Collaborating with Patients

Join us as this presenter discusses this poster live on Tuesday, August 11, 2020 | Track A at 5:35 PM Mountain

PRESENTER
RACHAEL KENNEY
Health Science Specialist, Veterans Health Administration
BACKGROUND
The number of Veterans (Vets) that the Veterans Health Administration (VA) treated for Opioid Use Disorder (OUD) nearly tripled from 25,000 in 2003 to over 69,000 in 2017. In 2019, the Consortium to Disseminate and Understand Implementation of OUD Treatment (CONDUIT) formed to address this challenge. CONDUIT is a compilation of seven projects focused on increasing treatment for OUD in various settings. One aspect of CONDUIT is an Opioid Addiction and Recovery Veteran Engagement Board (OAR-VEB). The board will meet in person for a kick-off (planned for Spring 2020) and then meet monthly by phone. On each one-hour call, investigators from a project will present a challenge to troubleshoot. This Pragmatic Methods and Evaluation abstract describes the development of this board.
POPULATION
The Denver Veteran Engagement Core (VEC) is selecting OAR-VEB members from each CONDUIT site (Table 1). Members are Vets who identify as being “in recovery” from OUD.
METHODS
The VEC utilized tools from the VA and the Health Care Systems Research Network to guide board development. The VEC created tools, conducted outreach in Denver, and assisted local contacts at the remaining CONDUIT sites with their outreach. Local contacts performed local outreach and screened Vets before providing contact information to the VEC. Outreach was conducted at VA (e.g., substance use clinics) and non-VA (e.g., Vet-Centers) entities.
RESULTS
To date, 22 interested Vets were identified. Three interested Vets were not in recovery from OUD, two withdrew, one was not affiliated with a CONDUIT site, and one was unreachable. The remaining 15 were interviewed. None withdrew their interest after the interview. To date, 15 Vets have been interviewed and eight members from four sites have agreed to participate. (Table 1)
CONCLUSIONS
Identifying local outreach contacts was key; this allowed the VEC to focus on candidates who met the membership criteria. Advertisements, email blasts, and reaching out to outside organizations did not yield a high response.
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Discharge Today: The Efficacy of a Multidisciplinary Electronic Discharge Readiness Tool

Join us as this presenter discusses this poster live on Tuesday, August 11, 2020 | Track A at 5:15 PM Mountain

PRESENTER
ANGELA KENISTON, MSPH
Director, Data and Analytics/Instructor, University of Colorado
BACKGROUND
Commonly used discharge communication workflows hinder timely and efficient discharge. Studies exploring the use of the EHR for discharge planning have been limited to electronic reports constructed from EHR data elements, including barriers to discharge documented at admission, care management data, and discharge criteria or other targeted interventions such as improving discharge summaries for patients or medication reconciliation at discharge. To address these deficits, we developed an innovative EHR tool to facilitate communication in real-time between hospitalists and other clinicians about discharge readiness and barriers to discharge.
POPULATION
All clinicians who were scheduled to be on an inpatient Hospital Medicine service were trained and asked to use the Discharge Today tool with all patients assigned to their team.
METHODS
This study is a prospective, single center, pragmatic, interrupted time series study. Clinicians were asked to update patient discharge readiness (Definite, Possible, Tomorrow, In 24-48 hours, > 48 hours) and any barriers to discharge, every morning and anytime patient status changed. Primary outcomes were time of day the clinician enters the discharge order, time of day the patient leaves the hospital, and hospital length of stay. Secondary outcomes were proportion of patients with a discharge order before 11 am and proportion of patients discharged before 11 am. We used linear mixed modeling and generalized linear mixed modeling with team and discharging provider included in all models to account for patients cared for by the same team and the same provider.
RESULTS
We found that, after adjusting for pre-specified confounders and effect modifiers, for every one patient increase in the morning census, there was a statistically significant reduction in the time of day the discharge order was entered into the EHR by the discharging physician (3 minutes per patient (95% CI: 42 seconds, 6 minutes), p=0.0245) for the pilot implementation period compared to the pre-implementation period, though not for the post-implementation period (p=0.4526). We also found a statistically significant reduction in hospital length of stay for the pilot implementation period compared to the pre-implementation period (56 minutes (95% CI: 52 minutes, 1 hour), p=0.0047), though not for the post-implementation period (p=0.4342).
CONCLUSIONS
Our Discharge Today tool is a real-time communication tool, created by hospitalists and other healthcare professionals who participate in discharge planning to not only document and communicate what tasks need to be completed before a patient can be discharged but also to help clinicians, nursing, and other staff to prioritize their work in real-time. Our analysis suggests this tool is useful for improving discharge timing, particularly as the number of patients being cared for by a team increases.
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VIDEO


Improving Measurement of Patient Responsiveness Using a Mixed Methods Approach

Join us as this presenter discusses this poster live on Tuesday, August 11, 2020 | Track C at 5:45 PM Mountain

PRESENTER
NICOLE WAGNER
PhD, Kaiser Permanente Colorado Institute for Health Research
BACKGROUND
Uptake of a new health intervention is dependent on patient acceptance and responsiveness. 1) Patient report is frequently used to assess patient responsiveness due to the low cost and limited labor requirements. 2) However, patient report is prone to errors, such as over-reporting and missing data. 2), 3) Incorporating measures of patient responsiveness from multiple sources presents an opportunity to sustain the low cost and labor requirements while increasing data accuracy and comprehensiveness. Measures from multiple sources may also provide an opportunity to identify strategies for adaptation. 4) To assess the value of a mixed methods approach, this study describes the use of electronic health record (EHR) data, patient report, and implementor logs for measuring patient responsiveness in a pragmatic intervention trial.
POPULATION
Just in Case (JIC) was a cluster randomized intervention trial, designed to increase the uptake of naloxone, an overdose antagonist medication, in patients on chronic opioid therapy. JIC was conducted between 2017 and 2019 at Denver Health Medical Center, a safety net health system serving the Denver metro area. Patients 18 years and older filling chronic opioid medications were eligible to receive naloxone co-dispensing. Eligible patients were recruited to complete surveys at baseline, 4 months, and 8 months (patient report). Pharmacy staff in the intervention arm recorded naloxone co-dispensing events and reasons for nonacceptance in a pharmacy fidelity log (implementor log).
METHODS
Patient responsiveness measures included naloxone uptake and barriers to naloxone uptake. Naloxone uptake was captured in the EHR using naloxone dispensing data. Reasons for not accepting naloxone were coded for common themes in patient report surveys and implementor log.
RESULTS
Using EHR pharmacy records, 527 eligible patients were identified with 204 naloxone fills. Barriers to naloxone uptake from patient report included lack of knowledge on naloxone (40%), not thinking they needed it (88%), and fear of repercussions from pharmacists or doctors (20%). Barriers to naloxone uptake from implementors included patients already had naloxone (36%) and weren’t willing to pay for naloxone (28%).
CONCLUSIONS
A mixed methods approach using measures from the EHR, patients, and implementors provides a comprehensive assessment of patient responsiveness with increased accuracy. Each measure contributed unique data to inform potential opportunities for improvement and adaptation. EHR data contributed accurate counts of acceptance. Patient report identified a knowledge gap indicating a more robust education program may be needed. Implementors reported cost issues indicating a potential protocol modification could improve uptake.
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