Search

Background: Childhood and adolescent obesity are persistent public health issues in the United States. Childhood obesity Electronic Health Record (EHR) tools strengthen provider-patient relationships and improve outcomes, but there are currently limited EHR tools that are linked to adolescent mHealth apps. This study is part of a larger study entitled, CommitFit, which features both an adolescent-targeted mobile health application (mHealth app) and an ambulatory EHR tool. The CommitFit mHealth app was designed to be paired with the CommitFit EHR tool for integration into clinical spaces for shared decision-making with patients and clinicians. Objectives: The objective of this sub-study was to identify the functional and design needs and preferences of healthcare clinicians and professionals for the development of the CommitFit EHR tool, specifically as it relates to childhood and adolescent obesity management. Methods: We utilized a user-centered design process with a mixed-method approach. Focus groups were used to assess current in-clinic practices, deficits, and general beliefs and preferences regarding the management of childhood and adolescent obesity. A pre- and post-focus group survey helped assess the perception of the design and functionality of the CommitFit EHR tool and other obesity clinic needs. Iterative design development of the CommitFit EHR tool occurred throughout the process. Results: A total of 12 healthcare providers participated throughout the three focus group sessions. Two themes emerged regarding EHR design: (1) Functional Needs, including Enhancing Clinical Practices and Workflow, and (2) Visualization, including Colors and Graphs. Responses from the surveys (n = 52) further reflect the need for Functionality and User-Interface Design by clinicians. Clinicians want the CommitFit EHR tool to enhance in-clinic adolescent lifestyle counseling, be easy to use, and presentable to adolescent patients and their caregivers. Additionally, we found that clinicians preferred colors and graphs that improved readability and usability. During each step of feedback from focus group sessions and the survey, the design of the CommitFit EHR tool was updated and co-developed by clinicians in an iterative user-centered design process. Conclusion: More research is needed to explore clinician actual user analytics for the CommitFit EHR tool to evaluate real-time workflow, design, and function needs. The effectiveness of the CommitFit mHealth and EHR tool as a weight management intervention needs to be evaluated in the future. [ABSTRACT FROM AUTHOR]

Background: Childhood and adolescent obesity are persistent public health issues in the United States. Childhood obesity Electronic Health Record (EHR) tools strengthen provider-patient relationships and improve outcomes, but there are currently limited EHR tools that are linked to adolescent mHealth apps. This study is part of a larger study entitled, CommitFit, which features both an adolescent-targeted mobile health application (mHealth app) and an ambulatory EHR tool. The CommitFit mHealth app was designed to be paired with the CommitFit EHR tool for integration into clinical spaces for shared decision-making with patients and clinicians. Objectives: The objective of this sub-study was to identify the functional and design needs and preferences of healthcare clinicians and professionals for the development of the CommitFit EHR tool, specifically as it relates to childhood and adolescent obesity management. Methods: We utilized a user-centered design process with a mixed-method approach. Focus groups were used to assess current in-clinic practices, deficits, and general beliefs and preferences regarding the management of childhood and adolescent obesity. A pre- and post-focus group survey helped assess the perception of the design and functionality of the CommitFit EHR tool and other obesity clinic needs. Iterative design development of the CommitFit EHR tool occurred throughout the process. Results: A total of 12 healthcare providers participated throughout the three focus group sessions. Two themes emerged regarding EHR design: (1) Functional Needs, including Enhancing Clinical Practices and Workflow, and (2) Visualization, including Colors and Graphs. Responses from the surveys (n = 52) further reflect the need for Functionality and User-Interface Design by clinicians. Clinicians want the CommitFit EHR tool to enhance in-clinic adolescent lifestyle counseling, be easy to use, and presentable to adolescent patients and their caregivers. Additionally, we found that clinicians preferred colors and graphs that improved readability and usability. During each step of feedback from focus group sessions and the survey, the design of the CommitFit EHR tool was updated and co-developed by clinicians in an iterative user-centered design process. Conclusion: More research is needed to explore clinician actual user analytics for the CommitFit EHR tool to evaluate real-time workflow, design, and function needs. The effectiveness of the CommitFit mHealth and EHR tool as a weight management intervention needs to be evaluated in the future. [ABSTRACT FROM AUTHOR]

