This umbrella literature review is driven by the pressing need for research to transform the elderly care sector, which is challenged by a rapidly ageing population and constrained resources. In this context, datadriven decision-making emerges as a promising approach to facilitate the necessary transformation. The aim this this review was to explore how socio-technical aspects are discussed in research on the use of data-driven decision-making in elderly care settings. Our analysis suggests that the majority of current research places disproportionate focus on technological development while neglecting the social, structural, and systemic contexts that are critical for successful and sustainable implementation. A more balanced and contextually aware approach-grounded in socio-technical systems thinking-is essential to advance the field and ensure that digital innovations truly enhance elderly care.
This umbrella literature review is driven by the urgent need to transform the elderly care sector,
which faces challenges from an ageing population and limited resources. Digital technologies—such
as safety alarms, monitoring devices, sensors, assistive robots, and positioning systems can support
a shift from traditional to data-driven care models, which has been highlighted as a promising
direction [
While digital technologies are already present in elderly care, their adoption tends to focus
narrowly on implementation and short-term cost savings—through process automation and
efficiency gains. This limited perspective reflects a narrow view of digitalization, rather than a
broader digital transformation [
In other sectors, the use of data—commonly termed business intelligence or data-driven
decisionmaking—relies on data collection, integration, analysis, and visualization to enhance
decisions [
However, research [
Against this backdrop, the aim of this umbrella review is to explore how socio-technical aspects are discussed in research on the use of data-driven decision-making in elderly care settings.
Rest of the paper is structured as followed. The next section outlines current research on the use of digital technologies and data in elderly care. This is followed by a presentation of our analytical framework and research method. Section 5 details the results of our analysis. The paper sends with a discussion and conclusion.
While a substantial body of literature explores the development, implementation, and use of WT in
elderly care, much of it focuses on technological design—such as monitoring systems [
Furthermore, recent studies shed light on collaboration-related barriers and enablers between
various stakeholders in the healthcare ecosystem. For instance, Khalil [
This section presents the socio-technical conceptual framework used to understand the complexity
in elderly care ecosystem utilizing digital technologies and data generated by these technologies. A
sociotechnical perspective recognizes the complex interactions between people, technology, and the
surrounding environment in shaping how an information system (digital technologies used in
elderly care) supports work and activities in organizational context [
In designing sociotechnical systems, both technical aspects—related to tools and tasks—and social
dimensions—such as people, organizational roles, and structures—must be considered. From this
perspective, an organization is viewed as a complex sociotechnical system influenced not only by
internal dynamics but also by external forces, including regulatory frameworks and other contextual
factors [
Technology refers to the tools, software and hardware, design methods, and IT infrastructure used within the system. Its characteristics include functional dimensions (such as production, coordination, control, and adaptability), level of specialization, functional scope and integration, and system qualities like reliability, performance, and user-friendliness.
Problems may arise if the technology selected or implemented is unsuitable or insufficient for a
given task (Technology–Task), if the users are not capable of managing, using, or adapting the
technology (Technology–Actors), or if the technology has not been adjusted or tailored to fit the
organizational structure (Technology–Structure). The greater the imbalance caused by unreliable,
inefficient, non-standardized, or limited technology, the more likely it is that the overall system will
be destabilized [
Actors are individuals or groups involved in, affected by, or benefiting from the system and its
changes. Relevant characteristics include personal attributes, level of engagement, competence,
stakeholder diversity, unrealistic expectations, misconceptions, absence or reluctance of key actors,
unethical professional behavior, staff turnover, opportunism, and personal agendas. Problems may
occur when actors are expected to carry out tasks for which they are unqualified or untrained
(Actor– Task), when they do not understand or accept the technology (Actor–Technology), or
when they are unaware of, resist, or misalign with the organizational structure (Actor–Structure).
