Dataset Information

Organizational Factors Affect Safety-Net Hospitals' Breast Cancer Treatment Rates.

ABSTRACT: To identify key organizational approaches associated with underuse of breast cancer care.Nine New York City area safety-net hospitals.Mixed qualitative-quantitative, cross-sectional cohort.We used qualitative comparative analysis (QCA) of key stakeholder interviews, defined organizational "conditions," calibrated conditions, and identified solution pathways. We defined underuse as no radiation after lumpectomy in women <75 years or mastectomy in women with ?4 positive nodes, or no systemic therapy in women with tumors ?1 cm. We used hierarchical models to assess organizational and patient factors' impact on underuse.Underuse varied by hospital (8-29 percent). QCA found lower underuse sites designated individuals to track and follow-up no-shows; shared clinical information during handoffs; had fully integrated electronic medical records enabling transfer of responsibility across specialties; had strong system support; allocated resources to cancer clinics; had a patient-centered culture paying close organizational attention to clinic patients. High underuse sites lacked these characteristics. Multivariate modeling found that hospitals with strong approaches to follow-up had low underuse rates (RR = 0.28; 0.08-0.95); individual patient characteristics were not significant.At safety-net hospitals, underuse of needed cancer therapies is associated with organizational approaches to track and follow-up treatment. Findings provide varying approaches to safety nets to improve cancer care delivery.

SUBMITTER: Bickell NA

PROVIDER: S-EPMC5682131 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Organizational Factors Affect Safety-Net Hospitals' Breast Cancer Treatment Rates.

Bickell Nina A NA Moss Alexandra DeNardis AD Castaldi Maria M Shah Ajay A Sickles Alan A Pappas Peter P Lewis Theophilus T Kemeny Margaret M Arora Shalini S Schleicher Lori L Fei Kezhen K Franco Rebeca R McAlearney Ann Scheck AS

Health services research 20161114 6

<h4>Objective</h4>To identify key organizational approaches associated with underuse of breast cancer care.<h4>Setting</h4>Nine New York City area safety-net hospitals.<h4>Study design</h4>Mixed qualitative-quantitative, cross-sectional cohort.<h4>Methods</h4>We used qualitative comparative analysis (QCA) of key stakeholder interviews, defined organizational "conditions," calibrated conditions, and identified solution pathways. We defined underuse as no radiation after lumpectomy in women <75 ye ...[more]

PMID: 27861833

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Project description:ImportanceIn the US, federal value-based incentive programs are more likely to penalize safety-net institutions than non-safety-net institutions. Whether these programs differentially change the rates of targeted health care-associated infections in safety-net vs non-safety-net hospitals is unknown.ObjectiveTo assess the association of Hospital-Acquired Condition Reduction Program (HACRP) and Hospital Value-Based Purchasing (HVBP) implementation with changes in rates of targeted health care-associated infections and disparities in rates among safety-net and non-safety-net hospitals.Design, setting, and participantsThis interrupted time series included all US acute care hospitals enrolled in the Preventing Avoidable Infectious Complications by Adjusting Payment study that participated in mandatory reporting to the National Healthcare Safety Network from January 1, 2013, through June 30, 2018. Hospital characteristics were obtained from the 2015 American Hospital Association annual survey. Penalty statuses for 2015 to 2018 were obtained from Hospital Compare. Data were analyzed between July 9, 2018, and October 1, 2019.ExposuresHACRP and HVBP implementation in fiscal year 2015 or 2016.Main outcomes and measuresThe primary outcomes were rates of 4 health care-associated infections: central line-associated bloodstream infection (CLABSI), catheter-associated urinary tract infection (CAUTI), surgical site infection (SSI) after colon surgical procedures, and SSI after abdominal hysterectomy procedures. Regression models were fit using generalized estimating equations to assess the association of HACRP and HVBP implementation with health care-associated infection rates and disparities in infection rates.ResultsOf the 618 acute care hospitals included in this study, 473 (76.5%) were non-safety net and 145 (23.5%) were considered safety net. In these hospitals, HACRP and HVBP implementation was not associated with improvements in level or trend for any health care-associated infection examined (eg, CAUTI in safety-net hospitals: incidence rate ratio [IRR] for level change, 0.98 [95% CI, 0.79-1.23; P = .89]; IRR for change in slope, 1.00 [95% CI, 0.97-1.03; P = .80]). Before program implementation, infection rates were statistically significantly higher for safety-net than for non-safety-net hospitals for CLABSI (IRR, 1.23; 95% CI, 1.07-1.42; P = .004), CAUTI (IRR, 1.38; 95% CI, 1.16-1.64; P < .001), and SSI after colon surgical procedure (odds ratio [OR], 1.26; 95% CI, 1.06-1.50; P = .009). The disparity persisted over time when comparing the last year of the study with the first year (CLABSI: ratio of ratios [ROR], 0.93 [95% CI, 0.77-1.13; P = .48]; CAUTI: ROR, 0.90 [95% CI, 0.73-1.10; P = .31]; SSI after colon surgical procedures: ROR, 0.96 [95% CI, 0.78-1.20; P = .75]). Rates of SSI after abdominal hysterectomy procedure were similar in safety-net and non-safety-net hospitals before implementation (OR, 1.13; 95% CI, 0.91-1.40; P = .27) but higher after implementation (OR, 1.43; 95% CI, 1.11-1.83; P = .006), although this change was not significant (ROR, 1.20; 95% CI, 0.91-1.59; P = .20).Conclusions and relevanceThis study found that HACRP and HVBP implementation was not associated with any improvements in targeted health care-associated infections among safety-net or non-safety-net hospitals or with changes in disparities in infection rates. Given the persistent health care-associated infection rate disparities, these programs appear to function as a disproportionate penalty system for safety-net hospitals that offer no measurable benefits for patients.

