Ebook: Integrated Citizen Centered Digital Health and Social Care

This volume presents the proceedings of the EFMI Special Topic Conference 2020 organized in November 2020 as the first virtual EFMI conference. This conference focused on citizen centered aspects of health informatics. The conference invited papers particularly from the following topics:

• Tools and technologies to support citizen centered digital services

• Capacity building to enhance the development and use of digital services

• Confidentiality, data integrity and data protection to guarantee trustworthy services

• Citizen safety in digital services

• Effectiveness and impacts of citizen digital and integrated health and social services

• Evaluation approaches and methods for digital services

• Usability, usefulness and user acceptance of digital services

• Guidelines for successful implementation of digital services for citizen

This volume shows what kind of a collection of papers received the highest marks in the peer review process. The most popular track among the authors was the track Tools and technologies to support citizen centered digital services. The papers in this track cover a wide area of applications. Surprisingly, quite few authors addressed the second main theme of the conference, Citizens as data producers and service co-creators. This may indicate that the progress in this area is not yet as fast as expected. Usability was, however, addressed by several authors. Privacy and security are – and given the developing security threat landscape, will be an important topic, as well. Some of the papers are related to the COVID-19 epidemic – the phenomenon of year 2020.

The local organization committee which became a part of the scientific program committee (SPC), had representatives from the Finnish Social and Health Informatics Association, University of Eastern Finland, University of Turku and Tampere University. The other SPC members were representatives from the EFMI working groups Citizen and Health Data, Education, Assessment of Health Information Systems, and Security, Safety and Ethics. The SPC consisted of the following people: Jaime Delgado, Parisis Gallos, Maria Hägglund, Kristiina Häyrinen (vice chair), Ulla-Mari Kinnunen, Louise Pape-Haugaard, Laura-Maria Peltonen, Kaija Saranto, Philip Scott, and Alpo Värri (chair).

On behalf of the scientific program committee I would like to warmly thank all the authors who submitted their papers to the conference. Many thanks also to the reviewers whose voluntary work contributed to the quality of the conference, not forgetting the scientific program committee itself that put the whole conference together in its 20+ meetings and individual work.

Alpo Värri

Chair of Scientific Programme Committee

Tampere, October 2020

Though a preventable risk, the management of pressure ulcers (PUs) in nursing homes is not satisfactory due to inadequate prevention and complex care plans. PUs early detection and wound assessment require to know the patient condition and risk factors and to have a good knowledge of best practices. We built a guideline-based clinical decision support system (CDSS) for the prevention, the assessment, and the management of PUs. Clinical practice guidelines have been modeled as decision trees and formalized as IF-THEN rules to be triggered by electronic health record (EHR) data. From PU assessment yielded by the CDSS, we propose a synthetic visualization of PU current and previous stages as a gauge that illustrates the different stages of PU continuous evolution. This allows to display PU current and previous stages to inform health care professionals of PU updated assessment and support their evaluation of previously delivered care efficiency. The CDSS will be integrated in NETSoins nursing homes EHR where gauges for several health problems constitute a patient dashboard.

Social determinants of health (SDoH) are the factors which lie outside of the traditional health system, such as employment or access to nutritious foods, that influence health outcomes. Some efforts have focused on identifying vulnerable populations during the COVID-19 pandemic, however, both the short- and long-term social impacts of the pandemic on individuals and populations are not well understood. This paper presents a pipeline to discover health outcomes and related social factors based on trending SDoH at population-level using Google Trends. A knowledge graph was built from a corpus of research literature (PubMed) and the social determinants that trended high at the start of the pandemic were examined. This paper reports on related social and health concepts which may be impacted by the COVID-19 outbreak and may be important to monitor as the pandemic evolves. The proposed pipeline should have wider applicability in surfacing related social or clinical characteristics of interest, outbreak surveillance, or to mine relations between social and health concepts that can, in turn, help inform and support citizen-centred services.

Patient-generated health data (PGHD), when shared with the provider, provides potential as an approach to improve quality of care. Based on interviews and a focus group with stakeholders involved in PGHD integration in the electronic medical record (EMR), we explore the benefits, barriers and possible risks. We propose solutions to address liability concerns, such as clarifying patient and provider expectations for the analyses of PGHD and emphasize considerations for future steps, which include the need to screen PGHD for patient safety.