BackgroundChildhood and adolescent obesity are persistent public health issues in the United States. Childhood obesity Electronic Health Record (EHR) tools strengthen provider-patient relationships and improve outcomes, but there are currently limited EHR tools that are linked to adolescent mHealth apps. This study is part of a larger study entitled, CommitFit, which features both an adolescent-targeted mobile health application (mHealth app) and an ambulatory EHR tool. The CommitFit mHealth app was designed to be paired with the CommitFit EHR tool for integration into clinical spaces for shared decision-making with patients and clinicians.ObjectivesThe objective of this sub-study was to identify the functional and design needs and preferences of healthcare clinicians and professionals for the development of the CommitFit EHR tool, specifically as it relates to childhood and adolescent obesity management.MethodsWe utilized a user-centered design process with a mixed-method approach. Focus groups were used to assess current in-clinic practices, deficits, and general beliefs and preferences regarding the management of childhood and adolescent obesity. A pre- and post-focus group survey helped assess the perception of the design and functionality of the CommitFit EHR tool and other obesity clinic needs. Iterative design development of the CommitFit EHR tool occurred throughout the process.ResultsA total of 12 healthcare providers participated throughout the three focus group sessions. Two themes emerged regarding EHR design: (1) Functional Needs, including Enhancing Clinical Practices and Workflow, and (2) Visualization, including Colors and Graphs. Responses from the surveys (n = 52) further reflect the need for Functionality and User-Interface Design by clinicians. Clinicians want the CommitFit EHR tool to enhance in-clinic adolescent lifestyle counseling, be easy to use, and presentable to adolescent patients and their caregivers. Additionally, we found that clinicians preferred colors and graphs that improved readability and usability. During each step of feedback from focus group sessions and the survey, the design of the CommitFit EHR tool was updated and co-developed by clinicians in an iterative user-centered design process.ConclusionMore research is needed to explore clinician actual user analytics for the CommitFit EHR tool to evaluate real-time workflow, design, and function needs. The effectiveness of the CommitFit mHealth and EHR tool as a weight management intervention needs to be evaluated in the future.

Introduction: The digitalisation of the German healthcare system enables a wide range of opportunities to utilize healthcare data. The implementation of the EHR in January 2021 was a significant step, but compared to other European countries, the implementation of the EHR in the German healthcare system is still at an early stage. The aim of this paper is to characterise the structural factors relating to the adoption of the EHR in more detail from the perspective of representatives of stakeholders working in the German healthcare system and to identify existing barriers to implementation and the need for change. Methods: Qualitative expert interviews were conducted with one representative from each of the stakeholder groups health insurance, pharmacies, healthcare research, EHR development and panel doctors. Results: The interviews with the various stakeholders revealed that the implementation process of the EHR is being delayed by a lack of a viable basis for decision-making, existing conflicts of interest and insufficient consideration of the needs of patients and service providers, among other things. Discussion: The current status of EHR implementation is due to deficiency in legal regulations as well as structural problems and the timing of the introduction. For instance, the access rights of various stakeholders to the EHR data and the procedure in the event of a technical failure of the telematics infrastructure are remain unclear. In addition, insufficient information and communication measures have not led to the desired acceptance of EHR use among patients and service providers. [ABSTRACT FROM AUTHOR]

Many diverse phenomena in nature often inherently encode both short- and long-term temporal dependencies, which especially result from the direction of the flow of time. In this respect, we discovered experimental evidence suggesting that interrelations of these events are higher for closer time stamps. However, to be able for attention-based models to learn these regularities in short-term dependencies, it requires large amounts of data, which are often infeasible. This is because, while they are good at learning piece-wise temporal dependencies, attention-based models lack structures that encode biases in time series. As a resolution, we propose a simple and efficient method that enables attention layers to better encode the short-term temporal bias of these data sets by applying learnable, adaptive kernels directly to the attention matrices. We chose various prediction tasks for the experiments using Electronic Health Records (EHR) data sets since they are great examples with underlying long- and short-term temporal dependencies. Our experiments show exceptional classification results compared to best-performing models on most tasks and data sets.

