Implications for sustainable healthcare operations in embracing telemedicine services during a pandemic

[Influence of telemedicine on the operation of public hospitals in China]

OBJECTIVE

Telemedicine, as an information-based tool, is widely recognized as an effective solution for compensating for the imbalanced allocation of medical resources in China. This study specifi-cally aimed to analyze the impact of telemedicine functions on the operational efficiency of public hospitals, with a particular focus on their heterogeneous effects on hospitals of different levels.

METHODS

A cross-sectional research design was used based on the 2022 Health Informatization Statistical Survey data, and 8 944 public hospitals were used as research objects to analyze the impact of telemedicine on hospital revenues and business capacity. Multivariate linear model, propensity score matching (PSM), and grouped regression methods were employed to evaluate the impact of telemedicine on hospital revenues, number of consultations, and the number of discharges.

RESULTS

The descriptive results showed that telemedicine was available in 35.51% of public hospitals. The analysis also demonstrated that various factors, such as hospital level, academic category, area of the hospital, administrational level and number of beds all had a significant influence on the operation of the hospital. Moreover, the regression results showed that opening telemedicine could increase hospital revenues by 0.140 (P < 0.01), hospital consultations by 0.136 (P < 0.01), and the number of discharges by 0.316 (P < 0.01). After correcting for endogeneity using the propensity score matching, the results showed that the effect of opening telemedicine on hospital revenues, consultations, and the number of discharges was 0.191 (P < 0.01), 0.216 (P < 0.01), and 0.353 (P < 0.01), respectively. Further heterogeneity analysis was conducted to explore the differential effects of telemedicine on hospitals of different levels. Grouped regression showed that telemedicine had a positive impact on the income of secondary hospitals, with a coefficient of 0.088 (P < 0.05), and it had a more significant positive impact on hospital consultations in secondary hospitals, with a coefficient of 0.127 (P < 0.01). An even greater impact on the number of discharges in primary hospitals, with a coefficient of 1.203 (P < 0.01). Telemedicine, on the other hand, did not have a significant positive impact on the overall revenue and operational capacity of tertiary hospitals.

CONCLUSION

Telemedicine had a significant promoting effect on hospital revenues, hospital consultations and the number of discharges, and this effect was differentiated between hospitals of different levels. Through the construction of telemedicine, primary hospitals were able to significantly improve their business capacity and revenue, which played a positive role in improving the operation of primary public hospitals.

The Perception of Health Care Practitioners Regarding Telemedicine During COVID-19 in Saudi Arabia: Mixed Methods Study

BACKGROUND

Telemedicine is a rapidly evolving field that uses information and communication technology to provide remote health care services, such as diagnosis, treatment, consultation, patient monitoring, and medication delivery. With advancements in technology, telemedicine has become increasingly popular during the COVID-19 lockdown and has expanded beyond remote consultations via telephone or video to include comprehensive and reliable services. The integration of telemedicine platforms can enable patients and health care providers to communicate more efficiently and effectively.

OBJECTIVE

This study aims to investigate the awareness, knowledge, requirements, and perceptions of health care practitioners in Saudi Arabia during the pandemic health crisis from the end-user perspective. The findings of this study will inform policy makers regarding the sustainability of telemedicine and how it affects the process of provision of health care and improves the patients' journey.

METHODS

This study adopted a mixed methods design with a quantitative-based cross-sectional design and qualitative interviews to assess the perceptions of various health care professionals working in outpatient departments that have a telemedicine system that was used during the COVID-19 pandemic. For both approaches, ethics approval was obtained, and informed consent forms were signed. In total, 81 completed questionnaires were used in this study. In the second phase, general interviews were conducted with managerial staff and health care professionals to obtain their view of telemedicine services in their hospitals.

RESULTS

The study revealed that most participants (67/81, 83%) were familiar with telemedicine technology, and the study proved to be statistically significant at P<.05 with a proportion of the participants (52/81, 64%) believing that continuous training was essential for its effective use. The study also found that consultations (55/153, 35.9%) and monitoring patients (35/153, 22.9%) were the major components of telemedicine used by health care professionals, with telephones being the most commonly used mode of interaction with patients (74/117, 63.2%). In addition, 54% (44/81) of the respondents expressed concerns about patient privacy and confidentiality, highlighting this as a major issue. Furthermore, the majority of participants (58/81, 72%) reported the necessity of implementing national standards essential for telemedicine technology in Saudi Arabia. The interviews conducted as part of the study revealed 5 major themes: culture, barriers and difficulties, communication, implementation, and evaluation. These themes highlighted the importance of a culture of acceptance and flexibility, effective communication, and ongoing evaluation of telemedicine technologies in health care systems.

CONCLUSIONS

This study provides a crucial message with insights into the perceptions and experiences of health care professionals with telemedicine during the COVID-19 pandemic in Saudi Arabia.

