A systematic review of barriers to data sharing in public health
Open accessAbstract
BACKGROUND: In the current information age, the use of data has become essential for decision making in public health at the local, national, and global level. Despite a global commitment to the use and sharing of public health data, this can be challenging in reality. No systematic framework or global operational guidelines have been created for data sharing in public health. Barriers at different levels have limited data sharing but have only been anecdotally discussed or in the context of specific case studies. Incomplete systematic evidence on the scope and variety of these barriers has limited opportunities to maximize the value and use of public health data for science and policy. METHODS: We conducted a systematic literature review of potential barriers to public health data sharing. Documents that described barriers to sharing of routinely collected public health data were eligible for inclusion and reviewed independently by a team of experts. We grouped identified barriers in a taxonomy for a focused international dialogue on solutions. RESULTS: Twenty potential barriers were identified and classified in six categories: technical, motivational, economic, political, legal and ethical. The first three categories are deeply rooted in well-known challenges of health information systems for which structural solutions have yet to be found; the last three have solutions that lie in an international dialogue aimed at generating consensus on policies and instruments for data sharing. CONCLUSIONS: The simultaneous effect of multiple interacting barriers ranging from technical to intangible issues has greatly complicated advances in public health data sharing. A systematic framework of barriers to data sharing in public health will be essential to accelerate the use of valuable information for the global good.
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Existing medical data is not fully exploited by machine learning primarily because it sits in data silos and privacy concerns restrict access to this data.
“Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data.”
From The future of digital health with federated learning (2020) · cites this paper as [6]
The Background describes routinely collected public-health data as underused, and Results > Legal barriers says privacy concerns can lead to restrictive data-access policies. Results > Technical barriers describes difficulties integrating datasets. The paper does not establish that machine learning is the affected use or that data silos and privacy are the primary reasons medical data is underexploited. (LLM, conf=0.87)
Experimental — accuracy not yet measuredHealth data is highly sensitive and its usage is tightly regulated, making large databases for AI training hard to obtain.
“Data like this is hard to obtain, because health data is highly sensitive and its usage is tightly regulated6.”
From The future of digital health with federated learning (2020) · cites this paper as [6]
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- Authors
- van Panhuis, W. G., Paul, P., Emerson, C., Grefenstette, J., Wilder, R., Herbst, A. J., Heymann, D., & Burke, D. S.
- Affiliations
- University of Pittsburgh (US)
- University Health Network (CA)
- Gates Foundation (US)
- Chatham House (GB)
- Venue
- BMC Public Health
- Published
- 2014-11-05
- Volume
- 14
- Issue
- 1
- Pages
- 1144-1144
- ISSN
- 1471-2458 (ISSN-L)
- PMC
- PMC4239377
- OpenAlex
- W2123737882
- Cited by
- 579
- References
- 71
- Citations by year
- 2026: 53 · 2025: 77 · 2024: 76 · 2023: 86 · 2022: 51 · 2021: 49 · 2020: 48 · 2019: 40 · 2018: 39 · 2017: 34 · 2016: 19 · 2015: 7
- FWCI
- 7.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- D-Scholarship@Pitt (University of Pittsburgh) (acceptedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- Funders
- Bill and Melinda Gates Foundation (49276)
- Wellcome Trust (097410)
- National Institutes of Health (5u54gm088491-10)
- National Institute of General Medical Sciences (U54 GM088491, 5U54GM088491)
- Topics
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › General Health Professions › Public Health Policies and Education
- Keywords
- public health data sharing, barriers to data sharing, routinely collected health data, health information systems, data sharing policy
- MeSH terms
- Communication Barriers, Humans, Public Health, Global Health, Information Dissemination
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