Interaction of 2 Continuous Variables in Logistic Regression

Hello,
I am running several logistic regressions and have a few significant interactions among 2 continuous variables. I am wondering if someone can lead me in the right direction for how to interpret what this means.

Here is a snipet: The model accounted for 7.5% of the variance in treatment decision (χ2(9)=11.74, p=.23).The results suggest a significant interaction of EMSxIAT (β = 1.08, p<.05; OR 2.932) and a nonrelevant unique effect of EMS (β = -.73, p<.05) on treatment decision. There were no significant effects of IMS (β = .182, p=.47), IMSxIAT (β = -.06, p=.89), or IMSxEMS (β = .32, p=.06) on treatment decision. These results suggest XXXXXXX. Interestingly when patient race was factored in, for those who saw an African American patient, the same significant interaction occurred (β = 1.36, p<.05; OR 3.908), on treatment decisions. However, for physicians that saw a White patient, the IMSxEMS interaction had a significant effect on treatment decisions (β = .92, p<.05; OR 2.509), (and the EMS had a nonrelevant unique effect, β = -1.42, p<.05), when controlling for physician demographics suggesting XXXXXXX.

(I know this is not the correct way to report an Odds Ratio but just in case anyone that would like to help me, I've included it)

some background: i am looking at potential predictors of medical treatment decisions. IAT: implicit bias; IMS: internal motivation to control prejudice; EMS: external motivation to control prejudice; Treatment Decision: Yes/No

Thank you so much!!!
Neena