Insurance Categories Research Articles

Strengths, weaknesses, opportunities, and threats (SWOT) of the electronic prescribing systems executed in Iran from the physician’s viewpoint: a qualitative study

BackgroundElectronic prescribing (e-prescribing) is an essential technology in the modern health system. This technology has made many changes in the prescription process, which have advantages and disadvantages and have created opportunities for transforming the health system. This study aimed to investigate the strengths, weaknesses, opportunities, and threats of the e-prescribing system implemented in Iran from the physician’s viewpoint.MethodsThis phenomenological qualitative study was conducted in 2022. The participants were 15 Iranian specialist physicians working at Urmia University of Medical Sciences, selected purposively and deliberately. Data was collected through in-depth semi-structured interviews using an interview guide comprising 16 questions. Interviews were conducted until data saturation was reached. The audio data was transcribed into text and analyzed using the thematic analysis. To ensure the validity and reliability of the findings, the criteria introduced by Lincoln and Guba were employed.ResultsThe results of this study showed that the e-prescribing system executed in Iran has diverse and multidimensional strengths, weaknesses, opportunities, and threats. In the strengths section, the analysis of the interviews led to the extraction of semantic units in the categories of prescription process, prescriber, patient, technical, economic, communication, and insurance. Also, the weaknesses in the three categories of the prescriber, patient, and technical were debatable. The opportunities extracted from the narratives of the interviewees were placed in four categories including technical, national macro policies, Ministry of Health macro-policies, and socio-cultural issues. Finally, the discussed threats are classified into two technical and macro policies of the Ministry of Health categories. On the other hand, technical issues played an effective role in all aspects of the SWOT model.ConclusionThe e-prescribing system in Iran has strengths, weaknesses, opportunities, and threats. An overarching factor across all aspects of the SWOT model was technical infrastructure. A robust technical infrastructure is considered a strength and an opportunity for the growth of the electronic prescribing system in Iran. Conversely, any shortcomings in these systems are viewed as weaknesses and pose a threat to the system’s sustainability.

Impact of Medicaid expansion on kidney transplantation in the State Oklahoma.

There is no data evaluating the impact of Medicaid expansion on kidney transplants (KT) in Oklahoma. To investigate the impact of Medicaid expansion on KT patients in Oklahoma. The UNOS database was utilized to evaluate data pertaining to adult KT recipients in Oklahoma in the pre-and post-Medicaid eras. Bivariate analysis, Kaplan Meier analysis was used to estimate, and cox proportional models were utilized. There were 2758 pre- and 141 recipients in the post-Medicaid expansion era. Post-expansion patients were more often non-United States citizens (2.3% vs 5.7%), American Indian, Alaskan, or Pacific Islander (7.8% vs 9.2%), Hispanic (7.4% vs 12.8%), or Asian (2.5% vs 8.5%) (P < 0.0001). Waitlist time was shorter in the post-expansion era (410 vs 253 d) (P = 0.0011). Living donor rates, pre-emptive transplants, re-do transplants, delayed graft function rates, kidney donor profile index values, panel reactive antibodies levels, and insurance types were similar. Patients with public insurance were more frail. Despite increased early (< 6 months) rejection rates, 1-year patient and graft survival were similar. In Cox proportional hazards model, male sex, American Indian, Alaskan or Pacific Islander race, public insurance, and frailty category were independent risk factors for death at 1 year. Medicaid expansion was not associated with graft failure or patient survival (adjusted hazard ratio: 1.07; 95%CI: 0.26-4.41). Medicaid expansion in Oklahoma is associated with increased KT access for non-White/non-Black and non-United States citizen patients with shorter wait times. 1-year graft and patient survival rates were similar before and after expansion. Medicaid expansion itself was not independently associated with graft or patient survival outcomes. Ongoing research is necessary to determine the long-term effects of Medicaid expansion.

Does vertical integration increase the costs for primary care inpatients? Evidence from a national pilot programme in China

ObjectiveTo assess the impact of vertical integration (VI) within County-Level Integrated Health Organisations (CIHOs) on the costs of primary care inpatients.MethodsThis study assessed Xishui, a national pilot county for CIHOs, using inpatient claims data. The treatment group comprised 10,118 inpatients from 5 vertically integrated township health centres (THCs), while the control group consisted of 21,165 inpatients from 19 non-vertically integrated THCs. The periods from July 2020 to December 2021 and January 2022 to December 2022 were defined as pre- and post-policy intervention, respectively. The primary outcome variables were total health expenditures (THS), out-of-pocket (OOP) expenditures, and the proportion of OOP expenditures. Propensity score matching was employed to align inpatient demographics and disease characteristics between the groups, followed by a difference-in-differences analysis to evaluate the outcomes.FindingsVI significantly increased THS (β = 0.1337, p < 0.01) and OOP expenditures per case (β = 0.1661, p < 0.001), but the increase in the proportion of OOP expenditures per case was not significant (β = 0.0029, p > 0.05). For the basic medical insurance for urban and rural residents, THS per case (β = 0.1343, p < 0.01) and OOP expenditures (β = 0.1714, p < 0.001) significantly increased. For the basic medical insurance system for employees, THS per case also increased significantly (β = 0.1238, p < 0.01), but the change in OOP expenditure proportion per case was not significant (β = 0.1020, p > 0.05). The THS per case led by Xishui County People’s Hospital, the leading county medical sub-centre (CMSC), significantly increased (β = 0.1753, p < 0.01), whereas the increase led by Xishui County Traditional Chinese Medicine Hospital was not significant (β = 0.0742, p > 0.05). Increases in OOP expenditures per case were significant in CMSCs led by the People’s Hospital and the Traditional Chinese Medicine Hospital (β = 0.1782, p < 0.01 and β = 0.0757, p < 0.05, respectively).ConclusionVI significantly increased THS and OOP expenditures for primary care inpatients. However, VI could exacerbate economic disparities in disease burden across different insurance categories.

