Faculty Bibliography

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/UNASSIGNED:Current guidelines recommend less aggressive target hemoglobin A1c (HbA1c) levels based on older age and lower life expectancy for older adults with diabetes. The effectiveness of electronic health record (EHR) clinical decision support (CDS) in promoting guideline adherence is undermined by alert fatigue and poor workflow integration. Integrating behavioral economics (BE) and CDS tools is a novel approach to improving adherence to guidelines while minimizing clinician burden. Methods/UNASSIGNED: = 8), (2) a 2-h, design-thinking workshop to derive and refine initial module ideas, and (3) semi-structured group interviews at each site with clinic leaders and clinicians to elicit feedback on three proposed nudge module components (navigator section, inbasket refill protocol, medication preference list). Detailed field notes will be summarized by module idea and usability theme for rapid iteration. Frequency of firing and user action taken will be assessed in the first month of implementation via EHR reporting to confirm that module components and related reporting are working as expected as well as assess utilization. To assess the utilization and feasibility of the new tools and generate estimates of clinician compliance with the Choosing Wisely guideline for diabetes management in older adults, a 6-month, single-arm pilot study of the BE-EHR module will be conducted in six outpatient primary care clinics. Discussion/UNASSIGNED:We hypothesize that a low burden, user-centered approach to design will yield a BE-driven, CDS module with relatively high utilization by clinicians. The resulting module will establish a platform for exploring the ability of BE concepts embedded within the EHR to affect guideline adherence for other use cases.

BACKGROUND:Hospitalization and readmission rates have decreased in recent years, with the possible consequence that hospitals are increasingly filled with high-risk patients. OBJECTIVE:We studied whether readmission reduction has affected the risk profile of hospitalized patients and whether readmission reduction was similarly realized among hospitalizations with low, medium, and high risk of readmissions. DESIGN/METHODS:Retrospective study of hospitalizations between January 2009 and June 2015. PATIENTS/METHODS:Hospitalized fee-for-service Medicare beneficiaries, categorized into 1 of 5 specialty cohorts used for the publicly reported hospital-wide readmission measure. MEASUREMENTS/METHODS:Each hospitalization was assigned a predicted risk of 30-day, unplanned readmission using a risk-adjusted model similar to publicly reported measures. Trends in monthly mean predicted risk for each cohort and trends in monthly observed to expected readmission for hospitalizations in the lowest 20%, middle 60%, and highest 20% of risk of readmission were assessed using time series models. RESULTS:Of 47,288,961 hospitalizations, 16.2% (n = 7,642,161) were followed by an unplanned readmission within 30 days. We found that predicted risk of readmission increased by 0.24% (P = .03) and 0.13% (P = .004) per year for hospitalizations in the surgery/ gynecology and neurology cohorts, respectively. We found no significant increase in predicted risk for hospitalizations in the medicine (0.12%, P = .12), cardiovascular (0.32%, P = .07), or cardiorespiratory (0.03%, P = .55) cohorts. In each cohort, observed to expected readmission rates steadily declined, and at similar rates for patients at low, medium, and high risk of readmission. CONCLUSIONS:Hospitals have been effective at reducing readmissions across a range of patient risk strata and clinical conditions. The risk of readmission for hospitalized patients has increased for 2 of 5 clinical cohorts.

BACKGROUND: Interventions to reduce readmissions following acute heart failure hospitalization require early identification of patients. The purpose of this study was to develop and test accuracies of various approaches to identify patients with acute decompensated heart failure (ADHF) using data derived from the electronic health record. METHODS AND RESULTS: We included 37,229 hospitalizations of adult patients at a single hospital in 2013-2015. We developed four algorithms to identify hospitalization with a principal discharge diagnosis of ADHF: 1) presence of one of three clinical characteristics; 2) logistic regression of 31 structured data elements; 3) machine learning with unstructured data; 4) machine learning with both structured and unstructured data. In data validation, 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, while both machine learning algorithms had AUCs of 0.99. Based on a brief survey of three providers who perform chart review for ADHF, we estimated providers spent 8.6 minutes per chart review; using this this parameter, we estimated providers would spend 61.4, 57.3, 28.7, and 25.3 minutes on secondary chart review for each case of ADHF if initial screening was done with algorithms 1, 2, 3, and 4, respectively. CONCLUSION: Machine learning algorithms with unstructured notes had best performance for identification of ADHF and can improve provider efficiency for delivery of quality improvement interventions.

Objective: The purpose of this study was to determine whether an electronic health record-based sepsis alert system could improve quality of care and clinical outcomes for patients with sepsis. Materials and Methods: We performed a patient-level interrupted time series study of emergency department patients with severe sepsis or septic shock between January 2013 and April 2015. The intervention, introduced in February 2014, was a system of interruptive sepsis alerts triggered by abnormal vital signs or laboratory results. Primary outcomes were length of stay (LOS) and in-hospital mortality; other outcomes included time to first lactate and blood cultures prior to antibiotics. We also assessed sensitivity, positive predictive value (PPV), and clinician response to the alerts. Results: Mean LOS for patients with sepsis decreased from 10.1 to 8.6 days ( P < .001) following alert introduction. In adjusted time series analysis, the intervention was associated with a decreased LOS of 16% (95% CI, 5%-25%; P = .007, with significance of alpha = 0.006) and no change thereafter (0%; 95% CI, -2%, 2%). The sepsis alert system had no effect on mortality or other clinical or process measures. The intervention had a sensitivity of 80.4% and a PPV of 14.6%. Discussion: Alerting based on simple laboratory and vital sign criteria was insufficient to improve sepsis outcomes. Alert fatigue due to the low PPV is likely the primary contributor to these results. Conclusion: A more sophisticated algorithm for sepsis identification is needed to improve outcomes.

