scholarly journals A Novel Hash-Based File Clustering Scheme for Efficient Distributing, Storing, and Retrieving of Large Scale Health Records

2016 ◽  
Author(s):  
Thanh Dat Dang ◽  
Doan Hoang ◽  
Priyadarsi Nanda
Keyword(s):  
Large Scale ◽  
Health Records

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.

Privacy concerns of the Australian My Health Record: Implications for other large-scale opt-out personal health records

2020 ◽  
Vol 57 (6) ◽  
pp. 102364 ◽  
Author(s):  
Patrick Cheong-Iao Pang ◽  
Dana McKay ◽  
Shanton Chang ◽  
Qingyu Chen ◽  
Xiuzhen Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Personal Health Records ◽  
Personal Health ◽  
Health Record ◽  
Health Records ◽  
Privacy Concerns ◽  
Opt Out

Five analytic challenges in working with electronic health records data to support clinical trials with some solutions

2020 ◽  
Vol 17 (4) ◽  
pp. 370-376
Author(s):  
Benjamin A Goldstein
Keyword(s):  
Clinical Trials ◽  
Electronic Health Records ◽  
Large Scale ◽  
Point Of Care ◽  
Cross Sectional ◽  
Health Records ◽  
Data Resource ◽  
Electronic Health ◽  
Data Elements ◽  
Data Efficiency

Electronic health records data are becoming a key data resource in clinical research. Owing to issues of data efficiency, electronic health records data are being used for clinical trials. This includes both large-scale pragmatic trails and smaller—more focused—point-of-care trials. While electronic health records data open up a number of scientific opportunities, they also present a number of analytic challenges. This article discusses five particular challenges related to organizing electronic health records data for analytic purposes. These are as follows: (1) data are not organized for research purposes, (2) data are both densely and irregularly observed, (3) we don’t have all data elements we may want or need, (4) data are both cross-sectional and longitudinal, and (5) data may be informatively observed. While laying out these challenges, the article notes how many of these challenges can be addressed by careful and thoughtful study design as well as by integration of clinicians and informaticians into the analytic team.

scholarly journals A Large-Scale Observational Study on the Temporal Trends and Risk Factors of Opioid Overdose: Real-World Evidence for Better Opioids

2020 ◽  
Author(s):  
Jianyuan Deng ◽  
Wei Hou ◽  
Xinyu Dong ◽  
Janos Hajagos ◽  
Mary Saltz ◽  
...  
Keyword(s):  
Risk Factors ◽  
Large Scale ◽  
Care Setting ◽  
Temporal Trends ◽  
Prescription Opioid ◽  
Opioid Overdose ◽  
Prescription Opioids ◽  
Health Records ◽  
The Us ◽  
Increasing Trends

