Barriers and facilitators to implementation of the interoperable Survivorship Passport (SurPass) v2.0 in 6 European countries: a PanCareSurPass online survey study

PURPOSE

Long-term follow-up (LTFU) care for childhood cancer survivors (CCSs) is essential to improve and maintain their quality of life. The Survivorship Passport (SurPass) is a digital tool which can aid in the delivery of adequate LTFU care. During the European PanCareSurPass (PCSP) project, the SurPass v2.0 will be implemented and evaluated at six LTFU care clinics in Austria, Belgium, Germany, Italy, Lithuania and Spain. We aimed to identify barriers and facilitators to the implementation of the SurPass v2.0 with regard to the care process as well as ethical, legal, social and economical aspects.

METHODS

An online, semi-structured survey was distributed to 75 stakeholders (LTFU care providers, LTFU care program managers and CCSs) affiliated with one of the six centres. Barriers and facilitators identified in four centres or more were defined as main contextual factors influencing implementation of SurPass v2.0.

RESULTS

Fifty-four barriers and 50 facilitators were identified. Among the main barriers were a lack of time and (financial) resources, gaps in knowledge concerning ethical and legal issues and a potential increase in health-related anxiety in CCSs upon receiving a SurPass. Main facilitators included institutions' access to electronic medical records, as well as previous experience with SurPass or similar tools.

CONCLUSIONS

We provided an overview of contextual factors that may influence SurPass implementation. Solutions should be found to overcome barriers and ensure effective implementation of SurPass v2.0 into routine clinical care.

IMPLICATIONS FOR CANCER SURVIVORS

These findings will be used to inform on an implementation strategy tailored for the six centres.

Factors Influencing Implementation of the Survivorship Passport: The IT Perspective

Compared to the general population, childhood cancer survivors represent a vulnerable population as they are at increased risk of developing health problems, known as late effects, resulting in excess morbidity and mortality. The Survivorship Passport aims to capture key health data about the survivors and their treatment, as well as personalized recommendations and a care plan with the aim to support long-term survivorship care. The PanCareSurPass (PCSP) project building on the experience gained in an earlier implementation in Giannina Gaslini Institute, Italy, will implement and rigorously assess an integrated, HL7 FHIR based, implementation of the Survivorship Passport. The six implementation countries, namely Austria, Belgium, Germany, Italy, Lithuania, and Spain, are supported by different national or regional digital health infrastructures and Electronic Medical Record (EMR) systems. Semi structured interviews were carried out to explore potential factors affecting implementation, identify use cases, and coded data that can be semi-automatically transferred from the EMR to SurPass. This paper reflects on findings of these interviews and confirms the need for a multidisciplinary and multi-professional approach towards a sustainable and integrated large-scale implementation of the Survivorship Passport across Europe.

A computational method to differentiate rheumatoid arthritis patients using thermography data

BACKGROUND

The traditional rheumatoid arthritis (RA) diagnosis is very complicated because it uses many clinical and image data. Therefore, there is a need to develop a new method for diagnosing RA using a consolidated set of blood analysis and thermography data.

OBJECTIVE

The following issues related to RA are discussed: 1) Which clinical data are significant in the primary diagnosis of RA? 2) What parameters from thermograms should be used to differentiate patients with RA from the healthy? 3) Can artificial neural networks (ANN) differentiate patients with RA from the healthy?

METHODS

The dataset was composed of clinical and thermal data from 65 randomly selected patients with RA and 104 healthy subjects. Firstly, the univariate logistic regression model was proposed in order to find significant predictors. Next, the feedforward neural network model was used. The dataset was divided into the training set (75% of data) and the test set (25% of data). The Broyden-Fletcher-Goldfarb-Shanno (BFGS) and non-linear logistic function to transformation nodes in the output layer were used for training. Finally, the 10 fold Cross-Validation was used to assess the predictive performance of the ANN model and to judge how it performs.

RESULT

The training set consisted of the temperature of all fingers, patient age, BMI, erythrocyte sedimentation rate, C-reactive protein and White Blood Cells (10 parameters in total). High level of sensitivity and specificity was obtained at 81.25% and 100%, respectively. The accuracy was 92.86%.

CONCLUSIONS

This methodology suggests that the thermography data can be considered in addition to the currently available tools for screening, diagnosis, monitoring of disease progression.

Research on physical activity variability and changes of metabolic profile in patients with prediabetes using Fitbit activity trackers data

BACKGROUND

Monitoring physical activity with consumers wearables is one of the possibilities to control a patient's self-care and adherence to recommendations. However, clinically approved methods, software, and data analysis technologies to collect data and make it suitable for practical use for patient care are still lacking.

OBJECTIVE

This study aimed to analyze the potential of patient physical activity monitoring using Fitbit physical activity trackers and find solutions for possible implementation in the health care routine.

METHODS

Thirty patients with impaired fasting glycemia were randomly selected and participated for 6 months. Physical activity variability was evaluated and parameters were calculated using data from Fitbit Inspire devices.

RESULTS

Changes in parameters were found and correlation between clinical data (HbA1c, lipids) and physical activity variability were assessed. Better correlation with variability than with body composition changes shows the potential to include nonlinear variability parameters analysing physical activity using mobile devices. Less expressed variability shows better relationship with control of prediabetic and lipid parameters.

CONCLUSIONS

Evaluation of physical activity variability is essential for patient health, and these methods used to calculate it is an effective way to analyze big data from wearable devices in future trials.