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Horizon Scan on Related Policy for CeIC Sandpit.


This Policy Section provides summary detail on evidence reviewed, and details how it relates to the topic of interoperability linking to health informatics and policy. It is an opinion piece on material that the CeIC has reviewed over the past year as part of its research agenda, which draws heavily from related work in the health informatics standards community.  The resources therefore provide a snap shot of related policy rather than a comprehensive overview of policy in general. The purpose of this section as is the case with other sections in the sandpit is to provide the reader with some signposting on what we consider are useful resources to support why interoperability is a critical foundation for health informatics and emerging policy.

We have selected three key areas to present our insights from preparation and readiness for artificial intelligence, emerging digital and health service and health informatics policy and standards. I include a quote from Turing and think we have some ways to go before this statement is reality…..

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I believe that at the end of the {20th} century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted Alan Turing, 1912-54

Readiness for AI

The prevalence of an aging population and the associated increase in chronic disease and disability has led to policy makers recommending a shift in care delivery to self-management patient centric support models of care [1, 2]. Underpinning evidence, which signals a diminishing health workforce, is a rapid deployment in digital services and increasing recognition for deployment of Artificial Intelligence (AI), Machine Learning (ML) and Robotics to optimise service delivery [3, 4].  Implementation of such technologies to become mainstream in society raises a number of questions for targeted action.  Risk reduction particularly around consent or lack of control of data management, potential bias and discrimination are some examples of key issues, which policy administrators are rigoursly engaged on. Key principles underpinning the discussion include Trustful AI and Ethical AI, and there has been a significant number of European online workshops and discussions published on the topic on the past 12 months [5, 6]. Cross cutting themes to address these aforementioned principles include strong governance frameworks, which can provide guidance on regulation data privacy and security issues.  It is anticipated that AI will improve health systems performance in accuracy, productivity, and workflow, but what impact will it have on existing defined roles for health care professionals, and how many health care professional are actively engaged in debating and development of such systems determining how privacy, bias and security can be managed ethically [7,8]. 

Some recent policy actions instigated by the WHO include developing a standardised assessment framework for the evaluation of AI for health assessment diagnosis, and treatment [9, 10].  Within the domain of health informatics, the short to medium term goals are signposted to address data structuring, data quality and those related standards, which can act as a springboard for future AI adoption uptake and use in the health and social care. As healthcare systems become more complex integrated and connected, policy makers scan the Horizon and deliberate on how System of Systems (SoS) and Systems of Interest (SoI) will operate. Such  terms are new to health care professionals but briefly they relate to machine learning algorithms, which collect data, and which operate and interact with other systems in an operational environment which can combine the data from two or more SoI to provide a recommendation or result to the end user. Further information on this topic is available from Systems and software System of systems (SoS) considerations in life cycle stages of a system ISO/IEC/IEEE 21839:2019 [11,12]. If machine algorithms are making decisions based on the data in the SoI it is therefore critical that the data is of high quality and available. My experience in dealing with heterogeneous databanks to data would suggest that we have a long way to go before we can endorse such systems. 

EU Related Policy and Standards

Focusing on data and specifically data quality, the recently published European Strategy for Data identifies a single European Data Space as a priority where data can flow within the EU and across sectors [13]. The Commission describe the value proposition of this single market as an open data space where personal data as well as non-personal data are secure and businesses have access to high-quality industrial data. Both required as a critical stepping-stone to move beyond siloed systems, advance AI across EU member states, and decrease the development of heterogeneous databanks in the future. Some initial progress in this domain includes the DCAT Application Profile for Data Portals in Europe (DCAT-AP). DCAT is a specification based on the Data Catalogue Vocabulary (DCAT) developed by W3C facilities that data consumers can easily use to source datasets [14].  We include some papers that might be useful to provide a backdrop to the topic here and in the Research Section of this resource.

