Population Health and Big Data Collision

May 11, 2018

Chicago, Illinois

� How would you use Big Data?

� How would you improve organizational performance using Big Data?

� What ideas justify using Big Data?

� Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low latency. And it has one or more of the following characteristics: high volume, high velocity, or high variety.

(IBM-https://www.ibm.com/analytics/hadoop/big-data-analytics)

� Analyzing big data allows analysts, researchers, and business leaders to make better and faster decisions using data that was previously inaccessible or unusable.

� Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing.

� Untapped data resources – gaining new insights resulting in better and faster decisions

(IBM-https://www.ibm.com/analytics/hadoop/big-data-analytics)

� Population Health

� Quality improvement

� Optimization of healthcare processes

� Providing healthcare at a lower cost

� Health policy implications

� Analytics to improve leadership development

� Development of artificial intelligence

� Predictive Analysis

� The California Public Hospital Redesign and Incentives in Medi-Cal (PRIME) Program is a five-year initiative under the Medi-Cal 2020 section 1115 waiver that builds upon the Delivery System Reform Incentive Payment (DSRIP) program established under the Bridge to Reform waiver.

Jan 1, 2017

� Electronic Medical Record

� Data Sources and Fields

� Narrative Fields

� One Report Writer

� Robust Data / Big Data

� Data Analysis

� Statistics and Analytics Intelligence

� $4.7m x 5-years

� Looked to outside analytics and statistician support

� External Partnerships

� Implemented Data Governance

� Education and Training

� Restructuring Organizational Reporting Structure◦ Clinical Informatics and the CNO

� Safe De-Identification of Big Data◦ HIPAA Privacy Rule De-Identification Methods

� Expert Determination 164.514(b)(1)

� Apply statistical or scientific principles / very small risk

� Safe Harbor 164.514(b)(2)

� Removal of 18-types of identifiers / No actual knowledge residual information can identify individual

◦ Balance the benefits with the risks

◦ Privacy

� ANA’s Social Policy Statement

� The Chief Nurse’s Big Data Checklist

◦ Create a Data Culture

◦ Develop data competencies

◦ Create a data infrastructure

� Executive Decision-Making

� Englebright & Caspers. (2016). Nurse Leader

� Big Data is here to stay – it is the future

� Setting up a governance structure

� Establishing competencies, a culture, and Decision-Making is essential

� Englebright, J., Caspers, B. (2016). The role of the chief nurse executive in the big data revolution. Nurse Leader.

� Gaul, P. (2013). Big data. TD: Talent Development, 72(3), 28.

� Hassett, M.J. (2017). Quality improvement in the era of big data. Journal of Clinical Oncology, 35(28), 3178-3180.

� Jones, L.K., Pulk, R., Gionfriddo, M.R., Evans, M.A., Parry, D. (2018). Utilizing big data to provide better health at lower cost. American Journal of Health Systems Pharmacists, 75(7), 427-435

� Koufi, V., Malamateniou, F. (2015). A big data-driven model for the optimization of healthcare processes. European Federation for Medical Informatics. Doi: 10.3233/978-1-61499-512-8-697.

� Vayena, E., Dzenowagis, J., Brownstein, J.S., Sheikh, A. (2017). Policy implications of big data in the health sector. Bull World Health Organ, 99:66-68. Doi: http://dx.doi.org/10.2471/BLT.17.197426

� Warner, D. (2013). Safe de-identification of big data is critical to health care. Journal of Health

Compliance.