Faculty Bibliography

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

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: In 2012 The Joint Commission implemented new Tobacco Treatment (TOB) performance measures for hospitals. A study evaluated the impact of a hospital-based electronic health record (EHR) intervention on adherence to the revised TOB measures. METHODS: The study was conducted in two acute care hospitals in New York City. Data abstracted from the EHR were analyzed retrospectively from 4,871 smokers discharged between December 2012 and March 2015 to evaluate the impact of two interventions: an order set to prompt clinicians to prescribe pharmacotherapy and a nurse-delivered counseling module that automatically populated the nursing care plan for all smokers. The study estimated the relative odds of a patient being prescribed medication and/or receiving smoking cessation counseling in the intervention period compared to the baseline time period. RESULTS: There was a modest increase in medication orders (odds ratio [OR], 1.35). In contrast, rates of counseling increased 10-fold (OR, 10.54). Patients admitted through surgery were less likely to receive both counseling and medication compared with the medicine service. CONCLUSION: Hospitalization presents an important opportunity to engage smokers in treatment for primary and secondary prevention of tobacco-related illnesses. EHRs can be leveraged to facilitate integration of TOB measure requirements into routine inpatient care; however, the smaller effect on prescribing patterns suggests limitations in this approach alone in changing clinician behavior to meet this measure. The success of the nurse-focused EHR-driven intervention suggests an effective tool for integrating the cessation counseling component of the new measures and the importance of nursing's role in achieving the Joint Commission measure targets.

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: Hospital care on weekends has been associated with delays in care, reduced quality, and poor clinical outcomes. OBJECTIVE: The purpose of this study was to evaluate the impact of a weekend hospital intervention on processes of care and clinical outcomes. The multifaceted intervention included expanded weekend diagnostic services, improved weekend discharge processes, and increased physician and care management services on weekends. DESIGN AND PATIENTS: This was an interrupted time series observational study of adult non-obstetric patients hospitalized at a single academic medical center between January 2011 and January 2014. The study included 18 months prior to and 19 months following the implementation of the intervention. Data were analyzed using segmented regression analysis with adjustment for confounders. MAIN MEASURES: The primary outcome was average length of stay. Secondary outcomes included percent of patients discharged on weekends, 30-day readmission rate, and in-hospital mortality rate. KEY RESULTS: The study included 57,163 hospitalizations. Following implementation of the intervention, average length of stay decreased by 13 % (95 % CI 10-15 %) and continued to decrease by 1 % (95 % CI 1-2 %) per month as compared to the underlying time trend. The proportion of weekend discharges increased by 12 % (95 % CI 2-22 %) at the time of the intervention and continued to increase by 2 % (95 % CI 1-3 %) per month thereafter. The intervention had no impact on readmissions or mortality. During the post-implementation period, the hospital was evacuated and closed for 2 months due to damage from Hurricane Sandy, and a new hospital-wide electronic health record was introduced. The contributions of these events to our findings are not known. We observed a lower inpatient census and found differences in patient characteristics, including higher rates of Medicaid insurance and comorbidities, in the post-Hurricane Sandy period as compared to the pre-Sandy period. CONCLUSIONS: The intervention was associated with a reduction in length of stay and an increase in weekend discharges. Our longitudinal study also illuminated the challenges of evaluating the effectiveness of a large-scale intervention in a real-world hospital setting.

