The Quality of Health Care Providers

Obtaining better information on the quality of health care providers is one of the most pressing issues in health policy today. In this paper we (1) develop a new method for measuring quality of care that overcomes the key limitations of available quality measures, and (2) apply this method to estimating the quality of hospital care for elderly patients with heart disease. Our approach optimally combines information from all available current and past quality indicators in order to more accurately estimate and forecast each provider's quality level. For patients with heart disease, the method is able to predict and forecast differences in patient outcomes across hospitals remarkably well - far better than existing methods. Our approach also provides an empirical basis for choosing among potential quality indicators. In particular, we find that differences across hospitals in short-term mortality rates following a heart attack, adjusted for patient demographics, are excellent indicators of quality of care: They vary dramatically across hospitals, are persistent over time, are highly correlated with alternative quality indicators, and are highly correlated with mortality rates that adjust more extensively for patient severity. Thus, comparing quality of care across providers may be far more feasible than many now believe.

Is More Information Better? The Effects of 'Report Cards' on Health Care Providers

Health care report cards - public disclosure of patient health outcomes at the level of the individual physician and/or hospital - may address important informational asymmetries in markets for health care, but they may also give doctors and hospitals incentives to decline to treat more difficult, severely ill patients. Whether report cards are good for patients and for society depends on whether their financial and health benefits outweigh their costs in terms of the quantity, quality, and appropriateness of medical treatment that they induce. Using national data on Medicare patients at risk for cardiac surgery, we find that cardiac surgery report cards in New York and Pennsylvania led both to selection behavior by providers and to improved matching of patients with hospitals. On net, this led to higher levels of resource use and to worse health outcomes, particularly for sicker patients. We conclude that, at least in the short run, these report cards decreased patient and social welfare.

Charlson Comorbidity Index: A Critical Review of Clinimetric Properties

The present critical review was conducted to evaluate the clinimetric properties of the Charlson Comorbidity Index (CCI), an assessment tool designed specifically to predict long-term mortality, with regard to its reliability, concurrent validity, sensitivity, incremental and predictive validity. The original version of the CCI has been adapted for use with different sources of data, ICD-9 and ICD-10 codes. The inter-rater reliability of the CCI was found to be excellent, with extremely high agreement between self-report and medical charts. The CCI has also been shown either to have concurrent validity with a number of other prognostic scales or to result in concordant predictions. Importantly, the clinimetric sensitivity of the CCI has been demonstrated in a variety of medical conditions, with stepwise increases in the CCI associated with stepwise increases in mortality. The CCI is also characterized by the clinimetric property of incremental validity, whereby adding the CCI to other measures increases the overall predictive accuracy. It has been shown to predict long-term mortality in different clinical populations, including medical, surgical, intensive care unit (ICU), trauma, and cancer patients. It may also predict in-hospital mortality, although in some instances, such as ICU or trauma patients, the CCI did not perform as well as other instruments designed specifically for that purpose. The CCI thus appears to be clinically useful not only to provide a valid assessment of the patient’s unique clinical situation, but also to demarcate major diagnostic and prognostic differences among subgroups of patients sharing the same medical diagnosis

Improving Palliative Care with Deep Learning

Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life. We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center. The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care. Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients. We also present a novel interpretation technique which we use to provide explanations of the model's predictions.Comment: IEEE International Conference on Bioinformatics and Biomedicine 201

Quality of Health Care for Medicare Beneficiaries: A Chartbook

Provides the results of a review of recently published studies and reports about the quality of health care for elderly Medicare beneficiaries. Includes examples of deficiencies and disparities in care, and some promising quality improvement initiatives

The Concentration of Medical Spending: An Update

In the last two decades, Medicare spending has doubled in real terms despite the fact that the health of Medicare beneficiaries improved over this period. The goals of this paper are to document how trends in spending by age have changed among elderly Medicare beneficiaries in the last decade and to reconcile the decline in disability rates with rapid increases in spending among the elderly. First, we conclude that the trend of disproportionate spending growth among the oldest old has continued between 1985 and 1995. Spending among the younger elderly, those 65-69 rose by two percent annually in real per person terms. In contrast, spending for those over age 85 rose by four percent. Second we show that the reasons for the large increase in spending on the oldest elderly relative to the younger elderly is the rapid increase in the use of post-acute services such as home health care and skilled nursing care. Spending on post-acute care for the very old has risen 20 percent per year in the last decade.

Using Healthcare Data to Inform Health Policy: Quantifying Cardiovascular Disease Risk and Assessing 30-Day Readmission Measures

Health policy makers are struggling to manage health care and spending. To identify strategies for improving health quality and reducing health spending, policy makers need to first understand health risks and outcomes. Despite lacking some desirable clinical detail, existing health care databases, such as national health surveys and claims and enrollment data for insured populations, are often rich in information relating patient characteristics to heath risks and outcomes. They typically encompass more inclusive populations than can feasibly be achieved with new data collection and are valuable resources for informing health policy. This dissertation illustrates how the Medicare Current Beneficiary Survey (MCBS) and MassHealth data can be used to develop models that provide useful estimates of risks and health quality measures. It provides insights into: 1) the benefits of a proxy for the Framingham cardiovascular disease (CVD) risk score, that relies only on variables available in the MCBS, to target health interventions to policy-relevant subgroups, such as elderly Medicare beneficiaries, based on their risk of developing CVD, 2) the importance of setting appropriate risk-adjusted quality of care standards for accountable care organizations (ACOs) based on the characteristics of their enrolled members, and 3) the outsized effect of high- frequency hospital users on re-admission measures and possibly other quality measures. This work develops tools that can be used to identify and support care of vulnerable patients to both improve their health outcomes and reduce spending – an important step on the road to health equity