Competition and Integration of US Health Systems in the Post-COVID-19 New Normal: Cross-sectional Survey
Notice bibliographique
Résumé
BACKGROUND: How do health systems in the United States view the concept of merger and acquisition (M&A) in a post-COVID 19 "new normal"? How do new entrants to the market and incumbents influence horizontal and vertical integration of health systems? Traditionally, it has been argued that M&A activity is designed to reduce inequities in the market, shift toward value-based care, or enhance the number and quality of health care offerings in a given market. However, the recent history of M&A activity has yielded fewer noble results. As might be expected, the smaller the geographical region in which M&A activity is pursued, the higher the likelihood that monopolistic tendencies will result. OBJECTIVE: We focused on three types of competition perceptions, external environment uncertainty-related competition, technology disruption-driven competition, and customer service-driven competition, and two integration plans, vertical integration and horizontal integration. We examined (1) how health system characteristics help discern competition perceptions and integration decisions, and (2) how environment-, technology-, and service-driven competition aspects influence vertical and horizontal integration among US health systems in the post-COVID-19 new normal. METHODS: We used data for this study collected through a consultant from a robust group of health system chief executive officers (CEOs) across the United States from February to March 2021. Among the 625 CEOs, 135 (21.6%) responded to our survey. We considered competition and integration aspects from the literature and ratified them via expert consensus. We collected secondary data from the Agency for Healthcare Research and Quality (AHRQ) Compendium of the US Health Systems, leading to a matched data set for 124 health systems. We used inferential statistical comparisons to assess differences across health systems regarding competition and integration, and we used ordered logit estimations to relate competition and integration. RESULTS: Health systems generally have a high level of the four types of competition perceptions, with the greatest concern being technology disruption-driven competition rather than environment uncertainty-related competition and customer service-driven competition. The first set of estimation results showed that size, teaching status, revenue, and uncompensated care burden are the main contingent factors influencing the three competition perceptions. The second set of estimation results revealed the relationships between different competition perceptions and integration plans. For vertical integration, environment uncertainty-related competition had a significant positive influence (P<.001), while the influence of technology disruption-driven competition was significant but negative (P<.001). The influence of customer service-driven competition on vertical integration was not evident. For horizontal integration, the results were similar for environment uncertainty-related competition and technology disruption-driven competition; however, the significance of technology disruption-driven competition was weak (P=.05). The influence of customer service-driven competition in the combined model was significant and negative (P<.001). CONCLUSIONS: Competition-driven integration has subtle influences across health systems. Environment uncertainty-related competition is a significant factor, with underlying contingent factors such as revenue concerns and leadership as the leading causes of integration plans. However, technology disruption may hinder integrations. Undoubtedly, small- and low-revenue health systems facing a high level of competition are likely to merge to navigate the health care business successfully. This trend should be a focus of policy to avoid monopolistic markets.
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 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 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 ».