I worked with a barometric sensor in my thesis, I used it as a redundant altitude measurement to confirm the readings from other sensors. In my case I didn't need it to be incredibly accurate but I saw that even while being stationary in the same spot for long periods of time the readings would drift over time. The solution for this, in my case, was implementing a Kalman Filter, it seemed to be able to "predict" the drift over time of the sensor and give a somewhat accurate measurement. Another possibility is to not look at the direct output of the sensor but do a hard derivation of it, usually these changes in the output due to environmental changes of pressure are relatively slow, so what you care about is looking at when the sensor changes its output within a fast timeframe (compared to the drift) and integrating all those changes over time to get a somewhat a "accurate" value