Connecting Patients to Prescription Assistance Programs: Effects on Emergency Department and Hospital Utilization
Notice bibliographique
Résumé
BACKGROUND: Manufacturer prescription assistance programs (PAPs) have been developed to provide medications at little or no cost to eligible patients. There are over 200 PAPs available from pharmaceutical companies, and each may have different eligibility requirements and assistance guidelines. A formalized community-based patient prescription coordinator can help patients navigate these programs by reviewing an applicant's financial information and medication requirements to identify which PAPs are most appropriate. Little is known, however, about whether providing such guidance is associated with a reduction in acute care utilization. OBJECTIVE: To evaluate changes in emergency department and hospital utilization among patients who received care coordination and financial assistance with prescribed medications. METHODS: This single-cohort interrupted time-series study included participants in eastern Washington state who enrolled in the Spokane Prescription Assistance Network (SPAN) program between March 1, 2009, and August 31, 2012. Referrals to the SPAN patient prescription coordinator were made by a social service agency or medical provider for patients who may have difficulty paying for prescribed medications. Initial patient contact occurred while the patient was still being treated in a clinic or hospital or through a direct visit to the coordinator's community-based office. Participants were contacted 6 months after the initial appointment and then annually thereafter to review current medications and health status. A review of electronic health records provided information on hospitalizations and emergency department visits in the 12 months before and after program entry. RESULTS: Among SPAN participants (n = 310), emergency department and hospital encounters declined from 0.38 per participant in the year before enrollment to 0.20 encounters in the year following program entry. A repeated-measures mixed-effects model indicated SPAN participation was associated with a 51% decline in the rate of emergency department and hospital utilization (incidence rate ratio [IRR] = 0.49; 95% CI = 0.31-0.77; P = 0.002). Observed effects differed by prescription class. Factor interactions revealed significant reductions in utilization for participants with prescribed pulmonary medications (IRR = 0.58; 95% CI = 0.37-0.92; P = 0.019). Assistance with mental health (psychotropic) medications was associated with increased incidence of utilization (IRR = 2.07; 95% CI = 1.32-3.24; P = 0.001). At the time of SPAN enrollment, 60% of participants had prescriptions for psychotropic medications. CONCLUSIONS: A formalized patient prescription coordinator can help patients access prescribed medications at low cost and remain compliant with treatment plans. In a study of a coordination pilot program, reductions in hospital admissions and emergency department visits were observed following program participation. DISCLOSURES: This study was not supported by any outside funding. The authors declare no conflicts of interest. Study design was created by Burley, McPherson, and Daratha. Burley Daratha, Selinger, and Armstrong collected the data, with interpretation performed by Burley, Daratha, and Tuttle, assisted by McPherson. The manuscript was written by Burley, Daratha, and Selinger, with assistance from White, and revised by Burley, White, and Selinger, with assistance from Daratha and Tuttle.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».