Informatics methods in medical privacy

Privacy concerns among the users of a national patient portal: A cross-sectional population survey study

INTRODUCTION

Seeking and receiving care requires disclosure of personal information which is recorded as health data in electronic health records. Thereafter, restricting the flow of information is dependent on data protection, information security, ethical conduct, and law. Privacy concerns may arise as patients' options concerning privacy have been balanced to cater both the privacy of patients and the needs of healthcare, as well as secondary use of data.

METHODS

This study examined privacy concerns among the users of a national patient portal in a representative sample of Finnish adults aged 20 to 99 years old (n = 3,731). We used logistic regression analysis with population weights to seek answers to which factors are associated with privacy concerns. The cross-sectional survey data was collected in 2020.

RESULTS

Every third patient portal user had privacy concerns. Those who were 50 to 59 years old (p = 0.030) had privacy concerns more often than 20 to 49-year-olds. Those who had financial difficulties (p = 0.003) also had privacy concerns more often while those, who had good digital skills (p=<0.026), did not need guidance on telehealth service use (p=<0.001) and found telehealth service use to be beneficial (p = 0.008), had privacy concerns less often.

CONCLUSION

The usefulness of telehealth seems to play an important role in privacy concerns. Another important factor is the skills required to use telehealth services. We encourage providing guidance to those who lack the necessary skills for telehealth service use. We also encourage putting effort not only into data protection and information security measures of telehealth services, but also into providing transparent and comprehensible privacy information for the service users as privacy concerns are common.

Achieving trust in health-behavior-change artificial intelligence apps (HBC-AIApp) development: a multi-perspective guide

OBJECTIVE

Trust determines the success of Health-Behavior-Change Artificial Intelligence Apps (HBC-AIApp). Developers of such apps need theory-based practical methods that can guide them in achieving such trust. Our study aimed to develop a comprehensive conceptual model and development process that can guide developers how to build HBC-AIApp in order to support trust creation among the app's users.

METHODS

We apply a multi-disciplinary approach where medical informatics, human-centered design, and holistic health methods are integrated to address the trust challenge in HBC-AIApps. The integration extends a conceptual model of trust in AI developed by Jermutus et al., whose properties guide the extension of the IDEAS (integrate, design, assess, and share) HBC-App development process.

RESULTS

The HBC-AIApp framework consists of three main blocks: (1) system development methods that study the users' complex reality, hence, their perceptions, needs, goals and environment; (2) mediators and other stakeholders who are important for developing and operating the HBC-AIApp, boundary objects that examine users' activities via the HBC-AIApp; and (3) the HBC-AIApp's structural components, AI logic, and physical implementation. These blocks come together to provide the extended conceptual model of trust in HBC-AIApps and the extended IDEAS process.

DISCUSSION

The developed HBC-AIApp framework drew from our own experience in developing trust in HBC-AIApp. Further research will focus on studying the application of the proposed comprehensive HBC-AIApp development framework and whether applying it supports trust creation in such apps.

Identification and classification of DICOM files with burned-in text content

BACKGROUND

Protected health information burned in pixel data is not indicated for various reasons in DICOM. It complicates the secondary use of such data. In recent years, there have been several attempts to anonymize or de-identify DICOM files. Existing approaches have different constraints. No completely reliable solution exists. Especially for large datasets, it is necessary to quickly analyse and identify files potentially violating privacy.

METHODS

Classification is based on adaptive-iterative algorithm designed to identify one of three classes. There are several image transformations, optical character recognition, and filters; then a local decision is made. A confirmed local decision is the final one. The classifier was trained on a dataset composed of 15,334 images of various modalities.

RESULTS

The false positive rates are in all cases below 4.00%, and 1.81% in the mission-critical problem of detecting protected health information. The classifier's weighted average recall was 94.85%, the weighted average inverse recall was 97.42% and Cohen's Kappa coefficient was 0.920.

CONCLUSION

The proposed novel approach for classification of burned-in text is highly configurable and able to analyse images from different modalities with a noisy background. The solution was validated and is intended to identify DICOM files that need to have restricted access or be thoroughly de-identified due to privacy issues. Unlike with existing tools, the recognised text, including its coordinates, can be further used for de-identification.

Controlled searching in reversibly de-identified medical imaging archives

Nowadays, digital medical imaging in healthcare has become a fundamental tool for medical diagnosis. This growth has been accompanied by the development of technologies and standards, such as the DICOM standard and PACS. This environment led to the creation of collaborative projects where there is a need to share medical data between different institutions for research and educational purposes. In this context, it is necessary to maintain patient data privacy and provide an easy and secure mechanism for authorized personnel access. This paper presents a solution that fully de-identifies standard medical imaging objects, including metadata and pixel data, providing at the same time a reversible de-identifier mechanism that retains search capabilities from the original data. The last feature is important in some scenarios, for instance, in collaborative platforms where data is anonymized when shared with the community but searchable for data custodians or authorized entities. The solution was integrated into an open source PACS archive and validated in a multidisciplinary collaborative scenario.