Healthcare Analytics Research Articles

DyHealth

In National University Hospital (NUH) in Singapore, we conduct healthcare analytics that analyzes heterogeneous electronic medical records (EMR) to support effective clinical decision-making on a daily basis. Existing work mainly focuses on multimodality for extracting complementary information from different modalities, and/or interpretability for providing interpretable prediction results. However, real-world healthcare analytics has presented another major challenge, i.e., the available modalities evolve or change intermittently. Addressing this challenge requires deployed models to be adaptive to such dynamic modality changes. To meet the aforementioned requirement, we develop a modular, multimodal and interpretable framework DyHealth to enable dynamic healthcare analytics in clinical practice. Specifically, different modalities are processed within their respective data modules that adhere to the interface defined by DyHealth. The extracted information from different modalities is integrated subsequently in our proposed Multimodal Fusion Module in DyHealth. In order to better handle modality changes at runtime, we further propose exponential increasing/decreasing mechanisms to support modality "hot-plug". We also devise a novel modality-based attention mechanism for providing fine-grained interpretation results on a per-input basis. We conduct a pilot evaluation of DyHealth on the patients' EMR data from NUH, in which DyHealth achieves superior performance and therefore, is promising to roll out for hospital-wide deployment. We also validate DyHealth in two public EMR datasets. Experimental results confirm the effectiveness, flexibility, and extensibility of DyHealth in supporting multimodal and interpretable healthcare analytics.

HEART DISEASE DATA ANALYSIS USING EXPLORATORY DATA ANALYSIS METHOD

Health care industry companies generate a large volume of raw data, often called big data, which reveals hidden patterns and knowledge for decision making. Data-driven decisions are more accurate than intuition because they use massive data. In exploratory data analysis, mistakes are detected, data is identified, assumptions are checked, and the correlation between the variables is determined. Analyzing data without inferences or statistical modeling is considered Exploratory Data Analysis in the context. Analysts forecast the future and reveal hidden patterns in any profession. In the recent past, data analytics can be seen as a technology that is accessible, and it is essential to healthcare, especially current findings in outbreaks and emergency circumstances. Exploratory data analysis is a crucial step when analyzing data, and the application of analytics in healthcare improves treatment by enabling preventive care. The elements that lead to heart disease are predicted in this paper. The research uses two sets of publicly accessible heart disease data. Two datasets contain 303 records with 13 attributes and 4238 with 16 attributes.

Performance and Information Leakage in Splitfed Learning and Multi-Head Split Learning in Healthcare Data and Beyond.

Machine learning (ML) in healthcare data analytics is attracting much attention because of the unprecedented power of ML to extract knowledge that improves the decision-making process. At the same time, laws and ethics codes drafted by countries to govern healthcare data are becoming stringent. Although healthcare practitioners are struggling with an enforced governance framework, we see the emergence of distributed learning-based frameworks disrupting traditional-ML-model development. Splitfed learning (SFL) is one of the recent developments in distributed machine learning that empowers healthcare practitioners to preserve the privacy of input data and enables them to train ML models. However, SFL has some extra communication and computation overheads at the client side due to the requirement of client-side model synchronization. For a resource-constrained client side (hospitals with limited computational powers), removing such conditions is required to gain efficiency in the learning. In this regard, this paper studies SFL without client-side model synchronization. The resulting architecture is known as multi-head split learning (MHSL). At the same time, it is important to investigate information leakage, which indicates how much information is gained by the server related to the raw data directly out of the smashed data—the output of the client-side model portion—passed to it by the client. Our empirical studies examine the Resnet-18 and Conv1-D architecture model on the ECG and HAM-10000 datasets under IID data distribution. The results find that SFL provides 1.81% and 2.36% better accuracy than MHSL on the ECG and HAM-10000 datasets, respectively (for cut-layer value set to 1). Analysis of experimentation with various client-side model portions demonstrates that it has an impact on the overall performance. With an increase in layers in the client-side model portion, SFL performance improves while MHSL performance degrades. Experiment results also demonstrate that information leakage provided by mutual information score values in SFL is more than MHSL for ECG and HAM-10000 datasets by and , respectively.

