Variance in Hospital Charges and in-Patient Care for Liver Transplants: Examining Non-Clinical Predictors and Implications.

The significant and apparent variance in hospital charges and inpatient care in the U.S. has perplexed the general public including many stakeholders such as the healthcare regulators and insurers. While the clinical side of inpatient care has been undergoing tremendous progress and standardization, the overall cost of healthcare has been ballooning. The purpose of this research is to conduct statistical analyses that reveal the sources of variance in hospital charges and inpatient care using the annual data from the AHRQ's (Agency for Healthcare Research and Quality) HCUP's (Hospital Cost and Utilization Project) NIS (National Inpatient Sample) database. Our focus is on non-clinical factors such as patient age, gender, income and race and hospital location data as independent variables to investigate their impact on hospital charges and inpatient care. Our research sample is the liver transplant cases in 2019 sampled in the NIS 2019 database. Our regression results show patient age and gender as well as payer affect the number of diagnoses; and hospital charges are affected by age, payer and hospital location. Number of procedures was not affected by any of these non-clinical factors except the hospital location. Implications suggest that there is more room for standardization of the number of diagnoses and procedures across regions in the US. Results also reveal that race and income do not have any effect on hospital charges and inpatient care. Our study contributes to an empirical understanding of non-clinical factors in the explanation of variance in hospital charges and inpatient care.

Demystifying hospital charges for hospital readmissions in 2017 in the United States for psychosis (DRG = 885).

Existing research on hospital charges is primarily focused on hospital admissions, but not on hospital readmissions. Our research fills this gap. We utilize the 2017 Hospital Readmissions database from the Agency for Healthcare Research and Quality (AHRQ) to empirically study factors that impact hospital charges for hospital readmissions. We focus on psychosis (DRG = 885) which has 609,360 records in 2017 in the AHRQ database. We employ regression analyses using patient demographics, inpatient care variables, and hospital characteristics to explain variance in hospital charges. Results show that inpatient care (diagnoses, procedures, length of stay), hospital ownership, and younger patients result in higher hospital charges.

Patient demographics as determinants of where they go for hospitalization, what inpatient care they get, and what they are charged: A national study.

This study focuses on the impact of race, income, age, and gender on hospital charges in the US. The data include 28,133 discharge records for appendectomies from a stratified sample of 4,584 hospitals in the HCUP's (Hospital Cost and Utilization Project) NIS (National Inpatient Sample) database. Results show that race, income, and age were significant determinants of hospital charges. Gender was not significantly related to the variance in hospital charges. Additionally, hospital variables (ownership/control region, teaching status, size, and primary expected payer) had statistically significant effects on hospital charges. We conclude with implications for clinicians, hospital administrators, and policy makers.

Macro influencers of electronic health records adoption.

While adoption rates for electronic health records (EHRs) have improved, the reasons for significant geographical differences in EHR adoption within the USA have remained unclear. To understand the reasons for these variations across states, we have compiled from secondary sources a profile of different states within the USA, based on macroeconomic and macro health-environment factors. Regression analyses were performed using these indicator factors on EHR adoption. The results showed that internet usage and literacy are significantly associated with certain measures of EHR adoption. Income level was not significantly associated with EHR adoption. Per capita patient days (a proxy for healthcare need intensity within a state) is negatively correlated with EHR adoption rate. Health insurance coverage is positively correlated with EHR adoption rate. Older physicians (>60 years) tend to adopt EHR systems less than their younger counterparts. These findings have policy implications on formulating regionally focused incentive programs.

"Cost creep due to age creep" phenomenon: pattern analyses of in-patient hospitalization costs for various age brackets in the United States.

The expectation that aging leads to a progressive deterioration of biological functions leading to higher healthcare costs is known as the healthcare cost creep due to age creep phenomenon. The authors empirically test the validity of this phenomenon in the context of hospitalization costs based on more than 8 million hospital inpatient records from 1,056 hospitals in the United States. The results question the existence of cost creep due to age creep after the age of 65 years as far as average hospitalization costs are concerned. The authors discuss implications for potential knowledge transfer for cost minimization and medical tourism.

Economic recession and headache-related hospital admissions.

Incidence of headaches across different regions and its relationship to unemployment rates in the United States before and during an economic recession was evaluated. Years 2008 and 2009 were determined as recessionary period. Headache-related admissions, particularly the uncomplicated headaches, increased significantly during recession. Proportion of women with headaches has increased and the age group of 25-54 years was the most affected during the recession. The hospital charges have increased even though the average length and charge of stay decreased. These findings are consistent with our understanding of effects of stress and unemployment on psychological and physical health.

Hospital care and capacity in the tri-state region of Indiana, Kentucky, and Ohio: analysis and insights.

Hospitals are a significant part of the burgeoning healthcare sector in the United States (U.S.) economy. Despite the availability of what some describe as the world's best healthcare, the U.S. suffers from wide discrepancies in healthcare provision across hospitals and regions of the country. Specifically, capacity, utilization, quality, and even financial performance of hospitals vary widely. Based on secondary data from 533 hospitals in the adjoining states of Indiana, Kentucky, and Ohio, this study develops several comparative metrics that enable benchmarking, which, in turn, leads to several inferences and implications for hospital administrators. The paper concludes with implications for hospital administrators and suggestions for future research.