Bridging the Gap between Primary and Secondary Care: Use of a Clinical Pathway for the Investigation and Management of Deep Vein Thrombosis
Bibliographic record
Abstract
BACKGROUND: Clinical processes that span the boundary between primary and secondary care often suffer from poor standardization of practice and lines of communication. One example is the clinical management of suspected deep vein thrombosis (DVT). Making or excluding the diagnosis can be complex. Tools to investigate DVT were not available to family physicians so patients had to be referred to the hospital emergency department and endure a long wait which could exacerbate the condition. In addition, urgent referrals to the ultrasound department disrupted the scheduled list of patients. ASSESSMENT OF PROBLEM: A team of stakeholders including all relevant specialties and professions from primary and secondary care was established. After reviewing the literature and interviewing those involved in delivering care, an evidence-based scoring system to calculate the clinical probability of a DVT was adopted and introduced as part of a clinical pathway from primary to secondary care. Its introduction was based on the Plan, Do, Study, Act (PDSA) cycle. RESULTS: In the first year after the introduction of the clinical pathway, 70% of family physicians had used it and were very satisfied. Patients found it efficient (75%) with 96% very satisfied or satisfied. The waiting time in the emergency department fell from a mean of 379 minutes to 285 minutes. Although the incidence of initial investigations (D-Dimer tests) increased by 42%, the proportion of patients undergoing a subsequent ultrasound test found to have a DVT was unchanged (14%). This suggested the level of suspicion of a DVT in the community had risen. Referrals to the hospital's DVT clinic increased by 14% overall, driven partly by a large increase in those who had come from the new clinical pathway from primary care. LESSONS AND MESSAGES: Key lessons include the importance of including all relevant stakeholders and the benefits of using PDSA to make rapid changes during implementation. We are now seeking to extend the use of the clinical pathway to other hospitals and more family physicians. In addition, the clinical pathway approach will be applied to other conditions and interventions. We have demonstrated how a multidisciplinary group of stakeholders in a clinical care process can develop and introduce a clinical pathway that allows smooth transit of patients over the barriers between different sectors of the health care system and between independent disciplines. We have also demonstrated the use of untapped non-physician potential in the system to safely facilitate patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".