Maulik S. Joshi

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.

Small, Nonteaching, And Rural Hospitals Continue To Be Slow In Adopting Electronic Health Record Systems

To achieve the goal of comprehensive health information record keeping and exchange among providers and patients, hospitals must have functioning electronic health record systems that contain patient demographics, care histories, lab results, and more. Using national survey data on US hospitals from 2011, the year federal incentives for the meaningful use of electronic health records began, we found that the share of hospitals with any electronic health record system increased from 15.1 percent in 2010 to 26.6 percent in 2011, and the share with a comprehensive system rose from 3.6 percent to 8.7 percent. The proportion able to meet our proxy criteria for meaningful use also rose; in 2011, 18.4 percent of hospitals had these functions in place in at least one unit and 11.2 percent had them across all clinical units. However, gaps in rates of adoption of at least a basic record system have increased substantially over the past four years based on hospital size, teaching status, and location. Small, nonteaching, and rural hospitals continue to adopt electronic health record systems more slowly than other types of hospitals. In sum, this is mixed news for policy makers, who should redouble their efforts among hospitals that appear to be moving slowly and ensure that policies do not further widen gaps in adoption. A more robust infrastructure for information exchange needs to be developed, and possibly a special program for the sizable minority of hospitals that have almost no health information technology at all.

Patient and family engagement: a survey of US hospital practices

Background Patient and family engagement (PFE) in healthcare is an important element of the transforming healthcare system; however, the prevalence of various PFE practices in the USA is not known. Objective We report on a survey of hospitals in the USA regarding their PFE practices during 2013–2014. Results The response rate was 42%, with 1457 acute care hospitals completing the survey. We constructed 25 items to summarise the responses regarding key practices, which fell into three broad categories: (1) organisational practices, (2) bedside practices and (3) access to information and shared decision-making. We found a wide range of scores across hospitals. Selected findings include: 86% of hospitals had a policy for unrestricted visitor access in at least some units; 68% encouraged patients/families to participate in shift-change reports; 67% had formal policies for disclosing and apologising for errors; and 38% had a patient and family advisory council. The most commonly reported barrier to increased PFE was ‘competing organisational priorities’. Summary Our findings indicate that there is a large variation in hospital implementation of PFE practices, with competing organisational priorities being the most commonly identified barrier to adoption.

Variation in Quality of Care Within Health Systems

BACKGROUND Although many hospitals belong to health care systems, little is known about the quality of care provided by those systems, or whether characteristics of health care systems are related to the quality of care patients receive. Dimensions of the quality of care provided in 73 hospital systems were examined using hospital quality data publicly reported by the Centers for Medicare & Medicaid Services (CMS). The hospital systems consisted of six or more acute care hospitals and represented 1,510 hospitals. The study was designed to determine whether these dimensions of system quality could be reliably measured, to describe how systems varied with respect to quality of care, and to explore system characteristics potentially related to care quality. METHODS Data were made available by CMS for 19 indicators of care quality for pneumonia, surgical infection prevention, acute myocardial infarction (AMI), and congestive heart failure. RESULTS At the system level, reliable measures (alphas > .70) were constructed for each of the four clinical areas, and these measures were combined into a single measure of quality (alpha = .85). Variability in system quality was substantial, ranging from 94% to 70% on the combined quality measure. On the clinical area measures, the smallest range was for AMI (99%-85%), whereas the largest was for surgical infection prevention (95%-54%). System ownership and system centralization were significant predictors of quality, accounting for 30% of variance in the combined quality measure. Geographic region, inclusion of teaching hospitals, and system size were unrelated to quality. DISCUSSION Systems vary greatly in terms of quality of care in each of the four clinical areas, with for-profit and more decentralized systems appreciably lower in quality of care. System-level quality measures and data could be used to compare processes within systems and to drive improvement efforts.

Improving evaluations of value-based purchasing programs.

Although value-based purchasing (VBP) holds promise for encouraging quality improvement and addressing rising costs, currently there is limited evidence about how best to structure and implement VBP programs. In this commentary, we highlight several issues for improving evaluations of VBP programs. Implementation research can be enhanced through early and continuous assessment and greater variation in program designs. Impact research can be improved by creating better outcome measures, increasing the availability of linked patient-level data, and advancing synthesis research. We offer several recommendations for improving the foundation to conduct evaluations of VBP programs to better inform policy and practice.

