17,820 research outputs found
Electronic health records to facilitate clinical research
Electronic health records (EHRs) provide opportunities to enhance patient care, embed performance measures in clinical practice, and facilitate clinical research. Concerns have been raised about the increasing recruitment challenges in trials, burdensome and obtrusive data collection, and uncertain generalizability of the results. Leveraging electronic health records to counterbalance these trends is an area of intense interest. The initial applications of electronic health records, as the primary data source is envisioned for observational studies, embedded pragmatic or post-marketing registry-based randomized studies, or comparative effectiveness studies. Advancing this approach to randomized clinical trials, electronic health records may potentially be used to assess study feasibility, to facilitate patient recruitment, and streamline data collection at baseline and follow-up. Ensuring data security and privacy, overcoming the challenges associated with linking diverse systems and maintaining infrastructure for repeat use of high quality data, are some of the challenges associated with using electronic health records in clinical research. Collaboration between academia, industry, regulatory bodies, policy makers, patients, and electronic health record vendors is critical for the greater use of electronic health records in clinical research. This manuscript identifies the key steps required to advance the role of electronic health records in cardiovascular clinical research
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk
Guidelines for the management of atherosclerotic cardiovascular disease
(ASCVD) recommend the use of risk stratification models to identify patients
most likely to benefit from cholesterol-lowering and other therapies. These
models have differential performance across race and gender groups with
inconsistent behavior across studies, potentially resulting in an inequitable
distribution of beneficial therapy. In this work, we leverage adversarial
learning and a large observational cohort extracted from electronic health
records (EHRs) to develop a "fair" ASCVD risk prediction model with reduced
variability in error rates across groups. We empirically demonstrate that our
approach is capable of aligning the distribution of risk predictions
conditioned on the outcome across several groups simultaneously for models
built from high-dimensional EHR data. We also discuss the relevance of these
results in the context of the empirical trade-off between fairness and model
performance
Use of m-Health Technology for Preventive Interventions to Tackle Cardiometabolic Conditions and Other Non-Communicable Diseases in Latin America- Challenges and Opportunities
In Latin America, cardiovascular disease (CVD) mortality rates will increase by an estimated 145% from 1990 to 2020. Several challenges related to social strains, inadequate public health infrastructure, and underfinanced healthcare systems make cardiometabolic conditions and non-communicable diseases (NCDs) difficult to prevent and control. On the other hand, the region has high mobile phone coverage, making mobile health (mHealth) particularly attractive to complement and improve strategies toward prevention and control of these conditions in low- and middle-income countries. In this article, we describe the experiences of three Centers of Excellence for prevention and control of NCDs sponsored by the National Heart, Lung, and Blood Institute with mHealth interventions to address cardiometabolic conditions and other NCDs in Argentina, Guatemala, and Peru. The nine studies described involved the design and implementation of complex interventions targeting providers, patients and the public. The rationale, design of the interventions, and evaluation of processes and outcomes of each of these studies are described, together with barriers and enabling factors associated with their implementation.Fil: Beratarrechea, Andrea Gabriela. Instituto de Efectividad ClĂnica y Sanitaria; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; ArgentinaFil: Diez Canseco, Francisco. Universidad Peruana Cayetano Heredia; PerĂșFil: Irazola, Vilma. Instituto de Efectividad ClĂnica y Sanitaria; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; ArgentinaFil: Miranda, Jaime. Universidad Peruana Cayetano Heredia; PerĂșFil: Ramirez Zea, Manuel. Institute of Nutrition of Central America and Panama; GuatemalaFil: Rubinstein, Adolfo Luis. Instituto de Efectividad ClĂnica y Sanitaria; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentin
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Trends in virtual reality technologies for the learning patient
NextMed convened the Medicine Meets Virtual Reality 22 (MMVR 22) conference in 2016. Since 1992, the conference has brought together a diverse group of researchers to share creative solutions for the evolving challenge of integrating virtual reality tools into medical education. Virtual reality (VR) and its enabling technologies utilize hardware and software to simulate environments and encounters where users can interact and learn. The MMVR 22 symposium proceedings contain projects that support a variety of learners: medical students, practitioners, soldiers, and patients. This report will contemplate the trends in virtual reality technologies for patients navigating their medical and healthcare learning. The learning patient seeks more than intervention; they seek prevention. From virtual humans and environments to motion sensors and haptic devices, patients are surrounded by increasingly rich and transformative data-driven tools. Applied data enables VR applications to simulate experience, predict health outcomes, and motivate new behavior. The MMVR 22 presents investigations into the usability of wearable devices, the efficacy of avatar inclusion, and the viability of multi-player gaming. With increasing need for individualized and scalable programming, only committed open source efforts will align instructional designers, technology integrators, trainers, and clinicians.âCurriculum and InstructionCurriculum and Instructio
Evaluating openEHR for storing computable representations of electronic health record phenotyping algorithms
Electronic Health Records (EHR) are data generated during routine clinical
care. EHR offer researchers unprecedented phenotypic breadth and depth and have
the potential to accelerate the pace of precision medicine at scale. A main EHR
use-case is creating phenotyping algorithms to define disease status, onset and
severity. Currently, no common machine-readable standard exists for defining
phenotyping algorithms which often are stored in human-readable formats. As a
result, the translation of algorithms to implementation code is challenging and
sharing across the scientific community is problematic. In this paper, we
evaluate openEHR, a formal EHR data specification, for computable
representations of EHR phenotyping algorithms.Comment: 30th IEEE International Symposium on Computer-Based Medical Systems -
IEEE CBMS 201
An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders
The data mining along with emerging computing techniques have astonishingly
influenced the healthcare industry. Researchers have used different Data Mining
and Internet of Things (IoT) for enrooting a programmed solution for diabetes
and heart patients. However, still, more advanced and united solution is needed
that can offer a therapeutic opinion to individual diabetic and cardio
patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced
healthcare system for proficient diabetes and cardiovascular diseases have been
proposed. The hybridization of data mining and IoT with other emerging
computing techniques is supposed to give an effective and economical solution
to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining,
Internet of Things, chatbots, contextual entity search (CES), bio-sensors,
semantic analysis and granular computing (GC). The bio-sensors of the proposed
system assist in getting the current and precise status of the concerned
patients so that in case of an emergency, the needful medical assistance can be
provided. The novelty lies in the hybrid framework and the adequate support of
chatbots, granular computing, context entity search and semantic analysis. The
practical implementation of this system is very challenging and costly.
However, it appears to be more operative and economical solution for diabetes
and cardio patients.Comment: 11 PAGE
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