An online experiment to assess bias in professional medical coding

Table 2 Ordinary least squares regression models of level of service score (range: 1–5) assigned to six sample charts by professional medical coders, by patient demographic characteristics or social need^*

	Chart 1		Chart 2		Chart 3		Chart 4		Chart 5		Chart 6
	Coef	SE	Coef	SE	Coef	SE	Coef	SE	Coef	SE	Coef	SE
Experimental Arm 1 (Racial Bias)
White patient (ref)
African-American patient	−0.078	(0.13)	0.123	(0.15)	0.109	(0.14)	0.020	(0.13)	0.057	(0.15)	0.107	(0.15)
Experimental Arm 2 (Age Bias)
Middle-aged patient (ref)
Older adult patient	−0.006	(0.15)	0.284	(0.15)*	0.300	(0.13)**	−0.015	(0.12)	−0.140	(0.11)	−0.157	(0.23)
Experimental Arm 3 (Ability Bias)
Patient without disabilities (ref)
Patient with hearing/visual/physical disability	0.007	(0.13)	0.088	(0.13)	0.058	(0.12)	0.015	(0.12)	0.110	(0.12)	−0.020	(0.17)
Experimental Arm (Gender Bias)^†
Male patient (ref)
Female patient	−0.250	(0.13)*	−0.073	(0.13)	−0.200	(0.12)*	0.143	(0.12)	−0.024	(0.13)	0.181	(0.17)
Experimental Arm 5 (Social Need)
No social need (ref)
Housing Insecurity	0.118	(0.13)	0.055	(0.13)	0.105	(0.09)	−0.107	(0.12)	−0.055	(0.11)	0.038	(0.17)
Food Insecurity	0.076	(0.13)	0.076	(0.12)	0.026	(0.09)	0.015	(0.11)	0.023	(0.11)	0.121	(0.17)

Source: Original data from an online experiment of professional medical coders in the US, August–September, 2017. * Models control for respondent gender, age in years, race/ethnicity, and years worked as a professional medical coder. † Chart 6 contrasts a female patient to a patient with no identified gender *p < 0.10, **p < 0.05

ISSN: 1472-6947