The greater the misalignment between actors and the other system components, the greater the
risk of systemic imbalance [
Structure refers to the system of communication, authority, workflows, and organizational
arrangements. Its key characteristics include the level of formalization, centralization, control span
and depth, distribution of roles and responsibilities, geographic dispersion, and degree of functional
differentiation and specialization. Structural issues may arise if the current or defined structures do
not support actors in performing their tasks (Structure–Actors), if the structure is inadequate or
illsuited for the nature of the tasks (Structure–Task), or if it is misaligned with the technology or
fails to leverage its potential (Structure–Technology). The more misaligned the structure is with
the tasks or other components, the higher the likelihood of system disruption [
Work systems are always embedded within a broader context known as the organizational
environment [
We have chosen to conduct an umbrella review to get an overview of research considering the use
of data-driven decision-making in elderly care settings. The umbrella review combines the results
from multiple systematic reviews. Systematic literature reviews aggregate results from various
studies and provide synthesis of a specific research area by following a well-defined methodology
[
In brief, this umbrella review combines the results from multiple systematic reviews and
metaanalyses focusing on the use of data-driven decision-making approaches in elderly care settings [
Our article gets inspiration of the recent development in the field of AI and use AI in some parts of the review process to make it more efficient. To this end we tapped into the advanced natural language processing and machine learning capabilities of the Elicit AI tool to automate various steps of the review process, including literature search, screening, and data extraction. This umbrella review focuses on systematic reviews and meta-analyses that examined the use of data-driven decision-making approaches, such as artificial intelligence, machine learning, or data analytics, to support clinical decision making, care planning, and resource allocation in elderly care settings.
Our search strategy and inclusion or exclusion criteria were specific to the data driven decision making and data analytics in the elderly care context. Specific inclusion criteria were systematic reviews or meta-analyses that examined the use of data-driven decision-making approaches in elderly care settings; studies that evaluated the impact of these approaches on implementations and outcomes such as quality of care published in English since 2000. Exclusion criteria were studies focused only on technological development without examining impact on care outcomes, studies not focused on elderly population, and studies not accessible in full-text.
Two researchers independently screened titles, abstracts, and full-text articles based on the inclusion and exclusion criteria. Disagreements were resolved through discussions and proper documentation was maintained by keeping track of decisions made at each stage of the review process. Data extraction and analysis was done using the following, Use the capabilities and knowledge synthesized from Elicit AI to build a comprehensive evidence table, distilling key findings and insights on the use of data-driven decision-making in elderly care from the included systematic reviews and meta-analyses. Downloaded full-text PDFs of the included studies were carefully reviewed to extract relevant data and synthesize key findings. In total, 28 full-text articles were retrieved. After applying the inclusion and exclusion criteria, 10 articles were excluded, leaving 18 papers for full-text analysis. Exclusions were made if the paper focused on the development of technologies supporting automated decision-making, if the study only addressed decision-making in the context of elderly care (no automated decision-making), or if it did not explicitly focus on the elderly population. Researchers 1 and 2 screened the remaining 18 full-text articles, examining: the context and purpose of automated decision-making, the main findings, and the challenges discussed.
These aspects were summarized for each paper to support later analysis. Finally, Researchers 1 and 2 analyzed the material — including the context and purpose, key findings, and identified challenges — using the socio-technical framework presented and described in Section 4 (see Figure 1).
Given the well-known limitations of AI tools — including the tendency to produce so-called “hallucinations,” which may closely resemble original material — we used Elicit AI in Step 1 solely as a preliminary aid to form an initial impression of the key findings and insights on data-driven decisionmaking in elderly care, as reported in the included systematic reviews. The final analysis, based on a thorough screening of the 18 full-text articles, was conducted manually by the authors.
The analysis revealed that Technology was the most consistently addressed category: all 18 studies engaged with technological aspects, with 16 treating it as a primary focus (+++) and the remaining two as secondary (++). This reflects a strong emphasis on the design, functionality, and implementation of digital tools and systems in elderly care contexts. The Task category, relating to the functional objectives of the systems (e.g., monitoring, detection, support), was also represented across all studies, although only three studies treated it as a primary focus. Most studies (n = 15) discussed task-related elements as secondary (++), indicating a widespread but often complementary concern with what the technologies are designed to accomplish.
