-
Journal of the American Medical... Jun 2024This article proposes a framework for examining the ethical and legal concerns for using artificial intelligence (AI) in post-acute and long-term care (PA-LTC). It...
This article proposes a framework for examining the ethical and legal concerns for using artificial intelligence (AI) in post-acute and long-term care (PA-LTC). It argues that established frameworks on health, AI, and the law should be adapted to specific care contexts. For residents in PA-LTC, their social, psychological, and mobility needs should act as a gauge for examining the benefits and risks of integrating AI into their care. Using those needs as a gauge, 4 areas of particular concern are identified. First, the threat that AI poses to the autonomy of residents can undermine their core needs. Second, how discrimination and bias in algorithmic decision-making can undermine Medicare coverage for PA-LTC, causing doctors' recommendations to be ignored and denying residents the care they are entitled to. Third, privacy rules concerning data use may undermine developers' ability to train accurate AI systems, limiting their usefulness in PA-LTC contexts. Fourth, the importance of obtaining consent before AI is used and discussions about how that care should continue if there are concerns about an ongoing decline in cognition. Together, these considerations elevate existing frameworks and adapt them to the context-specific case of PA-LTC. It is hoped that future research will examine the legal implications of these matters in each of these specific cases.
PubMed: 38909630
DOI: 10.1016/j.jamda.2024.105105 -
BMC Medical Research Methodology Jun 2024Generating synthetic patient data is crucial for medical research, but common approaches build up on black-box models which do not allow for expert verification or...
BACKGROUND
Generating synthetic patient data is crucial for medical research, but common approaches build up on black-box models which do not allow for expert verification or intervention. We propose a highly available method which enables synthetic data generation from real patient records in a privacy preserving and compliant fashion, is interpretable and allows for expert intervention.
METHODS
Our approach ties together two established tools in medical informatics, namely OMOP as a data standard for electronic health records and Synthea as a data synthetization method. For this study, data pipelines were built which extract data from OMOP, convert them into time series format, learn temporal rules by 2 statistical algorithms (Markov chain, TARM) and 3 algorithms of causal discovery (DYNOTEARS, J-PCMCI+, LiNGAM) and map the outputs into Synthea graphs. The graphs are evaluated quantitatively by their individual and relative complexity and qualitatively by medical experts.
RESULTS
The algorithms were found to learn qualitatively and quantitatively different graph representations. Whereas the Markov chain results in extremely large graphs, TARM, DYNOTEARS, and J-PCMCI+ were found to reduce the data dimension during learning. The MultiGroupDirect LiNGAM algorithm was found to not be applicable to the problem statement at hand.
CONCLUSION
Only TARM and DYNOTEARS are practical algorithms for real-world data in this use case. As causal discovery is a method to debias purely statistical relationships, the gradient-based causal discovery algorithm DYNOTEARS was found to be most suitable.
Topics: Humans; Algorithms; Electronic Health Records; Markov Chains; Medical Informatics
PubMed: 38909216
DOI: 10.1186/s12874-024-02257-8 -
Scientific Reports Jun 2024Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely...
Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data, i.e., data generated through a randomised process that have similar statistical properties as the original data, but do not have a one-to-one correspondence with the original individual-level records. In this study, we use a state-of-the-art synthetic data generation method and perform in-depth quality analyses of the generated data for a specific use case in the field of nutrition. We demonstrate the need for careful analyses of synthetic data that go beyond descriptive statistics and provide valuable insights into how to realise the full potential of synthetic datasets. By extending the methods, but also by thoroughly analysing the effects of sampling from a trained model, we are able to largely reproduce significant real-world analysis results in the chosen use case.
Topics: Humans; Longitudinal Studies; Data Analysis; Artificial Intelligence
PubMed: 38909025
DOI: 10.1038/s41598-024-62102-2 -
JMIR MHealth and UHealth Jun 2024Hospital apps are increasingly being adopted in many countries, especially since the start of the COVID-19 pandemic. Web-based hospitals can provide valuable medical...
BACKGROUND
Hospital apps are increasingly being adopted in many countries, especially since the start of the COVID-19 pandemic. Web-based hospitals can provide valuable medical services and enhanced accessibility. However, increasing concerns about personal information (PI) and strict legal compliance requirements necessitate privacy assessments for these platforms. Guided by the theory of contextual integrity, this study investigates the regulatory compliance of privacy policies for internet hospital apps in the mainland of China.
