Governance of artificial intelligence and personal health information


Jenifer Sunrise Winter in Digital Policy, Regulation and Governance: “This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions….

This paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions.

Health data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security….(More).

The Role of Big Data Analytics in Predicting Suicide


Chapter by Ronald C. Kessler et al: “…reviews the long history of using electronic medical records and other types of big data to predict suicide. Although a number of the most recent of these studies used machine learning (ML) methods, these studies were all suboptimal both in the features used as predictors and in the analytic approaches used to develop the prediction models. We review these limitations and describe opportunities for making improvements in future applications.

We also review the controversy among clinical experts about using structured suicide risk assessment tools (be they based on ML or older prediction methods) versus in-depth clinical evaluations of needs for treatment planning. Rather than seeing them as competitors, we propose integrating these different approaches to capitalize on their complementary strengths. We also emphasize the distinction between two types of ML analyses: those aimed at predicting which patients are at highest suicide risk, and those aimed at predicting the treatment options that will be best for individual patients. We explain why both are needed to optimize the value of big data ML methods in addressing the suicide problem….(More)”.

See also How Search Engine Data Enhance the Understanding of Determinants of Suicide in India and Inform Prevention: Observational Study.

Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence


Paper by Huimin Xia et al in at Nature Medicine: “Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework.

Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing common childhood diseases. Our study provides a proof of concept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare providers are in relative shortage, the benefits of such an AI system are likely to be universal….(More)”.

Impact of a nudging intervention and factors associated with vegetable dish choice among European adolescents


Paper by Q. Dos Santos et al: “To test the impact of a nudge strategy (dish of the day strategy) and the factors associated with vegetable dish choice, upon food selection by European adolescents in a real foodservice setting.

A cross-sectional quasi-experimental study was implemented in restaurants in four European countries: Denmark, France, Italy and United Kingdom. In total, 360 individuals aged 12-19 years were allocated into control or intervention groups, and asked to select from meat-based, fish-based, or vegetable-based meals. All three dishes were identically presented in appearance (balls with similar size and weight) and with the same sauce (tomato sauce) and side dishes (pasta and salad). In the intervention condition, the vegetable-based option was presented as the “dish of the day” and numbers of dishes chosen by each group were compared using the Pearson chi-square test. Multivariate logistic regression analysis was run to assess associations between choice of vegetable-based dish and its potential associated factors (adherence to Mediterranean diet, food neophobia, attitudes towards nudging for vegetables, food choice questionnaire, human values scale, social norms and self-estimated health, country, gender and belonging to control or intervention groups). All analyses were run in SPSS 22.0.

The nudging strategy (dish of the day) did not show a difference on the choice of the vegetable-based option among adolescents tested (p = 0.80 for Denmark and France and p = 0.69 and p = 0.53 for Italy and UK, respectively). However, natural dimension of food choice questionnaire, social norms and attitudes towards vegetable nudging were all positively associated with the choice of the vegetable-based dish. Being male was negatively associated with choosing the vegetable-based dish.

The “dish of the day” strategy did not work under the study conditions. Choice of the vegetable-based dish was predicted by natural dimension, social norms, gender and attitudes towards vegetable nudging. An understanding of factors related to choosing vegetable based dishes is necessary for the development and implementation of public policy interventions aiming to increase the consumption of vegetables among adolescents….(More)”

Using Personal Informatics Data in Collaboration among People with Different Expertise


Dissertation by Chia-Fang Chung: “Many people collect and analyze data about themselves to improve their health and wellbeing. With the prevalence of smartphones and wearable sensors, people are able to collect detailed and complex data about their everyday behaviors, such as diet, exercise, and sleep. This everyday behavioral data can support individual health goals, help manage health conditions, and complement traditional medical examinations conducted in clinical visits. However, people often need support to interpret this self-tracked data. For example, many people share their data with health experts, hoping to use this data to support more personalized diagnosis and recommendations as well as to receive emotional support. However, when attempting to use this data in collaborations, people and their health experts often struggle to make sense of the data. My dissertation examines how to support collaborations between individuals and health experts using personal informatics data.

My research builds an empirical understanding of individual and collaboration goals around using personal informatics data, current practices of using this data to support collaboration, and challenges and expectations for integrating the use of this data into clinical workflows. These understandings help designers and researchers advance the design of personal informatics systems as well as the theoretical understandings of patient-provider collaboration.

