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.