Dr. Schleyer asks a number of important questions. These might be summarized as asking why, as a discipline, are we not focusing on improving the acquisition of structured data rather than going through computational acrobatics to extract codified representation from narrative text? Should we not be focusing our efforts to ensure a fully-structured record?
We agree with Dr. Schleyer that a fully-structured record is an important goal. In the context of a healthcare system with 10-minute visits, time of data entry using current technologies remains burdensome.1 Even if time of data entry were of no concern, the engineering of structured data acquisition interfaces to support the full richness of patient state captured by natural language remains a challenge. Most tellingly, there have been over four decades of research by leading informaticians in precisely the area that Dr. Schleyer urges us to explore and yet the volume of narrative text continues to grow in our leading academic centers.
Perhaps the greatest potential for developing a fully-structured medical record lies in patients' annotating their own record given their direct interest in accuracy and disposable time. Forty years ago, it was shown that patients could accurately present their symptoms in codified fashion2 and subsequent research suggests that they and their families can do equally well in reporting medications and even physiological state.3,4 With the increased popularity of personally-controlled health records, such capabilities are likely to be increasingly exploited. Notwithstanding these hopeful developments, it seems quite likely that the amount of narrative text describing patient states will continue to grow in the near to mid-term and the existing corpus will persist for at least the life of our patients. The obligation and the opportunity to provide the best possible care of our patients and the most informed research using such data will therefore continue to motivate segments of the academic and commercial informatics research communities in refining natural language processing techniques.