Evidence map›Paper›PMID 36112409›Full record

ArticleJMIR cancer2022

Public Deliberation Process on Patient Perspectives on Health Information Sharing: Evaluative Descriptive Study.

Minakshi Raj, Kerry Ryan, Paige Nong, Karen Calhoun, M Grace Trinidad, Raymond De Vries, Melissa Creary, Kayte Spector-Bagdady, Sharon L R Kardia, Jodyn Platt

Abstract read
In one paragraph

Article in JMIR cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Building trust: public priorities for health care AI labeling.The American journal of managed care · 2026
    Article
  5. Article
  6. Article
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  9. Public perspectives on the use of different data types for prediction in healthcare.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
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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

10 authors.

Minakshi RajDepartment of Kinesiology and Community Health, University of Illinois at Urbana Champaign, Champaign, IL, United States.ORCID https://orcid.org/0000-0002-1457-7850
Kerry RyanCenter for Bioethics and Social Sciences in Medicine, University of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-0582-3693
Paige NongSchool of Public Health, University of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-2849-9005
Karen CalhounMichigan Institute for Clinical & Health Research, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-5011-9718
M Grace TrinidadNational Hemophilia Program Coordinating Center, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-0615-767X
Raymond De VriesCenter for Bioethics and Social Sciences in Medicine, University of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0003-3087-3040
Melissa CrearySchool of Public Health, University of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0001-7904-3377
Kayte Spector-BagdadyCenter for Bioethics and Social Sciences in Medicine, University of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-5851-6224
Sharon L R KardiaSchool of Public Health, University of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-9853-3379
Jodyn PlattCenter for Bioethics and Social Sciences in Medicine, University of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0003-4902-4903

Funding

Public trust of artificial intelligence in the precision CDS health ecosystem - Administrative SupplementR01EB030492 · NIBIB · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI PLATT, JODYN ELIZABETH · 2021 to 2024
$3.1M
The Lifecycle of Health Data: Polices and PracticesR01CA214829 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KARDIA, SHARON L, PLATT, JODYN ELIZABETH · 2018 to 2021
$2.4M
NCI NIH HHS R01 CA214829NIBIB NIH HHS R01 EB030492
6 · The paper itself

Abstract

backgroundPrecision oncology is one of the fastest-developing domains of personalized medicine and is one of many data-intensive fields. Policy for health information sharing that is informed by patient perspectives can help organizations align practice with patient preferences and expectations, but many patients are largely unaware of the complexities of how and why clinical health information is shared.

objectiveThis paper evaluates the process of public deliberation as an approach to understanding the values and preferences of current and former patients with cancer regarding the use and sharing of health information collected in the context of precision oncology.

methodsWe conducted public deliberations with patients who had a current or former cancer diagnosis. A total of 61 participants attended 1 of 2 deliberative sessions (session 1, n=28; session 2, n=33). Study team experts led two educational plenary sessions, and trained study team members then facilitated discussions with small groups of participants. Participants completed pre- and postdeliberation surveys measuring knowledge, attitudes, and beliefs about precision oncology and data sharing. Following informational sessions, participants discussed, ranked, and deliberated two policy-related scenarios in small groups and in a plenary session. In the analysis, we evaluate our process of developing the deliberative sessions, the knowledge gained by participants during the process, and the extent to which participants reasoned with complex information to identify policy preferences.

resultsThe deliberation process was rated highly by participants. Participants felt they were listened to by their group facilitator, that their opinions were respected by their group, and that the process that led to the group's decision was fair. Participants demonstrated improved knowledge of health data sharing policies between pre- and postdeliberation surveys, especially regarding the roles of physicians and health departments in health information sharing. Qualitative analysis of reasoning revealed that participants recognized complexity, made compromises, and engaged with trade-offs, considering both individual and societal perspectives related to health data sharing.

conclusionsThe deliberative approach can be valuable for soliciting the input of informed patients on complex issues such as health information sharing policy. Participants in our two public deliberations demonstrated that giving patients information about a complex topic like health data sharing and the opportunity to reason with others and discuss the information can help garner important insights into policy preferences and concerns. Data on public preferences, along with the rationale for information sharing, can help inform policy-making processes. Increasing transparency and patient engagement is critical to ensuring that data-driven health care respects patient autonomy and honors patient values and expectations.

Indexed as

cancerdata sharinghealth informationhealth information exchangeinformationoncologypatient engagementpersonalized medicinepolicyprecision oncologypublic deliberationpublic preferencesharing

Identifiers

PMID36112409
PMCPMC9526123

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.