The potential of healthcare systems worldwide is expanding as new medical devices and data sources are regularly presented to healthcare providers which could be used to personalise, improve and revise treatments further. However, there is presently a large gap between the data collected, the systems that store the data, and any ability to perform big data analytics to combinations of such data. This paper suggests a novel approach to integrate data from multiple sources and formats, by providing a uniform structure to the data in a healthcare data lake with multiple zones reflecting how refined the data is: from raw to curated when ready to be consumed or used for analysis. The integration further requires solutions that can be proven to be secure, such as patient-centric data sharing agreements (smart contracts) on a blockchain, and novel privacy-preserving methods for extracting metadata from data sources, originally derived from partially-structured or from completely unstructured data. Work presented here is being developed as part of an EU project with the ultimate aim to develop solutions for integrating healthcare data for enhanced citizen-centred care and analytics across Europe.

The COVID-19 pandemic is broadly undercutting global health and economies, while disproportionally impacting socially disadvantaged populations. An impactful pandemic surveillance solution must draw from multi-dimensional integration of social determinants of health (SDoH) to contextually inform traditional epidemiological factors. In this article, we describe an Urban Public Health Observatory (UPHO) model which we have put into action in a mid-sized U.S. metropolitan region to provide near real-time analysis and dashboarding of ongoing COVID-19 conditions. Our goal is to illuminate associations between SDoH factors and downstream pandemic health outcomes to inform specific policy decisions and public health planning.

In emergency situations, every minute counts. Therefore, staff of emergency medical services (EMS) require easily accessible sources of information to organize and coordinate their work as quickly as possible. Digital dashboards can visualize various information at a glance and have thus potential to meet this need. We developed in cooperation with the Emmental Hospital a prototype of a dashboard, which aims to improve organizational aspects of the EMS.

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent COVID-19 clusters in patients with chronic lower respiratory diseases (CLRD). Patients who underwent testing for SARS-CoV-2 were identified from electronic medical records. The analytical dataset comprised 2,328 CLRD patients of whom 1,029 were tested COVID-19 positive. We used the factor analysis for mixed data method for preprocessing. It performed principle component analysis on numeric values and multiple correspondence analysis on categorical values which helped convert categorical data into numeric. Cluster analysis was an effective means to both distinguish subgroups of CLRD patients with COVID-19 as well as identify patient clusters which were adversely affected by the infection. Age, comorbidity index and race were important factors for cluster separations. Furthermore, diseases of the circulatory system, the nervous system and sense organs, digestive system, genitourinary system, metabolic diseases and immunity disorders were also important criteria in the resulting cluster analyses.

Making data Findable, Accessible, Interoperable and Reusable (FAIR) is a good approach when data needs to be shared. However, security and privacy are still critical aspects. In the FAIRification process, there is a need both for de-identification of data and for license attribution. The paper analyses some of the issues related to this process when the objective is sharing genomic information. The main results are the identification of the already existing standards that could be used for this purpose and how to combine them. Nevertheless, the area is quickly evolving and more specific standards could be specified.

Cognitive behavior therapy for insomnia (CBT-I) is the first-line treatment for patients with insomnia disorder, including patients with severe mental disorders and comorbid insomnia. However, CBT-I is not sufficiently implemented in acute psychiatry settings. To make this treatment more accessible, we are currently adapting CBT-I to the needs of patients with severe psychiatric disorders in the form of a treatment program entitled SLEEPexpert. A core element of SLEEPexpert is keeping a sleep diary and restricting time in bed to increase sleep pressure. Here, we present a mobile application which supports the implementation of SLEEPexpert. The app is kept very simple, specifically designed for the target user group, and offers four main functionalities: entering information into the sleep diary, calculating the sleep efficiency and adapting the sleep window, delivering information on sleep and sleep disorders and accessing the recorded data in the sleep diary. Currently, we are preparing a usability test for the app aiming at fixing usability issues before running a clinical trial to assess the efficacy of this mHealth intervention.

The belief that following rigorous inclusive methods will eliminate bias from ‘quality’ measures ignores the preferences necessarily embedded in any formative instrument. These preferences almost always reflect the interests of its developers when one uses the wide definition of ‘interest’ appropriate in healthcare research and provision. We focus on the International Patient Decision Aid Standards instrument, a popular normative measure of decision aid quality. Drawing on its application to a set of 23 breast cancer screening decision aids, we show the effects of modifications that reflect our own different interest-conflicted preferences. It is emphasised that the only objection is to the implication that any formative instrument should be promoted or treated as the ‘the gold standard’, without a conflict of interests disclaimer, and to the implication that other instruments cannot provide equally valid, high-quality measures.

