Evaluating the Role of Past Clinical Information on Risk Adjustment.

OBJECTIVE: The objective of this study was to evaluate whether incorporating historical clinical information beyond 1 year improves risk adjustment. DATA SOURCES: Administrative data from the Department of Veterans Affairs and Medicare (for veterans concurrently enrolled in Medicare) for fiscal years (FYs) 2011-2015. STUDY DESIGN: We regressed total annual costs on Medicare hierarchical condition category indicators and risk scores for FY 2015 in both a concurrent and a prospective model using 5-fold cross-validation. Regressions were repeated incorporating clinical information from FY 2011 to 2015. Model fit was appraised using R and mean squared predictive error (MSPE). DATA COLLECTION: All veterans affairs users (n=3,254,783) with diagnostic information FY 2011-2015. PRINCIPAL FINDINGS: In a concurrent model, adding additional years of historical clinical information (FY 2011-2014) did not result in substantive gains in fit (R from 0.671 to 0.673) or predictive capability (MSPE from 1956 to 1950). In a prospective model, adding additional years of historical clinical information also did not result in substantive gains in fit (R from 0.334 to 0.344) or predictive capability (MSPE from 3988 to 3940). CONCLUSION: Incorporating historical clinical information yielded no material gain in risk adjustment fit.

Persistence of High Health Care Costs among VA Patients.

OBJECTIVES: To examine high-cost patients in VA and factors associated with persistence in high costs over time. DATA SOURCES: Secondary data for FY2008-2012. DATA EXTRACTION: We obtained VA and Medicare utilization and cost records for VA enrollees and drew a 20 percent random sample (N = 1,028,568). STUDY DESIGN: We identified high-cost patients, defined as those in the top 10 percent of combined VA and Medicare costs, and determined the number of years they remained high cost over 4 years. We compared sociodemographics, clinical characteristics, and baseline utilization by number of high-cost years and conducted a discrete time survival analysis to predict high-cost persistence. PRINCIPAL FINDINGS: Among 105,703 patients with the highest 10 percent of costs at baseline, 68 percent did not remain high cost in subsequent years, 32 percent had high costs after 1 year, and 7 percent had high costs in all four follow-up years. Mortality, which was 47 percent by end of follow-up, largely explained low persistence. The largest percentage of patients who persisted as high cost until end of follow-up was for spinal cord injury (16 percent). CONCLUSION: Most high-cost patients did not remain high cost in subsequent years, which poses challenges to providers and payers to manage utilization of these patients.

Case-Mix for Performance Management: A Risk Algorithm Based on ICD-10-CM.

BACKGROUND: Accurate risk adjustment is the key to a reliable comparison of cost and quality performance among providers and hospitals. However, the existing case-mix algorithms based on age, sex, and diagnoses can only explain up to 50% of the cost variation. More accurate risk adjustment is desired for provider performance assessment and improvement. OBJECTIVE: To develop a case-mix algorithm that hospitals and payers can use to measure and compare cost and quality performance of their providers. METHODS: All 6,048,895 patients with valid diagnoses and cost recorded in the US Veterans health care system in fiscal year 2016 were included in this study. The dependent variable was total cost at the patient level, and the explanatory variables were age, sex, and comorbidities represented by 762 clinically homogeneous groups, which were created by expanding the 283 categories from Clinical Classifications Software based on ICD-10-CM codes. The split-sample method was used to assess model overfitting and coefficient stability. The predictive power of the algorithms was ascertained by comparing the R, mean absolute percentage error, root mean square error, predictive ratios, and c-statistics. RESULTS: The expansion of the Clinical Classifications Software categories resulted in higher predictive power. The R reached 0.72 and 0.52 for the transformed and raw scale cost, respectively. CONCLUSIONS: The case-mix algorithm we developed based on age, sex, and diagnoses outperformed the existing case-mix models reported in the literature. The method developed in this study can be used by other health systems to produce tailored risk models for their specific purpose.

