IT-Related Barriers and Facilitators to the Implementation of a New European eHealth Solution, the Digital Survivorship Passport (SurPass Version 2.0): Semistructured Digital Survey.

BACKGROUND: To overcome knowledge gaps and optimize long-term follow-up (LTFU) care for childhood cancer survivors, the concept of the Survivorship Passport (SurPass) has been invented. Within the European PanCareSurPass project, the semiautomated and interoperable SurPass (version 2.0) will be optimized, implemented, and evaluated at 6 LTFU care centers representing 6 European countries and 3 distinct health system scenarios: (1) national electronic health information systems (EHISs) in Austria and Lithuania, (2) regional or local EHISs in Italy and Spain, and (3) cancer registries or hospital-based EHISs in Belgium and Germany. OBJECTIVE: We aimed to identify and describe barriers and facilitators for SurPass (version 2.0) implementation concerning semiautomation of data input, interoperability, data protection, privacy, and cybersecurity. METHODS: IT specialists from the 6 LTFU care centers participated in a semistructured digital survey focusing on IT-related barriers and facilitators to SurPass (version 2.0) implementation. We used the fit-viability model to assess the compatibility and feasibility of integrating SurPass into existing EHISs. RESULTS: In total, 13/20 (65%) invited IT specialists participated. The main barriers and facilitators in all 3 health system scenarios related to semiautomated data input and interoperability included unaligned EHIS infrastructure and the use of interoperability frameworks and international coding systems. The main barriers and facilitators related to data protection or privacy and cybersecurity included pseudonymization of personal health data and data retention. According to the fit-viability model, the first health system scenario provides the best fit for SurPass implementation, followed by the second and third scenarios. CONCLUSIONS: This study provides essential insights into the information and IT-related influencing factors that need to be considered when implementing the SurPass (version 2.0) in clinical practice. We recommend the adoption of Health Level Seven Fast Healthcare Interoperability Resources and data security measures such as encryption, pseudonymization, and multifactor authentication to protect personal health data where applicable. In sum, this study offers practical insights into integrating digital health solutions into existing EHISs.

Barriers and facilitators to the implementation of a new European eHealth solution (SurPass v2.0): the PanCareSurPass Open Space study.

PURPOSE: To identify barriers and facilitators for implementing the Survivorship Passport (SurPass) v2.0 in six long-term follow-up (LTFU) care centres in Europe. METHODS: Stakeholders including childhood cancer survivors (CCSs), healthcare providers (HCPs), managers, information and technology (IT) specialists, and others, participated in six online Open Space meetings. Topics related to Care, Ethical, Legal, Social, Economic, and Information & IT-related aspects of implementing SurPass were evaluated. RESULTS: The study identified 115 barriers and 159 facilitators. The main barriers included the lack of standardised LTFU care in centres and network cooperation, uncertainty about SurPass accessibility, and uncertainty about how to integrate SurPass into electronic health information systems. The main facilitators included standardised and coordinated LTFU care in centres, allowing CCSs to conceal sensitive information in SurPass and (semi)automatic data transfer and filing. CONCLUSIONS: Key barriers to SurPass implementation were identified in the areas of care, ethical considerations, and information & IT. To address these barriers and facilitate the implementation on SurPass, we have formulated 27 recommendations. Key recommendations include using the internationally developed protocols and guidelines to implement LTFU care, making clear decisions about which parties have access to SurPass data in accordance with CCSs, and facilitating (semi)automated data transfer and filing using Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR). IMPLICATIONS FOR CANCER SURVIVORS: The findings of this study can help to implement SurPass and to ensure that cancer survivors receive high-quality LTFU care with access to the necessary information to manage their health effectively.

Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma Detection and Segmentation in MR Images.

OBJECTIVES: To externally validate and assess the accuracy of a previously trained fully automatic nnU-Net CNN algorithm to identify and segment primary neuroblastoma tumors in MR images in a large children cohort. METHODS: An international multicenter, multivendor imaging repository of patients with neuroblastic tumors was used to validate the performance of a trained Machine Learning (ML) tool to identify and delineate primary neuroblastoma tumors. The dataset was heterogeneous and completely independent from the one used to train and tune the model, consisting of 300 children with neuroblastic tumors having 535 MR T2-weighted sequences (486 sequences at diagnosis and 49 after finalization of the first phase of chemotherapy). The automatic segmentation algorithm was based on a nnU-Net architecture developed within the PRIMAGE project. For comparison, the segmentation masks were manually edited by an expert radiologist, and the time for the manual editing was recorded. Different overlaps and spatial metrics were calculated to compare both masks. RESULTS: The median Dice Similarity Coefficient (DSC) was high 0.997; 0.944-1.000 (median; Q1-Q3). In 18 MR sequences (6%), the net was not able neither to identify nor segment the tumor. No differences were found regarding the MR magnetic field, type of T2 sequence, or tumor location. No significant differences in the performance of the net were found in patients with an MR performed after chemotherapy. The time for visual inspection of the generated masks was 7.9 ± 7.5 (mean ± Standard Deviation (SD)) seconds. Those cases where manual editing was needed (136 masks) required 124 ± 120 s. CONCLUSIONS: The automatic CNN was able to locate and segment the primary tumor on the T2-weighted images in 94% of cases. There was an extremely high agreement between the automatic tool and the manually edited masks. This is the first study to validate an automatic segmentation model for neuroblastic tumor identification and segmentation with body MR images. The semi-automatic approach with minor manual editing of the deep learning segmentation increases the radiologist's confidence in the solution with a minor workload for the radiologist.

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.

Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images.

Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect and segment tumors on MR images. A retrospective multicenter study of 132 patients with neuroblastic tumors was performed. Dice Similarity Coefficient (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC) were used to compare segmentation sets. Two more metrics were elaborated to understand the direction of the errors: the modified version of False Positive (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented 46 tumors and a comparative study was performed. nnU-Net was trained-tuned with 106 cases divided into five balanced folds to perform cross-validation. The five resulting models were used as an ensemble solution to measure training (n = 106) and validation (n = 26) performance, independently. The time needed by the model to automatically segment 20 cases was compared to the time required for manual segmentation. The median DSC for manual segmentation sets was 0.969 (±0.032 IQR). The median DSC for the automatic tool was 0.965 (±0.018 IQR). The automatic segmentation model achieved a better performance regarding the FPRm. MR images segmentation variability is similar between radiologists and nnU-Net. Time leverage when using the automatic model with posterior visual validation and manual adjustment corresponds to 92.8%.

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.