Search

Abstract Background Protecting public health from infectious diseases often relies on the cooperation of citizens, especially when self-care interventions are the only viable tools for disease mitigation. Accordingly, social aspects related to public opinion have been studied in the context of the recent COVID-19 pandemic. However, a comprehensive understanding of the effects of opinion-related factors on disease spread still requires further exploration. Methods We propose an agent-based simulation framework incorporating opinion dynamics within an epidemic model based on the assumption that mass media channels play a leading role in opinion dynamics. The model simulates how opinions about preventive interventions change over time and how these changes affect the cumulative number of cases. We calibrated our simulation model using YouGov survey data and WHO COVID-19 new cases data from 15 different countries. Based on the calibrated models, we examine how different opinion-related factors change the consequences of the epidemic. We track the number of total new infections for analysis. Results Our results reveal that the initial level of public opinion on preventive interventions has the greatest impact on the cumulative number of cases. Its normalized permutation importance varies between 69.67% and 96.65% in 15 models. The patterns shown in the partial dependence plots indicate that other factors, such as the usage of the pro-intervention channel and the response time of media channels, can also bring about substantial changes in disease dynamics, but only within specific ranges of the dominant factor. Conclusions Our results reveal the importance of public opinion on intervention during the early stage of the pandemic in protecting public health. The findings suggest that persuading the public to take actions they may be hesitant about in the early stages of epidemics is very costly because taking early action is critical for mitigating infectious diseases. Other opinion-related factors can also lead to significant changes in epidemics, depending on the average level of public opinion in the initial stage. These findings underscore the importance of media channels and authorities in delivering accurate information and persuading community members to cooperate with public health policies.

Despite improvements in early detection and therapeutic interventions, the mortality rate for Black breast cancer patients is still significantly higher than that of White breast cancer patients. This study seeks to understand differences in the patient experience that lead to this disparity. Semi-structured interviews were conducted to understand the breast cancer treatment process and patient experiences. This study collected health services and timeline data from medical records. Based on these two data sources, the patient's journey in breast cancer treatment was mapped and a thematic analysis was conducted to identify challenges and barriers in the process. The cancer care continuum consists of four stages-diagnosis, surgery, chemotherapy/radiation, and follow-up care. The themes contributing to patient experiences and challenges were identified and compared in each stage for both Black and White patients. Both Black and White participants faced challenges related to financial constraints, treatment changes, lack of autonomy, and insufficient emotional support. However, Black participants additionally faced significant barriers in terms of cultural concordance, effective patient-provider communication, and delay in diagnosis. This study highlights the importance of incorporating effective provider-patient communication, navigation, and emotional support, especially for Black breast cancer patients throughout the cancer care continuum to address healthcare disparities.

Hospital beds are often assigned among several major groups called service lines, each aimed to provide care for patients with similar medical needs such as cancer, musculoskeletal disorders, vascular, surgical, medical, and women and children. Besides the benefits of service lines in streamlining operations, they can lead to unintended issues if they are not assessed and adjusted regularly. These issues include fragmentation and inconsistencies in care, offering overlapping services, uneven resources, and growth, disparities in access to care, imbalanced capacity utilization, and system-wide flow issues. We propose an interpretable clustering classification approach for assessing and adjusting existing service lines regarding size and patient distribution. Our approach is useable in practice as it uses data available during patient admission and generates interpretable rules for patient assignment among service lines. Our results from two academic hospitals suggest the need for further splitting service lines such as internal medicine, general surgery, and neurological disorders. Further, our findings support the idea of providing specialized services such as orthopedic surgery, cardiology, physical medicine, and pregnancy, childbirth and the puerperium. These findings have several practical implications related to patient mix, capacity planning, bed assignment, and hospital administration overall.

