Empowering quality data – the Gordian knot of bringing real innovation into healthcare system

Abstract Objectives The introduction of Personalised Medicine (PM) into healthcare systems could benefit from a clearer understanding of the distinct national and regional frameworks around the world. Recent engagement by international regulators on maximising the use of real-world evidence (RWE) has highlighted the scope for improving the exploitation of the treasure-trove of health data that is currently largely neglected in many countries. The European Alliance for Personalised Medicine (EAPM) led an international study aimed at identifying the current status of conditions. Methods A literature review examined how far such frameworks exist, with a view to identifying conducive factors – and crucial gaps. This extensive review of key factors across 22 countries and 5 regions revealed a wide variety of attitudes, approaches, provisions and conditions, and permitted the construction of a comprehensive overview of the current status of PM. Based on seven key pillars identified from the literature review and expert panels, the data was quantified, and on the basis of further analysis, an index was developed to allow comparison country by country and region by region. Results The results show that United States of America is leading according to overall outcome whereas Kenya scored the least in the overall outcome. Conclusions Still, common approaches exist that could help accelerate take-up of opportunities even in the less prosperous parts of the world.

[1]  J. Reis-Filho,et al.  Delivering precision oncology to patients with cancer , 2022, Nature Medicine.

[2]  Mahsa Shabani Will the European Health Data Space change data sharing rules? , 2022, Science.

[3]  D. Kalra,et al.  Factors Affecting Citizen Trust and Public Engagement Relating to the Generation and Use of Real-World Evidence in Healthcare , 2022, International journal of environmental research and public health.

[4]  F. Rockhold,et al.  Real-World Evidence for Regulatory Decision-Making: Guidance From Around the World. , 2022, Clinical therapeutics.

[5]  Kornelia M. Batko,et al.  The use of Big Data Analytics in healthcare , 2022, J. Big Data.

[6]  P. Schröder-Bäck,et al.  Trust and The Acquisition and Use of Public Health Information , 2021, Health Care Analysis.

[7]  D. Malone,et al.  Payer perceptions of the use of real-world evidence in oncology-based decision making , 2021, Journal of managed care & specialty pharmacy.

[8]  A. Hincapie,et al.  PNS118 Real World Evidence (RWE) Use in Latin America Healthcare Decision Making: An Stakeholder Survey Analysis. , 2021, Value in Health.

[9]  Hao Hu,et al.  Integrating Real-World Evidence in the Regulatory Decision-Making Process: A Systematic Analysis of Experiences in the US, EU, and China Using a Logic Model , 2021, Frontiers in Medicine.

[10]  Mariam M Hamza,et al.  The capacity of primary health care facilities in Saudi Arabia: infrastructure, services, drug availability, and human resources , 2021, BMC Health Services Research.

[11]  J. Byrnes,et al.  Health Technology Assessment in Australia: The Pharmaceutical Benefits Advisory Committee and Medical Services Advisory Committee. , 2021, Value in health regional issues.

[12]  J. Crompvoets,et al.  Open health data: Mapping the ecosystem , 2021, Digital health.

[13]  R. Dess,et al.  Integrated Survival Estimates for Cancer Treatment Delay Among Adults With Cancer During the COVID-19 Pandemic. , 2020, JAMA oncology.

[14]  Débora C. Muchaluat-Saade,et al.  Digital healthcare in Latin America , 2020, Commun. ACM.

[15]  Domingos Alves,et al.  Mapping, Infrastructure, and Data Analysis for the Brazilian Network of Rare Diseases: Protocol for the RARASnet Observational Cohort Study , 2020, JMIR research protocols.

[16]  C. Abicalaffe,et al.  Opportunities and Challenges of Value-Based Health Care: How Brazil Can Learn from U.S. Experience , 2020, Journal of managed care & specialty pharmacy.

[17]  Benjamin M Marlin,et al.  Digitizing clinical trials , 2020, npj Digital Medicine.

[18]  S. Germann,et al.  Realising the benefits of data driven digitalisation without ignoring the risks: health data governance for health and human rights. , 2020, mHealth.

[19]  Tomasz Janowski,et al.  Data governance: Organizing data for trustworthy Artificial Intelligence , 2020, Gov. Inf. Q..

[20]  G. Ciliberto,et al.  Propelling Health Care into the Twenties , 2020, Biomedicine Hub.

[21]  F. Cardoso,et al.  A multi-stakeholder approach in optimising patients’ needs in the benefit assessment process of new metastatic breast cancer treatments☆ , 2020, Breast.

[22]  L. Dayton How South Korea made itself a global innovation leader , 2020, Nature.

[23]  Gordon Liu,et al.  The Development of Health Technology Assessment in Asia: Current Status and Future Trends. , 2020, Value in health regional issues.

