Combat medical care relies on aeromedical evacuation (AE). Vital to AE is the validating flight surgeon (VFS) who warrants a patient is "fit to fly." To do this, the VFS considers clinical characteristics and inflight physiological stressors, often prescribing specific interventions such as a cabin altitude restriction (CAR). Unfortunately, limited information is available regarding the clinical consequences of a CAR. Consequently, a dual case-control study (CAR patients versus non-CAR patients and non-CAR patients flown with a CAR versus non-CAR patients) was executed. Data on 1,114 patients were obtained from TRANSCOM Regulating and Command and Control Evacuation System and Landstuhl Regional Medical Center trauma database (January 2007 to February 2008). Demographic and clinical factors essentially showed no difference between groups; however, CAR patients appeared more severely injured than non-CAR patients. Despite being sicker, CAR patients had similar clinical outcomes when compared with non-CAR patients. In contrast, despite an equivocal severity picture, the non-CAR patients flown with a CAR had superior clinical outcomes when compared with non-CAR patients. It appeared that the CAR prescription normalized severely injured to moderately injured and brought moderately injured into a less morbid state. These results suggest that CAR should be seriously considered when evacuating seriously ill/injured patients.

CAR rate was inversely correlated to PFC and PFC-100 rates. This finding suggests that aggressive prescribing of CARs may have a salutary effect on postflight complication rates and bears further investigation.

INTRODUCTION: Listening and acting upon women's comments is central to the relationship we have with patients. Doing this systematically and scientifically is difficult and sometimes involves thematic analyses which are time consuming and costly to perform. We present a new way of analyzing thousands of comments and seeing how each comment affects the HCAHPs rate hospital score (RHS). METHODS: We utilized text mining to analyze 27,335 free text comments. Comments were provided by women from Jan 2008 to June 2014 as supplemental comments to the HCAPHs survey. We used SAS text miner (v13.1). Similar words were combined. We then calculated the frequency of each word and the frequency of its relationships to other words using an expectation maximization algorithm. This produced a set of most frequently linked words which are statistically significantly related to each other. We then performed multiple regression analyses to determine magnitude of positive or negative effect on HCAHPs RHS. RESULTS: Negative comments have a larger effect on rate hospital score than positive comments. Comments regarding midwives had the highest positive effect on RHS. Topics generated were helpfulness of staff, cleanliness, feeding, pain control and postpartum care. All these areas had a statistically significant effect (p=0.05) on the RHS given by the person giving the comment. CONCLUSION: The systematic nature of this analysis is important. Our output is not subject to interpretation bias or comment reading fatigue. Whilst we present national level data the analysis could be performed on different subsets of data, providing results at regional or hospital level.