OBJECTIVE
In this paper, we aim to evaluate the regulatory compliance of privacy policies of internet hospital apps in the mainland of China and offer recommendations for improvement.
METHODS
We obtained 59 internet hospital apps on November 7, 2023, and reviewed 52 privacy policies available between November 8 and 23, 2023. We developed a 3-level indicator scale based on the information processing activities, as stipulated in relevant regulations. The scale comprised 7 level-1 indicators, 26 level-2 indicators, and 70 level-3 indicators.
RESULTS
The mean compliance score of the 52 assessed apps was 73/100 (SD 22.4%), revealing a varied spectrum of compliance. Sensitive PI protection compliance (mean 73.9%, SD 24.2%) lagged behind general PI protection (mean 90.4%, SD 14.7%), with only 12 apps requiring separate consent for processing sensitive PI (mean 73.9%, SD 24.2%). Although most apps (n=41, 79%) committed to supervising subcontractors, only a quarter (n=13, 25%) required users' explicit consent for subcontracting activities. Concerning PI storage security (mean 71.2%, SD 29.3%) and incident management (mean 71.8%, SD 36.6%), half of the assessed apps (n=27, 52%) committed to bear corresponding legal responsibility, whereas fewer than half (n=24, 46%) specified the security level obtained. Most privacy policies stated the PI retention period (n=40, 77%) and instances of PI deletion or anonymization (n=41, 79%), but fewer (n=20, 38.5%) committed to prompt third-party PI deletion. Most apps delineated various individual rights, but only a fraction addressed the rights to obtain copies (n=22, 42%) or to refuse advertisement based on automated decision-making (n=13, 25%). Significant deficiencies remained in regular compliance audits (mean 11.5%, SD 37.8%), impact assessments (mean 13.5%, SD 15.2%), and PI officer disclosure (mean 48.1%, SD 49.3%).
CONCLUSIONS
Our analysis revealed both strengths and significant shortcomings in the compliance of internet hospital apps' privacy policies with relevant regulations. As China continues to implement internet hospital apps, it should ensure the informed consent of users for PI processing activities, enhance compliance levels of relevant privacy policies, and fortify PI protection enforcement across the information processing stages.
Topics: China; Humans; Mobile Applications; Computer Security; COVID-19; Confidentiality; Internet; Pandemics
PubMed: 38904994
DOI: 10.2196/55061 -
Frontiers in Digital Health 2024With advancements in communication technologies and internet connectivity, avatar robots for children who cannot attend school in person due to illness or disabilities...
INTRODUCTION
With advancements in communication technologies and internet connectivity, avatar robots for children who cannot attend school in person due to illness or disabilities have become more widespread. Introducing these technologies to the classroom aims to offer possibilities of social and educational inclusion. While implementation is still at an experimental level, several of these avatars have already been introduced as a marketable service. However, various obstacles impede widespread acceptance.
METHODS
In our explorative qualitative case study we conducted semi-structured interviews with eight individuals involved in the implementation of the avatar robots AV1 in Germany and eleven participants involved with implementing OriHime in Japan. We analyzed and compared implementation processes, application areas, access and eligibility, and the potential and limitations of avatars at schools.
RESULTS
We identified structural similarities and differences in both countries. In the German cases the target is defined as temporary use for children who cannot attend school in person because of childhood illness, with the clear goal of returning to school. Whereas in Japan OriHime is also implemented for children with physical or developmental disabilities, or who cannot attend school in person for other reasons.
DISCUSSION
Our study suggests that avatar technologies bear high potential for children to stay socially and educationally connected. Yet, structures need establishing that grant equal access to avatar technologies. These include educational board regulations, budgets for funding avatar technologies and making them accessible to the public, and privacy protection standards that are adequate, yet do not create implementation hurdles that are too high. Furthermore, guidelines or training sessions on technical, educational and psychosocial aspects of including avatar technologies in the classroom for teachers are important for successful implementation. Since our Japanese cases suggest that expanding the area of application beyond childhood illness is promising, further research on the benefits for different groups is needed.
PubMed: 38904032
DOI: 10.3389/fdgth.2024.1273415 -
Frontiers in Artificial Intelligence 2024The prevention of crime is a multifaceted challenge with legal, political, and cultural implications. Surveillance technologies play a crucial role in assisting law...
