Background: The New Public Management movement strove for transparency so that policy makers and citizens could gain insight into the work and performance of health care. As the use of the electronic health record (EHR) started to diffuse, a foundation was laid for enhanced transparency within and between health care organizations. Now we appear to be experiencing a new kind of transparency in the health care sector. Many health care providers offer their patients online access to their EHRs (here referred to as Open Notes). The Open Notes system enables and strives for transparency between the health care organization and the patient. Hence, this study investigates health care professional (HCP) perceptions of Open Notes and deepens the understanding of the transparency that Open Notes implies.

Objective: Based on two survey studies of HCP perceptions of Open Notes, this paper aims to deepen the academic writing on the type of transparency that is connected to Open Notes.

Methods: HCPs in adult psychiatry in Region Skåne, Sweden, were surveyed before and after implementation of Open Notes. The empirical material presented consists of 1554 free-text answers from two Web surveys. A qualitative content analysis was performed.

Results: The theoretically informed analysis pivots around the following factors connected to transparency: effectiveness; trust; accountability; autonomy and control; confidentiality, privacy, and anonymity; fairness; and legitimacy. The results show that free-text answers can be sorted under these factors as trade-offs with transparency. According to HCPs, trade-offs affect their work, their relationship with patients, and not least, their work tool, the EHR. However, since many HCPs also state that they have not met many patients, and in some cases none, who have read their EHRs, these effects seem to be more connected to the possibility (or threat) of transparency than to the actual effectuated transparency.

Conclusions: The implementation (or reform) of Open Notes is policy driven while demanding real-time transparency on behalf of citizens/patients and not the authorities, which makes this particular form of transparency quite unique and interesting. We have chosen to call it governed individual real-time transparency. The effects of Open Notes may vary between different medical specialties relative to their sensitivity to both total and real-time transparency. When HCPs react by changing their ways of writing notes, Open Notes can affect the efficiency of the work of HCPs and the service itself in a negative manner. HCP reactions are aimed primarily at protecting patients and their relatives as well as their own relationship with the patients and secondly at protecting themselves. Thus, governed individual real-time transparency that provides full transparency of an actual practice in health care may have the intended positive effects but can also result in negative trade-offs between transparency and efficiency of the actual practice. This may imply that full transparency is not always most desirable but that other options can be considered on a scale between none and full transparency.

© Gudbjörg Erlingsdóttir, Lena Petersson, Karin Jonnergård. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.09.2019.

Background: Information on how behavior change strategies have been used to design digital interventions (DIs) to improve blood pressure (BP) control or medication adherence (MA) for patients with hypertension is currently limited.

Objective: Hypertension is a major modifiable risk factor for cardiovascular diseases and can be controlled with appropriate medication. Many interventions that target MA to improve BP are increasingly using modern digital technologies. This systematic review was conducted to discover how DIs have been designed to improve MA and BP control among patients with hypertension in the recent 10 years. Results were mapped into a matrix of change objectives using the Intervention Mapping framework to guide future development of technologies to improve MA and BP control.

Methods: We included all the studies regarding DI development to improve MA or BP control for patients with hypertension published in PubMed from 2008 to 2018. All the DI components were mapped into a matrix of change objectives using the Intervention Mapping technique by eliciting the key determinant factors (from patient and health care team and system levels) and targeted patient behaviors.

Results: The analysis included 54 eligible studies. The determinants were considered at two levels: patient and health care team and system. The most commonly described determinants at the patient level were lack of education, lack of self-awareness, lack of self-efficacy, and forgetfulness. Clinical inertia and an inadequate health workforce were the most commonly targeted determinants at the health care team and system level. Taking medication, interactive patient-provider communication, self-measurement, and lifestyle management were the most cited patient behaviors at both levels. Most of the DIs did not include support from peers or family members, despite its reported effectiveness and the rate of social media penetration.

Conclusions: This review highlights the need to design a multifaceted DI that can be personalized according to patient behavior(s) that need to be changed to overcome the key determinant(s) of low adherence to medication or uncontrolled BP among patients with hypertension, considering different levels including patient and healthcare team and system involvement. © Kobra Etminani, Arianna Tao Engström, Carina Göransson, Anita Sant’Anna, Sławomir Nowaczyk.

Background: Significant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice.

Objective: This paper aims to identify the implementation frameworks used to understand the application of AI in health care practice.

