David A Katerndahl1, Robert Wood, Carlos Roberto Jaén. 1. Family & Community Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas 78229-3900, USA. katerndahl@uthscsa.edu
Abstract
PURPOSE: We wanted to demonstrate a method for calculating the relative complexity of ambulatory clinical encounters. METHODS: Measures of complexity should reflect the complexity of the typical encounter and across encounters. If inputs represent the information transferred from the patient to the physician, then inputs include history, physical examination, testing, diagnoses, and patient demographics. Outputs include medications prescribed and other therapies used, including education and counseling, procedures performed, and disposition. The complexity of each input/output is defined as the mean input/output quantity per clinical encounter weighted by its inter-encounter diversity (range of possibilities used) and variability (visit-to-visit change). In complex systems, as the information in the input increases linearly, the complexity of the system increases exponentially. To assess the impact of the complexity of the encounter on the physician, we adjusted the estimated complexity by the duration-of-visit. RESULTS: Using the 2000 NAMCS database, we calculated input and output complexities for 3 specialties. Construct validity was affirmed by comparing the relative rankings of complexity against relative rankings using other complexity-related measures. Although total relative complexity was similar for family medicine (44.04 +/- 0.0024 SE) and cardiology (42.78 +/- 0.0004 standard error [SE]), when adjusted for duration-of-visit, family medicine had a greater complexity density per hour (167.33 +/- 0.0095 SE) than either cardiology (125.4 +/- 0.0117 SE) or psychiatry (31.21 +/- 0.0027 SE). CONCLUSIONS: This method estimates complexity based on the amount of care provided weighted by its diversity and variability. Such estimates could have broad use for interphysician comparisons as well as longitudinal applications.
PURPOSE: We wanted to demonstrate a method for calculating the relative complexity of ambulatory clinical encounters. METHODS: Measures of complexity should reflect the complexity of the typical encounter and across encounters. If inputs represent the information transferred from the patient to the physician, then inputs include history, physical examination, testing, diagnoses, and patient demographics. Outputs include medications prescribed and other therapies used, including education and counseling, procedures performed, and disposition. The complexity of each input/output is defined as the mean input/output quantity per clinical encounter weighted by its inter-encounter diversity (range of possibilities used) and variability (visit-to-visit change). In complex systems, as the information in the input increases linearly, the complexity of the system increases exponentially. To assess the impact of the complexity of the encounter on the physician, we adjusted the estimated complexity by the duration-of-visit. RESULTS: Using the 2000 NAMCS database, we calculated input and output complexities for 3 specialties. Construct validity was affirmed by comparing the relative rankings of complexity against relative rankings using other complexity-related measures. Although total relative complexity was similar for family medicine (44.04 +/- 0.0024 SE) and cardiology (42.78 +/- 0.0004 standard error [SE]), when adjusted for duration-of-visit, family medicine had a greater complexity density per hour (167.33 +/- 0.0095 SE) than either cardiology (125.4 +/- 0.0117 SE) or psychiatry (31.21 +/- 0.0027 SE). CONCLUSIONS: This method estimates complexity based on the amount of care provided weighted by its diversity and variability. Such estimates could have broad use for interphysician comparisons as well as longitudinal applications.
Authors: Samuel T Edwards; Miguel Marino; Bijal A Balasubramanian; Leif I Solberg; Steele Valenzuela; Rachel Springer; Kurt C Stange; William L Miller; Thomas E Kottke; Cynthia K Perry; Sarah Ono; Deborah J Cohen Journal: J Gen Intern Med Date: 2018-10-01 Impact factor: 6.473