Results of a Regional Effort to Improve Warfarin Management.

BACKGROUND: Improved anticoagulation control with warfarin reduces adverse events and represents a target for quality improvement. No previous study has described an effort to improve anticoagulation control across a health system. OBJECTIVE: To describe the results of an effort to improve anticoagulation control in the New England region of the Veterans Health Administration (VA). METHODS: Our intervention encompassed 8 VA sites managing warfarin for more than 5000 patients in New England (Veterans Integrated Service Network 1 [VISN 1]). We provided sites with a system to measure processes of care, along with targeted audit and feedback. We focused on processes of care associated with site-level anticoagulation control, including prompt follow-up after out-of-range international normalized ratio (INR) values, minimizing loss to follow-up, and use of guideline-concordant INR target ranges. We used a difference-in-differences (DID) model to examine changes in anticoagulation control, measured as percentage time in therapeutic range (TTR), as well as process measures and compared VISN 1 sites with 116 VA sites located outside VISN 1. RESULTS: VISN 1 sites improved on TTR, our main indicator of quality, from 66.4% to 69.2%, whereas sites outside VISN 1 improved from 65.9% to 66.4% (DID 2.3%, P < 0.001). Improvement in TTR correlated strongly with the extent of improvement on process-of-care measures, which varied widely across VISN 1 sites. CONCLUSIONS: A regional quality improvement initiative, using performance measurement with audit and feedback, improved TTR by 2.3% more than control sites, which is a clinically important difference. Improving relevant processes of care can improve outcomes for patients receiving warfarin.

Measuring readmissions after surgery: do different methods tell the same story?

BACKGROUND: Readmission is widely used as a quality metric to assess hospital performance. However, different methods to calculate readmissions may produce various results, leading to differences in classification with respect to hospital performance. This study compared 2 commonly used approaches to measure surgical readmissions: the 30-day all-cause hospital-wide readmissions (HWRs) and the potentially preventable readmissions (PPRs). METHODS: We examined the correlation between hospitals' risk-adjusted HWR and PPR rates and whether there was agreement in categorizing hospital performance between these measures among 111 hospitals with inpatient surgical programs in the Veterans Health Administration. RESULTS: We found that hospitals' HWR and PPR rates were highly correlated (r = .85, P < .0001). The overall agreement between these 2 methods in categorizing hospital performance was 82% for all surgeries, 82% for colectomy, 84% for coronary bypass, and 87% for hip/knee replacement, respectively. CONCLUSIONS: Despite differences in methodologies, the HWR and the PPR measures provided relatively consistent perceptions of hospitals' performance on surgical readmissions.

Do pneumonia readmissions flagged as potentially preventable by the 3M PPR software have more process of care problems? A cross-sectional observational study.

BACKGROUND: In the USA, administrative data-based readmission rates such as the Centers for Medicare and Medicaid Services' all-cause readmission measures are used for public reporting and hospital payment penalties. To improve this measure and identify better quality improvement targets, 3M developed the Potentially Preventable Readmissions (PPRs) measure. It matches clinically related index admission and readmission diagnoses that may indicate readmissions resulting from admission- or post-discharge-related quality problems. OBJECTIVE: To examine whether PPR software-flagged pneumonia readmissions are associated with poorer quality of care. METHODS: Using a retrospective observational study design and Veterans Health Administration (VA) data, we identified pneumonia discharges associated with 30-day readmissions, and then flagged cases as PPR-yes or PPR-no using the PPR software. To assess quality of care, we abstracted electronic medical records of 100 random readmissions using a tool containing explicit care processes organised into admission work-up, in-hospital evaluation/treatment, discharge readiness and post-discharge period. We derived quality scores, scaled to a maximum of 25 per section (maximum total score=100) and compared cases by total and section-specific mean scores using t tests and effect size (ES) to characterise the clinical significance of findings. RESULTS: Our abstraction sample was selected from 11,278 pneumonia readmissions (readmission rate=16.5%) during 1 October 2005-30 September 2010; 77% were flagged as PPR-yes. Contrary to expectations, total and section mean quality scores were slightly higher, although non-significantly, among PPR-yes (N=77) versus PPR-no (N=23) cases (respective total scores, 71.2±8.7 vs 65.8±11.5, p=0.14); differences demonstrated ES >0.30 overall and for admission work-up and post-discharge period sections. CONCLUSIONS: Among VA pneumonia readmissions, PPR categorisation did not produce the expected quality of care findings. Either PPR-yes cases are not more preventable, or preventability assessment requires other data collection methods to capture poorly documented processes (eg, direct observation).

What is the value of adding Medicare data in estimating VA hospital readmission rates?

