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Professional Fee Ratios for US Hospital Discharge Data.

ABSTRACT: US hospital discharge datasets typically report facility charges (ie, room and board), excluding professional fees (ie, attending physicians' charges).We aimed to estimate professional fee ratios (PFR) by year and clinical diagnosis for use in cost analyses based on hospital discharge data.The subjects consisted of a retrospective cohort of Truven Health MarketScan 2004-2012 inpatient admissions (n=23,594,605) and treat-and-release emergency department (ED) visits (n=70,771,576).PFR per visit was assessed as total payments divided by facility-only payments.Using ordinary least squares regression models controlling for selected characteristics (ie, patient age, comorbidities, etc.), we calculated adjusted mean PFR for admissions by health insurance type (commercial or Medicaid) per year overall and by Major Diagnostic Category (MDC), Diagnostic Related Group, Healthcare Cost and Utilization Project Clinical Classification Software, and primary International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) diagnosis, and for ED visits per year overall and by MDC and primary ICD-9-CM diagnosis.Adjusted mean PFR for 2012 admissions, including preceding ED visits, was 1.264 (95% CI, 1.264, 1.265) for commercially insured admissions (n=2,614,326) and 1.177 (1.176, 1.177) for Medicaid admissions (n=816,503), indicating professional payments increased total per-admission payments by an average 26.4% and 17.7%, respectively, above facility-only payments. Adjusted mean PFR for 2012 ED visits was 1.286 (1.286, 1.286) for commercially insured visits (n=8,808,734) and 1.440 (1.439, 1.440) for Medicaid visits (n=2,994,696). Supplemental tables report 2004-2012 annual PFR estimates by clinical classifications.Adjustments for professional fees are recommended when hospital facility-only financial data from US hospital discharge datasets are used to estimate health care costs.

SUBMITTER: Peterson C

PROVIDER: S-EPMC4681390 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Professional Fee Ratios for US Hospital Discharge Data.

Peterson Cora C Xu Likang L Florence Curtis C Grosse Scott D SD Annest Joseph L JL

Medical care 20151001 10

<h4>Background</h4>US hospital discharge datasets typically report facility charges (ie, room and board), excluding professional fees (ie, attending physicians' charges).<h4>Objectives</h4>We aimed to estimate professional fee ratios (PFR) by year and clinical diagnosis for use in cost analyses based on hospital discharge data.<h4>Subjects</h4>The subjects consisted of a retrospective cohort of Truven Health MarketScan 2004-2012 inpatient admissions (n=23,594,605) and treat-and-release emergency ...[more]

PMID: 26340662

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Project description:ImportanceThe Centers for Medicare & Medicaid Services recently identified 30-day readmission after discharge from inpatient rehabilitation facilities as a national quality indicator. Research is needed to determine the rates and factors related to readmission in this patient population.ObjectiveTo determine 30-day readmission rates and factors related to readmission for patients receiving postacute inpatient rehabilitation.Design, setting, and patientsRetrospective cohort study of records for 736,536 Medicare fee-for-service beneficiaries (mean age, 78.0 [SD, 7.3] years) discharged from 1365 inpatient rehabilitation facilities to the community in 2006 through 2011. Sixty-three percent of patients were women, and 85.1% were non-Hispanic white.Main outcomes and measuresThirty-day readmission rates for the 6 largest diagnostic impairment categories receiving inpatient rehabilitation. These included stroke, lower extremity fracture, lower extremity joint replacement, debility, neurologic disorders, and brain dysfunction.ResultsMean rehabilitation length of stay was 12.4 (SD, 5.3) days. The overall 30-day readmission rate was 11.8% (95% CI, 11.7%-11.8%). Rates ranged from 5.8% (95% CI, 5.8%-5.9%) for patients with lower extremity joint replacement to 18.8% (95% CI, 18.8%-18.9%). for patients with debility. Rates were highest in men (13.0% [ 95% CI, 12.8%-13.1%], vs 11.0% [95% CI, 11.0%-11.1%] in women), non-Hispanic blacks (13.8% [95% CI, 13.5%-14.1%], vs 11.5% [95% CI, 11.5%-11.6%] in whites, 12.5% [95% CI, 12.1%-12.8%] in Hispanics, and 11.9% [95% CI, 11.4%-12.4%] in other races/ethnicities), beneficiaries with dual eligibility (15.1% [95% CI, 14.9%-15.4%], vs 11.1% [95% CI, 11.0%-11.2%] for no dual eligibility), and in patients with tier 1 comorbidities (25.6% [95% CI, 24.9%-26.3%], vs 18.9% [95% CI, 18.5%-19.3%] for tier 2, 15.1% [95% CI, 14.9%-15.3%] for tier 3, and 9.9% [95% CI, 9.9%-10.0%] for no tier comorbidities). Higher motor and cognitive functional status were associated with lower hospital readmission rates across the 6 impairment categories. Adjusted readmission rates by state ranged from 9.2% to 13.6%. Approximately 50% of patients rehospitalized within the 30-day period were readmitted within 11 days of discharge. Medicare Severity Diagnosis-Related Group codes for heart failure, urinary tract infection, pneumonia, septicemia, nutritional and metabolic disorders, esophagitis, gastroenteritis, and digestive disorders were common reasons for readmission.Conclusions and relevanceAmong postacute rehabilitation facilities providing services to Medicare fee-for-service beneficiaries, 30-day readmission rates ranged from 5.8% to 18.8% for selected impairment groups. Further research is needed to understand the causes of readmission.

