Personas for Better Targeted eHealth Technologies:User-Centered Design Approach

BACKGROUND: The full potential of eHealth technologies to support self-management and disease management for patients with chronic diseases is not being reached. A possible explanation for these lacking results is that during the development process, insufficient attention is paid to the needs, wishes, and context of the prospective end users. To overcome such issues, the user-centered design practice of creating personas is widely accepted to ensure the fit between a technology and the target group or end users throughout all phases of development. OBJECTIVE: In this study, we integrate several approaches to persona development into the Persona Approach Twente to attain a more holistic and structured approach that aligns with the iterative process of eHealth development. METHODS: In 3 steps, a secondary analysis was carried out on different parts of the data set using the Partitioning Around Medoids clustering method. First, we used health-related electronic patient record data only. Second, we added person-related data that were gathered through interviews and questionnaires. Third, we added log data. RESULTS: In the first step, 2 clusters were found, with average silhouette widths of 0.12 and 0.27. In the second step, again 2 clusters were found, with average silhouette widths of 0.08 and 0.12. In the third step, 3 clusters were identified, with average silhouette widths of 0.09, 0.12, and 0.04. CONCLUSIONS: The Persona Approach Twente is applicable for mixed types of data and allows alignment of this user-centered design method to the iterative approach of eHealth development. A variety of characteristics can be used that stretches beyond (standardized) medical and demographic measurements. Challenges lie in data quality and fitness for (quantitative) clustering

Enhanced Visualization of Optimal Cerebral Perfusion Pressure Over Time to Support Clinical Decision Making.

OBJECTIVE: Cerebrovascular reactivity can provide a continuously updated individualized target for management of cerebral perfusion pressure, termed optimal cerebral perfusion pressure. The objective of this project was to find a way of improving the optimal cerebral perfusion pressure methodology by introducing a new visualization method. DATA SOURCES: Four severe traumatic brain injury patients with intracranial pressure monitoring. DATA EXTRACTION: Data were collected and pre-processed using ICM+ software. DATA SYNTHESIS: Sequential optimal cerebral perfusion pressure curves were used to create a color-coded maps of autoregulation - cerebral perfusion pressure relationship evolution over time. CONCLUSIONS: The visualization method addresses some of the main drawbacks of the original methodology and might bring the potential for its clinical application closer.Marcel Aries received an unrestricted grant from the Dutch Society of Intensive Care. Joseph Donnelly is supported by a Woolf Fisher Trust Scholarship. The software for brain monitoring ICM+® (www.neurosurg.cam.ac.uk/imcplus) is licensed by the University of Cambridge (Cambridge Enterprise).This is the author accepted manuscript. It is currently embargoed pending publication by Wolters Kluwer

eHealth development: a holistic approach

This presentation was held during the course 'eHealth development a holistic approach' at the University of Twente. The presentation included information about the project Quantified Self at Work and specifically the approach of co-creation during eHealth design with important stakeholders

Visualisation of the 'Optimal Cerebral Perfusion' Landscape in Severe Traumatic Brain Injury Patients.

OBJECTIVE: An 'optimal' cerebral perfusion pressure (CPPopt) can be defined as the point on the CPP scale corresponding to the greatest autoregulatory capacity. This can be established by examining the pressure reactivity index PRx-CPP relationship, which is approximately U-shaped but suffers from noise and missing data. In this paper, we present a method for plotting the whole PRx-CPP relationship curve against time in the form of a colour-coded map depicting the 'landscape' of that relationship extending back for several hours and to display this robustly at the bedside.This is a short version of a full paper recently published in Critical Care Medicine (2016) containing some new insights and details of a novel bedside implementation based on a presentation during Intracranial Pressure 2016 Symposium in Boston. METHODS: Recordings from routine monitoring of traumatic brain injury patients were processed using ICM+. Time-averaged means for arterial blood pressure, intracranial pressure, cerebral perfusion pressure (CPP) and pressure reactivity index (PRx) were calculated and stored with time resolution of 1 min. ICM+ functions have been extended to include not just an algorithm of automatic calculation of CPPopt but also the 'CPPopt landscape' chart. RESULTS: Examining the 'CPPopt landscape' allows the clinician to differentiate periods where the autoregulatory range is narrow and needs to be targeted from periods when the patient is generally haemodynamically stable, allowing for more relaxed CPP management. This information would not have been conveyed using the original visualisation approaches. CONCLUSIONS: We describe here a natural extension to the concept of autoregulatory assessment, providing the retrospective 'landscape' of the PRx-CPP relationship extending over the past several hours. We have incorporated such visualisation techniques online in ICM+. The proposed visualisation may facilitate clinical evaluation and use of autoregulation-guided therapy