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Strata Big DataFeb. 26, 2013 Strata Big Data Feb. 26, 2013 Let the Data Decide: Predictive Analytics in Healthcare Eugene Kolker

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Presentation on theme: "Strata Big DataFeb. 26, 2013 Strata Big Data Feb. 26, 2013 Let the Data Decide: Predictive Analytics in Healthcare Eugene Kolker"— Presentation transcript:

1 Strata Big DataFeb. 26, 2013 Strata Big Data Feb. 26, 2013 Let the Data Decide: Predictive Analytics in Healthcare Eugene Kolker eugene.kolker@seattlechildrens.orgeugene.kolker@seattlechildrens.org, eugene.kolker@seattlechildrens.org gnklkr@yahoo.com

2 1.Appetizer: Introduction 2. Main Course: Prioritized Improvements (US News & World Report Metrics) 3. Second Course: Personnel & Reduction of Waste (Nurses’ Turnover Trends) 4. Dessert: General Observations Thanks to Edd Dumbill, Alistair Croll, O’Reilly Media, the Organizers, & You! Outline

3 1. Why Should You Care? Data Knowledge Action Benefits Our motto: Accelerating and optimizing your work through intuitive, reliable and powerful analytics

4 What is Seattle Children’s? Seattle Children’s: Hospital – Research – Foundation SCH: Non-profit, network, tertiary, 100 y.o. $0.8 Bln/yr, 5,000 FTEs, 350 beds (plans: 600) SCH covers 5 States (WWAMI region), 0-21 y.o. 6th on USNWR ranking of Ch. Hospitals 5th on Federal ranking of Ch. Research Institutes

5 Chief Data Officer Chief Data Officer @ SC DirectorBioinformatics & High-throughput Analysis Lab Director, Bioinformatics & High-throughput Analysis Lab, SC Research Institute Affiliate Professor Affiliate Professor @ Depts of Biomedical Informatics & Medical Education and Pediatrics, University of Washington MS in Applied Mathematics & Computer Sciences, PhD in Structural Molecular Biology (Bioinformatics) + Business School Executive and Founding Editor of: “OMICS A Journal of Integrative Biology Big Data ” “OMICS A Journal of Integrative Biology” and “ Big Data ” Who is EK?

6 In today’s data-driven age, healthcare is transitioning from opinion-based decisions to informed decisions based on data and analytics. Analyzing the data reveals trends and knowledge that may run contrary to our assumptions causing a shift in ultimate decisions that in turn will better serve both patients and healthcare enterprises. Abstract

7 Helmet laws “helmet laws are associated with a 13% reduction in bicycle-related head injuries, a 9% reduction in non-head bicycle-related injuries, and an 11% increase in all types of injuries from the wheeled sports.“ Buying habits “conservatives like established national brands—and are significantly less likely to try new items” Sober vs. intoxicated eye witnesses “Intoxicated eyewitnesses are no less reliable than sober ones, and neither is very good at picking kidnappers out of a lineup” WSJ Data news: Last weekend, Feb. 16-17

8 Volume, Veracity, Velocity, Variety, and Value Banking/Marketing/IT: Volume, Velocity, and Value Healthcare/Life Sciences: Veracity, Variety, and Value 5 Vs of Big Data

9 This talk illustrates our collaborative work with key stakeholders, including executive leadership, and describes a few representative, data-driven, and cost-effective projects. Abstract, Cont.