The Actor category was present in all 18 studies as well, with three highlighting it as a primary focus (+++), 14 as secondary (++), and one addressing it marginally (+). This suggests that while most studies recognized the role of end-users—including older adults, caregivers, and healthcare professionals—few placed primary emphasis on user engagement, competence, or interaction dynamics. In contrast, the Structure category was less prominently featured: although 14 studies touched on structural aspects such as workflow or integration into care practices, these were rarely central. Only three studies included structure as a secondary focus, and 10 as low (+), while five studies did not address it at all. Lastly, the Organizational Environment—covering broader systemlevel influences like policy, regulation, and institutional readiness—was the least addressed dimension. Only five studies considered it, and even then, only at a low level (+), indicating a significant gap in the literature regarding the contextual factors that shape or constrain the implementation of digital interventions in elderly care.
According to the socio-technical systems perspective, effective implementation of digital technologies requires consideration of both technical components—such as tools (technology), and tasks and social components, including human actors, organizational roles, and workflows (structures). In our analysis of 18 studies, we identified eight studies that substantially addressed both technical (Technology and Task) and social (Actor and Structure) dimensions. These studies recognized that technological solutions do not operate in isolation, but are shaped by and embedded within social practices and organizational structures.
Specifically, the studies by Abdellatif et al. [
These studies underscore the importance of aligning technological interventions with the social context in which they are deployed, echoing socio-technical principles that emphasize the interdependence of systems and people. Conversely, the majority of the other studies in the dataset engaged only marginally or not at all with structural or organizational concerns, revealing a gap in research that prioritizes technical functionality over contextual fit and user integration.
A notable finding from the analysis is the widespread neglect of the organizational environment dimension across the reviewed studies. Only a small minority (4 out of 18) addressed aspects of the environment to any meaningful degree, and none engaged with it as a primary or even strong secondary focus. This omission is striking given that information systems (IS) interventions, particularly in healthcare, are inherently embedded in broader organizational and systemic contexts.
Table 2 shows summary of the analysis regarding technology. Technology emerged as a dominant focus in the majority of the reviewed studies, with 15 out of 18 articles assigning it primary importance. These studies concentrated on the design, type, functionality, and limitations of digital systems developed for healthcare and aging populations.
Several studies provided in-depth evaluations of Clinical Decision Support Systems (CDSS) and
their technical features. For instance, Abdellatif et al. [
Artificial Intelligence (AI) and algorithm-driven tools were also frequently discussed. Cresswell
et al. [
A substantial portion of the literature focused on sensor- and IoT-based technologies designed
for elderly care. Stavropoulos et al. [
Other studies, such as Haslam-Larmer et al. [16] and Lapp et al. [
Only three studies—Cardona-Morrell et al. [
Taken together, the reviewed literature reflects a high level of technological innovation and diversity in applications—ranging from predictive analytics to telemonitoring and smart robotics— but also reveals persistent technical shortcomings such as lack of validation, integration barriers, and underdeveloped user-centered design.
Publication Abdellatif, A., et al. (2020)
Description
Primary: The article centers on clinical CDSS, evaluating their types, clinical functionalities (e.g. nutrition, pressure ulcers, medication management), and technical challenges like lack of interoperability, poor system descriptions, and alert fatigue Alharbi, M., et al.
(2019)
Primary: The article focuses on wearable devices (e.g., Fitbit, EKG patches) as technological tools for data collection and health monitoring. It evaluates their validity, accuracy, and usability, and discusses technical challenges like algorithm opacity and device turnover Secondary: The article reviews various formats of decision aids—print, audio, video, digital—and notes technical aspects like self-administration vs. providerguided use. However, it does not delve deeply into system design or digital
infrastructure, which makes technology relevant but not central
Primary: The article centers on AI-based decision support systems (CDS), particularly those using data-driven algorithms like Bayesian networks, Kalman filters, and neural networks. It evaluates their effectiveness and technical
implementation in healthcare.
Primary: The article focuses on CDSS as technological interventions in hospital
care for older patients. It assesses system design, implementation, and effectiveness. It also explores the role of workflow integration and points out
that none of the systems used predictive or machine learning models.
Primary: The study is centered around digital health technologies (e.g., smart wearables, IoT devices, digital health monitors) and identifies which measurable
health parameters are effective for predictive monitoring and outcomes.
Primary: The article focuses extensively on telemonitoring systems, sensors (e.g.
PIR, accelerometers, bed/stool sensors), and physiological data collection devices. Technological performance issues like false alarms, short battery life,
and wireless signal limitations are central to the analysis.