Methods: A scoping review was conducted using the Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases to identify publications that reported frameworks, models, and theories concerning AI implementation in health care. This review focused on studies published in English and investigating AI implementation in health care since 2000. A total of 2541 unique publications were retrieved from the databases and screened on titles and abstracts by 2 independent reviewers. Selected articles were thematically analyzed against the Nilsen taxonomy of implementation frameworks, and the Greenhalgh framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health care technologies.

Results: In total, 7 articles met all eligibility criteria for inclusion in the review, and 2 articles included formal frameworks that directly addressed AI implementation, whereas the other articles provided limited descriptions of elements influencing implementation. Collectively, the 7 articles identified elements that aligned with all the NASSS domains, but no single article comprehensively considered the factors known to influence technology implementation. New domains were identified, including dependency on data input and existing processes, shared decision-making, the role of human oversight, and ethics of population impact and inequality, suggesting that existing frameworks do not fully consider the unique needs of AI implementation.

Conclusions: This literature review demonstrates that understanding how to implement AI in health care practice is still in its early stages of development. Our findings suggest that further research is needed to provide the knowledge necessary to develop implementation frameworks to guide the future implementation of AI in clinical practice and highlight the opportunity to draw on existing knowledge from the field of implementation science. ©Fábio Gama, Daniel Tyskbo, Jens Nygren, James Barlow, Julie Reed, Petra Svedberg. 

Background: There is a shortage of electronic Health (eHealth) services for children 6-12 years old, which promotes their participation in healthcare. Children with long-term diseases want to be more involved in their healthcare, and have the right to receive information, to be listened to, to express their opinions and to participate in decision-making in healthcare.

Objective: The aim of this study was to investigate children’s participation during appointments with pediatricians at pediatric oncology clinics, with or without the use of the eHealth service Sisom.

Method: A quasi-experimental design with mixed methods was used. Twenty-seven appointments with pediatricians for 14 children aged 6-12 years (mean 8.3) with a cancer diagnosis were filmed and analyzed. The intervention group consisted of children who used an eHealth service prior their appointments with pediatricians at a pediatric oncology clinic and the control group consisted of children during their appointments with pediatricians at four other pediatric oncology clinics. The data from the observations from the films were analyzed with quantitative and qualitative analysis. The quantitative analysis included manual calculations of how many times the pediatricians spoke directly to the children, of the proportion of the appointment time that the children were talking and their levels of participation. The qualitative analysis included directed content analysis included observations of the video films to assess the children´s levels of participation manifested themselves.

Results:  A greater proportion of what the pediatrician said in the intervention group was addressed to the child than occurred in the control group, but the proportion of the appointment time the children talked was almost the same for both the intervention and the control groups. The levels of participation corresponded to the first three levels of Shier´s participation model: Children were listened to, Children were supported to express their views and Children´s views were taken into account. The results showed an increased level of the children´s participation in the intervention group. Several codes were found about information, which did not fit into any of the existing categories, and a new category was thus formed: Children received information. Situations were also identified where children were actively excluded from participation; these were presented as negative codes.

Conclusions: This study shows that the eHealth service Sisom can increase children´s participation during appointments with healthcare professionals. Future research should focus on evaluating outcomes on individual and organizational levels and in different healthcare contexts. © The authors. All rights reserved

BACKGROUND: Participatory Design (PD), albeit an established approach in User-Centered Design, comes with specific challenges when working with older adults as research participants. Addressing these challenges relates to the reflection and negotiation of the positionalities of the researchers and research participants and includes various acts of giving and receiving help. During the COVID-19 pandemic, facets of positionalities and (mutual) care became particularly evident in qualitative and participatory research settings. OBJECTIVE: The aim of this paper was to systematically analyze care practices of participatory (design) research, which are to different extents practices of the latter. Using a multiyear PD project with older people that had to take place remotely over many months, we specify different practices of care; how they relate to collaborative work in the design project; and represent foundational practices for sustainable, long-term co-design. Our research questions were "How can digitally-mediated PD work during COVID-19 and can we understand such digital PD as 'care'?" METHODS: Our data comes from the Joint Programming Initiative "More Years, Better Lives" (JPI MYBL), a European Union project that aims to promote digital literacy and technology appropriation among older adults in domestic settings. It targeted the cocreation, by older adults and university researchers, of a mobile demo kit website with cocreated resources, aimed at improving the understanding of use options of digital tools. Through a series of workshops, a range of current IT products was explored by a group of 21 older adults, which served as the basis for joint cocreative work on generating design ideas and prototypes. We reflect on the PD process and examine how the actors enact and manifest care. RESULTS: The use of digital technology allowed the participatory project to continue during the COVID-19 pandemic and accentuated the digital skills of older adults and the improvement of digital literacy as part of "care." We provide empirically based evidence of PD with older adults developing digital literacy and sensitizing concepts, based on the notion of care by Tronto for differentiating aspects and processes of care. The data suggest that it is not enough to focus solely on the technologies and how they are used; it is also necessary to focus on the social structures in which help is available and in which technologies offer opportunities to do care work. CONCLUSIONS: We document that the cocreation of different digital media tools can be used to provide a community with mutual care. Our study demonstrates how research participants effectively enact different forms of care and how such "care" is a necessary basis for a genuinely participatory approach, which became especially meaningful as a form of support during COVID-19. We reflect on how notions of "care" and "caring" that were central to the pandemic response are also central to PD. ©Richard Paluch, Katerina Cerna, Dennis Kirschsieper, Claudia Müller. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.07.2023.

