Identifying Complex Scheduling Patterns Among Patients With Cancer With Transportation and Housing Needs: Feasibility Pilot Study.

Background: Patients with cancer 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 or patient navigators is largely determined on a case-by-case basis and is resource intensive.

Objective: This study aims to propose a novel algorithm to use scheduling data to identify complex scheduling patterns among 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. Schedule sequence complexity is the degree to which appointments are scheduled and arrived to in a nonchronological order. Resolution complexity is the degree of no shows or canceled appointments. Location complexity reflects the proportion of appointment dates at 2 or more different locations. Schedule complexity captures deviations from chronological order, unresolved appointments, and coordination across multiple locations. We apply the scheduling complexity algorithm to scheduling data from 38 patients with breast cancer enrolled in a 6-month comorbidity management intervention at an urban hospital in the Washington, DC area that serves low-income patients. We compare the scheduling complexity metric with count-based metrics: arrived ratio, rescheduled ratio, canceled ratio, and no-show ratio. We defined an aggregate count-based adjustment metric as the harmonic mean of rescheduled ratio, canceled ratio, and no-show ratio. A low count-based adjustment metric would indicate that a patient has fewer disruptions or changes in their appointment scheduling.

Results: The patients had a median of 88 unique appointments (IQR 60.3), 62 arrived appointments (IQR 47.8), 13 rescheduled appointments (IQR 13.5), 9 canceled appointments (IQR 10), and 1.5 missed appointments (IQR 5). There was no statistically significant difference in count-based adjustments and scheduling complexity bins (χ24=6.296, P=.18). In total, 5 patients exhibited high scheduling complexity with low count-based adjustments. A total of 2 patients exhibited high count-based adjustments with low scheduling complexity. Out of the 15 patients that indicated transportation or housing insecurity issues in conversations with community health workers, 86.7% (13/15) patients were identified as medium or high scheduling complexity while 60% (9/15) were identified as medium or high count-based adjustments.

Conclusions: Scheduling complexity identifies patients with complex but nonchronological 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.