Healthcare Analytics: Applications and Challenges

Present day decision making in healthcare has become a data-intensive process leading to the rise of healthcareanalytics. These analytics involve the integration and analysis of data collected from multiple heterogenoussources including biomedical data (electronic health records), experimental (R&D) data, patient lifestyle andbehavior data (store records, social media, wearable electronic devices etc) and financial data. Healthcareanalytics is further being transformed by the application of current - omics technologies (genomics,epigenomics, transcriptomics, proteomics, metabolomics, pharmacogenomics etc), shifting healthcare to asystem that is “predictive, preventive, participatory, and personalized”, referred to as the P4 medicineparadigm. The omics revolution is generating a huge amount of data that needs to be collected, stored,transformed, analyzed and ultimately delivered in a format that is understandable to the clinicians. Properlyapplied, healthcare analytics provide actionable insights that can lead to improvement in healthcaremanagement and delivery, clinical outcomes, health policies and allocation of health resources to communitiesin need.
 Medical institutions worldwide are facing many challenges. There is a shortage of trained human resource,patients are more demanding and regulators are becoming more stringent. The healthcare systems are underpressure to provide better and safer healthcare that is also more cost-effective. This is leading to thedevelopment of a whole new healthcare analytics industry to capture, store and analyze big data for healthcareproviders, health system leaders, and those in government health and human services to improve healthcaredelivery and outcomes. A report by McKinsey Global Institute published in 2011 estimated that if US healthcarewere to use big data creatively and effectively, the sector could create more than $300 billion in value everyyear.1
 While innovative new technologies and platforms are making it possible to capture vast amounts ofinformation of individual patient over a large timescale, a large amount of data remains underutilized andwasted. It is estimated that healthcare providers discard around 90% of the data that they produce.1 One of thegreatest challenges in healthcare analytics is harvesting the data from multiple complex and heterogenoussources spread across different healthcare systems, health insurers, scientists, government bodies etc. andtransforming it to generate meaningful information. Moreover, the personal and financial data and medicalrecords of patients required to contribute to effective analytical platforms may be confidential and private andtherefore inaccessible. Hospitals are also reluctant to share their financial and clinical audits. There is the issuealso of ownership of the data and in many countries patient data is legally possessed by the patient. Even so thehealthcare industry, slow to transition towards data mining and IT solutions, has now begun to adapt to thedigital data age. Algorithms for collecting, compressing, anonymizing and sharing medical data are beingdeveloped by different groups in addition to various platforms specific to healthcare analytics.
 The implementation of healthcare analytics, particularly in developing countries like Pakistan where mosthealthcare facilities lack even the most basic form of EHRs is a formidable challenge. Pakistan has a robust ITcommunity with a special interest and investment by the government sector in IT and Artificial Intelligenceplatforms. Pakistan government last year announced seed money of Rs 1.1 billion, to carry out a three-yearproject on AI, through the Higher Education Commission to be conducted by six universities. One of theseprojects, related to medical imaging and diagnostics, is a step in the right direction. However, we still have a long way to go if we are to reap the benefits of healthcare analytics in Pakistan. There is a need for the involvement and commitment of all stakeholders including healthcare facilities, academicians, researchers and industry etc to contribute to this endeavor. There is no shortage of qualified human resource in the country. All that is required is the will of the health sector to invest in this golden opportunity for healthcare analytics to play a role in improving the delivery of cost-effective care, save lives and define health policies in Pakistan.
 Editor-in-Chief 
 How to cite this: Alamgir W, Mohyuddin A . Healthcare Analytics: Applications and Challenges. Life and Science. 2022; 3(3): 97-98. doi: http://doi.org/10.37185/LnS.1.1.263

Exploring the Path to Big Data Analytics Success in Health Care

Like Oxygen, the world is surrounded by data today. The quantity of data that we harvest and eat up is thriving aggressively in the digitized world. Increasing use of new innovations and social media generate vast amount of data that can earn splendid information if properly analyzed. This large dataset generally known as big data, do not fit in traditional databases because of its rich size. Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Organizations need to manage and analyze big data for better decision making and outcomes. So, big data analytics is receiving a great deal of attention today. In healthcare, big data analytics has the possibility of advanced patient care and clinical decision support. In this paper, we review the background and the various methods of big data analytics in healthcare. This paper also elaborates various platforms and algorithms for big data analytics and discussion on its advantages and challenges. This survey winds up with a discussion of challenges and future directions.

Big Data

Abstract: Big data refers to data sets that are overlarge or complex to be restrained by traditional processing apclication software. Data with many fields offer greater statistical power, while data with higher complexity maynlead to a better false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally related to three key concepts: volume, variety, and velocity. The analysis of huge data presents challenges in sampling, and thus previously with only observations and samling. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an appropriate time and value. Current usage of the term big data tends to sit down with the utilization of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from ig data, and infrequently to a specific size of knowledge set. “There is no doubt that the quantities of knowledge now available are indeed large. But that’s not the foremost relevant chracteristic of this new data ecosystem.” Analysis of information sets can find new correlations to ” spot business trends, prevent diseases, combat crime and then on”.Scientists, business executives, medical practitioners, advertising and governments alike regularly meet difficulties with large data-sets in areas including internet searches, fintech, healthcare analytics, geographic information systems, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology, and environmental research.The size and number of obtainable data sets have grown rapidly as data is collected by devices like mobile devices, cheap and diverse information – sensing Internet of things devices, aerial, software logs, cameras, microphones, radio-frequency identification(RFID) readers and wireless sensor networks. The world’s technological percapita capacity to store information has roughly doubled every 40 months since the 1980s: as of 2012, a day 2.5 exabytes(2.5*2^60 bytes) of knowledge are generated. supported an IDC report prediction, the worldwide data volume was predicteed to grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts there'll be 163 zettabytes of knowledge.

The Future of Healthcare Data Intelligence: Ethical Insights and Evolutionary Pathway

Data intelligence technologies have brought about transformative advancements in patient care, research, and healthcare management within the United States healthcare sector. This is particularly significant because numerous academic and research institutions in the United States are pioneering healthcare data research, making it an attractive location for in-depth investigations. This paper delves into the expansive realm of Data Intelligence in Healthcare, scrutinizing its applications, challenges, ethical considerations, and emerging trends. The applications of Data Intelligence encompass a range of technologies that are explicitly designed to efficiently collect, process, analyze, and interpret data. These applications empower healthcare practitioners to make more informed decisions, predict health outcomes, manage population health, personalize treatment, streamline workflows, facilitate research, enhance data security, and advance healthcare analytics. Nevertheless, the utilization of data intelligence applications gives rise to concerns and issues related to data privacy, fairness, transparency, data quality, accountability, equitable data access, adherence to regulatory requirements, and striking the right balance between automation and human judgment. Emerging themes in this field comprise the dominance of AI and machine learning, the establishment of more robust ethical and regulatory frameworks, the rise of edge and quantum computing, the democratization of data, the application of sustainability principles, and the evolution of human-machine collaboration. It is essential to recognize that data intelligence has a far-reaching impact that extends beyond healthcare delivery, influencing decision-making, scientific discoveries, education, and economic growth. Therefore, understanding its potential and embracing ethical responsibilities is crucial, as data-driven insights redefine healthcare excellence and extend their influence across various sectors.

Machine Learning Methods in Health Economics and Outcomes Research—The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force

Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR.The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation—helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis.Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results.To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.

How can Big Data Analytics Support People-Centred and Integrated Health Services: A Scoping Review.

