Data and well-constructed measures quantify suboptimal quality in health care and play a crucial role in improving quality. Measures are useful for three major purposes: (1) driving improvements in outcomes of care by prioritizing and selecting appropriate interventions, (2) developing comparative quality reports for consumer and payer decision making and health system accountability, and (3) creating incentives that pay for performance. This article describes the current landscape for measurement in pediatrics compared to adult care, provides a case study of the development and application of a publicly available and federally funded pediatric indicator set using routinely collected hospital discharge data, and addresses challenges and opportunities in selecting and using measures as a function of intended purpose.

Despite recommendations for voluntary HIV screening, few medical centres have implemented screening programmes. The objective of the study was to determine whether an intervention with computer-based reminders and feedback would increase screening for HIV in a Department of Veterans Affairs (VA) health-care system. The design of the study was a randomized controlled trial at five primary care clinics at the VA Palo Alto Health Care System. All primary care providers were eligible to participate in the study. The study intervention was computer-based reminders to either assess HIV risk behaviours or to offer HIV testing; feedback on adherence to reminders was provided. The main outcome measure was the difference in HIV testing rates between intervention and control group providers. The control group providers tested 1.0% (n = 67) and 1.4% (n = 106) of patients in the preintervention and intervention period, respectively; intervention providers tested 1.8% (n = 98) and 1.9% (n = 114), respectively (P = 0.75).In our random sample of 753 untested patients, 204 (27%) had documented risk behaviours. Providers were more likely to adhere to reminders to test rather than with reminders to perform risk assessment (11% versus 5%, P < 0.01). Sixty-one percent of providers felt that lack of time prevented risk assessment. In conclusion, in primary care clinics in our setting, HIV testing rates were low. Providers were unaware of the high rates of risky behaviour in their patient population and perceived important barriers to testing. Low-intensity clinical reminders and feedback did not increase rates of screening.

BACKGROUND: Previous research has provided evidence that socioeconomic status has an impact on invasive treatments use after acute myocardial infarction. In this paper, we compare the socioeconomic inequality in the use of high-technology diagnosis and treatment after acute myocardial infarction between the US, Quebec and Belgium paying special attention to financial incentives and regulations as explanatory factors.

METHODS: We examined hospital-discharge abstracts for all patients older than 65 who were admitted to hospitals during the 1993-1998 period in the US, Quebec and Belgium with a primary diagnosis of acute myocardial infarction. Patients' income data were imputed from the median incomes of their residential area. For each country, we compared the risk-adjusted probability of undergoing each procedure between socioeconomic categories measured by the patient's area median income.

RESULTS: Our findings indicate that income-related inequality exists in the use of high-technology treatment and diagnosis techniques that is not justified by differences in patients' health characteristics. Those inequalities are largely explained, in the US and Quebec, by inequalities in distances to hospitals with on-site cardiac facilities. However, in both Belgium and the US, inequalities persist among patients admitted to hospitals with on-site cardiac facilities, rejecting the hospital location effect as the single explanation for inequalities. Meanwhile, inequality levels diverge across countries (higher in the US and in Belgium, extremely low in Quebec).

CONCLUSIONS: The findings support the hypothesis that income-related inequality in treatment for AMI exists and is likely to be affected by a country's system of health care.

Objective. To compare safety climate between diverse U.S. hospitals and Veterans Health Administration (VA) hospitals, and to explore the factors influencing climate in each setting.

Data Sources. Primary data from surveys of hospital personnel; secondary data from the American Hospital Association's 2004 Annual Survey of Hospitals.

Study Design. Cross-sectional study of 69 U.S. and 30 VA hospitals.

Data Collection. For each sample, hierarchical linear models used safety-climate scores as the dependent variable and respondent and facility characteristics as independent variables. Regression-based Oaxaca–Blinder decomposition examined differences in effects of model characteristics on safety climate between the U.S. and VA samples.

Principal Findings. The range in safety climate among U.S. and VA hospitals overlapped substantially. Characteristics of individuals influenced safety climate consistently across settings. Working in southern and urban facilities corresponded with worse safety climate among VA employees and better safety climate in the U.S. sample. Decomposition results predicted 1.4 percentage points better safety climate in U.S. than in VA hospitals: −0.77 attributable to sample-characteristic differences and 2.2 due to differential effects of sample characteristics.

Conclusions. Results suggest that safety climate is linked more to efforts of individual hospitals than to participation in a nationally integrated system or measured characteristics of workers and facilities.

Objective

To determine whether eligible extremely-low-birth-weight children (<1000g) were enrolled in the federally enacted, state-coordinated Early Intervention (EI) program intended to help children with developmental delay or disability regardless of parental income, and the factors associated with enrollment.

Methods

Retrospective analysis of 884 EI-eligible ELBW children born in South Carolina with birth weight 401 to 999g, gestation ≥24 weeks, and survival for the first 120 days of life. We created a linked data set with data from Early Intervention (1996–2001), Vital Records (1996–1998), death certificates, and Medicaid. Each child was followed from birth to 3 years old, the program eligibility period.

Results

A total of 54% of ELBW children were enrolled in EI at any time from birth to 36 months. Even among children ever enrolled in Medicaid (83% of all ELBW children), only 63% were enrolled in EI. Being born in a multiple gestational birth, having heavier birth weight (750 to 999g), and having ever enrolled in Medicaid were positively associated with EI enrollment. Among Medicaid patients for whom perinatal data were available, additional risk adjustment showed that EI enrollment was more likely with birth in level 3 hospitals, birth weight 750 to 999g, Neonatal Medical Index severity level V (most severe), and longer initial length of hospital stay.

