Dataset Information

Comparing patient outcomes across payer types: implications for using hospital discharge records to assess quality.

ABSTRACT:

Objective

To explain observed differences in patient outcomes across payer types using hospital discharge records. Specifically, we address two mechanisms: hospital-payer matching versus unobserved patient heterogeneity.

Data source

Florida's hospital discharge records (1996-2000) of major surgery patients with private health insurance between the ages of 18 and 65, Health Maintenance Organization (HMO) market penetration data, hospital systems data, and the Area Resource File.

Study design

The dependent variable is occurrence of one or more in-hospital complications as identified by the Complication Screening Program. The key independent variable is patients' primary-payer type (HMO, Preferred Provider Organization, and fee-for-service). We estimate five different logistic regression models, each representing a different assumption about the underlying factors that confound the causal relationship between the payer type and the likelihood of experiencing complications.

Principal finding

We find that the observed differences in complication rates across payer types are largely driven by unobserved differences in patient health, even after adjusting for case mix using available data elements in the discharge records.

Conclusion

Because of the limitations inherent to hospital discharge records, making quality comparisons in terms of patient outcomes is challenging. As such, any efforts to assess quality in such a manner must be carried out cautiously.

SUBMITTER: Maeng DD

PROVIDER: S-EPMC3393018 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Publications

Comparing patient outcomes across payer types: implications for using hospital discharge records to assess quality.

Maeng Daniel D DD Martsolf Grant R GR

Health services research 20110620 6pt1

<h4>Objective</h4>To explain observed differences in patient outcomes across payer types using hospital discharge records. Specifically, we address two mechanisms: hospital-payer matching versus unobserved patient heterogeneity.<h4>Data source</h4>Florida's hospital discharge records (1996-2000) of major surgery patients with private health insurance between the ages of 18 and 65, Health Maintenance Organization (HMO) market penetration data, hospital systems data, and the Area Resource File.<h4 ...[more]

PMID: 21689096

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Project description:ContextThe Affordable Care Act resulted in unprecedented reductions in the uninsured population through subsidized private insurance and an expansion of Medicaid. Early estimates from the beginning of 2014 showed that the Medicaid expansion decreased uninsured discharges and increased Medicaid discharges with no change in total discharges.ObjectiveTo provide new estimates of the effect of the ACA on discharges for specific conditions.Design, setting, and participantsWe compared outcomes between states that did and did not expand Medicaid using state-level all-capture discharge data from 2009-2014 for 42 states from the Healthcare Costs and Utilization Project's FastStats database; for a subset of states we used data through 2015. We stratified the analysis by baseline uninsured rates and used difference-in-differences and synthetic control methods to select comparison states with similar baseline characteristics that did not expand Medicaid.Main outcomeOur main outcomes were total and condition-specific hospital discharges per 1,000 population and the share of total discharges by payer. Conditions reported separately in FastStats included maternal, surgical, mental health, injury, and diabetes.ResultsThe share of uninsured discharges fell in Medicaid expansion states with below (-4.39 percentage points (p.p.), -6.04 --2.73) or above (-7.66 p.p., -9.07 --6.24) median baseline uninsured rates. The share of Medicaid discharges increased in both small (6.42 p.p. 4.22-6.62) and large (10.5 p.p., 8.48-12.5) expansion states. Total and most condition-specific discharges per 1,000 residents did not change in Medicaid expansion states with high or low baseline uninsured rates relative to non-expansion states (0.418, p = 0.225), with one exception: diabetes. Discharges for that condition per 1,000 fell in states with high baseline uninsured rates relative to non-expansion states (-0.038 95% p = 0.027).ConclusionsEarly changes in payer mix identified in the first two quarters of 2014 continued through the Medicaid expansion's first year and are distributed across all condition types studied. We found no change in total discharges between Medicaid expansion and non-expansion states, however residents of states that should have been most affected by the Medicaid expansion were less likely to be hospitalized for diabetes.

Project description:BackgroundIdiopathic cardiomyopathy (ICM) is a rare disease affecting numerous physiological and biomolecular systems with multimorbidity. However, due to the small sample size of uncommon diseases, the whole spectrum of chronic disease co-occurrence, especially in developing nations, has not yet been investigated. To grasp the multimorbidity pattern, we aimed to present a multidimensional model for ICM and differences among age groups.MethodsHospital discharge records were collected from a rare disease centre of ICM inpatients (n = 1036) over 10 years (2012 to 2021) for this retrospective analysis. One-to-one matched controls were also included. First, by looking at the first three digits of the ICD-10 code, we concentrated on chronic illnesses with a prevalence of more than 1%. The ICM and control inpatients had a total of 71 and 69 chronic illnesses, respectively. Second, to evaluate the multimorbidity pattern in both groups, we built age-specific cosine-index-based multimorbidity networks. Third, the associated rule mining (ARM) assessed the comorbidities with heart failure for ICM, specifically.ResultsThe comorbidity burden of ICM was 78% larger than that of the controls. All ages were affected by the burden, although those over 50 years old had more intense interactions. Moreover, in terms of disease connectivity, central, hub, and authority diseases were concentrated in the metabolic, musculoskeletal and connective tissue, genitourinary, eye and adnexa, respiratory, and digestive systems. According to the age-specific connection, the impaired coagulation function was required for raising attention (e.g., autoimmune-attacked digestive and musculoskeletal system disorders) in young adult groups (ICM patients aged 20-49 years). For the middle-aged (50-60 years) and older (≥70 years) groups, malignant neoplasm and circulatory issues were the main confrontable problems. Finally, according to the result of ARM, the comorbidities and comorbidity patterns of heart failure include diabetes mellitus and metabolic disorder, sleeping disorder, renal failure, liver, and circulatory diseases.ConclusionsThe main cause of the comorbid load is aging. The ICM comorbidities were concentrated in the circulatory, metabolic, musculoskeletal and connective tissue, genitourinary, eye and adnexa, respiratory, and digestive systems. The network-based approach optimizes the integrated care of patients with ICM and advances our understanding of multimorbidity associated with the disease.

