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WG1 Presenters Connie White Delaney, PhD, RN, FAAN, FACMI, Professor & Dean Tom Clancy, PhD, MBA, RN, FAAN, Clinical Professor Associate.

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Presentation on theme: "WG1 Presenters Connie White Delaney, PhD, RN, FAAN, FACMI, Professor & Dean Tom Clancy, PhD, MBA, RN, FAAN, Clinical Professor Associate."— Presentation transcript:

1 WG1 Presenters Connie White Delaney, PhD, RN, FAAN, FACMI, Professor & Dean Delaney@umn.edu Tom Clancy, PhD, MBA, RN, FAAN, Clinical Professor Associate Dean for Faculty Practice, Partnership, Professional Development Clanc027@umn.edu Bonnie Westra, PhD, RN, FAAN, FACMI, Associate Professor Director, Center for Nursing Informatics Westr006@umn.edu Karen Monsen, PhD, RN, FAAN, Associate Professor Specialty Coordinator Doctorate of Nursing Practice in Nursing Informatics Mons0122@umn.edu Chih Lin Chi, PhD, MBA Assistant Professor cchi@umn.edu

2 Secondary Data Analysis, Big Data Science and Emerging Academic/Corporate Partnerships Thomas R. Clancy, PhD, MBA, RN, FAAN University of Minnesota School of Nursing Clinical Professor and Associate Dean for Partnerships, Practices and Professional Development

3 Disclosure I have no relevant financial interest to disclose nor am I endorsing any commercial products identified in this presentation.

4 Objectives Describe the drivers of large scale, complex datasets Review challenges of secondary data analysis using “big data” Provide exemplars of academic corporate partnerships to conduct big data science research.

5 Moores Law 1.Exponential growth in computer processing speed, 2.The digitization of everything, 3.Build-out of the Intranet, 4.Recombination of existing technologies. Courtesy of Ray Kurzweil and Kurzweil Technologies, Inc. - en:Image:PPTMooresLawai.jpg Accessed from Wikipedia on Sept. 17, 2014 at: http://en.wikipedia.org/wiki/Accelerating_change#mediaviewer/File:PPTMooresLawai.jpg

6 Big Data Drivers Electronic Health Record Health Insurance Claims Quantified Self Movement (1 trillion sensors) Geo-spatial Data Intranet of Things (IoT) Social Media (1.8 billion subscribers) eMobile Health (6 billion cellphones) Whole Gene Sequencing (6 billion diploid pairs/genome)

7 Public and Government Data Sets CMS – Medicare Claims Public Use Files CDC- National Center for Health Statistics AHRQ – Agency for Healthcare Research and Quality

8 CMS 2008 BSA Inpatient Claims Data Base 5% sample of 2008 Medicare beneficiaries (588,415 records) which include: 1.Gender of the beneficiary, 2.Age of the beneficiary at the end of 2008, 3.Base Diagnosis Related Group (DRG) for the inpatient stay, 4.Primary procedure performed during the inpatient stay 5.Number of days patient was hospitalized, 6.Average Medicare payment for low-, medium-, and high-payment stays within the DRG (defined below). https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and- Reports/BSAPUFS/Inpatient_Claims.html

9 CDC National Center for Health Statistics National Health Interview Survey National Health and Nutrition Examination Survey National Survey of Family Growth National Vital Statistics 42,000 households per year 5000 persons per year (all ages) 5000 men and women (all age groups) 4 million births, 2.4 million deaths annually http://www.cdc.gov/nchs/data/factsheets/factsh eet_summary.htm

10 Agency for Healthcare Quality and Research Medical Expenditure Panel Survey Household Insurance/Employer Medical Provider Healthcare Cost and Utilization Project Cost and quality of health services, Medical practice patterns, access to health care programs, Outcomes of treatments 589,666 sample providers (all years) 7 million hospital stays per year (National Inpatient Sample Database) http://www.ahrq.gov/research/data/index.html