Trends in heart failure costs for commercially insured patients in the United States (2006–2021)

BackgroundAlthough prior research has estimated the overarching cost burden of heart failure (HF), a thorough analysis examining medical expense differences and trends, specifically among commercially insured patients with heart failure, is still lacking. Thus, the study aims to examine historical trends and differences in medical costs for commercially insured heart failure patients in the United States from 2006 to 2021.MethodsA population-based, cross-sectional analysis of medical and pharmacy claims data (IQVIA PharMetrics® Plus for Academic) from 2006 to 2021 was conducted. The cohort included adult patients (age > = 18) who were enrolled in commercial insurance plans and had healthcare encounters with a primary diagnosis of HF. The primary outcome measures were the average total annual payment per patient and per cost categories encompassing hospitalization, surgery, emergency department (ED) visits, outpatient care, post-discharge care, and medications. The sub-group measures included systolic, diastolic, and systolic combined with diastolic, age, gender, comorbidity, regions, states, insurance payment, and self-payment.ResultsThe study included 422,289 commercially insured heart failure (HF) patients in the U.S. evaluated from 2006 to 2021. The average total annual cost per patient decreased overall from $9,636.99 to $8,201.89, with an average annual percentage change (AAPC) of -1.11% (95% CI: -2% to -0.26%). Hospitalization and medication costs decreased with an AAPC of -1.99% (95% CI: -3.25% to -0.8%) and − 3.1% (95% CI: -6.86–0.69%). On the other hand, post-discharge, outpatient, ED visit, and surgery costs increased by an AAPC of 0.84% (95% CI: 0.12–1.49%), 4.31% (95% CI: 1.03–7.63%), 7.21% (95% CI: 6.44–8.12%), and 9.36% (95% CI: 8.61–10.19%).ConclusionsThe study’s findings reveal a rising trend in average total annual payments per patient from 2006 to 2015, followed by a subsequent decrease from 2016 to 2021. This decrease was attributed to the decline in average patient costs within the Medicare Cost insurance category after 2016, coinciding with the implementation of the Medicare Access and CHIP Reauthorization Act (MACRA) of 2015. Additionally, expenses related to surgical procedures, emergency department (ED) visits, and outpatient care have shown substantial growth over time. Moreover, significant differences across various variables have been identified.

Survival Outcomes for US and Canadian Patients Diagnosed with Hodgkin Lymphoma before and after Brentuximab Vedotin Approval for Relapsed/Refractory Disease: A Retrospective Cohort Study.

Cost-effectiveness analyses are required for therapies within Canada's universal healthcare system, leading to delays relative to U.S. healthcare. Patients with Hodgkin lymphoma (HL) generally have an excellent prognosis, but those who relapse after or are ineligible for transplant benefit from novel therapies, including brentuximab vedotin (BV). BV was FDA-approved in 2011 but not Canadian-funded until 2014. To assess the impact of access delays, we compared changes in survival for U.S. (by insurer) and Canadian patients in periods pre/post-U.S. approval. Patients were 16-64 years, diagnosed with HL in 2007-2010 (Period 1) and 2011-2014 (Period 2) from the U.S. SEER and Canadian Cancer Registries. Approval date (surrogate) was utilized as therapy was unavailable in registries. Kaplan-Meier survival curves and adjusted Cox regression models compared survival between periods by insurance category. Among 12,003 U.S. and 4210 Canadian patients, survival was better in U.S. patients (adjusted hazard ratio (aHR) 0.87 (95%CI 0.77-0.98)) between periods; improvement in Canadian patients (aHR 0.84 (95%CI 0.69-1.03) was similar but non-significant. Comparisons between insurers showed survival was significantly worse for U.S. uninsured and Medicaid vs. U.S. privately insured and Canadian patients. Given the increasingly complex nature of oncologic funding, this merits further investigation to ensure equity in access to therapy developments.

Assessing breast cancer-related lymphedema screening and treatment gaps in a safety-net hospital.

