ABSTRACT
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.
Subject(s)
Telemedicine , Humans , Telemedicine/methods , Europe , Surveys and Questionnaires , Electronic Health Records , Cancer Survivors , Computer Security , SurvivorshipABSTRACT
BACKGROUND: Childhood cancer survivors (CCS), of whom there are about 500,000 living in Europe, are at an increased risk of developing health problems [1-6] and require lifelong Survivorship Care. There are information and knowledge gaps among CCS and healthcare providers (HCPs) about requirements for Survivorship Care [7-9] that can be addressed by the Survivorship Passport (SurPass), a digital tool providing CCS and HCPs with a comprehensive summary of past treatment and tailored recommendations for Survivorship Care. The potential of the SurPass to improve person-centred Survivorship Care has been demonstrated previously [10,11]. METHODS: The EU-funded PanCareSurPass project will develop an updated version (v2.0) of the SurPass allowing for semi-automated data entry and implement it in six European countries (Austria, Belgium, Germany, Italy, Lithuania and Spain), representative of three infrastructure healthcare scenarios typically found in Europe. The implementation study will investigate the impact on person-centred care, as well as costs and processes of scaling up the SurPass. Interoperability between electronic health record systems and SurPass v2.0 will be addressed using the Health Level Seven (HL7) International interoperability standards. RESULTS: PanCareSurPass will deliver an interoperable digital SurPass with comprehensive evidence on person-centred outcomes, technical feasibility and health economics impacts. An Implementation Toolkit will be developed and freely shared to promote and support the future implementation of SurPass across Europe. CONCLUSIONS: PanCareSurPass is a novel European collaboration that will improve person-centred Survivorship Care for CCS across Europe through a robust assessment of the implementation of SurPass v2.0 in different healthcare settings.
Subject(s)
Cancer Survivors , Survivorship , Humans , Child , Delivery of Health Care , Health Personnel , EuropeABSTRACT
BACKGROUND: The coronavirus disease 2019 (COVID-19) was a cause of concern in the healthcare system and increased the need for disease severity indicators. However, they still vary in use to evaluate in-hospital outcomes and severity. The National Early Warning Score (NEWS) is routinely used to evaluate patient health status at the hospital. Further research is needed to ensure if NEWS can be a good instrument for an overall health status assessment with or without additional information like laboratory tests, intensive care needs, and history of chronic diseases. OBJECTIVE: To evaluate if NEWS can be an indicator to measure COVID-19 patient status in-hospital. METHODS: We used the fully anonymized Electronic Health Records (EHR) characterizing patients admitted to the hospital with COVID-19. Data was obtained from Vilnius University Hospital Santaros Klinikos EHR system (SANTA-HIS) from 01-03-2020 to 31-12-2022. The study sample included 3875 patients. We created several statistical and machine learning models for discrimination between in-hospital death/discharge for evaluation NEWS as a disease severity measure for COVID-19 patients. In these models, two variable sets were considered: median NEWS and its combination with clinical parameters and medians of laboratory test results. Assessment of models' performance was based on the scoring metrics: accuracy, sensitivity, specificity, area under the ROC curve (AUC), and F1-score. RESULTS: Our analysis revealed that NEWS predictive ability for describing patient health status during the stay in the hospital can be increased by adding the patient's age at hospitalization, gender, clinical and laboratory variables (0.853 sensitivity, 0.992 specificity and F1-score - 0.859) in comparison with single NEWS (0.603, 0.995, 0.719, respectively). A comparison of different models showed that stepwise logistic regression was the best method for in-hospital mortality classification. Our findings suggest employing models like ours for advisory routine usage. CONCLUSION: Our model demonstrated incremental value for COVID-19 patient's status evaluation.
Subject(s)
COVID-19 , Early Warning Score , Humans , COVID-19/diagnosis , COVID-19/epidemiology , Electronic Health Records , Hospital Mortality , ROC Curve , Machine Learning , Retrospective StudiesABSTRACT
Patient-centered care is recognized as a key element in recent healthcare management strategies. However, the integrated collection of patient feedback capturing the entire journey of patients with complex medical conditions remains understudied. Herein, we aimed to describe the development of an instrument prototype for the collection of PROMs and PREMs that would encompass a whole patient journey at a single time point. We further describe the process of its integration into a hospital's information system (HIS) and the results of a pilot feasibility study in adult patients with kidney and hematological diseases. We developed an instrument consisting of original PREM and generic EQ-5D-5L questionnaires. E-questionnaires were handled with REDCap software (version 12.5.14) and integrated into the HIS. Patients refusing to use e-questionnaires (48%) were offered paper administration and were older (64 vs. 50 years). The overall response rate for e-questionnaires was 57.1% with a median completion time of 2.0 and 3.7 min for PROM and PREM, respectively. Psychological and social services and primary care setting (diagnosis establishment and involvement in continuous care) were identified as most problematic. The majority of PREM dimensions encompassing different levels of care significantly correlated with PROM responses. Our data indicate the feasibility and potential relevance of the proposed approach, although wider-scale studies in diverse settings are needed.
ABSTRACT
We aimed to explore the feasibility and potential relevance of integrated electronic collection of patient-reported outcome and experience measures (PROM and PREM) in children with special healthcare needs (CSHCN) by using the example of children with kidney and hematological diseases. We performed a cross-sectional, single-center study of children <18 years of age. Children (≥10 years) and their parents received Generic PedsQL Core Scale 4.0 and custom PREM surveys to their email addresses via the REDCap platform, and the results were integrated into the hospital's electronic health records system. A total of 192 patients (98 with kidney diseases and 94 with hematological diseases) were enrolled. The overall response rate was 51%, and the median time for completion of each proxy questionnaire was approximately three minutes. The lowest PROM scores were observed in the emotional and school functioning dimensions. More favorable experiences in the diagnosis establishment process were associated with higher scores in physical, social, school functioning, and total PROM scores. A better evaluation of the hospital's environment was associated with higher social functioning, while better information provision correlated with higher physical functioning and total PROM scores. Our data indicates that integrated electronic collection of PROMs and PREMs in the population of CSHCN is feasible, but efforts to increase the response rate are needed. The associations between PROMs and PREMs suggest that future studies exploring targeted interventions at the healthcare service level to improve subjective patient outcomes are needed.
ABSTRACT
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.
ABSTRACT
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.
Subject(s)
Cancer Survivors , Neoplasms , Child , Germany , Humans , Neoplasms/therapy , Survivors , SurvivorshipABSTRACT
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.
Subject(s)
Arthritis, Rheumatoid , Thermography , Arthritis, Rheumatoid/diagnosis , C-Reactive Protein , Humans , Neural Networks, Computer , Sensitivity and SpecificityABSTRACT
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.