Background: Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood.

Objective: The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible?

Methods: A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized.

Results: Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare.

Conclusions: Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.Keywords: artificial intelligence; health care; implementation; scoping review; technology adoption.©Malvika Sharma, Carl Savage, Monika Nair, Ingrid Larsson, Petra Svedberg, Jens M Nygren. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.10.2022.

Background: Lifestyle-related health problems is an important health concern in the transport service industry. Web- and telephone-based interventions could be suitable for this target group requiring tailored approaches.

Objective: To evaluate the effect of tailored web-based health feedback and optional telephone coaching with respect to improved lifestyle factors (Body Mass Index [BMI], dietary intake, physical activity, stress, sleep, tobacco- and alcohol consumption, disease history, self-perceived health, and motivation to change health habits), in comparison to no health feedback or telephone coaching.

Methods: 3,876 employees in the Swedish transport services were e-mailed a web-based questionnaire. They were randomized to either: A) control group (498 out of 1,238 answered, 40.2%) or B) intervention web (482 out of 1,305 answered, 36.9%), or C) intervention web+telephone (493 out of 1,333 answered, 37.0%). All groups received an identical questionnaire, only the interventions differed. Group B received tailored web-based health feedback and group C received tailored web-based health feedback + optional telephone coaching if the participants’ reported health habits did not meet the national guidelines, or if they expressed motivation to change health habits. The web-based feedback was fully automated. Telephone coaching was performed by trained health counsellors. Nine months later, all participants received a follow-up questionnaire and intervention web+telephone. Descriptive statistics, Chi-square test, analysis of variance, and generalized estimation equations (GEE) models were employed.

Results: 981 out of 1,473 (66.6%) employees participated at baseline (men: 66.7%, mean age: 44 years, mean BMI: 26.4 kg/m2) and at follow-up. No significant differences were found in reported health habits between the three groups over time. However, significant changes were found for motivation to change. The intervention groups reported higher motivation to improve dietary habits (n=144 out of 301 participants [47.8%] and n=165 out of 324 participants [50.9%] for B and C, respectively) and physical activity habits (n=181 out of 301 participants [60.1%] and n=207 out of 324 participants [63.9%] for B and C, respectively) compared to the control group A (n=122 out of 356 participants [34.3%] for diet and n=177 out of 356 participants [49.7%] for physical activity). At follow-up, the intervention groups had significantly decreased their motivation (group B: P<.001 for change in diet; P<.001 for change in physical activity; group C: P=.007 for change in diet; P<.001 for change in physical activity), whereas the control group reported significantly increased motivation to change diet and physical activity (P<.001 for change in diet; P<.001 for change in physical activity). © Madeleine Solenhill, Alessandra Grotta, Elena Pasquali, Linda Bakkman, Rino Bellocco, Ylva Trolle Lagerros.

Conclusions: Tailored web-based health feedback and the offering of optional telephone coaching did not have a positive health effect on employees in the transport services. However, our findings suggest an increased short-term motivation to change health behaviors related to diet and physical activity among those receiving tailored web-based health feedback.