Conclusions

Only about half of eligible ELBW children in South Carolina were enrolled—much lower than reported elsewhere. Efforts are needed to understand why eligible infants are not being enrolled and to develop strategies to remedy the situation.

Purpose. Mathematical and simulation models are increasingly used to plan for and evaluate health sector responses to disasters, yet no clear consensus exists regarding best practices for the design, conduct, and reporting of such models. The authors examined a large selection of published health sector disaster response models to generate a set of best practice guidelines for such models.

Methods. The authors reviewed a spectrum of published disaster response models addressing public health or health care delivery, focusing in particular on the type of disaster and response decisions considered, decision makers targeted, choice of outcomes evaluated, modeling methodology, and reporting format. They developed initial recommendations for best practices for creating and reporting such models and refined these guidelines after soliciting feedback from response modeling experts and from members of the Society for Medical Decision Making.

Results. The authors propose 6 recommendations for model construction and reporting, inspired by the most exemplary models: health sector disaster response models should address real-world problems, be designed for maximum usability by response planners, strike the appropriate balance between simplicity and complexity, include appropriate outcomes that extend beyond those considered in traditional cost-effectiveness analyses, and be designed to evaluate the many uncertainties inherent in disaster response. Finally, good model reporting is particularly critical for disaster response models.

Conclusions. Quantitative models are critical tools for planning effective health sector responses to disasters. The proposed recommendations can increase the applicability and interpretability of future models, thereby improving strategic, tactical, and operational aspects of preparedness planning and response.

The current focus of the health reform debate is rightfully beginning to shift to the need to transform the delivery system to contain the long run growth in costs. Although much of the debate still focuses on the role of a public plan, this ignores the need for fundamental change. None of the options on the table will transform the delivery system. If passed, the best the current proposals could do is to expand enrollment and perhaps contain federal costs, but on its own the public plan will be unable to make the delivery system more efficient.

To control health care costs, I propose a publicly chartered major risk pool, or MRP, that
will allow plans to pool risk, thereby eliminating the need for wasteful underwriting and
selective marketing costs. Participation in the MRP by both providers and insurers is
voluntary. It can be combined with any public option in an exchange implemented at the
federal or state level; it can even work without a public option. After a brief transition
period, the MRP requires no federal funds and will not be “on budget.” By allowing private plans to play a role in a transformed insurance and delivery system, the MRP can be politically attractive to a broader constituency than any of the current proposals.

The MRP addresses a key component of comprehensive health reform: restructuring the
delivery system. It is not a simple reinsurance pool that reimburses health plans for high costclaims. Instead, it creates a reformed payment system for both inpatient care and outpatient chronic care that will encourage efficiency and quality. The MRP will cover inpatient and similar short but expensive episodes, as well as chronic illness management. Its new payment approaches will achieve the efficiency goals promised by proposals for hospital medical staff-focused Accountable Care Organizations, but in an organizationally more plausible manner. Hospitals and physicians who focus on inpatient care and voluntarily form Care Delivery Teams will receive bundled episode-based payments, but the MRP will pay providers regardless of whether they belong to a Care Delivery Team, although at less attractive rates. Providers in these teams can use their bargaining power to charge the primary insurers more than the MRP pays. The MRP’s payments for monthly chronic illness management will give health plans and primary care physicians the incentives, flexibility, and information to more effectively compensate clinicians for the care they deliver and coordinate. By being publicly chartered, but independent of Congress, and by allowing options for all players, the MRP will be able to sidestep the ability of special interests to block change.

The prevalence of obesity has been rising dramatically in the U.S., leading to poor health and rising health care expenditures. The role of policy in addressing rising rates of obesity, however, is controversial. Policy recommendations for interventions intended to influence body weight decisions often assume the obesity creates negative externalities for the non-obese. We build on earlier work demonstrating that this argument depends on two important assumptions:

1. that the obese do not pay for their higher medical expenditures through differential payments for health care and health insurance, and
   
2. that body weight decisions are responsive to the incidence of medical care costs associated with obesity.
   

In this paper, we test the latter proposition – that body weight is influenced by insurance coverage - using two approaches. First, we use data from the Rand Health Insurance Experiment, in which people were randomly assigned to varying levels of health insurance, to examine the effect of generosity of insurance coverage on body weight along the intensive coverage margin. Second, we use instrumental variables methods to estimate the effect of type of insurance coverage (private, public and none) on body weight along the extensive margin. We explicitly address the discrete nature of the endogenous indicator of health insurance coverage by estimating a nonlinear instrumental variables model. We find weak evidence that more generous insurance coverage increases body mass index. We find stronger evidence that being insured increases body mass index and obesity.

Clinical research presents health care providers with information on the natural history and clinical presentations of disease as well as diagnostic and treatment options. Consumers, patients, and caregivers also require this information to decide how to evaluate and treat their conditions. All too often, the information necessary to inform these medical decisions is incomplete or unavailable, resulting in more than half of the treatments delivered today lacking clear evidence of effectiveness.

Comparative effectiveness research (CER) identifies what works best for which patients under what circumstances. Congress, in the American Recovery and Reinvestment Act (ARRA) of 2009, tasked the Institute of Medicine (IOM) to recommend national priorities for research questions to be addressed by CER and supported by ARRA funds. In its 2009 report, Initial National Priorities for Comparative Effectiveness Research, the authoring committee establishes a working definition of CER, develops a priority list of research topics to be undertaken with ARRA funding using broad stakeholder input, and identifies the necessary requirements to support a robust and sustainable CER enterprise. The full list of priorities and recommendations can be found in the below report brief.