Primary: The article focuses on smart home technologies for elderly care, including sensors, AI, machine learning, and assistive robots. It evaluates the type and functionality of technologies used, especially in terms of privacy-preserving
monitoring.
Primary: The article centers on RTLS as a specific technological solution. It details system components (sensors, tags, indoor receivers, software), functionalities (location tracking, activity monitoring), and limitations (lack of validation, data
interpretation). It critically examines technology use and maturity.
Secondary: highlights the use of data science and machine learning techniques in
LTC, as well as clinical technologies like Medicare data analytics and imaging methods for diagnosing LSS. However, technology is not the primary focus in all
included studies Primary: Technical integration (or lack thereof) with EHR systems The data
types used Digital innovations in care delivery Evidence base for tool development and effectiveness. Challenges: Limited integration with HER, often required manual data entry, Variation in performance and statistical significance,
Lack of robust validation and practical testing Masot, O., et al.
(2022) Nordin, S., et al.
(2021) Reena, J. K., & Parameswari, R.
(2019)
Sapci, A. H., & Sapci, H. A. (2019)
Primary: focus is on CCDSS—computerised clinical decision support systems—as technological tools designed to improve medication safety in LTC homes. The technical features like dosage recommendations and alert systems are central
Primary: The article focuses on digital decision support tools (DSTs) used to detect infections (e.g., UTI, sepsis, respiratory infections) in older adults. It evaluates tools based on input data, symptom algorithms, integration with
clinical records, and development/validation stages.
Primary: The article centers on ICT (e.g., online portals, teleconferencing, selfassessment apps), examining how these tools function, their usability, and their
role in supporting health-related decision-making among older adults.
Primary: The article centers on IoT-enabled healthcare systems, focusing on technologies such as wearable sensors, biometric devices, smart home systems, and data processing algorithms (e.g., Random Forest, ANN). It covers technical characteristics like accuracy, energy use, comfort, and integration challenges
(e.g., interoperability and real-time capabilities).
Primary: The article centers on emerging technologies used in aging-in-place
solutions, including AI-based monitoring, smart home systems, sensors, telehealth, and robotic support. It extensively discusses system types, algorithmic functions (e.g., ANN, Bayesian networks), device capabilities, and technological
limitations (e.g., integration, real-world deployment).
Primary: IoT technologies, especially wearables, sensors, and smart systems used in elderly care. Overview of various devices (e.g., smartwatches, biometric sensors, in-home systems) and technological parameters (e.g., battery life, comfort, interoperability). Challenges: Device comfort, battery life, lack of interoperability, unclear long-term effectiveness, and data privacy issues.
Secondary: While not the central focus, decision aids (digital or otherwise) are technological tools meant to support shared medical decision-making. The article discusses their delivery method (e.g., clinician-led vs. pre-visit) and the need for age-adapted tools. 5.2. Task Table 3 shows summary of the analysis regarding task. This category was prominently featured in the reviewed literature, with 4 studies assigning it a primary role and the majority (14) including it as a secondary focus. Across the studies, tasks were generally defined as the specific clinical, operational, or care-related functions that the technologies were intended to support—ranging from decisionmaking and symptom monitoring to fall detection and chronic care management.
Studies such as Abdellatif et al. [
The remaining studies treated task as a secondary yet still significant dimension. For example,
Marasinghe [
Notably, several articles such as Gochoo et al. [
Finally, van Weert et al. [
Taken together, the analysis shows that most technologies are closely tied to well-defined care tasks, particularly in clinical and home-based settings. However, many studies offered only limited exploration of task complexity or real-time performance constraints, suggesting the need for further research on optimizing task-technology alignment in aging care environments.
Primary: CDSS are intended to assist in key clinical tasks such as symptom monitoring, prescription, and disease management. The review examines how well these tasks are supported and how outcomes (e.g. reduced pressure ulcers) are affected.
Secondary: The wearables are used for specific health-monitoring tasks: tracking physical activity, heart rate, EKG, etc. The article addresses how reliably and
effectively these tasks are performed.
Primary: The core focus is on supporting decision-making tasks in end-of-life care, such as selecting treatment options and aligning care with patient preferences.
