Measuring Performance For Treating Heart Attacks And Heart Failure: The Case For Outcomes Measurement

Achieving Equity in Hospital Performance Assessments Using Composite Race-Specific Measures of Risk-Standardized Readmission and Mortality Rates for Heart Failure

BACKGROUND

The contemporary measures of hospital performance for heart failure hospitalization and 30-day risk-standardized readmission rate (RSRR) and risk-standardized mortality rate (RSMR) are estimated using the same risk adjustment model and overall event rate for all patients. Thus, these measures are mainly driven by the care quality and outcomes for the majority racial and ethnic group, and may not adequately represent the hospital performance for patients of Black and other races.

METHODS

Fee-for-service Medicare beneficiaries from January 2014 to December 2019 hospitalized with heart failure were identified. Hospital-level 30-day RSRR and RSMR were estimated using the traditional race-agnostic models and the race-specific approach. The composite race-specific performance metric was calculated as the average of the RSRR/RMSR measures derived separately for each race and ethnicity group. Correlation and concordance in hospital performance for all patients and patients of Black and other races were assessed using the composite race-specific and race-agnostic metrics.

RESULTS

The study included 1 903 232 patients (75.7% White [n=1 439 958]; 14.5% Black [n=276 684]; and 9.8% other races [n=186 590]) with heart failure from 1860 hospitals. There was a modest correlation between hospital-level 30-day performance metrics for patients of White versus Black race (Pearson correlation coefficient: RSRR=0.42; RSMR=0.26). Compared with the race-agnostic RSRR and RSMR, composite race-specific metrics for all patients demonstrated stronger correlation with RSRR (correlation coefficient: 0.60 versus 0.74) and RSMR (correlation coefficient: 0.44 versus 0.51) for Black patients. Concordance in hospital performance for all patients and patients of Black race was also higher with race-specific (versus race-agnostic) metrics (RSRR=64% versus 53% concordantly high-performing; 61% versus 51% concordantly low-performing). Race-specific RSRR and RSMR metrics (versus race-agnostic) led to reclassification in performance ranking of 35.8% and 39.2% of hospitals, respectively, with better 30-day and 1-year outcomes for patients of all race groups at hospitals reclassified as high-performing.

CONCLUSIONS

Among patients hospitalized with heart failure, race-specific 30-day RSMR and RSRR are more equitable in representing hospital performance for patients of Black and other races.

Structural components for the development of a heart failure network

Diagnosis and treatment of heart failure (HF) is challenging, and development of specialized HF networks is mandatory to warrant broad access to guideline directed therapies for patients. Numerous national cardiovascular societies recommend a three-level association of health care providers. This comprises tertiary academic centres, specialized HF clinics and specialized general cardiologists to cover the large spectrum of HF severity and entities. Although this idea of a multi-level care is widely accepted, optimal approach to build and implement a HF network service needs further definition. The core principle is that of network healthcare facilities that also consider regional peculiarities and that implements academic standards, quality indicators (QIs), interdisciplinarity and reimbursement strategies. These determinants of trans-sectoral healthcare need to be embedded in a network that provides sustainability and that incorporates QIs to objectify the efficacy of specific measures. The basis of a HF-network should be a certification system of the respective national HF association to warrant guideline standards and to prevent development of regional hierarchies or dependencies between members. This nationwide framework needs to be complemented by a federal system of regional networks, which also takes local demands into account. These regional units should incorporate digital communication and interaction pathways, structured educational programmes, certified telehealth concepts and follow-up algorithms to meet the requirements of sustainability and efficacy. We here summarize different components of HF networks and introduce the structure and development philosophy of the RUHR-HF-network that constitutes the first certified HF-clinics-network in the Ruhr area-the largest metropolitan area in Germany.

European Society of Cardiology methodology for the development of quality indicators for the quantification of cardiovascular care and outcomes

AIMS

It is increasingly recognized that tools are required for assessing and benchmarking quality of care in order to improve it. The European Society of Cardiology (ESC) is developing a suite of quality indicators (QIs) to evaluate cardiovascular care and support the delivery of evidence-based care. This paper describes the methodology used for their development.

METHODS AND RESULTS

We propose a four-step process for the development of the ESC QIs. For a specific clinical area with a gap in care delivery, the QI development process includes: (i) the identification of key domains of care by constructing a conceptual framework of care; (ii) the construction of candidate QIs by conducting a systematic review of the literature; (iii) the selection of a final set of QIs by obtaining expert opinions using the modified Delphi method; and (iv) the undertaking of a feasibility assessment by evaluating different ways of defining the QI specifications for the proposed data collection source. For each of the four steps, key methodological areas need to be addressed to inform the implementation process and avoid misinterpretation of the measurement results.

CONCLUSION

Detailing the methodology for the ESC QIs construction enables healthcare providers to develop valid and feasible metrics to measure and improve the quality of cardiovascular care. As such, high-quality evidence may be translated into clinical practice and the 'evidence-practice' gap closed.

Measuring and Improving Evidence-Based Patient Care Using a Web-Based Gamified Approach in Primary Care (QualityIQ): Randomized Controlled Trial

BACKGROUND

Unwarranted variability in clinical practice is a challenging problem in practice today, leading to poor outcomes for patients and low-value care for providers, payers, and patients.

OBJECTIVE

In this study, we introduced a novel tool, QualityIQ, and determined the extent to which it helps primary care physicians to align care decisions with the latest best practices included in the Merit-Based Incentive Payment System (MIPS).

METHODS

We developed the fully automated QualityIQ patient simulation platform with real-time evidence-based feedback and gamified peer benchmarking. Each case included workup, diagnosis, and management questions with explicit evidence-based scoring criteria. We recruited practicing primary care physicians across the United States into the study via the web and conducted a cross-sectional study of clinical decisions among a national sample of primary care physicians, randomized to continuing medical education (CME) and non-CME study arms. Physicians "cared" for 8 weekly cases that covered typical primary care scenarios. We measured participation rates, changes in quality scores (including MIPS scores), self-reported practice change, and physician satisfaction with the tool. The primary outcomes for this study were evidence-based care scores within each case, adherence to MIPS measures, and variation in clinical decision-making among the primary care providers caring for the same patient.

