Between Access and Privacy: Challenges in Sharing Health Data

Malin, Bradley; Goodman, Kenneth W.

doi:10.1055/s-0038-1641216

Cited by 25 publications

(14 citation statements)

References 13 publications

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“…This study has certain limitations. First, the multi-resource dataset for validating prediction module is relatively small and limited to the Chinese population, due to difficulties for assessing clinical records from centers in other countries 27 and limited cohorts tracking congenital cataract worldwide 28 . It is also essential to validate our system using datasets in other ethnic population or other countries in the future to achieve global generalization, as previously achieved in diabetic retinopathy 29 .…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing

Long

Chen

et al. 2020

npj Digit. Med.

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A challenge of chronic diseases that remains to be solved is how to liberate patients and medical resources from the burdens of long-term monitoring and periodic visits. Precise management based on artificial intelligence (AI) holds great promise; however, a clinical application that fully integrates prediction and telehealth computing has not been achieved, and further efforts are required to validate its real-world benefits. Taking congenital cataract as a representative, we used Bayesian and deep-learning algorithms to create CC-Guardian, an AI agent that incorporates individualized prediction and scheduling, and intelligent telehealth follow-up computing. Our agent exhibits high sensitivity and specificity in both internal and multi-resource validation. We integrate our agent with a web-based smartphone app and prototype a prediction-telehealth cloud platform to support our intelligent follow-up system. We then conduct a retrospective self-controlled test validating that our system not only accurately detects and addresses complications at earlier stages, but also reduces the socioeconomic burdens compared to conventional methods. This study represents a pioneering step in applying AI to achieve real medical benefits and demonstrates a novel strategy for the effective management of chronic diseases.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing

Long

Chen

et al. 2020

npj Digit. Med.

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“…Constant and evolving research improves privacy-protecting software. The 2018 IMIA Yearbook of Medical Informatics, for instance, included a special section ("Between Access and Privacy: Challenges in Sharing Health Data") that featured a suite of articles describing research devoted to improving digital privacy protection [11]. It is clear that the good guys write better software than the bad guys.…”

Section: Big Data Data Sharing and Privacymentioning

confidence: 99%

Ethics in Health Informatics

Goodman

2020

Yearb Med Inform

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SummaryContemporary bioethics was fledged and is sustained by challenges posed by new technologies. These technologies have affected many lives. Yet health informatics affects more lives than any of them. The challenges include the development and the appropriate uses and users of machine learning software, the balancing of privacy rights against the needs of public health and clinical practice in a time of Big Data analytics, whether and how to use this technology, and the role of ethics and standards in health policy. Historical antecedents in statistics and evidence-based practice foreshadow some of the difficulties now faced, but the scope and scale of these challenges requires that ethics, too, be brought to scale in parallel, especially given the size of contemporary data sets and the processing power of new computers. Fortunately, applied ethics affords a variety of tools to help identify and rank applicable values, support best practices, and contribute to standards. The bioethics community can in partnership with the informatics community arrive at policies that promote the health sciences while reaffirming the many and varied rights that patients expect will be honored.

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“…The reuse of health data is guided by a variety of controls from both legal and ethical perspectives, such as privacy rights, data protection regulations, and duties of confidentiality [12]. However, legal measures pose challenges for the effective reuse of clinical data [13,14].…”

Section: Health Data Reusementioning

confidence: 99%

Privacy-preserving architecture for providing feedback to clinicians on their clinical performance

Yigzaw

Budrionis

Marco-Ruiz

et al. 2020

BMC Med Inform Decis Mak

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Background: Learning from routine healthcare data is important for the improvement of the quality of care. Providing feedback on clinicians' performance in comparison to their peers has been shown to be more efficient for quality improvements. However, the current methods for providing feedback do not fully address the privacy concerns of stakeholders. Methods: The paper proposes a distributed architecture for providing feedback to clinicians on their clinical performances while protecting their privacy. The indicators for the clinical performance of a clinician are computed within a healthcare institution based on pseudonymized data extracted from the electronic health record (EHR) system. Group-level indicators of clinicians across healthcare institutions are computed using privacy-preserving distributed data-mining techniques. A clinician receives feedback reports that compare his or her personal indicators with the aggregated indicators of the individual's peers. Indicators aggregated across different geographical levels are the basis for monitoring changes in the quality of care. The architecture feasibility was practically evaluated in three general practitioner (GP) offices in Norway that consist of about 20,245 patients. The architecture was applied for providing feedback reports to 21 GPs on their antibiotic prescriptions for selected respiratory tract infections (RTIs). Each GP received one feedback report that covered antibiotic prescriptions between 2015 and 2018, stratified yearly. We assessed the privacy protection and computation time of the architecture. Results: Our evaluation indicates that the proposed architecture is feasible for practical use and protects the privacy of the patients, clinicians, and healthcare institutions. The architecture also maintains the physical access control of healthcare institutions over the patient data. We sent a single feedback report to each of the 21 GPs. A total of 14,396 cases were diagnosed with the selected RTIs during the study period across the institutions. Of these cases, 2924 (20.3%) were treated with antibiotics, where 40.8% (1194) of the antibiotic prescriptions were narrowspectrum antibiotics.

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Between Access and Privacy: Challenges in Sharing Health Data

Abstract: The papers illustrated that there is no one-size-fitsall solution to privacy while working with biomedical data. At the same time, the papers show that there are opportunities for leveraging newly emerging technologies to enable data use while minimizing privacy risks.

Cited by 25 publications

References 13 publications

Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing

Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing

Ethics in Health Informatics

Privacy-preserving architecture for providing feedback to clinicians on their clinical performance

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