Healthcare Providers’ perspectives on the adoption of Smart Care Technologies in Korean Long-Term Care Hospitals

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Ann Geriatr Med Res. 2026;30(2):208-216

Publication date (electronic) : 2026 May 14

doi : https://doi.org/10.4235/agmr.26.0019

Seok Gyu Kim¹, Soong-Nang Jang², Hyuk Ga^,3

¹Institute for Community Care and Health Equity, Chung-Ang University, Seoul, Republic of Korea

²Red Cross College of Nursing, Chung-Ang University, Seoul, Republic of Korea

³Institute of Geriatric Medicine, Incheon Eun-Hye Convalescent Hospital, Incheon, Republic of Korea

Corresponding Author: Hyuk Ga, MD, PhD Institute of Geriatric Medicine, Incheon Eun-Hye Convalescent Hospital, 21, Simgok-ro 132beon-gil, Seo-gu, Incheon 22711, Republic of Korea E-mail: gahyuk@gmail.com

Received 2026 January 28; Revised 2026 May 4; Accepted 2026 May 14.

Abstract

INTRODUCTION

Korean long-term care hospitals (LTCHs) care for highly dependent older adults with severe frailty and a high prevalence of malnutrition.1-3) LTCH care assistants (CAs), who provide most daily care, face workforce shortages, aging staff, and increasing reliance on foreign workers.4,5) Unlike nursing homes, LTCHs lack legally mandated staffing standards and dedicated reimbursement for caregiving services.6) This structural gap results in high patient-to-caregiver ratios, limiting the capacity to provide adequate and continuous care.7) In this context, there is growing interest in using smart care technologies in Korean long-term care (LTC) settings to support care delivery and relieve frontline strain. One study reported that healthcare providers perceive smart care technologies as potentially useful for reducing physical burden and improving care efficiency.8) This growing interest is consistent with international developments, where the introduction of assistive and smart care technologies has accelerated in response to population aging and chronic staffing shortages in LTC settings.9) Examples include fall detection sensors, pressure ulcer prevention smart mattresses, automated transfer devices, digital continence management systems, and artificial intelligence (AI) enabled monitoring tools, which are designed to reduce preventable adverse events and alleviate repetitive physical tasks.10,11) Qualitative research among nurses further indicates that, while the potential benefits of technologies such as smart mattresses are acknowledged, actual use in clinical practice depends heavily on usability, compatibility with workflow, and prior experience.12)

The Integrated Care Support Act, scheduled to take effect in March, 2026 represents a major policy shift aimed at overcoming the fragmented, facility centered structure of Korea’s current LTC system. This legislation signals a transition from a treatment-oriented model focused on life prolongation to a community based, person-centered model that enables older adults to maintain dignity and age in place.13) Such policy changes require new criteria for evaluating smart care technologies, emphasizing not only technical performance but also alignment with real clinical workflows, staffing constraints, and the needs of frontline healthcare providers. These considerations parallel the broader gerontechnology literature, which highlights that successful adoption depends on user perceptions, organizational readiness, and the degree to which technologies address actual sources of work burden.14)

To the best of our knowledge, no previous study has examined occupation-specific preferences for smart care technologies across multiple LTCHs. Consequently, evidence remains limited regarding which categories of smart care technologies are perceived as most needed in LTCHs, how these preferences differ by occupation and institutional characteristics, and how work burden relates to these preferences. Although physicians, registered nurses (RNs), nurse assistants (NAs), and CAs perform distinct roles and experience different types and levels of work burden, no study to date has systematically examined how these occupation-specific burdens are related to perceived needs for smart care technologies in LTCHs. Clarifying these occupation-specific needs is essential for designing realistic, sustainable, and person-centered strategies for implementing smart care technologies.

To meet the growing care needs of an aging population, coordination between hospital- and community-based health, medical, and welfare services is essential.15) In this context, 10 smart care technologies were included here: smart mattresses, all-in-one patient transfer robots, smart monitoring systems, radar-based fall detection devices, leg-care devices, CARE-net, wrist-worn bio-signal monitoring devices, large language model (LLM)-based dementia diagnosis tools, multimodal imaging devices, and smart diapers. These technologies align with the World Health Organization’s global strategy emphasizing digital solutions for continuous monitoring, early detection, and integrated care for aging populations.16) Eight of these technologies addressed four key care domains in LTCHs: excretion management, pressure ulcer prevention, mobility support, and fall prevention. These domains were selected because they reflect common and clinically important care issues in LTCHs and are closely associated with nursing workload and patient safety risks.17,18) In addition, the LLM-based diagnostic tool and CARE-net, a digital platform were specifically included to reflect the broader needs of integrated care.

This study aimed to examine healthcare providers’ work burden and perceived needs for these smart care technologies in LTCHs, and to explore how these measures differ according to occupation and hospital characteristics.

