Consistency in performance evaluation reports and medical records.
Notice bibliographique
Résumé
BACKGROUND: In the health care market managed care has become the latest innovation for the delivery of services. For efficient implementation, the managed care organization relies on accurate information. So clinicians are often asked to report on patients before referrals are approved, treatments authorized, or insurance claims processed. What are clinicians responses to solicitation for information by managed care organizations? The existing health literature has already pointed out the importance of provider gaming, sincere reporting, nudging, and dodging the rules. AIMS OF THE STUDY: We assess the consistency of clinicians reports on clients across administrative data and clinical records. METHODS: For about 1,000 alcohol abuse treatment episodes, we compare clinicians reports across two data sets. The first one, the Maine Addiction Treatment System (MATS), was an administrative data set; the state government used it for program performance monitoring and evaluation. The second was a set of medical record abstracts, taken directly from the clinical records of treatment episodes. A clinician s reporting practice exhibits an inconsistency if the information reported in MATS differs from the information reported in the medical record in a statistically significant way. We look for evidence of inconsistencies in five categories: admission alcohol use frequency, discharge alcohol use frequency, termination status, admission employment status, and discharge employment status. Chi-square tests, Kappa statistics, and sensitivity and specificity tests are used for hypothesis testing. Multiple imputation methods are employed to address the problem of missing values in the record abstract data set. RESULTS: For admission and discharge alcohol use frequency measures, we find, respectively, strong and supporting evidence for inconsistencies. We find equally strong evidence for consistency in reports of admission and discharge employment status, and mixed evidence on report consistency on termination status. Patterns of inconsistency may be due to both altruistic and self-interest motives. DISCUSSION AND LIMITATIONS: Payment contracts based on performance may be subject to provider mis-reporting, which could seriously undermine its purpose. However, further analysis is needed to determine how much of the inconsistencies observed are results of clinician gaming in reporting. IMPLICATIONS FOR HEALTH POLICY: Increasing system accountability is becoming more and more important for health care policy makers. Results of this study will lead to a better understanding of physician reporting behavior. IMPLICATIONS FOR FUTURE RESEARCH: Our work in this paper on the data sets confirms the statistical significance of strategic reporting in alcohol addiction treatment. It will be of interest to confirm our finding in other data sets. Our on-going research will model the motives behind strategic reporting. We will hypothesize that both altruistic and financial incentives are present. Our empirical identification strategy will use Maine s Performance-Based Contracting system and client insurance sources to test how these incentives affect the direction of clinician s strategic reporting.
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 | 0,003 | 0,003 |
| 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) | 0,002 | 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 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 ».