Introduction:Health systems in high-income countries face a variety of challenges calling for a systemic approach to improve quality and efficiency. Putting people in the centre is the main idea of the WHO model of people-centred and integrated health services. Integrating health services is fuelled by an integration of health data with great potentials for decision support based on big data analytics. The research question of this paper is “How can big data analytics support people-centred and integrated health services?”Methods:A scoping review following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Scoping Review (PRISMA-ScR) statement was conducted to gather information on how big data analytics can support people-centred and integrated health services. The results were summarized in a role model of a people-centred and integrated health services platform illustrating which data sources might be integrated and which types of analytics might be applied to support the strategies of the people-centred and integrated health services framework to become more integrated across the continuum of care. Additional rapid literature reviews were conducted to generate frequency distributions of the most often used data types and analytical methods in the medical literature. Finally, the main challenges connected with big data analytics were worked out based on a content analysis of the results from the scoping literature review.Results:Based on the results from the rapid literature reviews the most often used data sources for big data analytics (BDA) in healthcare were biomarkers (39.3%) and medical images (30.9%). The most often used analytical models were support vector machines (27.3%) and neural networks (20.4%). The people-centred and integrated health services framework defines different strategic interventions for health services to become more integrated. To support all aspects of these interventions a comparably integrated platform of health-related data would be needed, so that a role model labelled as people-centred health platform was developed. Based on integrated data the results of the scoping review (n = 72) indicate, that big data analytics could for example support the strategic intervention of tailoring personalized health plans (43.1%), e.g. by predicting individual risk factors for different therapy options. Also BDA might enhance clinical decision support tools (31.9%), e.g. by calculating risk factors for disease uptake or progression. BDA might also assist in designing population-based services (26.4% by clustering comparable individuals in manageable risk groups e.g. mentored by specifically trained, non-medical professionals. The main challenges of big data analytics in healthcare were categorized in regulatory, (information-) technological, methodological, and cultural issues, whereas methodological challenges were mentioned most often (55.0%), followed by regulatory challenges (43.7%).Discussion:The BDA applications presented in this literature review are based on findings which have already been published. For some important components of the framework on people-centred care like enhancing the role of community care or establishing intersectoral partnerships between health and social care institutions only few examples of enabling big data analytical tools were found in the literature. Quite the opposite does this mean that these strategies have less potential value, but rather that the source systems in these fields need to be further developed to be suitable for big data analytics.Conclusions:Big data analytics can support people-centred and integrated health services e.g. by patient similarity stratifications or predictions of individual risk factors. But BDA fails to unfold its full potential until data source systems are still disconnected and actions towards a comprehensive and people-centred health-related data platform are politically insufficiently incentivized. This work highlighted the potential of big data analysis in the context of the model of people-centred and integrated health services, whereby the role model of the person-centered health platform can be used as a blueprint to support strategies to improve person-centered health care. Likely because health data is extremely sensitive and complex, there are only few practical examples of platforms to some extent already capable of merging and processing people-centred big data, but the integration of health data can be expected to further proceed so that analytical opportunities might also become reality in the near future.

Evaluating Present and Future Challenges in IoT Security

The internet of things is presently faced with huge challenges which presently create a major effect on the present and future organizational development. IoT proceeds with high concentrated security and thus is mainly confirmed with the major development due to maintaining data security. Providing IoT security has been faced many challenges such as: in healthcare service, data analytics, information technology and agricultural issues. The government body has taken better chances under the construction of carelessness and various fraud activities. The strong passwords and security codes should be provided at the time of better processing. Lack of scalability, lack of government support, interoperability, security and personal privacy, lack of treating patients, design base challenges are major issues faced by managing IoT security. There is high cultural effectiveness and huge drastic changes have been faced by the organization as technical upgradation has not been done in any sector. Hence, security personnel and many important human resources should have a better impact on future development. The application of advanced technicians can have a better impact on future growth. Keyword : Development, information, Internet of things, IoT security.

