The perfect storm coming to healthcare: value-based healthcare meets fraud and abuse
Notice bibliographique
Résumé
Purpose This manuscript addresses the emerging tension between healthcare providers and regulatory authorities as the delivery of healthcare transitions from Volume-based Healthcare to Value-based Healthcare (VBH). In Volume-based healthcare, the more patients a doctor sees, the more money she makes. The more expensive drugs and treatments the doctor provides, the more money she makes. In VBH, keeping patients healthy with a focus on patient outcomes offers the potential to deliver improved healthcare outcomes along with cost reduction. In Volume-based Healthcare, there is an incentive to induce referrals and offer remuneration seeking referrals subjecting healthcare providers to Fraud and Abuse and Antikickback regulations. In VBH, there is no incentive to do more to achieve more income. The problem is: where do you draw the line between helping providers and patients by offering services and goods that achieve quality and cost reduction without running afoul of the law. This emerging tension is exacerbated by the emergence of social determinants of healthcare (SDOH) that have more to do with the quality of one’s healthcare than direct clinical care, medicines and medical devices. Design/methodology/approach This manuscript is based entirely on a narrative review and secondary research. No primary research has been conducted. Findings VBH offers the potential to achieve The Triple Aim: improve patient healthcare outcomes; enhance patient access to healthcare and satisfaction; and reduce costs. SDOH such as poverty, food deserts, crime, education, homelessness, transportation and more have more of an impact on the quality of one’s healthcare than direct clinical healthcare. Healthcare marketers can move beyond just selling goods and services to offering value that addresses the SDOH that stand in the way of achieving good health. Research limitations/implications This emerging approach to healthcare delivery is relatively new. Government has set the theme for this transformation by announcing a “regulatory sprint toward value-based healthcare”. The regulatory authorities like the Department of Justice, The Office of Inspector General (OIG) and State Attorneys General recognize that Fraud and Abuse and Antikickback can be obstacles to providing VBH. This new approach to healthcare delivery formally launched in January 2021 so there is little research on strategy and marketing guidance. Practical implications The varied healthcare providers such as hospitals, doctors, nurses, pharmacists and contractual healthcare networks such as Accountable Care Organizations and Clinically Integrated Networks are just beginning to move forward on this new paradigm. Social implications Social implications are huge. SDOH provide a real-world context in attempting to achieve improved healthcare. Take the example of an older patient with Type 2 diabetes along with a number of additional comorbidities such as obesity, depression, and more. The patient needs insulin for her diabetes, but she is homeless and lives under a bridge. What good is the best doctor, best hospital, best medicines if the patient is homeless. Originality/value The research on this healthcare delivery transition is just beginning to emerge.
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 | 0,020 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| 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 ».