Empirical measures of ‘decision aid quality’, like normative ones, are of a formative construct and therefore embody interest-conflicted preferences in their criteria selection and weighting. The preferences of the International Patient Decision Aid Standards consortium distinguish the quality of the decision-making process and the quality of the choice that is made ‘(i.e., decision quality)’. The Decision Conflict Scale features heavily in their profile measure of the former and Decision Quality Instruments (DQIs), have been developed by members of the consortium to measure the latter. We confirm that both of these, and other components, like the higher-level measures, are preference-sensitive and interest-conflicted. Non-financial interest-conflicted preferences are endemic in healthcare research, policy-making, and practice. That they are inevitable means the main problem lies in the denial of this and attitude to and behaviour towards alternatives, equally interest-conflicted.

Mental disorders are widespread among the world’s population and place a high burden on both the people affected and the economy. In this area of health care and prevention major deficits can be found. Health-enabling technologies are being developed in order to provide support in the therapy and diagnostics of mental disorders. However, it is not clear whether patients are open to these technologies and what they expect from a suitable usage. The main goal of this study is to find out what opinions, hopes and fears mentally ill persons have towards a supporting treatment with health-enabling technologies. Personal interviews were conducted with psychiatric patients for that purpose. The evaluation of the interview data revealed a predominantly positive mindset of the participants. In addition to the general question according to the acceptance, requirements and expectations for the use of health-enabling technologies were acquired. In this context the concern of an invasion of privacy was exposed as a major barrier.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease, that can lead to joint damage but also affects quality of life (QoL) including aspects such as self-esteem, fatigue, and mood. Current medical management focuses on the fluctuating disease activity to prevent progressive disability, but practical constraints mean periodic clinic appointments give little attention to the patient’s experience of managing the wider consequences of chronic illness. The main aim of this study is to explore how to use patient-derived data both for clinical decision-making and for personalisation, with the first steps towards a platform for tailoring self-management advice to patients’ lifestyle changes. As a result, we proposed a Bayesian network model for personalisation and have obtained promising outcomes.

Digital technologies are transforming the health sector all over the world, however various aspects of this emerging field of science is yet to be properly understood. Ambiguity in the definition of digital health is a hurdle for research, policy, and practice in this field. With the aim of achieving a consensus in the definition of digital health, we undertook a quantitative analysis and term mapping of the published definitions of digital health. After inspecting 1527 records, we analyzed 95 unique definitions of digital health, from both scholar and general sources. The findings showed that digital health, as has been used in the literature, is more concerned about the provision of healthcare rather than the use of technology. Wellbeing of people, both at population and individual levels, have been more emphasized than the care of patients suffering from diseases. Also, the use of data and information for the care of patients was highlighted. A dominant concept in digital health appeared to be mobile health (mHealth), which is related to other concepts such as telehealth, eHealth, and artificial intelligence in healthcare.

Pulmonary rehabilitation [PR] has been successfully carried out via telemedicine however initial patient assessment has been traditionally conducted in PR centers. The first step in PR is assessment of patient’s exercise capacity which allows individualized prescription of safe and effective exercise program. With COVID-19 pandemics assessment of patients in PR centers has been limited resulting in significant reduction of patients undergoing life-saving PR. The goal of this pilot study was to introduce approaches for remote assessment of exercise capacity using videoconferencing platforms and provide initial usability assessment of this approach by conducing cognitive walkthrough testing. We developed a remote assessment system that supports comprehensive physical therapy assessment necessary for prescription of a personalized exercise program tailored to individual fitness level and limitations in gait and balance of the patient under evaluation. Usability was assessed by conducting cognitive walkthrough and system usability surveys. The usability inspection of the remote exercise assessment demonstrated overall high acceptance by all study participants. Our next steps in developing user-centered interface should include usability evaluation in different subgroups of patients with varying socio-economic background, different age groups, computer skills, literacy and numeracy.

Due to the large number of smartphone users, mHealth has become a popular support to foster users’ health behavior change Personalization is an important factor to increase the effectiveness of mHealth interventions. Based on a literature review, we have listed and categorized personalization concepts associated with behavior change in mHealth into 4 dimensions, users, system functionalities, information, and app properties. The users dimension refers to user-related characteristics such as personality, player profile, need for cognition and perception of social norms. The system functionalities contain the functionalities that can be found in applications such as reminders as well as gamification functionalities such as collectibles. The information dimension concerns the way information is transmitted, such as the source of the message must be expert or the type of feedback to be provided. Finally, there are app properties such as the aesthetics of the application. For the next part, it would be interesting to discover the links we can make between the dimensions.

