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Enregistrement W2766541433 · doi:10.1111/jgs.15169

Effect of Primary Care‐Based Memory Clinics on Referrals to and Wait‐Time for Specialized Geriatric Services

2017· letter· en· W2766541433 sur OpenAlex
Linda Lee, Loretta M. Hillier, Jane M. Wilson, S Gregg, Karim Fathi, Cathy Sturdy Smith, Matt J. Smith

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Notice bibliographique

RevueJournal of the American Geriatrics Society · 2017
Typeletter
Langueen
DomaineBusiness, Management and Accounting
ThématiqueHealthcare Systems and Technology
Établissements canadiensCanadian Mental Health AssociationHamilton Health SciencesHealth Sciences CentreMcMaster UniversityCentre for Family Medicine
Organismes subventionnairesCanadian Mental Health Association
Mots-clésMedicineReferralGeriatricsFamily medicinePrimary careDementiaNursingMemory clinicPopulationHealth careIntervention (counseling)GerontologyDiseasePsychiatryEnvironmental health

Résumé

récupéré en direct d'OpenAlex

To the Editor: Primary care collaborative memory clinics (PCCMC), were developed in Ontario, Canada, starting in 2006 to address challenges and build capacity for dementia care at a primary care level. These family physician–led interprofessional clinics provide comprehensive assessment and management within a shared care approach and with support from local geriatrics specialists.1 The PCCMC model features elements of person-centered care, considered the criterion standard for the care of older adults.2 There are more than 100 PCCMCs across the province, and this is expected to increase over time. There is much anecdotal evidence from previous evaluations of this care model that the early identification and intervention that the PCCMCs offer contribute to more efficient use of existing specialist resources.1, 3-5 PCCMCs have consistently demonstrated referral rates to specialists of approximately 10%,1, 4, 5 compared with typical referral rates of up to 82% from family physicians for persons with memory concerns,6 which is of concern given the critical shortage of geriatricians in Canada.7 Nevertheless, there has been limited empirical evidence demonstrating effect on system efficiency in the use of specialists, primarily because of difficulties accessing valid system-level data. The purpose of this current study was to examine the effect of the PCCMCs on referrals to and wait-time for specialist consultation. In the Wellington-Dufferin-Guelph region of southern Ontario (population base of 265,240),8 PCCMCs were established in 2 large, rural primary care settings, (Clinics A and B, located in Family Health Teams), serving 41 medical practices with a combined patient base of 65,000. In this region, geriatrician consultation is accessed through the Canadian Mental Health Association—Waterloo Wellington (CMHA-WW) Specialized Geriatric Services (SGS). The CMHA-WW generated data from their information system on the number of referrals to SGS for memory concerns from the practice settings of both these clinics in the years before and after launch and median wait-time (days), defined as the difference between the date of referral and date of first assessment, for all referrals to SGS and from each clinic practice setting, regardless of reason. These data were provided for each year from 2008 to 2014; data were not provided beyond these years because, in 2015, a number of new initiatives (e.g., nurse-led assessment programs) were implemented that also affected referral rates for specialist consultation. Referrals to SGS for memory concerns from Clinic A's practice setting were lower each year after the implementation of the PCCMC (2009, n = 94; 2010, n = 67; 2011, n = 78; 2012, n = 73; 2013, n = 56; 2014, n = 33) than in the year prior (2008, n = 100), representing a 67% reduction in referrals to SGS in 2014 from 2008. Referrals to SGS from Clinic B's practice setting were higher in the year of implementation of the PCCMC (2013, n = 189) but lower the year after (2014, n = 145) than in the year before launch (2012, n = 183)—a 21% reduction in referrals to SGS from 2012 to 2014. After the launch of the clinics, median wait-time for SGS consultation decreased for referrals from both clinics’ practice setting (32% reduction for Clinic A, 47% for Clinic B in wait-times in the year after launch from the year before), as well as for all referrals to SGS (63% reduction in 2014 from 2008; Figure 1). These results provide some preliminary evidence suggesting that memory clinics help reduce reliance on referrals to specialists for memory concerns and shorten overall wait-times for specialist consultation. These are the first data demonstrating direct system effect on efficiency of use of limited geriatrician resources, particularly in rural communities where access to geriatricians is limited. By managing the majority of memory concerns in primary care, consistent with chronic disease management models,9 specialist resources are reserved for more complex cases and those who most urgently need it. There are several limitations to the interpretation of these data. A direct, causal relationship between the PCCMCs and SGS referrals cannot be presumed; there may be other factors contributing to the reduction in referrals to SGS that are unknown. These data are from a relatively small region of the province and may not reflect referral and wait-time patterns across the province. Despite these limitations, these data provide some evidence of the effect of the memory clinics on health system use. Further research is needed to study the effects of PCCMCs on the system of care for older adults. Conflict of Interest: No funding was received for this study. Author Contribution: LL, JMW, SG: study concept and design. KF, CSS: data collection. LL, LMH: letter writing. All authors: data interpretation, letter review and approval. Sponsor's Role: Not applicable.

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Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,649
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,018
Tête enseignante GPT0,293
Écart entre enseignants0,274 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle