One Health: Insights from Organizational & Social, Technology Assessment and Human Factors Perspectives.

OBJECTIVES: To offer diverse but complementary perspectives on how biomedical and health informatics can be informed by and help to achieve the vision of One Health. METHODS: Overview of key considerations and critical discussion of common themes, barriers and opportunities, based on collaborative review by International Medical Informatics Association (IMIA) working group members active in related fields. RESULTS: Health and care systems are complex sociotechnical systems that need explicit design and implementation strategies to align with the goals of One Health. The evidence-based health informatics paradigm and associated frameworks for evaluation of digital health technologies need to broaden their scope to take full account of the One Health approach. Informatics has specific contributions to make to One Health, for example by improved user experience reducing energy consumption and effective app design enhancing medication adherence. CONCLUSIONS: One Health is inherently intertwined with ergonomic, sociotechnical and evaluation perspectives in biomedical and health informatics. Health is a planetary issue that requires interdisciplinary collaborative action. The theories and principles of biomedical and health informatics offer many opportunities to transform digital health technology to better serve the One Health agenda.

The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision.

Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology. We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes. Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.

IT risk management for medical devices in hospital IT networks: a catalogue of measures and indicators.

OBJECTIVES: Connecting medical devices to hospital IT networks can create threats that must be covered by IT risk management. In practice, implementing such risk management is not trivial because the IEC 80001-1, as the existing state-of-the-art, do not describe sufficiently concrete implementation measures or evaluation indicators. The aim of the present work was to develop and evaluate a catalogue of measures and indicators to help hospitals implement and evaluate risk management in accordance with IEC 80001-1. METHODS: We conducted a Delphi study with 22 experts. In the first round, we performed interviews to identify implementation measures and evaluation indicators using qualitative content analysis. In the second round, a quantitative experts' survey confirmed the results of the first survey round and identified relationships between the measures and indicators. Based on these results, we then developed a catalogue containing the identified measures and indicators. Finally, we performed a case study to verify the practicability of this catalogue. RESULTS: We developed and verified a catalogue of 49 measures and 18 indicators to help hospitals implement and evaluate risk management following IEC 80001-1. The case study confirmed the practicability of the catalogue. DISCUSSION: Compared with IEC 80001-1, our catalogue goes into further detail to offer hospitals a stepwise implementation and evaluation approach. However, the catalogue must be tested in further case studies and evaluated in terms of generalisation. CONCLUSIONS: The catalogue will enable hospitals to overcome recent difficulties in implementing and evaluating IT risk management for medical devices according to IEC 80001-1.

The Role of Formative Evaluation in Promoting Digitally-based Health Equity and Reducing Bias for Resilient Health Systems: The Case of Patient Portals.

OBJECTIVES: Patient portals are increasingly implemented to improve patient involvement and engagement. We here seek to provide an overview of ways to mitigate existing concerns that these technologies increase inequity and bias and do not reach those who could benefit most from them. METHODS: Based on the current literature, we review the limitations of existing evaluations of patient portals in relation to addressing health equity, literacy and bias; outline challenges evaluators face when conducting such evaluations; and suggest methodological approaches that may address existing shortcomings. RESULTS: Various stakeholder needs should be addressed before deploying patient portals, involving vulnerable groups in user-centred design, and studying unanticipated consequences and impacts of information systems in use over time. CONCLUSIONS: Formative approaches to evaluation can help to address existing shortcomings and facilitate the development and implementation of patient portals in an equitable way thereby promoting the creation of resilient health systems.

Desiderata for a New Generation of EHRs for Inclusive Healthcare.

