The health conditions and the health care consumption of the uninsured

Differences in Health Professionals' Engagement With Electronic Health Records Based on Inpatient Race and Ethnicity

Importance

US health professionals devote a large amount of effort to engaging with patients' electronic health records (EHRs) to deliver care. It is unknown whether patients with different racial and ethnic backgrounds receive equal EHR engagement.

Objective

To investigate whether there are differences in the level of health professionals' EHR engagement for hospitalized patients according to race or ethnicity during inpatient care.

Design, Setting, and Participants

This cross-sectional study analyzed EHR access log data from 2 major medical institutions, Vanderbilt University Medical Center (VUMC) and Northwestern Medicine (NW Medicine), over a 3-year period from January 1, 2018, to December 31, 2020. The study included all adult patients (aged ≥18 years) who were discharged alive after hospitalization for at least 24 hours. The data were analyzed between August 15, 2022, and March 15, 2023.

Exposures

The actions of health professionals in each patient's EHR were based on EHR access log data. Covariates included patients' demographic information, socioeconomic characteristics, and comorbidities.

Main Outcomes and Measures

The primary outcome was the quantity of EHR engagement, as defined by the average number of EHR actions performed by health professionals within a patient's EHR per hour during the patient's hospital stay. Proportional odds logistic regression was applied based on outcome quartiles.

Results

A total of 243 416 adult patients were included from VUMC (mean [SD] age, 51.7 [19.2] years; 54.9% female and 45.1% male; 14.8% Black, 4.9% Hispanic, 77.7% White, and 2.6% other races and ethnicities) and NW Medicine (mean [SD] age, 52.8 [20.6] years; 65.2% female and 34.8% male; 11.7% Black, 12.1% Hispanic, 69.2% White, and 7.0% other races and ethnicities). When combining Black, Hispanic, or other race and ethnicity patients into 1 group, these patients were significantly less likely to receive a higher amount of EHR engagement compared with White patients (adjusted odds ratios, 0.86 [95% CI, 0.83-0.88; P < .001] for VUMC and 0.90 [95% CI, 0.88-0.92; P < .001] for NW Medicine). However, a reduction in this difference was observed from 2018 to 2020.

Conclusions and Relevance

In this cross-sectional study of inpatient EHR engagement, the findings highlight differences in how health professionals distribute their efforts to patients' EHRs, as well as a method to measure these differences. Further investigations are needed to determine whether and how EHR engagement differences are correlated with health care outcomes.

Integrating Population Health Strategies into Primary Care: Impact on Outcomes and Hospital Use for Low-Income Adults

Objective

Our objectives were two-fold: 1) To evaluate the benefits of population health strategies focused on social determinants of health and integrated into the primary care medical home (PCMH) and 2) to determine how these strategies impact diabetes and cardiovascular disease outcomes among a low-income, primarily minority community. We also investigated associations between these outcomes and emergency department (ED) and inpatient (IP) use and costs.

Design

Retrospective cohort.

Setting

Community-based PCMH: Baylor Scott & White Health and Wellness Center (BSW HWC).

Patients/Participants

All patients who attended at least two primary care visits at BSW HWC within a 12-month time span from 2011-2015.

Methods

Outcomes for patients participating in PCMH only (PCMH) as compared to PCMH plus population health services (PCMH+PoPH) were compared using electronic health record data.

Main Outcomes

Diastolic and systolic blood pressure, hemoglobin A1c, ED visits and costs, and IP hospitalizations and costs were examined.

Results

From 2011-2015, 445 patients (age=46±12 years, 63% African American, 61% female, 69.5% uninsured) were included. Adjusted regression analyses indicated PCMH+PoPH had greater improvement in diabetes outcomes (prediabetes HbA1c= -.65[SE=.32], P=.04; diabetes HbA1c= -.74 [SE=.37], P<.05) and 37% lower ED costs than the PCMH group (P=.01). Worsening chronic disease risk factors was associated with 39% higher expected ED visits (P<.01), whereas improved chronic disease risk was associated with 32% fewer ED visits (P=.04).

Conclusions

Integrating population health services into the PCMH can improve chronic disease outcomes, and impact hospital utilization and cost in un- or under-insured populations.

Associations between new health conditions and healthcare service utilizations among older adults in the United Kingdom: effects of COVID-19 risks, worse financial situation, and lowered income

Background

Health services are critically important for older adults, particularly during the Coronavirus disease-19 (COVID-19) pandemic. However, COVID-19 risks, worse financial situation, and lowered income may seriously impact health services by feasibility and accessibility. Therefore, the aim of the present study was empirically to explore how health-seeking behaviors are influenced by new health conditions through COVID-19 risks, worse financial situation, and lowered income.

Methods

Data were from ELSA COVID-19 waves 1 and 2 which included a sample of 6952 and 6710 older adults in the United Kingdom, respectively. The frequency distribution analyses were conducted by Chi-square analysis by gender groups. Zero-inflated Poisson regressions were used to examine how worse financial situation and lowered income were associated with COVID-19 risks and new health conditions. Logistic regressions were employed to examine the associations of COVID-19 risks, worse financial situation, and lowered income with treatment cancellation and accessible care. Cross-sectional mediation models, cross-sectional moderation models, longitudinal mediation models, and longitudinal moderation models were conducted based on Hayes model 6, Hayes model 29, Montoya model 1, and Montoya model 2, respectively.

