Meritus Health: Driving Data-Informed Decision-Making at Every Level.

With so much data available, health system leaders are challenged with sifting through it all to find the most useful information for decision-making. Meritus Health implemented effective approaches to understand, use, and communicate large amounts of data to alleviate some of this burden. These processes include system-wide daily huddles, dashboards, and standardized communication write-ups.

Differences in Perioperative Outcomes and Complications Between African American and White Patients After Total Joint Arthroplasty.

BACKGROUND: Racial disparities in healthcare utilization and outcomes have been reported and have wide-reaching implications for individual patient and healthcare system; as providers we bear an ethical burden to address this disparity and provide culturally competent care. This study will examine the influence of race on length of stay, discharge disposition, and complications requiring reoperation following total joint arthroplasty (TJA). METHODS: Single institution retrospective analysis of a consecutive series of 7208 primary TJA procedures performed between July 2013 and June 2017 was conducted. Chi-squared and t-tests were used to quantify differences between the groups and multiple logistic regression was used to identify race as an independent risk factor. RESULTS: In total, 6182 (84.3%) white and 1026 (14.0%) African American (AA) patients were included. AA patients were younger (63.62 vs 66.84 years, P < .001), more likely female (68.8% vs 57.0%, P < .001), had a longer length of stay (2.19 vs 2.00 days, P < .001), more likely to experience septic complications (1.3% vs 0.5%, P = .002) and manipulation under anesthesia (3.9% vs 1.8%, P < .001), and less likely to discharge home (67.1% vs 81.1%, P < .001). Multiple logistic regression showed that AA patients were more likely to discharge to a facility (adjusted odds ratio 2.63, 95% confidence interval 2.19-3.16, P < .001) and experience a manipulation under anesthesia (adjusted odds ratio 1.90, 95% confidence interval 1.26-2.85, P = .002). CONCLUSION: AA patients undergoing TJA were younger with longer length of stay and a higher rate of nonhome discharge; AA race was identified as an independent risk factor. Further study is required to understand the differences identified in this study. Targeted interventions should be developed to attempt to eliminate the disparity.

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.

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.

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.

Accountable Care Organizations and Population Health Organizations.

Accountable care organizations (ACOs) and hospitals are investing in improving "population health," by which they nearly always mean the health of the "population" of patients "attributed" by Medicare, Medicaid, or private health insurers to their organizations. But population health can and should also mean "the health of the entire population in a geographic area." We present arguments for and against ACOs and hospitals investing in affecting the socioeconomic determinants of health to improve the health of the population in their geographic area, and we provide examples of ACOs and hospitals that are doing so in a limited way. These examples suggest that ACOs and hospitals can work with other organizations in their community to improve population health. We briefly present recent proposals for such coalitions and for how they could be financed to be sustainable.

Faster by a power of 10: a PLAN for accelerating national adoption of evidence-based practices.

Despite a decade of heightened focus on quality improvement, evidence continues to move slowly and incompletely into practice. To improve the quality of care, national improvement efforts must accelerate the spread of evidence-based practices. We propose an ambitious goal: to increase the speed of adoption of evidence-based practices by a power of 10, from 17 years to 1.7 years, and present a 4-step PLAN to achieve this. The PLAN components are: Performance-identify performance gaps and set specific, measurable aims; Leadership-hospital and health system leaders support implementation and dissemination of evidence-based practices in their facilities; Alignment-align education and dissemination efforts with national policy drivers; and Next-continue to refine the implementation process to successfully address the next improvement opportunity.

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.

Adoption of electronic health records grows rapidly, but fewer than half of US hospitals had at least a basic system in 2012.

The US health care system is in the midst of an enormous change in the way health care providers and hospitals document, monitor, and share information about health and care delivery. Part of this transition involves a wholesale, but currently uneven, shift from paper-based records to electronic health record (EHR) systems. We used the most recent longitudinal survey of US hospitals to track how they are adopting and using EHR systems. Only 44 percent of hospitals report having and using what we define as at least a basic EHR system. And although 42.2 percent meet all of the federal stage 1 "meaningful-use" criteria, only 5.1 percent could meet the broader set of stage 2 criteria. Large urban hospitals continue to outpace rural and nonteaching hospitals in adopting EHR systems. The increase in adoption overall suggests that the positive and negative financial incentives currently in place across the US health care system are working as intended. However, achieving a nationwide health information technology infrastructure may require efforts targeted at smaller and rural hospitals.

Hospitals on the path to accountable care: highlights from a 2011 national survey of hospital readiness to participate in an accountable care organization.

Accountable care organizations (ACOs) are forming in communities across the country. In ACOs, health care providers take responsibility for a defined patient popu­lation, coordinate their care across settings, and are held jointly accountable for the quality and cost of care. This issue brief reports on results from a survey that assesses hospitals' readiness to participate in ACOs. Results show we are at the beginning of the ACO adop­tion curve. As of September 2011, only 13 percent of hospital respondents reported partici­pating in an ACO or planning to participate within a year, while 75 percent reported not considering participation at all. Survey results indicate that physician-led ACOs are the second most common governance model, far exceeding payer-led models, highlighting an encouraging paradigm shift away from acute care and toward primary care. Findings also point to significant gaps, including the infrastructure needed to take on financial risks and to manage population health.

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.

Progress toward meaningful use: hospitals' adoption of electronic health records.

OBJECTIVES: To update the status of electronic health record (EHR) adoption in US hospitals and assess their readiness for "Meaningful Use" (MU). STUDY DESIGN: We used data from the 2010 American Hospital Association Annual Information Technology Survey. The survey was first conducted in 2007 and is made available both online and through the mail to all non-federal acute-care hospitals in the United States. METHODS: We measure the percentages of applicable hospitals that have adopted "basic" and "comprehensive" EHRs as defined in previous literature. Additionally, we report the percentage of hospitals planning to apply for MU in the near term, and assess hospitals' readiness for the program and how readiness varies by key characteristics. RESULTS: We received responses from 2902 hospitals (64% of all non-federal acute-care hospitals). More than 15% have adopted at least a "basic" EHR, representing nearly 75% growth since 2008. Approximately two-thirds plan to apply for MU before 2013; however, only 4.4% had implemented each of the "core" MU functionalities we measured. Hospitals closer to achieving MU are more likely to be larger non-profits (P <.001) and vary by other key characteristics. Certain functionalities included in MU, such as computerized provider order entry, electronic generation of quality measures, and electronic access to records for patients are proving more challenging to implement for all hospitals. CONCLUSIONS: Broad enthusiasm exists among hospitals for participation in MU. However, adoption will have to accelerate above its current pace for readiness to match intention. Gaps in adoption show bringing all hospitals along is the key policy challenge.

Hospitals of the future.

Hospitals and health systems face unprecedented demand to change in both the near- and longer-term future, ranging from demographic changes to increasing reliance on value-based payment, and to the uncertainty surrounding governmental reform. the American Hospital Association Board Committee on Performance Improvement embarked on an initiative to identify the top ten strategies all hospitals must adopt in order to be successful care systems of the future. As a result of the committee's survey research, four top strategies were identified: (1) Aligning hospitals, physicians, and other providers across the continuum of care; (2) Using evidenced-based practices to improve quality and patient safety; (3) Improving efficiency through productivity and financial management; and (4) Developing integrated information systems. This article summarizes ten strategies and the measures to assess the accomplishment of these strategies.

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.