OBJECTIVES: This research addresses several factors relevant to inequity in healthcare that may be susceptible to being addressed in a new generation of electronic health records (EHRs). METHODS: Through a scoping review of the literature, inequities related to ethnicity, gender, and socioeconomic aspects in healthcare in general and, more specifically in EHRs, have been considered. Papers have been identified between 2011 and 2022 in three categories: EHR, gender inequalities, and ethnicity inequalities. RESULTS: Twenty-two recommendations have been identified within the scope of the three categories indicated above. These exposed requirements focus on two spheres: (1) technical sphere, mainly focused on the characteristics and tools that the EHR should develop from taking into account the studied inequalities; and (2) clinical sphere, which mainly affects patients, health professionals, and health providers. CONCLUSIONS: Ethnic and gender inequalities are essential factors to take into account when diagnosing, monitoring, preventing, and treating a patient. These factors give us the keys to discovering recommendations for a new generation of EHRs to help mitigate these needs.

Managing Pandemic Responses with Health Informatics - Challenges for Assessing Digital Health Technologies.

OBJECTIVES: To highlight the role of technology assessment in the management of the COVID-19 pandemic. METHOD: An overview of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. RESULTS: Evaluation of digital health technologies for COVID-19 should be based on their technical maturity as well as the scale of implementation. For mature technologies like telehealth whose efficacy has been previously demonstrated, pragmatic, rapid evaluation using the complex systems paradigm which accounts for multiple sociotechnical factors, might be more suitable to examine their effectiveness and emerging safety concerns in new settings. New technologies, particularly those intended for use on a large scale such as digital contract tracing, will require assessment of their usability as well as performance prior to deployment, after which evaluation should shift to using a complex systems paradigm to examine the value of information provided. The success of a digital health technology is dependent on the value of information it provides relative to the sociotechnical context of the setting where it is implemented. CONCLUSION: Commitment to evaluation using the evidence-based medicine and complex systems paradigms will be critical to ensuring safe and effective use of digital health technologies for COVID-19 and future pandemics. There is an inherent tension between evaluation and the imperative to urgently deploy solutions that needs to be negotiated.

Adult patient access to electronic health records.