10 US News & World Report (USNWR) Metrics Sponsors: David Fisher, SVP, Medical Director and Tom Hansen, CEO Objective: to prioritize enterprise-wide improvements based on USNWR Metrics (utilized as Hospital & Departmental Metrics) 2. 2. Prioritized Improvements

11 Prioritized Improvements, Cont. Three key recommendations: 1. Focus on care and outcomes for A1 and A2 Departments (medical service lines) 2.A1 and A2 Department-specific Marketing 3.Implement 1-Day Immunization Reporting This work is described in Kolker E. & Kolker E., Chief Data Officer in Healthcare: Predictive Analytics Transforms Data to Knowledge to Action, In: Chief Data Officer: Enterprise Data Solution for Business Challenges, MIT Press, 2013, in press. Since 2007, SC moved from 11th to 6th rank

12 USNWR 2012 Honor Roll RankHospitalPointsDepartments 1Boston Children's Hospital2010 1Children's Hospital of Philadelphia2010 3Cincinnati Children's Hospital Medical Center1910 4Texas Children's Hospital, Houston138 5Children's Hospital Los Angeles65 6Seattle Children's Hospital54 7Nationwide Children's Hospital, Columbus, Ohio43 7Children's Hospital Colorado, Denver43 9Children's Hospital of Pittsburgh of UPMC33 9Johns Hopkins Children's Center, Baltimore33 9Ann and Robert H. Lurie Children's Hospital of Chicago 33 9St. Louis Children’s Hospital- Washington University 33

13 Model for C1 department Reconstructed model based on provided data Empirically determined transformation applied to data

14 FY12-F11 Changes Department2012 rank2011 rankChangePoints B378+11 B11115+40 C21920+10 C11419+50 B21722+50 C442-22 A1810+21 C32217-50 A211 00 B457+21 Overall67+15

15 Prioritizing Improvements DepartmentPercent score increase needed for goal Percent score increase needed for stretch goal A1*+0.8% A2*1.4%7.8% B1**4.2%8.4% B2**4.4%12.0% B3**+4.4% B4**+5.3% C18.8%24.6% C210.7%18.3% C317.1%26.3% C4++ *First and **Second priority improvements

16 Guiding Improvements Categories broken down for each department Calculated as: maximum possible increase needed increase Need total of 100 points in a column to reach goal Still, reputation has major influence in every department, however, there are numerous important factors to be improved

17 A1 Department CategoryPoints for goal Points for stretch goal Reputation--2,438 Nurse-patient ratio--377 X1 management--222 Clinic volume--102 X2 treatment volume--96 Commitment to best practices--89 Surgical volume--87 Overall infection prevention--46 Specialized clinics and programs--38 Advanced clinical services--32 Subspecialist availability--24

18 A2 Department CategoryPoints for goal Points for stretch goal Reputation1,177213 Preventing deaths of Y1 patients27249 Success in reducing ICU infections18132 Y2 management16029 Y3 management14927 Y4 management13624 Nurse-patient ratio11019 Y5 management9317 Patient volume7012 Overall infection prevention6712 Nonsurgical procedure volume5910 Commitment to best practices295 Advanced clinical services132

19 Recurring Categories CategoryNumber of departments Reputation9 Advanced clinical services9 Nurse-patient ratio9 Overall infection prevention8 Commitment to best practices8 Patient volume6 Surgical volume5 Success in reducing ICU infections5

20 2. Implement 1-Day Immunization Reporting All categories have both general & departmental measurements 1. Advanced clinical services -Services and programs organized around a particular diagnosis, disease, need, or age group 2. Overall infection prevention -Hospital commitment to reducing infection risk (tracking infections, immunization reporting, etc.) 3. Commitment to best practices - Includes participating in conferences, safety procedure guidelines, database tracking, etc. 4. Success in reducing ICU infections -Rates of infection in ICUs

21 Nurses’ Turnover Trends Sponsors: Lisa Brandenburg, Hospital President and Steven Hurwitz, VP, HR ASK: something is happening with nurses WHAT? WHY? and HOW to deal with it? 3. Personnel + Reduction of Waste

22 Nurses’ Turnover Trends Findings: 1. Termination rates higher after Magnet Status 2. After Magnet Status more experienced nurses leaving more often 3. Overall termination decreases with experience, especially Involuntary termination

23 Nurses’ Turnover Trends, Cont. 4. Termination higher for VPs A & E, lower for others 5. Higher termination for nurses living in Seattle 6. No difference in termination for night versus day shifts

