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.

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.

Veterans Affairs initiative to prevent methicillin-resistant Staphylococcus aureus infections.

BACKGROUND: Health care-associated infections with methicillin-resistant Staphylococcus aureus (MRSA) have been an increasing concern in Veterans Affairs (VA) hospitals. METHODS: A "MRSA bundle" was implemented in 2007 in acute care VA hospitals nationwide in an effort to decrease health care-associated infections with MRSA. The bundle consisted of universal nasal surveillance for MRSA, contact precautions for patients colonized or infected with MRSA, hand hygiene, and a change in the institutional culture whereby infection control would become the responsibility of everyone who had contact with patients. Each month, personnel at each facility entered into a central database aggregate data on adherence to surveillance practice, the prevalence of MRSA colonization or infection, and health care-associated transmissions of and infections with MRSA. We assessed the effect of the MRSA bundle on health care-associated MRSA infections. RESULTS: From October 2007, when the bundle was fully implemented, through June 2010, there were 1,934,598 admissions to or transfers or discharges from intensive care units (ICUs) and non-ICUs (ICUs, 365,139; non-ICUs, 1,569,459) and 8,318,675 patient-days (ICUs, 1,312,840; and non-ICUs, 7,005,835). During this period, the percentage of patients who were screened at admission increased from 82% to 96%, and the percentage who were screened at transfer or discharge increased from 72% to 93%. The mean (±SD) prevalence of MRSA colonization or infection at the time of hospital admission was 13.6±3.7%. The rates of health care-associated MRSA infections in ICUs had not changed in the 2 years before October 2007 (P=0.50 for trend) but declined with implementation of the bundle, from 1.64 infections per 1000 patient-days in October 2007 to 0.62 per 1000 patient-days in June 2010, a decrease of 62% (P<0.001 for trend). During this same period, the rates of health care-associated MRSA infections in non-ICUs fell from 0.47 per 1000 patient-days to 0.26 per 1000 patient-days, a decrease of 45% (P<0.001 for trend). CONCLUSIONS: A program of universal surveillance, contact precautions, hand hygiene, and institutional culture change was associated with a decrease in health care-associated transmissions of and infections with MRSA in a large health care system.

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.

Liberation and animation: strategies to minimize brain dysfunction in critically ill patients.

Acute brain dysfunction, usually manifested as delirium, occurs in up to 80% of critically ill patients. Delirium increases costs of hospitalizations and affects short-term outcomes such as duration of mechanical ventilation, intensive care unit (ICU) length of stay, and the hospital length of stay. Long-term consequences-cognitive impairment and increased risk of death-can be devastating. For adequate recognition and management it is imperative to implement a successful delirium monitoring and assessment strategy. A liberation and animation strategy can reduce both the incidence and the duration of delirium. Liberation aims to reduce the harmful effects of sedative exposure through use of target-based sedation protocols, spontaneous awakening trials, and proper choice of sedative as well as liberation from the ventilator and the ICU. Animation refers to early mobilization, which reduces delirium and improves neurocognitive outcomes. Delirium is a serious problem with important consequences and can be prevented or improved using the information that we have learned in the last decade.

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.

Incidence and outcomes of acute kidney injury in intensive care units: a Veterans Administration study.

OBJECTIVES: : To examine the effect of severity of acute kidney injury or renal recovery on risk-adjusted mortality across different intensive care unit settings. Acute kidney injury in intensive care unit patients is associated with significant mortality. DESIGN: : Retrospective observational study. SETTING: : There were 325,395 of 617,927 consecutive admissions to all 191 Veterans Affairs ICUs across the country. PATIENTS: : Large national cohort of patients admitted to Veterans Affairs ICUs and who developed acute kidney injury during their intensive care unit stay. MEASUREMENTS AND MAIN RESULTS: : Outcome measures were hospital mortality, and length of stay. Acute kidney injury was defined as a 0.3-mg/dL increase in creatinine relative to intensive care unit admission and categorized into Stage I (0.3 mg/dL to <2 times increase), Stage II (> or =2 and <3 times increase), and Stage III (> or =3 times increase or dialysis requirement). Association of mortality and length of stay with acute kidney injury stages and renal recovery was examined. Overall, 22% (n = 71,486) of patients developed acute kidney injury (Stage I: 17.5%; Stage II: 2.4%; Stage III: 2%); 16.3% patients met acute kidney injury criteria within 48 hrs, with an additional 5.7% after 48 hrs of intensive care unit admission. Acute kidney injury frequency varied between 9% and 30% across intensive care unit admission diagnoses. After adjusting for severity of illness in a model that included urea and creatinine on admission, odds of death increased with increasing severity of acute kidney injury. Stage I odds ratio = 2.2 (95% confidence interval, 2.17-2.30); Stage II odds ratio = 6.1 (95% confidence interval, 5.74, 6.44); and Stage III odds ratio = 8.6 (95% confidence interval, 8.07-9.15). Acute kidney injury patients with sustained elevation of creatinine experienced higher mortality risk than those who recovered. INTERVENTIONS: : None. CONCLUSIONS: : Admission diagnosis and severity of illness influence frequency and severity of acute kidney injury. Small elevations in creatinine in the intensive care unit are associated with increased risk-adjusted mortality across all intensive care unit settings, whereas renal recovery was associated with a protective effect. Strategies to prevent even mild acute kidney injury or promote renal recovery may improve survival.

Veterans Affairs intensive care unit risk adjustment model: validation, updating, recalibration.

BACKGROUND: A valid metric is critical to measure and report intensive care unit (ICU) outcomes and drive innovation in a national system. OBJECTIVES: To update and validate the Veterans Affairs (VA) ICU severity measure (VA ICU). RESEARCH DESIGN: A validated logistic regression model was applied to two VA hospital data sets: 36,240 consecutive ICU admissions to a stratified random sample of moderate and large hospitals in 1999-2000 (cohort 1) and 81,964 cases from 42 VA Medical Centers in fiscal years 2002-2004 (cohort 2). The model was updated by adding diagnostic groups and expanding the source of admission variables. MEASURES: C statistic, Hosmer-Lemeshow goodness-of-fit statistic, and Brier's score measured predictive validity. Coefficients from the 1997 model were applied to predictors (fixed) in a logistic regression model. A 10 x 10 table compared cases with both VA ICU and National Surgical Quality Improvement Performance metrics. The standardized mortality ratios divided observed deaths by the sum of predicted mortality. RESULTS: The fixed model in both cohorts had predictive validity (cohort 1: C statistic = 0.874, Hosmer-Lemeshow goodness-of-fit C statistic chi-square = 72.5; cohort 2: 0.876, 307), as did the updated model (cohort 2: C statistic = 0.887, Hosmer-Lemeshow goodness-of-fit C statistic chi-square = 39). In 7,411 cases with predictions in both systems, the standardized mortality ratio was similar (1.04 for VA ICU, 1.15 for National Surgical Quality Improvement Performance), and 92% of cases matched (+/-1 decile) when ordered by deciles of mortality. The VA ICU standardized mortality ratio correlates with the National Surgical Quality Improvement Performance standardized mortality ratio (r2 = .74). Variation in discharge and laboratory practices may affect performance measurement. CONCLUSION: The VA ICU severity model has face, construct, and predictive validity.

Using human factors methods to design a new interface for an electronic medical record.

The Veterans Health Administration (VHA) is a leader in development and use of electronic patient records and clinical decision support. The VHA is currently reengineering a somewhat dated platform for its Computerized Patient Record System (CPRS). This process affords a unique opportunity to implement major changes to the current design and function of the system. We report on two human factors studies designed to provide input and guidance during this reengineering process. One study involved a card sort to better understand how providers tend to cognitively organize clinical data, and how that understanding can help guide interface design. The other involved a simulation to assess the impact of redesign modifications on computerized clinical reminders, a form of clinical decision support in the CPRS, on the learnability of the system for first-time users.

Evidence-based practice to reduce central line infections.

BACKGROUND: In 2003, through the Greater Cincinnati Health Council nine health care systems agreed to participate and fund 50% of a two-year project to reduce hospital-acquired infections among patients in intensive care units (ICU) and following surgery (SIP). METHODS: Hospitals were randomized to either the CR-BSI or SIP project in the first year, adding the alternative project in year 2. Project leaders, often the infection control professionals, implemented evidence-based practices to reduce catheter-related blood stream infections (CR-BSIs; maximal sterile barriers, chlorhexidine) at their hospitals using a collaborative approach. Team leaders entered process information in a secure deidentifled Web-based database. RESULTS: Of the four initial sites randomized to CR-BSI reduction, all reduced central line infections by 50% (CR-BSI, 1.7 to 0.4/1000 line days, p < .05). At the project midpoint (3 quarters of 2004), adherence to evidence-based practices increased from 30% to nearly 95%. DISCUSSION: The direct role of hospital leadership and development of a local community of practice, facilitated cooperation of physicians, problem solving, and success. Use of forcing functions (removal of betadine in kits, creation of an accessory pack and a checklist for line insertion) improved reliability. The appropriate floor for central line infections in ICUs is < 1 infection /1,000 line days.

Compliance with intended use of Bar Code Medication Administration in acute and long-term care: an observational study.

OBJECTIVE: To identify the types and extent of workaround strategies with the use of Bar Code Medication Administration (BCMA) in acute care and long-term care settings. BACKGROUND: Medication errors are the most commonly documented cause of adverse events in hospital settings. Scanning of bar codes to verify patient and medication information may reduce medication errors. METHOD: A prospective ethnographic study was conducted using targeted observation. Fifteen acute care and 13 long-term care nurses were directly observed during medication administration at small, medium, and large Veterans Administration hospitals to detect workaround strategies. RESULTS: Noncompliance with recommended practices was observed in all settings and facilities. A larger proportion of acute care nurses than long-term care nurses scanned bar-coded wristbands to identify patients (53% vs. 8%, p = .016). A larger proportion of acute care nurses than long-term care nurses administered bar-coded medications immediately after scanning (93% vs. 23%, p < .001). CONCLUSION: Workaround strategies were employed with BCMA that increased efficiency but created new potential paths to adverse events. There was a significant difference in the rate of use of workaround strategies between acute and long-term care. APPLICATION: The extent of workaround strategies varied by care setting and facility. BCMA should be tailored to the long-term care setting, including increasing the efficiency of use. Hospitals implementing bar coding should facilitate the intended use through equipment procurement, implementation, and quality improvement strategies.

Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure.

OBJECTIVE: To quantify the variability in risk-adjusted mortality and length of stay of Veterans Affairs intensive care units using a computer-based severity of illness measure. DESIGN: Retrospective cohort study. SETTING: A stratified random sample of 34 intensive care units in 17 Veterans Affairs hospitals. PARTICIPANTS: A consecutive sample of 29,377 first intensive care unit admissions from February 1996 through July 1997. INTERVENTIONS: Standardized mortality ratio (observed/expected deaths) and observed minus expected length of stay (OMELOS) with 95% confidence intervals were estimated for each unit using a hierarchical logistic (standardized mortality ratio) or linear (OMELOS) regression model with Markov Chain Monte Carlo simulation. We adjusted for patient characteristics including age, admission diagnosis, comorbid disease, physiology at admission (from laboratory data), and transfer status. MEASUREMENTS AND MAIN RESULTS: Mortality across the intensive care units for the 12,088 surgical and 17,289 medical cases averaged 11% (range, 2-30%). Length of stay in the intensive care units averaged 4.0 days (range, mean unit length of stay 3.0-5.9). Standardized mortality ratio of the intensive care units varied from 0.62 to 1.27; the standardized mortality ratio and 95% confidence interval were <1 for four intensive care units and >1.0 for seven intensive care units. OMELOS of the intensive care units ranged from -0.89 to 1.34 days. In a random slope hierarchical model, variation in standardized mortality ratio among intensive care units was similar across the range of severity, whereas variation in length of stay increased with severity. Standardized mortality ratio was not associated with OMELOS (Pearson's r = .13). CONCLUSIONS: We identified intensive care units whose indicators for mortality and length of stay differ substantially using a conservative statistical approach with a severity adjustment model based on data available in computerized clinical databases. Computerized risk adjustment employing routinely available data may facilitate research on the utility of intensive care unit profiling and analysis of natural experiments to understand process and outcome links and quality efforts.

Exploring barriers and facilitators to the use of computerized clinical reminders.

OBJECTIVE: Evidence-based practices in preventive care and chronic disease management are inconsistently implemented. Computerized clinical reminders (CRs) can improve compliance with these practices in outpatient settings. However, since clinician adherence to CR recommendations is quite variable and declines over time, we conducted observations to determine barriers and facilitators to the effective use of CRs. DESIGN: We conducted an observational study of nurses and providers interacting with CRs in outpatient primary care clinics for two days in each of four geographically distributed Veterans Administration (VA) medical centers. MEASUREMENTS: Three observers recorded interactions of 35 nurses and 55 physicians and mid-level practitioners with the CRs, which function as part of an electronic medical record. Field notes were typed, coded in a spreadsheet, and then sorted into logical categories. We then integrated findings across observations into meaningful patterns and abstracted the data into themes, such as recurrent strategies. Several of these themes translated directly to barriers and facilitators to effective CR use. RESULTS: Optimally using the CR system for its intended purpose was impeded by (1) lack of coordination between nurses and providers; (2) using the reminders while not with the patient, impairing data acquisition and/or implementation of recommended actions; (3) workload; (4) lack of CR flexibility; and (5) poor interface usability. Facilitators included (1) limiting the number of reminders at a site; (2) strategic location of the computer workstations; (3) integration of reminders into workflow; and (4) the ability to document system problems and receive prompt administrator feedback. CONCLUSION: We identified barriers that might explain some of the variability in the use of CRs. Although these barriers may be difficult to overcome, some strategies may increase user acceptance and therefore the effectiveness of the CRs. These include explicitly assigning responsibility for each CR to nurses or providers, improving visibility of positive results from CRs in the electronic medical record, creating a feedback mechanism about CR use, and limiting the overall number of CRs.

An irreplaceable safety culture.

Intensive care unit (ICU) clinicians are sources of errors and of resilience. When they learn how to juggle many competing goals, remain vigilant, and tell safety stories--all in the context of changing technologies and demand--they can create safe settings of care. Other strategies (eg, using computerized tools and implementing safety procedures) are important, but alone they are not sufficient. An ICU needs a safety culture that is rooted in a committed leadership, the acknowledgment that error is inevitable, a reporting system, and continuous learning. The all too common norm, "no harm no foul," is an obstacle. ICU leaders can use a campaign strategy to spread the safety practices that sustain a safety culture. They should attend to the political, marketing, and military aspects of such campaigns and recognize that people's time and attention are limited and built projects from existing ongoing pilots. Pilots can compete for people's attention; it has pull when it exemplifies a moral idea, simplifies work, and gives the health care professional more control and feedback. Under these conditions, the campaign will release individuals' passions and add energy and insight to the campaign itself.

Value for taxpayers' dollars: what VA care would cost at medicare prices.

Critics charge that Veterans Health Administration (VA) medical centers are inefficient and the cost of veteran health care would be reduced if VA purchased care for its patients directly from private-sector providers. This analysis compares VA medical care expenditures with estimates of total payments under a hypothetical Medicare fee-for-service payment system reimbursing providers for the same counts of each service VA medical centers provided in fiscal 1999. At six study sites, hypothetical payments were more than 20 percent greater than actual budgets. Nationally, this represented more than 3 billion US dollars in 1999 and more than 5 billion US dollars in 2003. Data limitations suggest the estimate is conservative. Less than half of the difference is due to VA's low pharmacy costs. The study demonstrates the potential savings to patients and taxpayers of the VA health care system.