Assessment of the Medicare Advantage Risk Adjustment Model for Measuring Veterans Affairs Hospital Performance.

Importance: Policymakers and consumers are eager to compare hospitals on performance metrics, such as surgical complications or unplanned readmissions, measured from administrative data. Fair comparisons depend on risk adjustment algorithms that control for differences in case mix. Objective: To examine whether the Medicare Advantage risk adjustment system version 21 (V21) adequately risk adjusts performance metrics for Veterans Affairs (VA) hospitals. Design, Setting, and Participants: This cohort analysis of administrative data from all 5.5 million veterans who received VA care or VA-purchased care in 2012 was performed from September 8, 2015, to October 22, 2018. Data analysis was performed from January 22, 2016, to October 22, 2018. Exposures: A patient's risk as measured by the V21 model. Main Outcomes and Measures: The main outcome was total cost, and the key independent variable was the V21 risk score. Results: Of the 5 472 629 VA patients (mean [SD] age, 63.0 [16.1] years; 5 118 908 [93.5%] male), the V21 model identified 694 706 as having a mental health or substance use condition. In contrast, a separate classification system for psychiatric comorbidities identified another 1 266 938 patients with a mental health condition. The V21 model missed depression not otherwise specified (396 062 [31.3%]), posttraumatic stress disorder (345 338 [27.3%]), and anxiety (129 808 [10.2%]). Overall, the V21 model underestimated the cost of care by $2314 (6.7%) for every person with a mental health diagnosis. Conclusions and Relevance: The findings suggest that current aspirations to engender competition by comparing hospital systems may not be appropriate or fair for safety-net hospitals, including the VA hospitals, which treat patients with complex psychiatric illness. Without better risk scores, which is technically possible, outcome comparisons may potentially mislead consumers and policymakers and possibly aggravate inequities in access for such vulnerable populations.

PA and NP productivity in the Veterans Health Administration.

This study assessed the 2014 clinical productivity of 5,959 physician assistants (PAs) and nurse practitioners (NPs) in the US Department of Veterans Affairs' Veterans Health Administration (VHA). Total work relative value units divided by the direct clinical full-time equivalent measured annual productivity, and correlated factors were examined using weighted analysis of variance. PAs and NPs in adult primary care roles were more productive than those in other specialties. Both providers were more productive in rural than in nonrural settings and less productive in teaching than nonteaching hospitals. Men were slightly more productive than women but age and years of VHA employment were not correlates of productivity. PAs were more productive when their scope of practice allowed significant autonomy; NP productivity was unaffected by supervisory requirements. PAs and NPs are an important component of the VHA provider workforce, and their productivity correlates with a number of factors. More organizational research is necessary to better understand the contributing roles PAs and NPs provide in a rapidly evolving, vertically integrated, national health delivery system.

Risk Adjustment Tools for Learning Health Systems: A Comparison of DxCG and CMS-HCC V21.

OBJECTIVE: To compare risk scores computed by DxCG (Verisk) and Centers for Medicare and Medicaid Services (CMS) V21. RESEARCH DESIGN: Analysis of administrative data from the Department of Veterans Affairs (VA) for fiscal years 2010 and 2011. STUDY DESIGN: We regressed total annual VA costs on predicted risk scores. Model fit was judged by R-squared, root mean squared error, mean absolute error, and Hosmer-Lemeshow goodness-of-fit tests. Recalibrated models were tested using split samples with pharmacy data. DATA COLLECTION: We created six analytical files: a random sample (n = 2 million), high cost users (n = 261,487), users over age 75 (n = 644,524), mental health and substance use users (n = 830,832), multimorbid users (n = 817,951), and low-risk users (n = 78,032). PRINCIPAL FINDINGS: The DxCG Medicaid with pharmacy risk score yielded substantial gains in fit over the V21 model. Recalibrating the V21 model using VA pharmacy data-generated risk scores with similar fit statistics to the DxCG risk scores. CONCLUSIONS: Although the CMS V21 and DxCG prospective risk scores were similar, the DxCG model with pharmacy data offered improved fit over V21. However, health care systems, such as the VA, can recalibrate the V21 model with additional variables to develop a tailored risk score that compares favorably to the DxCG models.

