Financial Risk Relationships and Adoption of Management Strategies in Physician Groups for Self-Administered Injectable Drugs
Notice bibliographique
Résumé
OBJECTIVE: To consider the extent, nature, and range of risk arrangements between physician groups and health maintenance organizations (HMOs) for self-administered injectable (SAI) drugs; to examine types and frequencies of SAI drug-use management strategies adopted by physician groups; and to explore the relationship between locus and level of financial risk for SAIs and physician group strategy adoption. METHODS: We used a multiple case-study design to select physician groups and their health maintenance organization (HMO) contractual partners in 4 markets in the United States (Northwest, Northeast, Midwest, Southwest). Physician groups in these markets were chosen based on size (e50 physicians) and experience with drug risk (e1 year). Physician groups were asked to identify their 3 major HMO contractual partners in each market. Telephone interviews were conducted from January 2000 to June 2001, with the resulting purposive sample of 37 individuals representing 20 physician groups. RESULTS: We found that the level and locus of SAI financial risk were related to the adoption of management strategies. Physician groups with higher financial risk for SAIs adopted more strategies than lower-risk groups. Groups with SAI financial risk in the medical services capitation (MSC) adopted 9.2 strategies per group. In contrast, groups with SAI financial risk in the pharmacy-risk budget (PRB) averaged 1.5 strategies per group. Groups with SAI financial risk in both the MSC and PRB fell in-between, averaging 4.5 strategies per group. The most frequently adopted strategy was designing evidenced-based therapeutic guidelines, i.e., protocols based on evidence from the peer-reviewed literature used to guide physicians in the treatment of typically chronic conditions (9 groups, 45% of sample). The second most common strategy involved adapting the existing utilization management system to process SAIs (7 groups, 35%) and the establishment of office procedures for internal authorization (5 groups, 25%). The least frequently used strategies were determining amount paid to out-of-group physician providers (1 group, 5%) and hiring personnel (e.g., pharmacists) in claims or utilization management departments to implement and manage SAI programs (1 group, 5%). We also identified potential factors that increased the likelihood of strategy adoption and that could slow the rate of SAI cost increases. CONCLUSION: Our findings suggest that adoption of SAI drug-use management strategies may be more likely to occur when there is a minimum level of risk for SAI drug costs. Likewise, both the adoption of strategies and the opportunity to slow the rate of SAI cost increases may be more likely to occur when 3 additional factors are present: a contractual environment conducive to controlling SAI drug costs, the ability to implement SAI drug-use management strategies, and power in negotiations with drug manufacturers to reduce SAI prices. A sustainable and affordable SAI financial risk management program maximizing these factors while minimizing the financial burden for patients will require collaboration among all stakeholders, payers, providers, drug manufacturers, and patients.
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 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 | 0,003 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».