An evaluation of a referral management and triage system for oral surgery referrals from primary care dentists: a mixed-methods study
Bibliographic record
Abstract
Background Oral surgery referrals from dentists are rising and putting increased pressure on finite hospital resources. It has been suggested that primary care specialist services can provide care for selected patients at reduced costs and similar levels of quality and patient satisfaction. Research questions Can an electronic referral system with consultant- or peer-led triage effectively divert patients requiring oral surgery into primary care specialist settings safely, and at a reduced cost, without destabilising existing services? Design A mixed-methods, interrupted time study (ITS) with adjunct diagnostic test accuracy assessment and health economic evaluation. Setting The ITS was conducted in a geographically defined health economy with appropriate hospital services and no pre-existing referral management or primary care oral surgery service. Hospital services included a district general, a foundation trust and a dental hospital. Participants Patients, carers, general and specialist dentists, consultants (both surgical and Dental Public Health), hospital managers, commissioners and dental educators contributed to the qualitative component of the work. Referrals from primary care dental practices for oral surgery procedures over a 3-year period were utilised for the quantitative and health economic evaluation. Interventions A consultant- then practitioner-led triage system for oral surgery referrals embedded within an electronic referral system for oral surgery with an adjunct primary care service. Main outcome measures Diagnostic test accuracy metrics for sensitivity and specificity were calculated. Total referrals, numbers of referrals sent to primary care and the cost per referral are reported for the main intervention. Qualitative findings in relation to patient experience and whole-system impact are described. Results In the diagnostic test accuracy study, remote triage was found to be highly specific (mean 88.4, confidence intervals 82.6 and 92.8) but with lower values for sensitivity. The implementation of the referral system and primary care service was uneventful. During consultant triage in the active phases of the study, 45% of referrals were diverted to primary care, and when general practitioner triage was used this dropped to 43%. Only 4% of referrals were sent from specialist primary care to hospital, suggesting highly efficient triage of referrals. A significant per-referral saving of £108.23 [standard error (SE) £11.59] was seen with consultant triage, and £84.13 (SE £11.56) with practitioner triage. Cost savings varied according the differing methods of applying the national tariff. Patients reported similar levels of satisfaction for both settings, and speed of treatment was their over-riding concern. Conclusions Implementation of electronic referral management in primary care can lead, when combined with triage, to diversions of appropriate cases to primary care. Cost savings can be realised but are dependent on tariff application by hospitals, with a risk of overestimating where hospitals are using day case tariffs extensively. Study limitations The geographical footprint of the study was relatively small and, hence, the impact on services was minimal and could not be fully assessed across all three hospitals. Future work The findings suggest that the intervention should be tested in other localities and disciplines, especially those, such as dermatology, that present the opportunity to use imaging to triage. Funding The National Institute for Health Research Health Services and Delivery Research programme.
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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 | 0.056 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".