Date of Service Provision and Date of Payment in Claims Data: Dealing with Time Differences
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".