Data protection, data management, and data sharing: Stakeholder perspectives on the protection of personal health information in South Africa

The Protection of Personal Information Act (POPIA) 2013 came into force in South Africa on 1 July 2020. It seeks to strengthen the processing of personal information, including health information. While POPIA is to be welcomed, there are concerns about the impact it will have on the processing of health information. To ensure that the National Health Laboratory Service [NHLS] is compliant with these new strict processing requirements and that compliance does not negatively impact upon its current screening, treatment, surveillance and research mandate, it was decided to consider the development of a NHLS POPIA Code of Conduct for Personal Health. As part of the process of developing such a Code and better understand the challenges faced in the processing of personal health information in South Africa, 19 semi-structured interviews with stakeholders were conducted between June and September 2020. Overall, respondents welcomed the introduction of POPIA. However, they felt that there are tensions between the strengthening of data protection and the use of personal information for individual patient care, treatment programmes, and research. Respondents reported a need to rethink the management of personal health information in South Africa and identified 5 issues needing to be addressed at a national and an institutional level: an understanding of the importance of personal information; an understanding of POPIA and data protection; improve data quality; improve transparency in data use; and improve accountability in data use. The application of POPIA to the processing of personal health information is challenging, complex, and likely costly. However, personal health information must be appropriately managed to ensure the privacy of the data subject is protected, but equally that it is used as a resource in the individual’s and wider public interest.

Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes

SummaryPhenotypic plasticity is essential to the immune system, yet the factors that shape it are not fully understood. Here, we comprehensively analyze immune cell phenotypes including morphology across human cohorts by single-round multiplexed immunofluorescence, automated microscopy, and deep learning. Using the uncertainty of convolutional neural networks to cluster the phenotypes of 8 distinct immune cell subsets, we find that the resulting maps are influenced by donor age, gender, and blood pressure, revealing distinct polarization and activation-associated phenotypes across immune cell classes. We further associate T-cell morphology to transcriptional state based on their joint donor variability, and validate an inflammation-associated polarized T-cell morphology, and an age-associated loss of mitochondria in CD4+ T-cells. Taken together, we show that immune cell phenotypes reflect both molecular and personal health information, opening new perspectives into the deep immune phenotyping of individual people in health and disease.

Patient Portal Use Near the End-of-Life

Use of patient portals, personal health information websites linked to electronic health records, in seriously ill populations is unknown, as is use by caregivers. We described portal use patterns among adults with serious illness nearing end-of-life and their caregivers within Kaiser Permanente Colorado. Inclusion criteria were: 1) seriously ill patients (defined by KP’s “Care Group”), □18 years of age, who were registered for the portal, and died between 1/1/2016-6/30/2019; and 2) caregivers of these patients, □18 years of age, registered for a proxy account. Data included user characteristics and portal use metrics summarized monthly over the 12-month period prior to death. Models included an unadjusted linear trend of the days used by month using a generalized estimating equation Poisson model with a log link and an autoregressive correlation structure of order 1. We identified 6,517 seriously ill patients with portal registrations; 163 of these patients had proxy caregivers. Patient users were 77 years old, mostly frail and White, and caregivers were predominantly female. Average days of use among patients was 42.4 days and <1 day among their caregivers. Number of days used significantly increased by 0.7% per month from twelve months to one month prior to death (95% CI: 0.4%-1.0%; p-value <.0001) and peaked 3 months prior to the patient’s death. Average use was high in comparison to previous portal research and suggests that as the patient approaches death portal use increases. Future research should explore how portals may serve as indicators for identifying and addressing end-of-life care needs.

Towards Designing Personal Health Information Management System Based on Java

Health information management systems help gather, compile, and analyze health data to help manage population health and reduce healthcare costs. It can support the clinical decision, help diagnose individual patients, and improve patient care. In this study, a personal health information management system based on Java is presented. Based on the Java platform, the overall hierarchical structure of the system is designed including a health information management module, personal health data synchronization module, and full-text retrieval module, to realize the functions of the system. To improve the security of personal health information, the data encryption standard (DES) algorithm is implemented to encrypt and protect personal healthcare information. The system is evaluated in terms of health information acquisition accuracy, information security, and system response time. To verify the robustness of the proposed health information management system, it is compared with two related studies. The maximum health information acquisition accuracy of the system is 99%, the safety factor reported is above 0.9, and the maximum response time is only 0.8 s. The experimental results show that the designed personal health information management system can collect health information more accurately and ensure the security of information, and the overall response time is shorter.

Medical Care at California Wildfire Incident Base Camps

Objective: The California Emergency Medical Services Authority manages and deploys California Medical Assistance Teams (CAL-MAT) to disaster medical incidents in the state. This analysis reviews diagnoses for ambulatory medical visits at multiple wildland fire incident base camp field sites in California during the 2020 fire season. Methods: Clinical data without personal health information were extracted retrospectively from patient care records from all patients seen by a provider. Results were entered into Excel spreadsheets with calculation of summary statistics. Results: During the 2020 fire season, CAL-MAT teams deployed 21 times for a total of 327 days to base camps supporting large fire incidents and cared for 1756 patients. Impacts of heat and environmental smoke are a constant factor near wildfires; however, our most common medical problem was rhus dermatitis (54.5%) due to poison oak. All 2020 medical missions were further complicated by prevention and management of coronavirus disease (COVID-19). Conclusions: There is very little literature regarding the acute medical needs facing responders fighting wildland fires. Ninety-five percent of clinical conditions presenting to a field medical team at the wildfire incident base camp during a severe fire season in California can be managed by small teams operating in field tents.