The prevention of crime is a multifaceted challenge with legal, political, and cultural implications. Surveillance technologies play a crucial role in assisting law enforcement and other relevant parties in this mission. Drones, cameras, and wiretaps are examples of such devices. As their use increases, it becomes essential to address related challenges involving various stakeholders and consider cultural, political, and legal aspects. The objective of this study was to analyze the impact of surveillance technologies and identify commonalities and differences in perspectives among social media users and researchers. Data extraction was performed from two platforms: Scopus (for academic research papers) and platform X (formerly known as Twitter). The dataset included 88,989 tweets and 4,874 research papers. Topic modeling, an unsupervised machine learning approach, was applied to analyze the content. The research results revealed that privacy received little attention across the datasets, indicating its relatively low prominence. The military applications and their usage have been documented in academic research articles as well as tweets. Based on the empirical evidence, it seems that contemporary surveillance technology may be accurately described as possessing a bi-directional nature, including both sousveillance and surveillance, which aligns with Deleuzian ideas on the Panopticon. The study's findings also indicate that there was a greater level of interest in actual applications of surveillance technologies as opposed to more abstract concepts like ethics and privacy.
PubMed: 38903156
DOI: 10.3389/frai.2024.1406361 -
BMC Medical Education Jun 2024Most Japanese medical schools likely continue to rely on peer physical examination (PPE) as a tool to for teaching physical examination skills to students. However, the...
BACKGROUND
Most Japanese medical schools likely continue to rely on peer physical examination (PPE) as a tool to for teaching physical examination skills to students. However, the attitudes of medical students in Japan toward PPEs have not be identified. Therefore, we evaluated students' attitudes toward PPE in a Japanese medical school as a preparation for developing a PPE policy tailored to the context of Japanese culture.
METHODS
We conducted a mixed-methods study with an explanatory sequential approach, in which qualitative data were used to interpret the quantitative findings. Surveys and interviews were conducted with medical students and junior residents at a Japanese university. A total of 63 medical students and 50 junior residents responded to the questionnaire. We interviewed 16 participants to reach theoretical saturation and investigated the attitudes of medical students toward PPE and the themes emerging from the interview data, providing detailed descriptions of the quantitative findings.
RESULTS
Female participants were significantly more likely than male participants to report varying degrees of resistance to being a model patient during PPE (male: 59.7%, female: 87%, p < 0.001). Most of the participants who took on the role of patients that involved undressing were males. The participants expected improvements in issues related to the guarantee of freedom to refuse to be a model patient and measures to protect confidentiality. Approximately 22% of the participants reported that they witnessed incidental findings (including variations within the normal range) in front of other students during PPE.
CONCLUSIONS
The findings imply that medical students expect high levels of autonomy and confidentiality when volunteering as model patients during PPE. Thus, developing a PPE policy suitable for Japanese culture may be effective in establishing a student-centered PPE environment.
Topics: Humans; Students, Medical; Japan; Physical Examination; Female; Male; Peer Group; Attitude of Health Personnel; Surveys and Questionnaires; Adult; Young Adult; Education, Medical, Undergraduate
PubMed: 38902752
DOI: 10.1186/s12909-024-05635-4 -
JMIR Human Factors Jun 2024Young adults in the United States exhibit some of the highest rates of substance use compared to other age groups. Heavy and frequent substance use can be associated...
BACKGROUND
Young adults in the United States exhibit some of the highest rates of substance use compared to other age groups. Heavy and frequent substance use can be associated with a host of acute and chronic health and mental health concerns. Recent advances in ubiquitous technologies have prompted interest and innovation in using technology-based data collection instruments to understand substance use and associated harms. Existing methods for collecting granular, real-world data primarily rely on the use of smartphones to study and understand substance use in young adults. Wearable devices, such as smartwatches, show significant potential as platforms for data collection in this domain but remain underused.
OBJECTIVE
This study aims to describe the design and user evaluation of a smartwatch-based data collection app, which uses ecological momentary assessments to examine young adult substance use in daily life.