Based on my formative work, I propose design and theoretical considerations regarding interactions between individuals and health experts mediated by personal informatics data. System designers and personal informatics researchers need to consider collaborations occurred throughout the personal tracking process. Patient-provider collaboration might influence individual decisions to track and to review, and systems supporting this collaboration need to consider individual and collaborative goals as well as support communication around these goals. Designers and researchers should also attend to individual privacy needs when personal informatics data is shared and used across different healthcare contexts. With these design guidelines in mind, I design and develop Foodprint, a photo-based food diary and visualization system. I also conduct field evaluations to understand the use of lightweight data collection and integration to support collaboration around personal informatics data. Findings from these field deployments indicate that photo-based visualizations allow both participants and health experts to easily understand eating patterns relevant to individual health goals. Participants and health experts can then focus on individual health goals and questions, exchange knowledge to support individualized diagnoses and recommendations, and develop actionable and feasible plans to accommodate individual routines….(More)”.

Facebook could be forced to share data on effects to the young


Nicola Davis at The Guardian: “Social media companies such as Facebook and Twitter could be required by law to share data with researchers to help examine potential harms to young people’s health and identify who may be at risk.

Surveys and studies have previously suggested a link between the use of devices and networking sites and an increase in problems among teenagers and younger children ranging from poor sleep to bullyingmental health issues and grooming.

However, high quality research in the area is scarce: among the conundrums that need to be looked at are matters of cause and effect, the size of any impacts, and the importance of the content of material accessed online.

According to a report by the Commons science and technology committee on the effects of social media and screen time among young people, companies should be compelled to protect users and legislation was needed to enable access to data for high quality studies to be carried out.

The committee noted that the government had failed to commission such research and had instead relied on requesting reviews of existing studies. This was despite a 2017 green paper that set out a consultation process on aUK internet safety strategy.

“We understand [social media companies’] eagerness to protect the privacy of users but sharing data with bona fide researchers is the only way society can truly start to understand the impact, both positive and negative, that social media is having on the modern world,” said Norman Lamb, the Liberal Democrat MP who chairs the committee. “During our inquiry, we heard that social media companies had openly refused to share data with researchers who are keen to examine patterns of use and their effects. This is not good enough.”

Prof Andrew Przybylski, the director of research at the Oxford Internet Institute, said the issue of good quality research was vital, adding that many people’s perception of the effect of social media is largely rooted in hype.

“Social media companies must participate in open, robust, and transparent science with independent scientists,” he said. “Their data, which we give them, is both their most valuable resource and it is the only means by which we can effectively study how these platforms affect users.”…(More)”

Not so gameful: A critical review of gamification in mobile energy applications


Paper by Ariane L.Beck et al in Energy Research & Social Sciences: “In order to help mitigate climate change and reduce the health-related consequences of air pollution, consumers need to be empowered to make better and more effective decisions regarding energy use. Utilities, government, and commercial entities offer numerous programs and consumer products to help individuals set or reach goals related to energy use.

Many of these interventions and products have related apps that use gamification in some capacity in order to improve the user experience, offer motivation, and encourage behavior change. We identified 57 apps from nearly 2400 screened apps that both target direct energy use and employ at least one element of gamification.

We evaluated these apps with specific focus on gamification components, game elements, and behavioral constructs. Our analysis shows that the average energy related app heavily underutilizes search engine optimization, gamification components, and game design elements, as well as the behavioral constructs known to impact energy-related decision-making and behavior. Our findings offer several insights for the design of more effective energy apps….(More)”.

“Giving something back”: A systematic review and ethical enquiry into public views on the use of patient data for research in the United Kingdom and the Republic of Ireland


Paper by Jessica Stockdale, Jackie Cassell and Elizabeth Ford: “The use of patients’ medical data for secondary purposes such as health research, audit, and service planning is well established in the UK, and technological innovation in analytical methods for new discoveries using these data resources is developing quickly. Data scientists have developed, and are improving, many ways to extract and process information in medical records. This continues to lead to an exciting range of health related discoveries, improving population health and saving lives. Nevertheless, as the development of analytic technologies accelerates, the decision-making and governance environment as well as public views and understanding about this work, has been lagging behind1.

