Determinants of Privacy Concerns and Intention to Share Personal Health Data on Electronic Health Records--Model

2021 ◽  
Author(s):  
Emna Cherif ◽  
Nora Bezaz ◽  
Manel Mzoughi
Keyword(s):  
Electronic Health Records ◽  
Health Data ◽  
Personal Health ◽  
Health Records ◽  
Privacy Concerns ◽  
Electronic Health

scholarly journals ‘I do not share it with others. No, it’s for me, it’s my care’: On sharing of patient accessible electronic health records

2020 ◽  
Vol 26 (4) ◽  
pp. 2554-2567 ◽  
Author(s):  
Leysan Nurgalieva ◽  
Åsa Cajander ◽  
Jonas Moll ◽  
Rose-Mharie Åhlfeldt ◽  
Isto Huvila ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
High Sensitivity ◽  
Health Data ◽  
Security And Privacy ◽  
Personal Health ◽  
Structured Interviews ◽  
Health Records ◽  
Educational Levels ◽  
Electronic Health ◽  
Privacy Issues

This study explores patients’ perspectives on sharing their personal health data, which is traditionally shared through discussions with peers and relatives. However, other possibilities for sharing have emerged through the introduction of online services such as Patient Accessible Electronic Health Records (PAEHR). In this article, we investigate strategies that patients adopt in sharing their PAEHR. Data were collected through a survey with 2587 patients and through 15 semi-structured interviews with cancer patients. Results show that surprisingly few patients share their information, and that older patients and patients with lower educational levels share more frequently. A large majority of patients trust the security of the system when sharing despite the high sensitivity of health information. Finally, we discuss the design implications addressing identified problems when sharing PAEHR, as well as security and privacy issues connected to sharing.

The process of sourcing and preparing electronic health records data to implement a machine-learning algorithm for early identification of maternal cardiovascular risk (Preprint)

2021 ◽  
Author(s):  
Nawar Shara ◽  
Kelley M. Anderson ◽  
Noor Falah ◽  
Maryam F. Ahmad ◽  
Darya Tavazoei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Risk ◽  
Electronic Health Records ◽  
Patient Care ◽  
Early Identification ◽  
Health Data ◽  
Patient Specific ◽  
Patient Records ◽  
Health Records ◽  
Electronic Health

BACKGROUND Healthcare data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes [6]. However, the differences that exist in each individual’s health records, combined with the lack of health-data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. While these problems exist throughout healthcare, they are especially prevalent within maternal health, and exacerbate the maternal morbidity and mortality (MMM) crisis in the United States. OBJECTIVE Maternal patient records were extracted from the electronic health records (EHRs) of a large tertiary healthcare system and made into patient-specific, complete datasets through a systematic method so that a machine-learning-based (ML-based) risk-assessment algorithm could effectively identify maternal cardiovascular risk prior to evidence of diagnosis or intervention within the patient’s record. METHODS We outline the effort that was required to define the specifications of the computational systems, the dataset, and access to relevant systems, while ensuring data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for its use by a proprietary risk-stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. RESULTS Patient records can be made actionable for the goal of effectively employing machine learning (ML), specifically to identify cardiovascular risk in pregnant patients. CONCLUSIONS Upon acquiring data, including the concatenation, anonymization, and normalization of said data across multiple EHRs, the use of a machine-learning-based (ML-based) tool can provide early identification of cardiovascular risk in pregnant patients. CLINICALTRIAL N/A

Privacy-Friendly Management of Electronic Health Records in the eHealth Context

2015 ◽  
pp. 251-264
Author(s):  
Milica Milutinovic ◽  
Bart De Decker
Keyword(s):  
Electronic Health Records ◽  
Large Scale ◽  
Patient Data ◽  
Health Records ◽  
Privacy Concerns ◽  
Ubiquitous Technology ◽  
Emergency Situations ◽  
User Centric ◽  
Electronic Health

Electronic Health Records (EHRs) are becoming the ubiquitous technology for managing patients' records in many countries. They allow for easier transfer and analysis of patient data on a large scale. However, privacy concerns linked to this technology are emerging. Namely, patients rarely fully understand how EHRs are managed. Additionally, the records are not necessarily stored within the organization where the patient is receiving her healthcare. This service may be delegated to a remote provider, and it is not always clear which health-provisioning entities have access to this data. Therefore, in this chapter the authors propose an alternative where users can keep and manage their records in their existing eHealth systems. The approach is user-centric and enables the patients to have better control over their data while still allowing for special measures to be taken in case of emergency situations with the goal of providing the required care to the patient.

scholarly journals Research using electronic health records: Balancing confidentiality and public good

2018 ◽  
Vol 10 (4) ◽  
pp. 288 ◽  
Author(s):  
Katharine A. Wallis ◽  
Kyle S. Eggleton ◽  
Susan M. Dovey ◽  
Sharon Leitch ◽  
Wayne K. Cunningham ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Ethical Issues ◽  
Personal Health Information ◽  
Personal Health ◽  
Quality And Safety ◽  
Patient Relationship ◽  
Health Records ◽  
Patient Consent ◽  
Information Research ◽  
Electronic Health

ABSTRACTGeneral practitioners are increasingly approached to participate in research and share de-identified patient information. Research using electronic health records has considerable potential for improving the quality and safety of patient care. Obtaining individual patient consent for the use of the information is usually not feasible. In this article we explore the ethical issues in using personal health information in research without patient consent including the threat to confidentially and the doctor-patient relationship, and we discuss how the risks can be minimised and managed drawing on our experience as general practitioners and researchers.

Sign in / Sign up

Export Citation Format

Share Document