Jongwha Chang

Introduction: Digital health techniques were adopted faster during COVID-19, but the gap remains. This study analyzes how the digital gap affected pandemic patient portal uptake during and after. Patient portals improve physician connections and patient health information access, increasing health outcomes. Digital divide—lack of internet, digital literacy, and SDOH—may inhibit fair adoption. One study found that the internet and IT affected people during and after the pandemic. Learn why this patient demographic uses online patient portals so much. Like finding demographic, socioeconomic, and behavioral characteristics that affect portal usage, specifically drivers and barriers for health-conscious patients. This project developed a health promotion model and implemented initiatives to increase patient portal use and reduce health inequities. Methods: In 2020 and 2022, we used Health Information National Trends Survey (HINTS) data. Patient portal use dominated. Our cross-sectional study assessed patient portal use and various predictors. Age, marital status, sex, mental health, education, income, urban/rural (urbanity), family size, trust, social media use, chronic condition, and health app use predict. We handled survey data collection biases with weighted analyses. Data was computed using survey weights for population representation. Logistic regression and weighted descriptive statistics utilized STATA SE 16.1. Results: Demographic, socioeconomic, and behavioral characteristics significantly affect online patient portal use. Portal usage is positively correlated with health app use, urbanity, higher income, sex, Asian/Native American race/ethnicity, and hospital trust. In addition, our data shows low adoption rates across the digital divide. Patients who trust others utilize patient portals, and previously established characteristics affect self-motivation for health improvement. Conclusion: Access obstacles and behavioral factors affect online patient portal adoption. Addressing the digital divide is crucial, but trust and self-motivation may improve patient portal utilization. Adopting health IT can improve patient participation and access to care, supporting equitable health outcomes.

Introduction: Individuals with gender dysphoria do not identify with their sex assigned at birth and face societal and cultural challenges, leading to increased risk for depression, anxiety, and suicide. Gender dysphoria is a DSM-5 diagnosis but is not necessary for transition therapy. Additionally, individuals with gender dysphoria or who identify as gender diverse/nonconforming may experience “minority stress” from increased discrimination, leading to a greater risk for mental health problems. This study aimed to identify possible health disparities in patients hospitalized for depression with gender dysphoria across the United States. Depression was selected because patients with gender dysphoria are at an increased risk for it. Various patient and hospital-related factors are explored for their association with changes in healthcare utilization for patients hospitalized with depression.Methods: The National Inpatient Sample was used to identify nationwide patients with depression (n = 378,552, weighted n = 1,892,760) from 2016 to 2019. We then examined the characteristics of the study sample and investigated how individuals’ gender dysphoria was associated with healthcare utilization measured by hospital cost per stay. Multivariate survey regression models were used to identify predictors.Results: Among the 1,892,760 total depression inpatient samples, 14,145 (0.7%) patients had gender dysphoria (per ICD-10 codes). Over the study periods, depression inpatients with gender dysphoria increased, but total depression inpatient rates remained stable. Survey regression results suggested that gender dysphoria, minority ethnicity or race, female sex assigned at birth, older ages, and specific hospital regions were associated with higher hospital cost per stay than their reference groups. Sub-group analysis showed that the trend was similar in most racial and regional groups.Conclusion: Differences in hospital cost per stay for depression inpatients with gender dysphoria exemplify how this community has been disproportionally affected by racial and regional biases, insurance denials, and economic disadvantages. Financial concerns can stop individuals from accessing gender-affirming care and risk more significant mental health problems. Increased complexity and comorbidity are associated with hospital cost per stay and add to the cycle.

Objective: The clinical aspects of lung cancer patients are well-studied. However, healthcare charge patterns have yet to be explored through a large-scale representative population-based sample investigating differences by socioeconomic factors and comorbidities. Aim: To identify how comorbidities associated with healthcare charges among lung cancer patients Methods: We examined the characteristics of the patient sample and the association between comorbidity status (diabetes, hypertension, or both) and healthcare charge. Multivariate survey linear regression models were used to estimate the association. We also investigated sub-group association through various patient and socioeconomic factors. Results: Of 212,745 lung cancer patients, 68.5% had diabetes and/or hypertension. Hospital charges were higher in the population with comorbidities. The results showed that lung cancer patients with comorbidities had 9.4%, 5.1%, and 12.0% (with diabetes, hypertension, and both, respectively) higher hospital charges than those without comorbidities. In sub-group analysis, Black patients also showed a similar trend across socioeconomic (i.e. household income and primary payer) and racial (i.e. White, Black, Hispanic, and Asian/Pacific Islander) factors. Discussion: Black patients may be significantly financially burdened because of the prevalence of comorbidities and low-income status. More work is required to ensure healthcare equality and promote access to care for the uninsured, low-income, and minority populations because comorbidities common in these populations can create more significant financial barriers.

Hypertension is so prevalent and requires strict adherence to medications to prevent further disease or death, but there is no study examining factors related to prescription drug non-adherence among 65 years old and older. This study aims to assess the likelihood of medication nonadherence among patients based on factors such as age, race, and socioeconomic status, with the goal of identifying strategies to enhance medication adherence and mitigate associated health risks. Using the 2020 Medicare Current Beneficiary Survey Public Use File to represent nationwide Medicare beneficiaries (unweighted n = 3917, weighted n = 27,134,782), medication non-adherence was related to multiple independent variables (i.e., age, sex, race/ethnicity, socioeconomic status, comorbidities, insurance coverage, and satisfaction with insurance). Cross-tabulations and Wald chi-square tests were used to determine how much each variable was related to non-adherence. Multivariate logistic regression was used to examine the association between medication non-adherence and factors such as prescription drug coverage satisfaction and cost-reducing behavior. Specific trends in medication non-adherence emerged among beneficiaries. Non-adherence was higher in older adults aged 65- to 74-year-olds and those with more chronic conditions (OR = 2.24; 95% CI = 1.74–2.89). If patients were dissatisfied with the medications on the insurance formulary or struggled to find a pharmacy that accepted their medication coverage, they had worse adherence (OR = 2.63; 95% CI = 1.80–3.84). Formulary and coverage must be expanded to improve adherence to antihypertensive medications in Medicare beneficiaries. Older adults aged 65 to 74 years may be less adherent to their medications because they do not see the seriousness of the disease and could benefit from further counseling. Patients with limited activities of daily living and more comorbidities may struggle with complex treatment regimens and should use adherence assistance tools.

Objective: The aim of the study is to identify vulnerable populations at risk of developing decubitus ulcers and their resultant increase in healthcare utilization to promote the use of early prevention methods. Methods: The National Inpatient Sample of the United States was used to identify hospitalized patients across the country who had a length of stay of 5 or more days (N = 9,757,245, weighted N = 48,786,216) from 2016 to 2020. We examined the characteristics of the entire inpatient sample based on the presence of decubitus ulcers, temporal trends, risk of decubitus ulcer development, and its association with healthcare utilization, measured by discounted hospital charges and length of stay. The multivariate survey logistic regression model was used to identify predictors for decubitus ulcer occurrence, and the survey linear regression model was used to measure how decubitus ulcers are associated with healthcare utilization. Results: Among 48,786,216 nationwide inpatients, 3.9% had decubitus ulcers. The percentage of inpatients with decubitus ulcers who subsequently experienced increased healthcare utilization rose with time. The survey logistic regression results indicate that patients who were Black, older, male, or those reliant on Medicare/Medicaid had a statistically significant increased risk of decubitus ulcers. The survey linear regression results demonstrate that inpatients with decubitus ulcers were associated with increased hospital charges and longer lengths of stay. Conclusions: Patients with government insurance, those of minority races and ethnicities, and those treated in the Northeast and West may be more vulnerable to pressure ulcers and subsequent increased healthcare utilization. Implementation of early prevention methods in these populations is necessary to minimize the risk of developing decubitus ulcers, even if upfront costs may be increased. For example, larger hospitals were found to have a lower risk of decubitus ulcer development but an increased cost of preventative care. Hence, it is imperative to explore and use universal, targeted preventative methods to improve patient safety.

Objective: The study utilized a cross-sectional dataset to identify demographic and health factors associated with patient utilization of mHealth applications for engaging with healthcare providers. The focus was on adults with chronic health conditions as the primary app user group. The goal was to reveal specific barriers and facilitators to app adoption among smartphone users, with the aim of highlighting opportunities for upgrades that promote patient empowerment as a prerequisite for shared decision-making (SDM). Methods: Data from the Health Information National Trends Survey (HINTS 5, Cycle 4, 2020) with 3865 respondents (≥ 18 years old) stratified analyses and weighted logistic regression were used. Results: The study found that individuals having a wellness app on a smartphone increased the likelihood (OR 2.68, CI: 2.02– 3.56, p-value < 0.0001) of discussing health conditions with providers. Furthermore, individuals with multiple chronic health conditions were more likely (OR 1.93, CI 1.26– 2.95, p-value < 0.01) to use apps to use mobile health applications to engage with healthcare providers. Other significant variables affecting app usage such as race, marital status, and educational level. Conclusion: Due to difficulties obtaining in-person healthcare, the COVID-19 epidemic forced a swift deployment of mHealth technologies. Even in the absence of a crisis, mobile health applications continue to be crucial for improving patient-provider engagement and developing novel approaches to healthcare delivery. During the pandemic, people with numerous chronic diseases used apps to stay in touch with doctors and maintain their reliance on these platforms. Nonetheless, different smartphone users continue to use mHealth application in different ways. The findings revealing barriers in mHealth app adoption among certain patient subgroups suggest opportunities for developers, in collaboration with users and providers, to enhance inclusion and acceptability when upgrading mHealth application platforms.