11 Top 10 Non-Profit Health Systems (by number of hospitals) 1. Ascension Health— 73 2. CHE - Trinity Health -45 3. Adventist Health System— 36 3. Kaiser Permanente— 36 4. Dignity Health -- 34 5. Catholic Health Initiatives — 32 6. Sutter Health (Sacramento) — 26 6. Providence Health and Services — 26 7. CHRISTUS Health — 22 8. UPMC — 20 9. Catholic Healthcare Partners — 17 9. Intermountain Health Care — 17 9. New York-Presbyterian — 17 9. SSM Health Care — 17 10. Banner Health — 16 http://www.beckershospitalreview.com/lists/30-largest- nonprofit-health-systems-2014.html

12 Private Data Sets Patient Centered Outcomes Research Institute (PCORI) Clinical Data Networks Patient Powered Research Networks NIH - Clinical Translational Science Institutes (CTSI) The HealthCare Cost Institute (HCCI) Optum Labs Data Warehouse (OLDW) 47 million lives of EHR and patient reported outcomes data 62 medical research institutions in 32 states 40 million lives claims data 150 million lives of claims and EHR data

13 Secondary Data Analysis Definition The use of existing data to test new hypotheses or answer new research questions 1. Nursing Studies 2 : 1997 to 2003 = 82 2003 to 2008 = 99 21% increase 1. Doolan, D. M., & Froelicher, E. S. (2009). Using an existing data set to answer new research questions: A methodological review. Research and Theory for Nursing Practice: An International Journal, 23, 203-215. doi:10.1891/1541-6577.23.3.20 2. Dunn,SL, Arslanian-Engoren, C, DeKoekkoek, T. Jadack, R. and Scott, LD. (2015). Secondary Data Analysis as an Efficient and Effective Approach to Nursing Research. Western Journal of Nursing Research Vol. 37(10) 1295– 1307

14 Comparison of Observational Studies to Secondary Analysis of Big Data Observational Studies Few data sources Limited set of variables (10’s – 1000’s) Demographics Clinical Insurance claims Census Small number of hypothesis Long, expensive data collection, analysis and evaluation cycle Secondary Data Analysis Multi-source, data mash-up Many variables (> million) EHR Imaging Social media Genomic Large number of hypothesis Short data collection, analysis and evaluation cycle

15 Challenges of Secondary Analysis of Big Data Database/Data Dictionaries Data Extraction Feature selection Data Cleansing Missing values, outliers, errors, redundancies, transformation… Analysis Exploratory Statistics, data mining Predictive Machine learning Algorithms Model Evaluation Testing on new data Extraction Cleansing Analysis Testing

16 Dealing with Databases!?#$%...! Sourcing public & private databases (DB) Data mash-ups? Data may not or may partially address the research question Accessing DB’s – Secure sandbox vs FTP Cost (public vs private) Data dictionary complexity!! Topol, E. (2015) The Patient Will See You Now. Basic Books, New York.

17 Types of Databases Relational Database Hierarchical Networked Object Oriented Hadoop

18 Healthcare Data Constraints Data takes on multiple forms : Structured & Unstructured text Audio (dictation) Images (PAC’s) Videos Waveforms (ECG) Streaming (Sensors) Sloanreview.mit.edu

19 Hardware and Software Central Processing Unit Speed, memory, data transfer rate, runtime Number of core processors needed Parallel computing Use of a supercomputer Database structure (relational or distributed, Hadoop/MapReduce) Software (SAS, MathLab)

