Date of Service Provision and Date of Payment in Claims Data: Dealing with Time Differences
Notice bibliographique
Résumé
Aim The paper quantifies discrepancies between date of payment and date of service provision when doing analyses in relation to date of death and also in relation to the end of a calendar year. In analyses of this type, time differences between service provision and payment can lead to both under- and overestimation of service use. We aim to capture these phenomena in claims data from different sectors (primary care, medication prescription, prescription of remedies and medical aids, hospital care). Method We have used pre-structured claims data from a scientific data warehouse of a large German statutory health insurance covering people that died in 2016. We investigated the discrepancies in time between date of service provision and date of payment for different outpatient and inpatient services based on data from 2015 to 2017. An exact date (dd/mm/yyyy) was only available for data covering prescriptions of remedies and medical aids. Data covering medication prescriptions were only exact to the month of payment (mm/yyyy), whereas data covering outpatient physician care were only exact to the quarter of payment (q/yyyy). Results For both outpatient physician care and hospital care, less than 1% had a payment date after the date of death. The share is considerably higher (28-31%) for prescriptions of remedies and medical aids. The majority of payments occurred within 3 months after death (93% for prescriptions of remedies and medical aids, 67% for primary care services). Less than 1% of outpatient physician care and about 18% of remedies had been paid after the end of the calender year 2015. Here too, the majority of payments were made within the first 3 months of 2016 (100% of prescriptions of remedies and medical aids, 65% of primary care services). Conclusions Discrepancies in time between date of service provision and date of payment pose a challenge and are a potential source of under-/overestimation of health service utilization when doing analyses in relation to date of death or the end of a calendar year. This needs to be taken into account when requesting the data, but also in preparing and analysing them. The primary recommendation is to ensure that services with a payment date after death are included explicitly.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,004 |
| Science ouverte | 0,001 | 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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