BACKGROUND: To support patient-centred care, healthcare organisations increasingly offer patients access to data stored in the institutional electronic health record (EHR). OBJECTIVES: Primary objective 1. To assess the effects of providing adult patients with access to electronic health records (EHRs) alone or with additional functionalities on a range of patient, patient-provider, and health resource consumption outcomes, including patient knowledge and understanding, patient empowerment, patient adherence, patient satisfaction with care, adverse events, health-related quality of life, health-related outcomes, psychosocial health outcomes, health resource consumption, and patient-provider communication. Secondary objective 1. To assess whether effects of providing adult patients with EHR access alone versus EHR access with additional functionalities differ among patient groups according to age, educational level, or different status of disease (chronic or acute). SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, PsycINFO, CINAHL, and Scopus in June 2017 and in April 2020. SELECTION CRITERIA: Randomised controlled trials and cluster-randomised trials of EHR access with or without additional functionalities for adults with any medical condition. DATA COLLECTION AND ANALYSIS: We used standard Cochrane methodological procedures. MAIN RESULTS: We included 10 studies with 78 to 4500 participants and follow-up from 3 to 24 months. Nine studies assessed the effects of EHR with additional functionalities, each addressing a subset of outcomes sought by this review. Five studies focused on patients with diabetes mellitus, four on patients with specific diseases, and one on all patients. All studies compared EHR access alone or with additional functionalities plus usual care versus usual care only. No studies assessing the effects of EHR access alone versus EHR access with additional functionalities were identified. Interventions required a variety of data within the EHR, such as patient history, problem list, medication, allergies, and lab results. In addition to EHR access, eight studies allowed patients to share self-documented data, seven offered individualised disease management functions, seven offered educational disease-related information, six supported secure communication, and one offered preventive reminders. Only two studies were at low or unclear risk of bias across domains. Meta-analysis could not be performed, as participants, interventions, and outcomes were too heterogeneous, and most studies presented results based on different adjustment methods or variables. The quality of evidence was rated as low or very low across outcomes. Overall differences between intervention and control groups, if any, were small. The relevance of any small effects remains unclear for most outcomes because in most cases, trial authors did not define a minimal clinically important difference. Overall, results suggest that the effects of EHR access alone and with additional functionalities are mostly uncertain when compared with usual care. Patient knowledge and understanding: very low-quality evidence is available from one study, so we are uncertain about effects of the intervention on patient knowledge about diabetes and blood glucose testing. Patient empowerment: low-quality evidence from three studies suggests that the intervention may have little or no effect on patient empowerment measures. Patient adherence: low-quality evidence from two studies suggests that the intervention may slightly improve adherence to the process of monitoring risk factors and preventive services. Effects on medication adherence are conflicting in two studies; this may or may not improve to a clinically relevant degree. Patient satisfaction with care: low-quality evidence from three studies suggests that the intervention may have little or no effect on patient satisfaction, with conflicting results. Adverse events: two small studies reported on mortality; one of these also reported on serious and other adverse events, but sample sizes were too small for small differences to be detected. Therefore, low-quality evidence suggests that the intervention may have little to no effect on mortality and other adverse events. Health-related quality of life: only very low-quality evidence from one study is available. We are uncertain whether the intervention improves disease-specific quality of life of patients with asthma. Health-related outcomes: low-quality evidence from eight studies suggests that the intervention may have little to no effect on asthma control, glycosylated haemoglobin (HbA1c) levels, blood pressure, low-density lipoprotein or total cholesterol levels, body mass index or weight, or 10-year Framingham risk scores. Low-quality evidence from one study suggests that the composite scores of risk factors for diabetes mellitus may improve slightly with the intervention, but there is uncertainty about effects on ophthalmic medications or intraocular pressure. Psychosocial health outcomes: no study investigated psychosocial health outcomes in a more than anecdotal way. Health resource consumption: low-quality evidence for adult patients in three studies suggests that there may be little to no effect of the intervention on different measures of healthcare use. Patient-provider communication: very low-quality evidence is available from a single small study, and we are uncertain whether the intervention improves communication measures, such as the number of messages sent. AUTHORS' CONCLUSIONS: The effects of EHR access with additional functionalities in comparison with usual care for the most part are uncertain. Only adherence to the process of monitoring risk factors and providing preventive services as well as a composite score of risk factors for diabetes mellitus may improve slightly with EHR access with additional functionalities. Due to inconsistent terminology in this area, our search may have missed relevant studies. As the overall quality of evidence is very low to low, future research is likely to change these results. Further trials should investigate the impact of EHR access in a broader range of countries and clinical settings, including more patients over a longer period of follow-up, as this may increase the likelihood of detecting effects of the intervention, should these exist. More studies should focus on assessing outcomes such as patient empowerment and behavioural outcomes, rather than concentrating on health-related outcomes alone. Future studies should distinguish between effects of EHR access only and effects of additional functionalities, and investigate the impact of mobile EHR tools. Future studies should include information on usage patterns, and consider the potential for widening health inequalities with implementation of EHR access. A taxonomy for EHR access and additional functionalities should be developed to promote consistency and comparability of outcome measures, and facilitate future reviews by better enabling cross-study comparisons.

International Comparison of Six Basic eHealth Indicators Across 14 Countries: An eHealth Benchmarking Study.