RESULTS

We found strong, scalable engagement with the tool, with 75% of participants (61 non-CME and 59 CME) completing at least 6 of 8 total cases. We saw significant improvement in evidence-based clinical decisions across multiple conditions, such as diabetes (+8.3%, P<.001) and osteoarthritis (+7.6%, P=.003) and with MIPS-related quality measures, such as diabetes eye examinations (+22%, P<.001), depression screening (+11%, P<.001), and asthma medications (+33%, P<.001). Although the CME availability did not increase enrollment in the study, participants who were offered CME credits were more likely to complete at least 6 of the 8 cases.

CONCLUSIONS

Although CME availability did not prove to be important, the short, clinically detailed case simulations with real-time feedback and gamified peer benchmarking did lead to significant improvements in evidence-based care decisions among all practicing physicians.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03800901; https://clinicaltrials.gov/ct2/show/NCT03800901.

Policy and Payment Challenges in the Postpandemic Treatment of Heart Failure: Value-Based Care and Telehealth

Increasing patient and therapeutic complexity have created both challenges and opportunities for heart failure care. Within this background, the coronavirus disease-2019 pandemic has disrupted care as usual, accelerating the need for transition from volume-based to value-based care, and demanding a rapid expansion of telehealth and remote care for heart failure. Patients, clinicians, health systems, and payors have by necessity become more invested in these issues. Herein we review recent changes in health care policy related to the movement from volume to value-based payment and from in-person to remote care delivery.

Validity of National Institutes of Health Stroke Scale for Severity of Stroke to Predict Mortality Among Patients Presenting With Symptoms of Stroke

Introduction

Cerebrovascular accident (CVA), also termed as stroke, is the third leading cause of mortality and the most common cause of disability globally. The National Institutes of Health Stroke Scale (NIHSS) is a valid assessment tool utilized to determine the severity of the stroke and can be used to prioritize patients to design treatment plans, rehabilitation, and better clinical outcomes. The primary objective of this study was to determine the validity of the NIHSS to predict mortality among patients presenting with symptoms of a stroke.

Material and methods

This was a descriptive case-series conducted over a period of six months between September 2019 and February 2020 at a tertiary care hospital in Nawabshah, Pakistan. The sample population included 141 patients admitted within 24 hours of the onset of symptoms of a stroke. A neurological examination of the patients was performed. On admission, stroke severity was evaluated with the NIHSS. After an initial clinical evaluation, patients underwent a non-enhanced computed tomography (CT) scan of the brain. The score of NIHSS and mortality at 72 hours were recorded on the pre-defined proforma by the investigators. All statistical analysis was performed using Statistical Package for Social Sciences (SPSS) version 23.0 (Armonk, NY: IBM Corp).

Results

The mean age of the participants was 52.37±8.61 years. 68.1% of patients were hypertensive, 29.1% were diabetic, and 36.9% of patients were found with hyperlipidemia. The mortality rate was 41.1%. The mean NIHSS score was 16.68±6.72 points. The findings of this study demonstrated that the score of 14.9% cases was good (0-6 points), the score of 29.1% cases was moderate (7-15 points), and the score of 56% cases was poor (≥16 points). There was a significant association of NIHSS score with mortality (p<0.001).

Conclusions

Baseline NIHSS score has a profound association with mortality after acute stroke. It can help clinicians decide whether to provide thrombolytic treatment, rehabilitation or a combination of both in these patients and decrease the mortality rate. However, more studies are needed to potentiate these conclusions.

Evaluating the Impact of Oncology Care Model Reporting Requirements on Biomarker Testing and Treatment

PURPOSE

The Oncology Care Model (OCM) is Medicare's first alternative payment model program for patients with cancer. As of October 2017, participating practices were required to report biomarker testing of patients with advanced non-small-cell lung cancer (aNSCLC). Our objective was to evaluate the effect of this OCM reporting requirement on quality of care.

METHODS

We selected patients with aNSCLC receiving care in practices in a nationwide de-identified electronic health record-derived database. We used an adjusted difference-in-differences (DID) logistic regression model to compare changes in biomarker testing rates (EGFR, ROS1, and ALK) and receipt of biomarker-guided therapy between patients in OCM versus non-OCM practices, before and after OCM implementation.

RESULTS

The analysis included 14,048 patients from 45 OCM practices (n = 8,151) and 105 non-OCM practices (n = 5,897). The overall unadjusted rates for biomarker testing and receipt of biomarker-guided therapy increased over the study period (2011-2018) in both OCM (55.5% v 71.6%; 89.8% v 94.6%, respectively) and non-OCM (55.2% v 69.7%; 90.1% v 95.2%, respectively) practices. In the adjusted DID model, the rates of biomarker testing (odds ratio [OR], 1.09 [95% CI, 0.88 to 1.34]; P = .45) and receipt of biomarker-guided therapy (OR, 0.87 [95% CI, 0.52 to 1.45]; P = .58) were similar between OCM and non-OCM practices.

CONCLUSION

OCM biomarker documentation and reporting requirements did not appear to increase the proportions of patients with aNSCLC who underwent testing or who received biomarker-guided therapy in OCM versus non-OCM practices.

Opioid Deaths and Local Healthcare Intensity: A Longitudinal Analysis of the U.S. Population, 2003-2014

INTRODUCTION

This study examines the association between local healthcare intensity and drug death rates.

METHODS

County-level drug death rates were computed for 2003-2014 using vital statistics data adjusted for incomplete reporting of drug involvement. A county-level healthcare intensity index was constructed using Dartmouth Atlas of Health Care data. Linear regression and dose-response models were estimated for all residents and for population subgroups to analyze the relationship between healthcare intensity and drug death rates, as well as for 7 indicators of healthcare quality. Data collection and analysis were conducted in 2018 and 2019.

