Background: Rapid changes in healthcare 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 the Medical University of South Carolina (MUSC) Health, a large academic medical center. A knowledge gap was identified, not only regarding billing practices of the APPs (NPs and physician assistants) but also in the use 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 that follows updated practice laws, compliance/legal standards, and hospital bylaws and 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 workforce growing, the implementation of electronic medical record systems, and today's healthcare financial constraints, healthcare systems must standardize their billing practices. The APP billing algorithm is a critical tool that will help to meet this demand.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/01.JAA.0000550293.01522.01 | DOI Listing |
Health Aff (Millwood)
January 2025
Jordan Everson, Office of the Assistant Secretary for Technology Policy, Washington, D.C.
Effective evaluation and governance of predictive models used in health care, particularly those driven by artificial intelligence (AI) and machine learning, are needed to ensure that models are fair, appropriate, valid, effective, and safe, or FAVES. We analyzed data from the 2023 American Hospital Association Annual Survey Information Technology Supplement to identify how AI and predictive models are used and evaluated for accuracy and bias in hospitals. Hospitals use AI and predictive models to predict health trajectories or risks for inpatients, identify high-risk outpatients to inform follow-up care, monitor health, recommend treatments, simplify or automate billing procedures, and facilitate scheduling.
View Article and Find Full Text PDFCurr Urol Rep
December 2024
Department of Urology, Lahey Hospital and Medical Center, MA, Burlington, USA.
Purpose Of Review: Artificial Intelligence (AI) has produced a significant impact across various industries, including healthcare. In the outpatient clinic setting, AI offers promising improvements in efficiency through Chatbots, streamlined medical documentation, and personalized patient education materials. On the billing side, AI technologies hold potential for optimizing the selection of appropriate billing codes, automating prior authorizations, and enhancing healthcare fraud detection.
View Article and Find Full Text PDFHeliyon
August 2024
Solar Energy Research Cell (SERC), School of Electrical Engineering, Vellore Institute Technology, Vellore, Tamil Nadu-632014, India.
In the present day electricity demand, demand response programs support mitigating the power demand and help to improve stability. Within this framework, the Home Energy Management System (HEMS) plays a critical role in optimizing energy consumption patterns by redistributing loads from peak to off-peak hours, thereby subsequently contributing to grid stability. The existing HEMS model often fails to simultaneously address the three important issues.
View Article and Find Full Text PDFPLoS One
December 2024
Faculty of Medicine, Department of Nephrology, University of Tsukuba, Tsukuba, Japan.
The billing database of the universal healthcare system in Japan potentially includes large-cohort data of patients with immunoglobulin A nephropathy, diagnosis codes aimed at billing should not be directly used for clinical research because of the risk of misdiagnosis. To solve this problem, we aimed to develop a novel method for identifying patients with immunoglobulin A nephropathy from billing data using machine learning. The medical records and bills of 3,743 patients who consulted nephrologists at a single center were extracted.
View Article and Find Full Text PDFInt J Popul Data Sci
December 2024
British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada.
The use of routinely collected administrative health data for research can provide unique insights to inform decision-making and, ultimately, support better public health outcomes. Yet, since these data are primarily collected to administer healthcare service delivery, challenges exist when using such data for secondary purposes, namely epidemiologic research. Many of these challenges stem from the researcher's lack of control over the quality and consistency of data collection, and - furthermore - a lessened understanding of the data being analyzed.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!