Factors Associated with Early and Periodic Screening, Diagnostic, and Treatment Services in a Medicaid Managed Care Pediatric Population.

Early and Periodic Screening, Diagnostic, and Treatment (EPSDT) services is a Medicaid benefit for children that addresses their health problems before they become advanced, debilitating, and expensive. We conducted a retrospective cross-sectional analysis of pediatric beneficiaries (newborn to younger than 21 years) enrolled in a Medicaid managed care organization to examine the factors associated with EPSDT screening services completion. We obtained 2018 administrative claims data for beneficiaries continuously enrolled for a minimum of 90 days (n=156,108). Completion of EPSDT screening services among our Medicaid managed care beneficiaries was low. Those having more emergency department visits and hospitalizations, having family medicine practitioners as primary care physicians, belonging to the racial/ethnic group Asian/Pacific Islander/Hawaiian/Alaskan Native/Native American, and 18 to younger than 21 years age group were less likely than others to complete EPSDT services. Our results provide information on segments of pediatric beneficiaries that can be targeted to increase EPSDT screening services completion.

When Agency Fails: An Analysis of the Association Between Hospital Agency Staffing and Quality Outcomes.

Introduction: Staffing is critical to hospital quality, but recent years have seen hospitals grappling with severe shortages, forcing them to rely on contract or agency staff for urgent patient care needs. This shift in staffing mix has raised questions about its impact on quality. Consequently, this study investigated whether the increased use of agency staff has affected healthcare quality in hospitals. Given the limited recent research on this topic, practitioners remain uncertain about the effectiveness of their staffing strategies and their potential impact on quality. Methods: Drawing from agency theory, data were obtained from Definitive Healthcare which consolidates information from numerous public access databases pertaining to hospitals such as the American Hospital Association Annual Survey (hospital profile) and the Hospital Value-Based Purchasing Program (quality data). We conducted a cross-sectional study using a multivariable linear regression model (2021-2022) with appropriate organizational and market- level control variables. Quality was measured across eight variables while the independent variable of interest was agency labor cost ratio operationalized as the percentage of net patient revenue consumed by agency labor expense. Results: Our results suggested that the employment of agency staff was significantly and negatively associated with six of eight quality measures tested, including the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) star rating, Hospital Compare rating, the hospital Total Performance Score (TPS), and three of the four sub-domains that comprise the TPS: clinical domain score, person and community engagement domain score, and the efficiency and cost reduction score. Discussion: Our results indicated that the increased use of agency labor may have a significant negative influence on quality outcomes at the hospital level. Our findings support the premise that interventions that promote full-time staffing may be more supportive of the quality of care delivered as well as patients' perceptions of care.

Examining the Effect of Contactless Intergenerational Befriending Intervention on Social Isolation Among Older Adults and Students' Attitude Toward Companionship: Content Analysis.

BACKGROUND: Intergenerational friendship, a mechanism of social support, is an effective intervention to reduce the increasing risk of social isolation (SI) and develop companionship in the older adult population. The COVID-19 pandemic provided a unique opportunity to examine the psychosocial intervention of befriending via technology use as a primary form of contactless socialization. OBJECTIVE: The study aims to explore the effectiveness of the befriending intervention through a contactless, intergenerational service-learning project on older adult emotions, especially boredom and loneliness as the key attributes of SI, and on students' attitude toward companionship. METHODS: During the months of January to April 2022 , undergraduate students enrolled in a health administration course with a special focus on culture were asked to be involved in a contactless, intergenerational service-learning project (n=46). In this study, contactless intervention meant communication using the telephone and apps such as FaceTime and Zoom. Students were paired with older adults to have at least a 30-minute weekly conversation, for 8 weeks, via telephone or an internet-based app such as FaceTime. Students were asked to write a half-page diary after each interaction and a 1-page reflection at the end of the fourth week and at the end of the service-learning project. At the completion of the project, the researchers also surveyed the older adults to assess the impact of the project using a 5-item open-ended questionnaire. Following a heuristic approach and content analysis, student artifacts (110,970 words; 118-page, single-spaced Microsoft Word document) and the older adult surveys were analyzed using MAXQDA, (VERBI GmbH). Qualitative data were extracted to assess the impact of service learning on SI by measuring the attributes of boredom and loneliness among 46 older adults. Students' attitudes toward companionship were also assessed using data from their diaries and reflections. RESULTS: Overall, three major constructs were identified: (1) meaningful engagement, defined as feeling safe, having increased confidence, and having reduced boredom; (2) internal motivation to participate in the weekly interaction, defined as discussion about daily life experience, level of happiness, and ability to exert personal control over the situation; and (3) intergenerational befriending, defined as perceived benefits from the friendly nature of the interaction, ability to comfortably connect with students, and positive feeling and attitude toward the student. CONCLUSIONS: The contactless, intergenerational befriending intervention reduced boredom and loneliness among older adults and enhanced positive attitude and confidence among university students. Students helped older adults to develop digital skills for the use of apps and social media. Older adults showed interest in the intervention and shared their daily life experiences with the students, which helped to reduce the gap between generations. Findings indicate the effectiveness of an intergenerational service-learning intervention on SI reduction and increased positive attitude among college students.

A Systematic Review of Artificial Intelligence Used to Predict Loneliness, Social Isolation, and Drug Use During the COVID-19 Pandemic.

