Demonstrating advanced practice provider value: Implementing a new advanced practice provider billing algorithm
Bibliographic record
Abstract
BACKGROUND: Rapid changes in health care are driving the adjustment of work flow by which providers serve patients in team-based care. Specifically, there is a need to develop more effective and efficient utilization with accurate attribution of advanced practice providers' (APPs) productivity. LOCAL PROBLEM: The Directors of the APP-Best Practice Center conducted assessments of each clinical area at MUSC Health, a large academic medical center. A knowledge gap was identified, not only regarding billing practices of the APPs (nurse practitioners/physician assistants) but also in the utilization of APPs to practice to the fullest extent of their license, education, and experience. METHODS: By substantiating APPs' contribution margin through the process of implementing a new standardized APP billing algorithm, a change in practice was accepted by senior leadership and a new APP billing algorithm was built while following updated practice laws, compliance/legal standards, and hospital bylaws/regulations. INTERVENTIONS: A new billing algorithm was implemented on July 1, 2017, and outcomes were evaluated 12 months after implementation. RESULTS: This project uncovered the work already performed by APPs while increasing relative value units, collections, and overall patient encounters by the APP/physician team. Findings suggest improved utilization and appropriate attribution of productivity. CONCLUSIONS: With the APP work force growing, the implementation of electronic medical record systems, and today's health care financial constraints, it is imperative that health care systems standardize their billing practices. The APP billing algorithm is a critical tool that will help to meet this demand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".