We evaluated the unmet breast cancer-related lymphedema(BCRL) treatment and education needs at New England's largest safety-net hospital serving a diverse population by assessing prevalence, risk factors, and treatment. This was a retrospective cohort study examining breast cancer surgery patients from September 2016 to September 2021. The primary outcome was BCRL diagnosis. Secondary outcomes included BCRL risk factors, and physical and occupational therapy (PT/OT)referral frequency and completion. Of 639 patients, 17% of patients had documented BCRL, which was significantly associated with axillary lymph node dissection (ALND).There were no racial, insurance, breast radiation, or BMI category differences between patients with and without a BCRL diagnosis. Of those with BCRL, 58% received a PT/OTreferral, and 56% completed their referral. There were no racial, insurance, or BMI category differences between those who received a PT/OTreferral and those who did not. In our high-risk population, rates of documented BCRL were higher than expected, approaching rates of ALND, despite the majority undergoing sentinel lymph node biopsy (SLNB).PT/OTreferral and completion rates were low, concerning for an unmet BCRL treatment and education need. No disparities in care delivery were seen, but a program that addresses treatment gaps and ensures accessible and patient-centered BCRL care is urgently needed.

Characterization of DNA damage repair mutations in lung cancer in an underserved urban patient population.

e20058 Background: DNA-damage repair (DDR) mutations in lung cancer have shown potential for use as therapeutic targets and predictive biomarkers. However, the relationship between DDR mutations and socioeconomic factors remains largely unknown. In this study, we characterize the distribution of DDR gene mutations in lung cancer patients at a single academic medical center serving a unique population consisting of primarily underserved, underinsured patients. Methods: All lung cancer patients seen at the University of Illinois Chicago (UIC) between 11/2/2015 and 9/11/2023 with TEMPUS genetic data available were included in the study (N = 235). DDR mutations of interest were ARID1A, ATM, ATR, ATRX, BAP1, BRCA1, BRCA2, BRIP1, CHEK2, ERCC3, FANCA, FANCC, MLH3, MRE11, MSH6, MUTYH, NBN, PALB2, and RAD50. The population was subdivided into a DDR mutation positive group and a DDR mutation negative group. Each group was compared with respect to lung cancer type, tumor mutational burden (TMB), and socioeconomic factors including sex, race, insurance category, and zip code. Results: The overall frequency of DDR mutations in the population was 28.5% (n = 67). The most common DDR mutations were ARID1A (20.7%), CHEK2 (12.8%), ATM (9.9%), BRCA2 (9.9%), ATRX (7.9%), and MUTYH (7.9%). The histological distribution of the DDR mutation positive group was adenocarcinoma (76.1%), squamous cell carcinoma (4.4%), adenosquamous carcinoma (2.9%), neuroendocrine carcinoma (5.9%), and other (10.4%). The histological distribution of the DDR negative group was adenocarcinoma (60.1%), squamous cell carcinoma (22.0%), adenosquamous carcinoma (1.1%), neuroendocrine carcinoma (4.1%), and other (12.5%). There was a significant effect in TMB, t(55) = 2.97, p = 0.004, with a higher mean TMB in the DDR positive group (M = 14.4, SD = 11.1) compared to the DDR negative group (M = 9.4, SD = 5.1). The most common zip codes in the DDR positive groupwere 60608 (9.0%), 60629 (9.0%), and 60616 (7.5%) compared to 60616 (8.3%), 60609 (5.3%), and 60624 (4.7%) in the DDR negative group. Demographic factors of the two groups are compared below. Conclusions: The frequency and distribution of DDR mutations in lung cancer at UIC appears similar to what has been previously reported in the literature. There is a higher tumor mutational burden in lung cancers with DDR mutations, which may suggest greater susceptibility to immunotherapy. There may be a correlation between the presence of DDR mutations and lung cancer type, insurance category, and patient geographic distribution. Future work will include obtaining further geographic data for the study population such as average income, pollution exposure, and smoking rate by neighborhood. [Table: see text]

Refining the 30-day emergency department visit after initial chemotherapy administration A-PROACCT model.

e23253 Background: This study attempted to address limitations in the A-PROACCT model developed by Stein et al. (JOP, 2023) to predict the rate of acute care events within 30 days (ACE30) of initial chemotherapy administration (ICA). The four most important predictors were drug category (immunotherapy), cancer type (gastrointestinal), age group ( &lt; 40), and BMI category (underweight). Only linear relationships were considered. For patients for whom an ACE30 was predicted by their first and second models, only 0.18 and 0.23 actually had an ACE30, a low positive predictive value (PPV). We attempted to improve the PPV with advanced modeling techniques. Methods: Using Orlando Health data between February of 2012 and April 2021, random sampling was used to split the dataset into training (10,838) and validation (4645). For the training data, resampling was employed to obtain a balanced quota, equal observations followed by an ACE30 and not followed by an ACE30. We included only variables used in Stein (drug category, age group, ED visits and hospitalizations in the prior year, cancer type, insurance category, number of anti-cancer agents, race, and BMI category). Logistic regression with L1-penalty, XGBoost (a non-linear nonparametric tree-based algorithm to account for nonlinear relationships), and artificial neural networks were used to develop and evaluate predictive models. Different sampling methods (bootstrap and SMOTE) as well as cuff-off thresholds for high-risk groups were tested. Results: Based on evaluation with validation data set, the best performing approach was an XGBoost model with SMOTE resampling in the training data. The four most important predictors were ED visits in the prior year, payor category (self-insured), cancer type (genitourinary) and hospitalization in past year. This model reported a PPV of 0.27. Many other combinations of methodologies described above were performed, and the PPV varied between 0.19 to 0.27. Of the 282 ICAs identified as high-risk by the best model, 76 (27.0%) had an ACE30. Of the 4346 ICAs identified as low-risk, 533 (12.3%) had an ACE30. The differences between high risk and low risk ACE30 rates were statistically significant (p &lt; 0.0001). In comparison, using Stein et al. models with our data reported a PPV of 0.27 and 0.25 which are basically at the same level of our best trained model. Conclusions: Using the variables included in A-PROACCT and a variety of machine learning models, advanced sampling methodologies and threshold cut-off limits, the best results were similar to those obtained using basic logistic regression. This suggests improvement in predicting acute care events following chemotherapy administration need to incorporate more extensive multimodal data, such as vital signs, performance status, patient reported outcomes, socioeconomic factors, laboratory and radiology results, remote patient monitoring and wearable device data.

