Barriers and Facilitators for Healthcare Providers Integrating Prevention into Connect Care EHR in Diverse Clinical Settings
Bibliographic record
Abstract
Background: This study aims to understand healthcare providers and facilitators to implement screening, brief intervention and referral (SBIR) for modifiable risk factors (alcohol, tobacco and physical inactivity) using electronic health record (EHR) across acute and ambulatory care settings. Approach Alberta Health Services (AHS) is implementing SBIR for modifiable risk factors using EHRs across hospital settings. We employed a multiple case studies approach with co-design and qualitative descriptions to understand the experiences of diverse healthcare providers on SBIR implementation. Prior to SBIR implementation: Clinical leaders across AHS were engaged and consulted. Clinical sites were recruited to implement SBIR based on their readiness to implement SBIR. Implementation facilitators (IF) completed observational clinic inventories, engaged healthcare providers to understand workflow, capacity, patient context and load. Healthcare provider recruitment to implement SBIR is opportunistic and on-going at each clinical site. During SBIR implementation: We used participant observations, descriptive field notes, iterative semi-structured interviews with 6 IFs. Additionally, opportunistic touch-base calls, surveys, and interviews with healthcare providers are underway. Data collection is guided by the Consolidated Framework for Implementation Research (CFIR). Participant observations and touch-base calls are recorded as descriptive field notes. Three readiness surveys were conducted to ensure our team could rapidly facilitate support at each phase of healthcare provider implementation. Qualitative data were transcribed, abductive thematic analysis completed in NVivo 2/4 and mapped back to the CFIR to systematically understand barriers and facilitators. Findings are presented back to the team on a three-month cycle to ensure accurate interpretation and agile application of strategies to resolve barriers. Results: Preliminary results of providers SBIR implementation revealed barriers and facilitators are based on ambulatory and acute care clinical contexts. Acute care clinics involved nurses with surgical / trauma patients. IFs reported the nurses barriers were their belief that SBIR does not make a difference; SBIR increased their workload; challenges integrating SBIR with workflow and patients critical health stopped intervention; fragmented screening tools in ERH make documentation difficult, follow-up; need for consistent smart phrases for data tracking; communication and coordination gap between IFs and nurses. Facilitators to SBIR implementation involve co-designing and refining SBIR to workflow, co-developing discharge checklist to support brief intervention and referral; co-developed smart-phrases for easy documentation; consistent engagement/communication with nurses and clinical leaders. Ambulatory clinicians are physicians and nurses with medically complex patients. IF reported clinicians barriers were challenges with documenting SBIR in EHR; preference for unique smart-phrases; and making referrals since referral programs are not currently using EHR. IFs reported facilitators were physician knowledge, motivation and capacity to implement; co-developed SBIR cheat sheets and orientation checklist to support casual nurses and residents training. Implications: SBIR implementation and adaptations are context specific that reflect ambulatory and acute care settings. Developing strong relationships with clinical teams facilitated the adaptation of training resources, co-designing checklists, discharge lists, and finding solutions that facilitate SBIR implementation in EHR for diverse clinical settings is vital for spread and scale. Our next steps involve incorporating lessons learned to spread, scale and sustainability of SBIR in EH
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".