Evidence map›Paper›PMID 38736506›Full record

ArticleHealth services research and managerial epidemiology

Self-scheduling Medical Visits in a Multispecialty, Multisite Medical Practice: Complexity, Challenges, and Successes.

Frederick North, Rebecca Buss, Elissa M Nelson, Matthew C Thompson, Jennifer Pecina, Gregory M Garrison, Brian A Crum

Abstract read
In one paragraph

Article in Health services research and managerial epidemiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

7 authors.

Frederick NorthDivision of Community Internal Medicine, Geriatrics, and Palliative Care, Mayo Clinic, Rochester, Minnesota, USA.ORCID https://orcid.org/0000-0002-3696-4595
Rebecca BussEnterprise Office of Access Management, Mayo Clinic, Rochester, Minnesota, USA.ORCID https://orcid.org/0000-0002-4583-4987
Elissa M NelsonEnterprise Office of Access Management, Mayo Clinic, Rochester, Minnesota, USA.ORCID https://orcid.org/0000-0002-4516-379X
Matthew C ThompsonEnterprise Office of Access Management, Mayo Clinic, Rochester, Minnesota, USA.ORCID https://orcid.org/0000-0003-0282-9181
Jennifer PecinaFamily Medicine, Mayo Clinic, Rochester, Minnesota, USA.ORCID https://orcid.org/0000-0003-1970-1344
Gregory M GarrisonFamily Medicine, Mayo Clinic, Rochester, Minnesota, USA.ORCID https://orcid.org/0000-0001-6903-4610
Brian A CrumDepartment of Neurology, Mayo Clinic, Rochester, Minnesota, USA.ORCID https://orcid.org/0000-0002-2624-0649

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Self-scheduling of medical visits is becoming more common but the complexity of applying multiple requirements for self-scheduling has hampered implementation. Mayo Clinic implemented self-scheduling in 2019 and has been increasing its portfolio of self-schedulable visits since then. Our aim was to show measures quantifying the complexity associated with medical visit scheduling and to describe how opportunities and challenges of scheduling complexity apply in self-scheduling. Methods: We examined scheduled visits from January 1, 2022, through August 24, 2023. For seven visit categories, we counted all unique visit types that were scheduled, for both staff-scheduled and self-scheduled. We examined counts of self-scheduled visit types to identify those with highest uptake during the study period. Results: There were 9555 unique visit types associated with 20.8 M (million) completed visits. Self-scheduled visit types accounted for 4.0% (838,592/20,769,699) of the completed total visits. Of seven visit categories, self-scheduled established patient visits, testing visits, and procedure visits accounted for 93.5% (784,375/838,592) of all self-scheduled visits. Established patient visits in primary care (10 visit types) accounted for 273,007 (32.6%) of all self-scheduled visits. Testing visits (blood and urine testing, 2 visit types) accounted for 183,870 (21.9%) of all self-scheduled visits. Procedure visits for screening mammograms, bone mineral density, and immunizations (8 visit types) accounted for 147,358 (17.6%) of all self-scheduled visits. Conclusion: Large numbers of unique visit types comprise a major challenge for self-scheduling. Some visit types are more suitable for self-scheduling. Guideline-based procedure visits such as screening mammograms, bone mineral density exams, and immunizations are examples of visits that have high volumes and can be standardized for self-scheduling. Established patient visits and laboratory testing visits also can be standardized for self-scheduling. Despite the successes, there remain thousands of specific visit types that may need some staff-scheduler intervention to properly schedule.

Indexed as

family medicinehealth economicsmedical informaticsoutpatient visitspediatricspractice managementpreventive screeningprimary careself-schedulewomen's health

Identifiers

PMID38736506
PMCPMC11085017

What OpenQuestion holds

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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.