RESULTS

Linear estimates indicated a positive correlation between healthcare intensity and opioid-involved drug death rates. Dose-response models revealed that the association was especially pronounced for the 2 highest healthcare intensity quintiles. Moving from the lowest to the highest healthcare intensity quintile was associated with a 2.14 (95% CI=1.56, 2.72) per 100,000 rise in opioid-involved drug death rates and a 25.1% (95% CI=18.3%, 31.9%) increase from the base rate of 8.54 per 100,000. Corresponding associations were larger in absolute terms for individuals who were male, white, aged 20-44 years, and not college educated than for their counterparts, but similar in percentages, except for 2 minority racial groups and seniors. Non-opioid drug death rates were unrelated to healthcare intensity. High healthcare intensity was associated with worse healthcare quality for 6 of 7 indicators.

CONCLUSIONS

In the U.S., between 2003 and 2014, high medical care intensity was associated with elevated opioid death rates and lower healthcare quality.

Retooling of Paper-based Outcome Measures to Electronic Format: Comparison of the NY State Public Risk Model and EHR-derived Risk Models for CABG Mortality

BACKGROUND

Risk adjustment is critical in the comparison of quality of care and health care outcomes for providers. Electronic health records (EHRs) have the potential to eliminate the need for costly and time-consuming manual data abstraction of patient outcomes and risk factors necessary for risk adjustment.

METHODS

Leading EHR vendors and hospital focus groups were asked to review risk factors in the New York State (NYS) coronary artery bypass graft (CABG) surgery statistical models for mortality and readmission and assess feasibility of EHR data capture. Risk models based only on registry data elements that can be captured by EHRs (one for easily obtained data and one for data obtained with more difficulty) were developed and compared with the NYS models for different years.

RESULTS

Only 6 data elements could be extracted from the EHR, and outlier hospitals differed substantially for readmission but not for mortality. At the patient level, measures of fit and predictive ability indicated that the EHR models are inferior to the NYS CABG surgery risk model [eg, c-statistics of 0.76 vs. 0.71 (P<0.001) and 0.76 vs. 0.74 (P=0.009) for mortality in 2010], although the correlation of the predicted probabilities between the NYS and EHR models was high, ranging from 0.96 to 0.98.

CONCLUSIONS

A simplified risk model using EHR data elements could not capture most of the risk factors in the NYS CABG surgery risk models, many outlier hospitals were different for readmissions, and patient-level measures of fit were inferior.

Comparative Effectiveness of New Approaches to Improve Mortality Risk Models From Medicare Claims Data

IMPORTANCE

Risk adjustment models using claims-based data are central in evaluating health care performance. Although US Centers for Medicare & Medicaid Services (CMS) models apply well-vetted statistical approaches, recent changes in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system and advances in computational capabilities may provide an opportunity for enhancement.

OBJECTIVE

To examine whether changes using already available data would enhance risk models and yield greater discrimination in hospital-level performance measures.

DESIGN, SETTING, AND PARTICIPANTS

This comparative effectiveness study used ICD-9-CM codes from all Medicare fee-for-service beneficiary claims for hospitalizations for acute myocardial infarction (AMI), heart failure (HF), or pneumonia among patients 65 years and older from July 1, 2013, through September 30, 2015. Changes to current CMS mortality risk models were applied incrementally to patient-level models, and the best model was tested on hospital performance measures to model 30-day mortality. Analyses were conducted from April 19, 2018, to September 19, 2018.

MAIN OUTCOMES AND MEASURES

The main outcome was all-cause death within 30 days of hospitalization for AMI, HF, or pneumonia, examined using 3 changes to current CMS mortality risk models: (1) incorporating present on admission coding to better exclude potential complications of care, (2) separating index admission diagnoses from those of the 12-month history, and (3) using ungrouped ICD-9-CM codes.

RESULTS

There were 361 175 hospital admissions (mean [SD] age, 78.6 [8.4] years; 189 225 [52.4%] men) for AMI, 716 790 hospital admissions (mean [SD] age, 81.1 [8.4] years; 326 825 [45.6%] men) for HF, and 988 225 hospital admissions (mean [SD] age, 80.7 [8.6] years; 460 761 [46.6%] men) for pneumonia during the study; mean 30-day mortality rates were 13.8% for AMI, 12.1% for HF, and 16.1% for pneumonia. Each change to the models was associated with incremental gains in C statistics. The best model, incorporating all changes, was associated with significantly improved patient-level C statistics, from 0.720 to 0.826 for AMI, 0.685 to 0.776 for HF, and 0.715 to 0.804 for pneumonia. Compared with current CMS models, the best model produced wider predicted probabilities with better calibration and Brier scores. Hospital risk-standardized mortality rates had wider distributions, with more hospitals identified as good or bad performance outliers.

CONCLUSIONS AND RELEVANCE

Incorporating present on admission coding and using ungrouped index and historical ICD-9-CM codes were associated with improved patient-level and hospital-level risk models for mortality compared with the current CMS models for all 3 conditions.

Readmissions after colorectal surgery: not all are equal

PURPOSE

This study aims to assess factors associated with preventable readmissions after colorectal resection.

METHODS

All readmissions following colorectal resection from May 2013 to May 2016 at an academic medical center were reviewed. Readmissions that could be prevented were identified. Factors associated with preventable readmission were assessed using logistic regression.

RESULTS

Of 686 patients discharged during the study period, there were 75 patients (11%) with unplanned readmission. Twenty-nine readmissions (39%) were preventable-these readmissions were due to dehydration or acute kidney injury, pain, ostomy complications, and gastrointestinal bleeding. On regression analysis, the strongest preoperative risk factors associated with preventable readmission were urgent or emergent operation (OR 4.0, 95% CI 1.6-9.9), recent myocardial infarction (OR 2.9, 95% CI 1.0-9.0), total or subtotal colectomy (OR 2.8, 95% CI 1.1-7.3), and American Society of Anesthesiologist score ≥ 3 (OR 2.2, 95% CI 1.0-4.7). Intraoperative risk factors associated with preventable readmission included intraoperative stapler complication (OR 24.2, 95% CI 1.5-397). Postoperative risk factors associated with preventable readmission included postoperative arrhythmia (OR 5.6, 95% CI 2.0-16.1), and postoperative anemia (OR 2.6, 95% CI 1.2-5.7). On multivariable analysis while controlling for procedure type, urgent or emergent operation (OR 2.9, 95% CI 1.1-8.2), intraoperative stapler complication (OR 37.5, 95% CI 2.3-627.8), and postoperative arrhythmia (OR 4, 95% CI 1.3-12.8) remained statistically significant.