BACKGROUND: Many countries adopt eHealth applications to support patient-centered care. Through information exchange, these eHealth applications may overcome institutional data silos and support holistic and ubiquitous (regional or national) information logistics. Available eHealth indicators mostly describe usage and acceptance of eHealth in a country. The eHealth indicators focusing on the cross-institutional availability of patient-related information for health care professionals, patients, and care givers are rare. OBJECTIVES: This study aims to present eHealth indicators on cross-institutional availability of relevant patient data for health care professionals, as well as for patients and their caregivers across 14 countries (Argentina, Australia, Austria, Finland, Germany, Hong Kong as a special administrative region of China, Israel, Japan, Jordan, Kenya, South Korea, Sweden, Turkey, and the United States) to compare our indicators and the resulting data for the examined countries with other eHealth benchmarks and to extend and explore changes to a comparable survey in 2017. We defined "availability of patient data" as the ability to access data in and to add data to the patient record in the respective country. METHODS: The invited experts from each of the 14 countries provided the indicator data for their country to reflect the situation on August 1, 2019, as date of reference. Overall, 60 items were aggregated to six eHealth indicators. RESULTS: Availability of patient-related information varies strongly by country. Health care professionals can access patients' most relevant cross-institutional health record data fully in only four countries. Patients and their caregivers can access their health record data fully in only two countries. Patients are able to fully add relevant data only in one country. Finland showed the best outcome of all eHealth indicators, followed by South Korea, Japan, and Sweden. CONCLUSION: Advancement in eHealth depends on contextual factors such as health care organization, national health politics, privacy laws, and health care financing. Improvements in eHealth indicators are thus often slow. However, our survey shows that some countries were able to improve on at least some indicators between 2017 and 2019. We anticipate further improvements in the future.

IT-Assisted Process Management in Healthcare.

Clinical processes need to be well understood before a new health IT tool can be introduced. Observations, interviews, surveys, or documentation analysis are carried out to systematically collect information to better understand a clinical process. To aggregate and visualize the collected information about a clinical process, use case diagrams can build a basis. Formal process models such as process chain diagrams or BPMN diagrams are well suited to model the process in detail. The objective of this chapter is to discuss these methods for analyzing and modeling clinical processes, as this is an important precondition for systematic process management in health care.

Multidisciplinary Approach for Education in Healthcare Management: Case Study from Montenegro.

Modern education of healthcare managers is one of key preconditions in making reforms towards increasing quality of care and making the provision of health services more cost-effective and efficient. This paper presents unique multidisciplinary approach for education of healthcare managers developed and accredited in Montenegro with elaboration results of the first year of its implementation. The results confirm the interest of experts of different profiles in multidisciplinary studies, as well as the optimality in using resources at national level in Montenegro.

Implementing National Electronic Health Records in Nursing Homes in Tyrol: A Nursing Management Perspective.

BACKGROUND: Health information technologies as electronic health records (EHR) have the potential to improve the quality and efficiency of health care. Implementing national EHR in nursing homes in Tyrol is a required task within the next years. OBJECTIVES: To explore and analyze the current situation of information exchange in nursing homes in Tyrol as well as expected potentials and challenges of an ELGA implementation from a nursing management perspective. METHODS: Semi-structured interviews with nine nursing managers of nursing homes in Tyrol were conducted and analyzed using qualitative content analysis. RESULTS: The explorative interviews offered a glance at the actual information exchange with other institutions and at the perception of nursing managers regarding potentials (e.g. decreasing organizational effort) and challenges (e.g. incompleteness of available patient information) of the nationally mandated implementation of ELGA in the nursing homes in Tyrol. CONCLUSION: The interviews with the nine nursing managers of nursing homes in Tyrol revealed some hopes (e.g. the reduction of double documentation) that ELGA will probably not be able to fulfill.

Technology Acceptance Models in Health Informatics: TAM and UTAUT.

Both the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) aim at understanding better why users accept or reject a given technology, and how user acceptance can be improved through technology design. Two case studies are presented where TAM and UTAUT were successfully used in a health care setting to predict technology adoption. Both models have found popularity in health care. However, recent reviews show that TAM and UTAUT failed to provide stable predictive capabilities for acceptance and use of technologies in health care. Reasons for this may be the specific context of health care, where not only the technology, but also socio-organizational and cultural factors influence technology acceptance.

Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications.

