Faculty Bibliography

Background: Discharge to skilled nursing facilities (SNF) is common in patients with heart failure (HF). The goal of a SNF stay is to improve functional status to allow patients to return home safely. However, the second transition from SNF to home may also be risky. Here, we examine the association between receipt of home health care (HHC) and readmission risk among patients discharged from SNF to home following HF hospitalization.
Method(s): We examined all Medicare fee-for-service beneficiaries 65 and older admitted 2012-2015 with a HF diagnosis discharged to SNF then subsequently discharged home. The primary outcome was unplanned read-mission within 30 days of SNF to home discharge, using CMS's HF read-mission methodology. We plotted time to readmission with Kaplan-Meier curves and compared these groups with a log-rank test. Then, we compared time to readmission using an adjusted Cox model; this model included a frailty term to account for correlation of patient outcome by SNF.
Result(s): There were 67,585 HF hospitalizations discharged to SNF and subsequently discharged home; 13,257 (19.6%) were discharged with HHC, 54,328 (80.4%) without. Patients discharged home from SNF with HHC had lower 30-day readmission rates than patients discharged without HHC (22.8% vs 24.5%, p< 0.0001). Kaplan-Meier curves demonstrated that patients discharged home from SNF with HHC have a longer unadjusted time to readmission. Of those readmitted within 30 days, median time to readmission for those discharged home from SNF with HHC was 11 days and 9 days for those discharged home without HHC (p< 0.0001). After risk-adjustment, patients discharged home with HHC still had a lower hazard of 30-day readmission.
Conclusion(s): Patients who received HHC were less likely to be readmitted within 30 days compared to those discharged home without HHC. This is unexpected as patients discharged with HHC likely have more functional impairments and therefore at higher readmission risk. Since patients requiring a SNF stay after hospital discharge may have additional needs, they may be especially likely to benefit from restorative therapy through HHC; however only about 20% received such services

Background: Electronic health record (EHR) documentation may contribute to burnout, especially for those with substantial clinical effort. We assessed whether clinical effort is associated with working in the EHR after work hours.
Method(s): We included all ambulatory physicians in a medicine specialty continuously practicing at any NYU Langone Health Faculty Group Practice site between May 1 and October 31, 2018. We quantified minutes logged into the EHR on days without scheduled appointments, and minutes logged into the EHR 30 minutes before and after appointments on days with scheduled appointments. We termed this time " work after work." We categorized physicians by their average number of days with appointments per week. Data were analyzed using SAS 9.4 (SAS Institute, Cary, NC). We calculated least squares means of fixed effects to account for heterogeneous variances, and compared means using Tukey's multiple comparison test. This study met institutional review board criteria for quality improvement work.
Result(s): We included 300 physicians, of whom 28.6% were general internists. The average physician had 3 days/week with scheduled appointments, spent 114.9 min in the EHR on days without appointments, and spent 21.7 min in the EHR after work hours on days with appointments. Time spent in the EHR on days without appointments increased with the number of appointment days per week (14.7 min/unscheduled day for 1 day/week vs. 193.8 min/unscheduled day for > 4 days/week, p< 0.001). Time spent in the EHR after hours on days with scheduled appointments did not significantly differ (Table 1).
Conclusion(s): All ambulatory physicians spend a substantial amount of time working in the EHR after hours and on unscheduled days (including weekends), but physicians with more clinical time were disproportionately burdened. The most clinically active spent an average of 2.8 hours in the EHR each unscheduled day. These findings add to concerns about EHR usability and documentation burden, particularly for busier clinicians. Our institution is now building dashboards to track work after work, offloading tasks to ancillary team members to reduce physician work burden, and exploring whether outliers would benefit from personalized technical assistance and training. Work after work analyses could be employed elsewhere to motivate similar improvements

