Exchanging personal health data with electronic health records: A standardized information model for patient generated health data and observations of daily living

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.

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

10.2196/preprints.34932 ◽

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

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Research using electronic health records: Balancing confidentiality and public good

Journal of Primary Health Care ◽

10.1071/hc18040 ◽

2018 ◽

Vol 10 (4) ◽

pp. 288 ◽

Cited By ~ 2

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.

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Archetype-Based Electronic Health Records: A Literature Review and Evaluation of Their Applicability to Health Data Interoperability and Access

Health Information Management Journal ◽

10.1177/183335830903800202 ◽

2009 ◽

Vol 38 (2) ◽

pp. 7-17 ◽

Cited By ~ 23

Author(s):

Dennis Wollersheim ◽

Anny Sari ◽

Wenny Rahayu

Keyword(s):

Literature Review ◽

Electronic Health Records ◽

Health Data ◽

Data Interoperability ◽

Health Records ◽

Electronic Health

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What is the Impact of Electronic Health Records on the Quality of Health Data?

Health Information Management Journal ◽

10.1177/183335831404300106 ◽

2014 ◽

Vol 43 (1) ◽

pp. 42-43 ◽

Cited By ~ 6

Author(s):

Joanne Callen

Keyword(s):

Electronic Health Records ◽

Health Data ◽

Health Records ◽

Electronic Health ◽

The Impact

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A nursing information model process for interoperability

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocu026 ◽

2015 ◽

Vol 22 (3) ◽

pp. 608-614 ◽

Cited By ~ 9

Author(s):

Marilyn Chow ◽

Murielle Beene ◽

Ann O’Brien ◽

Patricia Greim ◽

Tim Cromwell ◽

...

Keyword(s):

Electronic Health Records ◽

Information Model ◽

Health Records ◽

Quality Reporting ◽

Nursing Leaders ◽

Common Information ◽

Common Information Model ◽

Nursing Data ◽

Electronic Health ◽

Hospital Acquired

Abstract The ability to share nursing data across organizations and electronic health records is a key component of improving care coordination and quality outcomes. Currently, substantial organizational and technical barriers limit the ability to share and compare essential patient data that inform nursing care. Nursing leaders at Kaiser Permanente and the U.S. Department of Veterans Affairs collaborated on the development of an evidence-based information model driven by nursing practice to enable data capture, re-use, and sharing between organizations and disparate electronic health records. This article describes a framework with repeatable steps and processes to enable the semantic interoperability of relevant and contextual nursing data. Hospital-acquired pressure ulcer prevention was selected as the prototype nurse-sensitive quality measure to develop and test the model. In a Health 2.0 Developer Challenge program from the Office of the National Coordinator for Health, mobile applications implemented the model to help nurses assess the risk of hospital-acquired pressure ulcers and reduce their severity. The common information model can be applied to other nurse-sensitive measures to enable data standardization supporting patient transitions between care settings, quality reporting, and research.

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Facilitating the ethical use of health data for the benefit of society: electronic health records, consent and the duty of easy rescue

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2016.0130 ◽

2016 ◽

Vol 374 (2083) ◽

pp. 20160130 ◽

Cited By ~ 29

Author(s):

Sebastian Porsdam Mann ◽

Julian Savulescu ◽

Barbara J. Sahakian

Keyword(s):

Informed Consent ◽

Electronic Health Records ◽

Data Science ◽

Real Life ◽

Health Data ◽

Sensitive Information ◽

Minimal Risk ◽

Sensitive Data ◽

Health Records ◽

Electronic Health

Advances in data science allow for sophisticated analysis of increasingly large datasets. In the medical context, large volumes of data collected for healthcare purposes are contained in electronic health records (EHRs). The real-life character and sheer amount of data contained in them make EHRs an attractive resource for public health and biomedical research. However, medical records contain sensitive information that could be misused by third parties. Medical confidentiality and respect for patients' privacy and autonomy protect patient data, barring access to health records unless consent is given by the data subject. This creates a situation in which much of the beneficial records-based research is prevented from being used or is seriously undermined, because the refusal of consent by some patients introduces a systematic deviation, known as selection bias, from a representative sample of the general population, thus distorting research findings. Although research exemptions for the requirement of informed consent exist, they are rarely used in practice due to concerns over liability and a general culture of caution. In this paper, we argue that the problem of research access to sensitive data can be understood as a tension between the medical duties of confidentiality and beneficence. We attempt to show that the requirement of informed consent is not appropriate for all kinds of records-based research by distinguishing studies involving minimal risk from those that feature moderate or greater risks. We argue that the duty of easy rescue—the principle that persons should benefit others when this can be done at no or minimal risk to themselves—grounds the removal of consent requirements for minimally risky records-based research. Drawing on this discussion, we propose a risk-adapted framework for the facilitation of ethical uses of health data for the benefit of society. This article is part of the themed issue ‘The ethical impact of data science’.

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