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We set out to develop a real-time computerised decision support system (CDSS) embedded in the electronic health record (EHR) with information on risk factors, estimated risk, and guideline-based advice on treatment strategy in order to improve adherence to cardiovascular risk management (CVRM) guidelines with the ultimate aim of improving patient healthcare.
We defined a project plan including the scope and requirements, infrastructure and interface, data quality and study population, validation and evaluation of the CDSS.
In collaboration with clinicians, data scientists, epidemiologists, ICT architects, and user experience and interface designers we developed a CDSS that provides ‘live’ information on CVRM within the environment of the EHR. The CDSS provides information on cardiovascular risk factors (age, sex, medical and family history, smoking, blood pressure, lipids, kidney function, and glucose intolerance measurements), estimated 10-year cardiovascular risk, guideline-compliant suggestions for both pharmacological and non-pharmacological treatment to optimise risk factors, and an estimate on the change in 10-year risk of cardiovascular disease if treatment goals are adhered to. Our pilot study identified a number of issues that needed to be addressed, such as missing data, rules and regulations, privacy, and patient participation.
Development of a CDSS is complex and requires a multidisciplinary approach. We identified opportunities and challenges in our project developing a CDSS aimed at improving adherence to CVRM guidelines. The regulatory environment, including guidance on scientific evaluation, legislation, and privacy issues needs to evolve within this emerging field of eHealth.
Piepoli MF, Hoes AW, Agewall S, et al. 2016 European guidelines on cardiovascular disease prevention in clinical practice. Rev Esp Cardiol (engl Ed). 2016;69(10):939.
Nederlands Huisartsen Genootschap. Nederlands Huisartsen Genootschap. Multidisciplinaire richtlijn cardiovasculair risicomanagement. Houten: Bohn Stafleu van Loghum; 2011.
Zozus MN, Richesson R, Hammond WE, Simon GE. Acquiring and using electronic health record data. 2015. http://rethinkingclinicaltrials.org/resources/acquiring-and-using-electronic-health-record-data/#EHR. Accessed: 25 June 2018.
Berkelmans GFN, Gudbjornsdottir S, Visseren FLJ, et al. Prediction of individual life-years gained without cardiovascular events from lipid, blood pressure, glucose, and aspirin treatment based on data of more than 500 000 patients with Type 2 diabetes mellitus. Eur Heart J. 2019. https://doi.org/10.1093/eurheartj/ehy839 CrossRefPubMed
European Parliament, Council of the European Communities. Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EEC. Official Journal of the European Union 2017;60(L117):1–175
Sonnenberg FA, Beck JB. Markov models in medical decision making: a practical guide. Med Decis Making. 1993;13:322–38. CrossRef
Foley T, Fairmichael F. The potential of learning healthcare systems. 2015. http://www.learninghealthcareproject.org/LHS_Report_2015.pdf. Accessed: 5 July 2019.
- A computerised decision support system for cardiovascular risk management ‘live’ in the electronic health record environment: development, validation and implementation—the Utrecht Cardiovascular Cohort Initiative
T. K. J. Groenhof
Z. H. Rittersma
M. L. Bots
J. J. L. Jacobs
D. E. Grobbee
W. W. van Solinge
F. L. J. Visseren
F. W. Asselbergs
Members of the UCC-CVRM Study Group
- Bohn Stafleu van Loghum