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Using Semantic Technology to Drive Agile Analytics - SLIDES

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How do you accelerate data warehousing to meet the demands of the data-driven economy? Semantic technology provides an agile platform to bring data together, focus on data that matters and ultimately …

How do you accelerate data warehousing to meet the demands of the data-driven economy? Semantic technology provides an agile platform to bring data together, focus on data that matters and ultimately derive a target data model that can be easily extended. This webinar will present a semantically-based data federation case study and highlight the semantic components that facilitate agile data federation in the enterprise.

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  • 1. Using Semantic Technology to Drive Agile Analytics Semantics, Analytics and Data Unleashed™ May 14, 2014
  • 2. Presenters Overview (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 2 • Architecture • Data Security • Innovation David Read • Database Design • Warehousing • Semantics Scott Van Buren
  • 3. Webinar Goal • Demonstrate how semantic technology enables you to make better decisions by providing: – the right data – in the right form – at the right time (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 3
  • 4. Agenda • Introduce semantic terminology • Describe how semantic technology simplifies the data federation process • Present a case study analyzing post- discharge cost of care for heart attack patients to illustrate how semantic technology and agile analytics lead us to an unexpected and surprising result! (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 4
  • 5. Data Federation Agility: Iterative Process (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 5 Define Iteration Goal Identify Data FederateExplore Result
  • 6. SEMANTIC TECHNOLOGY A brief look at the semantic technology underpinnings that agile data federation leverages (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 6
  • 7. • Standards → RDF/RDFS/OWL/SPARQL • Definitions → Ontologies • Storage → Triple Stores • Data Access → SPARQL – Federation is assumed • Inferencing → Reasoners What Is Semantic Technology? (in this case) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 7
  • 8. Subject Predicate Object What’s Different About Semantic Technology? • Structure is (mostly) logical not physical – Triple – Directed Graph (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 8 Lisa bioMotherOf Michael friendOf Carl favoriteSport Bowling
  • 9. Lisa bioMotherOf Michael The Physical Structure is Flexible by Design • Relationships can be added or removed as they are found, explored, accepted or discredited (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 9 friendOf Carl Iced Tea favoriteDrink
  • 10. Clm… Bridging And Federating Data (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 10 Source_PvdrSource_Clm Clm_1 Pv1 nameid dos lamt ClaimHeader ClaimLine Provider NPI ClaimId ch1ch… prv1 PatNm BillAmtServDt Pv… NatId “Jones”“AB12” 2/3/14 405.00 “G403” cl1 cl… prv… Clm… Pv…Pv…Clm… Line Pvdr Prov
  • 11. Directed Graph: Domain Class & Individuals (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 11
  • 12. Drilling Into Individuals (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 12
  • 13. Federating Example (merged individuals) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 13 Source 1 Source 2
  • 14. CASE STUDY Semantically-enabled analytic agility at a healthcare plan (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 14
  • 15. Case Study Description • Healthcare Plan – Medicare Administrative Contractor (MAC) • Hypothesis – Inconsistent treatment plans for heart attack patients leads to varying costs and outcomes • Opportunity – Determine optimal post-discharge plans to improve outcome and reduce costs to the Medicare Trust Fund (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 15
  • 16. Infrastructure (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 16 Part A Claims • Headers • Lines Part B Claims • Headers • Lines General Info • Members • Providers Medical Review • Checklists • Denials
  • 17. Database Schemas (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 17 Part A Part B Providers and Beneficiaries Medical Review
  • 18. ITERATION 1 Validate the hypothesis (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 18
  • 19. I1: Business Definition and Ontology • Iteration Goal (Problem Statement) – Determine the overall cost of Part B claims directly related to patients discharged with DRG 280, 281 and 282 coded Part A claims • Identify Data (Terms and Relationships) – Interested in Part A and B claim headers • Part A: Patient Id, DRG, admit date, discharge date, paid amount • Part B: total paid across episode of care [EOC] (based on dates and prior history) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 19