Public opinion and data use

A range of small studies canvassing patient views, mainly in the USA, have found an overall positive orientation to the use of patient data for societal benefit27. However, recent case studies, like NHS England’s ill-fated Care.data scheme, indicate that certain schemes for secondary data use can prove unpopular in the UK. Launched in 2013, Care.data aimed to extract and upload the whole population’s general practice patient records to a central database for prevalence studies and service planning8. Despite the stated intention of Care.data to “make major advances in quality and patient safety”8, this programme was met with a widely reported public outcry leading to its suspension and eventual closure in 2016. Several factors may have been involved in this failure, from the poor public communication about the project, lack of social licence9, or as pressure group MedConfidential suggests, dislike of selling data to profit-making companies10. However, beyond these specific explanations for the project’s failure, what ignited public controversy was a concern with the impact that its aim to collect and share data on a large scale might have on patient privacy. The case of Care.data indicates a reluctance on behalf of the public to share their patient data, and it is still not wholly clear whether the public are willing to accept future attempts at extracting and linking large datasets of medical information. The picture of mixed opinion makes taking an evidence-based position, drawing on social consensus, difficult for legislators, regulators, and data custodians who may respond to personal or media generated perceptions of public views. However, despite differing results of studies canvassing public views, we hypothesise that there may be underlying ethical principles that could be extracted from the literature on public views, which may provide guidance to policy-makers for future data-sharing….(More)”.

Can I Trust the Data I See? A Physician’s Concern on Medical Data in IoT Health Architectures


Conference Paper by Fariha Tasmin Jaigirdar, Carsten Rudolph, and Chris Bain: “With the increasing advancement of Internet of Things (IoT) enabled systems, smart medical devices open numerous opportunities for the healthcare sector. The success of using such devices in the healthcare industry depends strongly on secured and reliable medical data transmission. Physicians diagnose that data and prescribe medicines and/or give guidelines/instructions/treatment plans for the patients. Therefore, a physician is always concerned about the medical data trustworthiness, because if it is not guaranteed, a savior can become an involuntary foe! This paper analyses two different scenarios to understand the real-life consequences in IoT-based healthcare (IoT-Health) application. Appropriate sequence diagrams for both scenarios show data movement as a basis for determining necessary security requirements in each layer of IoT-Health.

We analyse the individual entities of the overall system and develop a system-wide view of trust in IoT-Health. The security analysis pinpoints the research gap in end-to-end trust and indicates the necessity to treat the whole IoT-Health system as an integrated entity. This study highlights the importance of integrated cross-layer security solutions that can deal with the heterogeneous security architectures of IoT healthcare system and finally identifies a possible solution for the open question raised in the security analysis with appropriate future research directions….(More)”.

All of Us Research Program Expands Data Collection Efforts with Fitbit


NIH Press Release: “The All of Us Research Program has launched the Fitbit Bring-Your-Own-Device (BYOD) project. Now, in addition to providing health information through surveys, electronic health records, and biosamples, participants can choose to share data from their Fitbit accounts to help researchers make discoveries. The project is a key step for the program in integrating digital health technologies for data collection.

Digital health technologies, like mobile apps and wearable devices, can gather data outside of a hospital or clinic. This data includes information about physical activity, sleep, weight, heart rate, nutrition, and water intake, which can give researchers a more complete picture of participants’ health. The All of Us Research Program is now gathering this data in addition to surveys, electronic health record information, physical measurements, and blood and urine samples, working to make the All of Us resource one of the largest and most diverse data sets of its kind for health research.

“Collecting real-world, real-time data through digital technologies will become a fundamental part of the program,” said Eric Dishman, director of the All of Us Research Program. “This information, in combination with many other data types, will give us an unprecedented ability to better understand the impact of lifestyle and environment on health outcomes and, ultimately, develop better strategies for keeping people healthy in a very precise, individualized way.”…

All of Us is developing additional plans to incorporate digital health technologies. A second project with Fitbit is expected to launch later in the year. It will include providing devices to a limited number of All of Us participants who will be randomly invited to take part, to enable them to share wearable data with the program. And All of Us will add connections to other devices and apps in the future to further expand data collection efforts and engage participants in new ways….(More)”.