Evidence map›Paper›PMID 38498782›Full record

ArticlePrehospital emergency care2024

Factors Impacting the Implementation of Mobile Integrated Health Programs for the Acute Care of Older Adults.

Laurel O'Connor, Stephanie Behar, Jade Refuerzo, Xhenifer Mele, Elsa Sundling, Sharon A Johnson, Jamie M Faro, Peter K Lindenauer, Kristin M Mattocks

Open access · greenAbstract read
In one paragraph

Article in Prehospital emergency care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.5field-weighted citation impact, top 12% of its field
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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Observational
  3. 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

9 authors at 4 institutions in 2 countries.

Laurel O'ConnorDepartment of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.
Stephanie BeharDepartment of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.
Jade RefuerzoDepartment of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.
Xhenifer MeleDepartment of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.
Elsa SundlingDepartment of Industrial Management, KTH Royal Institute of Technology, Stockholm, Sweden.
Sharon A JohnsonRobert A. Foisie School of Business, Worcester Polytechnic Institute, Worcester, Massachusetts.
Jamie M FaroDepartment of Population Health and Quantitative Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts.
Peter K LindenauerDepartment of Healthcare Delivery and Population Sciences and Department of Medicine, University of Massachusetts Chan Medical School-Baystate, Springfield, Massachusetts.
Kristin M MattocksDepartment of Population Health and Quantitative Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts.
University of Massachusetts Chan Medical School · USBaystate Health · USKTH Royal Institute of Technology · SEWorcester Polytechnic Institute · US

Funding

University of Massachusetts Center for Clinical and Translational ScienceKL2TR001455 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI KIEFE, CATARINA I. · 2015 to 2024
$5.0M
University of Massachusetts Center for Clinical and Translational ScienceTL1TR001454 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LEWIS, BRIAN C · 2015 to 2024
$3.6M
NCATS NIH HHS KL2 TR001455NCATS NIH HHS TL1 TR001454
6 · The paper itself

Abstract

objectiveEmergency services utilization is increasing in older adult populations. Many such encounters may be preventable with better access to acute care in the community. Mobile integrated health (MIH) programs leverage mobile resources to deliver care and services to patients in the out-of-hospital environment and have the potential to improve clinical outcomes and decrease health care costs; however, they have not been widely implemented. We assessed barriers, potential facilitators, and other factors critical to the implementation of MIH programs with key vested partners.

methodsProfessional and community-member partners were purposefully recruited to participate in recorded structured interviews. The study team used the Practical Robust Implementation and Sustainability Model (PRISM) framework to develop an interview guide and codebook. Coders employed a combination of deductive and inductive coding strategies to identify common themes across partner groups.

resultsThe study team interviewed 22 participants (mean age 56, 68% female). A cohort of professional subject matter experts included physicians, paramedics, public health personnel, and hospital administrators. A cohort of lay community partners included patients and caregivers. Coders identified three prominent themes that impact MIH implementation. First, MIH is disruptive to existing clinical workflows. Second, using MIH to improve patients' experience during acute care encounters is key to intervention adoption. Finally, legislative action is needed to augment central financial and regulatory policies to ensure the adoption of MIH programs.

conclusionsCommon themes impacting the implementation of MIH programs were identified across vested partner groups. Multilevel strategies are needed to address patient adoption, clinical partners' workflow, and legislative policies to ensure the success of MIH programs.

Indexed as

Emergency Medical ServicesAgedDelivery of Health Care, IntegratedFemaleHumansInterviews as TopicMaleMiddle AgedQualitative ResearchTelemedicine

Identifiers

PMID38498782
PMCPMC11436480
OpenAlexW4392928694

What OpenQuestion holds

Textmetadata
LicenceTDM
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.