Project description:BACKGROUND: Administrative data are widely used to study health systems and make important health policy decisions. Yet little is known about the influence of coder characteristics on administrative data validity in these studies. Our goal was to describe the relationship between several measures of validity in coded hospital discharge data and 1) coders' volume of coding (> or = 13,000 vs. <13,000 records), 2) coders' employment status (full- vs. part-time), and 3) hospital type. METHODS: This descriptive study examined 6 indicators of face validity in ICD-10 coded discharge records from 4 hospitals in Calgary, Canada between April 2002 and March 2007. Specifically, mean number of coded diagnoses, procedures, complications, Z-codes, and codes ending in 8 or 9 were compared by coding volume and employment status, as well as hospital type. The mean number of diagnoses was also compared across coder characteristics for 6 major conditions of varying complexity. Next, kappa statistics were computed to assess agreement between discharge data and linked chart data reabstracted by nursing chart reviewers. Kappas were compared across coder characteristics. RESULTS: 422,618 discharge records were coded by 59 coders during the study period. The mean number of diagnoses per record decreased from 5.2 in 2002/2003 to 3.9 in 2006/2007, while the number of records coded annually increased from 69,613 to 102,842. Coders at the tertiary hospital coded the most diagnoses (5.0 compared with 3.9 and 3.8 at other sites). There was no variation by coder or site characteristics for any other face validity indicator. The mean number of diagnoses increased from 1.5 to 7.9 with increasing complexity of the major diagnosis, but did not vary with coder characteristics. Agreement (kappa) between coded data and chart review did not show any consistent pattern with respect to coder characteristics. CONCLUSIONS: This large study suggests that coder characteristics do not influence the validity of hospital discharge data. Other jurisdictions might benefit from implementing similar employment programs to ours, e.g.: a requirement for a 2-year college training program, a single management structure across sites, and rotation of coders between sites. Limitations include few coder characteristics available for study due to privacy concerns.

Project description:BackgroundNeonatal morbidity is associated with lifelong impairments, but the absence of a consensual definition and the need for large data sets limit research.ObjectivesTo inform initiatives to define standard outcomes for research, we reviewed composite neonatal morbidity indicators derived from routine hospital discharge data.Data sourcesPubMed (updated on October 12, 2018). The search algorithm was based on three components: "morbidity," "neonatal," and "hospital discharge data."Study selection and data extractionStudies investigating neonatal morbidity using a composite indicator based on hospital discharge data were included. Indicators defined for specific conditions (eg congenital anomalies, maternal addictions) were excluded. The target population, objectives, component morbidities, diagnosis and procedure codes, validation methods, and prevalence of morbidity were extracted.SynthesisFor each study, we assessed construct validity by describing the methods used to select the indicator components and evaluated whether the authors assessed internal and external validity. We also calculated confidence intervals for the prevalence of the morbidity composite.ResultsSeventeen studies fulfilled inclusion criteria. Indicators targeted all (n = 4), low-/moderate-risk (n = 9), and very preterm (VPT, n = 4) infants. Components were similar for VPT infants, but domains and diagnosis codes within domains varied widely for all and low-/moderate-risk infants. Component selection was described for 8/17 indicators and some form of validation reported for 12/17. Neonatal morbidity prevalence ranged from 4.6% to 9.0% of all infants, 0.4% to 8.0% of low-/moderate-risk infants, and 17.8% to 61.0% of VPT infants.ConclusionsMultiple neonatal morbidity indicators based on hospital discharge data have been used for research, but their heterogeneity limits comparisons between studies. Standard neonatal outcome measures are needed for benchmarking and synthesis of research results.