ß-Blockade and Operative Mortality in Noncardiac Surgery: Harmful or Helpful?

IMPORTANCE: The use of perioperative pharmacologic ß-blockade in patients at low risk of myocardial ischemic events undergoing noncardiac surgery (NCS) is controversial because of the risk of stroke and hypotension. Published studies have not found a consistent benefit in this cohort. OBJECTIVE: To determine the effect of perioperative ß-blockade on patients undergoing NCS, particularly those with no risk factors. DESIGN, SETTING, AND PARTICIPANTS: This is a retrospective observational analysis of patients undergoing surgery in Veterans Affairs hospitals from October 1, 2008, through September 31, 2013. METHODS: ß-Blocker use was determined if a dose was ordered at any time between 8 hours before surgery and 24 hours postoperatively. Data from the Veterans Affairs electronic database included demographics, diagnosis and procedural codes, medications, perioperative laboratory values, and date of death. A 4-point cardiac risk score was calculated by assigning 1 point each for renal failure, coronary artery disease, diabetes mellitus, and surgery in a major body cavity. Previously validated linear regression models for all hospitalized acute care medical or surgical patients were used to calculate predicted mortality and then to calculate odds ratios (ORs). MAIN OUTCOMES AND MEASURES: The end point was 30-day surgical mortality. RESULTS: There were 326,489 patients in this cohort: 314,114 underwent NCS and 12,375 underwent cardiac surgery. ß-Blockade lowered the OR for mortality significantly in patients with 3 to 4 cardiac risk factors undergoing NCS (OR, 0.63; 95% CI, 0.43-0.93). It had no effect on patients with 1 to 2 risk factors. However, ß-blockade resulted in a significantly higher chance of death in patients (OR, 1.19; 95% CI, 1.06-1.35) with no risk factors undergoing NCS. CONCLUSIONS AND RELEVANCE: In this large series, ß-blockade appears to be beneficial perioperatively in patients with high cardiac risk undergoing NCS. However, the use of ß-blockers in patients with no cardiac risk factors undergoing NCS increased risk of death in this patient cohort.

Denominator doesn't matter: standardizing healthcare-associated infection rates by bed days or device days.

OBJECTIVE: To examine the impact on infection rates and hospital rank for catheter-associated urinary tract infection (CAUTI), central line-associated bloodstream infection (CLABSI), and ventilator-associated pneumonia (VAP) using device days and bed days as the denominator DESIGN: Retrospective survey from October 2010 to July 2013 SETTING: Veterans Health Administration medical centers providing acute medical and surgical care PATIENTS: Patients admitted to 120 Veterans Health Administration medical centers reporting healthcare-associated infections METHODS: We examined the importance of using device days and bed days as the denominator between infection rates and hospital rank for CAUTI, CLABSI, and VAP for each medical center. The relationship between device days and bed days as the denominator was assessed using a Pearson correlation, and changes in infection rates and device utilization were evaluated by an analysis of variance. RESULTS: A total of 7.9 million bed days were included. From 2011 to 2013, CAUTI decreased whether measured by device days (2.32 to 1.64, P=.001) or bed days (4.21 to 3.02, P=.006). CLABSI decreased when measured by bed days (1.67 to 1.19, P=.04). VAP rates and device utilization ratios for CAUTI, CLABSI, and VAP were not statistically different across time. Infection rates calculated with device days were strongly correlated with infection rates calculated with bed days (r=0.79-0.94, P<.001). Hospital relative performance measured by ordered rank was also strongly correlated for both denominators (r=0.82-0.96, P<.001). CONCLUSIONS: These findings suggest that device days and bed days are equally effective adjustment metrics for comparing healthcare-associated infection rates between hospitals in the setting of stable device utilization.

