Repeat intervals for 97 common laboratory tests.

<p>(a) Frequency distribution of repeat intervals for all labs. Vertical bars indicate the boundaries used in the entropy calculations to convert repeat intervals to one of 20 discrete categories. (b) Median repeat interval for each of 97 tests. Vertical bars indicate the 25th and 75th percentiles.</p

Summary of repeat intervals for 97 laboratory tests.

<p>Listed for each test are the LOINC (Logical Observation Identifiers Names and Code) name and code; the median repeat interval and standard deviation for all 100,000 repeat intervals; the minimum and maximum median repeat intervals of the 20 value bins and their ratio; the entropy; and the category: “bad-good” (BG), “bad-good-bad” (BGB), “good-bad” (GB), “good-bad-good” (GBG), and “good-bad-good-bad” (GBGB). Repeat intervals are given in days.</p

Entropy time periods.

<p>Listed are the start of each time period and the most common repeat interval (peak).</p

Factors that affect repeat interval, including (a) age and (b) inpatient vs outpatient setting.

<p>Error bars represent the 25th and 75th percentiles.</p

The median repeat interval for different initial (a) WBC, (b) HDLc, (c) HbA1c, and (d) hCG values.

<p>Error bars represent the 25th and 75th percentiles. Triangles indicate reference values for BWH (black) and MGH (gray).</p

Visualizing Patient Population Overlap Using the Redeveloped i2b2 Web Client

For the latest release of i2b2 (version 1.8.0) we made significant redesign and implementation changes to the user interface (the i2b2 “web client”).To demonstrate the new web client plugin architecture, we created a new plugin that uses area-proportional Venn diagrams to display the overlap of several previously saved patient cohorts (i2b2 “patient sets”). One example of how this visualization can be used is to quickly identify the intersection sizes of three patient populations: patients with autism, patients with gastrointestinal disorders, and patients without food allergies. First, the three groups are saved as separate patient sets. The user then drags and drops the patient sets onto the visualization plugin loaded on the right side of the application (Figure 1). Another use case is optimizing clinical trial design. By visualizing the overlap between inclusion and exclusion criteria, the user can see where to make adjustments to increase the number of patients eligible for the trial.Open-source code for the plugin can be accessed from Neomancy at https://github.com/Neomancy/i2b2-venn-plugin</p