CONCLUSION

Approximately 40% of readmissions following colorectal surgery are potentially preventable. Since specific patients and factors that are associated with preventable readmission can be identified, resources should be targeted to factors associated with preventable readmissions.

Mortality Measures to Profile Hospital Performance for Patients With Septic Shock

OBJECTIVES

Sepsis care is becoming a more common target for hospital performance measurement, but few studies have evaluated the acceptability of sepsis or septic shock mortality as a potential performance measure. In the absence of a gold standard to identify septic shock in claims data, we assessed agreement and stability of hospital mortality performance under different case definitions.

DESIGN

Retrospective cohort study.

SETTING

U.S. acute care hospitals.

PATIENTS

Hospitalized with septic shock at admission, identified by either implicit diagnosis criteria (charges for antibiotics, cultures, and vasopressors) or by explicit International Classification of Diseases, 9th revision, codes.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We used hierarchical logistic regression models to determine hospital risk-standardized mortality rates and hospital performance outliers. We assessed agreement in hospital mortality rankings when septic shock cases were identified by either explicit International Classification of Diseases, 9th revision, codes or implicit diagnosis criteria. Kappa statistics and intraclass correlation coefficients were used to assess agreement in hospital risk-standardized mortality and hospital outlier status, respectively. Fifty-six thousand six-hundred seventy-three patients in 308 hospitals fulfilled at least one case definition for septic shock, whereas 19,136 (33.8%) met both the explicit International Classification of Diseases, 9th revision, and implicit septic shock definition. Hospitals varied widely in risk-standardized septic shock mortality (interquartile range of implicit diagnosis mortality: 25.4-33.5%; International Classification of Diseases, 9th revision, diagnosis: 30.2-38.0%). The median absolute difference in hospital ranking between septic shock cohorts defined by International Classification of Diseases, 9th revision, versus implicit criteria was 37 places (interquartile range, 16-70), with an intraclass correlation coefficient of 0.72, p value of less than 0.001; agreement between case definitions for identification of outlier hospitals was moderate (kappa, 0.44 [95% CI, 0.30-0.58]).

CONCLUSIONS

Risk-standardized septic shock mortality rates varied considerably between hospitals, suggesting that septic shock is an important performance target. However, efforts to profile hospital performance were sensitive to septic shock case definitions, suggesting that septic shock mortality is not currently ready for widespread use as a hospital quality measure.

Comparing Hospital Processes and Outcomes in California Medicare Beneficiaries: Simulation Prompts Reconsideration

INTRODUCTION

This article is not a traditional research report. It describes how conducting a specific set of benchmarking analyses led us to broader reflections on hospital benchmarking. We reexamined an issue that has received far less attention from researchers than in the past: How variations in the hospital admission threshold might affect hospital rankings. Considering this threshold made us reconsider what benchmarking is and what future benchmarking studies might be like. Although we recognize that some of our assertions are speculative, they are based on our reading of the literature and previous and ongoing data analyses being conducted in our research unit. We describe the benchmarking analyses that led to these reflections.

OBJECTIVES

The Centers for Medicare and Medicaid Services' Hospital Compare Web site includes data on fee-for-service Medicare beneficiaries but does not control for severity of illness, which requires physiologic data now available in most electronic medical records.To address this limitation, we compared hospital processes and outcomes among Kaiser Permanente Northern California's (KPNC) Medicare Advantage beneficiaries and non-KPNC California Medicare beneficiaries between 2009 and 2010.

METHODS

We assigned a simulated severity of illness measure to each record and explored the effect of having the additional information on outcomes.

RESULTS

We found that if the admission severity of illness in non-KPNC hospitals increased, KPNC hospitals' mortality performance would appear worse; conversely, if admission severity at non-KPNC hospitals' decreased, KPNC hospitals' performance would appear better.

CONCLUSION

Future hospital benchmarking should consider the impact of variation in admission thresholds.

Systematic review and directors survey of quality indicators for the cardiovascular intensive care unit

BACKGROUND

Quality indicators (QIs) are increasingly used in cardiovascular care as measures of performance but there is currently no consensus on indicators for the cardiovascular intensive care unit (CICU).

METHODS

We searched Medline, CINAHL, EMBASE, and COCHRANE databases from inception until October 2016 and websites for organizations involved in quality measurement for QIs relevant to cardiovascular disease in an intensive or critical care setting. We surveyed 14 expert cardiac intensivist-administrators (7 European; 7 North American) on the importance and relevance of each indicator as a measure of CICU care quality using a scale of 1 (=lowest) to 10 (=highest). Indicators with a mean score ≥8/10 for both importance and relevance were included in the final set.

RESULTS

Overall, 108 QIs (70 process, 18 structural, 18 outcome, 1 patient engagement, and 1 covering multiple domains) were identified in 30 articles representing 23 agencies, organizations, and societies. Disease-specific QIs included myocardial infarction (n = 37), heart failure (n = 31), atrial fibrillation (n = 11), and cardiac rehabilitation (n = 1); general QIs represented about one-quarter (n = 28) of all measures. Fifteen QIs were selected for the final QI set: 7 process, 2 structural, and 6 outcome measures, including 6 general and 9 disease-specific measures. Outcome measures chosen to evaluate general CICU performance included overall CICU mortality, length of stay, and readmission rate.

CONCLUSIONS

Numerous QIs relevant to the CICU have been recommended by a variety of organizations. The indicators chosen by the cardiac intensivist-administrators could serve as a basis for future efforts to develop a standardized set of quality measures for the CICU.

The impact of care management information technology model on quality of care after percutaneous coronary intervention: "Bridging the Divides"

BACKGROUND

Reducing readmissions and improving metrics of care are a national priority. Supplementing traditional care with care management may improve outcomes. The Bridges program was an initial evaluation of a care management platform (CareLinkHub), supported by information technology (IT) developed to improve the quality and transition of care from hospital to home after percutaneous coronary intervention (PCI) and reduce readmissions.

