Evidence map›Paper›PMID 37670961›Full record

ArticleInternational journal of population data science2023

Developing a linked electronic health record derived data platform to support research into healthy ageing.

Nadine E Andrew, Richard Beare, Tanya Ravipati, Emily Parker, David Snowdon, Kim Naude, Velandai Srikanth

Abstract read
In one paragraph

Article in International journal of population data science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Observational
  6. Observational
  7. Article
  8. Feasibility of implementing a codesigned patient reported outcome measures (PROMs) collection system for older adults in acute and sub-acute hospital settings.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Healthcare consumer acceptability of routine use of the EQ-5D-5L in clinical care: a cross-sectional survey.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
    Article
  13. Using a multi-stakeholder co-design process to develop a health service organisation-wide patient reported outcome measure collection system.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
    Article
  14. Article
  15. Article
  16. Observational
  17. 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.

Nadine E AndrewNational Centre for Healthy Ageing, Frankston, Victoria, Australia.
Richard BeareNational Centre for Healthy Ageing, Frankston, Victoria, Australia.
Tanya RavipatiDepartment of Medicine, Peninsula Clinical School, Central Clinical School, Monash University, Frankston, Victoria, Australia.
Emily ParkerDepartment of Medicine, Peninsula Clinical School, Central Clinical School, Monash University, Frankston, Victoria, Australia.
David SnowdonNational Centre for Healthy Ageing, Frankston, Victoria, Australia.
Kim NaudeDepartment of Medicine, Peninsula Clinical School, Central Clinical School, Monash University, Frankston, Victoria, Australia.
Velandai SrikanthNational Centre for Healthy Ageing, Frankston, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Digitalisation of Electronic Health Record (EHR) data has created unique opportunities for research. However, these data are routinely collected for operational purposes and so are not curated to the standard required for research. Harnessing such routine data at large scale allows efficient and long-term epidemiological and health services research. Objectives: To describe the establishment a linked EHR derived data platform in the National Centre for Healthy Ageing, Melbourne, Australia, aimed at enabling research targeting national health priority areas in ageing. Methods: Our approach incorporated: data validation, curation and warehousing to ensure quality and completeness; end-user engagement and consensus on the platform content; implementation of an artificial intelligence (AI) pipeline for extraction of text-based data items; early consumer involvement; and implementation of routine collection of patient reported outcome measures, in a multisite public health service. Results: Data for a cohort of >800,000 patients collected over a 10-year period have been curated within the platform's research data warehouse. So far 117 items have been identified as suitable for inclusion, from 11 research relevant datasets held within the health service EHR systems. Data access, extraction and release processes, guided by the Five Safes Framework, are being tested through project use-cases. A natural language processing (NLP) pipeline has been implemented and a framework for the routine collection and incorporation of patient reported outcome measures developed. Conclusions: We highlight the importance of establishing comprehensive processes for the foundations of a data platform utilising routine data not collected for research purposes. These robust foundations will facilitate future expansion through linkages to other datasets for the efficient and cost-effective study of health related to ageing at a large scale.

Indexed as

Healthy AgingAgingArtificial IntelligenceAustraliaElectronic Health RecordsHumansageingbig datadata linkageelectronic health recordlongitudinal cohort

Identifiers

PMID37670961
PMCPMC10476553

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.