Amsterdam health & technology institute

Digital transformation in health care has a lot of opportunities to improve access and quality of care. However, in reality not all individuals and communities are benefiting equally from these innovations. People in vulnerable conditions, already in need of more care and support, are often not participating in digital health programs. Fortunately, numerous initiatives worldwide are committed to make digital health accessible to all citizens, stimulating the long-cherished global pursuit of universal health coverage. Unfortunately initiatives are not always familiar with each other and miss connection to jointly make a significant positive impact. To reach universal health coverage via digital health it is necessary to facilitate mutual knowledge exchange, both globally and locally, to link initiatives and apply academic knowledge into practice. This will support policymakers, health care providers and other stakeholders to ensure that digital innovations can increase access to care for everyone, leading towards Digital health for all.

BACKGROUND: Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use. OBJECTIVE: This review focuses on an affordable data-driven approach based on electroencephalographic recordings. Web-based applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from electroencephalographic recordings using various features and machine learning models. METHODS: To detect depression, we reviewed published detection studies based on resting-state electroencephalogram with final machine learning, and to predict therapy outcomes, we reviewed a set of interventional studies using some form of stimulation in their methodology. RESULTS: We reviewed 14 detection studies and 12 interventional studies published between 2008 and 2019. As direct comparison was not possible due to the large diversity of theoretical approaches and methods used, we compared them based on the steps in analysis and accuracies yielded. In addition, we compared possible drawbacks in terms of sample size, feature extraction, feature selection, classification, internal and external validation, and possible unwarranted optimism and reproducibility. In addition, we suggested desirable practices to avoid misinterpretation of results and optimism. CONCLUSIONS: This review shows the need for larger data sets and more systematic procedures to improve the use of the solution for clinical diagnostics. Therefore, regulation of the pipeline and standard requirements for methodology used should become mandatory to increase the reliability and accuracy of the complete methodology for it to be translated to modern psychiatry.

BACKGROUND: Hospitals are under increasing pressure to share indicator-based performance information. These indicators can also serve as a means to promote quality improvement and boost hospital performance. Our aim was to explore hospitals' use of performance indicators for internal quality management activities. METHODS: We conducted a qualitative interview study among 72 health professionals and quality managers in 14 acute care hospitals in The Netherlands. Concentrating on orthopaedic and oncology departments, our goal was to gain insight into data collection and use of performance indicators for two conditions: knee and hip replacement surgery and breast cancer surgery. The semi-structured interviews were recorded and summarised. Based on the data, themes were synthesised and the analyses were executed systematically by two analysts independently. The findings were validated through comparison. RESULTS: The hospitals we investigated collect data for performance indicators in different ways. Similarly, these hospitals have different ways of using such data to support their quality management, while some do not seem to use the data for this purpose at all. Factors like 'linking pin champions', pro-active quality managers and engaged medical specialists seem to make a difference. In addition, a comprehensive hospital data infrastructure with electronic patient records and robust data collection software appears to be a prerequisite to produce reliable external performance indicators for internal quality improvement. CONCLUSIONS: Hospitals often fail to use performance indicators as a means to support internal quality management. Such data, then, are not used to its full potential. Hospitals are recommended to focus their human resource policy on 'linking pin champions', the engagement of professionals and a pro-active quality manager, and to invest in a comprehensive data infrastructure. Furthermore, the differences in data collection processes between Dutch hospitals make it difficult to draw comparisons between outcomes of performance indicators.

