Purpose: The aim of this study was to report on the impact of COVID-19 "Unlock-I" on Network of Eye Centers in Southern India.
Methods: Our eye health pyramid model has a network of eye care centers in four Indian states. The network constitutes a center of excellence (CoE) at the apex followed by tertiary care centers (TC) located in urban areas, secondary care centers (SC), and primary care vision centers (VC) at the base located in rural areas. We collected data on patients seen between June 2019 and June 2020, which included age, gender, total patients seen (new or follow-up), and socioeconomic status (paying and nonpaying). A comparative study was done between the data for outpatients and surgeries performed pre-COVID-19 and during Unlock-I in COVID-19 period.
Results: There was a 36.71% reduction in the overall outpatients seen in June 2020 (n = 83,161) compared to June 2019 (n = 131,395). The reduction was variable across different levels of the pyramid with the highest reduction in CoE (54.18%), followed by TCs (40.37%), SCs (30.49%) and VCs (18.85%). Similar pattern was seen for new paying patients with the highest reduction in CoE (54.22%), followed by TCs (25.86%) and SCs (4.9%). A 43.67% reduction was noted in the surgeries performed in June 2020 (n = 6,168), compared to June 2019 (n = 10,950). Reduction in paying services was highest in CoE (47.52%), followed by TCs (15.17%) and SCs (4.87%). There was no significant change in the uptake of services by gender in the network.
Conclusion: Highest reduction in patient footfalls during "Unlock-1" was noted in urban centers. Going forward, there is a need to develop strategies to provide eye care closer to the doorsteps.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7942085 | PMC |
http://dx.doi.org/10.4103/ijo.IJO_3143_20 | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.
Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.
J Nurs Adm
December 2024
Author Affiliations: Research Associate (Dr Keys), The Center for Health Design, Concord, California; National Senior Director (Dr Fineout-Overholt), Evidence-Based Practice and Implementation Science, at Ascension in St. Louis, MO.
Objective: Relationships among coworker and patient visibility, reactions to physical work environment, and work stress in ICU nurses are explored.
Background: Millions of dollars are invested annually in the building or remodeling of ICUs, yet there is a gap in understanding relationships between the physical layout of nursing units and work stress.
Methods: Using a cross-sectional, correlational, exploratory, predictive design, relationships among variables were studied in a diverse sample of ICU nurses.
J Neurosurg Anesthesiol
November 2024
Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA.
This systematic review aimed to identify and describe best practice for the intraoperative anesthetic management of patients undergoing emergent/urgent decompressive craniotomy or craniectomy for any indication. The PubMed, Scopus, EMBASE, and Cochrane databases were searched for articles related to urgent/emergent craniotomy/craniectomy for intracranial hypertension or brain herniation. Only articles focusing on intraoperative anesthetic management were included; those investigating surgical or intensive care unit management were excluded.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!