How does the hospital make a safe and stable elective surgery plan during COVID-19 pandemic?

Intra- and post-pandemic impact of the COVID-19 outbreak on Stanford Health Care

Stanford Health Care, which provides about 7% of overall healthcare to approximately 9 million people in the San Francisco Bay Area, has undergone significant changes due to the opening of a second hospital in late 2019 and, more importantly, the COVID-19 pandemic. We examine the impact of these events on anatomic pathology (AP) cases, aiming to enhance operational efficiency in response to evolving healthcare demands. We extracted historical census, admission, lab tests, operation, and AP data since 2015. An approximately 45% increase in the volume of laboratory tests (P < 0.0001) and a 17% increase in AP cases (P < 0.0001) occurred post-pandemic. These increases were associated with progressively increasing (P < 0.0001) hospital census. Census increase stemmed from higher admission through the emergency department (ED), and longer lengths of stay mostly for transfer patients, likely due to the greater capability of the new ED and changes in regional and local practice patterns post-pandemic. Higher census led to overcapacity, which has an inverted U relationship that peaked at 103% capacity for AP cases and 114% capacity for laboratory tests. Overcapacity led to a lower capability to perform clinical activities, particularly those related to surgical procedures. We conclude by suggesting parameters for optimal operations in the post-pandemic era.

The indirect impact of the COVID-19 pandemic on people with type 2 diabetes mellitus and without COVID-19 infection: Systematic review and meta-analysis

BACKGROUND

The effect directly from the coronavirus disease 2019 (COVID-19) infection on health and fatality has received considerable attention, particularly among people with type 2 diabetes mellitus (T2DM). However, evidence on the indirect impact of disrupted healthcare services during the pandemic on people with T2DM is limited. This systematic review aims to assess the indirect impact of the pandemic on the metabolic management of T2DM people without a history of COVID-19 infection.

METHODS

PubMed, Web of Science, and Scopus were systematically searched for studies that compared diabetes-related health outcomes between pre-pandemic and during-pandemic periods in people with T2DM and without the COVID-19 infection and published from January 1, 2020, to July 13, 2022. A meta-analysis was performed to estimate the overall effect on the diabetes indicators, including hemoglobin A1c (HbA1c), lipid profiles, and weight control, with different effect models according to the heterogeneity.

RESULTS

Eleven observational studies were included in the final review. No significant changes in HbA1c levels [weighted mean difference (WMD), 0.06 (95% CI -0.12 to 0.24)] and body weight index (BMI) [0.15 (95% CI -0.24 to 0.53)] between the pre-pandemic and during-pandemic were found in the meta-analysis. Four studies reported lipid indicators; most reported insignificant changes in low-density lipoprotein (LDL, n = 2) and high-density lipoprotein (HDL, n = 3); two studies reported an increase in total cholesterol and triglyceride.

CONCLUSIONS

This review did not find significant changes in HbA1c and BMI among people with T2DM after data pooling, but a possible worsening in lipids parameters during the COVID-19 pandemic. There were limited data on long-term outcomes and healthcare utilization, which warrants further research.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022360433.

Elective surgery scheduling under uncertainty in demand for intensive care unit and inpatient beds during epidemic outbreaks

Amid the epidemic outbreaks such as COVID-19, a large number of patients occupy inpatient and intensive care unit (ICU) beds, thereby making the availability of beds uncertain and scarce. Thus, elective surgery scheduling not only needs to deal with the uncertainty of the surgery duration and length of stay in the ward, but also the uncertainty in demand for ICU and inpatient beds. We model this surgery scheduling problem with uncertainty and propose an effective algorithm that minimizes the operating room overtime cost, bed shortage cost, and patient waiting cost. Our model is developed using fuzzy sets whereas the proposed algorithm is based on the differential evolution algorithm and heuristic rules. We set up experiments based on data and expert experience respectively. A comparison between the fuzzy model and the crisp (non-fuzzy) model proves the usefulness of the fuzzy model when the data is not sufficient or available. We further compare the proposed model and algorithm with several extant models and algorithms, and demonstrate the computational efficacy, robustness, and adaptability of the proposed framework.