BACKGROUND: Digital health technologies (DHTs) have become increasingly commonplace as a means of delivering primary care. While DHTs have been postulated to reduce inequalities, increase access, and strengthen health systems, how the implementation of DHTs has been realized in the sub-Saharan Africa (SSA) health care environment remains inadequately explored. OBJECTIVE: This study aims to capture the multidisciplinary experiences of primary care professionals using DHTs to explore the strengths and weaknesses, as well as opportunities and threats, regarding the implementation and use of DHTs in SSA primary care settings. METHODS: A combination of qualitative approaches was adopted (ie, focus groups and semistructured interviews). Participants were recruited through the African Forum for Primary Care and researchers' contact networks using convenience sampling and included if having experience with digital technologies in primary health care in SSA. Focus and interviews were conducted, respectively, in November 2021 and January-March 2022. Topic guides were used to cover relevant topics in the interviews, using the strengths, weaknesses, opportunities, and threats framework. Transcripts were compiled verbatim and systematically reviewed by 2 independent reviewers using framework analysis to identify emerging themes. The COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist was used to ensure the study met the recommended standards of qualitative data reporting. RESULTS: A total of 33 participants participated in the study (n=13 and n=23 in the interviews and in focus groups, respectively; n=3 participants participated in both). The strengths of using DHTs ranged from improving access to care, supporting the continuity of care, and increasing care satisfaction and trust to greater collaboration, enabling safer decision-making, and hastening progress toward universal health coverage. Weaknesses included poor digital literacy, health inequalities, lack of human resources, inadequate training, lack of basic infrastructure and equipment, and poor coordination when implementing DHTs. DHTs were perceived as an opportunity to improve patient digital literacy, increase equity, promote more patient-centric design in upcoming DHTs, streamline expenditure, and provide a means to learn international best practices. Threats identified include the lack of buy-in from both patients and providers, insufficient human resources and local capacity, inadequate governmental support, overly restrictive regulations, and a lack of focus on cybersecurity and data protection. CONCLUSIONS: The research highlights the complex challenges of implementing DHTs in the SSA context as a fast-moving health delivery modality, as well as the need for multistakeholder involvement. Future research should explore the nuances of these findings across different technologies and settings in the SSA region and implications on health and health care equity, capitalizing on mixed-methods research, including the use of real-world quantitative data to understand patient health needs. The promise of digital health will only be realized when informed by studies that incorporate patient perspective at every stage of the research cycle.

There is strong clinical evidence from the current literature that certain psychological and physiological indicators are closely related to mood changes. However, patients with mental illnesses who present similar behavior may be diagnosed differently, which is why a personalized study of each patient is necessary. Following previous promising results in the detection of depression, in this work, supervised machine learning (ML) algorithms were applied to classify the different states of patients diagnosed with bipolar depressive disorder (BDD). The purpose of this study was to provide relevant information to medical staff and patients’ relatives in order to help them make decisions that may lead to a better management of the disease. The information used was collected from BDD patients through wearable devices (smartwatches), daily self-reports, and medical observation at regular appointments. The variables were processed and then statistical techniques of data analysis, normalization, noise reduction, and feature selection were applied. An individual analysis of each patient was carried out. Random Forest, Decision Trees, Logistic Regression, and Support Vector Machine algorithms were applied with different configurations. The results allowed us to draw some conclusions. Random Forest achieved the most accurate classification, but none of the applied models were the best technique for all patients. Besides, the classification using only selected variables produced better results than using all available information, though the amount and source of the relevant variables differed for each patient. Finally, the smartwatch was the most relevant source of information.

BACKGROUND: eHealth offers opportunities to improve health and healthcare systems and overcome primary care challenges in low-resource settings (LRS). LRS has been typically associated with low- and middle-income countries (LMIC), but they can be found in high-income countries (HIC) when human, physical or financial resources are constrained. Adopting a concept of LRS that applies to LMIC and HIC can facilitate knowledge interchange between eHealth initiatives while improving healthcare provision for socioeconomically disadvantaged groups across the globe. OBJECTIVES: To outline the contributions and challenges of eHealth in low-resource primary care settings. STRATEGY: We adopt a socio-ecological understanding of LRS, making LRS relevant to LMIC and HIC. To assess the potential of eHealth in primary care settings, we discuss four case studies according to the WHO 'building blocks for strengthening healthcare systems'. RESULTS AND DISCUSSION: The case studies illustrate eHealth's potential to improve the provision of healthcare by i) improving the delivery of healthcare (using AI-generated chats); ii) supporting the workforce (using telemedicine platforms); iii) strengthening the healthcare information system (through patient-centred healthcare information systems), and iv) improving system-related elements of healthcare (through a mobile health financing platform). Nevertheless, we found that development and implementation are hindered by user-related, technical, financial, regulatory and evaluation challenges. We formulated six recommendations to help anticipate or overcome these challenges: 1) evaluate eHealth's appropriateness, 2) know the end users, 3) establish evaluation methods, 4) prioritise the human component, 5) profit from collaborations, ensure sustainable financing and local ownership, 6) and contextualise and evaluate the implementation strategies.

Due to the impact respiratory viruses have on human health, a lot of data has been collected and visualised in tools such as dashboards that provide retrospective insights into the course of an epidemic or pandemic. Two well-known respiratory viruses, influenza virus and SARS-CoV-2, are the causative agents of influenza and COVID-19, respectively. A scoping review was performed using Embase including data from January 2000 until April 2021 to identify individual and environmental health parameters that affect transmission of influenza virus and SARS-CoV-2, as well as disease severity (morbidity (hospitalisation) and mortality) of influenza and COVID-19. Summary data was extracted from published articles. A total of 2280 unique articles were identified by the search, 484 articles were analysed, and 149 articles were included. The information of included articles was combined with data from Dutch databases to create prospective interactive maps that visualise risk areas in the Netherlands on health region, municipality, and neighbourhood-level. Included health parameters are contacts per day, mixing patterns, household composition, presence of certain indoor public spaces, urbanity, meteorological values, average income, age, ethnicity, comorbidity, sex, and smoking habits. The impact and input of these parameters are adjustable by users allowing a fit-for-purpose approach. These maps can be used to corroborate local policy decisions in times of health crisis, or in pandemic preparedness plans, serving as an instant visualisation tool of risk areas in the country. Despite limitations caused by data unavailability, simplification steps, and lack of validation, these interactive maps provide an important basis that can be elaborated on by further research that integrates both individual and environmental parameters.

BACKGROUND AND AIMS: The path to hepatitis C virus (HCV) elimination is complicated by individuals who become lost to follow-up (LTFU) during care, particularly before receiving effective HCV treatment. We aimed to determine factors contributing to LTFU and whether LTFU is associated with mortality. METHODS: In this secondary analysis, we constructed a database including individuals with HCV who were either LTFU (data from the nationwide HCV retrieval project, CELINE) or treated with directly acting antivirals (DAA) (data from Statistics Netherlands) between 2012 and 2019. This database was linked to mortality data from Statistics Netherlands. Determinants associated with being LTFU versus DAA-treated were assessed using logistic regression, and mortality rates were compared between groups using exponential survival models. These analyses were additionally stratified on calendar periods: 2012-2014, 2015-2017 and 2018-2019. RESULTS: About 254 individuals, LTFU and 5547 DAA-treated were included. Being institutionalized (OR = 5.02, 95% confidence interval (CI) = 3.29-7.65), household income below the social minimum (OR = 1.96, 95% CI = 1.25-3.06), receiving benefits (OR = 1.74, 95% CI = 1.20-2.52) and psychiatric comorbidity (OR = 1.51, 95% CI = 1.09-2.10) were associated with LTFU. Mortality rates were significantly higher in individuals LTFU compared to those DAA-treated (2.99 vs. 1.15/100 person-years (PY), p < .0001), while in those DAA-treated, mortality rates slowly increased between 2012-2014 (.22/100PY) and 2018-2019 (2.25/100PY). CONCLUSION: In the Netherlands, individuals who are incarcerated/institutionalized, with low household income, or with psychiatric comorbidities are prone to being LTFU, which is associated with higher mortality. HCV care needs to be adapted for these vulnerable individuals.

There is lively discussion regarding the potential and pitfalls of artificial intelligence (AI) and machine learning (ML) for public policy. This debate tends to focus on replacing human decision-making with (semi-)automated processes and the unique challenges such applications pose for policymakers and society more generally. As this paper argues, particularly ML could be used in a more direct and less controversial way: to improve policy analysis and inform evidence-based policymaking. ML methods can be used to identify sub-groups in a population that differ in their policy effect in a data-driven way, which might otherwise be missed in standard policy analysis. In doing so, a more complete picture of a policy’s impact on a population can be obtained. I illustrate how ML can complement our understanding of policy interventions by studying the nationwide 2015 decentralisation of the social domain in The Netherlands. This policy intervention delegated responsibilities to administer social care from the national to the municipal level. Using ML methods on entire population data in The Netherlands, I find the policy induced strongly heterogeneous effects that include evidence of local capture and strong urban/rural divides. Findings that are crucial for policymakers to assess whether the policy had the desired outcome.