Examining the Relationship Between Community Integration and Mental Health Characteristics of Individuals With Childhood Acquired Neurological Disability

Background: Many individuals with cerebral palsy (CP) or acquired brain injury (ABI) are at higher risk of lowered psychosocial functioning, poor mental health outcomes and decreased opportunities for community integration (CI) as they transition to adulthood. It is imperative to understand the characteristics of those at highest risk of dysfunction so that targeted interventions can be developed to reduce the impact.Methods: This quantitative, cross-sectional study examines current patients of the Living Independently Fully Engaged [(LIFEspan) Service], a tertiary outpatient hospital-based clinic. The Patient Health Questionnaire-4 (PHQ-4) and the Community Integration Questionnaire (CIQ) were administered to participants. Personal health information was also collected from participants' health charts, and participant interviews. Associations of sex and condition with the outcomes of screening for further assessment of depression, screening for further assessment of anxiety, and CI were calculated using t-tests and Chi-square tests.Results: 285 participants completed standardized screening tools for depression and anxiety (PHQ-4) and 283 completed the Community Integration Questionnaire (CIQ). Mean age was 23.4 (4.2) years; 59% were diagnosed with CP, 41% diagnosed with ABI, and 56% were male. A moderate proportion of the sample screened positive for further assessment of anxiety (28%) and depression (16%), and the overall mean score on the CIQ for the sample was 15.8 (SD 5.1). Participants that screened positive for further assessment of depression and anxiety on the PHQ-4 had lower scores on the Social Integration subscale of the CIQ (p = 0.04 and p = 0.036, respectively). Females were found to have significantly higher community integration than males (p = 0.0011) and those diagnosed with ABI were found to have significantly higher community integration than those with CP (p = 0.009), respectively. A weak negative association was found between age for the total sample and overall PHQ-4 score (p = 0.0417). Presence of an intellectual or learning disability/challenge was associated with a lower CIQ score (p = 0.0026).Conclusions: This current study, highlights the need for further research to explore the unique needs and barriers faced by this population. This study may inform assessments and interventions to support the mental health and community integration of this population.

How a Service User Knows the Level of Privacy and to Whom Trust in pHealth Systems?

pHealth is a data (personal health information) driven approach that use communication networks and platforms as technical base. Often it’ services take place in distributed multi-stakeholder environment. Typical pHealth services for the user are personalized information and recommendations how to manage specific health problems and how to behave healthy (prevention). The rapid development of micro- and nano-sensor technology and signal processing makes it possible for pHealth service provider to collect wide spectrum of personal health related information from vital signs to emotions and health behaviors. This development raises big privacy and trust challenges especially because in pHealth similarly to eCommerce and Internet shopping it is commonly expected that the user automatically trust in service provider and used information systems. Unfortunately, this is a wrong assumption because in pHealth’s digital environment it almost impossible for the service user to know to whom to trust, and what the actual level of information privacy is. Therefore, the service user needs tools to evaluate privacy and trust of the service provider and information system used. In this paper, the authors propose a solution for privacy and trust as results of their antecedents, and for the use of computational privacy and trust. To answer the question, which antecedents to use, two literature reviews are performed and 27 privacy and 58 trust attributes suitable for pHealth are found. A proposal how to select a subset of antecedents for real life use is also provided.

Cryptoanalysis on a Cloud-Centric Internet-of-Medical-Things-Enabled Smart Healthcare System

The interconnecting of the biomedical sensors (in healthcare system) with cloud for the internet-of-medical-things (IoMT) technology has great potential to ameliorate people's living conditions. The privacy-preserving of personal health information (PHI) and the mutual authentication between the sensors and other entities are two main factors that affect the further applications of cloud-centric IoMT technology. In the recent work [IEEE IoT Journal, vol. 7(10), 10650-10659, 2020], Kumar and Chand applied identity-based aggregate signcryption scheme to the smart healthcare system (KC-system, for short), which provides privacy-preserving of PHI and the mutual authentication function, simultaneously. However, in this paper, we carefully analyze the security of KCsystem and find out that the critical authentication keys of entities can be easily recovered from their communication contents. In other words, the mutual authentication function of KC-system can be easily broken. Moreover, the recovering of the keys will lead to the tedious processes, including obtaining partial private key (from network manager) and requesting for key-protection (from key-protection servers), become completely useless. Finally, we remark that it seems to be hard to remedy the current KC-system so that it is immune to our attack.

Provably Secure Data Sharing Approach for Personal Health Records in Cloud Storage Using Session Password, Data Access Key, and Circular Interpolation

Personal health record (PHR) system has become the most important platform to exchange health information, in which the patients can share and manage personal health information more effectively in cloud storage. However, the cloud server is unreliable, and the secure data of users may be disclosed. Therefore, a secure data sharing mechanism is developed in this research using the proposed session password, data access key, and circular interpolation (SKC)-based data-sharing approach for the secure sharing of PHR in the cloud. The proposed SKC-based data sharing approach provides high efficiency and high-security guarantee. It effectively satisfies various security properties, such as tamper resistance, openness, and decentralization. The proposed SKC-based data sharing approach is the reliable mechanism created for the doctors to share the PHR and to access the patient historical data while meeting the privacy preservation.