Privacy challenges in health monitoring
Because of the population aging, homecare monitoring systems and assisted living technologies have been promoted by researchers and industries to help patients and the elderly at home to get medical help in time. This blog provides a short summary of existing challenges involving patient’s privacy-related decision-making in health monitoring environment.
Keywords: privacy preferences, assisted living technologies, privacy-related decision-making
For patients and the elderly at home, installing sensing systems helps health care providers to provide medical help and evaluate whether the care given to these people is appropriate based on the recording data they collect from these systems. From one perspective, it eases caregivers’ burden in health care organizations. From another perspective, it respects most patient’s willingness to stay at home as long as possible. Therefore, homecare monitoring systems with different functionalities and devices have been developed to meet the requirements of patients at home.
These healthcare devices and services for home care can be categorized into three groups: First, stationary medical devices which are used to measure physiological parameters like blood pressure, electrocardiogram (ECG), and photoplethysmography (PPG) regularly. Second, embedded devices are used to raise alarms in case of a medical emergency or safety accident. Third, wearable sensors and sensor networks are used to monitor physiological parameters continuously. Apart from the technologies that can achieve the prevention of diseases, researchers are making efforts to substitute traditional sensors with smart textiles in health monitoring as well.
However, modern intelligent services have made preserving patient’s privacy increasingly tricky. The past decades have seen an increase of security breaches and privacy leakage in the field of healthcare IT. It is important to ensure the privacy and security of the patient’s personal data in healthcare. Studies before have explored patients’ attitudes on privacy. Not only should technicians encrypt the data processing procedure and ensure the confidentiality of the system properly, but also they need to consider the privacy preferences of each patient based on their different attitudes towards homecare sensing. Researchers found that older adults have seen the value of the monitoring systems, expressed their willingness to adopt in-home monitoring technologies, and had relatively few concerns about privacy or security, while younger adults seem to have more concerns about privacy.
What is privacy-related decision-making?
In detail, privacy-related decision-making reflects patients’ preferences under different conditions. It varies from person to person.
There are lots of factors that will influence patients’ privacy preferences and their adoption of homecare monitoring systems and assisted living technologies. The past studies stated that the most important factors include: (1) context and type of technology, (2) age, (3) health needs, (4) personal trust and the device’s usability, (5) tradeoff among privacy, autonomy, assistance, safety, or independence, (6) health status of patients, (7) region, (8) gender, (9) user roles, (10) sociocultural context, (11) emotion, (12) previous knowledge, (13) personality, (14) potential use of personal data. The above factors will also influence patients’ understanding of the urgent and privacy level of the medical incidents.
What are the challenges in privacy-related decision-making?
Patient’s privacy-related decision-making in medical incidents, especially emergencies, will consequently influence the medical help they get from the health care providers. A patient who concerns his privacy might refuse to disclose more information, and consequently, he cannot get the best treatment sometimes. Therefore, professional decisions are still required. To help with better decision-making while protecting privacy of patients, shared decision-making (SDM) is put forward today, which requires the joint decision-making of clinicians and patients. The shared decision-making mechanism is one step towards transferring the medical services to patient centric.
Another challenge would be the decision-making in urgent cases when patients lose their cognitive capabilities, which will involve lots of legal and ethical disputes regarding privacy. Even if there were already existing regulations like the General Data Protection Regulation (GDPR) enabling access of sensitive personal data in emergency cases, these regulations cannot cover all specific cases and lack the flexibility of making decisions based on the patients’ own privacy preferences.
Last but not least, cognitive change is regarded as one of the barriers towards privacy-related decision-making. As privacy covers lots of subjects, patients may easily change their decisions from their experience. In the long run, the dynamic changes of patients’ privacy preferences add up to the difficulty of decision-making. Limited number of longitudinal studies have been conducted currently to investigate patients’ privacy-related decision-changes over time.
Patients’ attitudes on privacy will greatly influence their adoption of health monitoring technologies and decision-making in medical incidents. It never involves privacy-preserving technologies simply. Instead, patients’ privacy-related decision-making relies on longitudinal studies on different perspectives of privacy, including legal privacy, informational privacy, psychological privacy.
- “shared decision-making”: https://www.nice.org.uk/about/what-we-do/our-programmes/nice-guidance/nice-guidelines/shared-decision-making
- Rosenberg, R. S. (2013). The social impact of computers. Elsevier.
- Rostad, H. M., & Stokke, R. (2021). Integrating Welfare Technology in Long-term Care Services: Nationwide Cross-sectional Survey Study. Journal of medical Internet research, 23(8), e22316.
This blogpost was written by Luyi Sun. Since 2020, she has been working in the eHealth and Welfare Security group in the Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Norway. Her research topic is AI-based Privacy Agent towards Personalized Laws.