BIG DATA ANALYTICS & ARTIFICIAL INTELLIGENCE IN MANAGEMENT OF HEALTHCARE: IMPACTS & CURRENT STATE

Background: Healthcare is a very complex area that is ingesting data at an unprecedented speed. Within the health care industry, there’s an abundance of data; data from physicians and hospitals, medical research, medical insurance, medical equipment as well as life sciences keep flowing at a high rate. The improvement of technologies opens doors to huge possibilities for using the data found in health care for transforming the management of healthcare. Research gap: A lot of research have been conducted to study the significance of data analytics in general healthcare, but there’s a gap to research the importance of data analytics in the field of healthcare management as well as its current state of development. Novelty: The study, through employing structural mapping, focuses on evaluating the available research to offer proof that will encourage further application of data analytics in the healthcare field. Methodology: An organized structural mapping analysis of 1241 studies was carried out to identify as well as evaluate the application of Big Data Analytics and Artificial Intelligence in healthcare. Future scope: This study is going to identify the areas which need further study for the use of data analytics in healthcare. This study will likely provide a potential base for future researchers to conduct further research.

Investigating the dimensions, components, and key indicators of the use of big data in the health industry

Using big data analytics in healthcare has positive as well as life-saving results. Big data refers to the vast amounts of information generated by the digitization of everything that is synthesized and analyzed by specific technologies. Here Big Data uses health services to use specific health data of a population (or a specific individual) and potentially help prevent disease pandemics, treat diseases, reduce costs, and more. In the field of health, big data covers a wide range of information, including physiological, behavioral, molecular, clinical, medical imaging, disease management, medication history, nutrition, or exercise parameters. Big Data Analysis In the field of health, it is a complex process of examining big data to discover information. This information includes hidden patterns, market trends, unknown correlations, and customer preferences. Information analysis can help organizations make informed business and clinical decisions. The medical data-driven industry is the most complex among industries. Not only is this data available from a variety of sources, but it must also comply with government regulations. This process is difficult and delicate and requires some level of security and communication. Due to the importance of this issue, in this article, after introducing the types of data available in the health industry, the characteristics and sources of big data in health are defined and an analytical model for the use of large data in the health industry is presented. This model helps to understand the dimensions, components, and key elements of using big data in the health industry.

ROLEX: A Novel Method for Interpretable Machine Learning Using Robust Local Explanations

Recent developments in big data technologies are revolutionizing the field of healthcare predictive analytics (HPA), enabling researchers to explore challenging problems using complex prediction models. Nevertheless, healthcare practitioners are reluctant to adopt those models as they are less transparent and accountable due to their black-box structure. We believe that instance-level, or local, explanations enhance patient safety and foster trust by enabling patient-level interpretations and medical knowledge discovery. Therefore, we propose the RObust Local EXplanations (ROLEX) method to develop robust, instance-level explanations for HPA models in this study. ROLEX adapts state-of-the-art methods and ameliorates their shortcomings in explaining individual-level predictions made by black-box machine learning models. Our analysis with a large real-world dataset related to a prevalent medical condition called fragility fracture and two publicly available healthcare datasets reveals that ROLEX outperforms widely accepted benchmark methods in terms of local faithfulness of explanations. In addition, ROLEX is more robust since it does not rely on extensive hyperparameter tuning or heuristic algorithms. Explanations generated by ROLEX, along with the prototype user interface presented in this study, have the potential to promote personalized care and precision medicine by providing patient-level interpretations and novel insights. We discuss the theoretical implications of our study in healthcare, big data, and design science.

Big Data Analytics in Cloud Computing for Scientific Analytics

Abstract: Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Keywords: Big data, Analytics, Hadoop, Healthcare, Framework, Methodology.

Health Informatics & Data Analytics as A Career Choice

Data analytics and informatics both have become essential for the success and reputation of healthcare organizations and with the current increasing demand for the same by such organisations, a career path seems to be full of success and blooming opportunities. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various -omic data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data.

