Consistency in performance evaluation reports and medical records.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.237 | 0.584 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.026 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".