The Relationship of Engagement in Improvement Practices to Outcome Measures in Large-Scale Quality Improvement Initiatives.

Hospital engagement networks (HENs) are part of the largest health care improvement initiative ever undertaken. This article explores whether engagement in improvement activities within a HEN affected quality measures. Data were drawn from 1174 acute care hospitals. A composite quality score was created from 10 targeted topic area measures multiplied by the number of qualifying topics. Scores improved from 5.4 (SD = 6.8) at baseline to 4.6 (5.9) at remeasurement; P < .0001. Hospitals with higher baseline scores demonstrated greater improvement (P < .0001) than hospitals with lower baseline scores. Hospitals with larger Medicaid populations (P = .023) and micropolitan (P = .034) hospitals tended to have greater improvement, whereas hospitals in the West (P = .0009) did not improve as much as hospitals in other regions. After adjusting for hospital characteristics, hospitals with improvement champions (P = .008), a higher level of engagement with their state association (P = .001), and more leadership involvement (P = .005) in HEN demonstrated greater improvement.

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.

If You Don't Know Where You Are Going, You Might Not Get There

Hall-of-Fame baseball player Yogi Berra is well known for his witty quotes. Of his many yogiisms, a label for his humorous comments, Berra is credited with saying, “You’ve got to be very careful if you don’t know where you are going, because you might not get there.” This statement may describe the journeys of healthcare organizations today. With so much activity in the healthcare landscape—the testing and implementation of multiple forms of payment and care delivery redesign programs—healthcare organizations must focus on “where we are going” and how to “get there.” Although there are many unknowns in healthcare today, we know we all are moving toward a proliferation of new models of payment and care delivery, which are being tested in all sectors of the U.S. healthcare system. Accountable care organizations, patient-centered medical homes, and bundled payments are examples of programs and initiatives in the pilot phase. We have much to learn from the early implementation of these new models of both care redesign and payment redesign, before we get to the transformation in healthcare that we all seek. However, this activity requires strategy. To achieve a new level of performance in cost and quality in our healthcare system, program implementation must be done strategically with the end goal in mind—that is, knowing where we are going. We are “going” to a system that rewards high-quality and high-efficiency healthcare organizations. We are “going” to a system of coordinated care, with a focus on the health of populations and a swift, continuous drive to measurable, improved outcomes. We are “going” to a system that will reach new levels of performance through collaboration, evolutionary performance measurement, and health information connectivity. This Journal for Healthcare Quality (JHQ) special issue features a collection of eight peerreviewed articles that provide insights and learning on where we are headed and how to get there. The articles highlight quantitative and qualitative research findings of evolving quality measurement, care coordination programs, large-scale improvement efforts, and the implementation of multifaceted qualityimprovement interventions. Three articles, McDonald et al., Perla et al., and DuGoff et al., offer astute, applied research analysis in the development and use of quality measures for the future. McDonald et al. in “Can the Care Transitions Measure Predict Re-hospitalization Risk or Home Health Nursing Use of Home Healthcare Patients?” test the applicability of the Care Transitions Measure (CTM) to the home health setting. Although the authors’ study did not find evidence that the CTM could predict patients at elevated risk for emergent care, rehospitalization, or high consumption of home health nurse resources, the focus on identifying effective, predictive measurement in transitional care is a fundamental building block for assessing and using services effectively in a future coordinated care system. Perla et al. in “Whole-Patient Measure of Safety: Using Administrative Data to Assess the Probability of Highly Undesirable Events During Hospitalization” review a new model that uses administrative data to measure hospitallevel safety of care. In their article, Perla et al. discuss identifying highly undesirable events to develop a Whole-Patient Measure of Safety. DuGoff et al. in “Setting Standards at the Forefront of Delivery System Reform: Aligning Care Coordination Quality Measures for Multiple Chronic Conditions” determine that quality measures currently used or proposed for three major new programs aiming to improve care coordination primarily address only one key area: continuity of care. Other key categories of care coordination—communication, care transitions, patient-centered care, and inclusion of measures that can apply to multiple conditions—were not as frequently captured by these major programs. In addition, there was little overlap in the quality measures used by these programs and the NQF care coordination measurement set. Two articles, Taylor et al. and Madan et al., discuss the positive impact of programs that emphasize care coordination for patient results. Taylor et al. in “Implementing a Care Coordination Program for Children with Journal for Healthcare Quality Vol. 35, No. 5, pp. 5–6