OBJECTIVES: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance. METHOD: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. RESULTS: There is a rich history and tradition of evaluating AI in healthcare. While evaluators can learn from past efforts, and build on best practice evaluation frameworks and methodologies, questions remain about how to evaluate the safety and effectiveness of AI that dynamically harness vast amounts of genomic, biomarker, phenotype, electronic record, and care delivery data from across health systems. This paper first provides a historical perspective about the evaluation of AI in healthcare. It then examines key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance. Practical aspects of evaluating AI in healthcare, including approaches to evaluation and indicators to monitor AI are also discussed. CONCLUSION: Commitment to rigorous initial and ongoing evaluation will be critical to ensuring the safe and effective integration of AI in complex sociotechnical settings. Specific enhancements that are required for the new generation of AI-enabled clinical decision support will emerge through practical application.

The Safe and Effective Use of Shared Data Underpinned by Stakeholder Engagement and Evaluation Practice.

OBJECTIVES: The paper draws attention to: i) key considerations involving the confidentiality, privacy, and security of shared data; and ii) the requirements needed to build collaborative arrangements encompassing all stakeholders with the goal of ensuring safe, secure, and quality use of shared data. METHOD: A narrative review of existing research and policy approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Care and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. RESULTS: The technological ability to merge, link, re-use, and exchange data has outpaced the establishment of policies, procedures, and processes to monitor the ethics and legality of shared use of data. Questions remain about how to guarantee the security of shared data, and how to establish and maintain public trust across large-scale shared data enterprises. This paper identifies the importance of data governance frameworks (incorporating engagement with all stakeholders) to underpin the management of the ethics and legality of shared data use. The paper also provides some key considerations for the establishment of national approaches and measures to monitor compliance with best practice. CONCLUSION: Data sharing endeavours can help to underpin new collaborative models of health care which provide shared information, engagement, and accountability amongst all stakeholders. We believe that commitment to rigorous evaluation and stakeholder engagement will be critical to delivering health data benefits and the establishment of collaborative models of health care into the future.

Identification of Measures and Indicators for the IT Security of Networked Medical Devices: A Delphi Study.

The networking of medical devices or systems in a hospital network is the foundation for modern medical diagnostics and therapy. This, however, makes possible numerous hazards that could lead to risks for patients, clinical processes or data and information. The aim of the work was to develop a catalogue of measures and indicators for the effective support of the IT risk management process in a health facility. Through a qualitative and quantitative Delphi study among 21 experts, it was possible to identify an initial 51 practice-relevant measures of IT risk management that a hospital should implement. Additionally, 27 indicators were defined which can be used to measure the impact of these measures. Of the 51 measures, 35 were seen as especially important, particularly organizational measures. Of the 27 indicators, six were seen as especially important, particularly indicators to measure networking effectiveness. The study also investigated the impact of the measures on the indicators. A case study is planned to investigate the practicability of the identified measures and indicators.

Steps in Moving Evidence-Based Health Informatics from Theory to Practice.

OBJECTIVES: To demonstrate and promote the importance of applying a scientific process to health IT design and implementation, and of basing this on research principles and techniques. METHODS: A review by international experts linked to the IMIA Working Group on Technology Assessment and Quality Development. RESULTS: Four approaches are presented, linking to the creation of national professional expectations, adherence to research-based standards, quality assurance approaches to ensure safety, and scientific measurement of impact. CONCLUSIONS: Solely marketing- and aspiration-based approaches to health informatics applications are no longer ethical or acceptable when scientifically grounded evidence-based approaches are available and in use.

HerzMobil Tirol network: rationale for and design of a collaborative heart failure disease management program in Austria.

Heart failure (HF) is approaching epidemic proportions worldwide and is the leading cause of hospitalization in the elderly population. High rates of readmission contribute substantially to excessive health care costs and highlight the fragmented nature of care available to HF patients. Disease management programs (DMPs) have been implemented to improve health outcomes, patient satisfaction, and quality of life, and to reduce health care costs. Telemonitoring systems appear to be effective in the vulnerable phase after discharge from hospital to prevent early readmissions. DMPs that emphasize comprehensive patient education and guideline-adjusted therapy have shown great promise to result in beneficial long-term effects. It can be speculated that combining core elements of the aforementioned programs may substantially improve long-term cost-effectiveness of patient management.We introduce a collaborative post-discharge HF disease management program (HerzMobil Tirol network) that incorporates physician-controlled telemonitoring and nurse-led care in a multidisciplinary network approach.