Chronic Kidney Disease Prediction Using Naïve Bayesian Classifier and K-NN Machine-Learning Algorithms

Long-term renal damage is a critical issue that has to be addressed using healthcare analytics. It is a kind of kidney disease where the kidney's functionality will be degraded over months or years. Hence, accurate prediction needs to be done so that patients can undergo proper treatment at the right time. The machine learning techniques help to accomplish this. The proposed research will examine the effectiveness of supervised or guided classification algorithms such as Naive Bayesian and K-Nearest Neighbor in predicting the disorders on the basis of accuracy. A web application will be implemented that helps doctors and patients identify the disease and undergo medication with a proper diet plan.

Coriell Life Sciences: empowering the most precise medical care for a healthier world.

Since its founding in 2013, Coriell Life Sciences (CLS) has built technologies leveraging genetic testing to empower physicians and has invested in research that advances the field of personalized medicine. The company focuses on development, implementationand research with expertise in medication safety, pharmacogenomics, infectious diseasesand healthcare analytics. CLS works with healthcare institutions, laboratories, pharmacy benefit management companies, self-insured employers andpublic sector entities and actively contributes to scientific and clinical consortia. This overview summarizes the CLS service architecture and delivery capabilities for medication safety and risk reporting for pharmacogenomics, comprehensive medication management and infectious diseases. It includes the development and ongoing curation of genetic and non-genetic knowledge repositories, technology infrastructure and endpoints andresearch endeavors and it reviews economic, clinical and humanistic outcomes of CLS' pharmacogenomics-enriched comprehensive management program.

Lung cancer disease detection using service-oriented architectures and multivariate boosting classifier

Big data analytics in healthcare is emerging as a promising field to extract valuable information from large databases and enhance results with fewer costs. Although numerous methods have been proposed for big data analytics in the medical field, an authorized entity is required to access data, inhibiting diagnosis accuracy and efficiency. Particularly, the detection of lung cancer is critical as it is the third most common type of cancer occurring in both males and females in the US and a leading cause of cancer-related deaths worldwide, the detection of lung cancer. Therefore, this study introduces the Multivariate Ruzicka Regressed eXtreme Gradient Boosting Data Classification (MRRXGBDC) technique and service-oriented architecture (SOA) to improve the prediction accuracy and reduce the prediction time of lung cancer in big data analytics. Service-oriented architectures (SOAs) provide a set of healthcare services, where patient data are stored in the database of a physician or other certified entity. After receiving the patient data as input, several multivariate Ruzicka logistic regression trees are constructed by the physician to calculate the relationship between the dependent and independent variables. With this regression analysis, the presence or absence of disease is discovered. The experimental results reveal that the MRRXGBDC technique performs better with 10% improvement in prediction accuracy, 50% reduction of false positives, and 11% faster prediction time for lung cancer detection compared to existing works.

Guest Editorial: Data Analytics for Public Health Care

The papers in this special section focus on data analytics for public health care. Growing and intensive attention for the use of data analytics in healthcare due to its proven benefits to gain insights on the macro and micro depths to support decisionmaking at the patient, clinical, bench side and business levels. Health data analytics contributed to the improvements to patient care, faster and more accurate diagnoses, preventive measures, more personalized treatment, narrow the gap between bedside and bench side and provide more informed decision-making. However, the recent pandemic of COVID-19 draw the world attention to need to extend the power of data analytics beyond the legacy data channels including Electronic Health Records (EHRs) and Patient Portals. Data analytics can assist population health management in improving patient outcomes, enhancing care management, and address social determinants of health. As the population health analytics continues to grow in the healthcare space, healthcare systems must be able to gather data from multiple sources, apply variety of data analytics (e.g., Descriptive Analytics, Predictive Analytics Prescriptive Analytics), and manage the provide insights for the population.

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data

PurposeNeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform...