Finally Discharged From Hospital Quotes

A couple of days after the letter arrived, I was discharged from the hospital, in the custody, so to speak, of about three yards of adhesive tape around my ribs. Then began a very strenuous week's campaign to get permission to attend the wedding. I was finally able to do it by laboriously ingratiating myself with my company commander, a bookish man by his own confession, whose favorite author, as luck had it, happened to be my favorite author-L. Manning Vines. Or Hinds. Despite this spiritual bond between us, the most I could wangle out of him was a three-day pass, which would, at best, give me just enough time to travel by train to New York, see the wedding, bolt a dinner somewhere, and then return damply to Georgia.

Fiscal Numbers (the latter uniquely identifies a particular hospitalization for patients who might have been admitted multiple times), which allowed us to merge information from many different hospital sources. The data were finally organized into a comprehensive relational database. More information on database merger, in particular, how database integrity was ensured, is available at the MIMIC-II web site [1]. The database user guide is also online [2]. An additional task was to convert the patient waveform data from Philips’ proprietary format into an open-source format. With assistance from the medical equipment vendor, the waveforms, trends, and alarms were translated into WFDB, an open data format that is used for publicly available databases on the National Institutes of Health-sponsored PhysioNet web site [3]. All data that were integrated into the MIMIC-II database were de-identified in compliance with Health Insurance Portability and Accountability Act standards to facilitate public access to MIMIC-II. Deletion of protected health information from structured data sources was straightforward (e.g., database fields that provide the patient name, date of birth, etc.). We also removed protected health information from the discharge summaries, diagnostic reports, and the approximately 700,000 free-text nursing and respiratory notes in MIMIC-II using an automated algorithm that has been shown to have superior performance in comparison to clinicians in detecting protected health information [4]. This algorithm accommodates the broad spectrum of writing styles in our data set, including personal variations in syntax, abbreviations, and spelling. We have posted the algorithm in open-source form as a general tool to be used by others for de-identification of free-text notes [5].