Digital data processing and analytics to support decision making in cardiac care

  • Aleeha Iftikhar

Student thesis: Doctoral Thesis


Cardiovascular disease (CVD) is the main cause of death globally. This PhD was carried out
to improve clinical decision making in cardiac care by enhancing how data is collected and
analysed. Firstly, the PhD includes the development and assessment of the usability of three different interactive digital health forms, namely, 1) a single page digital form, 2) a multi-page digital form, and 3) a conversational digital form (a chatbot). After comparing these three
digital form designs, it was discovered that healthcare professionals perform better when using a single page digital form (p(HCI)/form design suggestions for gathering high quality data that can be used to facilitate
reliable real world data analytics, which could, in turn, provide new useful and actionable
insights to improve clinical decision making. Moreover, given that data science/data analytics
is an emerging area to improve patient care, this PhD carried out series of analyses to elicit
beneficial insights from analysing referral datasets and pathways. These analyses contain a
series of analyses including time series analysis, supervised and unsupervised machine
learning and process mining, all applied to real-world data (datasets of patients who were
referred to the primary percutaneous coronary intervention (PPCI) service/Cath-Lab for
cardiac reperfusion therapy). The primary findings include that time series analysis of all the patient's data exhibit various fluctuations over time. Furthermore, cluster analysis was used to discover patient archetypes as well as new taxonomy for naming archetypical patients. Also, using the PPCI patient referral datasets, 30 days and 1-year mortality was predicted using
various ML algorithms. This PhD also illustrates the use of process mining methods for
detecting patient pathway patterns in cardiac care. Secondary data also allowed the PhD to
investigate how computers and humans make clinical decisions when interpreting
electrocardiograms (ECGs).
Date of AwardMay 2022
Original languageEnglish
SupervisorRaymond Bond (Supervisor), Victoria Mc Gilligan (Supervisor), Aaron Peace (Supervisor) & Stephen Leslie (Supervisor)


  • Digital forms
  • Health care
  • Usability evaluation
  • PPCI
  • Data analytics
  • Digital data processing

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