Enhancing the Value of Statewide Hospital Discharge Data: Improving Clinical Content and Race-Ethnicity Data.
Ontology highlight
ABSTRACT:
SUBMITTER: Andrews RM
PROVIDER: S-EPMC4545331 | biostudies-literature | 2015 Aug
REPOSITORIES: biostudies-literature
Publications
Enhancing the Value of Statewide Hospital Discharge Data: Improving Clinical Content and Race-Ethnicity Data.
Andrews Roxanne M RM Schulman Kevin A KA
Health services research 20150801
Similar Datasets
Project description:ObjectivesEight grant teams used Agency for Healthcare Research and Quality infrastructure development research grants to enhance the clinical content of and improve race/ethnicity identifiers in statewide all-payer hospital administrative databases.Principal findingsGrantees faced common challenges, including recruiting data partners and ensuring their continued effective participation, acquiring and validating the accuracy and utility of new data elements, and linking data from multiple sources to create internally consistent enhanced administrative databases. Successful strategies to overcome these challenges included aggressively engaging with providers of critical sources of data, emphasizing potential benefits to participants, revising requirements to lessen burdens associated with participation, maintaining continuous communication with participants, being flexible when responding to participants' difficulties in meeting program requirements, and paying scrupulous attention to preparing data specifications and creating and implementing protocols for data auditing, validation, cleaning, editing, and linking. In addition to common challenges, grantees also had to contend with unique challenges from local environmental factors that shaped the strategies they adopted.ConclusionsThe creation of enhanced administrative databases to support comparative effectiveness research is difficult, particularly in the face of numerous challenges with recruiting data partners such as competing demands on information technology resources. Excellent communication, flexibility, and attention to detail are essential ingredients in accomplishing this task. Additional research is needed to develop strategies for maintaining these databases when initial funding is exhausted.
| S-EPMC4545333 | biostudies-literature
Project description:ObjectiveThe availability of complete and accurate crash injury data is critical to prevention and intervention efforts. Relying solely on hospital discharge data or police crash reports may result in a biased undercount of injuries. Linking hospital data with crash reports may allow for a more robust identification of injuries and an understanding of which populations may be missed in an analysis of one source. We used the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse to examine the share of the entire crash-injured population identified in each of the two data sources, overall and by age, race/ethnicity, sex, injury severity, and road user type.MethodsWe utilized 2016-2017 data from the NJ-SHO warehouse. We identified crash-involved individuals in hospital discharge data by applying the ICD-10-CM external cause of injury matrix. Among crash-involved individuals, we identified those with injury- or pain-related diagnosis codes as being injured. We also identified crash-involved individuals via crash report data and identified injuries using the KABCO scale. We jointly examined the two sources; injuries in the hospital discharge data were documented as being related to the same crash as injuries found in the crash report data if the date of the crash report preceded the date of hospital admission by no more than two days.ResultsIn total, there were 262,338 crash-involved individuals with a documented injury in the hospital discharge data or on the crash report during the study period; 168,874 had an injury according to hospital discharge data, and 164,158 had an injury in crash report data. Only 70,694 (26.9%) had an injury in both sources. We observed differences by age, race/ethnicity, injury severity, and road user type: hospital discharge data captured a larger share of those ages 65+, those who were Black or Hispanic, those with higher severity injuries, and those who were bicyclists or motorcyclists.ConclusionsEach data source in isolation captures approximately two-thirds of the entire crash-injured population; one source alone misses approximately one-third of injured individuals. Each source undercounts people in certain groups, so relying on one source alone may not allow for tailored prevention and intervention efforts.
| S-EPMC9744954 | biostudies-literature
Project description:ObjectiveTo investigate new metrics to improve the reporting of patient race and ethnicity (R/E) by hospitals.Data sourcesCalifornia Patient Discharge Database (PDD) and birth registry, 2008-2009, Healthcare and Cost Utilization Project's State Inpatient Database, 2008-2011, cancer registry 2000-2008, and 2010 US Census Summary File 2.Study designWe examined agreement between hospital reported R/E versus self-report among mothers delivering babies and a cancer cohort in California. Metrics were created to measure root mean squared differences (RMSD) by hospital between reported R/E distribution and R/E estimates using R/E distribution within each patient's zip code of residence. RMSD comparisons were made to corresponding "gold standard" facility-level measures within the maternal cohort for California and six comparison states.Data collectionMaternal birth hospitalization (linked to the state birth registry) and cancer cohort records linked to preceding and subsequent hospitalizations. Hospital discharges were linked to the corresponding Census zip code tabulation area using patient zip code.Principal findingsOverall agreement between the PDD and the gold standard for the maternal cohort was 86 percent for the combined R/E measure and 71 percent for race alone. The RMSD measure is modestly correlated with the summary level gold standard measure for R/E (r = 0.44). The RMSD metric revealed general improvement in data agreement and completeness across states. "Other" and "unknown" categories were inconsistently applied within inpatient databases.ConclusionsComparison between reported R/E and R/E estimates using zip code level data may be a reasonable first approach to evaluate and track hospital R/E reporting. Further work should focus on using more granular geocoded data for estimates and tracking data to improve hospital collection of R/E data.
