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Enregistrement W4392202208 · doi:10.1016/j.hrthm.2024.02.049

Alert-based remote monitoring: A model for increased reimbursement to meet device clinic workload while achieving overall health system cost savings

2024· letter· en· W4392202208 sur OpenAlexafffundabout
Derek S. Chew, Jonathan P. Piccini, Camille Frazier‐Mills, Justin Michalski, Niraj Varma

Notice bibliographique

RevueHeart Rhythm · 2024
Typeletter
Langueen
DomaineHealth Professions
ThématiqueElectronic Health Records Systems
Établissements canadiensLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Organismes subventionnairesNational Institute on AgingCanadian Institutes of Health ResearchBoston Scientific CorporationHeart and Stroke Foundation of CanadaAmerican Heart Association
Mots-clésMedicineWorkloadReimbursementMedical emergencyHealth careOperating system

Résumé

récupéré en direct d'OpenAlex

Remote monitoring (RM) is recommended to follow patients with implantable cardioverter defibrillators (ICDs).1Ferrick AM, Raj SR, Deneke T, et al. 2023 HRS/EHRA/APHRS/LAHRS expert consensus statement on practical management of the remote device clinic. Heart Rhythm Sep 2023;20:e92-e144.Google Scholar RM enables early detection of critical conditions and reduces in-clinic evaluations. However, adoption lags. An acknowledged barrier is insufficient personnel, linked to insufficient reimbursement, to conduct the “invisible” clinic work associated with virtual care (e.g., triaging alerts, documentation, patient communication and scheduling).2Diamond J. Varma N. Kramer D.B. Making the Most of Cardiac Device Remote Management: Towards an Actionable Care Model.Circulation Arrhythmia and electrophysiology Mar. 2021; 14e009497Google Scholar This encourages a re-examination of the RM schedule and cost/reimbursement structure. Current RM implementation comprises scheduled appointments (remotely plus in-person (IPE)) with continuous monitoring, and management of unscheduled evaluations. The framework of routine assessment every three months is a vestige from earliest ICD technologies requiring frequent manual capacitor reforms and threshold and battery tests but rendered unnecessary with current ICD platforms. Now, only a minority (6.6%) of routine evaluations trigger clinical action, usually reprogramming or medication changes. Device integrity issues are better detected by RM. Therefore, routine follow-up (whether IPE or remote) during continuous monitoring imposes a large nonactionable service burden.3Varma N. Love C.J. Michalski J. Epstein A.E. Investigators T. Alert-Based ICD Follow-Up: A Model of Digitally Driven Remote Patient Monitoring.JACC Clin Electrophysiol Aug. 2021; 7: 976-987Google Scholar Alert-driven RM (i.e., device clinic visits prompted by alerts, rather than routinely scheduled at regular intervals) redresses this by avoiding the bulk of non-actionable, routine patient encounters. However, its financial implications are unknown. To better understand the economic impact of alert-driven RM from the US hospital perspective assuming current Medicare reimbursement structures, we conducted a budget impact analysis quantifying costs associated with (i) IPE only, (ii) RM-Conventional (IPE+RM), and (iii) RM-Alert, from a published economic model derived from the TRUST (The Lumos-T Safely Reduces Routine Office Device Follow-Up) trial.3Varma N. Love C.J. Michalski J. Epstein A.E. Investigators T. Alert-Based ICD Follow-Up: A Model of Digitally Driven Remote Patient Monitoring.JACC Clin Electrophysiol Aug. 2021; 7: 976-987Google Scholar,4Chew D.S. Piccini J.P. Au F. Frazier-Mills C.G. Michalski J. Varma N. Investigators T. Alert-driven vs scheduled remote monitoring of implantable cardiac defibrillators: A cost-consequence analysis from the TRUST trial.Heart Rhythm Mar. 2023; 20: 440-447Google Scholar Ethics approval was obtained from the Conjoint Health Research Ethics Board at the University of Calgary. TRUST randomized 1,339 ICD recipients 2:1 to RM or IPE alone and measured scheduled and unscheduled evaluations.3Varma N. Love C.J. Michalski J. Epstein A.E. Investigators T. Alert-Based ICD Follow-Up: A Model of Digitally Driven Remote Patient Monitoring.JACC Clin Electrophysiol Aug. 2021; 7: 976-987Google Scholar,4Chew D.S. Piccini