Background: Hospitalized patients often undergo interventional radiology (IR) procedures, many of which require individualized pre-procedure preparation and post-procedure care. Internists caring for these patients may not be familiar with requirements for these patients, causing procedural delays or periprocedural adverse events. Clear communication between IR and internal medicine is therefore necessary, but is often lacking.
Method(s): We conducted qualitative interviews with hospitalists, house staff, nurses and IR staff to identify common breakdowns in periprocedural communication between IR and referring medicine units. Utilizing insights from these interviews, we identified essential elements for pre-procedure and post-procedure communication. These elements were added as fields in templated pre-and post-procedure IR notes (Table 1). Each standardized template contains 16 elements. Outcome measures included proportion of key elements included in IR notes, inpatient provider satisfaction, and frequency of phone calls into and out of IR before and after the intervention.
Result(s): Before implementation of the standardized templates, pre-procedure consult notes (N=25) contained an average of 3.5 key elements (typically a brief medical history, assessment and plan), while post-implementation (N=50), these notes contained an average of 15.3 elements (p< 0.001). Similarly, post-procedure notes (N=25) contained an average of 4.7 elements (typically the names of the IR providers, a brief procedure description and patient condition) pre-intervention versus a mean of 15.0 elements post-intervention (N=50) (p< 0.001). Surveys of hospitalists pre-(N=17) and post-intervention (N=10) showed no significant difference in lack of confidence in preparing patients for IR procedures (52.9% vs. 30.0%, p=0.40), ineffective collaboration with IR (11.8% vs. 0%, p=0.44), and not receiving clear recommendations (35.3% vs. 10%, p=0.67); however analyses were underpowered. Total calls into and out of VIR decreased 15.6% overall (mean decrease of 7.7 calls/weekday and 24.5 calls/weekend, p=.006).
Conclusion(s): Standardizing pre-and post-procedure documentation can effectively increase the inclusion of key content, and this content may reduce internal medicine physician questions and concerns regarding periprocedural patient care

OBJECTIVE:Discharge to skilled nursing facilities (SNFs) is common in patients with heart failure (HF). It is unknown whether the transition from SNF to home is risky for these patients. Our objective was to study outcomes for the 30 days after discharge from SNF to home among Medicare patients hospitalized with HF who had subsequent SNF stays of 30 days or less. DESIGN/METHODS:Retrospective cohort study. SETTING AND PARTICIPANTS/METHODS:All Medicare fee-for-service beneficiaries 65 and older admitted during 2012-2015 with a HF diagnosis discharged to SNF then subsequently discharged home. MEASURES/METHODS:Patients were followed for 30 days following SNF discharge. We categorized patients by SNF length of stay: 1 to 6 days, 7 to 13 days, and 14 to 30 days. For each group, we modeled time to a composite outcome of unplanned readmission or death after SNF discharge. Our model examined 0-2 days and 3-30 days post-SNF discharge. RESULTS:Our study included 67,585 HF hospitalizations discharged to SNF and subsequently discharged home. Overall, 16,333 (24.2%) SNF discharges to home were readmitted within 30 days of SNF discharge. The hazard rate of the composite outcome for each group was significantly increased on days 0 to 2 after SNF discharge compared to days 3 to 30, as reflected in their hazard rate ratios: for patients with SNF length of stay 1 to 6 days, 4.60 (4.23-5.00); SNF length of stay 7 to 13 days, 2.61 (2.45-2.78); SNF length of stay 14 to 30 days, 1.70 (1.62-1.78). CONCLUSIONS/IMPLICATIONS/CONCLUSIONS:The hazard rate of readmission after SNF discharge following HF hospitalization is highest during the first 2 days home. This risk attenuated with longer SNF length of stay. Interventions to improve postdischarge outcomes have primarily focused on hospital discharge. This evidence suggests that interventions to reduce readmissions may be more effective if they also incorporate the SNF-to-home transition.

