Eliciting meta consent for future secondary research use of health data using a smartphone application - a proof of concept study in the Danish population

Dermatologists rely on skin biopsies to diagnose cutaneous tumors and rashes. Skin biopsy sites should be accurately identified with conventional anatomical site descriptors in the pathology request form. Reliance upon free-text entries to describe these biopsy sites is prone to user error and can cause medical misadventures such as wrong-site follow-up surgery. We sought to determine whether a smartphone application (RightSite) could improve the precision of biopsy site labeling. We conducted a prospective proof-of-concept study of 100 smartphone-assisted skin biopsy site identifiers with matched comparison to 100 historical controls. Student’s t-test was used to identify significant differences in the precision of anatomic descriptors before and after adoption of the application. We found a 69% improvement in precision of anatomic site labeling with the RightSite smartphone application (P < 0.0001). These data show smartphone-assisted biopsy site labeling improves the precision of anatomic site descriptors. Integrating graphical user interfaces into the electronic health records system could improve health care by standardizing anatomic site nomenclature and site-specific descriptors.

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Framework for Privacy-Preserving Wearable Health Data Analysis: Proof-of-Concept Study for Atrial Fibrillation Detection

Applied Sciences ◽

10.3390/app11199049 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9049

Author(s):

Anamaria Vizitiu ◽

Cosmin-Ioan Nita ◽

Radu Miron Toev ◽

Tudor Suditu ◽

Constantin Suciu ◽

...

Keyword(s):

Atrial Fibrillation ◽

Data Analytics ◽

Wearable Devices ◽

Privacy Preserving ◽

Health Data ◽

Wearable Device ◽

Proof Of Concept ◽

Early Detection Of Disease ◽

Encrypted Data ◽

Concept Study

Medical wearable devices monitor health data and, coupled with data analytics, cloud computing, and artificial intelligence (AI), enable early detection of disease. Privacy issues arise when personal health information is sent or processed outside the device. We propose a framework that ensures the privacy and integrity of personal medical data while performing AI-based homomorphically encrypted data analytics in the cloud. The main contributions are: (i) a privacy-preserving cloud-based machine learning framework for wearable devices, (ii) CipherML—a library for fast implementation and deployment of deep learning-based solutions on homomorphically encrypted data, and (iii) a proof-of-concept study for atrial fibrillation (AF) detection from electrocardiograms recorded on a wearable device. In the context of AF detection, two approaches are considered: a multi-layer perceptron (MLP) which receives as input the ECG features computed and encrypted on the wearable device, and an end-to-end deep convolutional neural network (1D-CNN), which receives as input the encrypted raw ECG data. The CNN model achieves a lower mean F1-score than the hand-crafted feature-based model. This illustrates the benefit of hand-crafted features over deep convolutional neural networks, especially in a setting with a small training data. Compared to state-of-the-art results, the two privacy-preserving approaches lead, with reasonable computational overhead, to slightly lower, but still similar results: the small performance drop is caused by limitations related to the use of homomorphically encrypted data instead of plaintext data. The findings highlight the potential of the proposed framework to enhance the functionality of wearables through privacy-preserving AI, by providing, within a reasonable amount of time, results equivalent to those achieved without privacy enhancing mechanisms. While the chosen homomorphic encryption scheme prioritizes performance and utility, certain security shortcomings remain open for future development.

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Patient Perspective of Blockchain Technology in Clinical Trial Management: A Proof of Concept Study (Preprint)

10.2196/preprints.13597 ◽

2019 ◽

Author(s):