IntroductionThis study is part of the U.S. Food and Drug Administration (FDA)’s Biologics Effectiveness and Safety (BEST) initiative, which aims to improve the FDA’s postmarket surveillance capabilities by using real-world data (RWD). In the United States, using RWD for postmarket surveillance has been hindered by the inability to exchange clinical data between healthcare providers and public health organizations in an interoperable format. However, the Office of the National Coordinator for Health Information Technology (ONC) has recently enacted regulation requiring all healthcare providers to support seamless access, exchange, and use of electronic health information through the interoperable HL7 Fast Healthcare Interoperability Resources (FHIR) standard. To leverage the recent ONC changes, BEST designed a pilot platform to query and receive the clinical information necessary to analyze suspected AEs. This study assessed the feasibility of using the RWD received through the data exchange of FHIR resources to study post-vaccination AE cases by evaluating the data volume, query response time, and data quality.Materials and methodsThe study used RWD from 283 post-vaccination AE cases, which were received through the platform. We used descriptive statistics to report results and apply 322 data quality tests based on a data quality framework for EHR.ResultsThe volume analysis indicated the average clinical resources for a post-vaccination AE case was 983.9 for the median partner. The query response time analysis indicated that cases could be received by the platform at a median of 3 min and 30 s. The quality analysis indicated that most of the data elements and conformance requirements useful for postmarket surveillance were met.DiscussionThis study describes the platform’s data volume, data query response time, and data quality results from the queried postvaccination adverse event cases and identified updates to current standards to close data quality gaps.

IntroductionThe digitalisation of the German healthcare system enables a wide range of opportunities to utilize healthcare data. The implementation of the EHR in January 2021 was a significant step, but compared to other European countries, the implementation of the EHR in the German healthcare system is still at an early stage. The aim of this paper is to characterise the structural factors relating to the adoption of the EHR in more detail from the perspective of representatives of stakeholders working in the German healthcare system and to identify existing barriers to implementation and the need for change.MethodsQualitative expert interviews were conducted with one representative from each of the stakeholder groups health insurance, pharmacies, healthcare research, EHR development and panel doctors.ResultsThe interviews with the various stakeholders revealed that the implementation process of the EHR is being delayed by a lack of a viable basis for decision-making, existing conflicts of interest and insufficient consideration of the needs of patients and service providers, among other things.DiscussionThe current status of EHR implementation is due to deficiency in legal regulations as well as structural problems and the timing of the introduction. For instance, the access rights of various stakeholders to the EHR data and the procedure in the event of a technical failure of the telematics infrastructure are remain unclear. In addition, insufficient information and communication measures have not led to the desired acceptance of EHR use among patients and service providers.

It is well known that common variants in specific genes influence drug metabolism and response, but it is currently unknown what fraction of patients are given prescriptions over a lifetime that could be contraindicated by their pharmacogenomic profiles. To determine the clinical utility of pharmacogenomics over a lifetime in a general patient population, we sequenced the genomes of 300 deceased Marshfield Clinic patients linked to lifelong medical records. Genetic variants in 33 pharmacogenes were evaluated for their lifetime impact on drug prescribing using extensive electronic health records. Results show that 93% of the 300 deceased patients carried clinically relevant variants. Nearly 80% were prescribed approximately three medications on average that may have been impacted by these variants. Longitudinal data suggested that the optimal age for pharmacogenomic testing was prior to age 50, but the optimal age is greatly influenced by the stability of the population in the healthcare system. This study emphasizes the broad clinical impact of pharmacogenomic testing over a lifetime and demonstrates the potential application of genomic medicine in a general patient population for the advancement of precision medicine.

The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of “5 Vs”—variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.

