Models of Care in Hospital Medicine: An Analysis of Advance Practitioner Utilization Using Monte Carlo Methods

Physicians are facing a constant shortage, with market estimates of shortfalls only projected to increase as the population of the United States ages and health care employment needs increase. A lack of physicians has led to a steady increase in the number and scope of advanced practice practitioners (APPs) in the United States. Nurse practitioners and physician assistants have increased, in terms of percentage of the practitioner workforce, with varying degrees of autonomy. The authors describe a model of APPs to determine whether indirect supervision vs direct supervision is fiscally optimum in hospital medicine departments.

The authors utilized R software in R Studio to simulate, via Monte Carlo methods, APP-independent and supervised models of care. Publicly available salary data were used as the foundation to generate the models. The data were then used to simulate typical care teams of APPs and physicians in hospital medicine, while utilizing typical billing practices.

Analysis of the models of care generated demonstrated that APPs, given the current payer reimbursements, are an integral part of the hospital medicine care team. A range of scenarios are accounted for. This research demonstrates that direct supervision has a lower cost per work relative value unit cost to the employer, as opposed to indirect supervision.

APPs will only continue to increase their share of billed visits. In the setting of a hospital medicine workflow, given current payer and salary constraints, direct supervision is recommended to optimize the care team from a fiduciary perspective.