Big data and predictive analytics have immense potential to improve risk stratification, particularly in data-rich fields like oncology. This article reviews the literature published on use cases and challenges in applying predictive analytics to improve risk stratification in oncology. We characterized evidence-based use cases of predictive analytics in oncology into three distinct fields: (1) population health management, (2) radiomics, and (3) pathology. We then highlight promising future use cases of predictive analytics in clinical decision support and genomic risk stratification. We conclude by describing challenges in the future applications of big data in oncology, namely (1) difficulties in acquisition of comprehensive data and endpoints, (2) the lack of prospective validation of predictive tools, and (3) the risk of automating bias in observational datasets. If such challenges can be overcome, computational techniques for clinical risk stratification will in short order improve clinical risk stratification for patients with cancer.

The domain of healthcare acquired its influence by the impact of big data since the data sources involved in the healthcare organizations are well-known for their volume, heterogeneous complexity and high dynamism. Though the role of big data analytical techniques, platforms, tools are realized among various domains, their impact on healthcare organization for implementing and delivering novel use-cases for potential healthcare applications shows promising research directions. In the context of big data, the success of healthcare applications solely depends on the underlying architecture and utilization of appropriate tools as evidenced in pioneering research attempts. Novel research works have been carried out for deriving application specific healthcare frameworks that offer diversified data analytical capabilities for handling sources of data ranging from electronic health records to medical images. In this paper, we have presented various analytical avenues that exist in the patient-centric healthcare system from the perspective of various stakeholders. We have also reviewed various big data frameworks with respect to underlying data sources, analytical capability and application areas. In addition, the implication of big data tools in developing healthcare eco system is also presented.

The shortage of data scientists has restricted the implementation of big data analytics in healthcare facilities. This survey study explores big data tool and technology usage, examines the gap between the supply and the demand for data scientists through Diffusion of Innovations theory, proposes engaging academics to accelerate knowledge diffusion, and recommends adoption of curriculum-building models. For this study, data were collected through a national survey of healthcare managers. Results provide practical data on big data tool and technology skills utilized in the workplace. This information is valuable for healthcare organizations, academics, and industry leaders who collaborate to implement the necessary infrastructure for content delivery and for experiential learning. It informs academics working to reengineer their curriculum to focus on big data analytics. The paper presents numerous resources that provide guidance for building knowledge. Future research directions are discussed

The LexisNexis Risk Solution data and analytics team designed the COVID-19 data set by combining proprietary data assets and models from LexisNexis Health Care with data from the Johns Hopkins University Center for Systems Science and Engineering, which includes data from WHO, CDC, and other independent sources, and data from the American Hospital Association (AHA).

The COVID-19 Data Resource Center features heat maps with insights that identify at-risk populations and correlated gaps in provider coverage. Each county is assigned a percentile rank on a scale of 0 (low-risk) to 100 (high-risk) across various parameters. The map is updated regularly to quickly address the care needs of the community.

As the COVID-19 outbreak continues to surge, physicians and other healthcare providers are moving towards telehealth and remote patient monitoring to reduce in person contact and to stop the spread of the virus. To support physicians and other healthcare providers, CMS has also eased restrictions on telehealth reimbursements.

Join us for a panel discussion with Emily Yoder, an analyst with CMS and Phil Boucher, MD, a practicing pediatrician in Nebraska, regarding the use of telehealth to drive growth in his practice and how CMS reimbursement changes might affect those strategies in the future.

Presentation Slides can be found at the bottom of the page. 

Speakers:

Phil Boucher, MD

Lincoln Pediatrics Group

Phil Boucher, MD, is a board-certified pediatrician in private practice at Lincoln Pediatrics Group in Lincoln, Nebraska.  Phil is a husband and father of five young children. He helps private practice physicians build thriving practices and fulfilling lives through practical strategies paired with mindset shifts within his physician-only facebook group, The Private Practice Accelerator (privatepractice.show/join). He shares his strategies and interviews other thought leaders on his podcast, Private Practice Matters.

Andi Hila

Director of Strategy Consulting, Updox 

Andi Hila is Director of Strategy Consulting at Updox, where he also previously led product efforts for Patient Engagement solutions. Prior to Updox, Andi served in Product Management roles for Explorys and IBM Watson Health, with a focus on data analytics products for the population health, provider, and payer markets. Andi has a B.S. in Health Information Systems and Economics from The Ohio State University.

Emily Yoder

Centers for Medicare and Medicaid Services, Centers for Medicare/Hospital and Ambulatory Policy Group/Division of Practitioner Services​

Emily Yoder is an analyst in the Division of Practitioner Services (DPS) in the CMS Center for Medicare.  She has worked on Medicare Physician Fee Schedule rate setting and policy development since 2015, including primary care, evaluation and management visits, communication technology based services, and Medicare telehealth.  She holds graduate degrees from the University of Chicago and, as a Fulbright Fellow, from the University of Warwick, in the United Kingdom.