BACKGROUND:The Medicare Hospital Readmissions Reduction Program has led to fewer readmissions following hospitalizations with a principal diagnosis of heart failure (HF). Patients with HF are frequently hospitalized for other causes. OBJECTIVES/OBJECTIVE:This study sought to compare trends in Medicare risk-adjusted, 30-day readmissions following principal HF hospitalizations and other hospitalizations with HF. METHODS:This was a retrospective study of 12,973,853 Medicare hospitalizations with a principal or secondary diagnosis of HF between January 2008 and June 2015. Hospitalizations were categorized as follows: principal HF hospitalizations; principal acute myocardial infarction or pneumonia hospitalizations with secondary HF; and other hospitalizations with secondary HF. The study examined trends in risk-adjusted, 30-day, all-cause readmission rates for each cohort and trends in differences in readmission rates among cohorts by using linear spline regression models. RESULTS:Before passage of the Affordable Care Act in March 2010, risk-adjusted, 30-day readmission rates were stable for all 3 cohorts, with mean monthly rates of 26.1%, 24.9%, and 24.4%, respectively. Risk-adjusted readmission rates started declining after passage of the Affordable Care Act by 1.09% (95% confidence interval [CI]: 0.51% to 1.68%), 1.24% (95% CI: 0.92% to 1.57%), and 1.05% (95% CI: 0.52% to 1.58%) per year, respectively, until implementation of the Hospital Readmissions Reduction Program in October 2012 and then stabilized for all 3 cohorts. CONCLUSIONS:Patients with HF are often hospitalized for other causes, and these hospitalizations have high readmission rates. Policy changes led to decreases in readmission rates for both principal and secondary HF hospitalizations. Readmission rates in both groups remain high, suggesting that initiatives targeting all hospitalized patients with HF continue to be warranted.

BACKGROUND:Temporal changes in the readmission rates for patient groups and conditions that were not directly under the purview of Hospital Readmissions Reduction Program (HRRP) can help assess whether efforts to lower readmissions extended beyond targeted patients and conditions. METHODS:Using Nationwide Readmissions Database (2010-2015), we assessed trends in all-cause readmission rates for one of the 3 HRRP conditions (acute myocardial infarction, heart failure, pneumonia) or conditions not targeted by HRRP in 6 age-insurance groups defined by age-groups (≥65 or <65 years) and payer (Medicare, Medicaid, or private insurance). RESULTS:In the ≥65-year age-group, readmission rates for those covered by Medicare, private-insurance, and Medicaid decreased annually for acute myocardial infarction (risk-adjusted odds ratio, OR [95%CI], Medicare 0.94 [0.94-0.95], private-insurance 0.95 [0.93-0.97], and Medicaid 0.93 [0.90-0.97]), heart failure (ORs, 0.96 [0.96-0.97], 0.97 [0.96-0.99], and 0.96 [0.94-0.98], for the 3 payers, respectively), and pneumonia (ORs, 0.96 [0.96-0.97), 0.96 [0.95-0.97], and 0.94 [0.92-0.96], respectively). In the <65-year age-group, there was a similar decline in 30-day readmission rates for acute myocardial infarction (risk-adjusted ORs for yearly decrease, Medicare 0.97 [0.96-0.98], private-insurance 0.93 [0.92-0.94], and Medicaid 0.94 [0.92-0.95]), heart failure (ORs, 0.98 [0.97-0.98], 0.97 [0.95-0.98], and 0.96 [0.96-0.97], for the 3 payers, respectively), and pneumonia (ORs, 0.98 [0.97-0.99], 0.98 [0.97-1.00], and 0.98 [0.97-0.99], respectively). In comparison to the targeted conditions, there was a relatively small, but significant, decrease in readmission rates for non-target conditions across all age-insurance groups. CONCLUSION/CONCLUSIONS:There was a significant decline in readmission rates across patient age-insurance groups for the 3 target conditions under the HRRP, as well as a decline in readmission rates for non-target conditions. There appears to be a systematic improvement in readmission rates for patient groups beyond the population of fee-for-service, older, Medicare beneficiaries included in the HRRP.

BACKGROUND:Hospital readmission rates are publicly reported by the Centers for Medicare & Medicaid Services (CMS); however, the implications of emergency department (ED) visits following hospital discharge on readmissions are uncertain. We describe the frequency, diagnoses, and hospital-level variation in ED visitation following hospital discharge, including the relationship between risk-standardized ED visitation and readmission rates. METHODS:This is a cross-sectional analysis of Medicare beneficiaries hospitalized for acute myocardial infarction (AMI), heart failure, and pneumonia between July 2011 and June 2012. We used Medicare Standard Analytic Files to identify admissions, readmissions, and ED visits consistent with CMS measures. Postdischarge ED visits were defined as treat-and-discharge ED services within 30 days of hospitalization without readmission. We utilized hierarchical generalized linear models to calculate hospital risk-standardized postdischarge ED visit rates and readmission rates. RESULTS:We included 157,035 patients hospitalized at 1656 hospitals for AMI, 391,209 at 3044 hospitals for heart failure, and 342,376 at 3484 hospitals for pneumonia. After hospitalization for AMI, heart failure, and pneumonia, there were 14,714 (9%), 31,621 (8%), and 26,681 (8%) ED visits, respectively. Hospital-level variation in postdischarge ED visit rates was substantial: AMI (median: 8.3%; 5th and 95th percentile: 2.8%-14.3%), heart failure (median: 7.3%; 5th and 95th percentile: 3.0%-13.3%), and pneumonia (median: 7.1%; 5th and 95th percentile: 2.4%-13.2%). There was statistically significant inverse correlation between postdischarge ED visit rates and readmission rates: AMI (-0.23), heart failure (-0.29), and pneumonia (-0.18). CONCLUSIONS:Following hospital discharge, ED treatand- discharge visits are half as common as readmissions for Medicare beneficiaries. There is wide hospital-level variation in postdischarge ED visitation, and hospitals with higher ED visitation rates demonstrated lower readmission rates.