20 Data Dictionaries Column_NameBusiness NameColumn Description Design ated as Quasi- identifi er DataStand ard Views DatatypesValid Values ADJUSTED_HCCC_CODEHCCC CodesIdentifies a UnitedHealth Group categorization of health care services. NStandardVARCHAR(5 ) Adjusted_HCCC_Code coding schema is provided by Adjusted_HCCC_Desc. ADJUSTED_HCCC_DESCHCCC Codes DescriptionHCCC Codes Description.NStandardVARCHAR(2 2) (null),UNKNOWN HCCC CODE 00,UNKNOWN HCCC CODE 01,PRIMARY CARE PHYSICIAN 02,SPECIALTY PHYSICIAN 03,ALLIED HEALTH PROVIDER 04,04 - FUTURE USE 05,MEDICAL PLAN PHARMACY 06,INPATIENT FACILITY 07,OUTPATIENT FACILITY 08,08 - FUTURE USE 09,DENTAL 10,10 - FUTURE USE 11,UNKNOWN ADJUSTED_HCCC_GROUP_D ESC HCCC Codes Group description HCCC Codes Group description.NStandardVARCHAR(2 2) ADJUSTED_HCCC_SUMMAR Y_DESC HCCC Summary Description HCCC Summary Description.NStandardVARCHAR(1 2) UPDATE_DATEUpdate DateDate that the update to this record occurred.NStandardDATE FORMAT 'yyyy-mm-dd' ADMIT_CHANNEL_CODEAdmit Channel CodeThis code identifies how the inpatient stay was initiated. NStandardVARCHAR(1 6) For codes and descriptions refer to the 'ADMIT CHANNEL' tab. ADMIT_CHANNEL_DESCAdmit Channel Description This description identifies how the inpatient stay was initiated. NStandardVARCHAR(1 00) For codes and descriptions refer to the 'ADMIT CHANNEL' tab. ADMIT_SOURCE_CATEGORY _DESC Categorizes Admission Source Codes into high- level groups identifying how the inpatient stay was initiated. NStandardVARCHAR(1 00) For codes and descriptions refer to the 'ADMIT CHANNEL' tab. UB92_ADMIT_TYPE_CODE Describes the formula or process used to calculate the copay or coinsurance. NStandardVARCHAR(2 ) ADMIT_TYPE_CODE coding schema is provided by ADMIT_TYPE_DESC 1500 Rows

21 Data Extraction Process of retrieving data out of (usually unstructured) or (poorly structured) data sources for further data processing or data storage. Data Types Columns/rows Text Web pages, Emails, Documents, PDFs, Scanned document Streaming data

22 Feature Extraction Dimensionality Reduction The process of transforming raw data into a reduced set of features. Eliminates redundant and irrelevant data. Reduces the inputs to machine learning algorithms to improve efficiency.

23 Data Cleansing Process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database. May include data transformation, harmonization and/or standardization of data.

24 Statistical Analysis Inferential Analysis Parametric vs non parametric statistical analysis Correlation Regression ANOVA Wilcoxon Other Types of Studies Population observation Explanatory Confirmatory

25 Knowledge Discovery: Data Mining The computational process of discovering patterns in large, complex datasets. Goal of KDD: Extract information and transform it into an understandable structure (knowledge). Exploratory studies Pattern recognition Data visualization

26 Data Visualization: Clinical and Business Intelligence Dashboards Performance Analytics Dashboard Image: http://www.dashboardinsight.com/dashboards/screenshots/datawatch-healthcare- dashboard.aspx

27 Machine Learning The science and technology of systems that learn from data (i.e., typically how to solve non trivial problems, but also the structure of the data generating process).

28 Analysis : Data Mining & Machine Learning Decision trees Association rules Artificial neural networks Support vector machines Clustering Bayesian networks Genetic algorithms

29 Example: Predictive Systems: Modified Early Warning System (MEWS) Scoring is based on: Respiratory rate Heart rate Systolic blood pressure Conscious level Temperature Hourly urine output (for previous 2 hours) Image: http://www.ihi.org/resources/Pages/ImprovementStorie s/EarlyWarningSystemsScorecardsThatSaveLives.aspx