Translational research on complex, multifactorial mental health disorders, such as bipolar disorder, major depressive disorder, schizophrenia, and substance use disorders requires databases with large-scale, harmonized, and integrated real-world and research data. The Munich Mental Health Biobank (MMHB) is a mental health-specific biobank that was established in 2019 to collect, store, connect, and supply such high-quality phenotypic data and biosamples from patients and study participants, including healthy controls, recruited at the Department of Psychiatry and Psychotherapy (DPP) and the Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital of the Ludwig-Maximilians-University (LMU), Munich, Germany. Participants are asked to complete a questionnaire that assesses sociodemographic and cross-diagnostic clinical information, provide blood samples, and grant access to their existing medical records. The generated data and biosamples are available to both academic and industry researchers. In this manuscript, we outline the workflow and infrastructure of the MMHB, describe the clinical characteristics and representativeness of the sample collected so far, and reveal future plans for expansion and application. As of 31 October 2021, the MMHB contains a continuously growing set of data from 578 patients and 104 healthy controls (46.37% women; median age, 38.31 years). The five most common mental health diagnoses in the MMHB are recurrent depressive disorder (38.78%; ICD-10: F33), alcohol-related disorders (19.88%; ICD-10: F10), schizophrenia (19.69%; ICD-10: F20), depressive episode (15.94%; ICD-10: F32), and personality disorders (13.78%; ICD-10: F60). Compared with the average patient treated at the recruiting hospitals, MMHB participants have significantly more mental health-related contacts, less severe symptoms, and a higher level of functioning. The distribution of diagnoses is also markedly different in MMHB participants compared with individuals who did not participate in the biobank. After establishing the necessary infrastructure and initiating recruitment, the major tasks for the next phase of the MMHB project are to improve the pace of participant enrollment, diversify the sociodemographic and diagnostic characteristics of the sample, and improve the utilization of real-world data generated in routine clinical practice. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual's susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care. [ABSTRACT FROM AUTHOR]

Recently, there has been an emphasis on collecting large datasets in the field of sports medicine. While there have been great advances in areas of sport performance and sport epidemiology, there have been fewer efforts dedicated to understanding the effectiveness and impact of athletic healthcare, including injury prevention programs and rehabilitation interventions provided at the point-of-care. In 2009, the Athletic Training Practice-Based Research Network (AT-PBRN) was launched to address this need, with the mission of improving the quality of care provided by athletic trainers. Unlike other research efforts in sports and medicine, such as sport epidemiology, there are fewer methodological best practices specifically related to clinical data in athletic healthcare. As a result, the AT-PBRN has encountered several methodological challenges during its tenure and has established guidelines based on various sources within the fields of sports and medicine to address these challenges. Therefore, the purpose of this perspective is to identify the challenges and describe strategies to address these challenges related to characterizing athletic healthcare using a large database. Specifically, challenges related to data entry (data quality and reliability) and data extraction and processing (data variability and missing data) will be discussed. Sharing challenges and perspectives on solutions for collecting and reporting on athletic healthcare data may facilitate a greater consistency in the approach used to collect, analyze, and report on clinical data in athletic healthcare, with the goal of improving patient outcomes and the quality of care provided by athletic trainers.

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual’s susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care.

The number of treatment options for patients with metastatic renal cell carcinoma (mRCC) has significantly grown in the last 15 years. Although randomized controlled trials are fundamental in investigating mRCC treatment efficacy, their external validity can be limited. Therefore, the efficacy of the different treatment options should also be evaluated in clinical practice. We performed a chart review of electronic health records using text mining software to study the current treatment patterns and outcomes. mRCC patients from two large hospitals in the Netherlands, starting treatment between January 2015 and May 2020, were included. Data were collected from electronic health records using a validated text mining tool. Primary endpoints were progression-free survival (PFS) and overall survival (OS). Statistical analyses were performed using the Kaplan–Meier method. Most frequent first-line treatments were pazopanib (n = 70), sunitinib (n = 34), and nivolumab with ipilimumab (n = 28). The overall median PFS values for first-line treatment were 15.7 months (95% confidence interval [95%CI], 8.8–20.7), 16.3 months (95%CI, 9.3–not estimable [NE]) for pazopanib, and 6.9 months (95% CI, 4.4–NE) for sunitinib. The overall median OS values were 33.4 months (95%CI, 28.1–50.9 months), 39.3 months (95%CI, 29.5–NE) for pazopanib, and 28.1 months (95%CI, 7.0–NE) for sunitinib. For nivolumab with ipilimumab, median PFS and median OS were not reached. Of the patients who finished first- and second-line treatments, 64 and 62% received follow-up treatments, respectively. With most patients starting on pazopanib and sunitinib, these real-world treatment outcomes were most likely better than in pivotal trials, which may be due to extensive follow-up treatments. [ABSTRACT FROM AUTHOR]

BackgroundDrug challenge tests serve to evaluate whether a patient is allergic to a medication. However, the allergy list in the electronic health record (EHR) is not consistently updated to reflect the results of the challenge, affecting clinicians' prescription decisions and contributing to inaccurate allergy labels, inappropriate drug-allergy alerts, and potentially ineffective, more toxic, and/or costly care. In this study, we used natural language processing (NLP) to automatically detect discrepancies between the EHR allergy list and drug challenge test results and to inform the clinical recommendations provided in a real-time allergy reconciliation module.MethodsThis study included patients who received drug challenge tests at the Mass General Brigham (MGB) Healthcare System between June 9, 2015 and January 5, 2022. At MGB, drug challenge tests are performed in allergy/immunology encounters with routine clinical documentation in notes and flowsheets. We developed a rule-based NLP tool to analyze and interpret the challenge test results. We compared these results against EHR allergy lists to detect potential discrepancies in allergy documentation and form a recommendation for reconciliation if a discrepancy was identified. To evaluate the capability of our tool in identifying discrepancies, we calculated the percentage of challenge test results that were not updated and the precision of the NLP algorithm for 200 randomly sampled encounters.ResultsAmong 200 samples from 5,312 drug challenge tests, 59% challenged penicillin reactivity and 99% were negative. 42.0%, 61.5%, and 76.0% of the results were confirmed by flowsheets, NLP, or both, respectively. The precision of the NLP algorithm was 96.1%. Seven percent of patient allergy lists were not updated based on drug challenge test results. Flowsheets alone were used to identify 2.0% of these discrepancies, and NLP alone detected 5.0% of these discrepancies. Because challenge test results can be recorded in both flowsheets and clinical notes, the combined use of NLP and flowsheets can reliably detect 5.5% of discrepancies.ConclusionThis NLP-based tool may be able to advance global delabeling efforts and the effectiveness of drug allergy assessments. In the real-time EHR environment, it can be used to examine patient allergy lists and identify drug allergy label discrepancies, mitigating patient risks.

The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients' population reflects into the healthcare dynamics of the hospital, to investigate how patients' sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns. This is a process-oriented pipeline which integrates process mining algorithms, and trajectory mining by topological data analyses and pseudo time approaches. Data was collected for 1,179 COVID-19 positive patients, hospitalized at the Italian Hospital “Istituti Clinici Salvatore Maugeri” in Lombardy, integrating different sources including text admission letters, EHR and hospital infrastructure data. We identified five temporal phenotypes, from laboratory values trajectories, which are characterized by statistically significant different death risk estimates. The process mining algorithms allowed splitting the data in sub-cohorts as function of the pandemic waves and of the temporal trajectories showing statistically significant differences in terms of events characteristics.

