In this issue: prerequisites for precision medicine are genomics, computerised medical record systems and big data analytics

Simon de Lusignan

Professor of Primary Care and Clinical Informatics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK

Editor-in-Chief, Journal of Innovation in Health Informatics

Copyright © 2017 The Author(s). Published by BCS, The Chartered Institute for IT under Creative Commons license

Keywords: medical records systems, computerised, telemedicine, information science

Your editor’s choice in this issue of the journal is a paper by Ronquillo et al.1 The key message from this thought-provoking paper is that genomics, computerised medical record (CMR) systems and big data analytics are prerequisites for the delivery of precision medicine (Figure 1). All three need to be more interoperable if we are to make progress. They urge us to plug the gaps in our systems if we are to deliver the allure of personalised medicine. For example, there are a large number of genetic tests that are impossible to code in the systematised nomenclature for medicine – clinical terms (SNOMED CT). The strengths and limitations of the coding systems area is something we have previously discussed in this journal.2,3

Figure 1 The prerequisites to deliver precision medicine are interoperable genomics big and CMR systems and the application of big data analytics


This issue of the Journal of Innovation in Health Informatics comes out in the same month as we see the launch of the Faculty of Clinical Informatics (FCI).4 Your editor is proud to be one of the foundation fellows of this new faculty. FCI provides the opportunity to give our discipline a further boost. My Editorial team and I would very much like this journal to take a role in promoting learning from the new FCI, and for this knowledge to be disseminated though the pages of this journal.


Thomas describes how he made use of Google’s free cloud-based services to create a rota management system for a complex on-call system. This was piloted over a two-year period. Leave requests and swaps appear to be so much more straightforward… …customised free tools may provide a cost-effective alternative to the more traditional approach of developing an SQL database.5


An excellent Canadian primary care paper describes how the rate of referral of females is one-and-a-half times that of males. Importantly, they conclude that 86% of the variation in referral is explained by the patient level and only 16% is by the practice level factors. These findings have important implications for health service management. There appears to be relatively little scope to affect the referral numbers by practice level interventions.6


We commend the use of formal modelling methods – which make observations and findings about clinical findings much more accessible to software engineers.7 We publish a paper that takes a modelling/use case approach about how to incorporate eHealth data into CMR systems.8


Chaudhry et al. 9 report how UK hospital data are used more and more in research. They are used on their own, but more and more studies use hospital data linked to primary care data. Primary care data are good for providing information about the process of care, but hospital data are useful in identifying health outcomes and costs.


The second study from Canada sets out how there are perhaps more permutations/different combinations of multi-morbid disease that perhaps we might anticipate.10 From a database of over a million people they found around 6,000 different combinations in females and 4,000 in men. When they looked at more detailed permutations, the numbers were approximately 15,000 and 10,000, respectively, for females and males. Knowing there is such a large number of different combinations is important and has implications for our potential to offer stratified, let alone precision, medicine. The CMR systems we look to support the delivery care to people with multi-morbidity – must not only offer more minimally disruptive medicine,11 they must also potentially collate data from vast numbers of possible combinations of conditions.


1. Ronquillo JG, Weng C and Lester WT. Assessing the readiness of precision medicine interoperability: an exploratory study of the National Institutes of Health Genetic Testing Registry. Journal of Innovation in Health Informatics 2017;24(4):323–328.

2. de Lusignan S. Codes, classifications, terminologies and nomenclatures: definition, development and application in practice. Informatics in Primary Care 2005;13(1):65–70.

3. de Lusignan S, Chan T and Jones S. Large complex terminologies: more coding choice, but harder to find data--reflections on introduction of SNOMED CT (Systematized Nomenclature of Medicine--Clinical Terms) as an NHS standard. Informatics in Primary Care 2011;19(1):3–5.

4. de Lusignan S, Barlow J and Scott PJ. Genesis of a UK Faculty of Clinical Informatics at a time of anticipation for some, and ruby, golden and diamond celebrations for others. Journal of Innovation in Health Informatics 2017;24(4):344–346.

5. Thomas PBM. Bespoke automation of medical workforce rostering using Google’s free cloud applications. Journal of Innovation in Health Informatics 2017;24(4):334–338.

6. Ryan BL, Shadd J, Maddocks H, Stewart M, Thind A and Terry AL. Methods to describe referral patterns in a Canadian primary care electronic medical record database: modelling multi-level count data. Journal of Innovation in Health Informatics 2017;24(4):311–316.

7. de Lusignan S, Cashman J, Poh N, Michalakidis G, Mason A, Desombre T, et al. Conducting requirements analyses for research using routinely collected health data: a model driven approach. Studies in Health Technology and Informatics 2012;180:1105–7.

8. Paterson M, McAulay A and McKinstry B. Integrating third-party telehealth records with the general practice electronic medical record system: a use case approach. Journal of Innovation in Health Informatics 2017;24(4):317–322.

9. Chaudhry Z, Mannan F, Gibson-White A, Syed U, Majeed A and Ahmed S. Research outputs of England’s Hospital Episode Statistics (HES) database: a bibliometric analysis. Journal of Innovation in Health Informatics 2017;24(4):329–333.

10. Nicholson K, Bauer M, Terry AL, Fortin M, Williamson T and Thind A. The multimorbidity cluster analysis tool: identifying combinations and permutations of multiple chronic diseases using a record-level computational analysis. Journal of Innovation in Health Informatics 2017;24(4):339–343.

11. Schattner P, Barker F and de Lusignan S. Minimally disruptive medicine is needed for patients with multimorbidity: time to develop computerised medical record systems to meet this requirement. Journal of Innovation in Health Informatics 2015;22(1):250–54. doi: 10.14236/jhi.v22i1.136.


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