How smart medication systems are used to support older people's drug regimens: A systematic literature review

Improved Medication Adherence of an Elderly Diabetic Patient at a Dwelling Home Using a Pill Dispenser and Personal Health Records

Fookkun® is a pill dispenser in which single doses of several medications intended to be taken simultaneously are sealed in single film bags rolled onto a medication rotating drum. The system makes musical alert sounds when it is time for the patient to take the medications. If the patient misses a dose, a designated contact, such as the patient's child, is alerted. We conducted an experiment monitoring the use of a pill dispenser (Fookkun®) by an older patient. The participant was a 71-year-old woman with diabetes living in a dwelling home. The experiment lasted approximately 6 months. Fookkun® and the prototype data transmitter were installed at the patient's home. Fookkun®'s medication history data are displayed on the electronic medication record book (E-MRB) and the patient's pharmacist checks the patient's medication history on the E-MRB. The Fookkun® was effective in facilitating medication adherence. The pharmacist and the patient's daughter did not need to check the E-MRB because Fookkun® alerted them when the patient missed her medication. We believe that if the medication history data linked between a pill dispenser and an E-MRB can be shared among medical staff, this will contribute to a medical digital transformation in Japan in the future.

Effect of robot for medication management on home care professionals' use of working time in older people's home care: a non-randomized controlled clinical trial

BACKGROUND

Medication management has a key role in the daily tasks of home care professionals delivered to older clients in home care. The aim of this study was to examine the effect of using a robot for medication management on home care professionals´ use of working time.

METHODS

A pragmatic non-randomized controlled clinical trial was conducted. The participants were home care professionals who carried out home care clients' medication management. Home care clients were allocated into intervention groups (IG) and control groups (CG) (n = 64 and 46, respectively) based on whether or not they received the robot. Data were collected using the Working Time Tracking Form prior to and 1 and 2 months after introducing the intervention. The t-test was used to compare the groups at each three timepoints. Analysis of Covariance was used to examine the groups' differences for the total time for medications as the number of visits per day as the covariate.

RESULTS

With robot use, the total amount of home visits decreased by 89.4% and 92.4% after 1 and 2 months of intervention use, respectively, compared to pre-intervention (p < 0.001). The total working time used for medication management considering the number of visits per day decreased from 54.2 min (95% CI 37.4-44.3) to 34.9 min (31.4-38.3), i.e., by slightly over 19 min (p < 0.001) in the IG group. During the follow-up, the total working time used for medication management considering the number of visits per day remained the same in the CG group.

CONCLUSION

Using a robot for medication management had a notable effect on decreasing the use of working time of home care professionals. For health services, decreased use of working time for medication management means that the time saved can be assigned to services that cannot be replaced otherwise. More digital solutions should be developed based on home care clients' and professionals' needs to meet the challenge of the growing number of older people in need of home care and ensure their safety.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05908604 retrospectively registered (18/06/2023).

[Keyword Network Analysis and Topic Modeling of News Articles Related to Artificial Intelligence and Nursing]

PURPOSE

The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing.

METHODS

After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4.

RESULTS

As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.'

CONCLUSION

The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.

Older home care clients' experiences of digitalisation: A qualitative study of experiences of the use of robot for medicines management

BACKGROUND

Home-living older people with multiple medications are a key target group for medication robots. However, our understanding of how robots for medicines management work in older people's daily lives is limited.

AIM

The aim of this study was to describe older home care clients' experiences of the implementation and use of a robot for medicines management at home.

DESIGN

A qualitative interview study.

METHODS

Data were collected during spring and autumn 2021 using semi-structured individual interviews with older home care clients (n = 38). The data were analysed using inductive content analysis.

RESULTS

The older home care clients had positive experiences with the use of technology for the medication process, but they also faced challenges in their daily life activities. Implementation and use of the robot required open-mindedness, satisfaction with the implementation, and the opportunity to practice the use of the robot with a nurse. However, the current design and size of the robot for medicines management in home care still need development, given that our research participants did not feel included in the robot development process.

CONCLUSION

The use of digital solutions will increase older people's home care. Therefore, there is a need to deepen our understanding of the implementation and use of digital solutions to prevent digital challenges and to provide a more comprehensive picture of this phenomenon. In addition, research focusing on whether the use of the robot affects medication administration incidents and medication adherence should be conducted to improve the safety of medicines management.

Chinese adult segmentation according to health skills and analysis of their use for smart home: a cross-sectional national survey

BACKGROUND

Digital health has become a heated topic today and smart homes have received much attention as an important area of digital health. Smart home is a device that enables automation and remote control in a home environment via the internet. However, most of the existing studies have focused on discussing the impact of smart home on people. Only few studies have focused on relationship between health skills and use of smart home.

AIMS

To analyze the health skills of Chinese adults and segment them to compare and analyze the use of smart home for each group.

METHODS

We used data from 11,031 participants aged 18 and above. The population was clustered based on five health skills factors: perceived social support, family health, health literacy, media use, and chronic diseases self-behavioral management. A total of 23 smart homes were categorized into three sub-categories based on their functions: entertainment smart home, functional smart home, and health smart home. We analyzed demographic characteristics and utilization rate of smart home across different cluster. Each groups' features and the differences in their needs for smart home functions were compared and analyzed.