MATERIALS AND METHODS

RESULTS

Participant and Hospital Characteristics

This study included 114 healthcare professionals working in eight LTCHs across Korea. The sample consisted of 16 physicians, 53 RNs, 23 NAs, and 22 CAs. The majority of participants were female (84%). Detailed participant characteristics are presented in Table 1. The eight participating LTCHs showed substantial institutional heterogeneity. Bed capacity ranged from 132 to 524 beds (mean 279±116), and five hospitals (62.5%) operated a rehabilitation services. Information on bed composition, rehabilitation services, and average monthly family fees is summarized in Table 2.

Table 1.

Healthcare providers’ characteristics (n=114)

Table 2.

LTCHs characteristics (n=8)

Work Burden by Occupation

Perceived work burden differed by occupational group (Table 3). Using the neutral midpoint of the 5-point Likert scale as a reference, physicians reported significantly higher burden across all four domains, including mental burden, communication with patients, communication with staff, and communication with patients’ families (all p<0.01). Among these domains, mental burden showed the highest mean score. RNs also reported significantly higher burden in all domains (p<0.001), except for communication with staff (p=0.727). CAs reported significantly higher burden in the mental domains only (both p<0.01). NAs showed no significant deviation from the neutral point in most domains, although they reported a significantly lower burden in communication with staff (p=0.025).

Table 3.

Healthcare providers' work burden

Occupational Differences in Technology Preference Rankings

A one-way ANOVA showed no statistically significant differences in mean preference scores for any of the 10 smart care technologies across occupational groups (all p > 0.05), indicating similarly high overall levels of preference. However, analyses of within-occupation ranking patterns using the Friedman test revealed significant differences in technology prioritization among physicians (χ²(9)=18.39, p=0.031), RNs (χ²(9)=24.07, p=0.004), and CAs (χ²(9)=27.32, p=0.001). NAs showed a marginal trend toward significance (χ²(9)=15.20, p=0.086). Mean preference scores for each technology by occupation are shown in Fig. 1. Physicians most highly ranked smart monitoring systems, leg-care devices, and CARE-net. RNs ranked smart mattresses, CARE-net, and all-in-one patient transfer robots highest. NAs showed the highest rankings for radar-based fall detection devices, smart monitoring systems, and wrist-worn bio-signal monitoring devices. CAs ranked all-in-one patient transfer robots, smart mattresses, and wrist-worn bio-signal monitoring devices highest.

Fig. 1.

LTCHs healthcare providers’ perceived needs for smart care technologies. (A) Among physicians, the highest perceived needs were observed for smart monitoring systems (4.19), leg-care devices (4.19), and CARE-net (4.13). (B) Among registered nurses, the highest perceived needs were observed for smart mattresses (4.11), CARE-net (4.02), and all-in-one patient transport robots (4.00). (C) Among nurse assistants, the highest perceived needs were observed for radar-based fall detection devices (4.22), smart monitoring systems (4.17), and wrist-worn biosignal monitoring devices (4.13). (D) Among care assistants, the highest perceived needs were observed for all-in-one patient transport robots (4.36), smart mattresses (4.32), and wrist-worn biosignal monitoring devices (4.27). Scores were measured using a 5-point Likert scale from 1 (not at all) to 5 (very much).

Hospital-Level Variation in Technology Demand

At the hospital level, smart mattresses and all-in-one patient transfer robots consistently ranked among the top three technologies across all eight LTCHs, with smart monitoring systems or radar-based fall detection devices also appearing among the highest-ranked technologies in several hospitals (Fig. 2). Exploratory regression analyses identified institutional characteristics associated with overall technology demand. LTCHs without a rehabilitation services showed higher perceived demand (β=0.329, p<0.001), explaining 10.8% of the variance (R²=0.108). The average monthly family fee explained 42.8% of the variance in overall technology demand (R²=0.428). Hospital-level contrasts were particularly evident between Hospital E and Hospital B. Hospital E, which had no rehabilitation services and the lowest monthly family fee, showed the highest overall technology demand score (4.57). In contrast, Hospital B, which operated a rehabilitation services and had the highest monthly family fee, showed the lowest overall demand (3.49).

Fig. 2.

Perceived needs for smart care technologies of each long-term care hospital (LTCH). Mean perceived need scores for 10 smart care technologies across eight LTCHs. Colored lines (A–H) represent perceived need scores reported by each hospital, and the gray vertical bars indicate the overall ranking of technologies based on the mean scores across all hospitals. Scores were measured using a 5-point Likert scale from 1 (not at all) to 5 (very much).