30 Machine Learning Applications Adaptive websitesGame playingRobot locomotion Affective computingInformation retrievalSearch engines BioinformaticsInternet fraud detection Sentiment analysis (or opinion mining) Brain-machine interfacesMachine perceptionSequence mining CheminformaticsMedical diagnosisSoftware engineering Classifying DNA sequences Natural language processing Speech and handwriting recognition Computational finance Optimization and metaheuristic Stock market analysis Computer vision, including object recognition Online advertising Structural health monitoring Detecting credit card fraud Recommender systemsSyntactic pattern recognition Machine Learning accessed on November 1 at: https://en.wikipedia.org/wiki/Machine_learning P30

31 Examples in Nursing Classifying data into dashboards Classification of data into diagnosis (medical, nursing) Optimizing best practices (clinical pathways) Comparative effectiveness (drugs, technology, practice) Prediction (risk profiling: diabetes, stroke, MI, readmission, pressure ulcers, falls) Personalized medicine (genomic, claims, EHR, social media, GPS, wearable technology…) P31

32 Top 20 Skills: Data Scientist http://101.datascience.community/2015/12/21/the-most-popular-skills-and-degrees-of-todays-data-scientists/

33 Swami Chandrasekaran at http://nirvacana.com/thoughts/becoming-a-data-scientist/

34 Top 20 Backgrounds: Data Scientists http://101.datascience.community/2015/12/21/the-most-popular-skills-and-degrees-of-todays-data- scientists/

35 Gaps In Nurse Data Scientist Training Mathematics for modeling. Big data framework Software programming Data munging/ingestion Machine learning Data visualization tools Evaluation methods http://nirvacana.com/thoughts/becoming-a- data-scientist/

36 PhD NursingMasters in Data ScienceDoctor of Nursing Practice School of Nursing Department of Computer Science and Engineering, Department of Electrical and Computer Engineering, School of Statistics and Division of Biostatistics. Specialty: Nursing Informatics University of Minnesota School of Nursing Core Courses Principles of Database Systems HInf 5510 Applied Health Care Databases: Database Principles and Data Evaluation Graduate Statistics Course IApplied Regression AnalysisStatistics** Graduate Statistics Course IISTAT 5401 - Applied Multivariate Methods Nursing and Nursing Theory CoreCSCI 5523 - Introduction to Data MiningNurs 6105 Systems Analysis and Design NURS 8121: Health Behavior and Illness CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and ProgrammingNurs 7300 Program Evaluation NURS 8134: Interventions and OutcomesEE 5239 - Introduction to Nonlinear OptimizationNurs 7400 Health Policy Leadership NURS 8172: Theory and Theory Development for ResearchElectiveNurs 5116 Consumer Health Informatics Research Methodology CoreElectiveNurs 6200 Science of Nursing Intervention NURS 8171: Qualitative Research Design and MethodsCapstone Project (first half Nurs 7600 Nursing Research and Evidence Based Practice NURS 8173: Principles and Methods of Implementing ResearchCapstone Project (second half)Nurs 7113 Clinical Decision Support: Theory NURS 8175: Quantitative Research Design and Methods Nurs 7105 Knowledge Representation and Interoperability NURS 8177: Research Practicum HINF 8406 User Interface Design and Usability in Healthcare NURS 8180: Doctoral Pro-Seminar: Scholarly Development Nurs 7200 Economics of Health Care NURS 8152: Scholarship in Healthcare Ethics Nurs 7112 DNP Project Direction III: Evaluation NURS 8190: Critical University of Minnesota Review of Health Research Nurs 7108 Population Health Informatics Nursing Electives ( 1 or 2 courses; see below for examples) Nurs 6110 Epidemiology in Nursing Nurs 7610 Health Innovation and Leadership Nurs 7202 Moral and Ethical Positions and Actions in Nursing Nurs 5115 Interdisciplinary Healthcare Informatics

37 Team Science Project Manager Team Development Project Manage Funding/Budget Communication Data Scientist Building Models Validation/Testing Algorithms Knowledge of: Statistics Linear Algebra Machine Learning R, MatLab,SAS Programming Data Engineer Big Data Framework Data Ingestion/Munging Manage platforms Programming Java Python C++ Hadoop/MapReduce Domain Expert Deep Domain Knowledge Data Visualization Data Exploration Hypothesis Testing Pattern Discovery Correlations Serendipitous Discovery Data Analyst Knowledge of Data Dictionary Report Generation/Data Visualization SQL Searchs Pre-Processing data