Predicting potentially avoidable hospitalizations.

BACKGROUND: Hospitalizations due to ambulatory care sensitive conditions (ACSCs) are widely accepted as an indicator of primary care access and effectiveness. However, broad early intervention to all patients in a health care system may be deemed infeasible due to limited resources. OBJECTIVE: To develop a predictive model to identify high-risk patients for early intervention to reduce ACSC hospitalizations, and to explore the predictive power of different variables. METHODS: The study population included all patients treated for ACSCs in the VA system in fiscal years (FY) 2011 and 2012 (n=2,987,052). With all predictors from FY2011, we developed a statistical model using hierarchical logistic regression with a random intercept to predict the risk of ACSC hospitalizations in the first 90 days and the full year of FY2012. In addition, we configured separate models to assess the predictive power of different variables. We used a random split-sample method to prevent overfitting. RESULTS: For hospitalizations within the first 90 days of FY2012, the full model reached c-statistics of 0.856 (95% CI, 0.853-0.860) and 0.856 (95% CI, 0.852-0.860) for the development and validation samples, respectively. For predictive power of the variables, the model with only a random intercept yielded c-statistics of 0.587 (95% CI, 0.582-0.593) and 0.578 (95% CI, 0.573-0.583), respectively; with patient demographic and socioeconomic variables added, the c-statistics improved to 0.725 (95% CI, 0.720-0.729) and 0.721 (95% CI, 0.717-0.726), respectively; adding prior year utilization and cost raised the c-statistics to 0.826 (95% CI, 0.822-0.830) and 0.826 (95% CI,0.822-0.830), respectively; the full model was reached with HCCs added. For the 1-year hospitalizations, only the full model was fitted, which yielded c-statistics of 0.835 (95% CI, 0.831-0.837) and 0.833 (95% CI, 0.830-0.837), respectively, for development and validation samples. CONCLUSIONS: Our analyses demonstrate that administrative data can be effective in predicting ACSC hospitalizations. With high predictive ability, the model can assist primary care providers to identify high-risk patients for early intervention to reduce ACSC hospitalizations.

Impact of patient-level risk adjustment on the findings about nurse staffing and 30-day mortality in veterans affairs acute care hospitals.

BACKGROUND: Studies about nurse staffing and patient outcomes often lack adequate risk adjustment because of limited access to patient information. OBJECTIVE: The aim of this study was to examine the impact of patient-level risk adjustment on the associations of unit-level nurse staffing and 30-day inpatient mortality. METHODS: This retrospective cross-sectional study included 284,097 patients discharged during 2007-2008 from 446 acute care nursing units at 128 Veterans Affairs medical centers. The association of nurse staffing with 30-day mortality was assessed using hierarchical logistic models under three levels of risk-adjustment conditions: using no patient information (low), using patient demographics and diagnoses (moderate), or using patient demographics and diagnoses plus physiological measures (high). RESULTS: Discriminability of the models improved as the level of risk adjustment increased. The c-statistics for models of low, moderate, and high risk adjustment were 0.64, 0.74, and 0.88 for non-ICU patients and 0.66, 0.76, and 0.88 for ICU patients. For non-ICU patients, higher RN skill mix was associated with lower 30-day mortality across all three levels of risk adjustment. For ICU patients, higher total nursing hours per patient day was strongly associated with higher mortality with moderate risk adjustment (p = .0002), but this counterintuitive association was not significant with low or high risk adjustment. DISCUSSION: Inadequate risk adjustment may lead to biased estimates about nurse staffing and patient outcomes. Combining physiological measures with commonly used administrative data is a promising risk-adjustment approach to reduce potential biases.

Degree of Acute Kidney Injury before Dialysis Initiation and Hospital Mortality in Critically Ill Patients.

