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Expected impact for the practitioners

Expected Impact: Emerging data-driven analytics and advanced simulation methods to study causal mechanisms and improve forecasts of ill-health, identification of disease trajectories and
relapse;
Expected Impact: Better and faster means of high-quality response to prevent or timely address development of new medical conditions and/or improve the quality of life.
Expected Impact: Better knowledge for improved patient counselling as well as to improve follow-up of patients; PERSIST big data model will contribute to create entirely new knowledge and understanding on how patient behaviours and various patient-specific co-morbidities may influence health outcomes. This knowledge will support the expected impact by:

I. Providing an improved forecast of ill health by predicting individual patient trajectories.

PERSIST will contribute with specific impact on data-driven analytics over personal health data, providing a unique approach for predicting individual patient trajectories based on user profile modelling (including newly sensed prospective data). The cohorts learned from data and the developed trajectories will be evaluated as to their ability for reconstructing patients’ events and measuring health status changes as well as quantifying risks.

Indicator: (1) Patient profile model accuracy according to its capacity to effectively anticipate trajectory events, and risks; (2) Data analytics scalability through the increased and sustainable data processing and analytics over large and heterogeneous datasets comprising EHR; sensor data sources and other supporting information; (3) Provision of reliable forecasted information for the creation of dynamic follow-up strategies for oncologist to provide equality and fair attention to any patient based on factual data.

II. Providing a clinical decision support system to facilitate timely medical interventions and improve patient counselling.

PERSIST will enable to process and represent evidence to support the clinical decisions regarding diagnosis, treatment and follow-up data of cancer patients based on each individual patient data model, predicted trajectories and the risk stratification. This will allow detecting anomalies in their personal predicted trajectory – new risks not detected with current practices – and practitioners will have a better insight on which focus areas of improvement they/the patient should focus at individual level. This allows faster response to timely address patient safety. These outcomes will mainly influence the provision of personalised survivorship care plans and follow up strategies based on each individual patient profile to improve patient counselling.

Indicator: (1) Time-to-completion for specified tasks related to intervention before and after CDSS roll-out; (2) Rate at which practitioners override alerts; (3) Adherence to clinical guidelines for selected care pathways; (4) Time-to-order for critical medications/procedures; (5) Time-to-order for critical procedures; (6) #diagnosis of recurrence cancer; (7) #secondary diseases detected; (8) Sensitivity and specificity of the CDSS; (9) Usability perception of the associated risk factors and the wellbeing/lifestyle factors.

As a major impact, PERSIST has the potential to dramatically improve the selection of the best treatments, avoiding the harming of patients and the use of ineffective therapies. PERSIST contributes to the provision of evidence-based methods to increase health care delivery efficiency and to increase follow-up efficacy. Additionally, it will pave the way for new scientific research by presenting new evidence-based treatment methods and approaches.

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