24 Methods For this initial look, we broke down nurses’ turnover into 3 categories: Active, Involuntary, and Voluntary terminations. We initially looked at differences in age, gender, years since hired, whether they had been rehired, department, ethnicity, and FTE. We have added comparisons on reporting VP, Seattle residency status, and shift. We also compared 2 time periods: Before and After Magnet Status

25 Methods, Cont. For all comparisons except time period, odds ratios (with 95% Confidence Interval) were calculated for each variable: Odds = P(termination)/P(active) Odds Ratio (OR)=odds(Male)/odds(Female), e.g. Hence, an OR = 1 implies no difference in termination rates, OR > 1, Males (or whatever category) has higher termination rate, OR < 1 lower termination rate Analyses were done unadjusted as well as with an adjustment for age and adjustments for age and experience (years since hire).

26 Conclusion 6: SHIFT (Night vs. Day) Termination looks higher on night shift, but the difference gone after adjusting for age and experience.

27 Conclusion 5: ZIP in Seattle (Seattle vs. Other areas) Termination higher for nurses living in Seattle.

28 Conclusion 4: VPs (vs. other VPs) Involuntary Termination Involuntary termination higher for A and E. Note – OR = 1 for D (Unadj. and Age Adj.). UnadjustedAge Adj.Age and Exp. Adj. 0.1 0.2 0.5 1.0 2.0 5.0 10.0 A E D C B

29 29 Conclusion 2: Before and After Magnet Status Experience of Terminated Nurses Experience of terminated nurses is higher After Magnet Status (Age Adj.). Mean Experience Involuntary Termination Voluntary Termination Before Magnet0.140.63 After Magnet0.81.5

30 3. Three Follow-up Actions 1.Discussions with (experienced) nurses 2.Bringing external consultant in-house (psychology, sociology, nursing) 3. Hiring re-adjustments

31 Bottom Line for 2. & 3. Do you want to: A: Improve the health of your patients B: Cut huge amounts of waste C: Increase your rankings? How about all three?

32 Working Together 4. General Observations

33 EXA_3: Improve Care + Cost Savings Summary: 1. Medically Complex Patients (2+ Chronic Diseases), 80-20 rule 2. Question: Number of Medications? Answer: 5+ 3. Extremely complicated model with simple Q&A Sponsor: Mark Del Beccarro, VP, Medical Affairs

34 EXA_4: Model of Seattle Downtown Champions: Blake Nordstrom, Matt Griffin, Jim Hendricks + DSA An index of Downtown vitality which examines An index of Downtown vitality which examines four categories: Live, Work, Shop, andPlay four categories: Live, Work, Shop, and Play Enables comparison of Downtown across time Enables comparison of Downtown across time 2005 is baseline with score of 100 2005 is baseline with score of 100

35 Vitality Index: Integrated Score

36 100% Dashboard: Integrated Score (Inflation Adj.) Live 75% Work IS Play Shop

37 EXA_5: DELSA Global, delsaglobal.org Data-Enabled Life Sciences Alliance (DELSA Global) Data Knowledge Action Benefits

38 Ronald Fisher “To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: s/he may be able to say what the experiment died of.” Ronald Fisher, Cambridge U, 1938 Our motto: Accelerating and optimizing your work through intuitive, reliable and powerful analytics Big data, Predictive analytics, Computational modeling: From Data through Knowledge & Action to Outcomes & Benefits 4. Bottom Line