Objective: The primary objective of this study is to conduct a comparative analysis of telemedicine utilization patterns among adult populations residing in both rural and urban areas and evaluate the probability of telemedicine adoption among adults dwelling in both rural and urban areas amid the backdrop of the COVID-19 pandemic. Methods: Our study has attained sample populations (n = 279,260, National Weighted Estimates = 2,391,188,373) through the secondary analysis of the National Health Interview Survey (NHIS) for the year 2021. We examined the relationship between the rural, regional, and racial variables using chi-square tests and binary logistic regression associated with telemedicine use in our multivariable analysis. Results: Telemedicine use by population decreased with decreasing urbanization level, from 40.2% among adults living in large central metropolitan to 29.7% among adults living in rural area (p< 0.0001). Regarding household income, adults with 400% or more of the federal poverty level (FPL) were significantly more likely to use telemedicine than adults with less than 100% of the FPL. Females were more likely than males to utilize telemedicine. In terms of region, adults living in the West were 1.25 times more likely to use telemedicine than adults living in the Northeast, and minority race/ethnicity groups (eg, Non-Hispanic Black, Hispanic, and other) are less likely to use the telemedicine rather than Non-Hispanic White. Conclusion: Health equity is attained when all demographic groups enjoy uniform access to healthcare services, but disparities emerge when there are discernible variations in access to treatment. Considering this study’s findings, it becomes evident that the distinctions in poverty rates, median income levels, and healthcare utilization patterns across racial and regional lines may serve as indicators of potential health equity concerns.

Background and Objective The number of hospitalizations due to opioid use disorders in the USA increased steadily from 62,010 in 1998–2000 to 136,240 in 2015–2016; however, no health care utilization of lung cancer patients with opioid use disorder has been reported. The purpose of this paper is to investigate health care utilization due to opioid use disorder among lung cancer patients and to investigate additional charge status due to this disorder. Methods The National Inpatient Sample of the USA was used to identify lung cancer patients (n = 11,418, weighted n = 557,090) from 2016 to 2020. The characteristics of patient samples, temporal trend of opioid use disorder, and its association with health care utilization measured by hospital charges were thoroughly examined by the multivariate survey linear regression model. Results Among 557,090 lung cancer patients, 2.4% had opioid use disorder. The proportion of opioid use disorder among lung cancer patients during the study periods had continuously grown. Hospital charges also continued to increase during the study period and were higher among lung cancer patients with opioid use disorder. Survey linear results showed that opioid use disorder was associated with 12.6% higher hospital charges. Analysis of subgroups revealed that this trend was similar across p < the majority of social groups; however, it was significantly higher among Caucasian individuals (0.001) and self-pay groups (p = 0.035) than among others. Conclusions Research conducted has identified gaps in care in rural and suburban areas and a lack of equal care given to minority and low-income patients. These vulnerable groups access health care less often, are charged more for the care they receive, and often face multiple barriers to treatment. Unless these issues are addressed with a focus on socioeconomic factors, race, and region, the opioid epidemic will continue to negatively decimate these populations.

Background: Pneumonia is one of the leading causes of hospital admission in the United States with a global health burden of about 6.8 million hospitalizations and 1.1 million deaths in patients over 65 years old in 2015. This study aimed to identify possible patient and hospital-related risk factors for in-hospital pneumonia death across US hospitals. Methods: The National Inpatient Sample (NIS) was used to identify nationwide pneumonia patients (n=374 766, weighted n=1 873 828) from 2016 to 2019. We examined the characteristics of the study sample and their association with in-hospital death. Multivariate survey logistic regression models were used to identify risk factors. Results: During the study periods, in-hospital death rates continuously decreased (2.45% in 2016 to 2.19% in 2019). Descriptive statistics showed that patient and hospital factors had varied in-hospital death rates. Survey logistic regression results suggested that male, very low income, non-Medicare, government hospitals, rural hospitals, and specific hospital regions were associated with higher in-hospital death rates than their reference groups. Conclusion: Socioeconomic factors, including income and insurance, are associated with pneumonia mortality. Census region, hospital ownership, and rural location are also related to in-hospital mortality. Such findings in underserved, impoverished, and rural areas to identify possible health disparities.

Objective This study explores differences in COVID-19 in-hospital mortality rates by patient and geographic factors to identify at-risk populations and analyze how strained health disparities were exacerbated during the pandemic. Methods The latest 2020 United States National Inpatient Sample (NIS) data was used to obtain a population-based estimate for patients with COVID-19. We conducted a cross-sectional retrospective data analysis, and sampling weights were used for all statistical analyses to represent nationwide in-hospital mortality of patients with COVID-19. We used multivariate logistic regression models to identify predictors for how patients with COVID-19 are associated with in-hospital death. Results Of 200,531 patients, 88.9% did not have an in-hospital death (n=178,369), and 11.1% had in-hospital death (n=22,162). Patients older than 70 were 10 times more likely to have an in-hospital death than patients younger than 40 (pppp<0.001). Patients with hypertension and diabetes were 69% and 29%, respectively, more likely to have in-hospital death than patients without hypertension and diabetes. Conclusion Health disparities in the COVID-19 pandemic occurred across races and regions and must be addressed to prevent future deaths. Age and comorbidities like diabetes have a well-established link to increased disease severity, and we have linked both to higher mortality risk. Low-income patients had a significantly increased risk of in-hospital death starting at over 40 years old.