38 Filling the Gaps Hire faculty with expertise into your department and/or consult with faculty in other departments. Internal consultation service (CTSI) External research collaborative Contract an external consulting service Data Science Computer Science Engineering Epidemiology Statistics Physics Mathematics Information Technology

39 Health Affairs Optum Labs: Building A Novel Node In The Learning Health Care System Paul J. Wallace,*, Nilay D. Shah, Taylor Dennen, Paul A. Bleicher and William H. Crown Abstract Unprecedented change in the US health care system is being driven by the rapid uptake of health information technology and national investments in multi-institution research networks comprising academic centers, health care delivery systems, and other health system components. An example of this changing landscape is Optum Labs, a novel network “node” that is bringing together new partners, data, and analytic techniques to implement research findings in health care practice. Partners Mayo Clinic AARP AMGA Boston Scientific Boston University Lehigh Valley Pfizer Inc. Rensselaer Polytechnic Tufts Medical Center UM School of Nursing Harvard Medical School Medica Research Institute Merck University of Maryland The Brown University School of Public Health Johns Hopkins Bloomberg School of Public Health MIT Sloan School of Management Novartis Pharmaceuticals Corporation ResMed http://content.healthaffairs.org/content/33/7/1187.full?ijkey=b8qVnVJW pdA4s&keytype=ref&siteid=healthaff

40 UnitedHealthcare UnitedHealth Group: A diversified managed health care company offering a spectrum of products and services to 70 million individuals through two operating businesses: UnitedHealthcare and Optum. UnitedHealthcare: The largest single health carrier in the United States. UnitedHealthcare Optum: One of the largest health information, technology, services and consulting companies in the world. Population health management, care delivery and improving the clinical and operating elements of the system.

41 Data Categories Demographics Pharmacy claims Physician and facility claims Lab test results Socioeconomic data EHR data (clinical) Health risk appraisal Date of death

42 Optum Labs Data Warehouse Approximately 150 million lives (40 million EHR) 3400 fields per life Claims and electronic health records data (~25% of data is linked) 20 + years of data Includes Medicare Advantage

43 Partnership Management Executive Sponsor Connie White Delaney, Professor and Dean Steering Committee Connie White Delaney Ann Garwick, Professor and Senior Executive Associate Dean for Research Thomas Clancy, Clinical Professor and Assistant Dean for Practices, Partnerships & Professional Development Partnership Management Thomas Clancy Research Review Committee Chih-Lin Chi, Assistant Professor Legal Arnie Frishman, Office of General Counsel Communications Barb Schlaefer, Director of Strategic Communications Research Assistant Jin Wang,

44 Research Tools Exploratory Sandbox Statistical Tools are available May add additional software applications SAS, MatLab, R) Data is statistically de- identified and cannot leave the sandbox Multiple partners may work on the same project simultaneously OLDW 150M Lives UM Nursing Sandbox Data NHD Research Views Unified (claims/EHR) Death Index SES (social/economic) Team Project Mgt Domain Ex Machine Learn Data Dic. Analyst Optum Labs Project Mgr Data Engineer Data Dic. Analyst AHC Medicine Pharmacy Public Health

45 Optum Labs Research Process 2 Study Starts 10 Monthly project status reports Invoicing as needed 45 7 12 Researcher completes Project Deliverables 11 Researcher and OL conduct Project Reconciliation, Reporting, and Archiving 1 3 Researchers Trainings OLRO submits project provisioning requests Researcher and OL conduct Assessment of: Dissemination, Translation, Product/Service Development 13 Project Closed Close-out Start-up Maintenance Researcher Submits PRA OL Reviews and approves Researcher Submits DRA OL Reviews and approves SOW negotiations and signing 10 working days 8 9 Project environment ready 6 Initial payments and project setup Response times are per cycle – e.g. 15 days for each review of DRA, last of which moves to SOW negotiations 15working days

