Julia C. Prentice

What are the consequences of waiting for health care in the veteran population

ABSTRACTNational health reform is expected to increase how long individuals have to wait between requests for appointments and when their appointment is scheduled. The increase in demand for care due to more widespread insurance will result in longer waits if there is not also a concomitant increase in supply of healthcare services. Long waits for healthcare are hypothesized to compromise health because less frequent outpatient visits result in delays in diagnosis and treatment. Research testing this hypothesis is scarce due to a paucity of data on how long individuals wait for healthcare in the United States. The main exception is the Veterans Health Administration (VA) that has been routinely collecting data on how long veterans wait for outpatient care for over a decade. This narrative review summarizes the results of studies using VA wait time data to answer two main questions: 1) How much do longer wait times decrease healthcare utilization and 2) Do longer wait times cause poorer health outcomes? Longer VA wait times lead to small, yet statistically significant decreases in utilization and are related to poorer health in elderly and vulnerable veteran populations. Both long-term outcomes (e.g. mortality, preventable hospitalizations) and intermediate outcomes such as hemoglobin A1C levels are worse for veterans who seek care at facilities with longer waits compared to veterans who visit facilities with shorter waits. Further research is needed on the mechanisms connecting longer wait times and poorer outcomes including identifying patient sub-populations whose risks are most sensitive to delayed access to care. If wait times increase for the general patient population with the implementation of national reform as expected, U.S. healthcare policymakers and clinicians will need to consider policies and interventions that minimize potential harms for all patients.

Home-Based Primary Care and the Risk of Ambulatory Care–Sensitive Condition Hospitalization Among Older Veterans With Diabetes Mellitus

IMPORTANCE Primary care services based at home have the potential to reduce the likelihood of hospitalization among older adults with multiple chronic diseases. OBJECTIVE To characterize the association between enrollment in Home-Based Primary Care (HBPC), a national home care program operated by the US Department of Veterans Affairs (VA), and hospitalizations owing to an ambulatory care-sensitive condition among older veterans with diabetes mellitus. DESIGN AND SETTING Retrospective cohort study. Patients admitted to VA and non-VA hospitals were followed up from January 1, 2006, through December 31, 2010. PARTICIPANTS Veterans 67 years or older who were fee-for-service Medicare beneficiaries, were diagnosed as having diabetes mellitus and at least 1 other chronic disease, and had at least 1 admission to a VA or non-VA hospital in 2005 or 2006. EXPOSURES Enrollment in HBPC, defined as a minimum of 2 HBPC encounters during the study period. MAIN OUTCOMES AND MEASURES Admission to VA and non-VA hospitals owing to an ambulatory care-sensitive condition, as measured by the Agency for Healthcare Research and Qualitys Prevention Quality Indicators in VA medical records and Medicare claims. Outcomes were analyzed using distance from the veterans residence to a VA facility that provides HBPC as an instrumental variable. RESULTS Among 56 608 veterans, 1978 enrolled in HBPC. These patients were older (mean age, 79.1 vs 77.1 years) and had more chronic diseases (eg, 59.2% vs 53.5% had congestive heart failure). Multivariable predictors for HBPC enrollment included paralysis (odds ratio [OR], 2.11; 95% CI, 1.63-2.74), depression (OR, 1.99; 95% CI, 1.70-2.34), congestive heart failure (OR, 1.36; 95% CI, 1.17-1.58), and distance from the nearest HBPC-providing VA facility (OR, 0.59; 95% CI, 0.50-0.70 for >10-30 vs <5 miles). After controlling for selection using an instrumental variable analysis, HBPC was associated with a significant reduction in the probability of experiencing a hospitalization owing to an ambulatory care-sensitive condition (hazard ratio, 0.71; 95% CI, 0.57-0.89), with an absolute reduction in the probability of hospitalization of 5.8% in 1 year. CONCLUSIONS AND RELEVANCE Home-Based Primary Care is associated with a decreased probability of ambulatory care-sensitive condition hospitalization among elderly veterans with diabetes mellitus. In accountable care models, HBPC may have an important role in the management of older adults with multiple chronic diseases.

Primary care and health outcomes among older patients with diabetes.

OBJECTIVE The aim of this study was to measure the relationship between days spent waiting for primary care and health outcomes among patients diagnosed with diabetes, especially among the elderly population. DATA SOURCE Secondary data from VA administrative databases and Medicare claims. STUDY DESIGN This is a retrospective observational study. Outcome variables include primary care utilization, mortality, heart attack, stroke, and ambulatory-care sensitive condition (ACSC) hospitalization. The main explanatory variable of interest is VA primary care wait time. Negative binomial models predict utilization and stacked logistic regression models predict the probability of experiencing each health outcome. Models are stratified by the presence of a selected health condition and age. PRINCIPAL FINDINGS Longer wait times were predicted to decrease utilization between 2 and 4 percent. There was no significant relationship between wait times and health outcomes for the overall sample. In stratified analyses, longer waits were associated with undesirable outcomes for those over age 70 with one of the selected health conditions or in certain narrower 5-year age groups, but the overall pattern of results does not indicate a systematic and significant effect. CONCLUSIONS There was a modest effect of long wait times on primary care utilization but no robust effect of longer wait times on health outcomes. Waiting for care did not significantly compromise long-term health outcomes for veterans with diabetes.

