Online Clinical Consultation as a Utility Tool for Managing Medical Crisis During a Pandemic: Retrospective Analysis on the Characteristics of Online Clinical Consultations During the COVID-19 Pandemic

Promoting new users' online health consultation services usage behavior strategically

Online consultation services have the potential to reduce the workload of healthcare staff, provide timely care to patients, and improve doctor-patient relationships. The COVID-19 pandemic has accelerated the development of these services and platforms, but it remains to be seen whether the general public will continue to use them after the pandemic is under control. This research proposes a framework to examine the factors contributing to UK adults' continued usage of online healthcare consultation services after COVID-19 restrictions have been lifted. A total of 430 new users completed surveys, and the results indicate that expectation confirmation, system quality, and information quality can positively impact users' self-efficacy toward using online consultation services. This, in turn, can influence their continued usage behavior. Furthermore, the results suggest that participants' perception of health risks can moderate the relationship between self-efficacy and continued usage behavior. The strategic implications of these findings are discussed.

A Bibliometric Review of Constituents, Themes, and Trends in Online Medical Consultation Research

As an emerging form of health care with accelerated growth in recent years, online medical consultation (OMC) has received extensive attention worldwide. Although the number of studies on OMC has increased substantially, few provide a comprehensive and up-to-date review of OMC's research constituents, themes, and trends. This study, therefore, extracted 1,801 OMC-related articles published in English from the Web of Science (WoS) Core Collection database during the past 30 years and employed a bibliometric analysis of WoS and CiteSpace to examine major constituents' distribution, collaboration relationships, themes, and trends. The results indicate that the United States, England, and China contributed the most to the proliferation of OMC studies. The United States had the greatest academic influence and the most collaborative connections, while China demonstrated the sharpest increase and most active development in recent years. However, there is a lack of substantial and close collaboration between researchers worldwide. The main themes of OMC research were Internet hospitals, COVID-19, mixed methods, online health community, and information technology. Researchers have recently shifted their attention to social media, management, efficacy, word of mouth, mental health, and anxiety. This review paper provides researchers and practitioners with a holistic and clear understanding of the features and trends of OMC research. It also identifies potential areas for future OMC research and sheds light on OMC practices.

Exploring the Economic Aspects of Hospitals: A Comprehensive Examination of Relevant Factors

Financial limitations in the hospital industry have the potential to exacerbate healthcare disparities, impede investments in cutting-edge medical treatments, as well as impair patient outcomes. The interdependent connection between a hospital economy and the general well-being of the community highlights the necessity of careful financial oversight and inventive healthcare policies. Effective collaboration among policymakers, healthcare administrators, and stakeholders is imperative in the development of sustainable economic models that give equal weight to fiscal prudence and optimal patient outcomes. This article aims to underscore the pivotal importance of strategic fund allocation guided by hospital administrators, accentuating several key initiatives capable of revolutionizing healthcare delivery and elevating the institution's stature within the medical community. The other important aspects discussed here are fund allocation in hospitals, the boom of online consultations, and emphasis on the use of sustainable and cost-effective modalities of energy.

Implications for implementation and adoption of telehealth in developing countries: a systematic review of China's practices and experiences

The rapid advancement of telehealth technologies has the potential to revolutionize healthcare delivery, especially in developing countries and resource-limited settings. Telehealth played a vital role during the COVID-19 pandemic, supporting numerous healthcare services. We conducted a systematic review to gain insights into the characteristics, barriers, and successful experiences in implementing telehealth during the COVID-19 pandemic in China, a representative of the developing countries. We also provide insights for other developing countries that face similar challenges to developing and using telehealth during or after the pandemic. This systematic review was conducted through searching five prominent databases including PubMed/MEDLINE, Embase, Scopus, Cochrane Library, and Web of Science. We included studies clearly defining any use of telehealth services in all aspects of health care during the COVID-19 pandemic in China. We mapped the barriers, successful experiences, and recommendations based on the Consolidated Framework for Implementation Research (CFIR). A total of 32 studies met the inclusion criteria. Successfully implementing and adopting telehealth in China during the pandemic necessitates strategic planning across aspects at society level (increasing public awareness and devising appropriate insurance policies), organizational level (training health care professionals, improving workflows, and decentralizing tasks), and technological level (strategic technological infrastructure development and designing inclusive telehealth systems). WeChat, a widely used social networking platform, was the most common platform used for telehealth services. China's practices in addressing the barriers may provide implications and evidence for other developing countries or low-and middle- income countries (LMICs) to implement and adopt telehealth systems.

