Evidence map›Paper›PMID 37145249›Full record

ArticleJournal of digital imaging2023

Fast and Frictionless: A Novel Approach to Radiology Appointment Scheduling Using a Mobile App and Recommendation Engine.

Ankur M Doshi, Dana Ostrow, August Gresens, Rachel Grimmelmann, Salman Mazhar, Eduardo Neto, Molly Woodriff, Michael Recht

Abstract read
In one paragraph

Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ankur M DoshiDepartment of Radiology, NYU Langone Health, 660 First Ave, 3rd Floor, New York, NY, 10016, USA. ankur.doshi@nyulangone.org.ORCID 0000-0002-9415-2763
Dana OstrowMedical Center IT, NYU Langone Health, One Park Ave, New York, NY, 10016, USA.
August GresensMedical Center IT, NYU Langone Health, One Park Ave, New York, NY, 10016, USA.
Rachel GrimmelmannMedical Center IT, NYU Langone Health, One Park Ave, New York, NY, 10016, USA.
Salman MazharMedical Center IT, NYU Langone Health, One Park Ave, New York, NY, 10016, USA.
Eduardo NetoMedical Center IT, NYU Langone Health, One Park Ave, New York, NY, 10016, USA.
Molly WoodriffMedical Center IT, NYU Langone Health, One Park Ave, New York, NY, 10016, USA.
Michael RechtDepartment of Radiology, NYU Langone Health, 660 First Ave, 3rd Floor, New York, NY, 10016, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many outpatient radiology orders are never scheduled, which can result in adverse outcomes. Digital appointment self-scheduling provides convenience, but utilization has been low. The purpose of this study was to develop a "frictionless" scheduling tool and evaluate the impact on utilization. The existing institutional radiology scheduling app was configured to accommodate a frictionless workflow. A recommendation engine used patient residence, past and future appointment data to generate three optimal appointment suggestions. For eligible frictionless orders, recommendations were sent in a text message. Other orders received either a text message for the non-frictionless app scheduling approach or a call-to-schedule text. Scheduling rates by type of text message and scheduling workflow were analyzed. Baseline data for a 3-month period prior to the launch of frictionless scheduling showed that 17% of orders that received an order notification text were scheduled using the app. In an 11-month period after the launch of frictionless scheduling, the rate of app scheduling was greater for orders that received a text message with recommendations (frictionless approach) versus app schedulable orders that received a text without recommendations (29% vs. 14%, p < 0.01). Thirty-nine percent of the orders that received a frictionless text and scheduled using the app used a recommendation. The most common recommendation rules chosen for scheduling included location preference of prior appointments (52%). Among appointments that were scheduled using a day or time preference, 64% were based on a rule using the time of the day. This study showed that frictionless scheduling was associated with an increased rate of app scheduling.

Indexed as

Mobile ApplicationsRadiologyText MessagingAppointments and SchedulesHumansOutpatientsDigital appointment schedulingFrictionless schedulingRecommendation algorithms

Identifiers

PMID37145249
PMCPMC10406780

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.