Background: Paper-based Early Recovery after Surgery (ERAS) path-ways have been shown to reduce length of stay, but there have been limited evaluations of electronic health record (EHR) based pathways. The objective of this study was to evaluate whether ERAS processes implemented with a novel pathway activity integrated with the EHR (E-Pathway) can reduce costs without resulting in increased 30 day readmissions. Methods: We performed a retrospective cohort study of surgical patients age>= 18 years hospitalized at an academic medical center from March 1, 2013 to August 31, 2016. The primary cohort consisted of patients admitted for elective colon surgery. We also studied a control group of patients undergoing elective procedures with similar length of stay as colon surgery (3-5 days). The E-Pathway was based on a pathway template developed by a common EHR vendor (Epic Systems, Verona, WI) with content developed by a multidisciplinary team based on ERAS principles. The E-Pathway was implemented on March 2, 2015. The primary outcome was variable costs per case. Secondary outcomes were observed to expected length of stay (O:E LOS) and 30 day readmissions to our hospital. For both groups, we performed an interrupted time series with segmented regression analysis with month being the unit of time. We used gamma regression for cost models and logistic models for the secondary outcomes. Results: We included 823 (470 and 353 in the pre-and post-intervention, respectively) colon surgery patients and 3415 (1,819 and 1,596 in the pre-and post-intervention) surgical control patients. Among the colon surgery cohort, we observed no changesin cost eitheratbaseline [-0.1% (95% CI-0.8%, 0.5%) per month] or with immediate introduction of the pathway. However, there was statistically significant (p = 0.040) decrease in costs of 1.3% (0.6% to 2.5%) per surgical encounter per month over the 18 month post intervention period. The surgical comparator group had no change in cost either at baseline or at the time of intervention and had a nonsignificant decrease in monthly costs of 0.6% (p = 0.231) post-intervention. There was statistically significant (p = 0.039) decrease in the O:E slope after the intervention of 1.49% per surgical encounter per month. The surgical comparator group had a nonsignificant (p = 0.761) increase in slope of 1.87%. For the 30 day readmission rates, there were no statistically significant changes in either the colon surgery or control groups. Conclusions: Our study is the first, to our knowledge, to report on the outcomes of a novel sophisticated E pathway integrated into an EHR. Following implementation of the E-pathway for colon surgery patients, we observed decreasing direct variable costs and O:E LOS over time. These improvements were not observed among comparable surgical patients. Consequently, as institutions continue to place increased emphasis on standardization of best practice care, E-pathways can be powerful vehicles to support those changes in the new EHR-centric care model

Background: Clinical decision support (CDS) has been shown to im-prove compliance with evidence-based care but its impact is often diminished due to issues such as poor usability, insufficient integration into workflow, and alert fatigue. Non-interruptive CDS may be less subject to alert fatigue but there has been little assessment of its usability. The purpose of this study was to perform usability testing on interruptive and non-interruptive versions of a CDS. Methods: We conducted a usability study ofa CDS tool that recommended prescribing an angiotensin converting enzyme (ACE) inhibitor for inpatients with heart failure. We developed two versions of the CDS that varied in its format: an interruptive alert, in which the CDS popped-up at the time of order entry, and a non-interruptive alert, which was displayed in a checklist section of the Electronic Health Record (EHR). We recruited inpatient providers to use both versions in a laboratory setting. We randomly assigned providers to first trigger the interruptive or non-interruptive alert. Providers were given a clinical scenario and asked to " think aloud" as they worked through the CDS; we then conducted a brief semi-structured interview about usability. We used a constant comparative analysis informed by the Five Rights of CDS framework to analyze the interviews. Inpatient providers from different disciplines were recruited until thematic saturation was reached. Results: Of 12 providers who participated in usability testing, seven used the interruptive followed by the non-interruptive CDS and five used the non-interruptive CDS initially. We categorized codes into four themes related to the Five Rights of CDS and determined some codes to be general to the CDS while others were specific to the interruptive or non-interruptive version (Table). Providers noted that the interruptive alert was readily noticed but generally impeded workflow. Providers found the non-interruptive CDS to be less annoying but had lower visibility; although they liked the ability to address the non-interruptive CDS at any time, some providers questioned whether it would ultimately be used. Conclusions: Providers expressed annoyance in working with an inter-ruptive CDS. Although the non-interruptive CDS was more appealing, providers admitted that it may not be used unless integrated with workflow. One potential solution was a combination of the two formats: supplementing a non-interruptive alert with an occasional, well-timed interruptive alert if uptake was insufficient