Big Data for Public Health Policy-Making: Policy Empowerment

被引:13
作者
Mahlmann, Laura [1 ,2 ]
Reumann, Matthias [1 ,3 ]
Evangelatos, Nikolaos [1 ,4 ]
Brand, Angela [1 ,5 ]
机构
[1] Maastricht Univ, United Nations Univ, Maastricht Econ & Social Res Inst Innovat & Techn, Maastricht, Netherlands
[2] Univ Basel, Psychiat Clin, Ctr Affect Stress & Sleep Disorders, Basel, Switzerland
[3] IBM Res, Zurich Lab, Ruschlikon, Switzerland
[4] Paracelsus Med Univ, Intens Care Med Unit, Dept Resp Med Allergol & Sleep Med, Nurnberg, Germany
[5] Maastricht Univ, Dept Int Hlth, Fac Hlth Med & Life Sci, Maastricht, Netherlands
关键词
Big data analytics; Cross-border healthcare; Data linkage; E-health; Ethical and regulatory frameworks; Health data cooperatives; Prevention; Public health policies; Secondary use of data; CARE;
D O I
10.1159/000486587
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Digitization is considered to radically transform healthcare. As such, with seemingly unlimited opportunities to collect data, it will play an important role in the public health policymaking process. In this context, health data cooperatives (HDC) are a key component and core element for public health policy-making and for exploiting the potential of all the existing and rapidly emerging data sources. Being able to leverage all the data requires overcoming the computational, algorithmic, and technological challenges that characterize today's highly heterogeneous data landscape, as well as a host of diverse regulatory, normative, governance, and policy constraints. The full potential of big data can only be realized if data are being made accessible and shared. Treating research data as a public good, creating HDC to empower citizens through citizen-owned health data, and allowing data access for research and the development of new diagnostics, therapies, and public health policies will yield the transformative impact of digital health. The HDC model for data governance is an arrangement, based on moral codes, that encourages citizens to participate in the improvement of their own health. This then enables public health institutions and policymakers to monitor policy changes and evaluate their impact and risk on a population level. (c) 2018 S. Karger AG, Basel
引用
收藏
页码:312 / 320
页数:9
相关论文
共 23 条
  • [1] [Anonymous], 2015, Health Data Governance: Privacy, Monitoring and Research
  • [2] [Anonymous], NATURE
  • [3] Becoming partners, retaining autonomy: ethical considerations on the development of precision medicine
    Blasimme, Alessandro
    Vayena, Effy
    [J]. BMC MEDICAL ETHICS, 2016, 17 : 1 - 8
  • [4] Buchholtz S, 2014, BIG OPEN DATA EUROPE, P10
  • [5] Spatio-temporal information and knowledge representation of disease incidence and respective intervention strategies
    Davis, Matthew
    Von Cavallar, Stefan
    Wyres, Kelly L.
    Reumann, Matthias
    Sepulveda, Martin J.
    Rogers, Priscilla
    [J]. E-HEALTH - FOR CONTINUITY OF CARE, 2014, 205 : 1173 - 1177
  • [6] Goudey B, 2015, HEALTH INF SCI SYST, V3, DOI 10.1186/2047-2501-3-S1-S3
  • [7] Health Data Cooperatives - Citizen Empowerment
    Hafen, E.
    Kossmann, D.
    Brand, A.
    [J]. METHODS OF INFORMATION IN MEDICINE, 2014, 53 (02) : 82 - 86
  • [8] European healthcare systems readiness to shift from 'one-size fits all' to personalized medicine
    Halfmann, Sebastian Schee Genannt
    Evangelatos, Nikolaos
    Schroeder-Baeck, Peter
    Brand, Angela
    [J]. PERSONALIZED MEDICINE, 2017, 14 (01) : 63 - 74
  • [9] Höchtl J, 2016, J ORG COMP ELECT COM, V26, P147, DOI 10.1080/10919392.2015.1125187
  • [10] Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients
    Jensen, Anders Boeck
    Moseley, Pope L.
    Oprea, Tudor I.
    Ellesoe, Sabrina Gade
    Eriksson, Robert
    Schmock, Henriette
    Jensen, Peter Bjodstrup
    Jensen, Lars Juhl
    Brunak, Soren
    [J]. NATURE COMMUNICATIONS, 2014, 5