The DAs are explicitly evaluated based on how well they support these highly
sensitive, high-stakes tasks Secondary: AI-CDS systems are designed to support clinical decision-making tasks like medication adherence, triage, and diagnosis—key functions in health and social care. The article highlights how the performance and relevance of the
technology vary by task context (e.g., acute care vs home care).
Primary: CDSS are evaluated in the context of tasks like medication review, delirium management, fall prevention, and discharge planning. The study Daniolou, S., et al.
(2021)
Gokalp, H., & Clarke, M. (2013) Gochoo, M., et al.
(2021) categorizes interventions based on whether they improved task-related process
and patient outcomes.
Secondary: The core task is health monitoring and prediction of outcomes (e.g., morbidity, mortality, hospitalization) based on specific measurable health data.
The article discusses how digital tech supports this health surveillance task.
Secondary: It investigates how monitoring of ADL can support care-related tasks
like detecting functional decline, managing chronic illness, and triggering interventions. The task complexity and data-action linkage are addressed but not deeply analyzed
Secondary: Technologies are implemented to support aging-in-place by automating or assisting with daily living tasks (ADLs), fall detection, and remote monitoring. Effectiveness depends on specific task requirements (e.g., monitoring in bathrooms or distinguishing between residents and pets).
Secondary: RTLS is employed for tasks such as activity monitoring, gait analysis, social interaction tracking, and detection of health changes or response to interventions. These are operational and clinical care tasks in elderly care
environments.
Primary: All studies address specific healthcare-related tasks: assessing longevity risks, setting collaborative care goals, applying data-driven risk prediction in LTC, and diagnosing/treating LSS-related back pain. The article shows how these tasks are defined, performed, and challenged in elder care settings.
Secondary: CDSTs are developed to support specific clinical tasks in long-term care settings—such as medication management, fall prevention, pressure ulcer prevention, and infection control. These are high-importance, operationally
critical tasks in elderly care environments.
Secondary: CCDSS supports critical care tasks like medication prescription, monitoring adverse drug reactions, and reducing preventable medication-related incidents, which are central tasks in LTC environments
Secondary: DSTs are intended to assist in clinical tasks such as early infection detection, triaging, and supporting treatment decisions. The article emphasizes how these tools can reduce hospitalizations by enabling timely interventions in
both hospitals and nursing homes.
Secondary: ICT is evaluated in the context of supporting complex everyday
decision-making tasks related to health, self-care, and navigating social/healthcare services—especially for those with functional impairments.
Reena, J. K., & Parameswari, R.
(2019)
Secondary: The article emphasizes core care tasks enabled by these technologies, including monitoring of Activities of Daily Living (ADL), fall detection, anomaly tracking, and home-based elderly care support. These tasks are operationally
Sapci, A. H., & Sapci, H. A. (2019)
Secondary: The technologies are explicitly linked to healthcare and caregiving tasks such as fall detection, chronic disease monitoring, functional assessment (e.g., dementia), telepresence, remote consultation, and daily living support.
These are concrete, high-criticality tasks relevant to elderly care.
Secondary: The article addresses how IoT technologies support tasks such as health monitoring, fall detection, cognitive assessment, and patient tracking, the operational tasks within elderly care. Challenges: Some technologies lacked
sufficient validation for their effectiveness in specific tasks.
Primary: The article’s core concern is whether decision aids support the task of informed health decision-making among older adults. It explores how they
impact decision quality, risk perception, and participation.
Table 4 shows summary of the analysis regarding actor. The “actor” category—concerning individuals or groups interacting with technologies, such as older adults, caregivers, clinicians, or broader care teams—was widely acknowledged across the dataset.
Three studies [
Many studies explored the roles, perceptions, and needs of older adults as direct technology
users. For instance, Nordin et al. [
Healthcare professionals—especially nurses and physicians—were another prominent actor
group. Studies like Abdellatif et al. [
Several articles discussed caregivers and informal support networks as intermediaries or
cousers. For example, Gochoo et al. [
Furthermore, the importance of actor–technology alignment was explicitly addressed in many
studies. These included challenges like mismatch between user capabilities and system complexity
[
Importantly, Hendriks et al. [
Overall, actor-related considerations were well represented, although most studies addressed them in a supporting role. User acceptance, interaction design, trust, and training emerged as recurring themes critical to technology uptake in both institutional and home-based elderly care contexts.
