scholarly journals Patient perspectives on health data privacy and implications for adverse drug event documentation and communication: A qualitative study (Preprint)

10.2196/21452 ◽  
2020 ◽  
Author(s):  
Serena S Small ◽  
Corinne M Hohl ◽  
Ellen Balka
Keyword(s):  
Qualitative Study ◽  
Adverse Drug Event ◽  
Data Privacy ◽  
Health Data ◽  
Patient Perspectives ◽  
Drug Event

scholarly journals Patient perspectives on health data privacy and implications for adverse drug event documentation and communication: A qualitative study (Preprint)

2020 ◽  
Author(s):  
Serena S Small ◽  
Corinne M Hohl ◽  
Ellen Balka
Keyword(s):  
Information Sharing ◽  
Information And Communication Technologies ◽  
Adverse Drug Event ◽  
Data Privacy ◽  
Adverse Drug Events ◽  
Communication Technologies ◽  
Health Data ◽  
Drug Event ◽  
Information And Communication ◽  
Improve Patient

BACKGROUND Adverse drug events are unintended and harmful events related to medication use. Using existing information and communication technologies to increase information sharing about adverse drug events may improve patient care, but can also introduce concerns about data privacy. OBJECTIVE The aim of our study was to examine patients’ and their caregivers’ views about data protection when using information and communication technologies to communicate adverse drug event information in order to improve patient safety. METHODS We conducted an exploratory qualitative study. We held four focus groups among patients who had experienced or were at risk of experiencing an adverse drug event, their family members, and their caregivers. We recruited participants through multiple avenues. We iteratively analyzed the data using situational analysis. RESULTS Of the 47 participants we recruited, 28 attended our focus groups. We identified three primary themes. First, participants felt that improved information sharing about adverse drug events within their circle of care would likely improve care. Second, participants were concerned about data handling and inappropriate access, but believed that the benefits of information sharing outweighed the risks of privacy breaches. Lastly, participants were more concerned about data privacy in the context of stigmatized health conditions. CONCLUSIONS Current conditions for maintaining health data privacy are consistent with participants’ preferences, despite the fact that health data are susceptible to breaches and mismanagement. Information sharing that increases patient safety may justify potential privacy risks. Greater attention to patient concerns and the effect of social and contextual concerns in the design and implementation of health information technologies may increase patient confidence in the privacy of their information. CLINICALTRIAL

scholarly journals Why Clinicians Don�t Report Adverse Drug Events: Qualitative Study (Preprint)

2017 ◽  
Author(s):  
Corinne M Hohl ◽  
Serena S Small ◽  
David Peddie ◽  
Katherin Badke ◽  
Chantelle Bailey ◽  
...  
Keyword(s):  
Qualitative Study ◽  
Adverse Drug Event ◽  
Adverse Drug Events ◽  
Community Pharmacists ◽  
Care Quality ◽  
Drug Event ◽  
Complex Nature ◽  
Safety Research ◽  
Patient Care Quality ◽  
Field Notes

BACKGROUND Adverse drug events are unintended and harmful events related to medications. Adverse drug events are important for patient care, quality improvement, drug safety research, and postmarketing surveillance, but they are vastly underreported. OBJECTIVE Our objectives were to identify barriers to adverse drug event documentation and factors contributing to underreporting. METHODS This qualitative study was conducted in 1 ambulatory center, and the emergency departments and inpatient wards of 3 acute care hospitals in British Columbia between March 2014 and December 2016. We completed workplace observations and focus groups with general practitioners, hospitalists, emergency physicians, and hospital and community pharmacists. We analyzed field notes by coding and iteratively analyzing our data to identify emerging concepts, generate thematic and event summaries, and create workflow diagrams. Clinicians validated emerging concepts by applying them to cases from their clinical practice. RESULTS We completed 238 hours of observations during which clinicians investigated 65 suspect adverse drug events. The observed events were often complex and diagnosed over time, requiring the input of multiple providers. Providers documented adverse drug events in charts to support continuity of care but never reported them to external agencies. Providers faced time constraints, and reporting would have required duplication of documentation. CONCLUSIONS Existing reporting systems are not suited to capture the complex nature of adverse drug events or adapted to workflow and are simply not used by frontline clinicians. Systems that are integrated into electronic medical records, make use of existing data to avoid duplication of documentation, and generate alerts to improve safety may address the shortcomings of existing systems and generate robust adverse drug event data as a by-product of safer care.

scholarly journals Using ActionADE to create information continuity to reduce re-exposures to harmful medications: study protocol for a randomized controlled trial

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jeffrey P. Hau ◽  
Penelope M. A. Brasher ◽  
Amber Cragg ◽  
Serena Small ◽  
Maeve Wickham ◽  
...  
Keyword(s):  
Health Information ◽  
Adverse Drug Event ◽  
Primary Outcome ◽  
Adverse Drug Events ◽  
Primary Objective ◽  
Drug Event ◽  
Controlled Trials ◽  
Drug Reactions ◽  
Main Trial ◽  
Randomized Controlled

Abstract Background Repeat exposures to culprit medications are a common cause of preventable adverse drug events. Health information technologies have the potential to reduce repeat adverse drug events by improving information continuity. However, they rarely interoperate to ensure providers can view adverse drug events documented in other systems. We designed ActionADE to enable rapid documentation of adverse drug events and communication of standardized information across health sectors by integrating with legacy systems. We will leverage ActionADE’s implementation to conduct two parallel, randomized trials: patients with adverse drug reactions in the main trial and those diagnosed with non-adherence in a secondary trial. Primary objective of the main trial is to evaluate the effects of providing information continuity about adverse drug reactions on culprit medication re-dispensations over 12 months. Primary objective of the secondary trial is to evaluate the effect of providing information continuity on adherence over 12 months. Methods We will conduct two parallel group, triple-blind randomized controlled trials in participating hospitals in British Columbia, Canada. We will enroll adults presenting to hospital with an adverse drug event to prescribed outpatient medication. Clinicians will document the adverse drug event in ActionADE. The software will use an algorithm to determine patient eligibility and allocate eligible patients to experimental or control. In the experimental arm, ActionADE will transmit information to PharmaNet, where adverse drug event information will be displayed in community pharmacies when re-dispensations are attempted. In the control arm, ActionADE will retain information in the local record. We will enroll 3600 adults with an adverse drug reaction into the main trial. The main trial’s primary outcome is re-dispensation of a culprit or same-class medication within 12 months; the secondary trial’s primary outcome will be adherence to culprit medication. Secondary outcomes include health services utilization and mortality. Discussion These studies have the potential to guide policy decisions and investments needed to drive health information technology integrations to prevent repeat adverse drug events. We present an example of how a health information technology implementation can be leveraged to conduct pragmatic randomized controlled trials. Trial registration ClinicalTrials.gov NCT04568668, NCT04574648. Registered on 1 October 2020.

scholarly journals Application of Bayesian networks to generate synthetic health data

2020 ◽  
Author(s):  
Dhamanpreet Kaur ◽  
Matthew Sobiesk ◽  
Shubham Patil ◽  
Jin Liu ◽  
Puran Bhagat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Data Privacy ◽  
Statistical Tests ◽  
Synthetic Data ◽  
Original Data ◽  
Health Data ◽  
Minimal Risk ◽  
Data Types ◽  
Automated Method

Abstract Objective This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data. Materials and Methods We employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning tasks, preservation of rare events, disclosure risk, and the ability of a machine learning classifier to discriminate between the real and synthetic data. Results Our Bayesian network model outperformed or equaled medBGAN in all key metrics. Notable improvement was achieved in capturing rare variables and preserving association rules. Discussion Bayesian networks generated data sufficiently similar to the original data with minimal risk of disclosure, while offering additional transparency, computational efficiency, and capacity to handle more data types in comparison to existing methods. We hope this method will allow healthcare organizations to efficiently disseminate synthetic health data to researchers, enabling them to generate hypotheses and develop analytical tools. Conclusion We conclude the application of Bayesian networks is a promising option for generating realistic synthetic health data that preserves the features of the original data without compromising data privacy.

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