Computer habits and digital literacy in geriatric patients: A survey

Objective

Among hospitalised geriatric patients, only half are computer users. However, many of them refrain from using telehealth solutions. This study aimed to investigate geriatric patients' computer and Internet habits and digital literacy and their associations with stress levels and frequency of Internet use.

Methods

Inpatients and outpatients aged 65 years or older, all computer users, were consecutively surveyed. Besides information about computer and Internet habits, computer support, and computer stress, the survey also collected information about digital literacy using the electronic Health Literacy Assessment toolkit.

Results

A total of 124 computer users with a mean age of 80.6 ± 7.4 years participated in the study from 1 October to 1 December 2019. Most patients received computer support from their children and grandchildren, whereas 6% did not seek support. They found themselves 'most familiar with using a keyboard' (79%), 59% 'were unfamiliar with the Copy Paste function', and only one-third 'were open to new ways of using computers'. Digital literacy was associated with the frequency of Internet use (P = 0.001), and higher digital literacy was associated with less computer stress (P = 0.01).

Conclusions

Geriatric computer users are challenged by their basic computer skills, which may influence their choice of participation in telehealth solutions. If telehealth solutions are to succeed among geriatric patients, individualised computer support based on their basic computer skills and user-friendly computer devices are a prerequisite. For ongoing support, it is also necessary to introduce people close to the patient to telehealth solutions.

Improving patient self-description in Chinese online consultation using contextual prompts

Background

Online health care consultation has been widely adopted to supplement traditional face-to-face patient-doctor interactions. Patients benefit from this new modality of consultation because it allows for time flexibility by eliminating the distance barrier. However, unlike the traditional face-to-face approach, the success of online consultation heavily relies on the accuracy of patient-reported conditions and symptoms. The asynchronous interaction pattern further requires clear and effective patient self-description to avoid lengthy conversation, facilitating timely support for patients.

Method

Inspired by the observation that doctors talk to patients with the goal of eliciting information to reduce uncertainty about patients' conditions, we proposed and evaluated a machine learning-based computational model towards this goal. Key components of the model include (1) how a doctor diagnoses (predicts) a disease given natural language description of a patient's conditions, (2) how to measure if the patient's description is incomplete or more information is needed from the patient; and (3) given the patient's current description, what further information is needed to help a doctor reach a diagnosis decision. This model makes it possible for an online consultation system to immediately prompt a patient to provide more information if it senses that the current description is insufficient.

Results

We evaluated the proposed method by using classification-based metrics (accuracy, macro-averaged F-score, area under the receiver operating characteristics curve, and Matthews correlation coefficient) and an uncertainty-based metric (entropy) on three Chinese online consultation corpora. When there was one consultation round, our method delivered better disease prediction performance than the baseline method (No Prompts) and two heuristic methods (Uncertainty-based Prompts and Certainty-based Prompts).

Conclusion

The disease prediction performance correlated with uncertainty of patients’ self-described symptoms and conditions. However, heuristic solutions ignored the context to decrease large amounts of uncertainty, which did not improve the prediction performance. By elaborate design, a machine-learning algorithm can learn the inner connection between a patient’s self-description and the specific information doctors need from doctor-patient conversations to provide prompts, which can enrich the information in patient self-description for a better performance in disease prediction, thereby achieving online consultation with fewer rounds of doctor-patient conversation.