Description Secondary: The article highlights nurses as the primary users and discusses their satisfaction, usability concerns, and team dynamics. Actor–Technology alignment
issues like alert fatigue and limited training are also discussed Secondary: Older adults are the end-users, and findings include user acceptance, barriers (e.g., discomfort, complexity), and behavior change (e.g., increased
physical activity). Actor–Technology fit is a core issue.
Secondary: The article addresses different user groups (patients, proxies, care providers), highlighting challenges with tool appropriateness, emotional burden, and information complexity. It emphasizes the need for human facilitation,
suggesting the tools must be aligned with actors’ abilities and contexts.
Secondary: Discusses the role of healthcare professionals and emphasizes that despite AI support, decision responsibility lies with humans. Also notes training
needs for actors to appropriately use AI tools.
Secondary: Nurses, physicians, and care teams are central to the use of CDSS.
Findings discuss how implementation success is linked to clinician workflow, user
satisfaction, and training (e.g., “alert fatigue” issues).
Secondary: While not the main focus, the article addresses clinicians and tech developers as key users/designers. It notes their roles in implementation and
clinical use of the technologies, and the need to interpret digital data.
Secondary: The review discusses older adults, caregivers, and healthcare providers in terms of their perceptions, acceptance, preferences, and concerns (e.g. privacy, stigma, usability). Actor-related barriers (trust, familiarity) are acknowledged.
Gochoo, M., et al. Primary: The study emphasizes the importance of user comfort, privacy concerns, (2021) and feedback from elderly individuals, caregivers, and service providers. It identifies challenges related to user acceptance, usability, and ethical issues.
Haslam-Larmer, L., et al. (2022)
Secondary: The technology supports caregivers and clinicians by offering objective data for monitoring. The article mentions discrepancies between caregiver assessments and RTLS data and the need for interpretability, indicating
relevance to user roles and interactions with the system.
Hendriks, A., et al. (2024)
Primary: Collaborative goal setting among older adults and care partners is a central topic. Additionally, actors such as clinicians, data scientists, patients, and caregivers are critical across all studies. The article discusses diverse stakeholder
involvement, particularly in LTC and goal formulation
Secondary: The tools are aimed primarily at healthcare and social care professionals. The article notes variation in user identification, and also highlights challenges related to user workload, data entry burdens, and lack of training—
indicating alignment issues between actors and tools Secondary: Nurses and physicians in LTC settings are the primary users. The article discusses challenges like “alert fatigue,” underutilization of alerts, and varying levels of trust and responsiveness to CCDSS-generated suggestions.
Secondary: The article discusses how DSTs are used by various healthcare staff— physicians, nurses, and multiprofessional teams—depending on setting. It also notes differences in usability between hospital and care home staff, and
highlights user-related barriers to implementation.
Primary: The study focuses on older adults as end-users of ICT, exploring their needs, experiences, and barriers to use (e.g., cognitive decline, digital literacy). It
emphasizes the role of user perception and competence
Secondary: User-related aspects are addressed through discussions on technology usability, comfort, and personalization for elderly users, particularly those with physical or cognitive impairments. It also includes caregivers and clinicians as indirect users, touching on the human-technology interface and
adoption barriers.
Secondary: The article discusses users including older adults with varying needs (e.g., dementia, mobility limitations), caregivers, and healthcare providers. It
addresses technology acceptance, personalization, comfort, and ethical concerns—highlighting actor–technology alignment challenges across settings.
Secondary: The article discusses patient comfort, usability, and acceptance challenges. It also refers to caregivers, health professionals, and their interactions
with the technologies, including barriers related to human-device interaction.
Challenges: User discomfort, cognitive and physical limitations, need for training
and trust-building.
Primary: Older adults are the main user group. Their cognitive ability, preferences, and willingness to engage in decisions are central. The article also
considers the role of clinicians and researchers delivering the aids
Table 5 shows summary of the analysis regarding structure. The “structure” category, which encompasses organizational hierarchies, workflows, formal roles, and institutional integration, was the least addressed among the socio-technical dimensions analyzed.