AbstractBackgroundThe United States is in the midst of an opioid overdose epidemic. We evaluated the temporal trends and risk factors of inpatient opioid overdose. Based on the opioid overdose patterns, we further examined the innate properties underlying less overdose events.MethodsWe conducted a retrospective cross-sectional study based a large-scale inpatient electronic health records database, Cerner Health Facts®. We included patients admitted between January 1, 2009 and December 31, 2017. Opioid overdose prevalence by year, demographics and prescription opioid exposures.ResultsA total of 4,720,041 patients with 7,339,480 inpatient encounters were retrieved from Cerner Health Facts®. Among them, 30.2% patients were aged 65+, 57.0% female, 70.1% Caucasian, 42.3% single, 32.0% from South and 80.8% in urban area. From 2009 to 2017, annual opioid overdose prevalence per 1,000 patients significantly increased from 3.7 to 11.9 with an adjusted odds ratio (aOR): 1.16, 95% confidence interval (CI): [1.15-1.16]. Comparing to the major demographic counterparts above, being in 1) age group: 41-50 (overall aOR 1.36, 95% CI: [1.31-1.40]) or 51-64 (overall aOR 1.35, 95% CI: [1.32-1.39]), marital status: divorced (overall aOR 1.19, 95% CI: [1.15-1.23]), 3) census region: West (overall aOR 1.32, 95% CI: [1.28-1.36]), were significantly associated with higher odds of opioid overdose. Prescription opioid exposures were also associated with increased odds of opioid overdose, such as meperidine (overall aOR 1.09, 95% CI: [1.06-1.13]) and tramadol (overall aOR 2.20. 95% CI: [2.14-2.27]). Examination on the relationships between opioid agonists’ properties and their association strengths, aORs, in opioid overdose showed that lower aORs values were significantly associated with 1) high molecular weight, 2) negative interaction with multi-drug resistance protein 1 (MDR1) or positive interaction with cytochrome P450 3A4 (CYP3A4) and 3) negative interaction with delta opioid receptor (DOR) or kappa opioid receptor (KOR).ConclusionsThe significant increasing trends of opioid overdose at the inpatient care setting from 2009 to 2017 indicated an ongoing need of efforts to combat the opioid overdose epidemic in the US. Risk factors associated with opioid overdose included patient demographics and prescription opioid exposures. Different prescription opioids were associated with opioid overdose to different extents, indicating a necessity to better differentiate them during prescribing practice. Moreover, there are physicochemical, pharmacokinetic and pharmacodynamic properties underlying less overdose events, which can be utilized to develop better opioids.Key PointsThere were significant increasing trends of opioid overdose at the US inpatient care setting from 2009 to 2017, showing an ongoing need for opioid overdose prevention.Different prescription opioids were associated with opioid overdose to different extents, indicating a necessity to differentiate prescription opioids during prescribing.The optimal properties underlying less overdose events mined from the large-scale, real-world electronic health records hold high potential to guide the development of better opioids with reduced overdose effects.

scholarly journals An Explainable Artificial Intelligence Approach for Predicting Cardiovascular Outcomes using Electronic Health Records

2021 ◽  
Author(s):  
Sergiusz Wesolowski ◽  
Gordon Howard Lemmon ◽  
Edgar J Hernandez ◽  
Alex Ryan Henrie ◽  
Thomas A Miller ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Large Scale ◽  
Cardiovascular Health ◽  
Cardiovascular Outcomes ◽  
Health Records ◽  
University Of Utah ◽  
Explainable Artificial Intelligence ◽  
Electronic Health ◽  
Discovery Method

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyzes.

scholarly journals Real-world effectiveness of Ad26.COV2.S adenoviral vector vaccine for COVID-19

2021 ◽  
Author(s):  
Juan Corchado-Garcia ◽  
David Puyraimond-Zemmour ◽  
Travis Hughes ◽  
Tudor Cristea-Platon ◽  
Patrick Lenehan ◽  
...  
Keyword(s):  
Single Dose ◽  
Infection Rate ◽  
Real World ◽  
Large Scale ◽  
Vaccination Campaign ◽  
Reduction Rate ◽  
Health Records ◽  
Vector Vaccine ◽  
Fold Reduction

In light of the massive and rapid vaccination campaign against COVID-19, continuous real-world effectiveness and safety assessment of the FDA-authorized vaccines is critical to amplify transparency, build public trust, and ultimately improve overall health outcomes. In this study, we leveraged large-scale longitudinal curation of electronic health records (EHRs) from the multi-state Mayo Clinic health system (MN, AZ, FL, WN, IA). We compared the infection rate of 2,195 individuals who received a single dose of the Ad26.COV2.S vaccine from Johnson & Johnson (J&J) to the infection rate of 21,950 unvaccinated, propensity-matched individuals between February 27th and April 14th 2021. Of the 1,779 vaccinated individuals with at least two weeks of follow-up, only 3 (0.17%) tested positive for SARS-CoV-2 15 days or more after vaccination compared to 128 of 17,744 (0.72%) unvaccinated individuals (4.34 fold reduction rate). This corresponds to a vaccine effectiveness of 76.7% (95% CI: 30.3-95.3%) in preventing SARS-CoV-2 infection with onset at least two weeks after vaccination. This data is consistent with the clinical trial-reported efficacy of Ad26.COV2.S in preventing moderate to severe COVID-19 with onset at least 14 days after vaccine administration (66.9%; 95% CI: 59.0-73.4%). Due to the recent authorization of the Ad26.COV2.S vaccine, there are not yet enough hospitalizations, ICU admissions, or deaths within this cohort to robustly assess the effect of vaccination on COVID-19 severity, but these outcomes will be continually assessed in near-real-time with our platform. Collectively, this study provides further evidence that a single dose of Ad26.COV2.S is highly effective in preventing SARS-CoV-2 infection and reaffirms the urgent need to continue mass vaccination efforts globally.