Results

Most of the sample was >65 years old, females, located in urban place, and involved in long-standing condition. Regression analysis showed that COVID-19 risks, worse financial situation, and lowered income were associated with treatment cancellation and accessible care. In the longitudinal mediations, effect coefficients of ‘X’ → (treatment cancellation in wave 1 (Tcn1)- treatment cancellation in wave 2 (Tcn2))(β = −.0451, p < .0001, low limit confidence interval (LLCI) = −.0618, upper limit confidence interval (ULCI) = −.0284), ‘X’ → (COVID-19 risks in wave 1 (Csk1)- COVID-19 risks in wave 2 (Csk2)) (β = .0592, p < .0001, LLCI = .0361, ULCI = .0824), and ‘X’ → (lowered income in wave 1 (CIn1)- lowered income in wave 2 (CIn2)) (β = −.0351, p = .0001, LLCI = -.0523, ULCI = -.0179) were significant. Additionally, effect coefficients of ‘X’ → (accessible care in wave 1 (Acr1)- accessible care in wave 2 (Acr2)) (β = .3687, p < .0001, LLCI = .3350, ULCI = .4025),'X’ → (Csk1- Csk2) (β = .0676, p = .0005, LLCI = .0294, ULCI = .1058), and ‘X’ → (worse financial situation in wave 1- worse financial situation in wave 2) (β = −.0369, p = .0102, LLCI = -.0650, ULCI = -.0087) were significant.

Conclusions

There were longitudinal mediating effects of COVID-19 risks, worse financial situation, and lowered income on the relationship between new health conditions and treatment cancellation and relationship between new health conditions and accessible care. These findings suggest that worse financial situation, lowered income, and COVID-19 risks exerted an influence on the relationship between new health conditions and treatment cancellation and relationship between new health conditions and accessible care among older adults. Findings suggest that longitudinal mediations may be important components of interventions aiming to meet service needs. Long-term health policy implications indicate the need for reducing COVID-19 risks, improving financial situation, and increasing income among the targeted population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-02995-8.

What drives different treatment choices? Investigation of hospital ownership, system membership and competition

BACKGROUND

Differences in ownership types have attracted considerable interest because of policy implications. Moreover, competition in hospital markets is promoted to reduce health care spending. However, the effects of system membership and competition on treatment choices of hospitals have not been considered in studying hospital ownership types. We examine the treatment choices of hospitals considering ownership types (not-for-profit, for-profit, and government), system membership, patient insurance status (insured, and uninsured) and hospital competition in the United States.

METHODS

We estimate the probability of according the procedure as the treatment employing logistic regression. We consider all procedures accorded at hospitals, controlling for procedure type and diagnosis as well as relevant patient and hospital characteristics. Competition faced by hospitals is measured using a distance-weighted approach separately for procedural groups. Patient records are obtained from State Inpatient Databases for 11 states and hospital characteristics come from American Hospital Association Annual Survey.

RESULTS

Not-for-profit hospitals facing low for-profit competition that are nonmembers of hospital systems, act like government hospitals, whereas not-for-profits facing high for-profit competition and system member not-for-profits facing low for-profit competition are not statistically significantly different from their for-profit counterparts in terms of treatment choices. Uninsured patients are on average 7% less likely to be accorded the procedure as the treatment at system member not-for-profit hospitals facing high for-profit competition than insured patients. System member not-for-profit hospitals, which account for over half of the observations in the analysis, are on average 16% more likely to accord the procedure as the treatment when facing high for-profit competition than low-for-profit competition.

CONCLUSIONS

We show that treatment choices of hospitals differ by system membership and the level of for-profit competition faced by the hospitals in addition to hospital ownership type and health insurance status of patients. Our results support that hospital system member not-for-profits and not-for-profits facing high for-profit competition are for-profits in disguise. Therefore, system membership is an important characteristic to consider in addition to market competitiveness when tax exemption of not-for-profits are revisited. Moreover, higher competition may lead to increasing health care costs due to more aggressive treatment choices, which should be taken into account while regulating hospital markets.

Understanding the Economic Behavior of the Medically Uninsured in the United States

Previous research defining the health consequences of being medically uninsured show worse access, poorer health outcomes, and higher rates of premature death as compared to their insured counterparts. Adding to this literature, the present study investigated the associative role of health insurance with personal finance health behaviors. In a representative sample of the general population, our adjusted models indicated significant relationships (both positive and negative) between being uninsured and these personal finance behaviors. Therefore, future work using longitudinal data must build upon the present study to accurately determine the relative financial risk an individual takes on by being uninsured.

Trends in Hospital Mortality for Uninsured Rural and Urban Populations, 2012-2016

PURPOSE

Rural-urban health disparities have received increasing scrutiny as rural individuals continue to have worse health outcomes. However, little is known about how insurance status contributes to urban-rural disparities. This study characterizes how rural uninsured patients compare to the urban uninsured, determines whether rurality among the uninsured is associated with worse clinical outcomes, and examines how clinical outcomes based on rurality have changed over time.

METHODS

We conducted a retrospective cohort study of the 2012-2016 National Inpatient Sample hospital discharge data including 1,478,613 uninsured patients, of which 233,816 were rural. Admissions were broken into 6 rurality categories. Logistic regression models were used to determine the independent association between rurality and hospital mortality.

FINDINGS

Demographic and clinical characteristics differed significantly between rural and urban uninsured patients: rural patients were more often white, lived in places with lower median household income, and were more often admitted electively and transferred. Rurality was associated with significantly higher in-hospital mortality rates (1.44% vs 1.89%, OR 1.32, P < .001). This association strengthened after adjusting for medical comorbidities and hospital characteristics. Further, disparities between urban and rural mortality were found to be growing, with the gap almost doubling between 2012 and 2016.

CONCLUSIONS

Rural and urban uninsured patients differed significantly, specifically in terms of race and median income. Among the uninsured, rurality was associated with higher in-hospital mortality, and the gap between urban and rural in-hospital mortality was widening. Our findings suggest the rural uninsured are a vulnerable population in need of informed, tailored policies to reduce these disparities.