Julia Feldman ◽

Laura Pugliese ◽

Katrina Mateo ◽

Stan Kachnowski

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Patient Perspective ◽

Health Data ◽

Management Tool ◽

Proof Of Concept ◽

Trial Management ◽

Concept Study ◽

Blockchain Technology ◽

Clinical Trial Management

BACKGROUND Blockchain is a technology that has emerged over the past 12 years with the potential to heighten security, data provenance, immutability and create a ‘patient centered experience’ when used in clinical trials. Although much of the recent literature discusses the potential for blockchain to benefit patients in these trials, no IRB-approved, independent study, has evaluated a blockchain-enabled clinical trial management tool from the patient perspective. OBJECTIVE The objective of this study was to determine the usability and feasibility of a blockchain-enabled clinical trials management platform with a connected activity tracker and blood pressure monitor through the perspective of patients. Specifically, this study aimed to assess the ability of the blockchain-enabled platform to support electronic consenting, participants’ engagement and compliance to study activities in the one-week period, and to assess the participants’ ability to successfully use and transmit health data via the connected devices. METHODS A rapid proof of concept study of a blockchain software platform used for patient eConsent, engagement and management in clinical trials was conducted. Participants were recruited using digital flyering on online forums (e.g. Craigslist) and by contacting participants from previous studies by the authors. To be eligible participants had to be native English speakers aged 18-75 who: 1) have been diagnosed with at least one chronic condition, and 2) possess an Android or iOS Smart Phone. Once enrolled, participants used the platform (webpage and smartphone app) and activity trackers (a Fitbit™ and iHealth™ devices) for a one-week period. Adherence data as well as perceptions of the platform were collected via semi-structured interviews and surveys at baseline and endline visits. Audio-recorded interviews were professionally transcribed and systematically coded. RESULTS 15 chronically ill individuals with a mean age of 37.7 participated. Themes on opinions of the key properties of the blockchain technology emerged. Participants expressed that they valued transparency features of the blockchain tool because it would make doctors more accountable and potentially more cautious about the care they provide, which was especially important for patients with many doctors. Participants valued the ability to easily access, share and collect data remotely via the app because it saves money and time. Participants were highly interested in sharing their health records for clinical trials or being “matched” into trials. The heightened security of blockchain did not emerge as a major value because most expressed that they weren’t worried about keeping their health data secure. CONCLUSIONS Testing highlighted participants’ overall positive experience with the tool and trust that it could support their adherence to activities in the clinical trial, and that they would recommend the application be used in future studies. Participants believed that blockchain can improve the quality of care in clinical trials and were open to adopting it.

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Self-Care Behaviors of Ovarian Cancer Patients Before Their Diagnosis: Proof-of-Concept Study (Preprint)

10.2196/preprints.10447 ◽

2018 ◽

Author(s):

James M Flanagan ◽

Hanna Skrobanski ◽

Xin Shi ◽

Yasemin Hirst

Keyword(s):

Ovarian Cancer ◽

Primary Care ◽

Cancer Patients ◽

Self Care ◽

Health Data ◽

Proof Of Concept ◽

Concept Study ◽

Symptom Appraisal ◽

High Street ◽

Data Linkages

BACKGROUND Longer patient intervals can lead to more late-stage cancer diagnoses and higher mortality rates. Individuals may delay presenting to primary care with red flag symptoms and instead turn to the internet to seek information, purchase over-the-counter medication, and change their diet or exercise habits. With advancements in machine learning, there is the potential to explore this complex relationship between a patient’s symptom appraisal and their first consultation at primary care through linkage of existing datasets (eg, health, commercial, and online). OBJECTIVE Here, we aimed to explore feasibility and acceptability of symptom appraisal using commercial- and health-data linkages for cancer symptom surveillance. METHODS A proof-of-concept study was developed to assess the general public’s acceptability of commercial- and health-data linkages for cancer symptom surveillance using a qualitative focus group study. We also investigated self-care behaviors of ovarian cancer patients using high-street retailer data, pre- and postdiagnosis. RESULTS Using a high-street retailer’s data, 1118 purchases—from April 2013 to July 2017—by 11 ovarian cancer patients and one healthy individual were analyzed. There was a unique presence of purchases for pain and indigestion medication prior to cancer diagnosis, which could signal disease in a larger sample. Qualitative findings suggest that the public are willing to consent to commercial- and health-data linkages as long as their data are safeguarded and users of this data are transparent about their purposes. CONCLUSIONS Cancer symptom surveillance using commercial data is feasible and was found to be acceptable. To test efficacy of cancer surveillance using commercial data, larger studies are needed with links to individual electronic health records.

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