Early warning and risk estimation methods based on unstructured text in electronic medical records to improve patient adherence and care.
In this paper we present risk-estimation models and methods for early detection of patient non-adherence based on unstructured text in patient records. The primary objectives are to perform early interventions on patients at risk of non-adherence and improve outcomes. We analyzed over 1.1 million visit notes corresponding to 30,095 Cancer patients, spread across 12 years of Oncology practice. Our risk analysis, based on a rich risk-factor dictionary, revealed that a staggering 30% of the patients were estimated to be at a high risk of non-adherence. Our risk classification showed that 2 distinct patient groups, between 26 and 38 (mean risk score, r=0.77, s=0.22), and 75 and 90 (r=0.81, s=0.19) years of age respectively, exhibited the highest risk of nonadherence when compared to the rest. The dominant risk-factors for these two groups, not surprisingly, included psychosocial (e.g. depression, lack of support), medical (e.g. side-effects such as pain) and financial issues (e.g. costs of treatment).
Sairamesh
Jakka
J
360 Fresh, Inc, CA, USA.
Rajagopal
Ram
R
Nemana
Ravi
R
Argenbright
Keith
K
D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D057286:Electronic Health Records; D006801:Humans; D008875:Middle Aged; D008960:Models, Psychological; D009323:Natural Language Processing; D010349:Patient Compliance / Q000523:psychology; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors