Racial disparity and regional variance in healthcare utilization among patients with lung cancer in US hospitals during 2016-2019.

BACKGROUND: Lung cancer health disparities are related to various patient factors. This study describes regional differences in healthcare utilization and racial characteristics to identify high-risk areas. This study aimed to identify regions and races at greater risk for lung cancer health disparities based on differences in healthcare utilization, measured here by hospital charges and length of stay. METHODS: The National Inpatient Sample of the United States was used to identify patients with lung cancer (n = 92,159, weighted n = 460,795) from 2016 to 2019. We examined the characteristics of the patient sample and the association between the racial and regional variables and healthcare utilization, measured by hospital charges and length of stay. The multivariate sample weighted linear regression model estimated how racial and regional variables are associated with healthcare utilization. RESULTS: Out of 460,795 patients, 76.4% were white, and 40.2% were from the South. The number of lung cancer patients during the study periods was stable. However, hospital charges were somewhat increased, and the length of stay was decreased during the study period. Sample weighted linear regression results showed that Hispanic & Asian patients were associated with 21.1% and 12.3% higher hospital charges than White patients. Compared with the Northeast, Midwest and South were associated with lower hospital charges, however, the West was associated with higher hospital charges. CONCLUSION: Minority groups and regions are at an increased risk for health inequalities because of differences in healthcare utilization. Further differences in utilization by insurance type may exacerbate the situation for some patients with lung cancer. Hospital managers and policymakers working with these patient populations in identified areas should strive to address these disparities through special prevention programs and targeted financial assistance.

Palliative care and healthcare utilization among metastatic breast cancer patients in U.S. Hospitals.

There is a lack of research focused on understanding the different characteristics and healthcare utilization of metastatic breast cancer patients by palliative care use. This study aims to investigate trend of in-patient palliative care and its association with healthcare utilization among hospitalized metastatic breast cancer patients in the US. National Inpatient Sample (NIS) was used to identify nationwide metastatic breast cancer patients (n = 5209, weighted n = 25,961) from 2010 to 2014. We examined the characteristics of the study sample by palliative care and its association with healthcare utilization, measured by discounted hospital charges and length of stay. Multivariable survey regression models were used to identify predictors. Among 26,961 breast cancer patients, 19.0% had palliative care. Percentage of receiving palliative care during the period were gradually increased. Social factors including race, insurance types were also associated with a receipt of palliative care. Survey linear regression results showed that patients with palliative care were associated with 31% lower hospital charges, however, length of stays were not significantly associated. This study found evidence of who was associated with the receipt of palliative care and its relationship with healthcare utilization. This study also emphasizes the importance of receiving palliative care in patients with breast cancer, paving the way for future research into ways to improve palliative care in cancer patients. This study also found social differences and gave evidence of programs that could be used to help vulnerable groups in future health policy decisions.

Does Geographic Location Affect the Quality of Care? The Difference in Readmission Rates Between the Border and Non-Border Hospitals in Texas.

Purpose: Previous literature has limited empirical evidence describing the association between border location and readmission rates among hospitals in the U.S.-Mexico border region. Thus, our study explores this novel connection in Texas border hospitals using a non-experimental longitudinal study design. Materials and Methods: Using longitudinal panel data (2013~2016) drawn from the American Hospital Association Annual Survey Database, Hospital Compare, and Area Health Resource File, a random-effects linear regression analysis was performed to quantify the impact of border location on the readmission rates of the same sample at multiple timed points. Results: We found a positive relationship between border location and 30-day hospital readmission rates for heart failure and pneumonia in Texas. Border hospitals in Texas had approximately a 4.17% higher heart failure readmission rate and a 3.46% higher pneumonia readmission rate than non-border hospitals. We also identified several hospital organizational and market factors associated (eg, registered nurse [RN]-to-patient ratio) with hospital readmission rates. Conclusion: The results suggest that improving RN staffing levels can be the most feasible action to lower the readmission rates among border Texas hospitals. Decreasing readmission rates by increasing RN staffing levels would also help them avoid reimbursement reduction under the Hospital Readmission Reduction Program (HRRP) and enhance overall health in Texas border communities. Further, to improve border health in Texas, decision-makers in state and local governments must consider incentivizing border hospitals to improve RN staffing levels and modulating the market factors affecting hospital readmission rates that are mostly beyond the control of hospitals.