Independent validation of 30-day emergency department visit after initial chemotherapy A-PROACCT model.

e23243 Background: This study assessed model performance and, more importantly, the clinical usefulness of the PROACCT model created by Grant et al. (JAMA, 2019), refined as A-PROACCT by Stein et al. (JOP, 2023) with data from Orlando Health. The predictive performance of models anticipating acute care events within thirty days (ACE30) of initial chemotherapy administration (ICA) were evaluated. Methods: Stein et al. models classified high-risk patients as those whose model output risk score was at or above the 90th percentile. They studied 5153 ICAs between July and November of 2021, of which 559 (10.8%) had an ACE30. Their first model used drug category, age group, ED visits and hospitalizations in the prior year, and cancer type, and had sensitivity (Sen), specificity, (Spec), C-Statistic (C-Stat), and positive predictive value (PPV) of 0.28, 0.93, 0.79 and 0.18 respectively. Their second model added insurance category, number of anti-cancer agents, race, and BMI category and reported Sen, Spec, C-Stat, and PPV of 0.30, 0.94, 0.80 and 0.23 respectively. Of the high-risk patients, 33.6% and 34.1% had an ACE30 respectively. Of the low-risk patients, 8.3% and 8.1% had an ACE30 respectively. Our data were propagated through the two models. Results: Between February of 2012 and April 2021, Orlando Health had 15,483 distinct patient visits with ICAs. Overall, 2046 (13.2%) had an ACE30. Model one had Sen, Spec, C-Stat, and PPV of 0.19, 0.92, 0.65 and 0.27 respectively. Model two had Sen, Spec, C-Stat, and PPV of 0.19, 0.91, 0.65 and 0.25 respectively. Both models had an accuracy of 0.82. This demonstrated our data fit the Stein model. Of the 1460 ICAs identified as high-risk by model one, 387 (26.5%) had an ACE30, and of the 14,023 ICAs identified as low-risk, 1659 (11.8%) had an ACE30. Of the 1549 ICAs identified as high-risk by model two, 393 (25.4%) had an ACE30, and of the 13,934 ICAs identified as low-risk, 1653 (11.9%) had an ACE30. The difference of ACE30 rates between high risk and low risk groups were statistically significant (p &lt; 0.0001) in both models. This demonstrated both models were clinically useful in identifying chemotherapy patients at high risk of acute care events. Conclusions: These models allowed for clinically relevant stratification of patients into meaningful high-risk and low-risk groups (26.5% vs 11.8% with our data) for risk of acute care events within 30 days of initial chemotherapy administration. Nevertheless, only 26.5% of these high-risk patients actually had an acute care event within thirty days of initial chemotherapy administration. Further research is needed to improve the precision of identifying high-risk patients, by potentially adding more clinical variables, using alternative sampling methods, and employing nonlinear and more complex machine learning methodologies.

Machine Learning Approaches for Insurance Pricing: A Case Study of Public Liability Coverage in Morocco

As data-driven technologies continue to advance, the importance of machine learning (ML) techniques in enhancing the accuracy of insurance premium calculations cannot be overstated. That is especially crucial because traditional actuarial methods often fail to incorporate individual risk factors fully. This limitation significantly impacts the insurance industry's capacity to accurately determine premium prices, which in turn affects financial stability and customer satisfaction. This research seeks to assess the efficiency insurance premium calculations through different regression models in ML, including polynomial, decision tree, random forest, and gradient boosting. The study employs rigorous analysis techniques using a comprehensive dataset from a Moroccan vehicle insurance company, including claim history and insurance categories. The dataset is split into training and testing sets to assess the accuracy of the ML models using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R-squared). Initial findings suggest that ML models greatly surpass traditional actuarial methods, indicating the potential for machine learning to transform premium pricing strategies. That could result in more customized and financially sustainable outcomes within the insurance industry. The findings of this paper are likely to contribute to national insurance premium policy and expand the existing literature on this subject in Morocco.