<sec> <title>BACKGROUND</title> Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use. </sec> <sec> <title>OBJECTIVE</title> This review focuses on an affordable data-driven approach based on electroencephalographic recordings. Web-based applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from electroencephalographic recordings using various features and machine learning models. </sec> <sec> <title>METHODS</title> To detect depression, we reviewed published detection studies based on resting-state electroencephalogram with final machine learning, and to predict therapy outcomes, we reviewed a set of interventional studies using some form of stimulation in their methodology. </sec> <sec> <title>RESULTS</title> We reviewed 14 detection studies and 12 interventional studies published between 2008 and 2019. As direct comparison was not possible due to the large diversity of theoretical approaches and methods used, we compared them based on the steps in analysis and accuracies yielded. In addition, we compared possible drawbacks in terms of sample size, feature extraction, feature selection, classification, internal and external validation, and possible unwarranted optimism and reproducibility. In addition, we suggested desirable practices to avoid misinterpretation of results and optimism. </sec> <sec> <title>CONCLUSIONS</title> This review shows the need for larger data sets and more systematic procedures to improve the use of the solution for clinical diagnostics. Therefore, regulation of the pipeline and standard requirements for methodology used should become mandatory to increase the reliability and accuracy of the complete methodology for it to be translated to modern psychiatry. </sec> <sec> <title>CLINICALTRIAL</title> <p /> </sec>

The aim of this study is to investigate how the poor, relative to the wealthier, benefitted from recent improvements in health insurance coverage, maternity care utilisation (modern contraceptive use, antenatal care visits, facility delivery, and skilled birth attendants), and under-five mortality in Kenya. The analysis relies on the latest two waves of the Kenya Demographic and Health Survey and a theoretical framework with three different inclusiveness (pro-poorness) concepts. Our results are quite robust to pro-poorness concepts and poverty definitions. The main result is that the poor experienced larger improvements in all investigated health aspects compared to the rich (irrespective of the poverty concept) when changes are measured in relative terms. When we investigate changes in absolute terms, we find a similar pattern, except in the case of health insurance coverage and the presence of a skilled birth attendant during delivery. Our analysis is expected to inform policy-making aiming to achieve universal health coverage.

Abstract EP3.5, e-Poster Terminal 3, September 5, 2025, 13:05 - 13:30 Mobile populations, including refugees, asylum seekers and undocumented migrants, face challenges in access, continuity and quality of healthcare, among others, due to lack of available health records. Our study aimed to examine the current landscape of Electronic Personal Health Records (EPHRs) developed for and used by mobile populations. A rapid systematic literature review was conducted, identifying relevant publications through searches in Embase, PubMed, Scopus, and grey literature. The literature search yielded 2303 articles, with 74 remaining after title and abstract screening. After full-text screening, 10 scientific articles and 9 grey literature records were included in a qualitative data synthesis. Six distinct EPHRs were identified, differing in how they centralize health records, ranging from ‘digital vaults‘ to comprehensive systems, from smartphone apps to web-based apps, and from offline functionalities to the necessity of being connected to the internet. Moreover, they differ in additional functionalities, and the level of patient autonomy granted. Limited evidence exists on their impact on health outcomes or continuity of care, and user adoption remains a critical challenge. Key elements in the development and implementation of EPHRs include ensuring a high level of data security and co-designing easy to use EPHRs. The review indicates a need for future research on user-experiences and their impact on the health outcomes of mobile populations.

Abstract The COVID-19 pandemic has led to severe reductions in non-COVID related healthcare use, but little is known whether this burden is shared equally across the population. This study investigates whether the reduction in administered care disproportionately affected certain sociodemographic strata, in particular marginalised groups. Using detailed medical claims data from the Dutch universal health care system and rich registry data that cover all residents in The Netherlands, we predict expected healthcare use based on pre-pandemic trends (2017– Feb 2020) and compare these expectations with observed healthcare use in 2020. Our findings reveal a substantial 10% decline in the number of weekly treated patients in 2020 relative to prior years. Furthermore, declines in healthcare use are unequally distributed and are more pronounced for individuals below the poverty line, females, the elderly, and foreign-born individuals, with cumulative relative risk ratios ranging from 1.09 to 1.22 higher than individuals above the poverty line, males, young, and native-born. These inequalities stem predominantly from declines in middle and low urgency procedures, and indicate that the pandemic has not only had an unequal toll in terms of the direct health burden of the pandemic, but has also had a differential impact on the use of non-COVID healthcare.