Digital and Health 

Other topics, which we include under this policy section , relate to Software as a Medical Device (SaMD). The pandemic has accelerated remote patient monitoring with both lifestyle and medical device use. Key to note is the new legislation, which will be applicable within the EU member states from May 26 2021 [15]. Additional sources of standards and regulations relating to the SaMD are available from the Health Products Regulatory Authority [16].   Some standards apply to all medical devices irrespective of the type of device or level of risk [17, 18].  For most products, quality management and risk management are very important and manufacturers must demonstrate suitable systems are in place to manage quality during design, development and production.  The evaluation of risk for the use of the device and during design and manufacture is also a key requirement [Source: NSAI HISC Group Work].   Other resources we recommend that you review include resources from the WHO classification of digital health interventions published in 2018, this classification and associated resources categorizes the different ways in which digital and mobile technologies are being used to support health system needs [19]. We conclude the policy section with a PESTEL analysis of what we consider key drivers and factors which influence our research in CeIC and which the linked resources on this Policy Section are related to. 

Diagram for Anne PESTEL 04.03.jpg

1. World Health Organisation. (2016). Framework for people centred integrated care services [Sixty Ninth World Health Assembly].

2. State of the World’s Nursing Report (2020.). Retrieved March 3rd, 2021, from 

3. Global Strategy on Human Resources for Health: Workforce 2030 (2016) Retrieved March 3rd , 2021 , from 

4. European Commission  WHITE PAPER On Artificial Intelligence - A European approach to excellence and trust (2020) Retrieved March 3rd , 2021 , from

5.CEN ELEC Artificial Intelligence in healthcare: paving the way with standardization Online Workshop Event Tuesday 27 October 2020 Podcasts and presentations available from

6. Wiegand, T., Krishnamurthy, R., Kuglitsch, M., Lee, N., Pujari, S., Salathé, M., Wenzel, M., & Xu, S. (2019). WHO and ITU establish benchmarking process for artificial intelligence in health. The Lancet, 394(10192), 9–11.

7. Risling, T., & Low, C. (2019). Advocating for Safe, Quality and Just Care: What Nursing Leaders Need to Know about Artificial Intelligence in Healthcare Delivery. Canadian Journal of Nursing Leadership, 32(2), 31–45.

8. Booth, R., Strudwick, G. McMurray, J., Chan, R. Cotton, K. and Cooke, S The Future of Nursing Informatics in a Digitally-Enabled World In Hussey, P., & Kennedy, M. A. (Eds.). (2021). Introduction to Nursing Informatics (5th ed.). Springer International Publishing. pp 395-417

9. World Health Organisation / ITU Focus Group WHO Retrieved March 3rd , 2021 , from

10. Wiegand, T., Krishnamurthy, R., Kuglitsch, M., Lee, N., Pujari, S., Salathé, M., Wenzel, M., & Xu, S. (2019). WHO and ITU establish benchmarking process for artificial intelligence in health. The Lancet, 394(10192), 9–11. Retrieved March 3rd , 2021 ,

11. International Standards Organisation ISO/IEC/IEEE 21839:2019
Systems and software engineering — System of systems (SoS) considerations in life cycle stages of a system

12. International Standards Organisation Technical Committee 215 AHG 2 Working Group Report Application of AI Technologies in Health Informatics September 2020  Retrieved from NSAI HISC Portal October 12th 2020 

13. European data strategy. (2020.). [Text]. European Commission - European Commission. Retrieved March 2, 2021, from

14. ALEKSANDROVA, Z. (2016, November 25). DCAT Application Profile for data portals in Europe [Text]. ISA2 - European Commission. Retrieved March 3rd 2021 from

15. CARDOEN, G. (2020, June 25). Overview [Text]. Public Health - European Commission. Retrieved March 3rd 2021 from 

16. Medical Devices. (2014). Retrieved March 3, 2021, from

17. International Standard  EN ISO 13485 – Medical Devices – Quality management systems – requirements for regulatory purposes

18. International Standard I.S EN ISO 14971 – Medical devices – Application of risk management to medical devices.

19. WHO | Classification of digital health interventions v1.0. (2019.). WHO. Retrieved March 2, 2021, from 

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