Patient portals are used as a means to facilitate communication, performing administrative tasks, or accessing one’s health record. In a retrospective analysis of real-world data from the Swedish National Patient Portal 1177.se, we describe the rate of adoption over time, as well as how patterns of device usage have changed over time. In Jan 2013, 53% of all visits were made from a computer, and 38% from a mobile phone. By June 2020, 77% of all visits were made from a mobile phone and only 20% from a computer. These results underline the importance of designing responsive patient portals that allow patients to use any device without losing functionality or usability.

This viewpoint paper presents a potential solution to the “information islands” that are holding back PHR/UHR from becoming truly effective diagnostic information care management tools for patients especially those who suffer from chronic diseases. The solution involves integrating patient portal with a diagnostic data interface layer to create a single access point for caregivers and patients.

Current technologies provide the ability to healthcare practitioners and citizens, to share and analyse healthcare information, thus improving the patient care quality. Nevertheless, European Union (EU) citizens have very limited control over their own health data, despite that several countries are using national or regional Electronic Health Records (EHRs) for realizing virtual or centralized national repositories of citizens’ health records. Health Information Exchange (HIE) can greatly improve the completeness of patients’ records. However, most of the current researches deal with exchanging health information among healthcare organizations, without giving the ability to the citizens on accessing, managing or exchanging healthcare data with healthcare organizations and thus being able to handle their own data, mainly due to lack of standardization and security protocols. Towards this challenge, in this paper a secure Device-to-Device (D2D) protocol is specified that can be used by software applications, aiming on facilitating the exchange of health data among citizens and healthcare professionals, on top of Bluetooth technologies.

The use of welfare technologies in the home setting has drawn increased attention in healthcare. From a historical perspective, medical technologies were designed for hospital settings. Digitalization and internet of things have changed the structure of our society. The aim of this paper is to describe the factors that determine a user’s intent to adopt new welfare technologies in the context of homecare. The phenomenon was being examined by the unified theory of acceptance and use of technology. This study was to show that performance expectancy, effort expectancy, and facilitating conditions are significant factors in determining a user’s intention to use new welfare technologies. While, the use of welfare technologies was rare in homecare.

Patient incident reporting is an important way to promote safer health care. The barriers for reporting can be organizational (leadership, culture, lack of feedback, etc.) or individual (time pressure, perceived competence, attitude, etc.). In this study, we examined what kinds of ICT-related incidents health professionals observe in Finland, how they react to them and the reasons for non-reporting. Our data was collected using a nationwide survey during the Spring of 2020. The theory of planned behaviour by Ajzen served as our framework for explaining non-reporting behaviour. While we found that attitudes, subjective norms and perceived behavioural control all explain non-reporting, our factor model based on our confirmatory factor analysis did not directly match Ajzen’s theory.

How textual clinical practice guidelines are written may have an impact on how they are formalized and on the kind of recommendations issued by the clinical decision support systems (CDSSs) that implement them. Breast cancer guidelines are mostly centered on the description of the different recommended therapeutic modalities, represented as atomic recommendations, but seldom provide comprehensive plans that drive care delivery. The objective of this work is to implement a knowledge-based approach to develop a care plan builder (CPB) that works on atomic recommendations to build patient-centered care plans as sequences of chronologically ordered therapeutic steps. The CPB uses the atomic recommendations issued by the guideline-based decision support system (GL-DSS) of the DESIREE project. The domain knowledge is represented as the list of all care plans that apply to breast cancer patients. Scenarios are introduced to locate the patient on these theoretical care plans. The CPB has been evaluated on a sample of 99 solved clinical cases leading to an overall performance of 89,8%.

The objective of this study was to test the feasibility of automatically extracting and exploiting data from the YouTube platform, with a focus on the videos produced by the French YouTuber HugoDécrypte during COVID-19 quarantine in France. For this, we used the YouTube API, which allows the automatic collection of data and meta-data of videos. We have identified the main topics addressed in the comments of the videos and assessed their polarity. Our results provide insights on topics trends over the course of the quarantine and highlight users sentiment towards on-going events. The method can be expanded to large video sets to automatically analyse high amount of user-produced data.

One of the most important challenges in the scenario of COVID-19 is to design and develop decision support systems that can help medical staff to identify a cohort of patients that is more likely to have worse clinical evolution. To achieve this objective it is necessary to work on collected data, pre-process them in order to obtain a consistent dataset and then extract the most relevant features with advanced statistical methods like principal component analysis. As preliminary results of this research, very influential features that emerged are the presence of cardiac and liver illnesses and the levels of some inflammatory parameters at the moment of diagnosis.