OBJECTIVE: To determine the effects of including diagnostic and utilization data from a secondary payer on readmission rates and hospital profiles. DATA SOURCES/STUDY SETTING: Veterans Health Administration (VA) and Medicare inpatient and outpatient administrative data for veterans discharged from 153 VA hospitals during FY 2008-2010 with a principal diagnosis of acute myocardial infarction, heart failure, or pneumonia. STUDY DESIGN: We estimated hospital-level risk-standardized readmission rates derived using VA data only. We then used data from both VA and Medicare to reestimate readmission rates and compared hospital profiles using two methods: Hospital Compare and the CMS implementation of the Hospital Readmissions Reduction Program (HRRP). DATA COLLECTION/EXTRACTION METHODS: Retrospective data analysis using VA hospital discharge and outpatient data matched with Medicare fee-for-service claims by scrambled Social Security numbers. PRINCIPAL FINDINGS: Less than 2 percent of hospitals in any cohort were classified discordantly by the Hospital Compare method when using VA-only compared with VA/Medicare data. In contrast, using the HRRP method, 13 percent of hospitals had differences in whether they were flagged as having excessive readmission rates in at least one cohort. CONCLUSIONS: Inclusion of secondary payer data may cause changes in hospital profiles, depending on the methodology used. An assessment of readmission rates should include, to the extent possible, all available information about patients' utilization of care.

Using estimated true safety event rates versus flagged safety event rates: does it change hospital profiling and payment?

OBJECTIVE: To assess whether use of the AHRQ Patient Safety Indicator (PSI) composite measure versus modified composite measures leads to changes in hospital profiles and payments. DATA SOURCES/STUDY SETTING: Retrospective analysis of 2010 Veterans Health Administration discharge data. STUDY DESIGN: We used the AHRQ PSI software (v4.2) to obtain PSI-flagged events and composite scores for all 151 hospitals in the database (n = 517,814 hospitalizations). We compared the AHRQ PSI composite to two modified composites that estimated "true safety events" from previous chart abstraction findings: one with modified numerators based on the positive predictive value (PPV) of each PSI, and one with similarly modified numerators but whose denominators were based on the expected fraction of PSI-eligible cases that remained after removing those PSIs that were present-on-admission (POA). PRINCIPAL FINDINGS: Although a small percentage (5-6 percent) of hospitals changed outlier status based on modified PSI composites, some of these changes were substantial; 30 and 19 percent of hospitals changed ≥20 ranks after adjustment for PPVs and POA flags, respectively. We estimate that 33 percent of hospitals would see a change of at least 10 percent in performance payments. CONCLUSIONS: Changes in hospital profiles and payments would be substantial for some hospitals if the PSI composite score used weights reflecting the relative prevalence of true versus flagged events.

Comparison of the Agency for Healthcare Research and Quality Patient Safety Indicator Rates Among Veteran Dual Users.

This study compares rates of 11 Agency for Healthcare Research and Quality Patient Safety Indicators (PSIs) among 266 203 veteran dual users (ie, those with hospitalizations in both the Veterans Health Administration [VA] and the private sector through Medicare fee-for-service coverage) during 2002 to 2007. PSI risk-adjusted rates were calculated using the PSI software (version 3.1a). Rates of pressure ulcer, central venous catheter-related bloodstream infections, and postoperative sepsis, areas in which the VA has focused quality improvement efforts, were found to be significantly lower in the VA than in the private sector. VA had significantly higher rates for 7 of the remaining 8 PSIs, although the rates of only 2 PSIs (postoperative hemorrhage/hematoma and accidental puncture or laceration) remained higher in the VA after sensitivity analyses were conducted. A better understanding of system-level differences in coding practices and patient severity, poorly documented in administrative data, is needed before conclusions about differences in quality can be drawn.

Medical and surgical readmissions in the Veterans Health Administration: what proportion are related to the index hospitalization?

BACKGROUND: Readmissions are an attractive quality measure because they offer a broad view of quality beyond the index hospitalization. However, the extent to which medical or surgical readmissions reflect quality of care is largely unknown, because of the complexity of factors related to readmission. Identifying those readmissions that are clinically related to the index hospitalization is an important first step in closing this knowledge gap. OBJECTIVES: The aims of this study were to examine unplanned readmissions in the Veterans Health Administration, identify clinically related versus unrelated unplanned readmissions, and compare the leading reasons for unplanned readmission between medical and surgical discharges. METHODS: We classified 2,069,804 Veterans Health Administration hospital discharges (Fiscal Years 2003-2007) into medical/surgical index discharges with/without readmissions per their diagnosis-related groups. Our outcome variable was "all-cause" 30-day unplanned readmission. We compared medical and surgical unplanned readmissions (n=217,767) on demographics, clinical characteristics, and readmission reasons using descriptive statistics. RESULTS: Among all unplanned readmissions, 41.5% were identified as clinically related. Not surprisingly, heart failure (10.2%) and chronic obstructive pulmonary disease (6.5%) were the top 2 reasons for clinically related readmissions among medical discharges; postoperative complications (ie, complications of surgical procedures and medical care or complications of devices) accounted for 70.5% of clinically related readmissions among surgical discharges. CONCLUSIONS: Although almost 42% of unplanned readmissions were identified as clinically related, the majority of unplanned readmissions were unrelated to the index hospitalization. Quality improvement interventions targeted at processes of care associated with the index hospitalization are likely to be most effective in reducing clinically related readmissions. It is less clear how to reduce nonclinically related readmissions; these may involve broader factors than inpatient care.