Social Deprivation Index Affects Time to MRI after knee injury in pediatric patients and is predicted by patient demographics

ABSTRACT Objectives This study aims to characterize the association between the timing of MRI ordering and completion for pediatric knee injuries and Social Deprivation Index (SDI), which is a comprehensive, validated, county-level, measure of socioeconomic variation in health outcomes based upon combining geography, income, education, employment, housing, household characteristics, and access to transportation. Methods A retrospective chart review was completed of patients 21 years old and younger from our institution with a history of knee sports injury (ligamentous/soft tissue injury, structural abnormality, instability, inflammation) evaluated with MRI between 5/26/2017 and 12/28/2020. Patients were from three states and attended to by physicians associated with an urban academic institution. Patients were assigned SDI scores based on their ZIP code. Excluded from the study were patients with a non-knee related diagnosis (hip, foot, or ankle), patients from ZIP codes with unknown SDI, and non-sports medicine diagnoses (tumor, infection, fracture). Results In a multivariate regression analysis of 355 patients, increased SDI was independently associated with increased time from clinic visit to MRI order (p = 0.044) and from clinic visit to MRI completion (p = 0.047). Each 10-point increase in SDI (0-100) was associated with a delay of 7.2 days on average. SDI itself was found to be associated with a patient’s race (p < 0.001), ethnicity (p < 0.001), and insurance category (p < 0.001). Conclusion Increased SDI is independently associated with longer time from clinic visit to knee MRI order and longer time from clinic visit to knee MRI completion in our pediatric population. Recognizing potential barriers to orthopedic care can help create the change necessary to provide the best possible care for all individual patients.

Clinician Risk Tolerance and Rates of Admission From the Emergency Department

Much remains unknown about the extent of and factors that influence clinician-level variation in rates of admission from the emergency department (ED). In particular, emergency clinician risk tolerance is a potentially important attribute, but it is not well defined in terms of its association with the decision to admit. To further characterize this variation in rates of admission from the ED and to determine whether clinician risk attitudes are associated with the propensity to admit. In this observational cohort study, data were analyzed from the Massachusetts All Payer Claims Database to identify all ED visits from October 2015 through December 2017 with any form of commercial insurance or Medicaid. ED visits were then linked to treating clinicians and their risk tolerance scores obtained in a separate statewide survey to examine the association between risk tolerance and the decision to admit. Statistical analysis was performed from 2022 to 2023. The ratio between observed and projected admission rates was computed, controlling for hospital, and then plotted against the projected admission rates to find the extent of variation. Pearson correlation coefficients were then used to examine the association between the mean projected rate of admission and the difference between actual and projected rates of admission. The consistency of clinician admission practices across a range of the most common conditions resulting in admission were then assessed to understand whether admission decisions were consistent across different conditions. Finally, an assessment was made as to whether the extent of deviation from the expected admission rates at an individual level was associated with clinician risk tolerance. The study sample included 392 676 ED visits seen by 691 emergency clinicians. Among patients seen for ED visits, 221 077 (56.3%) were female, and 236 783 (60.3%) were 45 years of age or older; 178 890 visits (46.5%) were for patients insured by Medicaid, 96 947 (25.2%) were for those with commercial insurance, 71 171 (18.5%) were Medicare Part B or Medicare Advantage, and the remaining 37 702 (9.8%) were other insurance category. Of the 691 clinicians, 429 (62.6%) were male; mean (SD) age was 46.5 (9.8) years; and 72 (10.4%) were Asian, 13 (1.9%) were Black, 577 (83.5%) were White, and 29 (4.2%) were other race. Admission rates across the clinicians included ranged from 36.3% at the 25th percentile to 48.0% at the 75th percentile (median, 42.1%). Overall, there was substantial variation in admission rates across clinicians; physicians were just as likely to overadmit or underadmit across the range of projected rates of admission (Pearson correlation coefficient, 0.046 [P = .23]). There also was weak consistency in admission rates across the most common clinical conditions, with intraclass correlations ranging from 0.09 (95% CI, 0.02-0.17) for genitourinary/syncope to 0.48 (95% CI, 0.42-0.53) for cardiac/syncope. Greater clinician risk tolerance (as measured by the Risk Tolerance Scale) was associated with a statistically significant tendency to admit less than the projected admission rate (coefficient, -0.09 [P = .04]). The other scales studied revealed no significant associations. In this cohort study of ED visits from Massachusetts, there was statistically significant variation between ED clinicians in admission rates and little consistency in admission tendencies across different conditions. Admission tendencies were minimally associated with clinician innate risk tolerance as assessed by this study's measures; further research relying on a broad range of measures of risk tolerance is needed to better understand the role of clinician attitudes toward risk in explaining practice patterns and to identify additional factors that may be associated with variation at the clinician level.

СТАН ТА ЦИФРОВІ ВЕКТОРИ РОЗВИТКУ РИНКУ СТРАХУВАННЯ УКРАЇНИ

The article examines the current state and digital vectors of the development of the insurance market of Ukraine and identifies promising directions for the digitalization of insurance. The essence of the insurance category is revealed. It is noted that the insurance market in Ukraine lags far behind developed countries. An analysis of the dynamics of the number of participants in the insurance market, the amount of assets of insurance companies, the structure of the insurance portfolio by types of insurance was carried out, and the main trends of the insurance market in Ukraine were revealed. The keys to the stable development of insurance activity in Ukraine have been defined as: quick response to new conditions and customer requests; the ability to adapt, accumulate positive experience, analyze and correct negative moments; develop harmoniously, find a balance between the accumulation of new knowledge and their practical application. Factors influencing and functioning of the domestic insurance market are determined. The features of modern trends in digital insurance tools, as an important mechanism for the growth of the insurance market, are revealed. It was found InsurTech that modern technologies are important for insurance companies. These InsurTech nclude artificial intelligence technologies, new inventions in the field of cyber security, big data analysis («bigdata), blockchain technologies, the Internet of Things (LoT ) and smartphone applications.