METHODS

CareLink is comprised of care managers, patient navigators, pharmacists and physicians. Information to guide care management is guided by a middleware layer to gather information, PLR (ColdLight Solutions, LLC) and presented to CareLink staff on a care management platform, Aerial™ (Medecision). An additional analytic engine [Neuron™ (ColdLight Solutions, LLC)] helps, evaluates and guide care.

RESULTS

The "Bridges" program enrolled a total of 2054 PCI patients with 2835 admission from April, 1st 2013 through March 1st, 2015. The data of the program was compared with those of 3691 PCI patients with 4414 admissions in the 3years prior to the program. No impact was seen with respect to inpatient and observation readmission, or emergency department visits. Similarly no change was noticed in LDL control. There was minimal improvement in BP control and only in the CTM-3 and SAQ-7 physical limitation scores in the patients' reported outcomes. Patient follow-up with physicians within 1week of discharge improved during the Bridges years.

CONCLUSIONS

The CareLink hub platform was successfully implemented. Little or no impact on outcome metrics was seen in the short follow-up time. The Bridges program suggests that population health management must be a long-term goal, improving preventive care in the community.

The impact of care management information technology model on quality of care after Coronary Artery Bypass Surgery: "Bridging the Divides"

BACKGROUND

Reducing readmissions and improving metrics of care are a national priority. Supplementing traditional care with care management may improve outcomes. The Bridges program was an initial evaluation of a care management platform (CareLinkHub), supported by information technology (IT) developed to improve the quality and transition of care from hospital to home after Coronary Artery Bypass Surgery (CABG) and reduce readmissions.

METHODS

CareLink is comprised of care managers, patient navigators, pharmacists and physicians. Information to guide care management is guided by a middleware layer to gather information, PLR (ColdLight Solutions, LLC) and presented to CareLink staff on a care management platform, Aerial™ (Medecision). In addition there is an analytic engine to help evaluate and guide care, Neuron™ (Coldlight Solutions, LLC).

RESULTS

The "Bridges" program enrolled a total of 716 CABG patients with 850 admissions from April 2013 through March 2015. The data of the program was compared with those of 1111 CABG patients with 1203 admissions in the 3years prior to the program. No impact was seen with respect to readmissions, Blood Pressure or LDL control. There was no significant improvement in patients' reported outcomes using either the CTM-3 or any of the SAQ-7 scores. Patient follow-up with physicians within 1week of discharge improved during the Bridges years.

CONCLUSIONS

The CareLink hub platform was successfully implemented. Little or no impact on outcome metrics was seen in the short follow-up time.

Educational level and 30-day outcomes after hospitalization for acute myocardial infarction in Italy

Background

There is a growing interest in the factors that influence short-term mortality and readmission after hospitalization for acute myocardial infarction (AMI) since such outcomes are commonly considered as hospital performance measures. Socioeconomic status (SES) is one of the factors contributing to healthcare outcomes after hospitalization for AMI. However, no study has been published on education and 30-day readmission in Europe. The objective of this study is to examine the association between educational level and 30-day mortality and readmission among patients hospitalized for AMI in Tuscany (Italy).

Methods

A retrospective cohort study using data from hospital discharge records was conducted. The analysis included all patients discharged with a principal diagnosis of AMI between January 1, 2011, and November 30, 2014, from all hospitals in Tuscany. Educational level was categorized as low (no middle school diploma), mid (middle school diploma) and high (high school diploma or more). Three multilevel models were developed, sequentially controlling for patient-level socio-demographic and clinical variables and hospital-level variables. Patients were stratified by age (≤75 and >75 years).

Results

Mortality analysis included 23,402 patients, readmission analysis included 22,181 patients. In both unadjusted and full-adjusted models, patients with a high education had lower odds of 30-day mortality compared to those patients with low education (OR age ≤ 75 years 0.67, 95% CI:0.47–0.94; OR age > 75 years 0.72, 95% CI:0.54–0.95). With regard to 30-day readmission, only patients aged over 75 years with a high education had lower odds of short-term readmission compared to those patients with low education (OR age > 75 0.73, 95% CI:0.58–0.93).

Conclusions

Among patients hospitalized in Tuscany for AMI, low levels of education were associated with increased odds of 30-day mortality for both age groups and increased odds of 30-day readmission only for patients aged over 75 years. Our findings suggest that the educational component should not be underestimated in order to improve short-term outcomes, which are considered as performance measures at the hospital level. Hospital managers might consider strategies that are sensitive to patients with low SES, such as providing post-hospitalization support to less-educated patients and promoting a healthier lifestyle, to improve both health equity and performance outcomes.

Predicting readmission risk following percutaneous coronary intervention at the time of admission

OBJECTIVE

To investigate whether a prediction model based on data available early in percutaneous coronary intervention (PCI) admission can predict the risk of readmission.

BACKGROUND

Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after PCI early in a hospitalization would enable hospitals to enhance discharge planning.

METHODS

We developed 3 different models to predict 30-day inpatient readmission to our institution for patients who underwent PCI between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from CathPCI Registry data. We used logistic regression and assessed the discrimination of each model using the c-index. The models were validated with testing on a different patient cohort who underwent PCI between May 2013 and September 2015.

RESULTS

Our cohort included 6717 PCI patients; 3739 in the derivation cohort and 2978 in the validation cohort. The discriminative ability of the admission model was good (C-index of 0.727). The c-indices for the discharge and cath PCI models were slightly better. (C-index of 0.751 and 0.752 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.720, 0.739 and 0.741 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.703, 0.725 and 0.719 respectively).

CONCLUSION

Simple models based on available demographic and clinical data may be sufficient to identify patients at highest risk of readmission following PCI early in their hospitalization.

Predicting readmission risk following coronary artery bypass surgery at the time of admission

BACKGROUND

Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after coronary artery bypass graft surgery (CABG) early in a hospitalization would enable hospitals to enhance discharge planning.

