Faculty Bibliography

INTRODUCTION: Orthopaedic care is shifting to alternative payment models. We examined whether New York University Langone Medical Center achieved savings under the Centers for Medicare and Medicaid Services Bundled Payments for Care Improvement initiative. METHODS: This study was a difference-in-differences study of Medicare fee-for-service patients hospitalized from April 2011 to June 2012 and October 2013 to December 2014 for lower extremity joint arthroplasty, cardiac valve procedures, or spine surgery (intervention groups), or for congestive heart failure, major bowel procedures, medical peripheral vascular disorders, medical noninfectious orthopaedic care, or stroke (control group). We examined total episode costs and costs by service category. RESULTS: We included 2,940 intervention episodes and 1,474 control episodes. Relative to the trend in the control group, lower extremity joint arthroplasty episodes achieved the greatest savings: adjusted average episode cost during the intervention period decreased by $3,017 (95% confidence interval [CI], -$6,066 to $31). For cardiac procedures, the adjusted average episode cost decreased by $2,999 (95% CI, -$8,103 to $2,105), and for spinal fusion, it increased by $8,291 (95% CI, $2,879 to $13,703). Savings were driven predominantly by shifting postdischarge care from inpatient rehabilitation facilities to home. Spinal fusion index admission costs increased because of changes in surgical technique. DISCUSSION: Under bundled payment, New York University Langone Medical Center decreased total episode costs in patients undergoing lower extremity joint arthroplasty. For patients undergoing cardiac valve procedures, evidence of savings was not as strong, and for patients undergoing spinal fusion, total episode costs increased. For all three conditions, the proportion of patients referred to inpatient rehabilitation facilities upon discharge decreased. These changes were not associated with an increase in index hospital length of stay or readmission rate. CONCLUSION: Opportunities for savings under bundled payment may be greater for lower extremity joint arthroplasty than for other conditions.

Background: Heart failure is among the most common reasons for admission to hospital and is associated with high rates of readmission. Studies have shown that the frequency of heart failure hospitalisation in temperate climates increases in winter months. While such findings suggest that the risk of hospitalisation is higher in winter as compared to summer months, there has been little evaluation of whether there is seasonal variation in the readmission risk of patients who are hospitalised for heart failure. Purpose: To examine seasonal variations in: 1) readmission risk for patients hospitalised with heart failure; and 2) frequency of heart failure hospitalisation for patients at different risk of readmission. Methods: We performed a retrospective study of United States Medicare beneficiaries age >=65 who were hospitalised for heart failure between January 1, 2009 and June 30, 2015. We used a predictive model for 30-day unplanned hospital readmission to assign each hospitalisation a predicted risk of readmission; this model adds demographic and prior utilization data to the hospital-wide readmission model used by the Centres for Medicare and Medicaid Services (CMS). Each hospitalisation was categorized as lowest 20%, middle 60%, and highest 20% of predicted readmission risk. We calculated rate of hospitalisations per calendar month, predicted readmission risk, and monthly rate of hospitalisations for each risk stratum in the study period; monthly hospitalisations were standardized to 30 days. When comparing risk strata, we divided the totals for the middle 60% stratum by three. Results: Among 2,661,837 heart failure hospitalisations, we observed the highest rates of hospitalisation in January through March (range 37,185-37,949 per month) and the lowest rates in July through September (range 29,901-30,603 per month). Conversely, predicted readmission rates were lowest in January and highest in August, with rates of 23.1% and 24.0%, respectively. The number of hospitalisations increased in winter months for patients in all three risk strata, with greatest variation in seasonal differences observed for patients in the lowest 20% of predicted risk (Figure). For example, hospitalisation rates for highest risk patients were 7058 per 30 days in January versus 6473 per 30 days in August; for lowest risk patients, these values were 7852 versus 5595, respectively. (Figure Presented) Conclusion: Readmission risk decreased in winter versus summer months for patients hospitalised for heart failure. Much of the seasonal variation in heart failure hospitalisations appears to be due to a large excess of hospitalisations of these low risk patients in winter months. Our results suggest that preventative measures, such as vaccinations or dietary education, that target lower risk patients in colder months may reduce overall utilisation

BACKGROUND: Interventions to reduce readmission following hospitalization for acute decompensated heart failure (ADHF) require early identification of patients. Electronic health record (EHR)-based approaches to identify patients can range in complexity from simple algorithms based on few data elements to machine learning algorithms using big data. The purpose of this study was to compare performance of a range of algorithms to identify patients with ADHF. Given the emphasis that hospitals currently place on patients with ADHF, we developed models with high sensitivity to avoid missed opportunities for care improvement; this approach assumed secondary chart review would be necessary to confirm a diagnosis. We therefore estimated the time needed for secondary review by providers following initial screening with each algorithm. METHODS: We performed a retrospective study of hospitalizations for patients age > 18 at an academicmedical center in 2013-2015. Using a random 75% development set, we developed four algorithms to identify hospitalizations with a principal diagnosis of ADHF using EHR data through the second midnight of hospitalization: 1) one of three characteristics: heart failure on the problem list, loop diuretic use, or brain natriuretic peptide > 500 pg/ml; 2) logistic regression of 31 clinically-relevant data elements; 3) machine learning approach using L1-regularization logistic regression and unstructured data, including provider notes and radiology reports; 4) machine learning with both structured and unstructured data. We assessed performance of each algorithm in the 25% validation set. We also conducted a brief survey of providers who perform chart review for ADHF to estimate time needed for secondary screening following primary screening with each algorithm. RESULTS: We included 37,229 hospitalizations in the study, of which 1,294 (3.5%) carried a principal diagnosis of ADHF. Algorithm 1 had a sensitivity of 0.98 and positive predictive value (PPV) of 0.14 for ADHF. Algorithm 2 had an area under the receiver operating characteristic curve (AUC) of 0.96 and a PPV of 0.15 when setting the sensitivity at 0.98. Both machine learning algorithms had AUCs of 0.99; with a sensitivity of 0.98, algorithms 3 and 4 had PPVs of 0.30 and 0.34, respectively. Based on survey of three providers, we estimated providers spent 8.6 min per chart review; using this this parameter, providers would spend 61.4, 57.3, 28.7, and 25.3 min on secondary chart review for each case of ADHF if initial screening was done with algorithms 1, 2, 3, and 4, respectively. CONCLUSIONS: Traditional approaches with structured data can accurately identify nearly all patients with ADHF but are limited by a large number of false positives. Machine learning algorithms with unstructured notes and radiology reports can retain this sensitivity while significantly reducing false positives, thereby improving provider efficiency for delivery of quality improvement interventions

BACKGROUND: Electronic health record (EHR)-based clinical decision support tools (CDS) incorporate individualized data to produce patient-specific recommendations at the point-of-care. However, these tools are often limited in their effectiveness, which may be due to poor consideration of usability. The purpose of this study was to evaluate the utilization of a CDS intervention to increase prescription of Angiotensin Converting Enzyme inhibitor (ACEi) or Angiotensin Receptor Blocker (ARB) for patients with heart failure. METHODS: We performed a retrospective study of hospitalized patients with heart failure from the time of CDS implementation, 7/10/13, through 11/30/15. The CDS that we investigated offers providers an opportunity to prescribe an ACEi or ARB or report a contraindication to therapy for patients with documented heart failure. All patients with an EF < 40% who were not on an ACEi or ARB at time of discharge were included in the study. We identified the number of patients for whom the CDS triggered; of those, we categorized provider response as: dismissed, ordered an ACEi/ARB, or contraindication reported. We then performed manual chart review to identify the CDS reported contraindication and structured chart abstraction with standard guidelines to identify gold standard contraindications. We compared each CDS contraindication to gold standard contraindications to determine their accuracy. RESULTS: Out of the 618 subjects who had an EF < 40% but no ACEi or ARB at the time or discharge, 435/618 (70%) had a triggered CDS. Of these 435 subjects for who a CDS was triggered, 180 (41%) were dismissed, 225 (52%) had a contraindication response and 30 (7%) had a prescription for an ACEi/ARB therapy. Overall the accuracy of the documented CDS was 42% (Table 1). CONCLUSIONS: The CDS that we reviewed was poorly utilized and contraindications documented in the tool poorly correlated with patient clinical status reflected elsewhere in the EHR. These findings identify this CDS as a possible impedance to user workflow. One way to improve CDS tools at the point of care is through thorough usability testing and consideration of physician workflow prior to implementation. (Table Presented)