METHODS
This study used a 2-phase iterative design and acceptability evaluation process with young adults (aged 18-25 y) reporting recent alcohol or cannabis use. In phase 1, participants (8/15, 53%) used the data collection app for 14 days on their Apple Watches to report their substance use patterns, social contexts of substance use, and psychosocial risk factors (eg, affect). After this 14-day deployment, the participants completed a user experience survey and a semistructured interview to record their perspectives and experiences of using the app. Formative feedback from this phase informed feature modification and refinement of the app. In phase 2, an additional cohort (7/15, 47%) used the modified app for 14 days and provided feedback through surveys and interviews conducted after the app use period.
RESULTS
Analyses of overall app use patterns indicated high, consistent use of the app, with participants using the app for an average of 11.73 (SD 2.60) days out of 14 days of data collection. Participants reported 67 instances of substance use throughout the study, and our analysis indicates that participants were able to respond to ecological momentary assessment prompts in diverse temporal and situational contexts. Our findings from the user experience survey indicate that participants found the app usable and functional. Comparisons of app use metrics and user evaluation scores indicate that the iterative app design had a measurable and positive impact on users' experience. Qualitative data from the participant interviews highlighted the value of recording substance use patterns, low disruption to daily life, minimal overall burden, preference of platforms (smartphones vs smartwatches), and perspectives relating to privacy and app use in social contexts.
CONCLUSIONS
This study demonstrated the acceptability of using a smartwatch-based app to collect intensive, longitudinal substance use data among young adults. The findings document the utility of smartwatches as a novel platform to understand sensitive and often-stigmatized behaviors such as substance use with minimal burden.
Topics: Humans; Male; Feasibility Studies; Mobile Applications; Female; Adult; Young Adult; Adolescent; Substance-Related Disorders; Ecological Momentary Assessment; Smartphone; United States
PubMed: 38901024
DOI: 10.2196/50795 -
Health Informatics Journal 2024Wearables have the potential to transform healthcare by enabling early detection and monitoring of chronic diseases. This study aimed to assess wearables' acceptance,...
Wearables have the potential to transform healthcare by enabling early detection and monitoring of chronic diseases. This study aimed to assess wearables' acceptance, usage, and reasons for non-use. Anonymous questionnaires were used to collect data in Germany on wearable ownership, usage behaviour, acceptance of health monitoring, and willingness to share data. Out of 643 respondents, 550 participants provided wearable acceptance data. The average age was 36.6 years, with 51.3% female and 39.6% residing in rural areas. Overall, 33.8% reported wearing a wearable, primarily smartwatches or fitness wristbands. Men (63.3%) and women (57.8%) expressed willingness to wear a sensor for health monitoring, and 61.5% were open to sharing data with healthcare providers. Concerns included data security, privacy, and perceived lack of need. The study highlights the acceptance and potential of wearables, particularly for health monitoring and data sharing with healthcare providers. Addressing data security and privacy concerns could enhance the adoption of innovative wearables, such as implants, for early detection and monitoring of chronic diseases.
Topics: Humans; Germany; Female; Male; Adult; Cross-Sectional Studies; Wearable Electronic Devices; Surveys and Questionnaires; Middle Aged; Monitoring, Physiologic; Monitoring, Ambulatory
PubMed: 38900846
DOI: 10.1177/14604582241260607 -
Human Brain Mapping Jun 2024With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has...
With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have been developed to de-identify imaging data using blurring, defacing or refacing. Completely removing facial structures provides the best re-identification protection but can significantly impact post-processing steps, like brain morphometry. As an alternative, refacing methods that replace individual facial structures with generic templates have a lower effect on the geometry and intensity distribution of original scans, and are able to provide more consistent post-processing results by the price of higher re-identification risk and computational complexity. In the current study, we propose a novel method for anonymized face generation for defaced 3D T1-weighted scans based on a 3D conditional generative adversarial network. To evaluate the performance of the proposed de-identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox). The aim was to evaluate (i) impact on brain morphometry reproducibility, (ii) re-identification risk, (iii) balance between (i) and (ii), and (iv) the processing time. The proposed method takes 9 s for face generation and is suitable for recovering consistent post-processing results after defacing.
Topics: Humans; Magnetic Resonance Imaging; Adult; Brain; Male; Female; Neural Networks, Computer; Imaging, Three-Dimensional; Neuroimaging; Data Anonymization; Young Adult; Image Processing, Computer-Assisted; Algorithms
PubMed: 38899549
DOI: 10.1002/hbm.26721