{"doi":"10.1093/jamia/ocaf014","title":"Patterns of willingness to share health data with key stakeholders in US consumers: a latent class analysis","abstract":"OBJECTIVE: To identify distinct patterns in consumer willingness to share health data with various stakeholders and analyze characteristics across consumer groups. MATERIALS AND METHODS: Data from the Rock Health Digital Health Consumer Adoption Survey from 2018, 2019, 2020, and 2022 were analyzed. This study comprised a Census-matched representative sample of U.S. adults. Latent class analysis (LCA) identified groups of respondents with similar data-sharing attitudes. Groups were compared by sociodemographics, health status, and digital health utilization. RESULTS: We identified three distinct LCA groups: (1) Wary (36.8%), (2) Discerning (47.9%), and (3) Permissive (15.3%). The Wary subgroup exhibited reluctance to share health data with any stakeholder, with predicted probabilities of willingness to share ranging from 0.07 for pharmaceutical companies to 0.34 for doctors/clinicians. The Permissive group showed a high willingness, with predicted probabilities greater than 0.75 for most stakeholders except technology companies and government organizations. The Discerning group was selective, willing to share with healthcare-related entities and family (predicted probabilities >0.62), but reluctant to share with other stakeholders (predicted probabilities <0.29). Individual characteristics were associated with LCA group membership. DISCUSSION: Findings highlight a persistent trust in traditional healthcare providers. However, the varying willingness to share with non-traditional stakeholders suggests that while some consumers are open to sharing, others remain hesitant and selective. Data privacy policies and practices need to recognize and respond to multifaceted and stakeholder-specific attitudes. CONCLUSION: LCA reveals significant heterogeneity in health data-sharing attitudes among U.S. consumers, providing insights to inform the development of data privacy policies.","journal":"Journal of the American Medical Informatics Association","year":2025,"id":530312,"datarank":0.28664296408489787,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.017879043700689604,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.017879043700689604,"corpus_percentile":43.60640519842191,"corpus_rank":7291,"citation_count":5,"citer_count":4,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.648,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1410034,"name":"Xi Zhu","orcid":"0000-0003-2194-7327","position":1,"is_corresponding":false},{"id":337052,"name":"Ashwini Nagappan","orcid":"0000-0001-5518-102X","position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":null,"created_at":"2026-07-19T02:51:05.836974Z","pmid":"39873672","pmcid":"PMC12005618","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}