METHODS

We developed different models to predict 30-day inpatient readmission to our institution in patients who underwent CABG between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from STS Registry data. We used logistic regression and assessed the discrimination of each model using the c-index. The models were validated with testing on a different patient cohort who underwent CABG between May 2013 and September 2015. Our cohort included 1277 CABG patients: 1159 in the derivation cohort and 1018 in the validation cohort.

RESULTS

The discriminative ability of the admission model was reasonable (C-index of 0.673). The c-indices for the discharge and STS models were slightly better. (C-index of 0.700 and 0.714 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.641, 0.659 and 0.670 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.573, 0.605 and 0.595 respectively).

CONCLUSIONS

Risk prediction models based on data available early on admission are predictive for readmission risk. Adding registry data did not improved the performance of these models. These simplified models may be sufficient to identify patients at highest risk of readmission following coronary revascularization early in the hospitalization.

Stroke Performance Measures Do Not Predict Functional Outcome

BACKGROUND AND PURPOSE

Poststroke functional outcome is critical to stroke survivors. We sought to determine whether adherence to current stroke performance measures is associated with better functional outcome 90 days after an ischemic stroke.

METHODS

Utilizing the Brain Attack Surveillance in Corpus Christi cohort, we examined adherence to 7 ischemic stroke performance measures from February 2009 to June 2012. Adherence to the measures was analyzed in aggregate using a binary defect-free score and an opportunity score, representing the proportion of eligible measures met. The opportunity score ranges from 0 to 1, with values closer to 1 implying better adherence. Functional outcome, defined by an activities of daily living and instrumental activities of daily living (ADL/IADL) score (range 1-4, higher scores worse), was ascertained at 90 days poststroke. Tobit regression models were fitted to examine the associations between the performance measures and functional outcome, adjusting for demographic and clinical characteristics, including stroke severity.

RESULTS

There were 565 patients with ischemic stroke included in the analysis. The median ADL/IADL score was 2.32 (interquartile range [IQR]: 1.41-3.41). The median opportunity score was 1 (IQR: 0.8-1), and 58.4% of the patients received defect-free care. After adjustment, the opportunity score (P = .67) and defect-free care (P = .92) were not associated with functional outcome.

CONCLUSION

In this population, adherence to a composite of current stroke performance measures was not associated with poststroke functional outcome after adjustment for other factors. Performance measures that are associated with improved functional outcome should be developed and incorporated into stroke quality measures.

Episode-Based Approaches to Measuring Health Care Quality

Most currently available quality measures reflect point-in-time provider tasks, providing a limited and fragmented assessment of care. The concept of episodes of care could be used to develop quality measurement approaches that reflect longer periods of care. With input from clinical experts, we constructed episode-of-care frameworks for six illustrative conditions and identified potential gaps and measure development priority areas. Episode-based measures could assess changes in health outcomes ("delta measures"), the amount of time during an episode in which a patient has suboptimal health status ("integral measures"), quality contingent upon events occurring previously ("contingent measures"), and composites of measures throughout the episode. This article identifies a number of challenges that will need to be addressed to advance operationalization of episode-based quality measurement.

Quality Measurement in Gastroenterology: Confessions of a Realist

Clinicians are required to report their performance on an ever-increasing number of quality measures. However, it is difficult to measure health care quality and it is unclear whether broadly applying accountability measures effectively improves care. This article considers these challenges and includes recommendations that may help gastroenterologists respond to demands for increased quality measurement.

Two-year mortality, complications, and healthcare use in children with medicaid following tracheostomy

OBJECTIVES/HYPOTHESIS

To assess patient characteristics associated with adverse outcomes in the first 2 years following tracheostomy, and to report healthcare utilization and cost of caring for these children.

STUDY DESIGN

Retrospective cohort study.

METHODS

Children (0-16 years) in Medicaid from 10 states undergoing tracheostomy in 2009, identified with International Classification of Diseases, Ninth Revision, Clinical Modification procedure codes and followed through 2011, were selected using the Truven Health Medicaid Marketscan Database (Truven Health Analytics, Inc., Ann Arbor, MI). Patient demographic and clinical characteristics were assessed with likelihood of death and tracheostomy complication using chi-square tests and logistic regression. Healthcare use and spending across the care continuum (hospital, outpatient, community, and home) were reported.

RESULTS

A total of 502 children underwent tracheostomy in 2009, with 34.1% eligible for Medicaid because of disability. Median age at tracheostomy was 8 years (interquartile range 1-16 years), and 62.7% had a complex chronic condition. Two-year rates of in-hospital mortality and tracheostomy complication were 8.9% and 38.8%, respectively. In multivariable analysis, the highest likelihood of mortality occurred in children age < 1 year compared with 13+ years (odds ratio [OR] 7.3; 95% confidence interval [CI], 3.2-17.1); the highest likelihood of tracheostomy complication was in children with a complex chronic condition versus those without a complex chronic condition (OR 3.3; 95% CI, 1.1-9.9). Total healthcare spending in the 2 years following tracheostomy was $53.3 million, with hospital, home, and primary care constituting 64.4%, 9.4%, and 0.5% of total spending, respectively.

CONCLUSION

Mortality and morbidity are high, and spending on primary and home care is small following tracheostomy in children with Medicaid. Future studies should assess whether improved outpatient and community care might improve their health outcomes.

LEVEL OF EVIDENCE

4. Laryngoscope, 126:2611-2617, 2016.

A national system for monitoring the performance of hospitals in Ethiopia

Many countries struggle to develop and implement strategies to monitor hospitals nationally. The challenge is particularly acute in low-income countries where resources for measurement and reporting are scarce. We examined the experience of developing and implementing a national system for monitoring the performance of 130 government hospitals in Ethiopia. Using participatory observation, we found that the monitoring system resulted in more consistent hospital reporting of performance data to regional health bureaus and the federal government, increased transparency about hospital performance and the development of multiple quality-improvement projects. The development and implementation of the system, which required technical and political investment and support, would not have been possible without strong hospital-level management capacity. Thorough assessment of the health sector’s readiness to change and desire to prioritize hospital quality can be helpful in the early stages of design and implementation. This assessment may include interviews with key informants, collection of data about health facilities and human resources and discussion with academic partners. Aligning partners and donors with the government’s vision for quality improvement can enhance acceptability and political support. Such alignment can enable resources to be focused strategically towards one national effort – rather than be diluted across dozens of potentially competing projects. Initial stages benefit from having modest goals and the flexibility for continuous modification and improvement, through active engagement with all stakeholders.