BACKGROUND: Many hospitalized patients have at least 1 chronic disease that is not optimally controlled. The purpose of this study was to explore inpatient provider attitudes about chronic disease management and, in particular, barriers and facilitators of chronic disease management in the hospital. METHODS: We conducted a qualitative study of semi-structured interviews of 31 inpatient providers from an academic medical center. We interviewed attending physicians, resident physicians, physician assistants, and nurse practitioners from various specialties about attitudes, experiences with, and barriers and facilitators towards chronic disease management in the hospital. Qualitative data were analyzed using constant comparative analysis. RESULTS: Providers perceived that hospitalizations offer an opportunity to improve chronic disease management, as patients are evaluated by a new care team and observed in a controlled environment. Providers perceived clinical benefits to in-hospital chronic care, including improvements in readmission and length of stay, but expressed concerns for risks related to adverse events and distraction from the acute problem. Barriers included provider lack of comfort with managing chronic diseases, poor communication between inpatient and outpatient providers, and hospital-system focus on patient discharge. A strong relationship with the outpatient provider and involvement of specialists were facilitators of inpatient chronic disease management. CONCLUSIONS: Providers perceived benefits to in-hospital chronic disease management for both processes of care and clinical outcomes. Efforts to increase inpatient chronic disease management will need to overcome barriers in multiple domains. Journal of Hospital Medicine 2017;12:162-167.

Importance: Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation. Objective: To develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital. Design, Setting, and Participants: We performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients 18 years or older who were admitted after January 1, 2013, and discharged before February 28, 2015, were included. From a random 75% sample of hospitalizations, we developed 5 algorithms for heart failure identification using electronic health record data: (1) heart failure on problem list; (2) presence of at least 1 of 3 characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide level of 500 pg/mL or higher; (3) logistic regression of 30 clinically relevant structured data elements; (4) machine-learning approach using unstructured notes; and (5) machine-learning approach using structured and unstructured data. Main Outcomes and Measures: Heart failure diagnosis based on discharge diagnosis and physician review of sampled medical records. Results: A total of 47119 hospitalizations were included in this study (mean [SD] age, 60.9 [18.15] years; 23 952 female [50.8%], 5258 black/African American [11.2%], and 3667 Hispanic/Latino [7.8%] patients). Of these hospitalizations, 6549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of a PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating characteristic curves of 0.953, 0.969, and 0.974, respectively. With a PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively. Conclusions and Relevance: The problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.