In a multicenter observational cohort of patients-admitted to intensive care units (ICU), we assessed whether creatinine elevation prior to dialysis initiation in acute kidney injury (AKI-D) further discriminates risk-adjusted mortality. AKI-D was categorized into four groups (Grp) based on creatinine elevation after ICU admission but before dialysis initiation: Grp I > 0.3 mg/dL to <2-fold increase, Grp II ≥2 times but <3 times increase, Grp III ≥3-fold increase in creatinine, and Grp IV none or <0.3 mg/dl increase. Standardized mortality rates (SMR) were calculated by using a validated risk-adjusted mortality model and expressed with 95% confidence intervals (CI). 2,744 patients developed AKI-D during ICU stay; 36.7%, 20.9%, 31.2%, and 11.2% belonged to groups I, II, III, and IV, respectively. SMR showed a graded increase in Grp I, II, and III (1.40 (95% CI, 1.29-1.42), 1.84 (1.66-2.04), and 2.25 (2.07-2.45)) and was 0.98 (0.78-1.20) in Grp IV. In ICU patients with AKI-D, degree of creatinine elevation prior to dialysis initiation is independently associated with hospital mortality. It is the lowest in those experiencing minor or no elevations in creatinine and may represent reversible fluid-electrolyte disturbances.

Despite variation in volume, Veterans Affairs hospitals show consistent outcomes among patients with non-postoperative mechanical ventilation.

OBJECTIVE: To assess the relationship between volume of nonoperative mechanically ventilated patients receiving care in a specific Veterans Health Administration hospital and their mortality. DESIGN: Retrospective cohort study. SETTING: One-hundred nineteen Veterans Health Administration medical centers. PATIENTS: We identified 5,131 hospitalizations involving mechanically ventilated patients in an intensive care unit during 2009, who did not receive surgery. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We extracted demographic and clinical data from the VA Inpatient Evaluation Center. For each hospital, we defined volume as the total number of nonsurgical admissions receiving mechanical ventilation in an intensive care unit during 2009. We examined the hospital contribution to 30-day mortality using multilevel logistic regression models with a random intercept for each hospital. We quantified the extent of interhospital variation in 30-day mortality using the intraclass correlation coefficient and median odds ratio. We used generalized estimating equations to examine the relationship between volume and 30-day mortality and risk-adjusted all models using a patient-level prognostic score derived from clinical data representing the risk of death conditional on treatment at a high-volume hospital. Mean age for the sample was 65 (SD 11) yrs, 97% were men, and 60% were white. The median VA hospital cared for 40 (interquartile range 19-62) mechanically ventilated patients in 2009. Crude 30-day mortality for these patients was 36.9%. After reliability and risk adjustment to the median patient, adjusted hospital-level mortality varied from 33.5% to 40.6%. The intraclass correlation coefficient for the hospital-level variation was 0.6% (95% confidence interval 0.1, 3.4%), with a median odds ratio of 1.15 (95% confidence interval 1.06, 1.38). The relationship between hospital volume of mechanically ventilated and 30-day mortality was not statistically significant: each 50-patient increase in volume was associated with a nonsignificant 2% decrease in the odds of death within 30 days (odds ratio 0.98, 95% confidence interval 0.87-1.10). CONCLUSIONS: Veterans Health Administration hospitals caring for lower volumes of mechanically ventilated patients do not have worse mortality. Mechanisms underlying this finding are unclear, but, if elucidated, may offer other integrated health systems ways to overcome the disadvantages of small-volume centers in achieving good outcomes.

A hybrid Centers for Medicaid and Medicare service mortality model in 3 diagnoses.

INTRODUCTION: Reliance on administrative data sources and a cohort with restricted age range (Medicare 65 y and above) may limit conclusions drawn from public reporting of 30-day mortality rates in 3 diagnoses [acute myocardial infarction (AMI), congestive heart failure (CHF), pneumonia (PNA)] from Center for Medicaid and Medicare Services. METHODS: We categorized patients with diagnostic codes for AMI, CHF, and PNA admitted to 138 Veterans Administration hospitals (2006-2009) into 2 groups (less than 65 y or ALL), then applied 3 different models that predicted 30-day mortality [Center for Medicaid and Medicare Services administrative (ADM), ADM+laboratory data (PLUS), and clinical (CLIN)] to each age/diagnosis group. C statistic (CSTAT) and Hosmer Lemeshow Goodness of Fit measured discrimination and calibration. Pearson correlation coefficient (r) compared relationship between the hospitals' risk-standardized mortality rates (RSMRs) calculated with different models. Hospitals were rated as significantly different (SD) when confidence intervals (bootstrapping) omitted National RSMR. RESULTS: The ≥ 65-year models included 57%-67% of all patients (78%-82% deaths). The PLUS models improved discrimination and calibration across diagnoses and age groups (CSTAT-CHF/65 y and above: 0.67 vs. 0. 773 vs. 0.761; ADM/PLUS/CLIN; Hosmer Lemeshow Goodness of Fit significant 4/6 ADM vs. 2/6 PLUS). Correlation of RSMR was good between ADM and PLUS (r-AMI 0.859; CHF 0.821; PNA 0.750), and 65 years and above and ALL (r>0.90). SD ratings changed in 1%-12% of hospitals (greatest change in PNA). CONCLUSIONS: Performance measurement systems should include laboratory data, which improve model performance. Changes in SD ratings suggest caution in using a single metric to label hospital performance.

Determining population based mortality risk in the Department of Veterans Affairs.

We develop a patient level hierarchical regression model using administrative claims data to assess mortality outcomes for a national VA population. This model, which complements more traditional process driven performance measures, includes demographic variables and disease specific measures of risk classified by Diagnostic Cost Groups (DCGs). Results indicate some ability to discriminate survivors and non-survivors with an area under the Receiver Operating Characteristic Curve (C-statistic) of .86. Observed to expected mortality ranges from .86 to 1.12 across predicted mortality deciles while Risk Standardized Mortality Rates (RSMRs) range from .76 to 1.29 across 145 VA hospitals. Further research is necessary to understand mortality variation which persists even after adjusting for case mix differences. Future work is also necessary to examine the role of personal behaviors on patient outcomes and the potential impact on population survival rates from changes in treatment policy and infrastructure investment.

Organizational factors associated with decreased mortality among Veterans Affairs patients with an ICU stay.

In-hospital mortality rates associated with an ICU stay are high and vary widely among units. This variation may be related to organizational factors such as staffing patterns, ICU structure, and care processes. We aimed to identify organizational factors associated with variation in in-hospital mortality for patients with an ICU stay. This was a retrospective observational cross-sectional study using administrative data from 34 093 patients from 171 ICUs in 119 Veterans Health Administration hospitals. Staffing and patient data came from Veterans Health Administration national databases. ICU characteristics came from a survey in 2004 of ICUs within the Veterans Health Administration. We conducted multilevel multivariable estimation with patient-, unit-, and hospital-level data. The primary outcome was in-hospital mortality. Of 34 093 patients, 2141 (6.3%)died in the hospital. At the patient level, risk of complications and having a medical diagnosis were significantly associated with a higher risk of mortality. At the unit level, having an interface with the electronic medical record was significantly associated with a lower risk of mortality. The finding that electronic medical records integrated with ICU information systems are associated with lower in-hospital mortality adds support to existing evidence on organizational characteristics associated with in-hospital mortality among ICU patients.

Variations in efficiency and the relationship to quality of care in the veterans health system.