Project description:BackgroundPrevious studies have identified disparities in readmissions among Medicare beneficiaries hospitalized for the Hospital Readmissions Reduction Program's (HRRP's) priority conditions. Evidence suggests timely follow-up is associated with reduced risk of readmission, but it is unknown whether timely follow-up reduces disparities in readmission.ObjectiveTo assess whether follow-up within 7 days after discharge from a hospitalization reduces risk of readmission and mitigates identified readmission disparities.DesignA retrospective cohort study using Cox proportional hazards models to estimate the associations between sociodemographic characteristics (race and ethnicity, dual-eligibility status, rurality, and area social deprivation), follow-up, and readmission. Mediation analysis was used to examine if disparities in readmission were mitigated by follow-up.ParticipantsWe analyzed data from 749,402 Medicare fee-for-service beneficiaries hospitalized for acute myocardial infarction, chronic obstructive pulmonary disease, heart failure, or pneumonia, and discharged home between January 1 and December 1, 2018.Main measureAll-cause unplanned readmission within 30 days after discharge.Key resultsPost-discharge follow-up within 7 days of discharge was associated with a substantially lower risk of readmission (HR: 0.52, 95% CI: 0.52-0.53). Across all four HRRP conditions, beneficiaries with dual eligibility and beneficiaries living in areas with high social deprivation had a higher risk of readmission. Non-Hispanic Black beneficiaries had higher risk of readmission after hospitalization for pneumonia relative to non-Hispanic Whites. Mediation analysis suggested that 7-day follow-up mediated 21.2% of the disparity in the risk of readmission between dually and non-dually eligible beneficiaries and 50.7% of the disparity in the risk of readmission between beneficiaries living in areas with the highest and lowest social deprivation. Analysis suggested that after hospitalization for pneumonia, 7-day follow-up mediated nearly all (97.5%) of the increased risk of readmission between non-Hispanic Black and non-Hispanic White beneficiaries.ConclusionsImproving rates of follow-up could be a strategy to reduce readmissions for all beneficiaries and reduce disparities in readmission based on sociodemographic characteristics.

Project description:BACKGROUND:Discharge planning is a routine feature of health systems in many countries. The aim of discharge planning is to reduce hospital length of stay and unplanned readmission to hospital, and to improve the co-ordination of services following discharge from hospital.This is the third update of the original review. OBJECTIVES:To assess the effectiveness of planning the discharge of individual patients moving from hospital. SEARCH METHODS:We updated the review using the Cochrane Central Register of Controlled Trials (CENTRAL) (2015, Issue 9), MEDLINE, EMBASE, CINAHL, the Social Science Citation Index (last searched in October 2015), and the US National Institutes of Health trial register (ClinicalTrials.gov). SELECTION CRITERIA:Randomised controlled trials (RCTs) that compared an individualised discharge plan with routine discharge care that was not tailored to individual participants. Participants were hospital inpatients. DATA COLLECTION AND ANALYSIS:Two authors independently undertook data analysis and quality assessment using a pre-designed data extraction sheet. We grouped studies according to patient groups (elderly medical patients, patients recovering from surgery, and those with a mix of conditions) and by outcome. We performed our statistical analysis according to the intention-to-treat principle, calculating risk ratios (RRs) for dichotomous outcomes and mean differences (MDs) for continuous data using fixed-effect meta-analysis. When combining outcome data was not possible because of differences in the reporting of outcomes, we summarised the reported data in the text. MAIN RESULTS:We included 30 trials (11,964 participants), including six identified in this update. Twenty-one trials recruited older participants with a medical condition, five recruited participants with a mix of medical and surgical conditions, one recruited participants from a psychiatric hospital, one from both a psychiatric hospital and from a general hospital, and two trials recruited participants admitted to hospital following a fall. Hospital length of stay and readmissions to hospital were reduced for participants admitted to hospital with a medical diagnosis and who were allocated to discharge planning (length of stay MD - 0.73, 95% CI - 1.33 to - 0.12, 12 trials, moderate certainty evidence; readmission rates RR 0.87, 95% CI 0.79 to 0.97, 15 trials, moderate certainty evidence). It is uncertain whether discharge planning reduces readmission rates for patients admitted to hospital following a fall (RR 1.36, 95% CI 0.46 to 4.01, 2 trials, very low certainty evidence). For elderly patients with a medical condition, there was little or no difference between groups for mortality (RR 0.99, 95% CI 0.79 to 1.24, moderate certainty). There was also little evidence regarding mortality for participants recovering from surgery or who had a mix of medical and surgical conditions. Discharge planning may lead to increased satisfaction for patients and healthcare professionals (low certainty evidence, six trials). It is uncertain whether there is any difference in the cost of care when discharge planning is implemented with patients who have a medical condition (very low certainty evidence, five trials). AUTHORS' CONCLUSIONS:A discharge plan tailored to the individual patient probably brings about a small reduction in hospital length of stay and reduces the risk of readmission to hospital at three months follow-up for older people with a medical condition. Discharge planning may lead to increased satisfaction with healthcare for patients and professionals. There is little evidence that discharge planning reduces costs to the health service.