46 Objective and Specific Aims What individual level information is valuable to augment current CVD risk prediction models? Aim 1: Determine the value of current CVD risk prediction models for predicting first CVD event in the Optum Labs dataset Aim 2: Augment current models using newer statistical techniques and variables from EHR and claims data Aim 3: Construct a preliminary set of putative causal models

47 Potential Impact Improve CVD risk prediction Develop CVD prevention interventions Improve individual outcomes Reduce health care costs Forecast population health status Future: CVD risk score in the EHR

48 Population of Interest First development of CVD Coronary heart disease (CHD)* Non-fatal myocardial infarction, angina pectoris, and/or heart failure Cerebrovascular disease Non-fatal stroke and transient ischemic attack Peripheral artery disease Intermittent claudication and critical limb ischemia Aortic atherosclerosis and thoracic or abdominal aortic aneurysm

49 Current Cardiovascular Disease Prediction Models Limitations: Multivariable regression equations with identical “inputs” Missing values Multi-morbidity Limited stratification capacity (poor discrimination) Classification accuracy (overfitting populations)

50 Potential Prediction Variables Age Gender Ethnicity Total cholesterol (mg/dL) HDL cholesterol (mg/dL) Lipid lowering treatment (yes/no) Systolic blood pressure (mmHg) Blood pressure treatment (yes/no) Diabetes mellitus (yes/no) Current smoking (yes/no) Family history of CVD in first degree relative aged < 60 years (yes/no) Chronic kidney disease Atrial fibrillation Rheumatoid arthritis Body mass index (kg/m2) ????

51 Exploratory Analysis: Sample Size

52 Odds Ratios Treatment vs Control Group

53 Preliminary Research Application Study Aims Feasibility of data Exploratory analysis Research design Methods (machine learning) Funding source Grant/Departmental IRB – Institutional Project Team

54 Project Teams University of Minnesota Project Team Project Manager Domain Experts Nurse scientist (PI) Cardiology (MD) Data scientist (Institute of Health Informatics) Predictive modeling (Epidemiology) Data dictionary analyst Optum Labs Project Team Project Manager Data engineers Prepare data for sandbox Views (claims, EHR, unified) Data dictionary analyst Deep dive into data

55 Other Considerations Intellectual property Commercialization of models Licensing and royalties Publications and academic freedom Conflicts with grantees

56 PartnersSample of UM/Optum Labs Research Studies Funding Source Otolaryngology Prediction model: causal factors in patients presenting with dizziness TBD Nursing Prediction model: Patients experiencing adverse effects of statin therapy UM Internal Prediction model: Cardiovascular disease risk prediction using EHR/claims data UM Internal AHC Seed Public Health Prediction model: Diffusion of knowledge from clinical trials to practice. NIH Comparative effectiveness of extended oral anticoagulant use PCORI Contemporary Venous Thromboembolism Treatment - NIH NIH School of Dentistry Exploratory Study: Investigating prescription of opioids by dentists. TBD Neurosurgery Constellation Study: Comparative effectiveness between surgical and non-surgical intervention of low back pain. Optum Labs

57 Traditional Model Clinical Trial Prospective Randomized (sample) Single Trial/Single Source 2-3 year study Avg. RO1 in 2013: $405,000 Avg. faculty 1-2 grants/year Annual funding: $800,000 Downstream revenue Limited Commercialization Sustainability Emerging Model Secondary Data Analysis Retrospective Population based Multiple Trials/Single Source 12-18 months Avg. grant for Optum: $300,000 Avg. faculty: 3 – 4 per year Annual funding: $1.2 million Downstream revenue: UM: commercialization/algorithms Optum: data licensing fees & royalties Emerging Academic/Corporate Business Model https://nexus.od.nih.gov/all/2014/01/10/fy2013-by-the-numbers/

