JASON A. HOPPE, DO
University of Colorado Anschutz Medical Campus

Providing appropriate, safe analgesia during the opioid crisis a challenge. Prescription Drug Monitoring Programs (PDMPs), an evidence-based intervention to improve prescribing decisions, are underutilized. Electronic health record (EHR) based clinical decision support (CDS) represents a pragmatic, patient-specific and scalable implementation strategy to promote behavior change. Our objective is to assess the development and early deployment of a pragmatic, cluster randomized trial of CDS tools to facilitate PDMP use.

UCHealth system including 14 hospitals with associated Emergency Departments (EDs), 12 free-standing EDs, and approximately 250 ambulatory clinics, approximately 2850 total prescribers, and over 3.6 million total patient visits in 2018.

This is an IRB-approved cluster randomized study. Using published best practices and a literature review, we developed two prototype logic-driven patient-specific CDS to identify patients at risk of opioid abuse/overdose and trigger an interruptive alert to review the PDMP:(1) PDMP risk criteria alone and (2) PDMP + EHR risk criteria. Iterative CDS modifications were informed by interviews with target adopters and organizational decision makers, including clinical observations. Following silent testing of the revised alerts, we randomized providers to receive one of the two alerts or a control alert (fires for all opioid/benzodiazepine e-prescriptions). Alerts were tested live in the academic ED (156 providers and 88k unique patients in 2020) for one month prior to activation in all system EDs. Inpatient and ambulatory settings were activated in a staggered fashion. Education was disseminated through presentations, emails from leadership, a featured article in the system EHR newsletter, and individual provider contacts.

Qualitative feedback from 20 interviews and 10 workflow observations identified concerns for interruptions, alert fatigue, and alert suppression. We completed 3 months of silent testing on ED discharges: control alert triggered 8.1% (95%CI 7.9-8.3%) of discharges, with variability across EDs (5.1-11.3%). PDMP only alert triggered 2.8% (95%CI 2.7-3.0%) before modification and 2.0% (95%CI 1.9-2.1%) after user-driven modification. PDMP + EHR alert triggered 4.2% (95%CI 4.1-4.4%) before modification and 3.7% (95%CI 3.5-3.9%) afterward. Study is ongoing: firing rates, adoption, and PDMP use for all 3 CDS tools in EDs, inpatient facilities and ambulatory clinics will be reported.

User-centered design with key stakeholder input and pilot testing on a large set of target patients helped refine CDS tools for deployment in a pragmatic, cluster randomized trial. Silent alert firing data can inform implementation decisions, validate monitoring tools, and give context to CDS education and messaging. Feedback from the target population can inform study design to improve buy-in.

ANJU SAHAY, PhD
Veterans Affairs Palo Alto Health Care System

The US Department of Veterans Affairs (VA) VISN 21 Pharmacy Benefits Program Service has developed the Clinical Dashboard for Patient Aligned Care Team (PACT) performance measures, which includes patient data showing whether the clinical care meets performance measures tracked by the VA. Embedded within the Clinical Dashboard is a decision support tool being developed by the Medication Safety (MedSafe) QUERI Program, the MedSafe Clinical Decision Support (CDS) system. It provides recommendations for the care of select patients who are not meeting VA performance measures.
 
We conducted a pre-implementation assessment of potential interest among PACT members in use of the MedSafe CDS system. We also examined barriers and facilitators to guide its implementation.

Participants (N=21) consisted of health professionals from PACT core teams at two sites: Primary Care Providers (PCPs) (n=7), PACT-nurses (n=8), and pharmacists (n=6).

A team of two interviewers conducted semi-structured phone interviews individually with each participant. All interviews were recorded, transcribed, and coded. We used an open iterative process to create the codebook; two coders adjudicated discrepancies.

Six participants (28.6%) expressed varied interest in using the CDS system to inform other PACT members about issues like medication adherence and goal attainment. PACT member role was important as a PACT-nurse said: “We really rely on the pharmacists to do more of the medication management and so nurses don’t typically focus on medication…” According to a pharmacist, the CDS system: “…keeps you up to date with the different formulary issues, with different drugs that doctors may not all be familiar with…all of those things are constantly changing so it can be overwhelming keeping up with it.”
 
Barriers to use included doubts about CDS recommendation alignment with their own/provider recommendations, time constraints, understaffing and potential technical difficulties. To implement the CDS system, significance of support from the members of the PACT was reflected as: “…just not having a proper PACT team set up and getting buy-in for everybody to still use it.” However, one PACT-nurse said: “I don’t really see any barriers to using it.”
 
Participants expected the recommendations from the CDS system would improve efficiency during patient visits. It could be used for panel management as a PCP stated: “…usually I would like to have a pre-visit planning done for my patients before my patient’s appointment…this is an ideal tool to figure out what can be done as a PACT team.” For population management purposes: “I think this really makes population management much more manageable being able to go in and have this information at your fingertips.”

JEREMY GRABER, PT, DPT
University of Colorado Anschutz Medical Campus

Physical therapists (PTs) use range of motion (ROM) and functional measures like the Timed Up and Go (TUG) to monitor patient recovery after total knee arthroplasty (TKA).1 We recently developed a clinical decision support tool which precisely predicts ROM and TUG recovery post-TKA; we believe this may augment PTs’ ability to monitor patient recovery. The purpose of this project was to assess PTs’ confidence and accuracy in monitoring post-TKA recovery prior to implementing our clinical decision support tool into practice.

This project is part of a quality improvement collaboration between ATI Physical Therapy and the University of Colorado. All data collection occurred at two outpatient clinics in Greenville, SC.

Eight PTs rated their confidence level in predicting ROM and TUG measurement in TKA rehabilitation; the survey was scored on a Likert scale ranging from 0 (not at all confident) to 3 (very confident). During standard rehabilitation, PTs regularly collected ROM, TUG, and other outcomes; these data were entered into a quality improvement database for all patients with TKA. At the first postoperative visit, PTs also estimated patients’ discharge knee flexion ROM and TUG values. We examined correlation (Pearson’s r) and agreement (Bland-Altman plots) between predicted and observed values for ROM and TUG.2 Observations recorded within 21 days of patients’ discharge date were eligible for inclusion. Patient records with an episode duration < 1 month were excluded to remove data associated with premature self-discharge.

Overall, PTs felt confident in their ability to predict patient outcomes. PTs reported feeling “confident” or “very confident” for both measures, except for two PTs who chose “somewhat confident” for TUG prediction. A total of 477 patient records were screened for inclusion in the accuracy assessment; only 25 were eligible for ROM assessment and 22 for TUG. The correlation between predicted and observed was moderate for ROM (r = 0.65) and weak for TUG (r = 0.29).3 The Bland-Altman limits of agreement were 0.8 + 16.3 degrees for knee flexion ROM and 0.3 + 4.2 seconds for TUG.

Although PTs rated themselves as confident in their ability to predict post-TKA ROM and TUG recovery, their accuracy suggests there is room for improvement. The Bland-Altman limits of agreement exceeded the minimal detectable change for both knee flexion ROM (6.4 – 7.1 degrees4) and TUG (2.49 seconds5). The observed correlations indicate PTs may benefit most from assistance monitoring TUG recovery. These results suggest our clinical decision support tool may provide PTs with a relative advantage-a key feature for disseminating innovations6-compared to standard practice. Our next steps will be to integrate the tool into the participating clinics to assess its effectiveness and implementation potential in outpatient TKA rehabilitation.