Failure mode and effects analysis of telehealth service of minority elderly for sustainable digital transformation

Research Trends and Hot Spots in Telemedicine for the Elderly: A Scientometric Analysis

BACKGROUND

As the elderly population rapidly grows, age-related health issues are increasing. Telemedicine helps older adults adapt by providing efficient and accessible health management and medical services.

OBJECTIVES

This study employs bibliometric analysis to examine research focus areas, emerging trends, and collaboration networks in telemedicine for older adults over the past three decades.

METHODS

The Web of Science Core Collection served as the primary data source for the publications on telemedicine and the elderly since the database's inception through June 2024. Using CiteSpace.6.2.R4 software, keyword and collaboration network visualizations were generated, including clusters, co-authors, and co-citations.

RESULTS

This study analyzed 586 papers from 252 countries or regions, which were published across 246 journals and written by 2750 authors.

CONCLUSIONS

The analysis revealed three primary research directions encompassing 42 clusters: (1) health literacy and technology adaptation; (2) telemedicine technology and health management; and (3) social interaction and economic impact. Research hotspots include elderly fitness, mobile health, technology acceptance, telemedicine, elderly care, and health literacy. Despite the potential benefit of telemedicine, challenges persist in areas such as technology acceptance, usability, effectiveness, service quality, and privacy concerns. This review provides a comprehensive overview of current research on telemedicine for the elderly and highlights emerging trends in the field.

Prospective systematic risk analysis of the digital technology use within pharmaceutical care

BACKGROUND

Digital technologies are present in every phase of a drug lifecycle, from drug design and development to its dispensing and use. However, given the rapid development and implementation of digital solutions, their monitoring, evaluation, and risk assessment are limited and lacking.

OBJECTIVE

This research is aiming to identify potential errors, quantify and prioritize associated risks in the context of certain technologies used in pharmaceutical care, as well as define corrective measures to improve patient safety and the quality of pharmaceutical care.

METHODS

A 10-member multidisciplinary team conducted Failure Mode and Effect Analysis (FMEA) to identify critical risks, their causes and effects, along with developing corrective measures within the selected digital health components: Telepharmacy, mHealth, Artificial intelligence (AI), and Software infrastructure and systems. Critical risks were determined by calculating risk priority numbers (RPNs) from severity, occurence, and detectability scores.

RESULTS

This study identified 42 risks regarding the 4 components. After calculating RPNs and the threshold RPN (RPN=30), 8 critical risks were identified. Corrective measures were proposed for these failure modes, after which the risks were re-evaluated (RPN sum was reduced from 414 to 156). The risk with the highest RPN value was Internet/identity fraud, while the rest included inadequate and incomplete data entry and management, flawed implementation, human and technology errors, and lack of transparency, personalization and infrastructure. For the critical risks, 42 different causes were recognized on a system, technological and individual level while their effects were discussed in terms of patient safety and business management in pharmacies.

CONCLUSION

Digitalization of pharmaceutical practice promises greater effectiveness of pharmaceutical care, but in order to achieve this, efforts, resources and initiatives must be directed toward timely identification of problems, appropriate monitoring, and building adequate infrastructure that can support safe implementation of digital tools and services despite the swift development of innovations.

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplantation is the only possible therapy, for instance for patients with end-stage liver diseases, and the preferred treatment, for instance for patients with end-stage renal diseases. However, this surgical procedure comes with inherent risks and effectively managing these risks to minimize the likelihood of complications arising from organ transplantation (maximizing life years from transplant and quality-adjusted life years) is crucial. To facilitate this process, risk ranking is used to identify and promptly address potential risks. Over recent years, considerable efforts have been made, and various approaches have been proposed to enhance Failure Modes and Effects Analysis (FMEA). In this study, taking into account the uncertainty in linguistic variables (F-FMEA), we introduce an approach based on Fuzzy Multi Criteria Decision Making (F-MCDM) for effectively evaluating scenarios and initial failure hazards. Nevertheless, the results of ranking failure modes generated by different MCDM methods may vary. This study is a retrospective study that suggests a comprehensive unified risk assessment model, integrating multiple techniques to produce a more inclusive ranking of failure modes. Exploring a broad spectrum of risks associated with organ transplant operations, we identified 20 principal hazards with the assistance of literature and experts. We developed a questionnaire to examine the impact of various critical factors on the survival of transplanted organs, such as irregularities in immunosuppressive drug consumption, inappropriate dietary habits, psychological disorders, engaging in strenuous activities post-transplant, neglecting quarantine regulations, and other design-related factors. Subsequently, we analyzed the severity of their effects on the durability of transplanted organs. Utilizing the Mamdani algorithm as a fuzzy inference engine and the Center of Gravity algorithm for tooling, we expressed the probability and severity of each risk. Finally, the failure mode ranking obtained from the F-FMEA method, three fuzzy MCDM methods, and the proposed combined method were identified. Additionally, the results obtained from various methods were evaluated by an expert team, demonstrating that the highest consistency and effectiveness among different methods are attributed to the proposed method, as it achieved a 91.67% agreement with expert opinions.

Design and Feasibility Analysis of a Smartphone-Based Digital Cognitive Assessment Study in the Framingham Heart Study

BACKGROUND

Smartphone-based digital technology is increasingly being recognized as a cost-effective, scalable, and noninvasive method of collecting longitudinal cognitive and behavioral data. Accordingly, a state-of-the-art 3-year longitudinal project focused on collecting multimodal digital data for early detection of cognitive impairment was developed.

METHODS AND RESULTS

A smartphone application collected 2 modalities of cognitive data, digital voice and screen-based behaviors, from the FHS (Framingham Heart Study) multigenerational Generation 2 (Gen 2) and Generation 3 (Gen 3) cohorts. To understand the feasibility of conducting a smartphone-based study, participants completed a series of questions about their smartphone and app use, as well as sensory and environmental factors that they encountered while completing the tasks on the app. Baseline data collected to date were from 537 participants (mean age=66.6 years, SD=7.0; 58.47% female). Across the younger participants from the Gen 3 cohort (n=455; mean age=60.8 years, SD=8.2; 59.12% female) and older participants from the Gen 2 cohort (n=82; mean age=74.2 years, SD=5.8; 54.88% female), an average of 76% participants agreed or strongly agreed that they felt confident about using the app, 77% on average agreed or strongly agreed that they were able to use the app on their own, and 81% on average rated the app as easy to use.

CONCLUSIONS

Based on participant ratings, the study findings are promising. At baseline, the majority of participants are able to complete the app-related tasks, follow the instructions, and encounter minimal barriers to completing the tasks independently. These data provide evidence that designing and collecting smartphone application data in an unsupervised, remote, and naturalistic setting in a large, community-based population is feasible.