Association of Socio-Demographic Factors, Social Determinants of Health, and Weekly Physical Activity in an Urban Hospital in Northwest Indiana

Background: Engaging in regular physical activity has been proven to have beneficial health effects such as preventing chronic diseases and improving mental health. Recent studies have demonstrated correlations between socio-demographic factors and physical activity levels. This study determined the associations between socio-demographic factors, social determinants of health and the amount of weekly physical activity in patients occupying an urban underserved area.&#x0D; Methods: This study retrospectively analyzed a dataset generated by St. Mary Medical Center from EPIC™ with demographic characteristics and physical activity levels partitioned by time per week for adult inpatient visits from January 2021 to March 2023. Patients were stratified into physical activity levels based upon published guidelines: inactive (no physical activity), insufficiently active (&lt;150 minutes per week) or sufficiently active (≥150 minutes per week). Data analysis was conducted in SPSS 28.0 using tests of association including Kruskal Wallis H and multivariate ordinal regression model. This study was exempted by Indiana University Human Research Protection Program (IRB # 14040).&#x0D; Results: The sample of individuals from the dataset who answered physical activity questions was comprised of 1498 patients. There was a statistically significant difference in physical activity level by age group (p&lt;0.001), sex (p&lt;0.05), insurance category (p&lt;0.001), and social connections risk score (p&lt;0.001); with race (p=0.057) and language (p=0.054) approaching significance. Multivariate analysis showed that age was the only significant factor when accounting for all variables, with higher age groups reporting lower proportions of physically active individuals.&#x0D; Conclusion: Determining how socio-demographic factors influence physical activity levels will direct efforts to form and implement new interventions in the Northwest Indiana urban area and support community health initiatives. This data makes it possible to inform practitioners of the demographics that are at risk of being insufficiently active and having them direct those patients to programs in place to help bridge the lapse.

FİNTECH İNOVASYON DALGASI FİNANSAL PİYASALARI, BANKACILIK SEKTÖRÜNÜ VE MÜŞTERİ DAVRANIŞINI NASIL ETKİLER?

This study investigate FinTech’s on financial markets, the banking sector, and consumers. It aims to examine the role and effects FinTech in the presentation and use of financial services. The study focuses on FinTech investment areas in countries such as America, Canada, Brazil, Germany, France, Israel, China, and India, which are prominent in FinTech investments, between 2012 and 2020, using the panel data method and fixed effects model. FinTech investments are grouped according to payment management, insurance, information technologies, software, financial services, and other categories, and the relationships between them have been empirically tested. The FinTech investment amount was used as the dependent variable Inflation, number of branches, unemployment, and GDP were considered as independent variables. The results show that the increase in FinTech investment is affected by inflation and the number of branches in a negative and statistically significant manner. However, the results concluded that the individual Internet usage variable positively affected the FinTech investment amount. These findings provide strong empirical evidence that FinTech investments can increase profitability levels in the finance and banking sectors. This study highlights the impact of FinTech on the transformation process in the financial sector, and it can offer valuable insights for financial service providers and policymakers. It may also be essential for understanding consumers' demands and expectations for financial technologies. Such studies can offer a valuable roadmap for understanding

Socioeconomic determinants impact quality of life at vestibular schwannoma diagnosis

BackgroundSocioeconomic variables including race, education, and income have been shown to affect vestibular schwannoma incidence, treatment, and outcomes. We sought to determine the impact of socioeconomic factors on quality of life at the time of vestibular schwannoma diagnosis. MethodsRetrospective cohort study conducted at a tertiary academic center. All patients evaluated for vestibular schwannoma from March 1, 2010 to December 31, 2021 who completed at least one Penn Acoustic Neuroma Quality of Life (PANQOL) questionnaire at presentation or prior to any intervention were included. PANQOL scores were compared across income quintiles, racial groups, and health insurance categories. ResultsTwo-hundred and ninety-six patients who had non-missing information on variables of interest were included. Compared to White/Caucasian patients (84.5 %), Black/African American patients (4.7 %) had significantly lower PANQOL total scores (b = -12.8[-21.7, −4.0], p = 0.005). Compared to patients with Commercial insurance (53 %), patients who were Uninsured/ Self-pay (1.7 %) had significantly lower PANQOL total scores (b = -16.7[-31.4, −1.9], p = 0.027). Patients in higher income quintiles had significantly higher PANQOL total scores (b = 11.7[3.9, 19.5], p = 0.004 comparing highest income quintile to lowest). After controlling for potential confounders, income quintile (b = 9.6[1.3, 17.9], p = 0.023 comparing highest quintile to lowest) and insurance (b = -17.0[-31.9, −21], p = 0.025 comparing Uninsured/Self-pay to Commercial insurance) remained predictors of total PANQOL score. ConclusionsSocioeconomic factors including race, health insurance, and income appear to contribute to quality of life at the time of vestibular schwannoma diagnosis. These variables are interrelated and the effects of race may be mediated in part by differences in income and health insurance coverage.