WITHDRAWN: Quality Measurement in Gastroenterology: Confessions of a Realist

The Publisher regrets that this article is an accidental duplication of an article that has already been published, http://dx.doi.org/10.1016/j.cgh.2015.07.033. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.

Senior clinician views regarding introduction of a 'time to specialist' quality measure for unselected emergency admissions

The reorganisation of hospital emergency care aims to promote rapid access to specialists. In this study, we sought views from senior clinicians regarding the introduction of a 'time to specialist' (TTS) measure to evaluate healthcare delivery. We conducted a thematic analysis of transcripts from semi-structured interviews (n = 13) with clinical leads in a large National Health Service (NHS) Foundation Trust. Three main themes were identified, each with two subcategories: TTS as an appropriate measure (utility and acceptability); recording of TTS information (defining specialist contact and collection of time data); and impact (patient care and service efficiency). Interviewees perceived that a TTS target might improve clinical care for patients with severe illness and service efficiency for milder presentations. There was uncertainty about other patient groups and the definition of 'specialist' in this context. Clinical leads recognised that TTS might be helpful for describing changes in the provision of services, but the impact for patients was unclear because of heterogeneity in presentation and severity of illness for unselected admissions, and challenges in the definition of 'specialist' relative to individual clinical need.

Hospital-level variation in angina and mortality at 1 year after myocardial infarction: insights from the Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patients' Health Status (TRIUMPH) Registry

BACKGROUND

Despite calls to expand measurement of acute myocardial infarction (AMI) outcomes to include symptom burden, little has been done to describe hospital-level variation in this patient-centered outcome, or its association with mortality. Understanding the relationship between symptoms and longer-term mortality could inform the importance of these outcomes for monitoring quality of care.

METHODS AND RESULTS

Among 4316 patients with AMI treated at 24 hospitals participating in the Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patients' Health Status (TRIUMPH) study, we assessed risk-standardized 1-year symptom burden as measured by the Seattle Angina Questionnaire Angina Frequency Score and mortality attributed to the hospital that provided AMI care. Median odds ratios were used to assess outcome variation and reflect the relative odds of an outcome for 2 patients with identical covariates at different, randomly selected, hospitals. We then evaluated the correlation between hospital-level mortality and angina. Finally, we determined the extent to which variation in mortality and angina was explained by achievement of AMI performance measures. We observed hospital variation in risk-adjusted 1-year mortality (range, 4.9%-8.6%; median odds ratio, 1.30; P=0.01) and angina (range, 17.7%-29.4%; median odds ratio, 1.34; P<0.001). At the hospital level, mortality and angina at 1 year were weakly correlated (r=0.40; 95% confidence interval, 0.00-0.68; P=0.05). Accounting for the quality of AMI care did not attenuate variation in risk-adjusted 1-year mortality or angina.

CONCLUSIONS

Symptom burden and mortality vary at the hospital level after AMI and are only weakly correlated. These findings suggest that symptom burden should be considered a separate quality domain that is not well captured by current quality metrics.

Accuracy of administrative and clinical registry data in reporting postoperative complications after surgery for oral cavity squamous cell carcinoma

BACKGROUND

The purpose of this study was to describe and compare how postoperative complications after oral cavity squamous cell carcinoma (SCC) surgery are reported in medical records, institutional billing claims, and national clinical registries.

METHODS

The medical records of 355 previously untreated patients who underwent surgery for oral cavity SCC at our institution were retrospectively reviewed for postoperative complications. Information was compared with claims and National Surgical Quality Improvement Program (NSQIP) data.

RESULTS

We identified 219 patients (62%) experiencing 544 complications (10% major). Billing claims identified 29% of these patients, 36% of overall complications, and 98% of major complications. Of overlapping patients, NSQIP identified 27% of patients, 33% of overall complications, and 100% of major complications noted on chart abstraction.

CONCLUSION

The incidence of minor postoperative complications after oral cavity SCC surgery is relatively high. Both claims data and NSQIP accurately recorded major complications, but were suboptimal compared to chart abstraction in capturing minor complications.

Clinical prediction model suitable for assessing hospital quality for patients undergoing carotid endarterectomy

Background

Assessing hospital quality in the performance of carotid endarterectomy (CEA) requires appropriate risk adjustment across hospitals with varying case mixes. The aim of this study was to develop and validate a prediction model to assess the risk of in‐hospital stroke or death after CEA that could aid in the assessment of hospital quality.

Methods and Results

Patients from National Cardiovascular Data Registry (NCDR)'s Carotid Artery Revascularization and Endarterectomy (CARE) Registry undergoing CEA without acute evolving stroke from 2005 to 2013 were included. In‐hospital stroke or death was modeled using hierarchical logistic regression with 20 candidate variables and accounting for hospital‐level clustering. Internal validation was achieved with bootstrapping; model discrimination and calibration were assessed. A total of 213 (1.7%) primary end point events occurred during 12 889 procedures. Independent predictors of stroke or death included age, prior peripheral artery disease, diabetes mellitus, prior coronary artery disease, having a symptomatic carotid lesion, having a contralateral carotid occlusion, or having New York Heart Association Class III or IV heart failure. The model was well calibrated and demonstrated moderate discriminative ability (c‐statistic 0.65). The NCDR CEA score was then developed to support simple, prospective risk quantification in the clinical setting.

Conclusions

The NCDR CEA score, comprising 7 clinical variables, predicts in‐hospital stroke or death after CEA. This model can be used to estimate hospital risk‐adjusted outcomes for CEA and to assist with the assessment of hospital quality.

Predictors of early readmission among patients 40 to 64 years of age hospitalized for chronic obstructive pulmonary disease

RATIONALE

Various causes can contribute to the high rates of readmission among patients hospitalized with chronic obstructive pulmonary disease (COPD).

