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Applied Health Analytics and Informatics Using SAS

Name: Applied Health Analytics and Informatics Using SAS
Author: Joseph M. Woodside
ISBN: 9781635266146

by Joseph M. Woodside

November 2018

Beginner to intermediate

378 pages

11h 3m

English

SAS Institute

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About this Book
Acknowledgments
Chapter 1: Introduction
IntroductionAudience AccessibilityLearning ApproachExperiential Learning Activity: Learning Journal
Chapter 2: Health Anamatics
Chapter SummaryChapter Learning GoalsHealth AnamaticsHealth InformaticsExperiential Learning Activity: TelemedicineHealth AnalyticsHealth Anamatics ArchitectureExperiential Learning Activity: Evidence-Based Practice and ResearchHealth Anamatics CareersExperiential Learning Activity: Health Anamatics CareersLearning Journal Reflection
Chapter 3: Sampling Health Data
Chapter SummaryChapter Learning GoalsHealth Anamatics ProcessHealth Anamatics ToolsSEMMA: Sample Process StepSAS OnDemand for Academics SetupExperiential Learning Application: Health and Nutrition SamplingExperiential Learning Application: Health and Nutrition Data PartitioningExperiential Learning Application: Claim Errors Rare-Event OversamplingLearning Journal Reflection
Chapter 4: Discovering Health Data Quality
Chapter SummaryChapter Learning GoalsHealthcare QualityExperiential Learning Activity: Healthcare Data Quality CheckHealthcare Data Quality Case StudySix Sigma Health Data QualityExperiential Learning Activity: Public Data ExplorationSEMMA: ExplorationExperiential Learning Activity: Health Data SurveillanceSEMMA: ModifyExperiential Learning Application: Heart Attack Payment DataExperiential Learning Application: Data Quality ExplorationLearning Journal Reflection
Chapter 5: Modeling Patient Data
Chapter SummaryChapter Learning GoalsPatientsPatient AnamaticsPatient DataHealthcare Technology DisruptionExperiential Learning Activity: Personal Health RecordsSEMMA: Model Process StepExperiential Learning Application: Caloric Intake Simple Linear RegressionExperiential Learning Application: Caloric Intake Multiple Linear RegressionModel SummaryExperiential Learning Application: mHealth Heart Rate AppExperiential Learning Application: Inpatient Utilization - HCUPReflection
Chapter 6: Modeling Provider Data
Chapter SummaryChapter Learning GoalsProvidersProvider AnamaticsProvider DataEHR ImplementationsEHR Implementation and Success FactorsEHR Implementation ProcessExperiential Learning Activity: Electronic Health RecordsSEMMA: ModelExperiential Learning Application: Hospital-Acquired ConditionsModel SummaryExperiential Learning Application: ImmunizationsLearning Journal Reflection
Chapter 7: Modeling Payer Data
Chapter SummaryChapter Learning GoalsPayersPayer AnamaticsPayer DataClaim FormsExperiential Learning Activity - Claim Forms BillingExperiential Learning Activity: Claims Adjudication ProcessingElectronic Data InterchangeExperiential Learning Activity: EDI TranslationSEMMA: ModelExperiential Learning Application: Patient Mortality IndicatorsModel SummaryExperiential Learning Application: Self-Reported General HealthLearning Journal Reflection
Chapter 8: Modeling Government Data
Chapter SummaryChapter Learning GoalsGovernment AgenciesGovernment Health AnamaticsGovernment RegulationsExperiential Learning Activity: Government Data SharingGovernment Billing and PaymentsExperiential Learning Activity: Billing Issues and Fraud and AbuseSEMMA: ModelExperiential Learning Application: Fraud DetectionModel SummaryExperiential Learning Application: Hospital ReadmissionsLearning Journal Reflection

Chapter 9: Health Administration and Assessment
Chapter SummaryChapter Learning GoalsHealth Anamatics AdministrationCode SetsSecurityPrivacyExperiential Learning Activity: HIPAA AdministrationSEMMA: AssessExperiential Learning Application: Health Risk ScoreAssess SummaryExperiential Learning Application: Hip Fracture RiskLearning Journal Reflection
Chapter 10: Modeling Unstructured Health Data
Chapter SummaryChapter Learning GoalsUnstructured Health AnamaticsSocial MediaExperiential Learning Activity: Social Media PolicySocial Media MaturityExperiential Learning Activity: Dr. GoogleText MiningExperiential Learning Application: U.S. Presidential SpeechesModel SummaryExperiential Learning Application: Healthcare Legislation TweetsLearning Journal Reflection
Chapter 11: Identifying Future Health Trends and High-Performance Data Mining
Chapter SummaryChapter Learning GoalsPopulation and Consumer ChangesArtificial Intelligence and Robotics AutomationExperiential Learning Activity: Robotic SurgeryHealthcare Globalization and GovernmentPublic HealthBig Data Health AnamaticsBig Data and High-Performance Data Mining ModelExperiential Learning Application: SIDSModel SummaryHealthcare Digital TransformationExperiential Learning Application: LifelogsLearning Journal ReflectionExperiential Learning Application: Health Anamatics Project
References
Index

Overview

Leverage health data into insight!

Applied Health Analytics and Informatics Using SAS describes health anamatics, a result of the intersection of data analytics and health informatics. Healthcare systems generate nearly a third of the world’s data, and analytics can help to eliminate medical errors, reduce readmissions, provide evidence-based care, demonstrate quality outcomes, and add cost-efficient care. This comprehensive textbook includes data analytics and health informatics concepts, along with applied experiential learning exercises and case studies using SAS Enterprise MinerTM within the healthcare industry setting. Topics covered include:

Sampling and modeling health data – both structured and unstructured
Exploring health data quality
Developing health administration and health data assessment procedures
Identifying future health trends
Analyzing high-performance health data mining models

Applied Health Analytics and Informatics Using SAS is intended for professionals, lifelong learners, senior-level undergraduates, graduate-level students in professional development courses, health informatics courses, health analytics courses, and specialized industry track courses. This textbook is accessible to a wide variety of backgrounds and specialty areas, including administrators, clinicians, and executives.

This book is part of the SAS Press program.

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Analysis of Observational Health Care Data Using SAS

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