ORCID Profile
0000-0003-3895-518X
Current Organisations
Københavns Universitet
,
Deakin University
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Publisher: Springer Science and Business Media LLC
Date: 06-2021
Publisher: Springer Science and Business Media LLC
Date: 18-03-2022
DOI: 10.1038/S41746-022-00573-1
Abstract: An abundant and growing supply of digital health applications (apps) exists in the commercial tech-sector, which can be bewildering for clinicians, patients, and payers. A growing challenge for the health care system is therefore to facilitate the identification of safe and effective apps for health care practitioners and patients to generate the most health benefit as well as guide payer coverage decisions. Nearly all developed countries are attempting to define policy frameworks to improve decision-making, patient care, and health outcomes in this context. This study compares the national policy approaches currently in development/use for health apps in nine countries. We used secondary data, combined with a detailed review of policy and regulatory documents, and interviews with key in iduals and experts in the field of digital health policy to collect data about implemented and planned policies and initiatives. We found that most approaches aim for centralized pipelines for health app approvals, although some countries are adding decentralized elements. While the countries studied are taking erse paths, there is nevertheless broad, international convergence in terms of requirements in the areas of transparency, health content, interoperability, and privacy and security. The sheer number of apps on the market in most countries represents a challenge for clinicians and patients. Our analyses of the relevant policies identified challenges in areas such as reimbursement, safety, and privacy and suggest that more regulatory work is needed in the areas of operationalization, implementation and international transferability of approvals. Cross-national efforts are needed around regulation and for countries to realize the benefits of these technologies.
Publisher: BMJ
Date: 12-04-2019
DOI: 10.1136/BMJ.L1657
Publisher: Frontiers Media SA
Date: 13-07-2021
DOI: 10.3389/FHUMD.2021.688152
Abstract: This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.
Publisher: SAGE Publications
Date: 18-12-2020
Abstract: In three days at the beginning of the COVID-19 pandemic, the Copenhagen Emergency Medical Services developed a digital diagnostic device. The purpose was to assess and triage potential COVID-19 symptoms and to reduce the number of calls to public health-care helplines. The device was used almost 150,000 times in a few weeks and was described by politicians and administrators as a solution and success. However, high usage cannot serve as the sole criterion of success. What might be adequate criteria? And should digital triage for citizens by default be considered low risk? This paper reflects on the uncertain aspects of the performance, risks and issues of accountability pertaining to the digital diagnostic device in order to draw lessons for future improvements. The analysis is based on the principles of evidence-based medicine (EBM), the EU and US regulations of medical devices and the taxonomy of uncertainty in health care by Han et al. Lessons for future digital devices are (a) the need for clear criteria of success, (b) the importance of awareness of other severe diseases when triaging, (c) the priority of designing the device to collect data for evaluation and (d) clear allocation of responsibilities. A device meant to substitute triage for citizens according to its own criteria of success should not by default be considered as low risk. In a pandemic age dependent on digitalisation, it is therefore important not to abandon the ethos of EBM, but instead to prepare the ground for new ways of building evidence of effect.
Publisher: BMJ
Date: 13-01-2022
Publisher: Frontiers Media SA
Date: 08-07-2021
DOI: 10.3389/FHUMD.2021.673104
Abstract: Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1 Z-Inspection ® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.
Publisher: BMJ
Date: 25-10-2019
Publisher: SAGE Publications
Date: 25-04-2023
DOI: 10.1177/03063127231164345
Abstract: People are increasingly able to generate their own health data through new technologies such as wearables and online symptom checkers. However, generating data is one thing, interpreting them another. General practitioners (GPs) are likely to be the first to help with interpretations. Policymakers in the European Union are investing heavily in infrastructures to provide GPs access to patient measurements. But there may be a disconnect between policy ambitions and the everyday practices of GPs. To investigate this, we conducted semi-structured interviews with 23 Danish GPs. According to the GPs, patients relatively rarely bring data to them. GPs mostly remember three types of patient-generated data that patients bring to them for interpretation: heart and sleep measurements from wearables and results from online symptom checkers. However, they also spoke extensively about data work with patient queries concerning measurements from the GPs’ own online Patient Reported Outcome system and online access to laboratory results. We juxtapose GP reflections on these five data types and between policy ambitions and everyday practices. These data require substantial recontextualization work before the GPs ascribe them evidential value and act on them. Even when they perceived as actionable, patient-provided data are not approached as measurements, as suggested by policy frameworks. Rather, GPs treat them as analogous to symptoms—that is to say, GPs treat patient-provided data as subjective evidence rather than authoritative measures. Drawing on Science and Technology Studies (STS) literature,we suggest that GPs must be part of the conversation with policy makers and digital entrepreneurs around when and how to integrate patient-generated data into healthcare infrastructures.
No related grants have been discovered for Christoffer Bjerre Haase.