Medication management
alert could be seamlessly embedded within existing ePrescribing workflows.
Interoperability standards Interoperability standards, such as FHIR, will play a critical role in making genomics a practicable and effective reality for frontline clinical services. FHIR will enable the sharing and distribution of structured and coded data across services and systems (e.g., EPR, laboratory, analytics, AI, and decision support) to allow for their processing, display, and communication in a contextual and meaningful manner. For example, a genomic scientist will want a different view of detail and presentation of a patient’s results compared to a frontline secondary care doctor or general practitioner. Figure 3 outlines a unified data and interoperability platform that would be required to fulfil the complexities and challenges presented by genomics and pharmacogenomics for frontline clinical services. While still evolving, the existing genomic FHIR
standards hold great potential and promise (HL7 FHIR Genomics Resource).3
Artificial intelligence The rapidly emerging and increasingly sophisticated Artificial Intelligence (AI) domain will also present a multitude of new opportunities to improve the safety and efficacy of medications for use by healthcare services and patients. For pharmacogenomics, Machine Learning (ML) shows promise for highlighting further insights for how specific genomic variations can impact the effectiveness of medications for patients by drawing upon frontline service datasets (e.g., such as those held by EPRs) and genomic data repositories. In addition, generative AI technologies may
help clinicians and patients to more easily formulate and communicate what a patient’s profile (e.g., genomic and phenotypical) means in relation to their personalised treatment plan. Figure 4 provides a high-level structure for the various AI domains and their relationship.
Conclusion
Digital medication management solutions have and continue to make a significant positive impact to mitigate and overcome many of the inherent risks and weaknesses associated with paper-based systems. However, through the emergence of new technologies and science relating to our understanding of how an individual’s DNA impacts their ability to respond to medications, there lies the opportunity to radically transform how disease and conditions are treated and managed. Drug expenditures for secondary care
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www.clinicalservicesjournal.com I June 2024
Don Woodlock, Head of Healthcare Solutions for InterSystems
Corporation, recently provided some thought provoking insights on the role AI could play in our healthcare services at a Global Summit Keynote, titled:
Caring for the Future. You can view his presentation online at: https://www.
intersystems.com/resources/caring- for-the-future/ or click on the QR code.
services typically represent the second largest capital expenditure after staff salaries. With only 30-60% of medication therapies being effective for individuals, there is also an overwhelming cost benefit analysis for the use of technologies to help make precision medicine routine for frontline clinical practice. While significant barriers to achieving the
full potential for pharmacogenomics remain, national healthcare services and government agencies across the world are awake to their potential and have strategies to help overcome the challenges. Software suppliers will also need to play their part in helping to forge this new path and capitalise on the opportunity to deliver more personalised, effective, and efficient healthcare care services. The future is bright, the future is genomic! CSJ
References 1. Pirmohamed M, James S, Meakin S, Green C, Scott A K, Walley T J , et
al.Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patientsBMJ 2004; 329 :15 doi:10.1136/ bmj.329.7456.15
2. Accessed at:
https://www.nhsbsa.nhs.uk/ statistical-collections/prescription-cost- analysis-england/prescription-cost-analysis- england-202122
3. Accessed at:
https://build.fhir.org/genomics. htm
SCAN ME
Figure 4: Different types of AI
About the author
Gary Mooney, Clinical Solution Executive at InterSystems has been with the company for ten years and has a background in academic medicine and digital clinical solutions. Starting his career in academic medicine with a focus on medical education, clinical decision-making and medicines safety, Gary went on to a secondment with the Department of Health to write strategy around the use of digital technology for national evidence bases and public health. While there, he also managed a digital team delivering against the policy agenda for the Our Healthier Nation White Paper. He also spent time through a travelling
Lectureship with a School of Medicine in South Africa to help with the adoption of digital technologies to support undergraduate medical education, before moving into commerce, where his focus was again on medicines management and patient safety, along with developing and delivering EPMA, pharmacy, and clinical EPR solutions in the UK and overseas.
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