58 Registry-based clinical trial puts heart treatment to the test A study challenging the value of a device used when unclogging blocked arteries is raising the prospect that inexpensive registry trials, which use observational data to assess outcomes, may soon be widely used to challenge expensive add-ons to medical procedures that may not improve patient care. Experts say the new model holds promise as a quick, low-cost way to conduct large-scale research from registries and patient records, whose results can be disseminated far more quickly than traditional clinical trials. The growing availability of electronic health records is spurring interest in such trials to determine if high-cost medical devices and drugs perform as well in actual medical settings as they did in the original randomized clinical trials that led to their regulatory approval.electronic health records http://www.modernhealthcare.com/article/20131102/MAGAZINE/311029968

59 Examples: Commercialized Algorithms Credit card and general fraud detection Information retrieval, filtering, ranking, and search Network intrusion detection Web site classification and filtering. Matchmaking Predict success of movies Predict outcomes of elections Predict prices or trends of stock markets Weather and other physical phenomena forecasting Recommend purchases Email spam detection Human Capital/Resources: recruitment, retention, task selection, compensation Evaluate and predict academic performance and impact Optimizing medical outcomes, safety, patient experience, cost, profit margin in healthcare systems Medical diagnosis, prognosis and risk assessment Detection of plagiarism Molecular profiling and sequencing based diagnostics, prognostics

60 Expert Systems: Algorithmic Symptom Checkers WebMD Mobile iTriageHealth Medscape Mobile Diagnosaurus DDx Symptoms TakTools iHeadache SignsSx Handbook Symptom Mate Differential Dx i-pocket STATworkUP eRoentgen Radiology DX Symptom Minder Pocket Symptom Analyzer Image: http://www.thinklabs.com/#!thinklink/cbor https://www.youtube.com/watch?v=CQqBMG578tA

61 UnitedHealthcare pushes 'wearable' wellness By Christopher Snowbeck Star Tribune MARCH 1, 2016 — 5:00AM UnitedHealthcare is announcing Tuesday a new program to outfit workers with fitness tracking devices, so they have a shot at earning up to $1,460 over the course of a year for meeting certain fitness goals. An employee and the employee's spouse can each earn up to $4 per day for hitting three distinct targets. If they take 300 steps within 5 minutes, six times per day, the health plan enrollee earns $1.50. The enrollee earns another $1.25 for walking 3,000 steps within a 30- minute period. Finally, walking a total of 10,000 steps in a day brings another $1.25 http://www.startribune.com/unitedhealthcare-pushes-wearable-wellness/370584581/

62 Resources Page NIH Big Data to Knowledge (BD2K) Workshops: https://datascience.nih.gov/bd2k/events/bd2kworkshops https://datascience.nih.gov/bd2k/events/bd2kworkshops NINR Advancing Nursing Research through Data Science http://www.ninr.nih.gov/training/online-developing-nurse- scientists#.VtdHJvkrLIU http://www.ninr.nih.gov/training/online-developing-nurse- scientists#.VtdHJvkrLIU University of Minnesota School of Nursing. Nursing Knowledge: Big Data Conference 2016: http://www.nursing.umn.edu/icnp/center- projects/big-data/2016-nursing-knowledge-big-data-science- conference/index.htmhttp://www.nursing.umn.edu/icnp/center- projects/big-data/2016-nursing-knowledge-big-data-science- conference/index.htm American Medical Informatics Association: https://www.amia.org/https://www.amia.org/ Health Information and Management Systems Society (HIMSS): http://www.himss.org/aboutHIMSS/ http://www.himss.org/aboutHIMSS/ Coursera: Six courses on data science: https://www.coursera.org/https://www.coursera.org/ Health Catalyst Knowledge Center: https://www.healthcatalyst.com/knowledge-center/ https://www.healthcatalyst.com/knowledge-center/

63 Thank You! Questions?


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