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NESS 2006 Annual Meeting
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Mid Level Practitioner (MLP) Work Force Analysis in a Multi Specialty Department of Surgery at a University-Affiliated, Tertiary Care Teaching Hospital with a General Surgery Residency Program and Sub-Specialty Fellowships Under the Constraints of the ACGME Mandated 80 Hour Work Week
Orlando C Kirton, Michael E. Ivy, Marilyn A Folcik, Rebecca Calabrese, Eric D Dobkin, Judith L Pepe, John W Mah, Kevin P Keating, Marc D Palter
Hartford Hospital, Connecticut, CT

Objective: To establish a methodology to quantify staffing requirements to meet coverage needs under constraints of the 80-hr work week for residents and fellows.
Design: Data extracted from Eclipsys Sunrise Decision Support Manager (SDSM), hospital financial & census reports, MLP, fellow and resident call schedules.
Setting: 867 bed urban teaching hospital, 5 surgical units (116) and 3 surgical ICUs (34 ICU and 12 SD beds).
Participants: Attending physicians, MLPs, nurse directors, residents and fellows.
Interventions: FY 2005 patient volume and hours of care were defined by MD service and/or patient location; duty time standards were developed and validated for MLPs, residents and fellows.
Main Outcome Measures: 1) MLP staffing hours available, 2) clinical coverage (in hours) needed for 24/7 operation, 3) patient care hours based on volume and time standards, 4) Coverage Index (the total staff hours [residents, fellows, MLPs] available divided by clinical coverage needs for 24/7 operation of unit, and 5) Workload Index/Staff Efficiency-total # of clinical hours needed for patient care activities divided by total hours of available staff.
Results: The analysis identified the Workload Index for 5 of the 8 units to be over 100% of the ideal workload. Our Coverage Index indicated 4 of the 8 units were in need of additional MLPs.
Conclusions: We developed a staffing methodology to quantify the number of MLP hours needed to provide patient care services which encompass the roles and availability of residents, fellows and mid-level practitioners based on patient volume and coverage schedules.

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