BACKGROUND: Sensitive and interpretable machine learning (ML) models can provide valuable assistance to clinicians in managing patients with heart failure (HF) at discharge by identifying individual factors associated with a high risk of readmission. In this cohort study, we delve into the factors driving the potential utility of classification models as decision support tools for predicting readmissions in patients with HF. OBJECTIVE: The primary objective of this study is to assess the trade-off between using deep learning (DL) and traditional ML models to identify the risk of 100-day readmissions in patients with HF. Additionally, the study aims to provide explanations for the model predictions by highlighting important features both on a global scale across the patient cohort and on a local level for individual patients. METHODS: The retrospective data for this study were obtained from the Regional Health Care Information Platform in Region Halland, Sweden. The study cohort consisted of patients diagnosed with HF who were over 40 years old and had been hospitalized at least once between 2017 and 2019. Data analysis encompassed the period from January 1, 2017, to December 31, 2019. Two ML models were developed and validated to predict 100-day readmissions, with a focus on the explainability of the model's decisions. These models were built based on decision trees and recurrent neural architecture. Model explainability was obtained using an ML explainer. The predictive performance of these models was compared against 2 risk assessment tools using multiple performance metrics. RESULTS: The retrospective data set included a total of 15,612 admissions, and within these admissions, readmission occurred in 5597 cases, representing a readmission rate of 35.85%. It is noteworthy that a traditional and explainable model, informed by clinical knowledge, exhibited performance comparable to the DL model and surpassed conventional scoring methods in predicting readmission among patients with HF. The evaluation of predictive model performance was based on commonly used metrics, with an area under the precision-recall curve of 66% for the deep model and 68% for the traditional model on the holdout data set. Importantly, the explanations provided by the traditional model offer actionable insights that have the potential to enhance care planning. CONCLUSIONS: This study found that a widely used deep prediction model did not outperform an explainable ML model when predicting readmissions among patients with HF. The results suggest that model transparency does not necessarily compromise performance, which could facilitate the clinical adoption of such models. © Amira Soliman, Björn Agvall, Kobra Etminani, Omar Hamed, Markus Lingman. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.10.2023.

BACKGROUND: Children's participation in health care is one of the most important components in the management of their disease. Electronic health (eHealth) services that are adapted to the needs of children have the potential for restructuring how children and professionals work together. Therefore, a digital interactive assessment and communication tool, Sisom, was developed to give children aged between 6 and 12 years a voice in their own health care. However, the implementation of eHealth services such as Sisom in daily practice in pediatric health care is rarely investigated. OBJECTIVE: The aim of this study was to explore the process of implementing Sisom for children in pediatric care in Sweden. More specifically, the study aimed to (1) evaluate whether the implementation strategy was conducted as planned, (2) understand the barriers and facilitators of the implementation strategy in pediatric care settings, (3) gain insight into how professionals work with the specific intervention, and (4) gain insight into the usefulness and effects of the intervention from the professionals' perspectives. METHODS: A process evaluation design was used to study the implementation of Sisom at 4 pediatric care centers in Sweden. An extensive amount of qualitative and quantitative data was collected before, during, and after the intervention through self-report checklists, memos, and interviews with professionals. In total, 46 children, aged between 6 and 13 years, participated. The children used Sisom on two occasions during 6 months. When they used Sisom, a printed report formed the basis for a forthcoming dialogue between professionals, children, and their parents. RESULTS: To our knowledge, this is the first implementation study of an eHealth communication tool aimed at strengthening children's participation in pediatric health care. Key factors for successful implementation were alignment of the solution with the values and goals of the organization, health care professionals' beliefs in the usefulness and usability of the solution, and health care professionals' willingness to change their professional roles guided by the solution. CONCLUSIONS: The results from the study show that it is possible to restructure health care delivery toward a child-centered approach, if there is a willingness and preparedness in the organization to implement an eHealth solution with the aim of restructuring the way of working with children's participation. ©Petra Svedberg, Susann Arvidsson, Ingrid Larsson, Ing-Marie Carlsson, Jens M Nygren.

Background: Misreporting food intake is common because most health screenings rely on self-reports. The more accurate methods (eg, weighing food) are costly, time consuming, and impractical.

Objectives: We developed a new instrument for reporting food intake—an Internet-based interactive virtual food plate. The objective of this study was to validate this instrument’s ability to assess lunch intake.

Methods: Participants were asked to compose an ordinary lunch meal using both a virtual and a real lunch plate (with real food on a real plate). The participants ate their real lunch meals on-site. Before and after pictures of the composed lunch meals were taken. Both meals included identical food items. Participants were randomized to start with either instrument. The 2 instruments were compared using correlation and concordance measures (total energy intake, nutritional components, quantity of food, and participant characteristics).