Project description:BackgroundDischarge planning is a routine feature of health systems in many countries that aims to reduce delayed discharge from hospital, and improve the co-ordination of services following discharge from hospital and reduce the risk of hospital readmission. This is the fifth update of the original review.ObjectivesTo assess the effectiveness of planning the discharge of individual patients moving from hospital.Search methodsWe searched CENTRAL, MEDLINE, Embase and two trials registers on 20 April 2021. We searched two other databases up to 31 March 2020. We also conducted reference checking, citation searching and contact with study authors to identify additional studies.Selection criteriaRandomised trials that compared an individualised discharge plan with routine discharge that was not tailored to individual participants. Participants were hospital inpatients.Data collection and analysisTwo review authors independently undertook data analysis and quality assessment using a pre-designed data extraction sheet. We grouped studies by older people with a medical condition, people recovering from surgery, and studies that recruited participants with a mix of conditions. We calculated risk ratios (RRs) for dichotomous outcomes and mean differences (MDs) for continuous data using fixed-effect meta-analysis. When combining outcome data it was not possible because of differences in the reporting of outcomes, we summarised the reported results for each trial in the text.Main resultsWe included 33 trials (12,242 participants), four new trials included in this update. The majority of trials (N = 30) recruited participants with a medical diagnosis, average age range 60 to 84 years; four of these trials also recruited participants who were in hospital for a surgical procedure. Participants allocated to discharge planning and who were in hospital for a medical condition had a small reduction in the initial hospital length of stay (MD - 0.73, 95% confidence interval (CI) - 1.33 to - 0.12; 11 trials, 2113 participants; moderate-certainty evidence), and a relative reduction in readmission to hospital over an average of three months follow-up (RR 0.89, 95% CI 0.81 to 0.97; 17 trials, 5126 participants; moderate-certainty evidence). There was little or no difference in participant's health status (mortality at three- to nine-month follow-up: RR 1.05, 95% CI 0.85 to 1.29; 8 trials, 2721 participants; moderate certainty) functional status and psychological health measured by a range of measures, 12 studies, 2927 participants; low certainty evidence). There was some evidence that satisfaction might be increased for patients (7 trials), caregivers (1 trial) or healthcare professionals (2 trials) (very low certainty evidence). The cost of a structured discharge plan compared with routine discharge is uncertain (7 trials recruiting 7873 participants with a medical condition; very low certainty evidence).Authors' conclusionsA structured discharge plan that is tailored to the individual patient probably brings about a small reduction in the initial hospital length of stay and readmissions to hospital for older people with a medical condition, may slightly increase patient satisfaction with healthcare received. The impact on patient health status and healthcare resource use or cost to the health service is uncertain.

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Professional Fee Ratios for US Hospital Discharge Data.

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Professional Fee Ratios for US Hospital Discharge Data.

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