An Analytical Data Model to Improve Benefits of the Comprehensive Health Insurance System By Data Mining Techniques

Health insurance represents a crucial safeguard against the myriad risks associated with individual health conditions, covering expenses related to examinations, diagnosis and treatment, as well as providing psychological and physical support. Until mid-2019, Egypt implemented a social health insurance system, and then moved to a renewed framework known as the comprehensive health insurance system in the latter half of 2019. The research at hand focuses on exploring a new mechanism through develop a new model, it can be help the General Authority for Health Insurance to attainment of maximum benefits, whether in terms of individuals medical services or the enhancement of the nation's overall income return.&#x0D; The researcher depends on using the data mining technique to conduct specialized analyzes of big data for the new model, to offer the numerous advantages, including empowering decision-makers to make accurate decisions based on reliable information and proficient data analysis, Based on the new model for the health insurance system that was prepared and implemented using the Weka program, version 3.8.6, through the classifier scheme (classifiers.trees.J48 algorithm), the applied pattern was used:&#x0D; (Scheme:weka.classifiers.misc.InputMappedClassifier -I -trim -Wweka.classifiers.trees.J48 -- -C 0.25 -M 2), Through the Weka program, a new model was designed that contains two parts. The first part is to classify the insured into insurance categories according to their monthly salaries, and the second part is to predict the classification and distribution of employees among insurance packages according to the new health insurance model. The model was prepared using Weka.classifiers.trees.J48. software to analysis the dataset of 2781 employees.&#x0D; One of the significant outcomes that the researcher emphasizes from implementing the new model is the attainment of maximum benefits, whether in terms of individual medical services or the enhancement of the nation's overall income return. Under the existing health insurance law in Egypt, the monthly subscription fee is a flat 1% for all employees, without accounting for other factors. The results of applying the new model showed the effectiveness of the model through a comparison between the current subscription fees and the new health insurance model, developed using the same dataset of 2781 employees, reveals that the total monthly subscription fees under the current health insurance system amount to 153057.97 Egyptian Pounds (L.E.), whereas the total subscription fees under the new health insurance system model reach 365998.91 L.E., The financial benefits realized from the new model amount to 212940.94 L.E., representing a percentage increase of up to 139.12%. This demonstrates a considerable improvement in financial outcomes, and the potential advantages of transitioning to the new health insurance model.

Retrospective Observational Study on Real-World Bortezomib Prescribing Patterns and Outcomes in Newly Diagnosed Multiple Myeloma

Introduction: Most induction regimens for multiple myeloma (MM) contain bortezomib. In most clinical trials, bortezomib has been prescribed either intravenously or subcutaneously twice per week in 21-day or 28-day cycles. However, several retrospective single-center studies have shown that once-weekly bortezomib has comparable efficacy and less peripheral neuropathy compared to twice-weekly bortezomib ( Sidana et al, PLoS One 2017; Cook et al, AJH 2021). These findings have also been confirmed by a secondary analysis of data from three prospective Phase 3 randomized studies ( Mateos et al, Haematologica 2014). As such, many clinicians have adopted once-weekly bortezomib regimens in routine care in the past decade. However, the real-world (RW) prevalence and efficacy of once-weekly vs twice-weekly bortezomib regimens in a larger population have not yet been characterized. Methods: This study used US nationwide longitudinal Flatiron Health electronic health record-derived de-identified data. We included patients aged 18-70 years with newly diagnosed MM between 2017-2022 with ≥ 6 months of follow-up and ≥ 2 clinical visits who were treated with a first-line (1L) bortezomib-containing regimen. Patients aged &amp;gt;70 were excluded a priori given the likelihood of altered practice patterns and more frequent comorbidities. Twice-weekly bortezomib was defined as patients for whom the most frequent interval between two doses of 1L bortezomib within a cycle was 3.0-4.9 days, while once-weekly bortezomib was defined as a corresponding interval of 5.0-9.9 days. We used multivariable logistic regression to assess demographic and clinical factors associated with once-weekly vs twice-weekly (reference) bortezomib. RW progression free survival (rwPFS) and RW overall survival (rwOS) from 1L treatment initiation were estimated using the Kaplan-Meier method. Outcomes were compared using Cox proportional hazards model including transplant as a time-varying covariate and adjustment for other demographic and clinical factors. Results: Of 2,522 patients included, 927 (36.8%) received twice-weekly while 1,522 (63.2%) received once-weekly bortezomib. Approximately half of patients (50.3%) were non-Latinx White, 21.7% non-Latinx Black, and 8.0% Latinx. Most patients were treated in the community setting (78.8%). Rates of once-weekly bortezomib administration were 66.2% and 62.1% among patients treated at academic and community practices, respectively. The frequency of weekly bortezomib use increased over time, from 58.0% between 2017-2019 to 68.8% between 2020-2022. In multivariate analysis, ECOG PS ≥ 2 (odds ratio [OR] 1.50; [95% CI 1.14 to 1.99] vs ECOG PS 0), and more recent year of diagnosis (OR 1.14; [95% CI 1.08 to 1.20] per year) were associated with higher odds of receiving once-weekly bortezomib. Conversely, Latinx patients vs non-Latinx White (OR 0.60 [95% CI 0.44 to 0.82]) and patients with ≥ 2 high-risk cytogenetic abnormalities (HRCA) vs 0 HRCA (OR 0.66 [95% CI 0.48 to 0.91]) had lower odds of receiving once-weekly bortezomib administration. No significant associations were found between gender, ISS stage, practice setting, insurance category, or baseline creatinine. There was no statistically significant difference in rwPFS among patients receiving once-weekly (median 37.3 months, 95% CI 33.8 to 42.5) vs twice-weekly bortezomib (median 39.2 months, 95% CI 32.9 to 42.8) (HR 0.87 [95% CI 0.77 to 1.00]) (Figure 1A). Similarly, there was no statistically significant difference in rwOS with median 25.5 months follow-up (Figure 1B). Median rwOS was not reached in either group (hazard ratio 0.89 [95% CI 0.75 to 1.07]). Discussion: We evaluated US bortezomib prescribing patterns for the first time in the modern era using a large nationwide RW-study. Whereas prescribing patterns have shifted towards once-weekly bortezomib over time, over 30% of patients diagnosed in the last three years received twice-weekly bortezomib. Importantly, once-weekly and twice-weekly bortezomib performed comparably with regard to rwPFS and rwOS. While our study did not analyze neuropathy or patient quality of life, once-weekly bortezomib is known to be associated with less neuropathy and less time in clinic. These data further support the conclusion that once-weekly bortezomib should be considered the standard of care for newly diagnosed patients with MM.

