CLAUDIA R. AMURA
Research Assistant Professor, CU College of Nursing

Opioid use disorders (OUD) are a huge burden for both those suffering of addiction and the community. Colorado ranks 12th nationally in non-medical use of opioids, with rural counties having the highest use and overdose-deaths. The Colorado Senate funded an evidence-based Medication for Opioid Used Disorders (MOUD) Program to increase access to care for Coloradoans with OUD in rural areas. We review implementation outcomes from this pilot program. Setting/population: SB17-74 focused on two counties with disproportionally high overdoses deaths, Pueblo and Routt. Three agencies were funded to either start or expand MOUD services. Patients served use either heroin, opioid drugs or prescription painkillers other than in as prescribed.

The grantees advertised services through local partnerships. From12/17 to 06/19, the agencies reported on services provided, outreach, barriers and successes, and submitted de-identified patient-level data via REDCap, both at baseline and after 6 months. The Addiction Severity Index was used to measure OUD’s impact across various life domains. Pre-post changes in patient outcomes were tested using 2 and t-tests.

Over the first 18 months, this pilot project added 15 Nurse Practitioners and Physician Assistants and served 1,005 individuals. Patients were mostly 25-44 y.o. (66%), high rate Hispanic (42%), not married (77%), uninsured (47%) or Medicaid (91%), unemployed (65%) or part-time worker (26%). The majority reported less than good health, and use of opioids (42%), heroin (48%), and other substances (32-38%). After 6 mo of MOUD, 28.7 % patients remained in treatment, with 30% missing data. In the previous month, patients used less heroin (13.0 vs. 3.68 d, p<.001, prescription opioids (3.66 vs. 1.86 d), p=0.029, and sedatives (2.59 vs. 1.10 d), p=0.001, and alcohol (3.12 vs 1.67 d), p=0.000. There was no difference in meth, barbiturates or cannabis use. After treatment, patients also had improved health (53.4% vs 68.2 %), p=0.036, with less days unable to carry out normal activities (8.69 vs. 6.51 d), p=0.016. The number of clients with symptoms significantly dropped (64.1% vs. 55.2%), p=0.000. They overall reported lower pain (p=0.000), worry about their health (p=0.000) or medical treatment (p=0.001). There were no changes in emergency room visits or incarcerations. Patient-centered approach, availability and referrals were successful strategies, while education was needed to reduce stigma.

While results are limited to patients in treatment with high lost to follow-up, this pilot study shows implementation success, decreased substance use, and improved health after treatment. Research is needed on retention and long-term effects. Lessons learned for barriers and facilitators encountered during implementation could inform new programs to address one of the state’s major public health crises.

RODGER KESSLER, PhD, ABPP
Professor, Arizona State University

This poster describes a strategy to develop shared understanding between research and practice teams. Often, during the conduct of on the ground practice research, issues arise from assumptions between research and clinical teams that interrupt and often threaten projects viability and impact. Many of these issues can be identified and addressed in the implementation planning and we generated a brief checklist to be completed collaboratively by research and practice team members, to generate shared assumptions and implementation expectations. We are currently testing this work in one community implementation project. Practice-based research often includes partners with limited capacity for research participation. There may be no protected time for research; IRB policies or communication lines between research administration and clinical management may not be clear. This may lead to different assumptions or expectations that may impede the project. Checklists are a common feature in manufacturing and a growing feature in health care systems, including research. However, most practice-based checklists do not mutually engage researchers and partners collaboratively, are lengthy, and do not examine and clarify assumptions and solutions to identified challenges. The purpose of this pragmatic checklist is to increase the chance of collaboration success by identifying and resolving unclear assumptions and expectations.

The application of this work is an Arizona Health System that owns and contracts with primary care practices. They initiated development of a primary care/ academic researcher team to engage in care and practice improvement by implementing evidence supported care pathways.

We generated a 12 item checklist based on a careful review of existing scientific literature, our own experience, and constructs from the Consolidated Framework for Implementation Research (CFIR). Table 1 Identifies the 12 content domains. It is completed together by research and practice teams, identifies areas of consensus and those needing further resolution and key members from both teams to further discuss and generate resolution strategies to then be reviewed and endorsed by the full team. It includes four crucial time events: Before Signing a Memorandum of Agreement; Before Submitting to the IRB; Before Entering the Setting; and Before Collecting Data.

Researcher teams can use the checklist to verify whether the relationship with the community partner is clear, and to identify any potential problems and support team consensus. We are currently using the checklist in an Arizona State University research team collaboration with a local primary care setting and will report on results.

ANDREW HAMMES
Research Instructor, Colorado School of Public Health

During a hospitalization, patients routinely receive blood draws (i.e. venipunctures) in the course of diagnosis and treatment of their conditions. From the patient perspective, frequent blood draws can be distressing and decrease patient satisfaction with care. From the hospital perspective, blood draws represent an expense to the hospital both in terms of personnel time and material cost. Even though venipunctures impact patient care and hospital expenditures, integrated aggregate data on the patients receiving blood draws, the personnel performing the blood draws and overall trends in volume are not currently available to decision makers at the University of Colorado Hospital. The University of Colorado SOM NavLab seeks to address this issue by developing a clinician-usable interface which will allow clinical leaders to examine the number and timing of venipunctures done to patients.

This study was performed at the University of Colorado Hospital, including inpatient data between March 2019 and April 2020. All inpatient venipunctures were potentially candidates to be included in the interface.

Data was acquired from the Clarity tabulation of the EPIC Electronic Health Record including all tests done on inpatient venipuncture blood draws between March 2019 and April 2020. Consultation with the Ancillary Health Technician leadership informed the aggregation of tests for summarization. Tests were collapsed into individual patient draws in a five-minute rolling window, that is any vials which were recorded as collected within five minutes of the previous vial within a contiguous patient and collector pairing were considered one stick. This was done to allow many potential tests from one draw to be correctly subset into a single draw. Individual patient sticks were then aggregated to yield the number during a calendar day per-patient, as well as totals by the collecting user. All data aggregation was done in R v 3.6.0.

Aggregated data was transferred from R to Microsoft Power BI for visualization and dissemination. Summaries of data including daily tracking of number of tests and sticks, sticks by patient, sticks by user, and comparisons of sticks between groups were built within Power BI. Having these within Power BI also allowed the use of filtering, such as by date, user, or department, to be done by the end-user without additional effort by the team. Updates to the completed Power BI will be automated using stored commands within EPIC and R to load updated data into the report.

Excessive venipunctures can impact patient satisfaction and hospital resources. To address this problem in a data-driven way data must be presented to decision makers. The University of Colorado NavLab produced a Power BI report which allows clinicians and leadership to examine data on their own in a dynamic way with informative visualizations of the data.