  • 20. I1: Relevant System Data (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 20 Part A Claims • Headers Part B Claims • Headers Semantic Environment • Ontologies (narrow) • Triple Store (in-memory/physical) • Reasoner • SPARQL Endpoint 2000 3000 4000 1.0 1.5 2.0 2.5 3.0 DRG Grouping PartACost($) DRG 280 281 282 Part A Cost by DRG 2500 5000 7500 10000 1.0 1.5 2.0 2.5 3.0 DRG Grouping PartBCost($) DRG 280 281 282 Part B Cost by DRG Costs Categorized by DRG Analytic Tools
  • 21. I1: Federated Data Sample (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 21
  • 22. I1: EOC Aggregated Costs by DRG and LOS (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 22 280 281 282 400010000 Total EOC Cost by DRG DRG TotalEOCCost($) 2 3 4 5 6 400010000 Total EOC Cost by Inpatient LOS LOS (Days) TotalEOCCost($)
  • 23. I1: Patient EOC Costs by DRG (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 23 2500 5000 7500 10000 12500 15000 1.0 1.5 2.0 2.5 3.0 DRG Grouping TotalEOCCost($) DRG 280 281 282 Total EOC Cost by DRG
  • 24. I1: Patient EOC Costs by DRG (Part A, Part B) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 24 2000 3000 4000 1.0 1.5 2.0 2.5 3.0 DRG Grouping PartACost($) DRG 280 281 282 Part A Cost by DRG 2500 5000 7500 10000 1.0 1.5 2.0 2.5 3.0 DRG Grouping PartBCost($) DRG 280 281 282 Part B Cost by DRG Costs Categorized by DRG
  • 25. I1: Patient EOC Costs by LOS (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 25 2500 5000 7500 10000 12500 15000 2 3 4 5 6 LOS (Days) TotalEOCCost($) DRG 280 281 282 Total EOC Cost by LOS 2000 3000 4000 2 3 4 5 6 LOS (Days) PartACost($) DRG 280 281 282 Part A Cost by LOS 2500 5000 7500 10000 2 3 4 5 6 LOS (Days) PartBCost($) DRG 280 281 282 Part B Cost by LOS Costs Categorized by LOS
  • 26. ITERATION 2 Investigate the Part B stratifications (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 26
  • 27. I2: Business Definition and Ontology • Iteration Goal – Understand the Part B cost stratification. Start by looking at the types of providers and facilities within the relative order of visits • Identify Additional Data – Interested in Part B claim headers, lines and providers (facilities) • Part B: total paid for each claim, provider associated with lines, provider specialty or facility type (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 27
  • 28. I2: Relevant System Data (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 28 Part A Claims • Headers Part B Claims • Headers • Lines Semantic Environment • Ontologies (broadened) • Triple Store (in-memory/physical) • Reasoner • SPARQL Endpoint General Info • Providers pcp car inp car phm nut phm cpt gpt nut pcp cpt car hom pcp edu gpt hom edu cpt pcp cpt car hom pcp cpt cpt gpt car pcp car Flow by Patient Count pcp car inp car phm nut phm cpt gpt nut pcp cpt car hom pcp edu gpt hom edu cpt pcp cpt car hom pcp cpt cpt gpt car pcp car Flow by Mean $ per Patient Analytic Tools
  • 29. I2: Federated Data Sample (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 29
  • 30. pcp car inp phm car nut phm gpt cpt nut pcp cpt hom car pcp edu gpt hom edu cpt pcp cpt hom car pcp cpt gpt cpt car pcp car Flow by Patient Count (DRG 282) pcp car inp phm car nut phm gpt cpt nut pcp cpt hom car pcp edu gpt hom edu cpt pcp cpt hom car pcp cpt gpt cpt car pcp car Flow by Patient Count (DRG 281) pcp car inp phm car nut phm gpt cpt nut pcp cpt hom car pcp edu gpt hom edu cpt pcp cpt hom car pcp cpt gpt cpt car pcp car Flow by Patient Count (DRG 280) I2: Part B Facility Flow (by DRG) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 30 1 1 2 3 2 3
  • 31. pcp car inp phm car nut phm gpt cpt nut pcp cpt hom car pcp edu gpt hom edu cpt pcp cpt hom car pcp cpt gpt cpt car pcp car Flow by Patient Count pcp car inp phm car nut phm gpt cpt nut pcp cpt hom car pcp edu gpt hom edu cpt pcp cpt hom car pcp cpt gpt cpt car pcp car Flow by Mean Aggregated $ per Patient I2: Part B Facility Flow (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 31
  • 32. I2: Average EOC Costs By DRG and Flow (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 32 280-PCP 280-CDO 280-REH 281-PCP 281-CDO 281-REH 282-PCP 282-CDO 282-REH Average Claim Costs DRG and Flow Dollars 0 2000 4000 6000 8000 10000 12000 Claim Type Part B Part A
  • 33. 280 281 282 4000800012000 Total EOC Cost by DRG with Rehab Hosp DRG TotalEOCCost($) 280 281 282 3000500070009000 Total EOC Cost by DRG without Rehab Hosp DRG TotalEOCCost($) I2:EOC Agg by DRG (Rehab Hosp Difference) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 33
  • 34. I2: Patient EOC by DRG (w/o Rehab Hosp) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 34 4000 6000 8000 1.0 1.5 2.0 2.5 3.0 DRG Grouping TotalEOCCost($) DRG 280 281 282 Total EOC Cost by DRG
