Anne-Marie J. Audet

Community Factors and Hospital Readmission Rates

OBJECTIVE To examine the relationship between community factors and hospital readmission rates. DATA SOURCES/STUDY SETTING We examined all hospitals with publicly reported 30-day readmission rates for patients discharged during July 1, 2007, to June 30, 2010, with acute myocardial infarction (AMI), heart failure (HF), or pneumonia (PN). We linked these to publicly available county data from the Area Resource File, the Census, Nursing Home Compare, and the Neilsen PopFacts datasets. STUDY DESIGN We used hierarchical linear models to assess the effect of county demographic, access to care, and nursing home quality characteristics on the pooled 30-day risk-standardized readmission rate. DATA COLLECTION/EXTRACTION METHODS Not applicable. PRINCIPAL FINDINGS The study sample included 4,073 hospitals. Fifty-eight percent of national variation in hospital readmission rates was explained by the county in which the hospital was located. In multivariable analysis, a number of county characteristics were found to be independently associated with higher readmission rates, the strongest associations being for measures of access to care. These county characteristics explained almost half of the total variation across counties. CONCLUSIONS Community factors, as measured by county characteristics, explain a substantial amount of variation in hospital readmission rates.

The Role of Care Coordinators in Improving Care Coordination: The Patient's Perspective

T he prevalence of chronic illness and increasing life expectancy is forcing all nations to consider models of care delivery that achieve desired outcomes at affordable costs. Nearly half of all Americans live with 1 chronic condition. One in 4 patients with a chronic condition will see at least 3 physicians, and the typical primary care physician coordinates care with 229 other physicians in 117 different practices. People with chronic conditions are at high risk of poor care coordination, leading to test duplications, medical errors, and adverse health outcomes. Promising solutions to improve care coordination include providing easy access to care when patients need it, establishing a stable relationship and effective communication between patients and their primary care practice, and using multidisciplinary care teams that include care coordinators to manage the care plan. To date, little is known about the impact of various approaches to improving care coordination from the patient’s perspective. Using data from a 2010 survey of adults in 11 countries, we report on what effects having a care coordinator, better access to primary care, and strong health care provider–patient communication have on care coordination.

Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges

Context: The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an urgent need for a systematic framework to guide the development and application of e-HPA to ensure that the field develops in a scientifically sound, ethical, and efficient manner. Objectives: Building upon earlier frameworks of model development and utilization, we identify the emerging opportunities and challenges of e-HPA, propose a framework that enables us to realize these opportunities, address these challenges, and motivate e-HPA stakeholders to both adopt and continuously refine the framework as the applications of e-HPA emerge. Methods: To achieve these objectives, 17 experts with diverse expertise including methodology, ethics, legal, regulation, and health care delivery systems were assembled to identify emerging opportunities and challenges of e-HPA and to propose a framework to guide the development and application of e-HPA. Findings: The framework proposed by the panel includes three key domains where e-HPA differs qualitatively from earlier generations of models and algorithms (Data Barriers, Transparency, and Ethics) and areas where current frameworks are insufficient to address the emerging opportunities and challenges of e-HPA (Regulation and Certification; and Education and Training). The following list of recommendations summarizes the key points of the framework: Data Barriers: Establish mechanisms within the scientific community to support data sharing for predictive model development and testing. Transparency: Set standards around e-HPA validation based on principles of scientific transparency and reproducibility. Ethics: Develop both individual-centered and society-centered risk-benefit approaches to evaluate e-HPA. Regulation and Certification: Construct a self-regulation and certification framework within e-HPA. Education and Training: Make significant changes to medical, nursing, and paraprofessional curricula by including training for understanding, evaluating, and utilizing predictive models.

Assessing Community Quality of Health Care.

OBJECTIVE To determine the agreement of measures of care in different settings-hospitals, nursing homes (NHs), and home health agencies (HHAs)-and identify communities with high-quality care in all settings. DATA SOURCES/STUDY SETTING Publicly available quality measures for hospitals, NHs, and HHAs, linked to hospital service areas (HSAs). STUDY DESIGN We constructed composite quality measures for hospitals, HHAs, and nursing homes. We used these measures to identify HSAs with exceptionally high- or low-quality of care across all settings, or only high hospital quality, and compared these with respect to sociodemographic and health system factors. PRINCIPAL FINDINGS We identified three dimensions of hospital quality, four HHA dimensions, and two NH dimensions; these were poorly correlated across the three care settings. HSAs that ranked high on all dimensions had more general practitioners per capita, and fewer specialists per capita, than HSAs that ranked highly on only the hospital measures. CONCLUSION Higher quality hospital, HHA, and NH care are not correlated at the regional level; regions where all dimensions of care are high differ systematically from regions which score well on only hospital measures and from those which score well on none.