Evidence map›Paper›PMID 38939111›Full record

ArticleHealth care science2023

Lessons learned from the hospital to home community care program in Singapore and the supporting AI multiple readmissions prediction model.

John Abisheganaden, Kheng Hock Lee, Lian Leng Low, Eugene Shum, Han Leong Goh, Christine Gia Lee Ang, Andy Wee An Ta, Steven M Miller

Abstract read
In one paragraph

Article in Health care science, 2023. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  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

8 authors.

John AbisheganadenDepartment of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital National Healthcare Group Singapore Singapore.
Kheng Hock LeeDepartment of Family Medicine and Continuing Care, Singapore General Hospital, SingHealth Group Singapore Singapore.
Lian Leng LowDepartment of Family Medicine and Continuing Care, Singapore General Hospital, SingHealth Group Singapore Singapore.
Eugene ShumOffice of Community Development Changi General Hospital, SingHealth Group Singapore Singapore.
Han Leong GohData Analytics and AI Department, Integrated Health Information Systems Singapore Singapore.
Christine Gia Lee AngData Analytics and AI Department, Integrated Health Information Systems Singapore Singapore.
Andy Wee An TaData Analytics and AI Department, Integrated Health Information Systems Singapore Singapore.
Steven M MillerSchool of Computing and Information Systems Singapore Management University Singapore Singapore.ORCID 0000-0001-6045-3552

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In a prior practice and policy article published in Healthcare Science, we introduced the deployed application of an artificial intelligence (AI) model to predict longer-term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home (H2H) program that has been operating since 2017. In this follow on practice and policy article, we further elaborate on Singapore's H2H program and care model, and its supporting AI model for multiple readmission prediction, in the following ways: (1) by providing updates on the AI and supporting information systems, (2) by reporting on customer engagement and related service delivery outcomes including staff-related time savings and patient benefits in terms of bed days saved, (3) by sharing lessons learned with respect to (i) analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants, (ii) balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables, and (iii) the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems, (4) by highlighting how this H2H effort supported broader Covid-19 response efforts across Singapore's public healthcare system, and finally (5) by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards. For the convenience of the reader, some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.

Indexed as

healthcare technologyhospital to home community carehospital to home lessons learnedintegrated caremachine learning in healthcaremultiple readmissions AI prediction modeltransitional care

Identifiers

PMID38939111
PMCPMC11080848

What OpenQuestion holds

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LicenceCC BY
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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.