Evaluation of multiple myeloma clinical decision support tool value for assessing treatment selection variation when populated with community health system data.

Abstract

e18781 Background: Clinical decision support (CDS) technology has the potential to improve health outcomes by offering physicians an informational resource to support review and application of best practices. The Multiple Myeloma Research Foundation (MMRF) and Intermountain Healthcare (IMH) conducted a study to assess the suitability of a single health system’s data for a myeloma-specific CDS tool that visualizes treatment pathways, and to assess the effort needed to support a CDS program. This research is part of a longer-term effort to explore how CDS technology can help: Increase awareness of and apply treatment guidelines by visualizing pathways for specific MM patient cohorts, Improve understanding of treatment variation for quality improvement within healthcare systems, Improve outcomes research by visualizing relationships between treatments and outcomes. This abstract focuses on the second use case, showing suitability of community health system data to assess treatment variability within the health system. Methods: IA12 data from the CoMMpass study was used to create a CDS tool prototype. These data were aggregated into state and transition maps to identify nodes and pathways with corresponding outcomes, including response, progression-free survival (PFS), and overall survival (OS). We also tested if EMR data from a community health system (i.e., IMH) could support such visualization. Inclusion criteria included patients with active MM between January 2016–June 2018; adult aged 18 years to 89 years at diagnosis of active or smoldering MM. An IMH-specific data dictionary was assessed for variable importance, quantity, and ease of acquisition. Results: Ninety-six of an initial 146 patients meeting eligibility criteria had sufficient data usable for the study, reflecting 44 unique drug combinations across 9 lines of therapy. The tool was able to associate and visualize all patients and their clinical states and transitions to their outcomes. Clinical data was typically complete (99% of the time), including key clinician-derived data, such as ECOG scores (78%) and treatment response (99%). Conclusions: The IMH portion of the study supports the hypothesis that a community health system can provide sufficiently high-quality information to power a CDS tool with priority features including display of treatment selection variability. Only 65% (96/146) of the initial study group had usable data because some patients had received partial care outside of the IMH integrated delivery network (IDN) leaving associated data inaccessible. Initial biostatistical analysis suggests that roughly 750-1000 complete patient records would be required for statistically significant outcomes research with granularly stratified cohorts. The MMRF plans to recruit additional large IDNs to obtain the patients to power more generalizable functionality.