Patient navigation based on predictive modeling decreases no-show rates in cancer care

Cancer. 2015 May 15;121(10):1662-70. doi: 10.1002/cncr.29236. Epub 2015 Jan 13.

Abstract

Background: Patient adherence to appointments is key to improving outcomes in health care. "No-show" appointments contribute to suboptimal resource use. Patient navigation and telephone reminders have been shown to improve cancer care and adherence, particularly in disadvantaged populations, but may not be cost-effective if not targeted at the appropriate patients.

Methods: In 5 clinics within a large academic cancer center, patients who were considered to be likely (the top 20th percentile) to miss a scheduled appointment without contacting the clinic ahead of time ("no-shows") were identified using a predictive model and then randomized to an intervention versus a usual-care group. The intervention group received telephone calls from a bilingual patient navigator 7 days before and 1 day before the appointment.

Results: Over a 5-month period, of the 40,075 appointments scheduled, 4425 patient appointments were deemed to be at high risk of a "no-show" event. After the patient navigation intervention, the no-show rate in the intervention group was 10.2% (167 of 1631), compared with 17.5% in the control group (280 of 1603) (P<.001). Reaching a patient or family member was associated with a significantly lower no-show rate (5.9% and 3.0%, respectively; P<.001 and .006, respectively) compared with leaving a message (14.7%: P = .117) or no contact (no-show rate, 21.6%: P = .857).

Conclusions: Telephone navigation targeted at those patients predicted to be at high risk of visit nonadherence was found to effectively and substantially improve patient adherence to cancer clinic appointments. Further studies are needed to determine the long-term impact on patient outcomes, but short-term gains in the optimization of resources can be recognized immediately.

Keywords: cancer care; disparities; no-show; patient navigation; predictive modeling.

Publication types

  • Randomized Controlled Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Appointments and Schedules*
  • Boston
  • Cancer Care Facilities / statistics & numerical data
  • Female
  • Humans
  • Insurance, Health
  • Male
  • Massachusetts
  • Middle Aged
  • Models, Statistical
  • Neoplasms / therapy*
  • Patient Compliance / statistics & numerical data*
  • Patient Navigation*
  • Prospective Studies
  • Reminder Systems
  • Sample Size
  • Telephone
  • Vulnerable Populations / statistics & numerical data*