MétaCan
Menu
Retour à la cohorte
Enregistrement W2589218517 · doi:10.2196/iproc.6151

A Comprehensive Survey of Managed Care Organization (MCO) Medication Adherence Intervention Programs

2016· article· en· W2589218517 sur OpenAlexvenueno aff
Clifford B. Jones, Fawad Piracha, Kenny Ng, Ian Sullivan, Michael Boice, David J. C. Coutts, Stefanie Mazlish, Aishwarya Nagarian, Slanix Paul Alex, Sudarshan Phani, Kalee Shah, Aneesh Sheth, Kamila Sip, T Van Kempen, U. Basu Roy

Notice bibliographique

RevueIproceedings · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueMedication Adherence and Compliance
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineIntervention (counseling)Health careSocioeconomic statusHealth literacyIntensive care medicineQuality of life (healthcare)Medication adherenceDiabetes mellitusAdverse effectMEDLINEFamily medicineNursingEnvironmental healthInternal medicinePopulation

Résumé

récupéré en direct d'OpenAlex

Background: Medication adherence is defined as the extent to which patients takes their medications as prescribed by their healthcare providers. Medication non-adherence is understood to be a costly and dangerous problem. In the United States alone, non-adherence is estimated to incur US $290 billion in avoidable healthcare expenditures each year. On an individual basis, annual spending for non-adherent patients with hypertension and diabetes is approximately US $4000 greater than for patients who are adherent to their medications. In addition, non-adherent patients suffering from heart failure are hospitalized 2.5 times more frequently than those who adhere to their medication regimens, resulting in a profoundly diminished quality of life, while driving up health care costs. This problem will continue to get worse as more patients will continue to take multiple medications to treat chronic conditions. While medication non-adherence is a serious problem, its origins are complex and not fully elucidated. Some of the more commonly accepted causes include complex treatment regimens, adverse drug reactions, forgetfulness, socioeconomic issues, health literacy, and personal beliefs. Objective: The purpose of this study is map the current landscape of managed care organization (MCO) medication adherence programs, review the common medication adherence interventions that are used by MCOs, and identify the need for more enhanced intervention targeting such as predictive analytics platforms and other non-predictive methodologies. Methods: This research survey was conducted in the spring of 2014 and involved 30 MCOs. Each MCO was asked about their current medication adherence intervention programs. Each MCO was represented by an employee who manages medication adherence programs within the organization. Each employee was interviewed during a 10-week long period by virtue of a detailed questionnaire that involved open-ended and multiple-choice questions. Questions addressed information about each MCO’s existing medication adherence programs and interventions. Information and insight into the effectiveness of existing interventions, the criteria by which patients are selected for interventions, and the processes by which adherence is measured by each MCO were summarized for this survey. The questionnaire was divided into three basic components: general questions regarding existing medication adherence programs, types of interventions used and their individual effectiveness, and how patients are selected to receive interventions. Respondents also discussed any unique services their programs provide and any future plans of expansion. All data was recorded by 5 interviewers and then reanalyzed by the authors. All 30 MCOs interviewed have medication adherence intervention programs that target their patient population. The MCOs vary in size and coverage demographics. A total of 19 of the organizations are classified as small MCOs (enrollment 200,000). The surveyed MCO populations encompass most of the continental United States as well as Puerto Rico. The coverage demographics of these MCOs include commercial, Medicare, and/or Medicaid. Overall, 53% of MCOs cover a commercial population, 80% cover a Medicare population, and 27% cover a Medicaid population. The highest coverage combinations are commercial and Medicare and Medicare and Medicaid, which account for 23% of the population and 17% of the population, respectively. Results: Most MCO medication adherence program interventions are directed at patients with chronic cardiovascular disease states (diabetes, hypertension, hyperlipidemia, and heart failure). Cardiovascular diseases are a primary concern for MCOs because of the chronic use of medications/therapies that are associated with the management of such disease states. Furthermore, there is a strong association with medication non-adherence and increased hospitalization rates. Most MCOs use triggers and retrospective adherence measures to select patients for interventions. MCOs seem to follow a rule-based approach (using specific demographic profiles and predefined events to trigger interventions) rather than treating each patient individually. One way to implement a more personalized approach is through predictive analytics. While only 7% of MCOs currently use predictive analytics, over half of the surveyed MCOs plan on incorporating some type of analytics platform. Many are interested in adopting a platform that identifies interventions most likely to engage patients and influence their behavior, avoiding wasteful spending on interventions with patients who will not need them. These enhanced programs are dynamic and self-learning and can rapidly adapt to new intervention techniques. MCOs focus on four intervention channels to improve patient adherence: telephone outreach, direct mail, provider-centric, and face-to-face visits. These interventions are conducted through a combination of in-house and outsourced techniques. MCOs perceive current approaches as only moderately effective because of a failure to intervene before patients are non-adherent and a failure to personalize interventions. This relative ineffectiveness of current interventions has been implicated in previous studies. Conclusions: The study results suggest that most MCO medication adherence programs target chronic, comorbid cardiovascular disease patients through a system of triggers and retrospective adherence measures. Most MCOs intervene using telephone outreach, direct mail, provider-centric, and face-to-face visits through a combination of in-house and outsourced methods. This approach is seen as only moderately effective as it fails to personalize interventions and intervene before a patient becomes non-adherent. In light of these findings, predictive analytics platforms can play an increasing role in addressing the needs and shortcomings of existing MCO medication adherence programs.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,258
Score d'incertitude au seuil0,635

Scores Codex et Gemma par catégorie

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

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 Opus0,059
Tête enseignante GPT0,316
Écart entre enseignants0,258 · 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; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueIproceedingsMême sujetMedication Adherence and ComplianceTravaux en français237 207