There is widespread belief that the US health care system could realize significant improvements in efficiency, savings, and patient outcomes if care were provided in a more integrated and accountable way. We examined efficiency and its relationship to quality of care for medical centers run by the Veterans Health Administration of the Department of Veterans Affairs (VA), a national, vertically integrated health care system that is accountable for a large patient population. After devising a statistical model to indicate efficiency, we found that VA medical centers were highly efficient. We also found only modest variation in the level of efficiency and cost across VA medical centers, and a positive correlation overall between greater efficiency and higher inpatient quality. These findings for VA medical centers suggest that efforts to drive integration and accountability in other parts of the US health care system might have important payoffs in reducing variations in cost without sacrificing quality. Policy makers should focus on what aspects of certain VA medical centers allow them to provide better care at lower costs and consider policies that incentivize other providers, both within and outside the VA, to adopt these practices.

Reduction of central line infections in Veterans Administration intensive care units: an observational cohort using a central infrastructure to support learning and improvement.

BACKGROUND: Elimination of hospital-acquired infections is an important patient safety goal. SETTING: All 174 medical, cardiac, surgical and mixed Veterans Administration (VA) intensive care units (ICUs). INTERVENTION: A centralised infrastructure (Inpatient Evaluation Center (IPEC)) supported the practice bundle implementation (handwashing, maximal barriers, chlorhexidinegluconate site disinfection, avoidance of femoral catheterisation and timely removal) to reduce central line-associated bloodstream infections (CLABSI). Support included recruiting leadership, benchmarked feedback, learning tools and selective mentoring. DATA COLLECTION: Sites recorded the number of CLABSI, line days and audit results of bundle compliance on a secure website. ANALYSIS: CLABSI rates between years were compared with incidence rate ratios (IRRs) from a Poisson regression and with National Healthcare Safety Network referent rates (standardised infection ratio (SIR)). Pearson's correlation coefficient compared bundle adherence with CLABSI rates. Semi-structured interviews with teams struggling to reduce CLABSI identified common themes. RESULTS: From 2006 to 2009, CLABSI rates fell (3.8-1.8/1000 line days; p<0.01); as did IRR (2007; 0.83 (95% CI 0.73 to 0.94), 2008; 0.65 (95% CI 0.56 to 0.76), 2009; 0.47 (95% CI 0.40 to 0.55)). Bundle adherence and CLABSI rates showed strong correlation (r = 0.81). VA CLABSI SIR, January to June 2009, was 0.76 (95% CI 0.69 to 0.90), and for all FY2009 0.88 (95% CI 0.80 to 0.97). Struggling sites lacked a functional team, forcing functions and feedback systems. CONCLUSION: Capitalising on a large healthcare system, VA IPEC used strategies applicable to non-federal healthcare systems and communities. Such tactics included measurement through information technology, leadership, learning tools and mentoring.

Infrastructure for quality transformation: measurement and reporting in veterans administration intensive care units.

BACKGROUND Veterans Health Administration (VA) intensive care units (ICUs) develop an infrastructure for quality improvement using information technology and recruiting leadership. METHODS Setting Participation by the 183 ICUs in the quality improvement program is required. Infrastructure includes measurement (electronic data extraction, analysis), quarterly web-based reporting and implementation support of evidence-based practices. Leaders prioritise measures based on quality improvement objectives. The electronic extraction is validated manually against the medical record, selecting hospitals whose data elements and measures fall at the extremes (10th, 90th percentile). results are depicted in graphic, narrative and tabular reports benchmarked by type and complexity of ICU. RESULTS The VA admits 103 689±1156 ICU patients/year. Variation in electronic business practices, data location and normal range of some laboratory tests affects data quality. A data management website captures data elements important to ICU performance and not available electronically. A dashboard manages the data overload (quarterly reports ranged 106-299 pages). More than 85% of ICU directors and nurse managers review their reports. Leadership interest is sustained by including ICU targets in executive performance contracts, identification of local improvement opportunities with analytic software, and focused reviews. CONCLUSION Lessons relevant to non-VA institutions include the: (1) need for ongoing data validation, (2) essential involvement of leadership at multiple levels, (3) supplementation of electronic data when key elements are absent, (4) utility of a good but not perfect electronic indicator to move practice while improving data elements and (5) value of a dashboard.