The Impact of Market Conditions on RN Staffing in Hospitals: Using Resource Dependence Theory and Information Uncertainty Perspective.

PURPOSE: Due to a limited number of studies with generalizable findings on the relationships between market conditions and RN staffing levels in hospitals, this study examined such relationships employing a longitudinal design with a representative national sample. MATERIALS AND METHODS: We used longitudinal panel datasets from 2006 to 2010, drawn from various datasets including the American Hospital Association Annual Survey Database and the Area Health Resource File. A random-effects linear regression model was used to measure the influence of market conditions on RN staffing levels. RESULTS: The results of this study showed that market conditions were significantly associated with RN staffing levels in hospitals. First, an increase in per capita income and being located in urban rather than rural areas were associated with a greater number of RNs per 1,000 inpatient days and a higher ratio of RNs to LPNs and nursing aides. In addition, an increase in the number of physician specialists was associated with an increase in the number of RNs per 1,000 inpatient days. Second, an increase in Medicare HMO penetration in the environment was related to an increase in the RNs to LPNs and nursing aides ratio. Lastly, an increase in market competition was associated with an increase in the number RNs per 1,000 inpatient days and the ratio of RNs to LPNs and nursing aides. CONCLUSION: The findings of this study suggest that staffing decision makers in hospitals should consider how to best align their RN staffing levels with their operating environment. In addition, health policy makers may improve the levels the RN supply in communities that needs more RNs by modulating external environmental forces (eg, specialist resources) that influence RN staffing levels in hospitals.

Payer mix and EHR adoption in hospitals.

Payers are known to influence the adoption of health information technology (HIT) among hospitals. However, previous studies examining the relationship between payer mix and HIT have not focused specifically on electronic health record systems (EHRs). Using data from the Nationwide Inpatient Sample and the American Hospital Association Annual Survey, we examine how Medicare, Medicaid, commercial insurance, and managed care caseloads are associated with EHR adoption in hospitals. Overall, we found a weak relationship between payer mix and EHR adoption. Medicare and, separately, Medicaid volumes were not associated with EHR adoption. Furthermore, commercial insurance volume was not associated with EHR adoption; however, a hospital located in the third quartile of managed care caseloads had a decreased likelihood of EHR adoption. We did not find empirical evidence to support the hypothesis that payer generosity and other indirect mechanisms influence EHR adoption in hospitals. The direct incentives embedded in the Health Information Technology for Economic and Clinical Health Act may have a positive influence on EHR adoption--especially for hospitals with high Medicare and/or Medicaid caseloads. However, it is still uncertain whether the available incentives will offset the barriers many hospitals face in achieving meaningful use of EHRs.

Environmental factors and health information technology management strategy.

BACKGROUND: : Previous studies have provided theoretical and empirical evidence that environmental forces influence hospital strategy. PURPOSES: : Rooted in resource dependence theory and the information uncertainty perspective, this study examined the relationship between environmental market characteristics and hospitals' selection of a health information technology (HIT) management strategy. METHODOLOGY/APPROACH: : A cross-sectional design is used to analyze secondary data from the American Hospital Association Annual Survey, the Healthcare Information and Management Systems Society Analytics Database, and the Area Resource File. Univariate and multinomial logistic regression analyses are used. FINDINGS: : Overall, 3,221 hospitals were studied, of which 60.9% pursed a single-vendor HIT management strategy, 28.9% pursued a best-of-suite strategy, and 10.2% used a best-of-breed strategy. Multivariate analyses controlling for hospital characteristics found that measures of environmental factors representing munificence, dynamism, and/or complexity were systematically associated with various hospital HIT management strategy use. Specifically, the number of generalist physicians per capita was positively associated with the single-vendor strategy (B = -5.64, p = .10). Hospitals in urban markets were more likely to pursue the best-of-suite strategy (B = 0.622, p < .001). Dynamism, measured as the number of managed care contracts for a given hospital, was negatively associated with the single-vendor strategy (B = 0.004, p = .049). Lastly, complexity, measured as market competition, was positively associated with the best-of-breed strategy (B = 0.623, p = .042). PRACTICE IMPLICATIONS: : By and large, environmental factors are associated with hospital HIT management strategies in mostly theoretically supported ways. Hospital leaders and policy makers interested in influencing the adoption of hospital HIT should consider how market conditions influence HIT management decisions as part of programs to promote meaningful use.