Evidence map›Paper›PMID 35767470›Full record

ArticleJournal of the American Geriatrics Society2022

Automated telephone follow-up programs after hospital discharge: Do older adults engage with these programs?

James D Harrison, Rebecca L Sudore, Andrew D Auerbach, Sachin Shah, Sandra Oreper, Margaret Meg Wheeler, Margaret C Fang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Geriatrics Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Predicting drug overdose and death after "before medically advised" hospital discharge.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2025
    Article
  5. 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

7 authors.

James D HarrisonDivision of Hospital Medicine, University of California San Francisco, San Francisco, California, USA.ORCID 0000-0002-7761-7039
Rebecca L SudoreDivision of Geriatrics, University of California San Francisco, San Francisco, California, USA.ORCID 0000-0003-4436-2209
Andrew D AuerbachDivision of Hospital Medicine, University of California San Francisco, San Francisco, California, USA.ORCID 0000-0001-8440-0527
Sachin ShahDivision of Hospital Medicine, University of California San Francisco, San Francisco, California, USA.ORCID 0000-0001-8400-6888
Sandra OreperDivision of Hospital Medicine, University of California San Francisco, San Francisco, California, USA.
Margaret Meg WheelerOffice of Population Health, UCSF Health, San Francisco, California, USA.
Margaret C FangDivision of Hospital Medicine, University of California San Francisco, San Francisco, California, USA.ORCID 0000-0003-3362-1543

Funding

Institutional Career Development Core SupplementKL2TR001870 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KANAYA, ALKA M., SARKAR, URMIMALA · 2016 to 2025
$19.7M
UCSF Learning Health System K12 Career Development ProgramK12HS026383 · AHRQ · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GONZALES, RALPH, SARKAR, URMIMALA · 2018 to 2022
$3.6M
Mentoring Researchers in Advance Care Planning for Underrepresented Older Adults at Risk for Alzheimer’s Disease and Related Dementias and Their CaregiversK24AG054415 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI REBECCA L SUDORE · 2017 to 2026
$1.7M
Use and outcomes of anticoagulants for the treatment and prevention of thrombosis among hospitalized patientsK24HL141354 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FANG, MARGARET C. · 2018 to 2022
$731k
AHRQ HHS K12 HS026383NCATS NIH HHS KL2 TR001870NHLBI NIH HHS K24 HL141354NIA NIH HHS K24 AG054415
6 · The paper itself

Abstract

backgroundHealth systems have developed automated telephone call programs to screen and triage patients' post-hospital discharge issues and concerns. The aims of our study were to determine whether and how older adults engage with automated post-hospital discharge telephone programs and to describe the prevalence of patient-reported post-discharge issues.

methodsWe identified all telephone calls made by an urban academic medical center as part of a post-hospital discharge program between May 1, 2018 and April 30, 2019. The program used automated telephone outreach to patients or their caregivers that included 11 distinct steps 3 days post-discharge. All adults discharged home from the hospital, were included, and we categorized patients into ≤64 years, 65-84 years, and ≥85 years age groups. We then compared call reach rate, completeness of 11-step calls and patient-reported issues between age groups.

resultsEighteen thousand and seventy six patients were included. More patients 65-84 years old were reached compared to patients ≤64 years old (84.3% vs. 78.9%, AME 5.52%; 95%CI: 3.58%-7.45%). Completion rates of automated calls for those ≥85 years old were also high. Patients ≥85 years old were more likely to have questions about their follow-up plans and need assistance scheduling appointments compared to those ≤64 years old (19.0% vs. 11.9%, AME 7.0% (95%CI: 2.7%-11.3%).

conclusionPost-hospital automated telephone calls are feasible and effective at reaching older adults. Future work should focus on improving discharge communication to ensure older adults are aware of their follow-up plan and appointments.

Indexed as

Continuity of Patient CarePatient DischargeAftercareAgedAged, 80 and overFollow-Up StudiesHospitalsHumansTelephonehealth services deliverytelemedicinetransitional care

Identifiers

PMID35767470
PMCPMC9588657

What OpenQuestion holds

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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.