The number of treatment options for patients with metastatic renal cell carcinoma (mRCC) has significantly grown in the last 15 years. Although randomized controlled trials are fundamental in investigating mRCC treatment efficacy, their external validity can be limited. Therefore, the efficacy of the different treatment options should also be evaluated in clinical practice. We performed a chart review of electronic health records using text mining software to study the current treatment patterns and outcomes. mRCC patients from two large hospitals in the Netherlands, starting treatment between January 2015 and May 2020, were included. Data were collected from electronic health records using a validated text mining tool. Primary endpoints were progression-free survival (PFS) and overall survival (OS). Statistical analyses were performed using the Kaplan–Meier method. Most frequent first-line treatments were pazopanib (n = 70), sunitinib (n = 34), and nivolumab with ipilimumab (n = 28). The overall median PFS values for first-line treatment were 15.7 months (95% confidence interval [95%CI], 8.8–20.7), 16.3 months (95%CI, 9.3–not estimable [NE]) for pazopanib, and 6.9 months (95% CI, 4.4–NE) for sunitinib. The overall median OS values were 33.4 months (95%CI, 28.1–50.9 months), 39.3 months (95%CI, 29.5–NE) for pazopanib, and 28.1 months (95%CI, 7.0–NE) for sunitinib. For nivolumab with ipilimumab, median PFS and median OS were not reached. Of the patients who finished first- and second-line treatments, 64 and 62% received follow-up treatments, respectively. With most patients starting on pazopanib and sunitinib, these real-world treatment outcomes were most likely better than in pivotal trials, which may be due to extensive follow-up treatments.

Objective: Although many clinical metrics are associated with proximity to decompensation in heart failure (HF), none are individually accurate enough to risk-stratify HF patients on a patient-by-patient basis. The dire consequences of this inaccuracy in risk stratification have profoundly lowered the clinical threshold for application of high-risk surgical intervention, such as ventricular assist device placement. Machine learning can detect non-intuitive classifier patterns that allow for innovative combination of patient feature predictive capability. A machine learning-based clinical tool to identify proximity to catastrophic HF deterioration on a patient-specific basis would enable more efficient direction of high-risk surgical intervention to those patients who have the most to gain from it, while sparing others. Synthetic electronic health record (EHR) data are statistically indistinguishable from the original protected health information, and can be analyzed as if they were original data but without any privacy concerns. We demonstrate that synthetic EHR data can be easily accessed and analyzed and are amenable to machine learning analyses.Methods: We developed synthetic data from EHR data of 26,575 HF patients admitted to a single institution during the decade ending on 12/31/2018. Twenty-seven clinically-relevant features were synthesized and utilized in supervised deep learning and machine learning algorithms (i.e., deep neural networks [DNN], random forest [RF], and logistic regression [LR]) to explore their ability to predict 1-year mortality by five-fold cross validation methods. We conducted analyses leveraging features from prior to/at and after/at the time of HF diagnosis.Results: The area under the receiver operating curve (AUC) was used to evaluate the performance of the three models: the mean AUC was 0.80 for DNN, 0.72 for RF, and 0.74 for LR. Age, creatinine, body mass index, and blood pressure levels were especially important features in predicting death within 1-year among HF patients.Conclusions: Machine learning models have considerable potential to improve accuracy in mortality prediction, such that high-risk surgical intervention can be applied only in those patients who stand to benefit from it. Access to EHR-based synthetic data derivatives eliminates risk of exposure of EHR data, speeds time-to-insight, and facilitates data sharing. As more clinical, imaging, and contractile features with proven predictive capability are added to these models, the development of a clinical tool to assist in timing of intervention in surgical candidates may be possible.

Over the last decade, our understanding of acute kidney injury (AKI) has evolved considerably. The development of a consensus definition standardized the approach to identifying and investigating AKI in children. As a result, pediatric AKI epidemiology has been refined and the consequences of renal injury are better established. Similarly, “big data” methodologies experienced a dramatic evolution and maturation, leading the critical care community to explore potential AKI/big data synergies. One such concept with tremendous potential is electronic health record (EHR) enabled informatics. Much of the promise surrounding these approaches is due to the unique position of the EHR which sits at the intersection of data accumulation and care delivery. EHR data is generated simply via the provision of routine clinical care and should be considered “big” from the standpoint of volume, variety, and velocity as a myriad of diverse elements accumulate rapidly in real time, spontaneously generating an immense dataset. This massive dataset interfaces directly with providers which creates tremendous opportunity. AKI can be diagnosed more accurately, AKI-related care can be optimized, and subsequent outcomes can be improved. Although applying big data concepts to the EHR has proven more challenging than originally thought, we have seen much success and continue to explore its potential. In this review article, we will discuss the EHR in the context of big data concepts, describe approaches applied to date, examine the challenges surrounding optimal application, and explore future directions.