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The expectations and acceptability of a smart nursing home model among Chinese elderly people: A mixed methods study protocol.

ABSTRACT: Nursing homes integrated with smart information such as the Internet of Things, cloud computing, artificial intelligence, and digital health could improve not only the quality of care but also benefit the residents and health professionals by providing effective care and efficient medical services. However, a clear concept of a smart nursing home, the expectations and acceptability from the perspectives of the elderly people and their family members are still unclear. In addition, instruments to measure the expectations and acceptability of a smart nursing home are also lacking. The study aims to explore and determine the levels of these expectations, acceptability and the associated sociodemographic factors. This exploratory sequential mixed methods study comprises a qualitative study which will be conducted through a semi-structured interview to explore the expectations and acceptability of a smart nursing home among Chinese elderly people and their family members (Phase I). Next, a questionnaire will be developed and validated based on the results of a qualitative study in Phase I and a preceding scoping review on smart nursing homes by the same authors (Phase II). Lastly, a nationwide survey will be carried out to examine the levels of expectations and acceptability, and the associated sociodemographic factors with the different categories of expectations and acceptability (Phase III). With a better understanding of the Chinese elderly people's expectations and acceptability of smart technologies in nursing homes, a feasible smart nursing home model that incorporates appropriate technologies, integrates needed medical services and business concepts could be formulated and tested as a solution for the rapidly ageing societies in many developed and developing countries.

SUBMITTER: Zhao Y

PROVIDER: S-EPMC8382194 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSmart nursing homes (SNHs) integrate advanced technologies, including IoT, digital health, big data, AI, and cloud computing to optimise remote clinical services, monitor abnormal events, enhance decision-making, and support daily activities for older residents, ensuring overall well-being in a safe and cost-effective environment. This study developed and validated a 24-item Expectation and Acceptability of Smart Nursing Homes Questionnaire (EASNH-Q), and examined the levels of expectations and acceptability of SNHs and associated factors among older adults in China.MethodsThis was an exploratory sequential mixed methods study, where the qualitative case study was conducted in Hainan and Dalian, while the survey was conducted in Xi'an, Nanjing, Shenyang, and Xiamen. The validation of EASNH-Q also included exploratory and confirmatory factor analyses. Multinomial logistic regression analysis was used to estimate the determinants of expectations and acceptability of SNHs.ResultsThe newly developed EASNH-Q uses a Likert Scale ranging from 1 (strongly disagree) to 5 (strongly agree), and underwent validation and refinement from 49 items to the final 24 items. The content validity indices for relevance, comprehensibility, and comprehensiveness were all above 0.95. The expectations and acceptability of SNHs exhibited a strong correlation (r = 0.85, p < 0.01), and good test-retest reliability for expectation (0.90) and acceptability (0.81). The highest tertile of expectations (X2=28.89, p < 0.001) and acceptability (X2=25.64, p < 0.001) towards SNHs were significantly associated with the willingness to relocate to such facilities. Older adults with self-efficacy in applying smart technologies (OR: 28.0) and those expressing a willingness to move to a nursing home (OR: 3.0) were more likely to have the highest tertile of expectations compared to those in the lowest tertile. Similarly, older adults with self-efficacy in applying smart technologies were more likely to be in the highest tertile of acceptability of SNHs (OR: 13.8).ConclusionsEASNH-Q demonstrated commendable validity, reliability, and stability. The majority of Chinese older adults have high expectations for and accept SNHs. Self-efficacy in applying smart technologies and willingness to relocate to a nursing home associated with high expectations and acceptability of SNHs.

Project description:BackgroundIn the home care industry, the assignment and tracking of care services are controlled by care centers that are centralized in nature and prone to inefficient information transmission. A lack of trust among the involved parties, information opaqueness, and large manual manipulation result in lower process efficiency.ObjectiveThis study aimed to explore and demonstrate the application of blockchain and smart contract technologies to innovate/renovate home care services for harvesting the desired blockchain benefits of process transparency, traceability, and interoperability.MethodsAn object-oriented analysis/design combined with a unified modeling language tool was used to construct the architecture of the proposed home care service system. System feasibility was evaluated via an implementation test, and a questionnaire survey was performed to collect opinions from home care service respondents knowledgeable about blockchain and smart contracts.ResultsAccording to the comparative analysis results, the proposed design outperformed the existing system in terms of traceability, system efficiency, and process automation. Moreover, for the questionnaire survey, the quantitative analysis results showed that the proposed blockchain-based system had significantly (P<.001) higher mean scores (when compared with the existing system) in terms of important factors, including timeliness, workflow efficiency, automatic notifications, insurance functionality, and auditable traceability. In summary, blockchain-based home care service participants will be able to enjoy improved efficiency, better transparency, and higher levels of process automation.ConclusionsBlockchain and smart contracts can provide valuable benefits to the home care service industry via distributed data management and process automation. The proposed system enhances user experiences by mitigating human intervention and improving service interoperability, transparency/traceability, and real-time response to home care service events. Efforts in exploring and integrating blockchain-based home care services with emerging technologies, such as the internet of things and artificial intelligence, are expected to provide further benefits and therefore are subject to future research.

Project description:BACKGROUND:The Pragmatic Trial of Video Education in Nursing Homes (PROVEN) is one of the first large pragmatic randomized clinical trials (pRCTs) to be conducted in U.S. nursing homes (N = 119 intervention and N = 241 control across two health-care systems). The trial aims to evaluate the effectiveness of a suite of videos to improve advance care planning (ACP) for nursing home patients. This report uses mixed methods to explore the optimal and suboptimal conditions necessary for implementation fidelity within pRCTs in nursing homes. METHODS:PROVEN's protocol required designated facility champions to offer an ACP video to long-stay patients every 6 months during the 18-month implementation period. Champions completed a video status report, stored within electronic medical records, each time a video was offered. Data from the report were used to derive each facility's adherence rate (i.e., cumulative video offer). Qualitative interviews held after 15 months with champions were purposively sampled from facilities within the highest and lowest adherence rates (i.e., those in the top and bottom quintiles). Two researchers analyzed interview data thematically using a deductive approach based upon six domains of the revised Conceptual Framework for Implementation Fidelity (CFIF). Matrices were developed to compare coded narratives by domain across facility adherence status. RESULTS:In total, 28 interviews involving 33 champions were analyzed. Different patterns were observed across high- versus low-adherence facilities for five CFIF domains. In low-adherence nursing homes, (1) there were limited implementation resources (Context), (2) there was often a perceived negative patient or family responsiveness to the program (Participant Responsiveness), and (3) champions were reticent in offering the videos (Recruitment). In high-adherence nursing homes, (1) there was more perceived patient and family willingness to engage in the program (Participant Responsiveness), (2) champions supplemented the video with ACP conversations (Quality of Delivery), (3) there were strategic approaches to recruitment (Recruitment), and (4) champions appreciated external facilitation (Strategies to Facilitate Implementation). CONCLUSIONS:Critical lessons for implementing pRCTs in nursing homes emerged from this report: (1) flexible fidelity is important (i.e., delivering core elements of an intervention while permitting the adaptation of non-core elements), (2) reciprocal facilitation is vital (i.e., early and ongoing stakeholder engagement in research design and, reciprocally, researchers' and organizational leaders' ongoing support of the implementation), and (3) organizational and champion readiness should be formally assessed early and throughout implementation to facilitate remediation. TRIAL REGISTRATION:ClinicalTrials.gov, NCT02612688. Registered on 19 November 2015.

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The expectations and acceptability of a smart nursing home model among Chinese elderly people: A mixed methods study protocol.

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