Table 3 Regression results for medical DRGs with one year preceding and lagged IT investment (unit of analysis: admission)
From: The impact of health information technology on disparity of process of care
Variables | Coefficient | Coefficient | |
|---|---|---|---|
(std. err) | (std. err) | ||
Age (years) | Ref (1-17) | ||
18 to 34 | −0.011 | −0.006 | |
(0.007) | (0.0060 | ||
35 to 64 | 0.049*** | 0.051*** | |
(0.006) | (0.006) | ||
65 and older | 0.092*** | 0.094*** | |
(0.007) | (0.006) | ||
Sex | Ref (Female) | ||
Male | −0.021*** | −0.018*** | |
(0.001) | (0.001) | ||
Payment source | Ref (Medicare) | ||
Medical1 | 0.028*** | 0.028*** | |
(0.002) | (0.002) | ||
Private | −0.117*** | −0.116*** | |
(0.002) | (0.002) | ||
Self | −0.107*** | −0.10***7 | |
(0.004) | (0.004) | ||
Other | −0.045*** | −0.046*** | |
(0.004) | (0.003) | ||
DRG weight | 0.183*** | 0.173*** | |
(0.001) | (0.001) | ||
Health IT (t + 1) | −0.003 | ||
(0.002) | |||
Health IT (t-1) | −0.004*** | ||
(0.002) | |||
Race | Non-White | 0.000 | 0.001 |
(0.002) | (0.002) | ||
Ownership | Ref (Profit) | ||
Not-for-profit | −0.062*** | −0.045*** | |
(0.010) | (0.010) | ||
Government | −0.068*** | −0.050*** | |
(0.014) | (0.013) | ||
Teaching status | −0.024 | −0.029 | |
(0.019) | (0.018) | ||
Network hospital | −0.008 | −0.004 | |
(0.011) | (0.010) | ||
Licensed beds | 0.0001*** | 0.0001*** | |
(0.000) | (0.000) | ||
Rural hospital | −0.085*** | −0.084*** | |
(0.015) | (0.014) | ||
Constant | 0.535*** | 0.547*** | |
(0.031) | (0.027) | ||