title: Exploiting time in electronic health record correlations.
abstract: OBJECTIVE
  To demonstrate that a large, heterogeneous clinical database can reveal fine temporal patterns in clinical associations; to illustrate several types of associations; and to ascertain the value of exploiting time.
  
  
  METHODS
  Lagged linear correlation was calculated between seven clinical laboratory values and 30 clinical concepts extracted from resident signout notes from a 22-year, 3-million-patient database of electronic health records. Time points were interpolated, and patients were normalized to reduce inter-patient effects.
  
  
  RESULTS
  The method revealed several types of associations with detailed temporal patterns. Definitional associations included low blood potassium preceding 'hypokalemia.' Low potassium preceding the drug spironolactone with high potassium following spironolactone exemplified intentional and physiologic associations, respectively. Counterintuitive results such as the fact that diseases appeared to follow their effects may be due to the workflow of healthcare, in which clinical findings precede the clinician's diagnosis of a disease even though the disease actually preceded the findings. Fully exploiting time by interpolating time points produced less noisy results.
  
  
  CONCLUSIONS
  Electronic health records are not direct reflections of the patient state, but rather reflections of the healthcare process and the recording process. With proper techniques and understanding, and with proper incorporation of time, interpretable associations can be derived from a large clinical database.
  
  
  CONCLUSIONS
  A large, heterogeneous clinical database can reveal clinical associations, time is an important feature, and care must be taken to interpret the results.
authors: Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA. hripcsak@columbia.edu
authors: George
authors: G
authors: Hripcsak
authors: David J
authors: DJ
authors: Albers
authors: Adler
authors: A
authors: Perotte
mesh_terms: D057225:Data Mining / Q000379:methods*; D019992:Databases as Topic; D057286:Electronic Health Records; D006801:Humans; D013995:Time