cost analysis] of digital technologies in nursing care and nursing education. Such technologies were required to i] either support the immediate action of a caregiver or ii] contribute to the self-reliance of the person in need of care in such a way that direct on-site care assistance can be waived, or iii] substitute the nursing support by using technology or iv] support the training or education of nurses. The assistance of the technology may relate to the person in need of care, formal caregivers, informal caregivers or organizational processes. It potentially involves a wide range of technical innovations. Target settings that have been included are residential long-term care, formal and informal care at home, hospital care, cross-sectoral care, palliative inpatient care, intensive care unit [ICU] care, day-care centre care.
We excluded studies i] without human participation; ii] situated in an emergency department, rehabilitation or surgery context; iii] comprising the following technologies: solely mechanical devices and aids, electrical devices that are not networked or that do not rely on sensors to detect the activity of the person in need of care or caregiver or their immediate vicinity, biotechnology, nanotechnology, medical devices [unless very closely related to nursing activities], imaging diagnostics, tissue engineering, devices with functional diagnostic focus, invasive technologies, mobile visits, telemedicine services, purely pleasure-oriented games, textile technology and technical evaluations of algorithms. Excluded settings and technologies were chosen in alignment with the underlying project.
Search Terms
The search terms selected were based on an initial literature review and the available knowledge of experts involved in this project. Each term has been adapted to the respective format of each database. German equivalents have been used for the two German databases [GeroLit and CarelLit]. All search queries can be provided upon request.
English search strategy
[Care OR Caring OR Nursing] AND [Technol* OR Robot* OR Intelligent OR Smart OR Assistive OR Decision Support System OR Ambient Assisted Living OR Sensor OR Wearable OR Virtual Reality OR Mixed Reality OR Tagging OR Tracking OR Remote Health Monitoring OR Fall Detection OR Human Computer Interaction OR Human Machine Interaction OR Gerontotechnology OR Gerontechnology OR Head Mounted Display OR Exoskeleton OR Augmented Reality OR Biomedical Monitoring] AND [Effectiveness OR Efficacy OR Effect OR Efficiency OR Acceptance OR Adoption OR Acceptability HTA OR Health Technology Assessment OR Evaluation OR Evaluations OR Cost-Benefit Analysis OR Cost Benefit OR Cost Effectiveness OR Cost Utility OR Cost Analysis OR Cost Analyses OR Cost Consequence OR Economic Evaluation OR Economic Evaluations OR Economic Analysis OR Economic Analyses OR Costs and Benefits OR Benefits and Costs OR Costs and Outcomes OR Marginal Analysis]
German search strategy
[Pflege] UND [Techn* ODER Technik ODER Robot* ODER Computer ODER Maschine ODER Smart ODER Intelligent ODER Assistive ODER Ambient assisted living ODER Sensor ODER Wearable ODER Virtual reality ODER Mixed reality ODER Ortung ODER Sturzerkennung ODER Mensch-Maschine-Interaktion ODER Gerontechnologie ODER Head mounted display ODER Exoskelett ODER Augmented reality ODER Biomedizinisches Monitoring] UND [Effektivität ODER Effektivität ODER Effizienz ODER Evaluation ODER Akzeptanz ODER Adoption ODER Technikakzeptanz ODER HTA ODER Health technology assessment ODER Kosten ODER Nutzen ODER Kosten-Nutzen-Analyse ODER Wirksamkeit ODER Gesundheitsökonomische Analyse ODER Marginalanalyse]
Identifying relevant studies
We imported all search results into EndNote X8 and reimported all titles and abstracts into the Excel screening workbook by VonVille [27]. Two researchers independently screened 100 titles and Cohen's kappa was calculated to verify agreement between the reviewers on the inclusion and exclusion criteria. The eligibility criteria were refined until a good agreement of 0.716 was reached. Two pairs of two reviewers each independently screened half of the titles and abstracts. A third person was consulted in case of disagreement on whether an article should be included. The eligibility criteria were then refined again before screening the full texts to reach a maximum consensus on criteria. Considering the large number of full texts to be screened in relation to the existing resources, we created a control scheme whereby each author screened a part of the full texts and, in case of exclusion, a further author checked whether the exclusion criteria matched.
Data extraction
A data extraction form was collectively drawn up in Excel and piloted to record authors, year, title, abstract, country, study design, number of study participants, technology category, outcome dimension, target setting, field of support of the technology and the addressed target groups. Patterns were filtered out from a digital, automated data analysis [28], as well as from previous interviews with experts and an initial literature search, to develop an optimal technology category system. We iteratively added categories if technologies were found that did not fit into any previously known pattern. Sixteen technology categories were drawn up to classify the technologies discussed in each article. Most of the categories still comprise a wide range of technologies. In a final step, the extraction form was optimized and adapted for all categories in an iterative team process. Four authors screened the full texts and extracted information. Each full text was reviewed once if it was clearly classified with the extraction form. If a text was excluded, a second author checked the reason and re-included if necessary.
Methodological quality appraisal
In line with guidelines for conducting a scoping review, no formal assessment of methodological quality of the included articles was performed [25, 26, 29].
Charting the data
During the analysis phase, we iteratively reviewed the results to find an adequate means of presenting the descriptive numerical data. Despite this process we observed that a non-overlapping categorization of individual technologies was not possible due to the complexity of the technologies and their interconnectedness. Since we were aware of this issue from the beginning, we refined the categories in many revision processes to guarantee the best possible classification system. Technologies were assigned to the most fitting category; for instance, although a robot presented in the study has multiple sensors, it is classified as a robot, not a sensor. The importance of all results for both the practical implementation and the study situation were then discussed in a team process [26].