Contrary to what is often assumed, the non-adherence problem is not exclusive to ‘real-world’ patients, but it also influences the strictly regulated setting of clinical drug registration trials. Of every hundred trial participants, four do not initiate a study drug. Each study day, 10–12% does not take their medication while still on treatment. In long-term studies, after one year, almost 40% of trial participants have stopped taking their medication . Novel digital adherence monitoring devices may offer a solution for patients who tend to forget their medication and for trial regulators to have granular data on the exact timing of medication use.

HIEs play a critical role in facilitating care coordination and real-time data exchange, making them pivotal during a public health crisis. Learn how three leading HIEs are confronting various data challenges head on, including aggregation, patient identification, and interoperability for accurate reporting and tracking of COVID-19 in their communities. Our panelists will also share their emergency response efforts, lessons learned, best practices and existing/foreseeable challenges in managing and surveilling the outbreak.

You can watch the video here. 

Speakers:

Kim Chaundy​

Senior Director  IT - External Customer Relations Geisinger Health System 

Kim currently serves as Senior Director of the Geisinger-owned Keystone Health Information Exchange, Inc. (KeyHIE), which is one of the oldest health information exchanges in the United States, serving over 6 million patients. Kim oversees and tracks all aspects of KeyHIE operations including, but not limited to: vendor management, participant outreach and implementation projects.  Kim also directs Geisinger's IT Integration Systems Support team, responsible for Geisinger's Rhapsody infrastructure.  Kim recently received her Masters of Business Administration degree from the University of Scranton in 2019. 

Daniel Cidon

​Chief Technology Officer, NextGate

Dan is responsible for shaping NextGate’s long-term technical vision and turning emerging technologies into leading-edge solutions. As a specialist in the intricate domain of pattern analysis and probabilistic matching algorithms, he brings innovative and pragmatic solutions to the company’s product portfolio. Dan is a credible industry thought leader, educator and mentor in the areas of healthcare interoperability, standards development and integration, frequently speaking and writing about the quality, operational and safety issues related to siloed and incomplete patient data in healthcare.Dan holds a Masters in Computer Science from the University of California, Davis and a B.S. in Mechanical Engineering from The University of Texas at Austin.

Cody Johanson

Director of Operations at UHIN

Cody Johansen serves as Director of Operations at UHIN, an HIE and clearinghouse.  His experience working with all levels of healthcare has helped him bridge the gap between healthcare and technology. He enjoys making the most complete information available to clinicians when treating patients. He holds a BS in Biology from BYU and an MPH in Health Services Administration from SDSU, where he was awarded the American Medical International Award and Foster G. McGaw Scholarship.

Bill Pearch

CIO from HealtheConnect Alaska

As CIO of healtheConnect Alaska, Bill Pearch provides strategic IT leadership and oversight for the HIE to ensure its members meet changing regulatory, organizational, clinical and population health-related demands with capable and innovative technology.
Prior to joining healtheConnect in 2017, Bill served CIO roles at Bristol Bay Area Health Corp. and YKHC. His areas of expertise include IT Governance and HIPAA.

Kevin Conway

Data Integrity Manager,Nebraska Health Information Initiative (NEHII)

Kevin has over 30 years of experience in health-care planning, finance and information technology with Nebraska organizations. Prior to joining NEHII, he was Vice President, Health Information for the Nebraska Hospital Association, and worked at Blue Cross and Blue Shield of Nebraska.

The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However, the lack of a comprehensive and systematic narrative motivated us to construct a literature review on this topic. In this paper, we present a review of the literature on healthcare analytics using data mining and big data. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a database search between 2005 and 2016. Critical elements of the selected studies—healthcare sub-areas, data mining techniques, types of analytics, data, and data sources—were extracted to provide a systematic view of development in this field and possible future directions. We found that the existing literature mostly examines analytics in clinical and administrative decision-making. Use of human-generated data is predominant considering the wide adoption of Electronic Medical Record in clinical care. However, analytics based on website and social media data has been increasing in recent years. Lack of prescriptive analytics in practice and integration of domain expert knowledge in the decision-making process emphasizes the necessity of future research.
 

This Paper surveys big data with highlighting big data analytics in medicine and healthcare. Big data characteristics: value, volume, variety, veracity, and variability are described.  Dig data analytics in medicine and healthcare covers integration and analysis of large amounts of complex heterogeneous data such ad various -omics data, biomedical data, and electronic health records. We underline the challenging issues about big data open-source distributed data processing software platform are given.