Only two studies [
Damoiseaux-Volman et al. [
Several studies acknowledged implementation issues or integration challenges (e.g., Lapp et
al. [
Most studies (e.g., Abdellatif et al. [
Only a few articles (e.g., Gokalp & Clarke [
In sum, structural considerations—though highly relevant in socio-technical systems thinking— remain underexplored in the reviewed literature. This signals a potential blind spot in implementation research, where technologies are often evaluated in isolation from the institutional settings that shape their use. Low: Although teamwork and care processes are mentioned, there’s little focus on broader structural integration, such as communication hierarchies or how
CDSS are embedded into formal workflows.
No addressed
Not addressed Low: There is limited discussion on how DAs are implemented within healthcare
workflows or institutional procedures. The importance of multidisciplinary facilitation is mentioned but not elaborated in terms of organizational roles or
hierarchies.
Secondary: The review analyzes workflow integration, implementation strategies (e.g., multifaceted interventions), and coordination between stakeholders, which
reflect on structural alignment of the CDSS within hospital systems Daniolou, S., et al. Low: The article does not explicitly discuss care delivery structures, workflows, or (2021) role distribution in health systems where these technologies are implemented Gochoo, M., et al.
(2021) Haslam-Larmer, L., et al. (2022) Hendriks, A., et
al. (2024) Lapp, L., et al.
(2022) Marasinghe, K. M.
(2015) Masot, O., et al.
(2022) Nordin, S., et al.
(2021) Reena, J. K., & Parameswari, R.
(2019)
Low: While the article briefly refers to organizational barriers like data ownership and who has control/access, it does not explore workflows, authority
distribution, or formal structures in depth
Secondary: While not a central focus, the review notes fragmented implementations and the absence of fully integrated smart home systems. This indirectly points to structural limitations in system integration and organizational
support across care settings.
Low: The article does not explicitly address organizational workflows, roles, or decision hierarchies. However, it briefly notes the lack of studies on the actual effect of RTLS on clinical decision-making and implementation, hinting at
structural gaps.
Low: There is little discussion on formal communication flows, role distribution, or organizational hierarchies. LTC facilities are mentioned, but not structurally
analyzed Low: The article mentions a need for organizational support and integration into workflows but does not deeply analyze structural aspects like hierarchies, formal roles, or process redesign. Structural concerns are recognized, but not explored
in detail.
Low: While organizational challenges (e.g., lack of implementation, stand-alone systems) are mentioned, there is little direct analysis of workflows, hierarchies,
or institutional coordination affecting CCDSS use.
Low: While the review touches on settings (hospital vs. care home) and variations in tool users, it does not systematically analyze clinical workflows, formal roles,
or decision hierarchies where DSTs are implemented.
Low: Organizational structures (e.g., healthcare workflows, formal care processes) are mentioned implicitly but not examined directly or in detail.
Low: The article only lightly touches on system integration within formal care
settings. Organizational workflows, institutional roles, or hierarchical arrangements are not a major focus, except for noting the lack of interoperability
and deployment barriers.
Sapci, A. H., & Low: While system types are discussed in various care environments (e.g., home, Sapci, H. A. (2019) nursing homes), the article lacks depth on formal organizational structures, roles, and care process integration. Integration into clinical workflows and authority
structures is not a core analytical dimension Stavropoulos, T.
G., et al. (2020)
Low: Mentioned but not central: Structural aspects like integration of systems or workflow adjustments are only briefly referenced in the context of interoperability Not addressed
Table 6 shows a summary of the analysis regarding organizational environment. The analysis of the
18 studies reveals a notable gap in the attention paid to the organizational environment, as defined
by Lyytinen and Newman [
Out of all the reviewed studies, most (n = 13) did not address the environment category at all,
making no reference to either inner or outer contextual factors. Only five articles made limited
reference to environmental aspects, but these discussions were brief and lacked depth. For example,
Gochoo et al. [
This limited engagement with the environment dimension suggests that most studies are focused narrowly on technology, task performance, or user perspectives, while omitting the broader contextual forces that shape the adoption, sustainability, and impact of digital health solutions in elderly care. Future research should more explicitly consider both inner and outer environmental contexts to understand how systemic factors enable or constrain the use of digital interventions in practice. Abdellatif, A., et
al. (2020) Alharbi, M., et al.