Prevalence of Computer Use among Geriatric In- and Outpatients

Objective: Despite its many benefits, tele-rehabilitation is not widely used by the older generations. This study aimed to investigate the opportunity to offer tele-rehabilitation in a geriatric population by determining the prevalence of computer use and to examine whether the patients’ characteristics affect computer use. Design: Cross-sectional study. Patients: Cognitive well-functioning in- and outpatients aged 65 years or older. Methods: Patients were consecutively included and surveyed. Results: A total of 249 patients participated in the survey. Among them, 124 were computer users. Four of these never went online. Compared to non-users, computer users were younger OR: 0.91 (95% CI: 0.87; 0.94) p = 0.001, less frail OR: 0.37 (95% CI: 0.25;0.55) p = 0.001, had a higher functional capacity OR: 1.02 (95% CI 1.01; 1.03) p = 0.001 and more often had an education at high school level or higher OR: 1.7 (95% CI: 1.41; 2.40). Conclusion: Only half of the geriatric patients are computer users. If tele-rehabilitation is to be adopted by a wider geriatric population challenged by reduced mobility, long transportation times, or frailty, computer training, user friendly devices and computer support should be considered beforehand.

Effectiveness of peer support interventions for adults with depressive symptoms: a systematic review and meta-analysis

BACKGROUND

Many adults suffer from depression and peer support has been shown to reduce depressive symptoms.

AIM

To examine the effectiveness of peer support interventions for adults with depressive symptoms.

METHODS

Five electronic databases were searched until February 2021. A random-effect meta-analysis was conducted. The I² statistics and Cochran's Q chi-squared test assessed heterogeneity. Quality appraisal was conducted by the Cochrane risk of bias tool and the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) approach at the study and outcome level, respectively.

RESULTS

Seventeen studies conducted in most Western countries and on the maternal population were included. Three types of peer support interventions (peer-delivered psychotherapy, individualized peer mentoring, and peer support group discussions) were identified. Significant effects of peer support interventions were found when compared to standard care. However, professional-led psychotherapy and exercise programs showed more effectiveness compared to peer support group discussions.

CONCLUSIONS

Future trials could be improved by engaging trained healthy peers (peers with and without prior history of depression) to deliver interventions, encouraging online methods of communication between participants and peers, and facilitating the building of deeper connections between participants and peers. Considering the low-quality rating by the GRADE approach, future research is needed to corroborate current findings.

The Covid Impact to Public Healthcare Utilization Among Urban Low-Income Subsidized Community in Klang Valley Malaysia

BACKGROUND

Appropriate level of healthcare utilization is one of the aims in translating health system inputs into improving the outcome of population health. Healthcare utilization services in Malaysia remains relatively low as compared to the rate in most high-income countries and some gaps exist across socioeconomic status. After the financial handouts deemed Household Living Aid (HLA) to low-income earners, Malaysia has recently implemented a financial health protection scheme toward for low-income group known as PeKa B40 to improve their access for healthcare services. This study aims to determine the healthcare utilization among the low-income population living in urban Klang Valley, and to explore the relationship between healthcare utilization with the demographic characteristics of this population.

MATERIAL AND METHODS

A cross-sectional study using face to face structured questionnaire. All 447 respondents included were low-income earners enrolled in the HLA. Chi-square analysis and multiple logistic regression were used to examine association between the risk factors and healthcare utilization.

RESULTS

The response rate was 93.5%. The healthcare utilization among the respondents during the partial lockdown period was 19.5% and 33.1% during the recovery lockdown period. Enrollment in the PeKa B40 scheme among the 7.6% respondents was not associated with healthcare utilization. After controlling for the variables, those aged 60 years and above [AOR: 1.87; 95% (CI): (1.07; 3.27)], self-rated poor health status [AOR: 2.16; 95% (CI): (1.07; 4.34)], having NCDs [AOR: 4.21; 95% (CI): (2.23; 7.94)], and being hospitalized in the past 12 months [AOR: 3.54; 95% (CI): (1.46; 8.62)], were more likely to utilize healthcare services as compared to their counterparts.

CONCLUSION

The results from this study is valuable for policy recommendations to improve on the coverage of the PeKa B40 scheme and healthcare access for the low-income population especially during the pandemic.