| S-EPMC4545337 | biostudies-literature
Project description:ImportanceTotal knee arthroplasty (TKA) is one of the most common elective procedures performed in adults with end-stage arthritis. Racial disparities in TKA outcomes have been described in the literature.ObjectivesTo assess the association of race/ethnicity with discharge disposition and hospital readmission after elective primary TKA and to assess the association of nonhome discharge disposition with hospital readmission risk.Design, setting, and participantsThis retrospective cohort study used data from the Pennsylvania Health Care Cost Containment Council Database, a large regional database that included demographic data from all discharges of patients who underwent elective primary TKA in 170 nongovernmental acute care hospitals in Pennsylvania from April 1, 2012, to September 30, 2015. Data analyses were conducted from September 29, 2017, to November 29, 2017.ExposuresPatient race/ethnicity and discharge disposition.Main outcomes and measuresDischarge disposition and 90-day hospital readmission.ResultsAmong 107 768 patients, 7287 (6.8%) were African American, 68 372 (63.4%) were women, 46 420 (43.1%) were younger than 65 years, and 60 636 (56.3%) were insured by Medicare. In multivariable logistic regression, among patients younger than 65 years, African American patients were more likely than white patients to be discharged to inpatient rehabilitation facility (IRF) (adjusted relative risk ratio [aRRR], 2.49 [95% CI, 1.42-4.36]; P = .001) or a skilled nursing facility (SNF) (aRRR, 3.91 [95% CI, 2.17-7.06]; P < .001) and had higher odds of 90-day hospital readmission (adjusted odds ratio [aOR], 1.30 [95% CI, 1.02-1.67]; P = .04). Compared with white patients 65 years or older, African American patients 65 years or older were more likely to be discharged to SNF (aRRR, 3.30 [95% CI, 1.81-6.02]; P < .001). In both age groups, discharge to an IRF (age <65 years: aOR, 3.62 [95% CI, 2.33-5.64]; P < .001; age ≥65 years: aOR, 2.85 [95% CI, 2.25-3.61]; P < .001) or SNF (age <65 years: aOR, 1.91 [95% CI, 1.37-2.65]; P < .001; age ≥65 years: aOR, 1.55 [95% CI, 1.27-1.89]; P < .001) was associated with higher odds of 90-day readmission.Conclusions and relevanceThis cohort study found that race/ethnicity was associated with higher odds of discharge to an IRF or SNF for postoperative care after primary TKA. Among patients younger than 65 years, African American patients were more likely than white patients to be readmitted to the hospital within 90 days. Discharge to an IRF or SNF for postoperative care and rehabilitation was also associated with a higher risk of readmission to an acute care hospital.
| S-EPMC6824220 | biostudies-literature
Project description:Hospital discharge (HD) records contain important information that is used in public health and health care sectors. It is becoming increasingly common to rely mostly or exclusively on HD data to assess and monitor severe maternal morbidity (SMM) overall and by sociodemographic characteristics, including race and ethnicity. Limited studies have validated race and ethnicity in HD or provided estimates on the impact of assessing health differences in maternity populations. This study aims to determine the differences in race and ethnicity reporting between HD and birth certificate (BC) data for maternity hospitals in Florida and to estimate the impact of race and ethnicity misclassification on state- and hospital-specific SMM rates. We conducted a population-based retrospective study of live births using linked BC and HD records from 2016 to 2019 (n = 783,753). BC data were used as the gold standard. Race and ethnicity were categorized as non-Hispanic (NH)-White, NH-Black, Hispanic, NH-Asian Pacific Islander (API), and NH-American Indian or Alaskan Native (AIAN). Overall, race and ethnicity misclassification and its impact on SMM at the state- and hospital levels were estimated. At the state level, NH-AIAN women were the most misclassified (sensitivity: 28.2%; positive predictive value (PPV): 25.2%) and were commonly classified as NH-API (30.3%) in HD records. NH-API women were the next most misclassified (sensitivity: 57.3%; PPV: 85.4%) and were commonly classified as NH-White (5.8%) or NH-other (5.5%). At the hospital level, wide variation in sensitivity and PPV with negative skewing was identified, particularly for NH-White, Hispanic, and NH-API women. Misclassification did not result in large differences in SMM rates at the state level for all race and ethnicity categories except for NH-AIAN women (% difference 78.7). However, at the hospital level, Hispanic women had wide variability of a percent difference in SMM rates and were more likely to have underestimated SMM rates. Reducing race and ethnicity misclassification on HD records is key in assessing and addressing SMM differences and better informing surveillance, research, and quality improvement efforts.
