Evidence map›Paper›PMID 31993507›Full record

ArticleNPJ digital medicine2020

SMART Markers: collecting patient-generated health data as a standardized property of health information technology.

Raheel Sayeed, Daniel Gottlieb, Kenneth D Mandl

Abstract read
In one paragraph

Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

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

  1. Remote symptom monitoring integrated into electronic health records: A systematic review.Journal of the American Medical Informatics Association : JAMIA · 2020
    Pooled it
  2. Trial
  3. Review
  4. A standards-based approach to digital health research: implementing the people heart study.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024
    Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. HL7 FHIR-based tools and initiatives to support clinical research: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  16. Article
  17. Article
  18. The technical landscape for patient-centered CDS: progress, gaps, and challenges.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  19. Article
  20. 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

3 authors.

Raheel Sayeed1Computational Health Informatics Program, Boston Children's Hospital, Boston, MA USA.ORCID 0000-0001-8810-2471
Daniel Gottlieb1Computational Health Informatics Program, Boston Children's Hospital, Boston, MA USA.
Kenneth D Mandl1Computational Health Informatics Program, Boston Children's Hospital, Boston, MA USA.ORCID 0000-0002-9781-0477

Funding

Instrumenting the Delivery System for a Genomics Research Information CommonsU01TR002623 · NCATS · BOSTON CHILDREN'S HOSPITAL · PI MANDL, KENNETH D. · 2019 to 2024
$8.6M
Improving Patient Reported Outcome Data for Research through Seamless Integration of the PROMIS Toolkit into EHR WorkflowsU01TR001806 · NCATS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI STARREN, JUSTIN B. · 2016 to 2019
$6.3M
NCATS NIH HHS U01 TR001806NCATS NIH HHS U01 TR002623
6 · The paper itself

Abstract

A patient-centered health system needs precise computable measurements to derive value. While validated patient-reported outcomes (PROs) are increasingly used in trials, their adoption in care remains limited and generally separated from the medical record. Further, absence of systematic processes for patient-led data submission excludes valuable data from digital devices that can potentially aid in contextualizing health status. With prior experience in developing apps for the Patient-Reported Outcomes Measurement Information System (PROMIS), we sought to make collecting patient-generated health data (PGHD) a fundamental property of health information technology at scale, and in an interoperable, standards-compliant fashion. We build upon the open SMART on FHIR (Fast Health Interoperability Resources) specification to create SMART Markers-a mobile device software framework encapsulating functionality needed for rapid deployment of both patient- and practitioner-facing PGHD apps. We refactored previously developed PROMIS apps to use SMART Markers for handling PGHD-request creation, on-device administration, and generation of a variety of PGHD types and submission of results to a FHIR server. Validation and conformance tests were performed on the generated output and app-reusability was demonstrated across two demo servers. App developers can import SMART Markers into their existing or new apps to readily leverage an interoperable PGHD capturing functionality out of the box, without having to reinvent the wheel. Our approach enables the creation of health system integrated, context-specific experiences for both patients and practitioners.

Indexed as

Health careTranslational research

Identifiers

PMID31993507
PMCPMC6978521

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.