An evaluation of a referral management and triage system for oral surgery referrals from primary care dentists: a mixed-methods study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,056 | 0,072 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».