Results
Search results
A total of 27.339 articles were retrieved for this review, including 27.278 from the databases and 61 from hand-search. After removing duplicates, 19.510 remained for screening the titles. 1.949 articles were chosen from screening the abstracts, yielding 1.044 full-texts eligible for full-text screening. 715 full texts were included for the data analysis [see PRISMA flow diagram in Fig. 1]. The studies included came from 69 different countries. A complete list of all contained studies can be found in Additional file 1.
Fig. 1
Search results and publication selection process
Full size image
Technology categories
We analysed the number of included studies on each technology area to identify which technology areas were most frequently explored in terms of all outcome dimensions [AEE], and which were least frequently researched. An overview of the distribution of included studies in terms of technology categories is presented in Table 1. The table is sorted by frequencies. A lack of universal definitions for different technology categories, was clearly noticeable during the analysis of the studies. The definitions we developed to differentiate the technologies in this review are included in Table 1. The most widely researched technology category is Information and Communication Technologies [ICT] [n = 147]. ICT comprises a wide range of technologies. In general, ICT are technologies that provide or document relevant information with a primary focus on improve interpersonal communication. Included technologies can be found in Table 1. Electronic Health Records [EHR]/ Electronic Medical Records [EMR], Hospital/Care Institution Information Systems [HIS] or monitoring technologies could also be included in the category ICT. Since these areas represent large fields of research, we have decided to present them separately. The second most frequently researched category is robots [n = 102]. We found that the robots under scrutiny here differ greatly in their focus. They provide support on numerous different levels, e.g. physical, psychological, social, organisational, security or educational and therapeutic. All types of robots that were called “robot” in the article are grouped in this category.
Table 1 Technology categories with included studies
Full size table
The third most frequently researched technology category is sensors [n = 83]. These sensors can either aim at measuring behaviour, movement, falls and other parameters or to measure in combination with controlling other devices like pumps or alarm systems. Many studies cover multiple technologies [n = 80] rather than one technology only. Most of them are reviews that focus on specific target groups or nursing problems. A large share of these studies are acceptance studies that comprise a range of different technologies. Only few studies actually provide research on the effectiveness or efficiency of technological systems comprising different types of technologies. Less researched technologies are virtual reality [VR] technologies [n = 11] that create a virtual world, tracking technologies [n = 9] intended to locate either people or objects, and serious games, which are used for learning purposes or to improve personal independence. We found only one study on personal medical records [PMR], which – in contrast to EMR – allows patients access to all their data. Still, depending on the classification system, PMR could also be subordinated to studies on EMR. This study should therefore not be given a special status. In summary, ICT, robot and sensor technologies can be stated as the most frequently explored areas of technology in terms of all outcome dimensions [AEE]. VR, tracking technologies and serious games are the least researched technologies.
Outcome dimensions and technologies
The inclusion criteria of this study comprise a broad understanding of the outcome dimensions “acceptance”, “effectiveness” and “efficiency”. This is reflected in the broad scope of conceptualizations of these outcome dimensions in the studies included and widely differing measurement concepts. Acceptance studies include the quantitative measurement of acceptance in accordance with a wide range of theoretical acceptance models as well as qualitatively described acceptance results. Effectiveness comprises results on the technical effectiveness or accuracy of technologies as well as personal health or care-related outcomes, organisational or learning outcomes. As there are only very few studies focussing on costs of technologies at all, studies categorized as efficiency-studies include simple cost analyses next to very few full economic evaluations.
With respect to the specific outcome dimensions [AEE], 60 % of all included studies [n = 427] analyse aspects of the effectiveness of care technologies, 59 % [n = 424] analyse acceptance and only 5,8 % [n = 42] analyse efficiency or at least included a cost analysis. Multiple counts of studies are possible, because some studies consider multiple outcome dimensions, which is why the percentage shares add up to more than 100 %. A detailed analysis by outcome dimension [Table 2] shows that acceptance studies are most frequently performed for ICT [n = 93], followed by robots [n = 64] and EMR/EHR [n = 48]. Studies on effectiveness have been most frequently carried out for ICT [n = 94]. Sensor technologies represent the second largest group [n = 68] and robotic technologies make up the third [n = 57]. Efficiency has been studied very rarely for all technologies. ICT [n = 9] can be highlighted for this category. Still, compared to the considerably high total number of ICT studies, only 6% of them cover efficiency or cost analyses. In summary, we have found a large number of effectiveness studies with a focus on ICT, robots and sensors, and a large number of acceptance studies focusing on ICT, robots, and EHR/EMR. Efficiency studies are very rare.
Table 2 Number of studies by technology category and study outcome dimensions
Full size table
Target settings and technologies
The most frequently researched technologies and their target settings are depicted in Table 3. Most of the included studies aim at hospital care [n = 169], which accounts for almost a quarter of all included studies [about 24%]. Studies on technologies for informal care at home represent 21% [n = 147] and studies on technologies for residential long-term care make up 17% of the studies included [n = 122]. Ninety-one articles left the setting undefined [13 %]. These are more or less explorative studies researching general aspects of the technology in question without considering specific applications. It is noticeable that technologies for formal care [n = 48] at home are much less intensively researched than technologies for informal care at home. Studies on technologies for formal care at home account for only 6.7% of all included studies. Hardly any studies focus on cross-sectoral care [