39 Thanks to Team: Roger HigdonNatali Kolker Bill Broomall Winn Haynes Chris MossBeth Stewart Greg Yandl Imre Janko Andrew Lowe Larissa Stanberry Maggie Lackey Randy Salomon Chris HowardSkylarJohnson Nate Anderson Courtney MacNealy-Koch Gerald van Belle Vural Ozdemir Matthias Hebrok Corinna Gries Biaoyang LinTodd Smith Geoffrey Fox Peter ArzbergerDan AtkinsDeborah Elvins Rob Arnold Jack Faris Evelyne & Ben Kolker David Fisher, Lisa Branderburg, Kelly Wallace, Skip Smith, Wes Wright, Mark Del Beccarro, Sandy Meltzer, Steven Hurwitz, Bruder Stapleton, Peter Tarczy-Hornoch, Troy McGuire, Judy Dougherty, Lee Hunstman Jim Hendricks Tom Hansen Support:NSF, NIH, SCRI, Robert McMillen Foundation, Gordon and Betty Moore Foundation

40 gnklkr@yahoo.com Contact EK: gnklkr@yahoo.com eugene.kolker@seattlechildrens.org or eugene.kolker@seattlechildrens.org kolkerlab.org For more info: kolkerlab.org anddelsaglobal.org Thank You! Any questions?

41 Additional Slides

42 Radom representation of data today: REGULATION (WSJ Feb. 15, Week in Ideas: Daniel Akst) Helmet Headwind American kids need more exercise, but are helmet laws making them ride their bicycles less? Two economists say that could be the case. Helmet laws, they found, are associated not only with fewer bike-related head injuries for children but also with fewer non-head biking injuries. More than 20 states have laws requiring bike helmets, with various age limits, as do localities. "For 5-19 year olds," the researchers write, "we find the helmet laws are associated with a 13% reduction in bicycle head injuries, but the laws are also associated with a 9% reduction in non-head bicycle related injuries and an 11% increase in all types of injuries from the wheeled sports." The increase in injuries from other wheeled sports suggests young riders might be shifting to skateboards and roller skates instead of bicycling. "Effects of Bicycle Helmet Laws on Children's Injuries," Pinka Chatterji and Sara Markowitz, National Bureau of Economic Research Working Paper 18773 (February) American kids need more exercise, but are helmet laws making them ride their bicycles less?

43 Radom representation of data today: MARKETING (WSJ Feb. 15, Week in Ideas: Daniel Akst) Buying Conservatively Bringing a new product to market? You'll have a harder time in conservative parts of the country, a paper implies. A trio of business professors studied six years of supermarket purchases in counties covering nearly half the U.S. population and found that, when it comes to groceries, conservatives like established national brands—and are significantly less likely to try new items. "These tendencies," the researchers wrote, "correspond with other psychological traits associated with a conservative ideology, such as preference for tradition and the status quo, avoidance of ambiguity and uncertainty, and skepticism about new experiences." Conservative ideology was measured in the study by Republican voting behavior and religiosity. In counties high on both measures, generic products fared worse and new products had lower penetration. "Ideology and Brand Consumption," Romana Khan, Kanishka Misra and Vishal Singh, Psychological Science (Feb. 4)

44 Radom representation of data today: CRIMINAL JUSTICE (WSJ Feb. 15, Week in Ideas: Daniel Akst) Unreliable, Sober or Not Intoxicated eyewitnesses are no less reliable than sober ones—but neither is very good at picking culprits out of a lineup. Researchers in Sweden gave screwdrivers to two groups of presumably eager volunteers with the aim of a 0.04 blood alcohol concentration in one, and 0.07 in the other—both above the 0.02 Swedish limit for driving but below the 0.08 level that is standard in the U.S. Then the participants, along with an alcohol-free control group, were shown a staged kidnapping on video. A week later the volunteers were asked to pick the kidnappers out of a lineup. All three groups of participants performed about the same—better than chance but poorly nonetheless. The poor showing was in keeping with prior studies. "Do Sober Eyewitnesses Outperform Alcohol Intoxicated Eyewitnesses in a Lineup?" Angelica Hagsand and four other authors, The European Journal of Psychology Applied to Legal Context (January) Intoxicated eyewitnesses are no less reliable than sober ones—but neither is very good at picking culprits out of a lineup.


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