Project description:ImportancePatient education at time of hospital discharge is critical for smooth transitions of care; however, empirical data regarding discharge communication are limited.ObjectiveTo describe whether key communication domains (medication changes, follow-up appointments, disease self-management, red flags, question solicitation, and teach-back) were addressed at the bedside on the day of hospital discharge, by whom, and for how long.Design, setting, and participantsThis quality improvement study was conducted from September 2018 through October 2019 at inpatient medicine floors in 2 urban, tertiary-care teaching hospitals and purposefully sampled patients designated as "discharge before noon." Data analysis was performed from September 2018 to May 2020.ExposuresA trained bedside observer documented all content and duration of staff communication with a single enrolled patient from 7 am until discharge.Main outcomes and measuresPresence of the key communication domains, role of team members, and amount of time spent at the bedside.ResultsDischarge days for 33 patients were observed. Patients had a mean (SD) age of 63 (18) years; 14 (42%) identified as White, 15 (45%) were female, and 6 (18%) had a preferred language of Spanish. Thirty patients were discharged with at least 1 medication change. Of these patients, 8 (27%) received no verbal instruction on the change, while 16 of 30 (53%) were informed but not told the purpose of the changes. About half of the patients (15 of 31, 48%) were not told the reason for follow-up appointments, and 18 of 33 (55%) were not given instructions on posthospital disease self-management. Most patients (27 of 33, 81%) did not receive guidance on red-flag signs. While over half of the patients (19 of 33, 58%) were asked if they had any questions, only 1 patient was asked to teach back his understanding of the discharge plan. Median (IQR) total time spent with patients on the day of discharge by interns, senior residents, attending physicians, and nurses was 4.0 (0.75-6.0), 1.0 (0-2.0), 3.0 (0.5-7.0), and 22.5 (15.5-30.0) minutes, respectively. Most of the time was spent discussing logistics rather than discharge education.Conclusions and relevanceIn this quality improvement study, patients infrequently received discharge education in key communication domains, potentially leaving gaps in patient knowledge. Interventions to improve the hospital discharge process should address the content, method of delivery, and transparency among team members regarding patient education.

Project description:IntroductionStroke is the second most common cause of death and disability-adjusted life years (DALYs) globally. However, the incidence and impact of stroke by ethnicity and gender is frequently distinct. This is particularly the case in Ecuador where geographic and economic marginalization are often correlated with ethnic marginalization and the extent to which females lack the same opportunities as their male counterparts. The aim of this paper is to investigate the differential impacts in terms of stroke diagnosis and burden of disease by ethnicity and gender, using hospital discharge records over the years 2015-2020.MethodsThis paper calculates stroke incidence, and fatality rates using hospital discharge and death records over the years 2015-2020. The DALY package in R was employed to calculate the Disability Adjusted Life Years lost due to stroke in Ecuador.ResultsThe results show that while the incidence rate of stroke in males (64.96 per 100,000 persons-year) is higher than that for females on average (57.84 per 100,000 persons-year), males accounted for 52.41% of all stroke cases and 53% of all surviving cases. Thus, hospital data suggests that females had a higher death rate when compared to males. Case fatality rates also differed significantly by ethnicity. The highest fatality rate corresponded to the Montubio ethnic group (87.65%), followed by Afrodescendants (67.21%). The estimated burden of disease of stroke calculated using Ecuadorian hospital records (2015-2020) varied from 1,468 to 2,991 DALY per 1,000 population on average.DiscussionDifferences in the burden of disease by ethnic group are likely to reflect differential access to care by region and socio-economic group, both of which are frequently correlated with ethnic composition in Ecuador. Equitable access to health services remains an important challenge in the country. The gender discrepancy in fatality rates suggests that there is a need for targeted educational campaigns to identify stroke signs early, especially in the female population.

Dataset Information

Comparing patient outcomes across payer types: implications for using hospital discharge records to assess quality.

Objective

Data source

Study design

Principal finding

Conclusion

Publications

Comparing patient outcomes across payer types: implications for using hospital discharge records to assess quality.

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