Project description:ImportanceAlthough readmission rates are declining under Medicare's Hospital Readmissions Reduction Program (HRRP), concerns remain that the HRRP will harm quality at safety-net hospitals because they are penalized more often. Disparities between white and black patients might widen because more black patients receive care at safety-net hospitals. Disparities may be particularly worse for clinical conditions not targeted by the HRRP because hospitals might reallocate resources toward targeted conditions (acute myocardial infarction, pneumonia, and heart failure) at the expense of nontargeted conditions.ObjectiveTo examine disparities in readmission rates between white and black patients discharged from safety-net or non-safety-net hospitals after the HRRP began, evaluating discharges for any clinical condition and the subsets of targeted and nontargeted conditions.Design, setting, and participantsCohort study conducting quasi-experimental analyses of patient hospital discharges for any clinical condition among fee-for-service Medicare beneficiaries from 2007 to 2015 after controlling for patient and hospital characteristics. Changes in disparities were measured within safety-net and non-safety-net hospitals after the HRRP penalties were enforced and compared with prior trends. These analyses were then stratified by targeted and nontargeted conditions. Analyses were conducted from October 1, 2017, through August 31, 2018.Main outcomes and measuresTrends in 30-day readmission rates among white and black patients by quarter and differences in trends across periods.ResultsThe study sample included 58 237 056 patient discharges (black patients, 9.8%; female, 57.7%; mean age [SD] age, 78.8 [7.9] years; nontargeted conditions, 50 372 806 [86.5%]). Within safety-net hospitals, disparities in readmission rates for all clinical conditions widened between black and white patients by 0.04 percentage point per quarter in the HRRP penalty period (95% CI, 0.01 to 0.07; P = .01). This widening was driven by nontargeted conditions (0.05 percentage point per quarter [95% CI, 0.01 to 0.08]; P = .006), whereas disparities for the HRRP-targeted conditions did not change (with an increase of 0.01 percentage point per quarter [95% CI, -0.07 to 0.10]; P = .74). Within non-safety-net hospitals, racial disparities remained stable in the HRRP penalty period across all conditions, whether the conditions were HRRP-targeted or nontargeted.Conclusions and relevanceFindings from this study suggest that disparities are widening within safety-net hospitals, specifically for non-HRRP-targeted conditions. Although increases in racial disparities for nontargeted conditions were modest, they represent 6 times more discharges in our cohort than targeted conditions.