Declining mortality following acute myocardial infarction in the Department of Veterans Affairs Health Care System.

BACKGROUND: Mortality from acute myocardial infarction (AMI) is declining worldwide. We sought to determine if mortality in the Veterans Health Administration (VHA) has also been declining. METHODS: We calculated 30-day mortality rates between 2004 and 2006 using data from the VHA External Peer Review Program (EPRP), which entails detailed abstraction of records of all patients with AMI. To compare trends within VHA with other systems of care, we estimated relative mortality rates between 2000 and 2005 for all males 65 years and older with a primary diagnosis of AMI using administrative data from the VHA Patient Treatment File and the Medicare Provider Analysis and Review (MedPAR) files. RESULTS: Using EPRP data on 11,609 patients, we observed a statistically significant decline in adjusted 30-day mortality following AMI in VHA from 16.3% in 2004 to 13.9% in 2006, a relative decrease of 15% and a decrease in the odds of dying of 10% per year (p = .011). Similar declines were found for in-hospital and 90-day mortality.Based on administrative data on 27,494 VHA patients age 65 years and older and 789,400 Medicare patients, 30-day mortality following AMI declined from 16.0% during 2000-2001 to 15.7% during 2004-June 2005 in VHA and from 16.7% to 15.5% in private sector hospitals. After adjusting for patient characteristics and hospital effects, the overall relative odds of death were similar for VHA and Medicare (odds ratio 1.02, 95% C.I. 0.96-1.08). CONCLUSION: Mortality following AMI within VHA has declined significantly since 2003 at a rate that parallels that in Medicare-funded hospitals.

Hyperglycemia-related mortality in critically ill patients varies with admission diagnosis.

OBJECTIVES: Hyperglycemia during critical illness is common and is associated with increased mortality. Intensive insulin therapy has improved outcomes in some, but not all, intervention trials. It is unclear whether the benefits of treatment differ among specific patient populations. The purpose of the study was to determine the association between hyperglycemia and risk- adjusted mortality in critically ill patients and in separate groups stratified by admission diagnosis. A secondary purpose was to determine whether mortality risk from hyperglycemia varies with intensive care unit type, length of stay, or diagnosed diabetes. DESIGN: Retrospective cohort study. SETTING: One hundred seventy-three U.S. medical, surgical, and cardiac intensive care units. PATIENTS: Two hundred fifty-nine thousand and forty admissions from October 2002 to September 2005; unadjusted mortality rate, 11.2%. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A two-level logistic regression model determined the relationship between glycemia and mortality. Age, diagnosis, comorbidities, and laboratory variables were used to calculate a predicted mortality rate, which was then analyzed with mean glucose to determine the association of hyperglycemia with hospital mortality. Hyperglycemia was associated with increased mortality independent of illness severity. Compared with normoglycemic individuals (70-110 mg/dL), adjusted odds of mortality (odds ratio, [95% confidence interval]) for mean glucose 111-145, 146-199, 200-300, and >300 mg/dL was 1.31 (1.26-1.36), 1.82 (1.74-1.90), 2.13 (2.03-2.25), and 2.85 (2.58-3.14), respectively. Furthermore, the adjusted odds of mortality related to hyperglycemia varied with admission diagnosis, demonstrating a clear association in some patients (acute myocardial infarction, arrhythmia, unstable angina, pulmonary embolism) and little or no association in others. Hyperglycemia was associated with increased mortality independent of intensive care unit type, length of stay, and diabetes. CONCLUSIONS: The association between hyperglycemia and mortality implicates hyperglycemia as a potentially harmful and correctable abnormality in critically ill patients. The finding that hyperglycemia-related risk varied with admission diagnosis suggests differences in the interaction between specific medical conditions and injury from hyperglycemia. The design and interpretation of future trials should consider the primary disease states of patients and the balance of medical conditions in the intensive care unit studied.