  • 35. I2: Patient EOC by DRG (w/o Rehab Hosp) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 35 2000 3000 4000 1.0 1.5 2.0 2.5 3.0 DRG Grouping PartACost($) DRG 280 281 282 Part A Cost by DRG 2000 3000 4000 5000 1.0 1.5 2.0 2.5 3.0 DRG Grouping PartBCost($) DRG 280 281 282 Part B Cost by DRG Costs Categorized by DRG
  • 36. pcp car phm nut gpt cpt pcp car cpt hom edu car pcp hom edu gpt cpt hom edu cpt pcp car cpt hom cpt car pcp car cpt gpt cpt pcp car Flow by Patient Count (DRG 282) pcp car phm nut gpt cpt pcp car cpt hom edu car pcp hom edu gpt cpt hom edu cpt pcp car cpt hom cpt car pcp car cpt gpt cpt pcp car Flow by Patient Count (DRG 281) pcp car phm nut gpt cpt pcp car cpt hom edu car pcp hom edu gpt cpt hom edu cpt pcp car cpt hom cpt car pcp car cpt gpt cpt pcp car Flow by Patient Count (DRG 280) I2: Part B Facility Flow (w/o Rehab Hosp) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 36
  • 37. pcp car phm nut gpt cpt pcp car cpt hom edu car pcp hom edu gpt cpt hom edu cpt pcp car cpt hom cpt car pcp car cpt gpt cpt pcp car Flow by Patient Count pcp car phm nut gpt cpt pcp car cpt hom edu car pcp hom edu gpt cpt hom edu cpt pcp car cpt hom cpt car pcp car cpt gpt cpt pcp car Flow by Mean Aggregated $ per Patient I2: Part B Facility Flow (w/o Rehab Hosp) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 37
  • 38. 280-PCP 280-CDO 281-PCP 281-CDO 282-PCP 282-CDO Average Claim Costs DRG and Flow Dollars 0 2000 4000 6000 8000 Claim Type Part B Part A I2: Average EOC Costs By DRG and Flow (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 38
  • 39. ITERATION 3 Refine the data set (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 39
  • 40. I3: Business Definition and Ontology • Iteration Goal – Remove claims that would be denied based on claim review history • Define Additional Data – Interested in claim review denials and relationship to claims in the study’s data set • Medical Review: adjudication status, claim information such as provider specialty and facility type (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 40
  • 41. I3: Relevant System Data (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 41 Part A Claims • Headers Part B Claims • Headers • Lines Semantic Environment • Ontologies (broadened) • Triple Store (in-memory/physical) • Reasoner • SPARQL Endpoint General Info • Providers Medical Review • Adjudication • Claim Details Analytic Tools 280-PCP 280-CDO 281-PCP 281-CDO 282-PCP 282-CDO Average Claim Costs DRG and Flow Dollars 0 1000 2000 3000 4000 5000 6000 Claim Type Part B Part A
  • 42. I3:EOC Agg by DRG (Denied Claims Diff) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 42 280 281 282 3000500070009000 Total EOC Cost by DRG with Denied Claims DRG TotalEOCCost($) 280 281 282 300050007000 Total EOC Cost by DRG without Denied Claims DRG TotalEOCCost($)
  • 43. I3: Patient EOC by DRG (w/o Denied Claims) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 43 3000 4000 5000 6000 7000 1.0 1.5 2.0 2.5 3.0 DRG Grouping TotalEOCCost($) DRG 280 281 282 Total EOC Cost by DRG
  • 44. 2000 3000 4000 1.0 1.5 2.0 2.5 3.0 DRG Grouping PartACost($) DRG 280 281 282 Part A Cost by DRG 1500 2000 2500 3000 3500 1.0 1.5 2.0 2.5 3.0 DRG Grouping PartBCost($) DRG 280 281 282 Part B Cost by DRG Costs Categorized by DRG I3: Patient EOC by DRG (w/o Denied Claims) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 44
  • 45. pcp car phm nut gpt cpt hom cpt edu pcp edu cpt gpt cpt edu car pcp hom pcp gpt pcp Flow by Patient Count pcp car phm nut gpt cpt hom cpt edu pcp edu cpt gpt cpt edu car pcp hom pcp gpt pcp Flow by Mean Aggregated $ per Patient I3: Part B Facility Flow (w/o Denied Claims) (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 45
  • 46. I3: Average EOC Costs By DRG and Flow (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 46 280-PCP 280-CDO 281-PCP 281-CDO 282-PCP 282-CDO Average Claim Costs DRG and Flow Dollars 0 1000 2000 3000 4000 5000 6000 Claim Type Part B Part A
  • 47. Conclusions • Data federation agility accelerates data analysis & reporting – Experts follow unexpected paths as they work with data – Predicting up-front what data will be useful • Error prone (too little  missed) • Heavyweight (too much  wasted effort) • Targeting federation at specific questions reduces the scope of data integration • Multiple iterations inform a broader data warehousing need (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 47
  • 48. Thank You (c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 48 We appreciate your spending time with us. If there are questions we do not cover during the Q&A time in the webinar, feel free to contact us at your convenience: David.Read@blueslate.net Scott.VanBuren@blueslate.net www.blueslate.net www.dataunleashed.com