BACKGROUND: Previous studies have suggested that weekend hospital care is inferior to weekday care and that this difference may be related to diminished care intensity. The purpose of this study was to determine whether a metric for measuring intensity of hospital care based on utilization of the electronic health record (EHR) was associated with patient-level outcomes. METHODS: We performed a cohort study of hospitalizations at an academic medical center. Intensity of care was defined as the hourly number of provider accessions of the electronic health record, termed "EHR interactions." Hospitalizations were categorized based on the mean difference in EHR interactions between the first Friday and Saturday of hospitalization. We used regression models to determine the association of these categories with patient outcomes after adjusting for covariates. RESULTS: EHR interactions decreased from Friday to Saturday in 77% of the 9,051 hospitalizations included in the study. As compared to hospitalizations with no change in Friday to Saturday EHR interactions, the relative lengths of stay for hospitalizations with a small, moderate, and large decrease in EHR interactions were 1.05 (95% CI 1.00-1.10), 1.11 (95% CI 1.05-1.17), and 1.25 (95% CI 1.15-1.35), respectively. Although a large decrease in EHR interactions was associated with in-hospital mortality, these findings were not significant after risk adjustment (odds ratio 1.74, 95% CI 0.93-3.25). CONCLUSIONS: Intensity of inpatient care, measured by EHR interactions, significantly diminished from Friday to Saturday, and this decrease was associated with length of stay. Hospitals should consider monitoring and correcting temporal fluctuations in care intensity.

BACKGROUND: Hospital care on weekends has been associated with reduced quality and poor clinical outcomes, suggesting that decreases in overall intensity of care may have important clinical effects. We describe a new measure of hospital intensity of care based on utilization of the electronic health record (EHR). METHODS: We measured global intensity of care at our academic medical center by monitoring the use of the EHR in 2011. Our primary measure, termed EHR interactions, was the number of accessions of a patient's electronic record by a clinician, adjusted for hospital census, per unit of time. Our secondary measure was percent of total available central processing unit (CPU) power used to access EHR servers at a given time. RESULTS: EHR interactions were lower on weekend days as compared to weekdays at every hour (P < 0.0001), and the daytime peak in intensity noted each weekday was blunted on weekends. The relative rate and 95% confidence interval (CI) of census-adjusted record accessions per patient on weekdays compared with weekends were: 1.76 (95% CI: 1.74-1.77), 1.52 (95% CI: 1.50-1.55), and 1.14 (95% CI: 1.12-1.17) for day, morning/evening, and night hours, respectively. Percent CPU usage correlated closely with EHR interactions (r = 0.90). CONCLUSIONS: EHR usage is a valid and easily reproducible measure of intensity of care in the hospital. Using this measure we identified large, hour-specific differences between weekend and weekday intensity. EHR interactions may serve as a useful measure for tracking and improving temporal variations in care that are common, and potentially deleterious, in hospital systems. Journal of Hospital Medicine 2013;8:513-518. (c) 2013 Society of Hospital Medicine.

OBJECTIVE: The aim of this study was to measure the effect of an electronic heparin-induced thrombocytopenia (HIT) alert on provider ordering behaviors and on patient outcomes. MATERIALS AND METHODS: A pop-up alert was created for providers when an individual's platelet values had decreased by 50% or to <100,000/mm(3) in the setting of recent heparin exposure. The authors retrospectively compared inpatients admitted between January 24, 2008 and August 24, 2008 to a control group admitted 1 year prior to the HIT alert. The primary outcome was a change in HIT antibody testing. Secondary outcomes included an assessment of incidence of HIT antibody positivity, percentage of patients started on a direct thrombin inhibitor (DTI), length of stay and overall mortality. RESULTS: There were 1006 and 1081 patients in the control and intervention groups, respectively. There was a 33% relative increase in HIT antibody test orders (p=0.01), and 33% more of these tests were ordered the first day after the criteria were met when a pop-up alert was given (p=0.03). Heparin was discontinued in 25% more patients in the alerted group (p=0.01), and more direct thrombin inhibitors were ordered for them (p=0.03). The number who tested HIT antibody-positive did not differ, however, between the two groups (p=0.99). The length of stay and mortality were similar in both groups. CONCLUSIONS: The HIT alert significantly impacted provider behaviors. However, the alert did not result in more cases of HIT being detected or an improvement in overall mortality. Our findings do not support implementation of a computerized HIT alert.