Evaluation of Health Plan Member Use of an Online Prescription Drug Price Comparison Tool
Notice bibliographique
Résumé
BACKGROUND: Health plans have implemented tiered copayment systems to incentivize members to use less expensive medications. However, members need drug price information to make comparisons among therapeutic alternatives. Many health plans and pharmacy benefit management companies have implemented online prescription drug price comparison tools to provide such information. There has been little published evaluation of these tools. OBJECTIVE: To evaluate use of an online price comparison tool- MyPharmacyTools (MPT)- by the measures of (a) the extent to which the tool was used, (b) changes in use over the first year after implementation, and (c) the types of members who were most likely to use the tool. METHODS: Data were provided by a 500,000-member integrated health plan with approximately 156,250 enrolled families. The sample included only families with continuous eligibility for all members from July 1, 2006, through June 30, 2008; use of 1 of 7 common copayment structures; and use of the pharmacy benefit in every quarter of the study period. Data collected on each member, using pharmacy claims for the time period July 1, 2007, through June 30, 2008, included annual drug costs (total, out-of-pocket, plan-paid, and mail order) and number of unique drugs and unique generic drugs taken during the third quarter of 2007. Data collected also included whether the member had each of several selected chronic diseases (as inferred from drug claims for the third quarter of 2007) and demographics. Age, gender, and family size were taken from eligibility files. Other demographic data were imputed to members from the demographics of the ZIP code in which they resided. MPT was made available to members on July 1, 2007. Use of MPT was measured as the number of times members logged into the site for each quarter during the subsequent year. Statistical analyses were conducted at the family rather than at the individual level, and families were defined as users if any family member used MPT at least once during the year. Between-group comparisons were evaluated with t-tests, Pearson chi-square tests, and analyses of variance. RESULTS: Data were analyzed for 8,909 families composed of 28,537 health plan members, of which 464 (5.2%) families used MPT at least once between July 2007 and June 2008. A total of 141 families used MPT in the first quarter it was available, 170 families used it in the second quarter, 185 families in the third quarter, and 182 families during the fourth quarter. Users had significantly higher mean [SD] total drug costs ($4,477 [$9,647] vs. $2,848 [$3,473], P < 0.001) and used significantly more unique drug products (7.7 [5.7] vs. 5.9 [4.5], P < 0.001) and unique generic drug products (5.0 [3.9] vs. 3.9 [3.2], P < 0.001) than did nonusers. Users were significantly more likely than nonusers to use drugs for behavioral diseases (47.0% vs. 39.7%, P = 0.002), hypercholesterolemia (35.8% vs. 27.0%, P < 0.001), gastric disorders (32.8% vs. 23.0%, P < 0.001), diabetes (18.3% vs. 12.8%, P < 0.001), epilepsy (21.1% vs. 10.6%, P < 0.001), cardiovascular problems (48.3% vs. 37.5%, P < 0.001), and asthma (14.0% vs. 10.7%, P = 0.025). Families that used MPT were less likely to have a female subscriber than were nonusers (39.7% vs. 49.0%, P < 0.001). Otherwise, here were no statistically significant demographic differences between users and nonusers. Families using MPT in more quarters of the year had higher out-of-pocket (P < 0.001) and mail order drug costs (P < 0.001), took a larger number of drugs (P = 0.003) and generic drugs (P = 0.019), were more likely to use drugs for diabetes (P = 0.049) and cardiovascular disease (P = 0.013), and used drugs for a greater number of chronic diseases (P = 0.049), compared with less frequent MPT users. CONCLUSIONS: About 5% of families in a sample from a large integrated health plan used an online prescription drug cost comparison tool during the first year it was available. Use increased over the year. Users were more likely to have several chronic diseases, took more prescription drugs, and had higher drug costs than nonusers. Further, users with more chronic diseases and more prescriptions were more likely to use the tool consistently throughout the year. These results indicate that the tool was successful in reaching health plan members who could most benefit from comparative prescription drug price information.
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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 | 0,002 | 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) | 0,001 | 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.
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