The TEP caregiver intervention resulted in short-term cost savings for veteran care recipients. Future studies of caregiver interventions should examine their effects on costs of care for the care recipients in addition to their effects on caregiver outcomes.

The implementation of the family support coordinator intervention increased family satisfaction across a range of parameters. Although there were decreases in length-of-stay and costs, they were not statistically significant. Further research is needed to determine whether intervention refinement could produce lower length-of-stay and costs.

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.

The rate of hospitalizations due to ambulatory care-sensitive conditions (ACSCs) has been widely accepted as an indicator of access and quality of primary care. This study aimed to examine the trends and geographic variation of ACSC hospitalizations in US veterans health-care system, to identify factors associated with ACSC hospitalizations and to develop a quality indicator that can monitor access and effectiveness of primary care at hospital level. Using fiscal years 1997-2007 data, we found total ACSC hospitalizations per 1000 ACSC patients decreased by 58%; ACSC hospitalizations as percentage of total hospitalizations decreased 9%. However, significant geographic variations of ACSC hospitalizations remained and we found that adjustment of case-mix or confounding factors was essential in making meaningful comparisons among hospitals in a health-care system. Further, this study also reveals that low-income veterans still had higher ACSC hospitalization rates and patient travel time less than 30 minutes to the nearest VA providers was associated with fewer ACSC hospitalizations, which possess important policy implications.

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.

Background In medical research and practice, the p-value is arguably the most often used statistic and yet it is widely misconstrued as the probability of the type I error, which comes with serious consequences. This misunderstanding can greatly affect the reproducibility in research, treatment selection in medical practice, and model specification in empirical analyses. By using plain language and concrete examples, this paper is intended to elucidate the p-value confusion from its root, to explicate the difference between significance and hypothesis testing, to illuminate the consequences of the confusion, and to present a viable alternative to the conventional p-value. Main text The confusion with p-values has plagued the research community and medical practitioners for decades. However, efforts to clarify it have been largely futile, in part, because intuitive yet mathematically rigorous educational materials are scarce. Additionally, the lack of a practical alternative to the p-value for guarding against randomness also plays a role. The p-value confusion is rooted in the misconception of significance and hypothesis testing. Most, including many statisticians, are unaware that p-values and significance testing formed by Fisher are incomparable to the hypothesis testing paradigm created by Neyman and Pearson. And most otherwise great statistics textbooks tend to cobble the two paradigms together and make no effort to elucidate the subtle but fundamental differences between them. The p-value is a practical tool gauging the “strength of evidence” against the null hypothesis. It informs investigators that a p-value of 0.001, for example, is stronger than 0.05. However, p-values produced in significance testing are not the probabilities of type I errors as commonly misconceived. For a p-value of 0.05, the chance a treatment does not work is not 5%; rather, it is at least 28.9%. Conclusions A long-overdue effort to understand p-values correctly is much needed. However, in medical research and practice, just banning significance testing and accepting uncertainty are not enough. Researchers, clinicians, and patients alike need to know the probability a treatment will or will not work. Thus, the calibrated p-values (the probability that a treatment does not work) should be reported in research papers.

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.

Purpose: The long-term effectiveness and efficiency of an outpatient geriatric evaluation and management (GEM) program was compared to usual primary care (UPC). Design and Method: A randomized controlled group design was used. Health care utilization, cost of care, and survival were assessed during a 48-month period among a sample of 160 male veterans age 55 and over who were above-average users of outpatient services. Results: The results indicate that GEM patients incurred significantly lower overall health care costs than UPC patients by 24 months and that cost savings plateaued during the 24- to 48-month period. Cost savings were due primarily to fewer hospital days of care. No significant differences were found in survival. Implications: Results of this follow-up study suggest that outpatient GEMoffers a specialized health delivery option for frail older persons that may reduce costs over the long term without having a negative impact on survival rates.