| S-EPMC10178402 | biostudies-literature
Project description:ImportanceAlthough discharges against medical advice (DAMA) are associated with greater morbidity and mortality, little is known about current racial and ethnic disparities in DAMA from the emergency department (ED) nationally.ObjectiveTo characterize current patterns of racial and ethnic disparities in rates of ED DAMA.Design, setting, and participantsThis cross-sectional study used data from the Nationwide Emergency Department Sample on all hospital ED visits made between January to December 2019 in the US.Main outcomes and measuresThe main outcome was odds of ED DAMA for Black and Hispanic patients compared with White patients nationally and in analysis adjusted for sociodemographic factors. Secondary analysis examined hospital-level variation in DAMA rates for Black, Hispanic, and White patients.ResultsThe study sample included 33 147 251 visits to 989 hospitals, representing the estimated 143 million ED visits in 2019. The median age of patients was 40 years (IQR, 22-61 years). Overall, 1.6% of ED visits resulted in DAMA. DAMA rates were higher for Black patients (2.1%) compared with Hispanic (1.6%) and White (1.4%) patients, males (1.7%) compared with females (1.5%), those with no insurance (2.8%), those with lower income (<$27 999; 1.9%), and those aged 35 to 49 years (2.2%). DAMA visits were highest at metropolitan teaching hospitals (1.8%) and hospitals that served greater proportions of racial and ethnic minoritized patients (serving ≥57.9%; 2.1%). Odds of DAMA were greater for Black patients (odds ratio [OR], 1.45; 95% CI, 1.31-1.57) and Hispanic patients (OR, 1.16; 95% CI, 1.04-1.29) compared with White patients. After adjusting for sociodemographic characteristics (age, sex, income, and insurance status), the adjusted OR (AOR) for DAMA was lower for Black patients compared with the unadjusted OR (AOR, 1.18; 95% CI, 1.09-1.28) and there was no difference in odds for Hispanic patients (AOR, 1.03; 95% CI, 0.92-1.15) compared with White patients. After additional adjustment for hospital random intercepts, DAMA disparities reversed, with Black and Hispanic patients having lower odds of DAMA compared with White patients (Black patients: AOR, 0.94 [95% CI, 0.90-0.98]; Hispanic patients: AOR, 0.68 [95% CI, 0.63-0.72]). The intraclass correlation in this secondary analysis model was 0.118 (95% CI, 0.104-0.133).Conclusions and relevanceThis national cross-sectional study found that Black and Hispanic patients had greater odds of ED DAMA than White patients in unadjusted analysis. Disparities were reversed after patient-level and hospital-level risk adjustment, and greater between-hospital than within-hospital variation in DAMA was observed, suggesting that Black and Hispanic patients are more likely to receive care in hospitals with higher DAMA rates. Structural racism may contribute to ED DAMA disparities via unequal allocation of health care resources in hospitals that disproportionately treat racial and ethnic minoritized groups. Monitoring variation in DAMA by race and ethnicity and hospital suggests an opportunity to improve equitable access to health care.
| S-EPMC10685883 | biostudies-literature
Project description:ObjectiveTo examine the impact of key laboratory and race/ethnicity data on the prediction of in-hospital mortality for congestive heart failure (CHF) and acute myocardial infarction (AMI).Data sourcesHawaii adult hospitalizations database between 2009 and 2011, linked to laboratory database.Study designCross-sectional design was employed to develop risk-adjusted in-hospital mortality models among patients with CHF (n = 5,718) and AMI (n = 5,703).Data collection/extraction methodsResults of 25 selected laboratory tests were requested from hospitals and laboratories across the state and mapped according to Logical Observation Identifiers Names and Codes standards. The laboratory data were linked to administrative data for each discharge of interest from an all-payer database, and a Master Patient Identifier was used to link patient-level encounter data across hospitals statewide.Principal findingsAdding a simple three-level summary measure based on the number of abnormal laboratory data observed to hospital administrative claims data significantly improved the model prediction for inpatient mortality compared with a baseline risk model using administrative data that adjusted only for age, gender, and risk of mortality (determined using 3M's All Patient Refined Diagnosis Related Groups classification). The addition of race/ethnicity also improved the model.ConclusionsThe results of this study support the incorporation of a simple summary measure of laboratory data and race/ethnicity information to improve predictions of in-hospital mortality from CHF and AMI. Laboratory data provide objective evidence of a patient's condition and therefore are accurate determinants of a patient's risk of mortality. Adding race/ethnicity information helps further explain the differences in in-hospital mortality.