Results: A total of 55 men (median age: 45 years, median body mass index [BMI]: 25.8 kg/m2) participated. We found an overall overestimation of reported median energy intake using the computer plate (3044 kJ, interquartile range [IQR] 1202 kJ) compared with the real lunch plate (2734 kJ, IQR 1051 kJ, P<.001). Spearman rank correlations and concordance correlations for energy intake and nutritional components ranged between 0.58 to 0.79 and 0.65 to 0.81, respectively.

Conclusion: Although it slightly overestimated, our computer plate provides promising results in assessing lunch intake. © Filippo Castiglione.

Background: Eating behaviors are essential components in weight loss programs, but limited research has explored eating behaviors in Web-based weight loss programs.

Objectives: The aim was to evaluate an interactive Web-based weight loss program on eating behaviors using the 18-item Three-Factor Eating Questionnaire Revised (TFEQ-R18) which measures uncontrolled eating, emotional eating, and cognitive restrained eating. Our Web-based weight loss program is comprised of information about healthy lifestyle choices, weekly chats with experts, social networking features, databases for recipe searches, and features allowing members to self-report and track their weight, physical activity, and dietary intake on the website.

Methods: On registering for the weight loss program, 23,333 members agreed to take part in the research study. The participants were then asked to complete the TFEQ-R18 questionnaire at baseline and after 3 and 6 months of participation. All data collection was conducted online, with no face-to-face contact. To study changes in TFEQ-R18 eating behaviors we restricted our study to those members who completed all 3 TFEQ-R18 questionnaires. These participants were defined as "completers" and the remaining as "noncompleters." The relationships between sex, change in eating behaviors, and total weight loss were studied using repeated measures ANOVA and Pearson correlation coefficient.

Results: In total, 22,800 individuals participated (females: 19,065/22,800, 83.62%; mean age 39.6, SD 11.4 years; BMI 29.0 kg/m2; males: 3735/22,800, 16.38%; mean age 43.2, SD 11.7 years; BMI 30.8 kg/m2). Noncompleters (n=22,180) were younger and reported a lower score of uncontrolled eating and a higher score of cognitive restrained eating. Over time, completers (n=620) decreased their uncontrolled eating score (from 56.3 to 32.0; P<.001) and increased their cognitive restrained eating (from 50.6 to 62.9; P<.001). Males decreased their emotional eating (from 57.2 to 35.9; P<.001), but no significant change was found among females. The baseline cognitive restrained eating score was significantly and positively associated with weight loss for completers in both men (P=.02) and women (P=.002).

Conclusions: To our knowledge, this is the largest TFEQ sample that has been documented. This Web-based weight loss intervention suggests that eating behaviors (cognitive restrained eating, uncontrolled eating, and emotional eating) measured by TFEQ-R18 were significantly changed during 6 months of participation. Our findings indicate differences in eating behaviors with respect to sex, but should be interpreted with caution because attrition was high.

BACKGROUND: Peer support services have the potential to support children who survive cancer by handling the physical, mental, and social challenges associated with survival and return to everyday life. Involving the children themselves in the design process allows for adapting services to authentic user behaviors and goals. As there are several challenges that put critical requirements on a user-centered design process, we developed a design method based on personas adapted to the particular needs of children that promotes health and handles a sensitive design context.

OBJECTIVE: The purpose of this study was to evaluate the effects of using child personas in the development of a digital peer support service for childhood cancer survivors.

METHODS: The user group's needs and behaviors were characterized based on cohort data and literature, focus group interviews with childhood cancer survivors (n=15, 8-12 years), stakeholder interviews with health care professionals and parents (n=13), user interviews, and observations. Data were interpreted and explained together with childhood cancer survivors (n=5) in three explorative design workshops and a validation workshop with children (n=7).

RESULTS: We present findings and insights on how to codesign child personas in the context of developing digital peer support services with childhood cancer survivors. The work resulted in three primary personas that model the behaviors, attitudes, and goals of three user archetypes tailored for developing health-promoting services in this particular use context. Additionally, we also report on the effects of using these personas in the design of a digital peer support service called Give Me a Break.

CONCLUSIONS: By applying our progressive steps of data collection and analysis, we arrive at authentic child-personas that were successfully used to design and develop health-promoting services for children in vulnerable life stages. The child-personas serve as effective collaboration and communication aids for both internal and external purposes.