OBJECTIVES

To determine the frequency and predictors of early readmission among patients aged 40-64 years, hospitalized with COPD.

METHODS

In a retrospective cohort study, using a large national commercial insurance database, we obtained the clinical information within 12 months of the index hospitalization and 30 days after discharge.

MEASUREMENTS AND MAIN RESULTS

Primary outcome was early readmission, defined as hospitalization within 30 days of discharge. We categorized predictor variables as patient, provider, and system factors, and compared these variables between patients readmitted and those not readmitted. Logistic regression was used for multivariable analysis. Of 8,263 patients who met the inclusion criteria, 741 (8.9%) had early readmission. Multivariable analysis showed patient factors (male, history of heart failure, lung cancer, osteoporosis, and depression), provider factors (no prior prescription of statin within 12 mo of the index hospitalization and no prescription of short-acting bronchodilator, oral steroid and antibiotic on discharge), and system factors (length of stay, <2 or >5 d and lack of follow-up visit after discharge) were associated with early readmission among patients hospitalized with COPD. The C-statistic of the model including patient characteristics was 0.677 (95% confidence interval, 0.656-0.697), which was improved to 0.717 (95% confidence interval, 0.702-0.732) after addition of provider- and system-based factors.

CONCLUSIONS

One of 11 patients hospitalized with COPD is readmitted within 30 days of discharge. Provider and system factors are important modifiable risk factors of early readmission.

Variations and practice in the care of patients with rheumatoid arthritis: quality and cost of care

INTRODUCTION

Variability in treatment is linked to lower quality of care and higher costs. Rheumatoid arthritis (RA) is a chronic inflammatory disease for which care and management may vary considerably among rheumatologists. The extent of this variability and its cost ramifications have not been widely studied. This prospective study evaluated the quality and variability in care and quantified the potential cost implications.

METHODS

We used Clinical Performance and Value® vignettes to measure the quality of RA care among community-based rheumatologists. Three online Clinical Performance and Value® vignettes--representing patients likely seen in practice with mild disease activity (case A), worsening disease activity (case B), and stable disease with a complicating comorbidity (case C)--were administered to each rheumatologist. Responses were scored against evidence-based criteria. Costs were computed using current (2011) Medicare pricing. Data were analyzed using t test and fixed-effects analysis of variance.

RESULTS

One hundred eight board-certified rheumatologists (72% were male; mean age, 49.1 years) completed the study. Overall quality scores averaged 61.3%. Those employed by a health system or in a multispecialty practice were more likely to score higher. Highest combined scores for diagnosis and treatment were evident with case A (61.7%) and lowest with case C (46.7%). Up to 79% of rheumatologists ordered at least 1 laboratory test that was considered unnecessary by study protocol criteria, incurring a mean excess cost of $37.85 per physician per case. Up to 26.9% rheumatologists prescribed biologic agents that were not indicated based on American College of Rheumatology treatment guidelines, resulting in additional costs of $2041 per patient per month.

CONCLUSION

In this study, we observed a wide range of reported practice variability by rheumatologists in the management of RA. This included unnecessary testing and use of biologic agents that increased the costs of treatment. Opportunities for quality improvement and cost control exist in the management of RA.

Derivation and validation of a risk standardization model for benchmarking hospital performance for health-related quality of life outcomes after acute myocardial infarction

BACKGROUND

Before outcomes-based measures of quality can be used to compare and improve care, they must be risk-standardized to account for variations in patient characteristics. Despite the importance of health-related quality of life (HRQL) outcomes among patients with acute myocardial infarction (AMI), no risk-standardized models have been developed.

METHODS AND RESULTS

We assessed disease-specific HRQL using the Seattle Angina Questionnaire at baseline and 1 year later in 2693 unselected AMI patients from 24 hospitals enrolled in the Translational Research Investigating Underlying disparities in acute Myocardial infarction Patients' Health status (TRIUMPH) registry. Using 57 candidate sociodemographic, economic, and clinical variables present on admission, we developed a parsimonious, hierarchical linear regression model to predict HRQL. Eleven variables were independently associated with poor HRQL after AMI, including younger age, previous coronary artery bypass graft surgery, depressive symptoms, and financial difficulties (R(2)=20%). The model demonstrated excellent internal calibration and reasonable calibration in an independent sample of 1890 AMI patients in a separate registry, although the model slightly overpredicted HRQL scores in the higher deciles. Among the 24 TRIUMPH hospitals, 1-year unadjusted HRQL scores ranged from 67-89. After risk-standardization, HRQL score variability narrowed substantially (range=79-83), and the group of hospital performance (bottom 20%/middle 60%/top 20%) changed in 14 of the 24 hospitals (58% reclassification with risk-standardization).

CONCLUSIONS

In this predictive model for HRQL after AMI, we identified risk factors, including economic and psychological characteristics, associated with HRQL outcomes. Adjusting for these factors substantially altered the rankings of hospitals as compared with unadjusted comparisons. Using this model to compare risk-standardized HRQL outcomes across hospitals may identify processes of care that maximize this important patient-centered outcome.

Criminal justice outcomes after engagement in outpatient substance abuse treatment

The relationship between engagement in outpatient treatment facilities in the public sector and subsequent arrest is examined for clients in Connecticut, New York, Oklahoma and Washington. Engagement is defined as receiving another treatment service within 14 days of beginning a new episode of specialty treatment and at least two additional services within the next 30 days. Data are from 2008 and survival analysis modeling is used. Survival analyses express the effects of model covariates in terms of "hazard ratios," which reflect a change in the likelihood of outcome because of the covariate. Engaged clients had a significantly lower hazard of any arrest than non-engaged in all four states. In NY and OK, engaged clients also had a lower hazard of arrest for substance-related crimes. In CT, NY, and OK engaged clients had a lower hazard of arrest for violent crime. Clients in facilities with higher engagement rates had a lower hazard of any arrest in NY and OK. Engaging clients in outpatient treatment is a promising approach to decrease their subsequent criminal justice involvement.