ABSTRACT
Background: The outbreak of coronavirus disease 2019 (COVID-19) has created significant challenges in the management of oncology patients, including patients with colorectal cancer (CRC). We suspect that the COVID-19 pandemic had a major impact on the number of CRC inpatients and outpatients, which might leave many CRC patients unable to get timely medical treatment. At the time, the most important task was to satisfy the imperious demand for rapid optimization of processes and the development of efficient and effective triage and treatment strategies, as well as emergency distant clinical reasoning. Methods: The number of outpatients and inpatients, as well as surgeries performed in Shanghai East Hospital from December 2019 to February 2020 were collected. Using December 2019 data as the baseline status before the pandemic, the changes during this period were analyzed which can reflect the impact of COVID-19 on the treatment of CRC patients. In addition, a triage system and management strategy for patients with CRC during COVID-19 were designed and implemented. To evaluate their effectiveness, we assessed COVID-19 infection rates among CRC patients in relation to total patients and healthcare staff. Result: Compared with the pre-COVID-19 period (December 2019), a drastic decline in the number of outpatient visits (2,789 to 120) and inpatient hospitalizations (207 to 50) for all the CRC patients, as well as in non-emergency colorectal surgeries (133 to 23), was observed in February 2020 at our hospital. Conclusion: A multidisciplinary triage strategy aligned with regional guidance and digital, artificial intelligence (AI)-technology solutions can help increase the efficacy in patient management, allow efficient access to care and reduce the incidence of COVID-19 among CRC patients. © 2022 The HealthBook Company Ltd. All Rights Reserved.
ABSTRACT
Study Objectives: A non-food-borne hepatitis A outbreak occurred in Michigan between August 2016 and September 2019, resulting in 920 cases, 738 hospitalizations, and 30 deaths. To support the Michigan Department of Health and Human Services' efforts to increase hepatitis A vaccination rates among high-risk individuals, our multicenter health system implemented an electronic medical record (EMR)-based vaccination intervention across its nine emergency departments (ED). The primary objective of this retrospective cohort and survey analysis was to quantitatively determine whether this intervention was successful in increasing vaccination rates. The secondary objective was to qualitatively assess the attitudes towards, and barriers to use of, the computerized vaccine reminder system.
ABSTRACT
In practical multiple attribute decision making (MADM) problems, the interest groups or individuals intentionally set attribute weights to achieve their own benefits. In this case, the rankings of different alternatives are changed strategically, which is called the strategic weight manipulation in MADM. Sometimes, the attribute values are given with imprecise forms. Several theories and methods have been developed to deal with uncertainty, such as probability theory, interval values, intuitionistic fuzzy sets, hesitant fuzzy sets, etc. In this paper, we study the strategic weight manipulation based on the belief degree of uncertainty theory, with uncertain attribute values obeying linear uncertain distributions. It allows the attribute values to be considered as a whole in the operation process. A series of mixed 0-1 programming models are constructed to set a strategic weight vector for a desired ranking of a particular alternative. Finally, an example based on the assessment of the performance of COVID-19 vaccines illustrates the validity of the proposed models. Comparison analysis shows that, compared to the deterministic case, it is easier to manipulate attribute weights when the attribute values obey the linear uncertain distribution. And a further comparative analysis highlights the performance of different aggregation operators in defending against the strategic manipulation, and highlights the impacts on ranking range under different belief degrees.