LEARNING FROM TEXT MINING IN MEDICAL CARE MANAGEMENT NOTES

Purpose: Using free text in medical care management notes, we explore and identify specific words and phrases that exhibit tight associations with each other and with future urgent and unplanned (emergency department or urgent clinic) medical care utilization.

Method: The HealthPlaNET System is a proprietary electronic platform used by medical care managers at UPMC Health Plan to record their interactions with Health Plan members for the purpose of managing and coordinating patient care.  The HealthPlaNET System is designed to support care that centers on the member as the primary focus of activity.  This electronic record contains both structured fields as well as a large volume of unstructured free text.  Free text notes include care managers’ comments about their interactions with patients as well as other care management notes and observations (chronic conditions, medications, caregiver information) and summaries of overall care coordination/management (service authorizations).  StatSoft^®’s Text Miner tool was used to perform text mining of these electronic care management notes using 440,000 notes created between July and December 2010. Associations between important words/phrases were then tested for a statistically significant increase with the cumulative utilization of unplanned care in 2011. To guide the contextual interpretation of the results, discussions were held with the nurses and care managers responsible for documenting in HealthPlaNET.

Result: : Distinct classes of words and phrases exhibited recognizable associations. Some phrases, such as a mention of “social worker”, were found to be reliable indictors of future unplanned care while other words, such as “mother” were associated with low future unplanned care. Per individual comment, the average unplanned future care was $4,885 while that figure was $2,375 for “mother” and $10,113 for “social worker.”

Conclusion: There is a large body of medical data that is contained in the form of unstructured textual information. Text Mining is seen to be a useful mechanism for incorporating this knowledge into advanced decision making processes.  This method holds promise for using electronic medical records to improve prospective medical management.