DISCUSSION

To our knowledge, this is the first study to examine healthcare providers’ perceptions of multiple smart care technologies in LTCHs using a survey supplemented by focus group interviews. Our findings suggest that distinct patterns of work burden across occupations are associated with different technology needs (e.g., decision-support technology is primarily required for physicians, management technology for RNs, and robotics technology for CAs). First, physicians showed a high preference for the smart monitoring system, LEG-care, and CARE-net. This pattern may reflect the fact that physicians’ work typically centers on assessing patients’ medical status and making treatment decisions. Second, RNs most preferred the Smart Mattress, CARE-net, and the all-in-one patient transport robot. This pattern may reflect the core components of nursing work in Korean LTCHs. Third, NAs preferred fall detection devices, smart monitoring, and wrist-worn biometric monitoring devices. They provide routine daily care while simultaneously assisting nurses by performing ongoing observation, recording patient status, and executing basic treatments. This is interpreted as leading to a high preference for technologies that allow for the immediate detection of patient danger and tracking changes in medical status. Lastly, CAs preferred the all-in-one patient transport robot, smart mattress, and wrist-worn biometric monitoring devices. CAs, who had the highest mean age among the job categories and were primarily responsible for high-intensity physical tasks such as patient transfer, diaper changes, and repositioning for pressure ulcer prevention, reported the greatest physical fatigue.

Importantly, the qualitative FGI findings significantly extended these quantitative survey results by providing the contextual mechanisms behind the occupation-specific technology preferences. While the quantitative data established the statistical trends between high work burdens and specific technology needs, the qualitative data explained the why. For instance, while the survey demonstrated that CAs highly prioritized transfer robots and smart mattresses, the FGIs extended this by revealing that this preference is not merely about general fatigue, but a strategic response to the repetitive physical strain of managing patient restraints during diaper changes. Similarly, the high mental burden reported by RNs quantitatively was qualitatively contextualized as liability-related stress concerning fall accidents, which perfectly explains their strong demand for continuous smart monitoring.19,20) Ultimately, the qualitative data did not challenge the quantitative findings, but rather enriched them, demonstrating that technology needs in LTCHs are driven by specific, structural clinical risks rather than simple convenience.

At the LTCH level, the pervasive decline in inpatients' ADLs necessitates preventive strategies.17) As shown in Fig. 2, smart mattresses, all-in-one patient transport robots, and smart monitoring systems consistently occupied the top tier of preferences across hospitals. This pattern likely reflects the clinical profile of LTCH inpatients, many of whom are bed-bound or have severe mobility limitations; consequently, healthcare providers showed particularly strong interest in technologies that can prevent pressure ulcers, support safe transfers between bed and wheelchair, and enable continuous monitoring at the bedside.

Taken together, these findings suggest that, in our sample of eight LTCHs, ward environment and cost structure may be more salient contextual factors for technology demand than the mere presence of a rehabilitation services. Although hospitals without a rehabilitation service tended to show higher demand in the regression analysis, Hospital E and Hospital B illustrate that “high-density, low-fee” versus “low-density, high-fee” configurations may shape staff perceptions more directly. Hospital E combined the lowest monthly family fee with an exclusively multi-bed ward structure and showed the highest overall technology demand, whereas Hospital B combined the highest monthly family fee with a relatively high proportion of private or semi-private rooms and showed the lowest demand. This contrast is consistent with the interpretation that crowded multi-bed environments and lower reimbursement levels may heighten concerns about workload and safety, increasing the perceived need for physical assistive technologies, while settings oriented toward more individualized care with higher per-patient fees may perceive less immediate pressure to adopt such technologies. However, these hospital-level analyses were exploratory and based on only eight facilities, and the observed associations should therefore be interpreted with caution.

Overall, these findings may be interpreted using concepts drawn from technology acceptance and implementation theories, including the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT).21) Across both the quantitative and qualitative findings, smart care technologies were more likely to be valued when they were perceived as useful for reducing care burden and supporting safer care in everyday practice.22) At the same time, adoption in LTCHs may depend not only on this perceived usefulness but also on facilitating conditions, such as staffing, budget, training, and organizational readiness. In this respect, technology demand in LTCHs appears to reflect both frontline clinical needs and the broader institutional context in which care is delivered.

This study has several limitations that should be considered when interpreting the findings.

First, the sample size was modest and based on convenience sampling of eight LTCHs. Second, most participants had limited or no direct experience with several of the smart care technologies evaluated in this survey. Subsequent research should examine changes in work burden and attitudes before and after real-world implementation of specific technologies in LTCH wards. Third, data were collected through a cross-sectional, self-administered online questionnaire, which is subject to self-report bias and social desirability effects. Fourth, although our questionnaire demonstrated adequate internal consistency, the work burden scale was newly developed for this study and did not undergo formal psychometric validation, including content validity assessment, pilot testing, or construct and criterion validity testing. Therefore, the work burden scores should be interpreted with caution as exploratory measures. Finally, the study focused on physicians, RNs, NAs, and CAs and did not include other key stakeholders; future research should incorporate a broader range of professional roles across different LTC systems.

In conclusion, healthcare providers in Korean LTCHs reported a high perceived need for smart care technologies, particularly prioritizing smart mattresses, all-in-one patient transfer robots, and smart monitoring systems. While overall demand was higher in institutions with lower fees and no rehabilitation services, specific technology preference rankings differed significantly by occupation according to their distinct work burdens.

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