On Tuesday 14 October 2014, 850 family members, friends, colleagues, prominent scientists and dignitaries from all over the world gathered in Amsterdam to pay tribute to the lives and legacies of Joep Lange and Jacqueline van Tongeren. The remembrance was held at the Amsterdam Medical Centre (AMC) where Joep and Jacqueline met and worked together for many years. The day was organized by the AMC, the Amsterdam Institute for Global Health and Development (AIGHD) and PharmAccess Foundation. The latter two were both founded by Joep. A morning symposium titled ‘Research in action: from AIDS to global health to impact’ highlighted Joep's scientific legacy (Figure 1). During the remembrance in the afternoon, a range of speakers shared memories of Joep and Jacqueline. As was the case during their lives, the personal and the professional were closely intertwined throughout the day. As Prof Peter Piot said, Joep and Jacqueline shared a common perspective on life: ‘La folie suprême est de voir la vie comme elle est et non comme elle devrait être.’ If there was one thing that defined them both, it was indeed that they saw life – and lived it – not as it was, but as it should be. ‘Joep's place in history is really as the visionary architect of combination therapy,’ Prof Piot stated, adding that ‘it cannot be stressed enough that he was ahead of his time, a true innovator.’ Joep's contribution didn't stop at science. Dr Khama Rogo of the World Bank explained that ‘it's not enough to be a doctor or a researcher if you're not also an activist.’ Joep fully understood the importance of translating research into action and generating impact for people. Prof Marcel Levi, chairman of the AMC, summarized the enormity of the impact Joep had on the world with the words ‘it's rare to know someone who has saved millions of lives.’ The scientific symposium traced Joep's career, starting in the early eighties with the treatment of the first AIDS patients and the design of antiretroviral therapy, moving towards the emerging field of global health and ending with his most recent focus: using knowledge derived from scientific research to improve access to quality health care in real-world settings. From Prof Françoise Barré-Sinoussi, who won the Nobel Prize for the discovery of HIV, to Prof Michael Merson, who founded Duke University's Global Health Institute, the list of presenters reads like a who's who of people involved at key moments in the history of HIV and global health (Figure 2). ‘And Joep,’ as Barré-Sinoussi said, ‘contributed to all eras of HIV.’ More memories of Joep and Jacqueline shared throughout the day are available at http://www.joepandjacqueline.org/remembrance/ .

Objectives Emerging evidence shows that health disparities contribute to an increased risk of severe asthma. Therefore, the study objectives were to explore how socioeconomic disparities influence the risk of severe paediatric asthma in the Netherlands. Methods In this nationwide cohort study, all children aged 2–17 years living in the Netherlands between 2018 and 2022 were included. Asthma definitions were based upon individually linked data from nonpublic Dutch registry databases on asthma-related health expenditures including hospital and paediatric intensive care unit (PICU) admissions. Geospatial analysis was used to identify hot spots based on the regions with the highest counts of severe asthma. Additionally, the impact of various socioeconomic variables ( e.g. housing, migration background and socioeconomic status) on the primary outcome of severe asthma was assessed using a linear probability model. Results The total study population consisted of 4 538 020 children. Children from the lowest income class had twice the odds of severe asthma (p&lt;0.001) and were 2.6 times more likely to be admitted to the PICU compared to the highest income class (p&lt;0.001). Other socioeconomic disadvantage factors for severe (acute) asthma are, in order of level of association, living in a rental house, having a migration background and having a lower socioeconomic status score based on income, education and employment history. Conclusions We identified several socioeconomic disparities that were increased in children with severe asthma and severe acute asthma at the PICU in the Netherlands. Comprehensive assessment and mitigation of these determinants may improve health equity in paediatric severe asthma and enhancement of asthma care.