BACKGROUND:The assessment of clinical guideline adherence for the evaluation of pulmonary embolism (PE) via computed tomography pulmonary angiography (CTPA) currently requires either labor-intensive, retrospective chart review or prospective collection of PE risk scores at the time of CTPA order. The recording of clinical data in a structured manner in the electronic health record (EHR) may make it possible to automate the calculation of a patient's PE risk classification and determine whether the CTPA order was guideline concordant. OBJECTIVES/OBJECTIVE:The objective of this study was to measure the performance of automated, structured-data-only versions of the Wells and revised Geneva risk scores in emergency department encounters during which a CTPA was ordered. The hypothesis was that such an automated method would classify a patient's PE risk with high accuracy compared to manual chart review. METHODS:We developed automated, structured-data-only versions of the Wells and revised Geneva risk scores to classify 212 emergency department (ED) encounters during which a CTPA was performed as "PE Likely" or "PE Unlikely." We then combined these classifications with D-dimer ordering data to assess each encounter as guideline concordant or discordant. The accuracy of these automated classifications and assessments of guideline concordance were determined by comparing them to classifications and concordance based on the complete Wells and revised Geneva scores derived via abstractor manual chart review. RESULTS:The automatically derived Wells and revised Geneva risk classifications were 91.5% and 92% accurate compared to the manually determined classifications, respectively. There was no statistically significant difference between guideline adherence calculated by the automated scores as compared to manual chart review (Wells: 70.8 vs. 75%, p = 0.33 | Revised Geneva: 65.6 vs. 66%, p = 0.92). CONCLUSION/CONCLUSIONS:The Wells and revised Geneva score risk classifications can be approximated with high accuracy using automated extraction of structured EHR data elements in patients who received a CTPA. Combining these automated scores with D-dimer ordering data allows for the automated assessment of clinical guideline adherence for CTPA ordering in the emergency department, without the burden of manual chart review.

PURPOSE: To better understand the decision making behind the ordering of CT pulmonary angiography (CTPA) for the diagnosis of pulmonary embolism (PE) in the emergency department. METHODS: We conducted semistructured interviews with our institution's emergency medicine (EM) providers and radiologists who read CTPAs performed in the emergency department. We employed the Theoretical Domains Framework-a formal, structured approach used to better understand the motivations and beliefs of physicians surrounding a complex medical decision making-to categorize the themes that arose from our interviews. RESULTS: EM providers were identified as the main drivers of CTPA ordering. Both EM and radiologist groups perceived the radiologist's role as more limited. Experience- and gestalt-based heuristics were the most important factors driving this decision and more important, in many cases, than established algorithms for CTPA ordering. There were contrasting views on the value of d-dimer in the suspected PE workup, with EM providers finding this test less useful than radiologists. EM provider and radiologist suggestions for improving the appropriateness of CTPA ordering consisted of making this process more arduous and incorporating d-dimer tests and prediction rules into a decision support tool. CONCLUSION: EM providers were the main drivers of CTPA ordering, and there was a marginalized role for the radiologist. Experience- and gestalt-based heuristics were the main influencers of CTPA ordering. Our findings suggest that a more nuanced intervention than simply including a d-dimer and a prediction score in each preimaging workup may be necessary to curb overordering of CTPA in patients suspected of PE.