ArticleBMC health services research2022
The development of a visual dashboard report to assess physician assistant and nurse practitioner financial and clinical productivity.
Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Current status and future outlook of artificial intelligence and automation in intravenous infusion systems.NPJ digital medicine · 2026Review
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- Nurse Practitioners and Physician Assistants: Building a Team and Optimizing Practice in the Medical ICU.Chest · 2025Review
- The Perceived Impact of the New Medicare Rules for Split/Shared Visits: A Survey of Advanced Practice Administrators.Cureus · 2023Article
- Optimal Use of Advanced Practice Providers at an Academic Medical Center: A First-Year Retrospective Review.Cureus · 2023Article
- Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The evolving COVID-19 pandemic has unevenly affected academic medical centers (AMCs), which are experiencing resource-constraints and liquidity challenges while at the same time facing high pressures to improve patient access and clinical outcomes. Technological advancements in the field of data analytics can enable AMCs to achieve operational efficiencies and improve bottom-line expectations. While there are vetted analytical tools available to track physician productivity, there is a significant paucity of analytical instruments described in the literature to adequately track clinical and financial productivity of physician assistants (PAs) and nurse practitioners (NPs) employed at AMCs. Moreover, there is no general guidance on the development of a dashboard to track PA/NP clinical and financial productivity at the individual, department, or enterprise level. At our institution, there was insufficient tracking of PA/NP productivity across many clinical areas within the enterprise. Thus, the aim of the project is to leverage our institution's existing visualization tools coupled with the right analytics to track PA/NP productivity trends using a dashboard report.MethodsWe created an intuitive and customizable highly visual clinical/financial analytical dashboard to track productivity of PAs/NPs employed at our AMC.ResultsThe APP financial and clinical dashboard is organized into two main components. The volume-based key performance indicators (KPIs) included work relative value units (wRVUs), gross charges, collections (payments), and payer-mix. The session utilization (KPIs) included (e.g., new versus return patient ratios, encounter type, visit volume, and visits per session by provider). After successful piloting, the dashboard was deployed across multiple specialty areas and results showed improved data transparency and reliable tracking of PAs/NPs productivity across the enterprise. The dashboard analytics were also helpful in assessing PA/NP recruitment requests, independent practice sessions, and performance expectations.ConclusionTo our knowledge, this is the first paper to highlight steps AMCs can take in developing, validating, and deploying a financial/clinical dashboard specific to PAs/NPs. However, empirical research is needed to assess the impact of qualitative and quantitative dashboards on provider engagement, revenue, and quality of care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.