[Nursing minimum data sets (NMDS) - a literature review relating to objective and data elements]. / Nursing Minimum Data Sets (NMDS) - eine Literaturübersicht bezüglich Zielsetzungen und Datenelementen.

BACKGROUND: In order to be able to follow the predicted changes in healthcare systems, there has long been a need for a unified database that could transparently compare nursing care data from different service providers. In Austria, a recommendation has been lacking thus far as to which nursing care data need to be documented as "basic data", and as a result of this, comparisons of nursing care data on a national basis have been hindered. The international development of Nursing Minimum Data Sets (NMDS) has demonstrated that nursing care data can be sufficiently compared. AIM: The aim of the systematic literature review is to raise the current level of knowledge regarding NMDS and to develop a structured description of NMDS which, above all else, can document the recorded data elements and the associated objectives with the use of NMDS. RESULTS: A total of seventy publications on the subject of NMDS were included in the literature overview. The analysis of the eight NMDS that were presented yielded six central objectives and six higher-level data elements. The identified objectives include: the description of the nursing care practice, distribution of the financial means, benchmarking, human resources planning, trend analyses, and quality assurance. The six data elements that were identified comprise operating data, demographic data, and the diagnoses, interventions, results (quality indicators), and intensity of nursing care (clinical data). DISCUSSION: What emerged was that no clear association is present between the objectives and the data elements of the minimum nursing care datasets that were studied.

Assessing the prognoses on Health care in the information society 2013--thirteen years after.

Health care and information technology in health care is advancing at tremendous speed. We analysed whether the prognoses by Haux et al. - first presented in 2000 and published in 2002 - have been fulfilled in 2013 and which might be the reasons for match or mismatch. Twenty international experts in biomedical and health informatics met in May 2013 in a workshop to discuss match or mismatch of each of the 71 prognoses. After this meeting a web-based survey among workshop participants took place. Thirty-three prognoses were assessed matching; they reflect e.g. that there is good progress in storing patient data electronically in health care institutions. Twenty-three prognoses were assessed mismatching; they reflect e.g. that telemedicine and home monitoring as well as electronic exchange of patient data between institutions is not established as widespread as expected. Fifteen prognoses were assessed neither matching nor mismatching. ICT tools have considerably influenced health care in the last decade, but in many cases not as far as it was expected by Haux et al. in 2002. In most cases this is not a matter of the availability of technical solutions but of organizational and ethical issues. We need innovative and modern information system architectures which support multiple use of data for patient care as well as for research and reporting and which are able to integrate data from home monitoring into a patient centered health record. Since innovative technology is available the efficient and wide-spread use in health care has to be enabled by systematic information management.

Past and next 10 years of medical informatics.

More than 10 years ago Haux et al. tried to answer the question how health care provision will look like in the year 2013. A follow-up workshop was held in Braunschweig, Germany, for 2 days in May, 2013, with 20 invited international experts in biomedical and health informatics. Among other things it had the objectives to discuss the suggested goals and measures of 2002 and how priorities on MI research in this context should be set from the viewpoint of today. The goals from 2002 are now as up-to-date as they were then. The experts stated that the three goals: "patient-centred recording and use of medical data for cooperative care"; "process-integrated decision support through current medical knowledge" and "comprehensive use of patient data for research and health care reporting" have not been reached yet and are still relevant. A new goal for ICT in health care should be the support of patient centred personalized (individual) medicine. MI as an academic discipline carries out research concerning tools that support health care professionals in their work. This research should be carried out without the pressure that it should lead to systems that are immediately and directly accepted in practice.