Abstract Background Thirteen essential maternal child health (MCH) commodities, identified by the UN Commission on Life-Saving Commodities for Women and Children, could save the lives of more than 6 million women and children in Low-and-Middle-Income Countries (LMICs) if made available at the point of care. To reduce stockout of those commodities and improve the health supply chains in LMICs, the Electronic TRAcking system for healthcare commodities (E+TRA Health), an all-in-one out-of-box solution, was developed to track and manage medical commodities at lower-level health facilities in rural areas. It aims to support real-time monitoring and decision-making to (1) reduce the time needed to prepare orders, (2) reduce stockout and overstock cases of targeted medical supplies, (3) help improve patient outcomes. In this study, we adopted an integrated approach to analyze the process of information flow, identify and address critical paths of essential supplies associated with maternal health in the Ugandan health system. Methods We apply system engineering principles and work with community partners in hospitals to develop care process workflow charts (based on essential services) for the lifecycle of maternal health continuum of care. Based on this chart, we develop a cloud-based offline-compatible smart sync platform named “E+TRA Health” to triangulate (1) patient admission, diagnoses, delivery information, testing reports from laboratories, (2) inventory information from main store, stores in MCH unit, and (3) lab, to identify the critical list of medical and laboratory supplies, their lead times for procurement and then generate reports and suggested procurement plans for real time decision-making. Results The E+TRA Health platform was piloted in two Healthcare Center IV facilities in Uganda over a period of 6 months. The system collected more than 5000 patient records and managed more than 500 types of medicines. The pilot study demonstrated the functionalities of E+TRA Health and its feasibility to sense demand from point of care. Conclusion E+TRA Health is the first to triangulate supply and demand data from three different departments (main store, lab, and MCH) to forecast and generate orders automatically to meet patient demands. It is capable of generating reports required by Ministry of Health in real time compared to one-week lead-time using paper-based systems. This prompts frontline stakeholders to generate efficient, reliable and sustainable strategic healthcare plans with real time data. This system improves patient outcomes through better commodity availability by sensing true patient demands.

The increasing demand for Intensive Care Unit (ICU) beds requires efficient admission, discharge, and care processes. These efforts require predictions of Length of Stay (LOS) values; however, it is unclear how accurate these predictions need to be. This study investigates the relationship between the accuracy level of LOS predictions and operational performance indicators. A discrete event simulation model is developed to model the ICU patient flows. A linear function of actual and simulated LOS values is used to measure the accuracy level of the predictions. Multiple configurations of patient mix and patient waiting threshold were included in the simulation scenarios. Performance indicators are the average waiting time of patients for an ICU bed and overall admission ratios. Further statistical tests were carried out to evaluate the significance of the results. Results suggest that inaccurate LOS predictions overestimated both the average waiting time of patients for an ICU bed and overall admission rates which can have several quality and performance implications for hospitals. The gaps increased when more elective patients were included in the patient mix. Moreover, higher waiting thresholds (i.e., the maximum amount of time a patient will wait for an ICU bed) yielded higher values in both performance indicators.

Abstract Background States have passed various legislative acts in an attempt to reduce opioid prescribing and corresponding doctor shopping, including prescription drug monitoring programs. This study seeks to determine the association between two state-based interventions enacted in Indiana and the level of doctor shopping among Medicaid-enrolled pregnant women. Methods Indiana Medicaid claims data over the period of January 2014 to March 2019 were used in a regression model to determine the longitudinal change in percentage of pregnant women engaged in doctor shopping based on passage of Indiana Administrative Code Title 884 in 2014 and Public Law 194 in 2018. The primary reasons for prescribing were also identified. Results There were 37,451 women that had both pregnancy and prescription opioid claims over the time horizon. Of these, 2130 women met the criteria for doctor shopping. Doctor shopping continued to increase over the time between the passage of the two interventions but decreased after passage of Public Law 194. Conclusion The decrease in doctor shopping among Medicaid-enrolled pregnant women after passage of Public Law 194 points to the importance of addressing this issue across a broad set of healthcare professionals including nurse practitioners and physician assistants. It is also possible that the potential punitive component in the Law for non-compliance played a role.