| S-EPMC4545336 | biostudies-literature
Project description:ObjectiveWe aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic patients from unstructured clinical notes and assessing differences between patients with or without structured race/ethnicity data.Materials and methodsUsing EHR notes for 16 665 patients with encounters at a primary care practice, we developed rule-based natural language processing (NLP) algorithms to classify patients as black/Hispanic. We evaluated performance of the method against an annotated gold standard, compared race and ethnicity between NLP-derived and structured EHR data, and compared characteristics of patients identified as black or Hispanic using only NLP vs patients identified as such only in structured EHR data.ResultsFor the sample of 16 665 patients, NLP identified 948 additional patients as black, a 26%increase, and 665 additional patients as Hispanic, a 20% increase. Compared with the patients identified as black or Hispanic in structured EHR data, patients identified as black or Hispanic via NLP only were older, more likely to be male, less likely to have commercial insurance, and more likely to have higher comorbidity.DiscussionStructured EHR data for race and ethnicity are subject to data quality issues. Supplementing structured EHR race data with NLP-derived race and ethnicity may allow researchers to better assess the demographic makeup of populations and draw more accurate conclusions about intergroup differences in health outcomes.ConclusionsBlack or Hispanic patients who are not documented as such in structured EHR race/ethnicity fields differ significantly from those who are. Relatively simple NLP can help address this limitation.
| S-EPMC6696506 | biostudies-literature
Project description:BackgroundAccording to the U.S. State Department's Refugee Processing Center and the U.S. Census Bureau, in the fiscal year 2016, among all states in the United States, Nebraska resettled the highest number of refugees per capita.ObjectivesThe objectives of this study were to determine the most common reasons for refugees utilizing hospital services in Nebraska between January 2011 and September 2015, and to examine whether refugee patients had increased risks for adverse health conditions compared to non-refugee patients.MethodsStatewide linkage was performed between Nebraska Medicaid Program's immigration data, and 2011-2015 Nebraska hospital discharge data inpatient and outpatient files. The linkage produced 3017, 5460, and 775 cases for emergency department visits, outpatient clinic visits, and inpatient care for the refugee sample, respectively.FindingsRefugee patients were at increased risk for a number of diagnoses or medical conditions, including pregnancy complications, abdominal pain, upper respiratory infections, viral infections, mood disorders, disorders of teeth and jaw, deficiency and anemia, urinary system disorders, headache, nausea and vomiting, limb fractures, spondylosis, essential hypertension, and uncomplicated diabetes mellitus.ConclusionsThe findings suggest a greater emphasis on preventive healthcare, especially in areas of maternal health and perinatal outcomes, psychological counseling, screening for infectious diseases, nutrition and healthy eating, and oral health. Additionally, culturally appropriate measures to address prevention, health screening, and treatments should be adopted by health providers who care for refugees.
| S-EPMC6748192 | biostudies-literature
Project description:We provide the largest compiled publicly available dictionaries of first, middle, and surnames for the purpose of imputing race and ethnicity using, for example, Bayesian Improved Surname Geocoding (BISG). The dictionaries are based on the voter files of six U.S. Southern States that collect self-reported racial data upon voter registration. Our data cover the racial make-up of a larger set of names than any comparable dataset, containing 136 thousand first names, 125 thousand middle names, and 338 thousand surnames. Individuals are categorized into five mutually exclusive racial and ethnic groups - White, Black, Hispanic, Asian, and Other - and racial/ethnic probabilities by name are provided for every name in each dictionary. We provide both probabilities of the form ℙ(race|name) and ℙ(name|race), and conditions under which they can be assumed to be representative of a given target population. These conditional probabilities can then be deployed for imputation in a data analytic task for which self-reported racial and ethnic data is not available.
| S-EPMC10199086 | biostudies-literature