Hospital dual-channel adoption decisions with telemedicine referral and misdiagnosis

With the rapid development of telemedicine and the impact of the COVID-19 pandemic, more and more patients are now resorting to using telemedicine channels for healthcare services. However, for hospitals, there exists a lack of managerial guidance in place to help them adopt telemedicine in a practical and standardized way. This study considers a hospital operating with both telemedicine (virtual) and face-to-face (physical) medical channels, and which allocates its capacity by also taking into account the possibility of both referrals and misdiagnosis. Methodologically, we construct a game model based on a queuing framework. We first analyze equilibrium strategies for patient arrivals. Then we propose the necessary conditions for a hospital to develop a telemedicine channel and to operate both channels simultaneously. Finally, we find the optimal decisions for the service level of telemedicine, which can also be regarded as the optimal proportion of diseases treated by telemedicine, and the optimal hospital capacity allocation ratio between the two channels. We also find that hospitals in a full coverage market (e.g., for certain small-scale hospitals and community hospitals or cancer hospitals) are more difficult to adopt telemedicine than hospitals in a partial coverage market (e.g., for comprehensive large-scale hospitals with many potential patients). Small-scale hospitals are more suited to operating telemedicine as a gatekeeper to help triage patients, while large hospitals are more prone to regard telemedicine as a medical channel for providing professional medical services to patients. We also analyze the effects of the telemedicine cure rate and the cost ratio of telemedicine to the physical hospital on the overall healthcare system performance, including the physical hospital arrival rate, patients' waiting time, total profit, and social welfare. Then we compare the performance, ex ante versus ex post, the implementation of telemedicine. It is shown that when the market is partially covered, the total social welfare is always higher than it was before the implementation. However, as far as the profit goes, if the telemedicine cure rate is low and the cost ratio is high, the total hospital profit may be lower than it was prior to using telemedicine. However, the profit and social welfare of hospitals in the full coverage market are always lower than it was before the implementation. In addition, the waiting time in the hospital is always higher than that before the implementation, which means that the implementation of telemedicine will make patients who must receive treatment in the physical hospital face even worse congestion than before. More insights and results are gleaned from a series of numerical studies.

Healthcare Facilities Redesign Using Multicriteria Decision-Making: Fuzzy TOPSIS and Graph Heuristic Theories

Background

Healthcare facilities are crucial assets that are necessary to be updated and evaluated regularly. One of the most pressing issues today is the renovation of healthcare facilities to match international standards. In large projects involving nations renovating healthcare facilities, it is necessary to rank the evaluated hospitals and medical centers in making optimal decisions for the redesign process.

Objective

This study presents the process of renovating old healthcare facilities to meet international standards, applying proposed algorithms for measuring compliance for redesign, and deciding whether or not the redesign process is beneficial.

Methods

The evaluated hospitals were ranked using a fuzzy technique for order of preference by similarity to ideal solution algorithm and a reallocation algorithm that calculates the layout score before and after applying the proposed algorithm for the redesign process using bubble plan and graph heuristics techniques. Results and Conclusion. The results of the methodologies applied to 10 evaluated hospitals as selected hospitals in Egypt showed that the hospital with the abbreviation (D) had the most required general hospital criteria, and the hospital with the abbreviation (I) had no cardiac catheterization laboratory and lacked the most international standard criteria. After applying the reallocation algorithm, one hospital's operating theater layout score improved by 32.5%. Proposed algorithms support decision-making by helping organizations redesign healthcare facilities.

Managing healthcare supply chain through artificial intelligence (AI): A study of critical success factors

Healthcare is one of the most critical sectors due to its importance in handling public health. With the outbreak of various diseases, more recently during Covid-19, this sector has gained further attention. The pandemic has exposed vulnerabilities in the healthcare supply chain (HSC). Recent advancements like the adoption of various advanced technologies viz. AI and Industry 4.0 in the healthcare supply chain are turning out to be game-changers. This study focuses on identifying critical success factors (CSFs) for AI adoption in HSC in the emerging economy context. Rough SWARA is used for ranking CSFs of AI adoption in HSC. Results indicate that technological (TEC) factors are the most influential factor that impacts the adoption of AI in HSC in the context of emerging economies, followed by institutional or environmental (INT), human (HUM), and organizational (ORG) dimensions.

The role of forecasting in preventing supply chain disruptions during the COVID-19 pandemic: a distributor-retailer perspective

Strengthen the resilience of supply chains was observed to be critical issue by firms to confront disruptions triggered by unprecedented demand and severe disasters. However, the extraordinarily challenging disruptions of COVID-19 pandemic, unlike any disasters seen in recent times. This study aims to provide a practical solution to supply chain (SC) disruptions by estimating the best forecasting models for demand fluctuations in the context of food and beverages. A method is proposed to predict SC disruptions and enhance SC resilience. Double exponential smoothing (DES) and the ARIMA model are adopted as forecasting approaches to estimate demand and optimum inventory quantities during three different periods of disruption associated with the COVID-19 outbreak. A downstream SC involving 2 distributors and 56 retailers is considered to elaborate inventory measurements (optimal inventory levels and total costs). The results demonstrate that distributors can reduce costs by dispensing with some retailers, particularly those who order low quantities and thus incur unjustified expenses. Furthermore, high accuracy is obtained, with minimal differences between the real data and the model’s forecast. Existing research has largely ignored supply disruptions in the distributor-retailer relationship. This study provides distributors and SC managers important knowledge on SC disruptions and identifies appropriate forecasting methods to increase SC resilience. It also provides distributors and other SC managers unprecedented insights on tackling crises of stability like COVID-19.