Comparing 2 methods of assessing 30-day readmissions: what is the impact on hospital profiling in the veterans health administration?

BACKGROUND: The Centers for Medicare and Medicaid Services' (CMS) all-cause readmission measure and the 3M Health Information System Division Potentially Preventable Readmissions (PPR) measure are both used for public reporting. These 2 methods have not been directly compared in terms of how they identify high-performing and low-performing hospitals. OBJECTIVES: To examine how consistently the CMS and PPR methods identify performance outliers, and explore how the PPR preventability component impacts hospital readmission rates, public reporting on CMS' Hospital Compare website, and pay-for-performance under CMS' Hospital Readmission Reduction Program for 3 conditions (acute myocardial infarction, heart failure, and pneumonia). METHODS: We applied the CMS all-cause model and the PPR software to VA administrative data to calculate 30-day observed FY08-10 VA hospital readmission rates and hospital profiles. We then tested the effect of preventability on hospital readmission rates and outlier identification for reporting and pay-for-performance by replacing the dependent variable in the CMS all-cause model (Yes/No readmission) with the dichotomous PPR outcome (Yes/No preventable readmission). RESULTS: The CMS and PPR methods had moderate correlations in readmission rates for each condition. After controlling for all methodological differences but preventability, correlations increased to >90%. The assessment of preventability yielded different outlier results for public reporting in 7% of hospitals; for 30% of hospitals there would be an impact on Hospital Readmission Reduction Program reimbursement rates. CONCLUSIONS: Despite uncertainty over which readmission measure is superior in evaluating hospital performance, we confirmed that there are differences in CMS-generated and PPR-generated hospital profiles for reporting and pay-for-performance, because of methodological differences and the PPR's preventability component.

Excess costs attributable to postoperative complications.

This article estimates excess costs associated with postoperative complications among inpatients treated in Veterans Health Administration (VA) hospitals. The authors conducted an observational study on 43,822 hospitalizations involving inpatient surgery in one of 104 VA hospitals during fiscal year 2007. Hospitalization-level cost regression analyses were performed to estimate the excess cost of each of 18 unique postoperative complications. The authors used generalized linear modeling techniques to account for the heavily skewed cost distribution. Costs were measured using an activity-based cost accounting system and complications were assessed based on medical chart review conducted by the VA 'National Surgical Quality Improvement Program. The authors found excess costs associated with postoperative complications ranging from $8,338 for "superficial surgical site infection" to $29,595 for "failure to wean within 24 hours in the presence of respiratory complications." The results obtained suggest that quality improvement efforts aimed at reducing postoperative complications can contribute significantly to lowering of hospital costs.

Blood pressure and survival in the oldest old.

OBJECTIVES: To determine the relationship between blood pressure (BP) and all-cause mortality in subjects aged 80 and older with hypertension. DESIGN: Retrospective cohort study with 5 years of follow-up. SETTING: Ten Veterans AFFAIRS (VA) sites. PARTICIPANTS: Four thousand seventy-one ambulatory patients aged 80 and older with hypertension. MEASUREMENTS: The outcome measure was likelihood of survival during the follow-up period. Vital status was obtained from VA and Social Security files. Variables collected for adjustment in Cox regression models were baseline BP, medications, demographics, diagnoses, and health-related quality of life (HRQoL); HRQoL information was available on 1,289 subjects based on Veterans Health Study Short From-36 (SF-36) questionnaire scores. RESULTS: Subjects with higher BP (up to a systolic BP (SBP) of 139 mmHg and a diastolic BP (DBP) of 89 mmHg) were less likely to die during follow-up than subjects with lower BP. After baseline adjustments, the hazard ratio for a 10-point increase in SBP was 0.82 (95% confidence interval (CI)=0.74-0.91), up to a SBP of 139 mmHg, and for DBP was 0.85 (95% CI=0.78-0.92), up to a DBP of 89 mmHg. There was no significant association between survival and BP levels in subjects with uncontrolled hypertension. CONCLUSION: In a cohort of very old, hypertensive veterans, in subjects with controlled BPs, subjects with lower BP levels had a lower 5-year survival than those with higher BPs. This suggests that clinicians should use caution in their approach to BP lowering in this age group.