BACKGROUND
Cancer patients frequently encounter complex treatment pathways, often characterized by challenges with coordinating and scheduling appointments at various specialty services and locations. Identifying patients who might benefit from scheduling and social support from community health workers (CHW) or patient navigators is largely determined on a case-by-case basis and is resource intensive.
OBJECTIVE
Our study proposes a novel algorithm to use scheduling data to identify complex scheduling patterns amongst patients with transportation and housing needs.
METHODS
We present a novel algorithm to calculate scheduling complexity from patient scheduling data. We define patient scheduling complexity as an aggregation of sequence, resolution, and facility components. We apply the scheduling complexity algorithm to 38 breast cancer patients’ scheduling data and compare this metric with common count-based metrics.
RESULTS
Five patients exhibited high scheduling complexity with low count-based adjustments. Two patients exhibited high count-based adjustments with low scheduling complexity. Of the 15 patients that indicated transportation or housing insecurity issues in conversations with CHWs, 86.7% (13 of 15) patients were identified as medium or high scheduling complexity while 60% (9 of 15) were identified as medium or high count-based adjustments.
CONCLUSIONS
Scheduling complexity identifies patients with complex, but non-chronical scheduling behaviors who would be missed by traditional count-based metrics. This study shows a potential link between transportation and housing needs with schedule complexity. Scheduling complexity can complement count-based metrics when identifying patients who might need additional care coordination support especially as it relates to transportation and housing needs.
CLINICALTRIAL
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