Which Outpatient Wait-Time Measures Are Related to Patient Satisfaction?

Long waits for appointments decrease patient satisfaction. Administrative wait-time measures are used by managers, but relationships between these measures and satisfaction have not been studied. Data from the Veterans Health Administration are used to examine the relationship between wait times and satisfaction. Outcome measures include patient-reported satisfaction and timely appointment access. Capacity and retrospective and prospective time stamp measures are calculated separately for new and returning patients. The time stamp measures consist of the date when the appointment was created in the scheduling system (create date [CD]) or the date the appointment was desired as the start date for wait-time computation. Logistic regression models predict patient satisfaction using these measures. The new-patient capacity, new-patient time stamp measures using CD, and the returning-patient desired-date prospective measure were significantly associated with patient satisfaction. Standard practices can be improved by targeting wait-time measures to patient subpopulations.

Identifying the independent effect of HbA1c variability on adverse health outcomes in patients with Type 2 diabetes

To characterize the relationship between HbA1c variability and adverse health outcomes among US military veterans with Type 2 diabetes.

Capitalizing on prescribing pattern variation to compare medications for type 2 diabetes.

BACKGROUND Clinical trials often compare hypoglycemic medications on the basis of glycemic control but do not examine long-term outcomes (e.g., mortality). This study demonstrates an alternative approach to lengthening clinical trials to assess these long-term outcomes. OBJECTIVE To use observational quasi-experimental methods using instrumental variables (IVs) to compare the effect of two hypoglycemic medications, sulfonylureas (SUs) and thiazolidinediones (TZDs), on long-term outcomes. METHODS This study used administrative data from the Veterans Health Administration and Medicare from 2000 to 2010. The study population included US veterans dually enrolled in Medicare who received a prescription for metformin and then initiated SUs or TZDs. Patients could either continue on or discontinue metformin after the initiation of the second agent. Treatment was defined as starting either a SU or a TZD. Local variations in SU prescribing rates were used as instruments in IV models to control for selection bias. Survival models predicted all-cause mortality, ambulatory care sensitive condition hospitalizations, and stroke or heart attack (acute myocardial infarction). RESULTS Starting on SUs compared to TZDs significantly increased the likelihood of experiencing mortality and ACSC hospitalization. The estimated hazard ratio for the effect of starting on SUs compared to TZDs was 1.50 (95% confidence interval [CI] 1.09-2.09) for all-cause mortality, 1.68 (95% CI 1.31-2.15) for ambulatory care sensitive condition hospitalization, and 1.15 (95% CI 0.80-1.66) for acute myocardial infarction or stroke. CONCLUSIONS Our findings suggest increased risk of major adverse events associated with SUs as a second-line agent. Quasi-experimental IV methods may be an important alternative to lengthening clinical trials to assess long-term outcomes.

Overcoming Challenges to Evidence-Based Policy Development in a Large, Integrated Delivery System

OBJECTIVE To describe a new Veterans Health Administration (VHA) program to foster the learning health system paradigm by rigorously evaluating health care initiatives and to report key lessons learned in designing those evaluations. PRINCIPAL FINDINGS The VHAs Quality Enhancement Research Initiative and its Health Services Research and Development Service are cooperating on several large, randomized program evaluations aimed at improving the care veterans receive and the efficiency with which it is delivered. The evaluations we describe involve collaborative design, outcomes assessment, and implementation science through partnerships between VHA operations and researchers. We review key factors to assess before committing to an evaluation. In addition to traditional design issues (such as ensuring adequate power and availability of data), these include others that are easily overlooked: the stability of intervention financing, means of controlling and commitment to adhering to randomized roll-out, degree of buy-in from key implementation staff, and feasibility of managing multiple veto points for interventions that span several programs, among others. CONCLUSIONS Successful program implementation and rigorous evaluation require resources, specialized expertise, and careful planning. If the learning health system model is to be sustained, organizations will need dedicated programs to prioritize resources and continuously adapt evaluation designs.

Preventing Hospitalization with Veterans Affairs Home-Based Primary Care: Which Individuals Benefit Most?

To examine how medical complexity modifies the relationship between enrollment in Department of Veterans Affairs (VA) home‐based primary care (HBPC) and hospitalization for ambulatory care–sensitive conditions (ACSC) for veterans with diabetes mellitus and whether the effect of HBPC on hospitalizations varies according to clinical condition.

Metrics That Matter.

Increasingly, performance metrics are seen as key components for accurately measuring and improving health care value. Disappointment in the ability of chosen metrics to meet these goals is exemplified in a recent Institute of Medicine report that argues for a consensus-building process to determine a simplified set of reliable metrics. Overall health care goals should be defined and then metrics to measure these goals should be considered. If appropriate data for the identified goals are not available, they should be developed. We use examples from our work in the Veterans Health Administration (VHA) on validating waiting time and mental health metrics to highlight other key issues for metric selection and implementation. First, we focus on the need for specification and predictive validation of metrics. Second, we discuss strategies to maintain the fidelity of the data used in performance metrics over time. These strategies include using appropriate incentives and data sources, using composite metrics, and ongoing monitoring. Finally, we discuss the VA’s leadership in developing performance metrics through a planned upgrade in its electronic medical record system to collect more comprehensive VHA and non-VHA data, increasing the ability to comprehensively measure outcomes.