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

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  • 1MedStar Health Research Institute, 3007 Tilden St, Washington, DC, 20008, United States, 1 202-244-9807.

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Summary
This summary is machine-generated.

A new scheduling complexity algorithm identifies cancer patients with complex needs, including those with transportation and housing challenges, who may be missed by traditional metrics. This approach aids in better care coordination for vulnerable patient populations.

Keywords:
algorithmbreast cancercancer patientcarecommunity health workerdatasethousing needmetastasisoncologypatient schedulingscheduling complexitiessocial determinant of healthsocial supporttemporal data miningtransportation

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Area of Science:

  • Health Services Research
  • Patient Navigation
  • Health Informatics

Background:

  • Cancer patients often face complex treatment schedules across multiple services and locations.
  • Identifying patients needing extra support for scheduling and social needs is resource-intensive and often subjective.
  • Current methods for assessing patient scheduling needs are limited in capturing true complexity.

Purpose of the Study:

  • To develop and validate a novel algorithm for quantifying patient scheduling complexity using electronic health record data.
  • To identify patients with complex scheduling patterns, particularly those with potential transportation and housing needs.
  • To compare the novel scheduling complexity metric against traditional count-based metrics.

Main Methods:

  • Developed a scheduling complexity algorithm aggregating sequence, resolution, and location components.
  • Defined schedule sequence complexity (non-chronological scheduling), resolution complexity (no-shows/cancellations), and location complexity (multiple sites).
  • Applied the algorithm to scheduling data of 38 breast cancer patients and compared results with count-based metrics (arrived, rescheduled, canceled, no-show ratios).

Main Results:

  • The scheduling complexity algorithm identified 5 patients with high complexity and low count-based adjustments, who might otherwise be overlooked.
  • A significant proportion (86.7%) of patients reporting transportation or housing insecurity were identified with medium or high scheduling complexity.
  • No statistically significant difference was found between count-based adjustments and scheduling complexity bins overall, highlighting the unique insights of the new metric.

Conclusions:

  • The novel scheduling complexity metric effectively identifies patients with complex, non-chronological scheduling behaviors missed by traditional metrics.
  • A potential link exists between transportation and housing needs and higher scheduling complexity.
  • Scheduling complexity can enhance care coordination efforts by complementing existing metrics for identifying patients requiring additional support.