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.

Materials and 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.

Discussion: 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.

Conclusion: A large, heterogeneous clinical database can reveal clinical associations, time is an important feature, and care must be taken to interpret the results.

Figure 1

Temporal interpolation. For a given patient, each measured point (solid circles on both curves) is mapped to an interpolated point on the opposite curve. Concepts are mapped to 0 and 1 (absent and present), and laboratory values are continuous. Interpolation is linear between points and constant at the ends of the curves.

Figure 2

Correlation of laboratory values and signout note concepts. Four blood laboratory values (graphs A–D) are correlated with clinical concepts extracted from physician signout notes (respective graph legends). Linear correlation (y axis) is plotted against time lag in days (x axis). Signal to the left of 0 days implies that changes in the laboratory value preceded changes in the concept, and signal to the right implies that changes in the laboratory value followed changes in the concept. Correlation greater than zero implies that a higher laboratory value was associated with presence of the concept. See text for interpretations.

Figure 3

Patients with few values. The linear correlation between blood potassium level and mention of spironolactone is shown, comparing all patients with relevant data versus only patients with 10 or fewer laboratory values. Linear correlation (y axis) is plotted against time lag in days (x axis).

Figure 4

Comparison of different temporal algorithms. Linear correlation of blood potassium with mention of spironolactone (A) and blood sodium with mention of hyponatremia (B) is shown, comparing four temporal algorithms (see text). Linear correlation (y axis) is plotted against time lag in days (x axis).

Figure 5

Inducing low correlation. For the hyponatremia–sodium correlation, a lower correlation is induced by scrambling a proportion of the patients. When 70% of the patients have their values scrambled in time, the height of the correlation drops approximately 70%. When 100% of the patients have their values scrambled, the correlation disappears.