Cardiometabolic disease, comorbidities and risk of death: findings using data from large-scale electronic health records

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
D Canoy ◽  
M Zottoli ◽  
J Tran ◽  
R Ramakrishnan ◽  
A Hasseine ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Large Scale ◽  
Excess Mortality ◽  
Cardiometabolic Disease ◽  
Funding Source ◽  
Combined Effects ◽  
Health Records ◽  
Risk Of Death ◽  
Individual Disease

Abstract Background Myocardial infarction (MI), stroke and diabetes are separately associated with increased risk of mortality but it is uncertain if their combined effects are proportional, amplified or less than the expected risk of each disease individually. In addition, patients with these conditions tend to also have other long-term comorbidities. How the relationship between cardiometabolic disease and risk of death is modified by the presence of comorbidity is unclear. Purpose We investigated the separate and combined effects of MI, stroke and diabetes on all-cause mortality, and examined the impact of comorbidity on these associations. Methods We selected a patient cohort of 2,007,731 (51% women) aged ≥16 years at registration with their general practice, using large-scale UK primary care electronic health records that were linked to the national death registry. We identified patients with a recorded diagnosis of MI, stroke, diabetes or none before 2005 (baseline), and classified the patient cohort into mutually exclusive categories of their baseline disease status. For each group, we also extracted information on another major 53 long-term conditions prior to baseline. The cohort was followed until death, deregistration from the practice or censored at the end of study (31 Dec 2014). We used Cox regression, and tested for departure from additivity and multiplicativity to assess interaction. Results At baseline, the mean age of the cohort was 51 (SD=18) years and 7% (N=145,910) had a cardiometabolic disease. Over an average follow-up of 7 (SD=3) years, 270,036 died (mean age of death=79 years). After adjusting for baseline age and sex, the hazard ratio (HR) (95% confidence interval [CI]), relative to those without cardiometabolic disease, were as follows: diabetes=1.53 (1.51 to 1.55), MI=1.54 (1.51 to 1.56), stroke=1.87 (1.84 to 1.90), diabetes and MI=2.16 (2.09 to 2.23), MI and stroke=2.39 (2.28 to 2.49), diabetes and stroke=2.56 (2.47 to 2.65), and all three=3.17 (2.95 to 3.41). After adjusting for the 53 comorbidities, the HR (95% CI) were attenuated: diabetes=1.37 (1.35 to 1.39), MI=1.25 (1.23 to 1.27), stroke=1.49 (1.46 to 1.52), diabetes and MI=1.60 (1.55 to 1.65), MI and stroke=1.52 (1.45 to 1.59), diabetes and stroke=1.91 (1.84 to 1.98), and all three=1.77 (1.64 to 1.91). The results did not materially changed with adjustment for smoking and deprivation level. Test for interaction revealed some minor synergistic effects when cardiometabolic disease co-occurred but excess risks were lower than expected for two combined vs individual disease effects; no significant interaction was seen for all three vs individual disease effects. Conclusion MI, stroke and diabetes are associated with excess mortality, which was partly due to associated chronic conditions. We found no evidence that the co-occurrence of these three conditions contribute to a higher excess mortality than expected from each of them separately. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): NIHR Oxford Biomedical Research Centre; Oxford Martin School, University of Oxford

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

2017 ◽  
pp. 528-542
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 Long‐Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large‐Scale Routine Electronic Health Records

2019 ◽  
Vol 8 (12) ◽  
Author(s):  
Jose Roberto Ayala Solares ◽  
Dexter Canoy ◽  
Francesca Elisa Diletta Raimondi ◽  
Yajie Zhu ◽  
Abdelaali Hassaine ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Electronic Health Records ◽  
Systolic Blood Pressure ◽  
Large Scale ◽  
Health Records ◽  
Link Type ◽  
Elevated Systolic Blood Pressure ◽  
Incident Cardiovascular Disease

See Editorial Ahmad and Oparil

Sign in / Sign up

Export Citation Format

Share Document