Cancer stage at diagnosis: Comparison of insurance status in SEER to the Department of Defense Cancer Registry.

Military individuals, retirees, and their families have free care or minimal out-of-pocket costs in the US military health system (MHS). In contrast, out-of-pocket costs in the US general population vary substantially. This study compared cancer patients with various insurance types in the general population to those in the MHS in cancer stage at diagnosis. Patients were identified from the US Department of Defense's (DoD) Automated Central Tumor Registry (ACTUR) and the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program. Tumor stage at diagnosis of breast, prostate, lung, and colon cancers during 2007-2013 was compared between ACTUR and SEER insurance categories of "insured," "insured-no specifics," "any Medicaid," and "uninsured," A multivariable logistic regression analysis estimated the odds ratio (OR) of late stage (Stages III and IV) versus early stage (Stages I and II) cancers comparing SEER insurance status to ACTUR. There were 18,440 eligible patients identified from ACTUR and 831,959 patients identified from SEER. For all cancer types, patients in the SEER-insured/no specifics, Medicaid, and uninsured groups had significantly greater likelihood of late stage diagnosis compared to ACTUR patients. The adjusted ORs were greatest among uninsured and Medicaid patients. The SEER-insured group also had a significantly higher odds of advanced stage disease than ACTUR patients for prostate cancer and lung cancer. Patients in the MHS with universal access to healthcare were diagnosed at an earlier stage than those in the general population. This difference was most evident compared to Medicaid and uninsured groups.

Factors influencing spiritual wellbeing among pancreatic ductal adenocarcinoma patients receiving chemotherapy.

Spiritual wellbeing emphasizes optimistic and positive attitudes while self-regulating negative emotions when coping with stress. However, there have only been a few small studies of spiritual wellbeing of pancreatic ductal adenocarcinoma (PDAC) patients undergoing chemotherapy. The core factors influencing spiritual wellbeing in this clinical population are still unclear. To identify factors influencing spiritual wellbeing among patients with PDAC receiving chemotherapy. A total of 143 PDAC patients receiving chemotherapy were enrolled from January to December 2022. Patients completed general information questionnaires including: Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being 12 Item Scale (FACIT-Sp-12), European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) and Zung's Self-rating Anxiety Scale (SAS). Independent sample t-test, one-way analysis of variance, Pearson's correlation analysis, and multiple linear regression analysis were adopted for statistical analyses. P < 0.05 (two-tailed) was considered statistically significant for all tests. Total spiritual wellbeing (FACIT-Sp-12) score was 32.16 ± 10.06 points, while dimension sub-scores were 10.85 ± 3.76 for faith, 10.55 ± 3.42 for meaning, and 10.76 ± 4.00 for peace. Total spiritual wellbeing score was negatively correlated with SAS score for anxiety and with the symptom domain of EORTC QLC-C30. Conversely, spiritual wellbeing score was positively correlated with global health status and EORTC QLQ-C30 role functioning domain score. Multivariate regression analysis identified educational level, health insurance category, symptom domain, functional role domain, and global health status as significant independent factors influencing spiritual wellbeing among PDAC patients undergoing chemotherapy (R2 = 0.502, P < 0.05). Individualized spiritual support is needed for PDAC patients. Health, daily functioning, emotional, cognitive, and social function status should be taken into account to promote implementation of spirituality in nursing practice.