RESULTS

As a result of the survey on health skills, three groups with different characteristics were clustered: good health skills, middle health skills, and poor health skills. The utilization rate of smart home was the highest was good health skills group (total smart home: 92.7%; entertainment smart home: 61.1%, functional smart home: 77.4%, and health smart home: 75.3%; P < 0.001). For entertainment smart home, smart TV had the highest utilization rate (good health skills: 45.7%; middle health skills: 43.5%, poor health skills: 33.4%, P < 0.001). For functional smart home, smart washing machine (good health skills: 37.7%, middle health skills: 35.11%, poor health skills: 26.5%; P < 0.001) and smart air conditioner (good health skills: 36.0%, middle health skills: 29.1%, poor health skills: 24.6%) were higher than other of this category. For health smart home, sports bracelet has the highest utilization rate (good health skills: 37.3%, middle health skills: 24.5%, poor health skills: 22.8%).

CONCLUSION

People can be divided into different categories based on health skill profiles, those with good health skills had a better utilization rate of smart home. The government and smart home companies need to focus on people with poor smart home use in various ways to promote their use of smart homes for personal health management.

Demand analysis of an intelligent medication administration system for older adults with chronic diseases based on the Kano model

Objectives

Gerontechnology has great potential in promoting older adults’ well-being. With the accelerated aging process, gerontechnology has a promising market prospect. However, most technological developers and healthcare professionals attached importance to products’ effectiveness, and ignored older adults’ demands and user experience, which reduced older adults' adoption intention of gerontechnology use. The inclusion of older adults in the design process of technologies is essential to maximize the effect. This study explored older adults’ demands for a self-developed intelligent medication administration system and proposed optimization schemes, thus providing reference to developing geriatric-friendly technologies and products.

Methods

A cross-sectional survey was conducted to explore older adults’ technological demands for the self-developed intelligent medication administration system, and data were analyzed based on the Kano model. A self-made questionnaire was administered from July 2020 to October 2020 after participants used this system for two weeks. The study was registered with the Chinese Clinical Trial Registry (ChiCTR2000040644).

Results

A total of 354 older adults participated in the survey. Four items, namely larger font size, simpler operation process, scheduled medication reminders and reliable hardware, were classified as must-be attributes; three items, namely searching drug instructions through WeChat, more sensitive system and longer battery life, as attractive attributes; one item, viewing disease-related information through WeChat, as the one-dimensional attribute; and the rest were indifferent attributes, including simple and beautiful displays, blocking advertisements automatically, providing user privacy protection protocol, viewing personal medical information only by logged-in users, recording all the medications, ordering medications through WeChat. The satisfaction values were between 0.24 and 0.69, and dissatisfaction values were between 0.06 and 0.94.

Conclusion

This study suggested that older adults had personalized technology demands. Including their technological demands and desire may assist in decreasing the digital divide and promoting the satisfaction of e-health and/or m-health. Based on older adults’ demands, our study proposed optimization schemes of the intelligent medication administration system, which may help developers design geriatric-friendly intelligent products and nurses to perform older adults-centered and efficient medication management.

Mapping of drug-related problems among older adults conciliating medical and pharmaceutical approaches

PURPOSE

To lay the fundamentals of drug-related problems (DRPs) in older adults, and to organize them according to a logical process conciliating medical and pharmaceutical approaches, to better identify the causes and consequences of DRPs.

MATERIALS AND METHODS

A narrative overview.

RESULTS

The causes of DRPs may be intentional or unintentional. They lie in poor prescription, poor adherence, medication errors (MEs) and substance use disorders (SUD). Poor prescription encompasses sub-optimal or off-label drug choice; this choice is either intentional or unintentional, often within a polypharmacy context and not taking sufficiently into account the patient's clinical condition. Poor adherence is often the consequence of a complicated administration schedule. This review shows that MEs are not the most frequent causes of DRPs. SUD are little studied in older adults and needs to be more investigated because the use of psychoactive substances among older people is frequent. Prescribers, pharmacists, nurses, patients, and caregivers all play a role in different causes of DRPs. The potential deleterious outcomes of DRPs result from adverse drug reactions and therapeutic failures. These can lead to a negative benefit-risk ratio for a given treatment regimen.

DISCUSSION/CONCLUSION

Interdisciplinary pharmacotherapy programs show significant clinical impacts in preventing or resolving adverse drug events and, suboptimal responses. New technologies also seem to be interesting solutions to prevent MEs. Better communication between healthcare professionals, patients and their caregivers would ensure greater safety and effectiveness of treatments.

A Simple Framework of Smart Geriatric Nursing considering Health Big Data and User Profile

The National Bureau of Statistics of China shows that the population over 65 years old in China exceeds 166 million accounting for 11.93% of the total population by the end of 2018. The importance and severity of taking care of the elderly are becoming increasingly prominent. High-quality and meticulous care for the daily life of the elderly needs helpful and advanced sciences and technologies. Smart geriatric nursing is a must. Basing on the professional knowledge of geriatric nursing, this paper proposes a framework of smart geriatric nursing which consists of three aspects of smart nursing: smart geriatric nursing in physical health using biosensor and advanced devices, smart geriatric nursing in mental health based on user profile, and smart geriatric nursing for daily life based on big data in health. The deployment of the proposed method relies on the technologies of the Internet of Things (IoT), user profile system, big data, and many other advanced information technologies. The framework of methods can provide a useful reference for the systematic technical scheme of smart geriatric nursing in an aging society.