Literature DB >> 25585595

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

Sanja Percac-Lima1, Patrick R Cronin, David P Ryan, Bruce A Chabner, Emily A Daly, Alexandra B Kimball.   

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.
© 2015 American Cancer Society.

Entities:  

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

Mesh:

Year:  2015        PMID: 25585595     DOI: 10.1002/cncr.29236

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  17 in total

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3.  Patient-Reported Attributions for Missed Colonoscopy Appointments in Two Large Healthcare Systems.

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4.  Targeted Reminder Phone Calls to Patients at High Risk of No-Show for Primary Care Appointment: A Randomized Trial.

Authors:  Sachin J Shah; Patrick Cronin; Clemens S Hong; Andrew S Hwang; Jeffrey M Ashburner; Benjamin I Bearnot; Calvin A Richardson; Blair W Fosburgh; Alexandra B Kimball
Journal:  J Gen Intern Med       Date:  2016-08-08       Impact factor: 5.128

5.  The effects of navigation and types of neighborhoods on timely follow-up of abnormal mammogram among black women.

Authors:  Sage Kim; Yamile Molina; Anne Elizabeth Glassgow; Nerida Berrios; Jenny Guadamuz; Elizabeth Calhoun
Journal:  Med Res Arch       Date:  2015-07

6.  Dynamic Scheduling for Veterans Health Administration Patients using Geospatial Dynamic Overbooking.

Authors:  Stephen Adams; William T Scherer; K Preston White; Jason Payne; Oved Hernandez; Mathew S Gerber; N Peter Whitehead
Journal:  J Med Syst       Date:  2017-10-12       Impact factor: 4.460

7.  Efficacy of the Competency-Based Oncology Patient Navigator Training.

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8.  Precision Patient Navigation to Improve Rates of Follow-up Colonoscopy, An Individual Randomized Effectiveness Trial.

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9.  Association between patient-reported barriers and HIV clinic appointment attendance: A prospective cohort study.

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Review 10.  Patient Navigation in Cancer: The Business Case to Support Clinical Needs.

Authors:  Ronald M Kline; Gabrielle B Rocque; Elizabeth A Rohan; Kris A Blackley; Cynthia A Cantril; Mandi L Pratt-Chapman; Howard A Burris; Lawrence N Shulman
Journal:  J Oncol Pract       Date:  2019-09-11       Impact factor: 3.840

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