(2019) Cardona-Morrell, M., et al. (2017) Cresswell, K., et
al. (2020)
DamoiseauxVolman, B. A., et
al. (2021) Daniolou, S., et al.
(2021)
Gokalp, H., & Clarke, M. (2013) Gochoo, M., et al.
(2021)
Description Not addressed Not addressed Not addressed Not addressed Not addressed Nor addressed
Not addressed
Low: The article does not explicitly discuss broader policy, regulatory, or economic environments influencing smart home tech deployment. There is
Low: There is limited discussion of systemic or regulatory factors. The article notes evidence quality issues and the need for implementation studies but does not explore external policy, reimbursement, or infrastructural enablers of
adoption.
Low: While the article notes broader issues like workforce shortages and unmet potential of digital tools, it does not explore regulatory, financial, or policy-level enablers/barriers in depth. The organizational environment is acknowledged but not systematically addressed.
Not addressed No addressed Not addressed van Weert, J. C.,
et al. (2016)
Drawing on the socio-technical systems theory proposed by Lyytinen and Newman [
One central issue is the frequent misalignment between technology and tasks. Although technologies are often well-developed from a functional perspective, only three studies in our sample treated the task dimension (i.e., what the technology is designed to accomplish) as a primary concern. This suggests a risk that technologies may be poorly matched to real-world care objectives, leading to inefficiencies or reduced relevance in practice. Similarly, the actor dimension, while present in nearly all studies, was typically discussed in a secondary manner.
Low: The article highlights implementation challenges but lacks detail on policy, legal, economic, or systemic conditions. It acknowledges a gap in evidence regarding real-world uptake and long-term integration of DSTs in care
environments.
Not addressed beside some ethical and privacy concerns
Not addressed beside some ethical and privacy concerns
Low discussion: Raises concerns about ethics, data security, and standard frameworks, but lacks deeper analysis of institutional, legal, or policy contexts
Not addressed Without deeper engagement with user needs, capacities, and interactions—including those of older adults, caregivers, and healthcare professionals—there is a risk that systems may be underutilized, misused, or even rejected due to poor usability or lack of training (Actor–Technology and Actor–Task misalignments).
The structure category, referring to care workflows, routines, and institutional arrangements, was even less emphasized. Ten studies addressed it only marginally, and five did not address it at all. This is concerning, as socio-technical theory emphasizes that organizational structures must support both users and the technologies they are expected to use. When this alignment is absent (Structure–Actor or Structure–Technology gaps), the risk of system disruption and failed implementation increases.
Moreover, our findings underscore a notable and under-researched area: the organizational environment. Only a minority of studies touched upon broader contextual factors such as policy, regulation, and institutional readiness—and even then, only at a superficial level. This omission is striking given that digital interventions, particularly in healthcare, are deeply embedded in systemic and regulatory contexts. Failing to consider these dimensions may hinder scalability, sustainability, and policy alignment of otherwise promising solutions.
Importantly, eight studies in our dataset—including Abdellatif et al. [
Based on these findings, several directions for future research emerge. First, there is a need for integrated socio-technical investigations that consider the interdependencies between digital tools, their users, the tasks they support, and the organizational settings into which they are introduced. Second, more attention should be paid to user-centered design and evaluation, involving both older adults and care professionals throughout the development process. Third, implementation research must address how digital systems can be embedded into existing workflows, including attention to training, communication, and change management. Fourth, future work should expand to examine system-level and policy-related factors, such as funding, regulation, and institutional readiness, which are essential for sustainable adoption. Finally, there is a need for longitudinal, real-world studies to evaluate the long-term impacts and systemic outcomes of digital health interventions in elderly care.
Our analysis shows that while technology is well-covered, and in most of cases discussed in relation to actors and task, structures, and environments are underexplored. From a socio-technical perspective, this imbalance increases the risk of implementation failure and solutions that do not improve care processes, as technologies do not operate in isolation but are deeply embedded in human and organizational systems. Future research must adopt a more holistic approach to ensure that digital innovations are not only functional but feasible, acceptable, and sustainable in real-world elderly care.
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