Digital health technologies offer significant opportunities to reshape current health care systems. From the adoption of electronic medical records to mobile health apps and other disruptive technologies, digital health solutions have promised a better quality of care at a more sustainable cost. However, the widescale adoption of these solutions is lagging behind. The most adverse scenarios often provide an opportunity to develop and test the capacity of digital health technologies to increase the efficiency of health care systems. Catalonia (Northeast Spain) is one of the most advanced regions in terms of digital health adoption across Europe. The region has a long tradition of health information exchange in the public health care sector and is currently implementing an ambitious digital health strategy. In this viewpoint, we discuss the crucial role digital health solutions play during the coronavirus disease (COVID-19) pandemic to support public health policies. We also report on the strategies currently deployed at scale during the outbreak in Catalonia.

Background: The System Usability Scale (SUS) is used to measure usability of internet-based Cognitive Behavioural Therapy (iCBT). However, whether the SUS is a valid instrument to measure usability in this context is unclear. The aim of this study is to assess the factor structure of the SUS, measuring usability of iCBT for depression in a sample of professionals. In addition, the psychometric properties (reliability, convergent validity) of the SUS were tested. Methods: A sample of 242 professionals using iCBT for depression from 6 European countries completed the SUS. Confirmatory Factor Analysis (CFA) was conducted to test whether a one-factor, two-factor, tone-model or bi-direct model would fit the data best. Reliability was assessed using complementary statistical indices (e.g. omega). To assess convergent validity, the SUS total score was correlated with an adapted Client Satisfaction Questionnaire (CSQ-3). Results: CFA supported the one-factor, two-factor and tone-model, but the bi-factor model fitted the data best (Comparative Fit Index = 0.992, Tucker Lewis Index = 0.985, Root Mean Square Error of Approximation = 0.055, Standardized Root Mean Square Residual = 0.042 (respectively χ²_diff (9) = 69.82, p < 0.001; χ²_diff (8) ₌ 33.04, p < 0.001). Reliability of the SUS was good (ω = 0.91). The total SUS score correlated moderately with the CSQ-3 (CSQ1 r_s = .49, p < 0.001; CSQ2 r_s = .46, p < 0.001; CSQ3 r_s = .38, p < 0.001), indicating convergent validity. Conclusions: Although the SUS seems to have a multidimensional structure, the best model showed that the total sumscore of the SUS appears to be a valid and interpretable measure to assess the usability of internet-based interventions when used by professionals in mental healthcare.

Objectives: To identify current patient identification techniques and approaches used worldwide in today’s healthcare environment. To identify challenges associated with improper patient identification. Methods: A literature review of relevant peer-reviewed and grey literature published from January 2015 to October 2019 was conducted to inform the paper. The focus was on: 1) patient identification techniques and 2) unintended consequences and ramifications of unresolved patient identification issues. Results: The literature review showed six common patient identification techniques implemented worldwide ranging from unique patient identifiers, algorithmic approaches, referential matching software, biometrics, radio frequency identification device (RFID) systems, and hybrid models. The review revealed three themes associated with unresolved patient identification: 1) treatment, care delivery, and patient safety errors, 2) cost and resource considerations, and 3) data sharing and interoperability challenges. Conclusions: Errors in patient identification have implications for patient care and safety, payment, as well as data sharing and interoperability. Different patient identification techniques ranging from unique patient identifiers and algorithms to hybrid models have been implemented worldwide. However, no current patient identification techniques have resulted in a 100% match rate. Optimizing algorithmic matching through data standardization and referential matching software should be studied further to identify opportunities to enhance patient identification techniques and approaches. Further efforts to improve patient identity management include adoption of patients’ photos at registration, naming conventions, and standardized processes for recording patients’ demographic data attributes.

BackgroundDepressive disorder is a major societal challenge. Despite the availability of clinically and cost-effective treatments including Internet interventions, the number of patients receiving treatment is limited. Evidence-based Internet interventions promise wide availability and high efficiency of treatments. However, these interventions often do not enter routine mental healthcare delivery at a large scale. The MasterMind project aims to provide insight into the factors that promote or hinder the uptake and implementation of evidence-based Internet interventions by mental healthcare practice. Internet-based Cognitive Behaviour Therapy (iCBT) and videoconferencing facilitating collaborative care (ccVC) will be implemented in routine mental healthcare. The services will be offered to 5230 depressed adults in 15 European regions. The current paper describes the evaluation protocol for this large-scale implementation project.DesignCurrent summative evaluation study follows a naturalistic one-group pretest–posttest design and assesses three distinct stakeholders: patients, mental healthcare professionals, and mental healthcare organisations. The Model for Assessment of Telemedicine applications (MAST) will be employed to structure the implementation and evaluation study. The primary focal points of interest are reach, clinical effect, acceptability, appropriateness, implementation costs, and sustainability of the interventions in practice. Mixed-methods are used to provide an understanding of what (quantitative) the implementation projects have achieved and their meaning to various stakeholders (qualitative).DiscussionThe use of Internet interventions in routine practice is limited. MasterMind attempts to bridge the gap between routine practice and effectiveness research by evaluating the implementation of evidence-based Internet interventions for depressive disorders in routine mental healthcare settings in Europe

Objectives Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario. Settings The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). Participants Responsible teams for regional data management in the five ACT regions. Primary and secondary outcome measures We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction. Results There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment. Conclusions The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation.

Background: Multimorbidity measures are useful for resource planning, patient selection and prioritization, and factor adjustment in clinical practice, research, and benchmarking. We aimed to compare the explanatory performance of the adjusted morbidity group (GMA) index in predicting relevant healthcare outcomes with that of other quantitative measures of multimorbidity.Methods: The performance of multimorbidity measures was retrospectively assessed on anonymized records of the entire adult population of Catalonia (North-East Spain). Five quantitative measures of multimorbidity were added to a baseline model based on age, gender, and socioeconomic status: the Charlson index score, the count of chronic diseases according to three different proposals (i.e., the QOF, HCUP, and Karolinska institute), and the multimorbidity index score of the GMA tool. Outcomes included all-cause death, total and non-scheduled hospitalization, primary care and ER visits, medication use, admission to a skilled nursing facility for intermediate care, and high expenditure (time frame 2017). The analysis was performed on 10 subpopulations: all adults (i.e., aged > 17 years), people aged > 64 years, people aged > 64 years and institutionalized in a nursing home for long-term care, and people with specific diagnoses (e.g., ischemic heart disease, cirrhosis, dementia, diabetes mellitus, heart failure, chronic kidney disease, and chronic obstructive pulmonary disease). The explanatory performance was assessed using the area under the receiving operating curves (AUC-ROC) (main analysis) and three additional statistics (secondary analysis).Results: The adult population included 6,224,316 individuals. The addition of any of the multimorbidity measures to the baseline model increased the explanatory performance for all outcomes and subpopulations. All measurements performed better in the general adult population. The GMA index had higher performance and consistency across subpopulations than the rest of multimorbidity measures. The Charlson index stood out on explaining mortality, whereas measures based on exhaustive definitions of chronic diagnostic (e.g., HCUP and GMA) performed better than those using predefined lists of diagnostics (e.g., QOF or the Karolinska proposal).Conclusions: The addition of multimorbidity measures to models for explaining healthcare outcomes increase the performance. The GMA index has high performance in explaining relevant healthcare outcomes and may be useful for clinical practice, resource planning, and public health research.