OBJECTIVES: This study sought to determine whether racial and ethnic differences exist among patients with similar access to care. We examined outcomes after heart failure hospitalization within a large municipal health system. BACKGROUND: Racial and ethnic disparities in heart failure outcomes are present in administrative data, and one explanation is differential access to care. METHODS: We performed a retrospective cohort study of 8,532 hospitalizations of adults with heart failure at 11 hospitals in New York City from 2007 to 2010. Primary exposure was ethnicity and race, and outcomes were 30- and 90-day readmission and 30-day and 1-year mortality rates. Generalized estimating equations were used to test for associations between ethnicity and race and outcomes with covariate adjustment. RESULTS: Of the number of hospitalizations included, 4,305 (51%) were for blacks, 2,449 (29%) were for Hispanics, 1,494 (18%) were for whites, and 284 (3%) were for Asians. Compared to whites, blacks and Asians had lower 1-year mortality, with adjusted odds ratios (aORs) of 0.75 (95% confidence interval [CI]: 0.59 to 0.94) and 0.57 (95% CI: 0.38 to 0.85), respectively, and rates for Hispanics were not significantly different (aOR: 0.81; 95% CI: 0.64 to 1.03). Hispanics had higher odds of readmission than whites (aOR: 1.27; 95% CI: 1.03 to 1.57) at 30 (aOR: 1.40; 95% CI: 1.15 to 1.70) and 90 days. Blacks had higher odds of readmission than whites at 90 days (aOR:1.21; 95% CI: 1.01 to 1.47). CONCLUSIONS: Racial and ethnic differences in outcomes after heart failure hospitalization were present within a large municipal health system. Access to a municipal health system may not be sufficient to eliminate disparities in heart failure outcomes.

Importance: Reduced rates of cardiac catheterization, percutaneous coronary intervention (PCI), and coronary artery bypass graft (CABG) are an unintended consequence of public reporting of cardiogenic shock outcomes in New York. Objectives: To evaluate whether the referral rates for cardiac catheterization, PCI, or CABG have improved in New York since cardiogenic shock was excluded from public reporting in 2008 and compare them with corresponding rates in Michigan, New Jersey, and California. Design, Setting, and Participants: Patients with cardiogenic shock complicating acute myocardial infarction from 2002 to 2011 were identified using the National Inpatient Sample. Propensity score matching was used to assemble a cohort of patients with cardiogenic shock with similar baseline characteristics in New York and Michigan. Main Outcomes and Measures: Percutaneous coronary intervention (primary outcome), invasive management (cardiac catheterization, PCI, or CABG), revascularization (PCI or CABG), and CABG were evaluated with reference to 3 calendar year periods: 2002-2005 (time 1: cardiogenic shock included in publicly reported outcomes), 2006-2007 (time 2: cardiogenic shock excluded on a trial basis), and 2008 and thereafter (time 3: cardiogenic shock excluded permanently) in New York and compared with Michigan. Results: Among 2126 propensity score-matched patients representing 10795 (weighted) patients with myocardial infarction complicated by cardiogenic shock in New York and Michigan, 905 (42.6%) were women and mean (SE) age was 69.5 (0.3) years. A significantly higher proportion of the patients underwent PCI (time 1 vs 2 vs 3: 31.1% vs 39.8% vs 40.7% [OR, 1.50; 95% CI, 1.12-2.01; P = .005 for time 3 vs 1]), invasive management (time 1 vs 2 vs 3: 59.7% vs 70.9% vs 73.8% [OR, 1.84; 95% CI, 1.37-2.47; P < .001 for time 3 vs 1]), or revascularization (43.1% vs 55.9% vs 56.3% [OR, 1.66; 95% CI, 1.26-2.20; P < .001 for time 3 vs 1]) after the exclusion of cardiogenic shock from public reporting in New York. However, during the same periods, a greater proportion of patients underwent PCI (time 1 vs 2 vs 3: 41.2% vs 52.6% vs 57.8% [OR, 1.93; 95% CI, 1.45-2.56; P < .001 for time 3 vs 1]), invasive management (time 1 vs 2 vs 3: 64.4% vs 80.5% vs 78.6% [OR, 2.01; 95% CI, 1.47-2.74; P < .001 for time 3 vs 1]), or revascularization (51.2% vs 65.8% vs 68.0% [OR, 2.00; 95% CI, 1.50-2.66; P < .001 for times 3 vs 1]) in Michigan. Results were largely similar in several sensitivity analyses comparing New York with New Jersey or California. Conclusions and Relevance: Although the rates of PCI, invasive management, and revascularization have increased substantially after the exclusion of cardiogenic shock from public reporting in New York, these rates remain consistently lower than those observed in other states without public reporting.