Data collection and analysis
The QI leader identified newly-diagnosed patients through an automated
new diagnosis banner in the EMR. We performed frequent monitoring of
newly-diagnosed patients to ensure that all cases were captured.
Standardized definitions for Vitamin D deficiency, insufficiency and
sufficiency were used.13 EMRs of patients aged 2–18
years old with newly-diagnosed cancer were reviewed, including provider
notes, lab testing orders and results, and prescriptions. Rates of
Vitamin D testing, supplementation and follow-up testing
post-supplementation were obtained at different time points, from
November 1, 2015 to January 31, 2016 (pre-intervention) and from
February 1, 2016 to June 30, 2016 (post-intervention), and averaged over
seven-day periods. To assess sustainability, we obtained data every two
months until June 2018.
Process measures of testing and supplementation were chosen to assess
the system improvement; supplementation was also identified as a
feasible proxy for clinical outcome improvement by the team. An
additional process measure of perceived utility of the decision-tree and
automated triggers was chosen to assess end-user buy-in.
Weekly documentation rates were plotted on a run chart during the study
phase of each PDSA cycle to identify non-random signals of change in
Vitamin D testing (Figure 4) and supplementation and follow-up testing
post-supplementation (Figure 5). For statistical process control, a
p-control chart was used to detect special cause variation. Both the run
chart and p-control chart were generated with Microsoft Excel QI Macros.
To assess process measures including use and perceived utility of the
decision-making tree and automated triggers, we conducted the survey
described above.