This systematic literature review evaluates the role of machine learning, artificial intelligence (AI), and social determinants of health (SDOH) in identifying loneliness during the COVID-19 pandemic. By examining various studies and articles through a comprehensive search of databases EBSCOhost, Medline Complete, Academic Search Complete, Directory of Open Access Journals, and Complementary Index, the research team sought to discern consistent themes and patterns. We identified four constructs central to understanding the impact of the pandemic on societal well-being: (1) the prediction of compliance with COVID-19 measures, (2) the prediction of loneliness and its effects, (3) the prediction of well-being and social inclusion, and (4) the prediction of drug use. Within these constructs, prevalent themes related to opioid overdose, stress levels, mental health, well-being, and cognitive decline emerged. The adherence to the PRISMA 2020 checklist has resulted in a PRISMA flow diagram that categorizes the selected literature. The findings of this review, including the proportion of studies predicting various attributes related to loneliness, demonstrate the critical intersections between machine learning, AI, SDOH, and the psychosocial phenomenon of loneliness amidst a global health crisis. The review results provide a summary of the occurrences and predictive percentages of each construct as determined by the literature, contributing to a nuanced understanding of the pandemic's multifaceted impact on loneliness, social isolation, and drug use. Using AI to predict these constructs has remarkable capabilities in identifying individuals at risk and facilitating timely interventions to mitigate adverse outcomes and promote mental health resilience in the face of challenges such as the COVID-19 pandemic. Moving forward, future research is warranted to refine AI algorithms, validate predictive models and utilize AI-based interventions in healthcare and mental health services while ensuring data security, and individuals' privacy.

Systematic Review and Meta-Analysis of the Financial Impact of 30-Day Readmissions for Selected Medical Conditions: A Focus on Hospital Quality Performance.

BACKGROUND: The Patient Protection and Affordable Care Act (ACA) established the Hospital Quality Initiative in 2010 to enhance patient safety, reduce hospital readmissions, improve quality, and minimize healthcare costs. In response, this study aims to systematically review the literature and conduct a meta-analysis to estimate the average cost of procedure-specific 30-day risk-standardized unplanned readmissions for Acute Myocardial Infarction (AMI), Heart Failure (HF), Pneumonia, Coronary Artery Bypass Graft (CABG), and Total Hip Arthroplasty and/or Total Knee Arthroplasty (THA/TKA). METHODS: Eligibility Criteria: This study included English language original research papers from the USA, encompassing various study designs. Exclusion criteria comprise studies lacking empirical evidence on hospital financial performance. INFORMATION SOURCES: A comprehensive search using relevant keywords was conducted across databases from January 1990 to December 2019 (updated in March 2021), covering peer-reviewed articles and gray literature. Risk of Bias: Bias in the included studies was assessed considering study design, adjustment for confounding factors, and potential effect modifiers. SYNTHESIS OF RESULTS: The review adhered to PRISMA guidelines. Employing Monte Carlo simulations, a meta-analysis was conducted with 100,000 simulated samples. Results indicated mean 30-day readmission costs: USD 16,037.08 (95% CI, USD 15,196.01-16,870.06) overall, USD 6852.97 (95% CI, USD 6684.44-7021.08) for AMI, USD 9817.42 (95% CI, USD 9575.82-10,060.43) for HF, and USD 21,346.50 (95% CI, USD 20,818.14-21,871.85) for THA/TKA. DISCUSSION: Despite the financial challenges that hospitals face due to the ACA and the Hospital Readmissions Reduction Program, this meta-analysis contributes valuable insights into the consistent cost trends associated with 30-day readmissions. CONCLUSIONS: This systematic review and meta-analysis provide comprehensive insights into the financial implications of 30-day readmissions for specific medical conditions, enhancing our understanding of the nexus between healthcare quality and financial performance.

The effect of COVID-19 on cancer incidences in the U.S.

Fundamental data analysis assists in the evaluation of critical questions to discern essential facts and elicit formerly invisible evidence. In this article, we provide clarity into a subtle phenomenon observed in cancer incidences throughout the time of the COVID-19 pandemic. We analyzed the cancer incidence data from the American Cancer Society [1]. We partitioned the data into three groups: the pre-COVID-19 years (2017, 2018), during the COVID-19 years (2019, 2020, 2021), and the post-COVID-19 years (2022, 2023). In a novel manner, we applied principal components analysis (PCA), computed the angles between the cancer incidence vectors, and then added lognormal probability concepts in our analysis. Our analytic results revealed that the cancer incidences shifted within each era (pre, during, and post), with a meaningful change in the cancer incidences occurring in 2020, the peak of the COVID-19 era. We defined, computed, and interpreted the exceedance probability for a cancer type to have 1000 incidences in a future year among the breast, cervical, colorectal, uterine corpus, leukemia, lung & bronchus, melanoma, Hodgkin's lymphoma, prostate, and urinary cancers. We also defined, estimated, and illustrated indices for other cancer diagnoses from the vantage point of breast cancer in pre, during, and post-COVID-19 eras. The angle vectors post the COVID-19 were 72% less than pre-pandemic and 28% less than during the pandemic. The movement of cancer vectors was dynamic between these eras, and movement greatly differed by type of cancer. A trend chart of cervical cancer showed statistical anomalies in the years 2019 and 2021. Based on our findings, a few future research directions are pointed out.