[24]  Tim Hulsen Sharing Is Caring—Data Sharing Initiatives in Healthcare , 2020, International journal of environmental research and public health.

[25]  N. Dreyer,et al.  Real‐world evidence to support regulatory decision‐making for medicines: Considerations for external control arms , 2020, Pharmacoepidemiology and drug safety.

[26]  Z. Kaló,et al.  Implementation of Health Technology Assessment in the Middle East and North Africa: Comparison Between the Current and Preferred Status , 2020, Frontiers in Pharmacology.

[27]  S. Shin,et al.  Views on Precision Medicine among Health Professionals in Korea: A Mixed Methods Study , 2020, Yonsei medical journal.

[28]  A. Skinner,et al.  A review of Kenya’s cancer policies to improve access to cancer testing and treatment in the country , 2020, Health Research Policy and Systems.

[29]  Prashant Goswami,et al.  A literature review of current technologies on health data integration for patient-centered health management , 2019, Health Informatics J..

[30]  Sean A Munson,et al.  Use of patient-generated health data across healthcare settings: implications for health systems , 2019, JAMIA open.

[31]  Sandeep Kaushik,et al.  Big data in healthcare: management, analysis and future prospects , 2019, Journal of Big Data.

[32]  M. Drummond,et al.  Real-World Evidence in Healthcare Decision Making: Global Trends and Case Studies From Latin America. , 2019, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[33]  Nader Al-Dewik,et al.  Genomics and Precision Medicine: Molecular Diagnostics Innovations Shaping the Future of Healthcare in Qatar , 2019, Advances in Public Health.

[34]  Alan R. Moody,et al.  From Big Data to Precision Medicine , 2019, Front. Med..

[35]  Fakhar Shahzad,et al.  Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change , 2019, Journal of Big Data.

[36]  K. Son Understanding the adoption of new drugs decided by several stakeholders in the South Korean market: a nonparametric event history analysis , 2018, Health Economics Review.

[37]  Liezel Cilliers,et al.  Electronic health record system in the public health care sector of South Africa: A systematic literature review , 2018, African journal of primary health care & family medicine.

[38]  Adrian Towse,et al.  Real-world evidence for coverage decisions: opportunities and challenges. , 2018, Journal of comparative effectiveness research.

[39]  P. Allotey,et al.  Current landscape of personalized medicine adoption and implementation in Southeast Asia , 2018, BMC Medical Genomics.

[40]  C. Ho MS26.02 Translation of Clinical Data to Real World - North America , 2018, Journal of Thoracic Oncology.

[41]  P. Cleary,et al.  Understanding the determinants of public trust in the health care system in China: an analysis of a cross-sectional survey , 2018, Journal of health services research & policy.

[42]  P. Burnel The introduction of electronic medical records in France: More progress during the second attempt. , 2018, Health policy.

[43]  Y. Joly,et al.  South Korea: in the midst of a privacy reform centered on data sharing , 2018, Human Genetics.

[44]  Niels Peek,et al.  Three controversies in health data science , 2018, International Journal of Data Science and Analytics.

[45]  Z. Kaló,et al.  HTA Implementation in Latin American Countries: Comparison of Current and Preferred Status. , 2017, Value in health regional issues.

[46]  N. Mulder Development to enable precision medicine in Africa. , 2017, Personalized medicine.

[47]  N. Monga,et al.  Review Of Real-World Evidence To Assess The Burden Of Illness Of Mantle Cell Lymphoma , 2017 .

[48]  E. Álava,et al.  Proposal for the creation of a national strategy for precision medicine in cancer: a position statement of SEOM, SEAP, and SEFH , 2017, Clinical and Translational Oncology.

[49]  Rachel Conrad Bracken,et al.  Trust and privacy in the context of user-generated health data , 2017, Big Data Soc..

[50]  Giuseppe De Pietro,et al.  Towards Interoperability of EHR Systems: The Case of Italy , 2016, ICT4AgeingWell.

[51]  P. Marschall,et al.  Personalized Medicine in the U.S. and Germany: Awareness, Acceptance, Use and Preconditions for the Wide Implementation into the Medical Standard , 2016, Journal of personalized medicine.

[52]  G. Rajagopal,et al.  The path from big data to precision medicine , 2016 .

[53]  Sang-Soo Lee,et al.  Medical device reimbursement coverage and pricing rules in Korea: current practice and issues with access to innovation. , 2014, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[54]  Golam Sorwar,et al.  Implementation of E-health record systems in Australia , 2013 .

[55]  Isabel de la Torre Díez,et al.  EHR Systems in the Spanish Public Health National System: The Lack of Interoperability between Primary and Specialty Care , 2013, Journal of Medical Systems.

[56]  D. ssiers Kenya , 1985, The Lancet.