Patients in intensive care units (ICUs) may lack decisional capacity and may depend on proxy decision makers (PDMs) to make medical decisions on their behalf. High-quality information-sharing with PDMs, including through such means as health information technology, could improve communication and decision making and could potentially minimize the psychological consequences of an ICU stay for both patients and their family members. However, alongside these anticipated benefits of information-sharing are risks of unwanted disclosure of sensitive information. Approaches to identifying the optimal balance between access to digital health information to facilitate engagement and protecting patient privacy are urgently needed. We identified eight themes that should be considered in balancing health information access and patient privacy: 1) potential benefits to patients from PDM data access; 2) potential harms to patients from such access; 3) the moral status of families within the patient-clinician relationship; 4) the scope of relevant information provided to PDMs; 5) issues around defining PDMs' authority; 6) methods for eliciting and documenting patient preferences about their family's information access; 7) the relevance of methods for ascertaining the identity of PDMs; and 8) the obligations of hospitals to prevent privacy breaches by PDMs. We conclude that PDMs should typically have access to health information from the current episode of care when the patient is decisionally impaired, unless the patient has previously expressed a clear preference that PDMs not have such access.

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocv182DOI Listing

Publication Analysis

Top Keywords

patient privacy
12
intensive care
8
pdms
7
access
7
patient
6
balancing digital
4
digital information-sharing
4
information-sharing patient
4
privacy
4
privacy engaging
4

Similar Publications

Comprehensive histopathological analysis of gastric cancer in European and Latin America populations reveals differences in PDL1, HER2, p53 and MUC6 expression.

Gastric Cancer

January 2025

Department of Medical Oncology, Hospital Clinico Universitario, INCLIVA, Biomedical Research Institute, University of Valencia, Avenida Menendez Pelayo nro 4 accesorio, Valencia, Spain.

Introduction: Gastric cancer (GC) burden is currently evolving with regional differences associated with complex behavioural, environmental, and genetic risk factors. The LEGACy study is a Horizon 2020-funded multi-institutional research project conducted prospectively to provide comprehensive data on the tumour biological characteristics of gastroesophageal cancer from European and LATAM countries.

Material And Methods: Treatment-naïve advanced gastroesophageal adenocarcinoma patients were prospectively recruited in seven European and LATAM countries.

View Article and Find Full Text PDF

Introduction And Hypothesis: This study was aimed at evaluating the therapeutic effects of a modified intravesical botulinum toxin injection technique (fewer injection sites under local anesthesia), in comparison with the conventional technique for patients with idiopathic detrusor overactivity, considering the urodynamic parameters.

Methods: In this double-blinded randomized clinical trial, 78 adult females with idiopathic detrusor overactivity were divided into two groups: conventional and modified groups. In the conventional method, patients received intradetrusor botulinum toxin injection at 20 sites under general or spinal anesthesia in a trigone-sparing fashion.

View Article and Find Full Text PDF

Prediction of pulmonary embolism by an explainable machine learning approach in the real world.

Sci Rep

January 2025

Department of Respiratory and Critical Care Medicine, Changhai Hospital, The Second Military Medical University, Shanghai, People's Republic of China.

In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge because of its high mortality, high disability, high missed and high misdiagnosed rates. To address this, we employed an artificial intelligence-based machine learning algorithm (MLA) to construct a robust predictive model for PE. We retrospectively analyzed 1480 suspected PE patients hospitalized in West China Hospital of Sichuan University between May 2015 and April 2020.

View Article and Find Full Text PDF

Gastrointestinal lesions of eosinophilic granulomatosis with polyangiitis: a prediction model and clinical patterns.

Arthritis Res Ther

January 2025

Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, the Ministry of Education Key Laboratory, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, Beijing, China.

Objective: Severe gastrointestinal lesions are associated with a poor prognosis in eosinophilic granulomatosis with polyangiitis (EGPA). The goal of this study was to develop an effective predictive model for gastrointestinal lesions and to examine clinical patterns, associated factors, treatment, and outcomes of gastrointestinal lesions in EGPA.

Methods: We retrospectively enrolled 165 EGPA patients.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!