Project description:ImportanceMedical patients discharged from safety-net hospitals (SNHs) experience higher readmission rates compared with those discharged from non-SNHs. However, little is known about whether this association persists for surgical patients.ObjectivesTo examine differences in readmission rates between SNHs and non-SNHs among surgical patients after discharge and determine whether hospital characteristics might account for some of the variation.Design, setting, and participantsThis observational retrospective study linked the Healthcare Cost and Utilization Project State Inpatient Databases of the Agency for Healthcare Research and Quality from January 1, 2011, through December 31, 2014, for 4 states (New York, Florida, Iowa, and Washington) with data from the 2014 American Hospital Association annual survey. After identifying surgical discharges, SNHs were defined as those with the top quartile of inpatient stays paid by Medicaid or self-paid. Hospital-level risk-standardized readmission rates (RSRRs) for surgical discharges were calculated. The association between hospital RSRRs and hospital characteristics was evaluated with bivariate analyses. An estimated multivariable hierarchical linear regression model was used to examine variation in hospital RSRRs, adjusting for hospital characteristics, state, year, and SNH status. Data were analyzed from June 1, 2017, through March 1, 2018.ExposuresSurgical care at an SNH.Main outcomes and measuresReadmission after an index surgical admission.ResultsA total of 1 252 505 patients across all 4 years and states were included in the analysis (51.7% women; mean [SD] age, 52.7 [18.1] years). Bivariate analyses found that SNHs had higher mean (SD) surgical RSRRs compared with non-SNHs; significant differences were found for New York (9.6 [0.1] vs 10.9 [0.1]; P < .001) and Florida (11.6 [0.1] vs 12.1 [0.1]; P = .001). The SNHs also had higher RSRRs in these 2 states when stratified by hospital funding (nonfederal government SNHs in New York, 11.9 [0.2]; for-profit, private SNHs in Florida, 13.1 [0.2]; P < .001 for both); however, bed size was a significant factor for higher mean (SD) RSRRs only for New York (200 to 399 beds, 12.0 [0.4]; P = .006). Similar results were found for multivariable linear regression models; RSRRs were 1.02% higher for SNHs compared with non-SNHs (95% CI, 0.75%-1.29%; P < .001). Increased RSRRs were observed for hospitals in New York and Florida, teaching hospitals, and investor-owned hospitals. Factors associated with reduced RSRRs included presence of an ambulatory surgery center, cardiac catheterization capabilities, and high surgical volume.Conclusions and relevanceAccording to results of this study, surgical patients treated at SNHs experienced slightly higher RSRRs compared with those treated at non-SNHs. This association persisted after adjusting for year, state, and hospital factors, including teaching status, hospital bed size, and hospital volume.

Project description:ObjectiveTo compare trends in readmission rates among safety net and non-safety net hospitals under the US Hospital Readmission Reduction Program (HRRP).DesignA retrospective time series analysis using Medicare administrative claims data from January 2008 to June 2015.SettingWe examined 3254 US hospitals eligible for penalties under the HRRP, categorised as safety net or non-safety net hospitals based on the hospital's proportion of patients with low socioeconomic status.ParticipantsAdmissions for Medicare fee-for-service patients, age ?65 years, discharged alive, who had a valid five-digit zip code and did not have a principal discharge diagnosis of cancer or psychiatric illness were included, for a total of 52 516 213 index admissions.Primary and secondary outcome measuresMean hospital-level, all-condition, 30-day risk-adjusted standardised unplanned readmission rate, measured quarterly, along with quarterly rate of change, and an interrupted time series examining: April-June 2010, after HRRP was passed, and October-December 2012, after HRRP penalties were implemented.Results58.0% (SD 15.3) of safety net hospitals and 17.1% (SD 10.4) of non-safety net hospitals' patients were in the lowest quartile of socioeconomic status. The mean safety net hospital standardised readmission rate declined from 17.0% (SD 3.7) to 13.6% (SD 3.6), whereas the mean non-safety net hospital declined from 15.4% (SD 3.0) to 12.7% (SD 2.5). The absolute difference in rates between safety net and non-safety net hospitals declined from 1.6% (95% CI 1.3 to 1.9) to 0.9% (0.7 to 1.2). The quarterly decline in standardised readmission rates was 0.03 percentage points (95%?CI 0.03 to 0.02, p<0.001) greater among safety net hospitals over the entire study period, and no differential change among safety net and non-safety net hospitals was found after either HRRP was passed or penalties enacted.ConclusionsSince HRRP was passed and penalties implemented, readmission rates for safety net hospitals have decreased more rapidly than those for non-safety net hospitals.

Dataset Information

Organizational Factors Affect Safety-Net Hospitals' Breast Cancer Treatment Rates.

Publications

Organizational Factors Affect Safety-Net Hospitals' Breast Cancer Treatment Rates.

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