J.P. Au F. Frazier-Mills C.G. Michalski J. Varma N. Investigators T. Alert-driven vs scheduled remote monitoring of implantable cardiac defibrillators: A cost-consequence analysis from the TRUST trial.Heart Rhythm Mar. 2023; 20: 440-447Google Scholar The total cost of health care resources utilized was contrasted among follow-up strategies over a 1-year time horizon (Figure). Costs were valued in 2021 USD, and included outpatient encounters (such as in-clinic visits or remote evaluation), inpatient hospitalization, physician fees, and nursing care.4Chew D.S. Piccini J.P. Au F. Frazier-Mills C.G. Michalski J. Varma N. Investigators T. Alert-driven vs scheduled remote monitoring of implantable cardiac defibrillators: A cost-consequence analysis from the TRUST trial.Heart Rhythm Mar. 2023; 20: 440-447Google Scholar In a prototypic device clinic following 1,000 patients, the estimated annual hospital budget would be $6.6 million (IPE), $6.3 million (RM-Conventional) and $5.7 million (RM Alert). The overall annual estimated cost-savings would be $292,000 using RM-conventional vs. conventional IPE, but $821,000 if transitioned to RM-Alert. In RM-Conventional vs. IPE, overall cost savings (-$292 per patient) were driven by reduced hospitalization costs (-$311 per patient) although clinic costs were marginally higher (+$70 per patient). In RM-Alert, cost savings (-$821 per patient) were driven by both reductions in clinic-associated (-$401) and inpatient (-$311) costs compared to IPE only. Regarding clinic costs alone, RM-Alert leads to a 66% reduction in annual costs (Figure). This reflects the enormous cost of non-actionable work. In summary, a strategy of alert-driven RM for patients with ICDs is associated with a significant projected annual cost-savings for hospital budgets. In the US, physicians and device clinics are largely reimbursed by private health insurers and Medicare/Medicaid through a fee-for-service system billing every 90 days. This is considered insufficient for staffing the current RM-Conventional follow up strategy. However, cost-savings with RM-Alert may be redirected to reimburse a full complement of device clinic staff, assuming total reimbursement remains unchanged. Their tasks may be switched from a predominantly non-actionable service burden to actionable work (i.e., unscheduled IPEs and high priority alerts) where patient need is greatest. Alert-based RM, while promising to decompress an overwhelmed system, is disruptive to traditional clinical care pathways and reimbursement models. Since the associated workload cannot be measured by patient-facing encounters, alternative payment models require consideration. For instance, device clinics may be reimbursed on an annual per-patient basis for maintaining remote connectivity regardless of volume of transmission and IPEs conducted (i.e., bundled payment model). This could incentivize a reduction in non-actionable routine follow ups and transition to continuous remote monitoring. Alert-based RM may be enhanced through use of artificial intelligence algorithms to reduce the load of unnecessary transmissions, and to link individual patient needs to treatment pathways that improve outcomes. From the perspective of the health system / hospital payer, our economic analysis suggests alert-driven RM programs would still provide overall cost-savings at the hospital system level, realized through fewer hospitalizations, even when reimbursement is increased to adequately support RPM device clinic staff.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

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)0,008
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesMéta-épidémiologie (sens strict), Intégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,065
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0080,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0030,007
Charge utile insuffisante (le modèle a refusé de juger)0,0000,002

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 Opus0,144
Tête enseignante GPT0,441
Écart entre enseignants0,297 · 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

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2024
Routes d'admission3
Résumé présentoui

Explorer davantage

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