title: Patient navigation based on predictive modeling decreases no-show rates in cancer care.
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.
authors.affiliation: Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, Massachusetts; Center for Community Health Improvement, Massachusetts General Hospital, Boston, Massachusetts.
authors.forename: Sanja
authors.initials: S
authors.lastname: Percac-Lima
authors.forename: Patrick R
authors.initials: PR
authors.lastname: Cronin
authors.forename: David P
authors.initials: DP
authors.lastname: Ryan
authors.forename: Bruce A
authors.initials: BA
authors.lastname: Chabner
authors.forename: Emily A
authors.initials: EA
authors.lastname: Daly
authors.forename: Alexandra B
authors.initials: AB
authors.lastname: Kimball
mesh_terms: D000328:Adult; D000368:Aged; D001071:Appointments and Schedules; D001900:Boston; D002173:Cancer Care Facilities / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007348:Insurance, Health; D008297:Male; D008404:Massachusetts; D008875:Middle Aged; D015233:Models, Statistical; D009369:Neoplasms / Q000628:therapy*; D010349:Patient Compliance / Q000706:statistics & numerical data*; D062526:Patient Navigation; D011446:Prospective Studies; D017010:Reminder Systems; D018401:Sample Size; D013689:Telephone; D035862:Vulnerable Populations / Q000706:statistics & numerical data*
keywords: cancer care; disparities; no-show; patient navigation; predictive modeling