Evidence map›Paper›PMID 38243666›Full record

ArticleJournal of hospital medicine2024

Development and evaluation of best practice advisory alert for patient eligibility in a hospital-at-home program: A multicenter retrospective study.

Michael J Maniaci, Ricardo A Torres-Guzman, Francisco R Avila, Karla Maita, John P Garcia, Antonio J Forte, Rachel Rutledge, Sagar B Dugani, Shannon M Campbell, Ingrid J Pritchard and 1 more

Abstract readMulticenter Study
In one paragraph

Article in Journal of hospital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Hospital-at-Home Patient Fall Predictors.Mayo Clinic proceedings. Innovations, quality & outcomes · 2025
    Article
  5. Observational
  6. Article
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

11 authors.

Michael J ManiaciDivision of Hospital Internal Medicine, Mayo Clinic, Jacksonville, Florida, USA.ORCID 0000-0002-2731-1787
Ricardo A Torres-GuzmanDivision of Plastic Surgery, Mayo Clinic, Jacksonville, Florida, USA.
Francisco R AvilaDivision of Plastic Surgery, Mayo Clinic, Jacksonville, Florida, USA.
Karla MaitaDivision of Plastic Surgery, Mayo Clinic, Jacksonville, Florida, USA.
John P GarciaDivision of Plastic Surgery, Mayo Clinic, Jacksonville, Florida, USA.
Antonio J ForteDivision of Plastic Surgery, Mayo Clinic, Jacksonville, Florida, USA.
Rachel RutledgeAdministrative Operations, Mayo Clinic, Jacksonville, Florida, USA.
Sagar B DuganiDivision of Hospital Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA.ORCID 0000-0001-7858-1317
Shannon M CampbellDivision of Hospital Internal Medicine, Mayo Clinic, Jacksonville, Florida, USA.
Ingrid J PritchardDivision of Hospital Internal Medicine, Mayo Clinic, Jacksonville, Florida, USA.
Margaret R PaulsonDivision of Hospital Internal Medicine, Mayo Clinic Health Systems, Eau Claire, Wisconsin, USA.

Funding

Rural Patient Risks and Exposures for Diabetes ConTrol (Rural PREDICT)K23MD016230 · NIMHD · MAYO CLINIC ROCHESTER · PI DUGANI, CHANDRASAGAR · 2021 to 2025
$763k
NIMHD NIH HHS K23 MD016230
6 · The paper itself

Abstract

backgroundHospital-at-home (HaH) is a growing model of care that has been shown to improve patient outcomes, satisfaction, and cost-effectiveness. However, selecting appropriate patients for HaH is challenging, often requiring burdensome manual screening by clinicians. To facilitate HaH enrollment, electronic health record (EHR) tools such as best practice advisories (BPAs) can be used to alert providers of potential HaH candidates.

objectiveTo describe the development and implementation of a BPA for identifying HaH eligible patients in Mayo Clinic's Advanced Care at Home (ACH) program, and to evaluate the provider response and the patient characteristics that triggered the BPA. DESIGN, SETTING, AND

participantsWe conducted a retrospective multicenter study of hospitalized patients who triggered the BPA notification for ACH eligibility between March and December 2021 at Mayo Clinic in Jacksonville, FL and Mayo Clinic Health System in Eau Claire, WI. We extracted demographic and diagnosis data from the patients as well as characteristics of the providers who received the BPA notification.

interventionThe BPA was developed based on the ACH inclusion and exclusion criteria, which were derived from clinical guidelines, literature review, and expert consensus. The BPA was integrated into the EHR and displayed a pop-up message to the provider when a patient met the criteria for ACH eligibility. The provider could choose to refer the patient to ACH, dismiss the notification, or defer the decision. MAIN OUTCOMES AND MEASURES: The main outcomes were the number and proportion of BPA notifications that resulted in a referral to ACH, and the number and proportion of referrals that were accepted by the ACH clinical team and transferred to ACH. We also analyzed the factors associated with the provider's decision to refer or not refer the patient to ACH, such as the provider's role, location, and specialty.

resultsDuring the study period, 8962 notifications were triggered for 2847 patients. Providers opted to refer 711 (11.4%) of the total notifications linked to 324 unique patients. After review by the ACH clinical team, 31 of the 324 referrals (9.6%) met clinical and social criteria and were transferred to ACH. In multivariable analysis, Wisconsin nurses, physician assistants, and in-training personnel had lower odds of referring the patients to ACH when compared to attending physicians.

Indexed as

Electronic Health RecordsHealth PersonnelConsensusHospitalsHumansRetrospective Studies

Identifiers

PMID38243666
PMCPMC10923137

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Registered trials

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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.