Presentation at 2010 California Institute of Mental Health Conference with Nathaniel Israel of San Francisco Department of Public Health

1. Combining Data Warehousing and Predictive Modeling to Open New Possibilities for Guiding Clinical And Management Decision Making Nathaniel Israel San Francisco Department of Public Healthnathaniel.israel@sfdph.org Stephen GuerinRedfish Group / Santa Fe Complexstephen@sfcomplex.org San FranciscoDepartment of Public Health

3. Describe a particular implementation of a decision support framework in SF County

4. Describe a particular visualization strategy, Agent Based Modeling (ABM)

6. Billing Data

7. Clinical Data CANS Data: System Snapshot of Needs and Functioning Agency -Level Trajectories re: sub-population needs and outcomes Client –Level Outcomes

8. System-Level Profile

9. System-Level Profile

10. Agency Trajectories

11. Client –Level Outcomes

12. Theory: Complex Systems Research Agent-Based Modeling Data Visualization Applied Examples: Emergency Response Planning Pharmaceutical R&D Portfolio Florida Case Study Ongoing San Francisco Project

13. Theory: Complex Systems Research Agent-Based Modeling Data Visualization Applied Examples Emergency Response Planning Pharmaceutical R&D Portfolio Florida Case Study Ongoing San Francisco Project

14. Complex Adaptive Systems How Systems Organize Why Systems Organize

15. Josh Thorp, stigmergic.net Flocking and Schooling

16. A simple agent’s behavior Attract Repel Josh Thorp, stigmergic.net

17. Ants and Pheromones

19. Theory: Complex Systems Research Agent-Based Modeling Data Visualization Applied Examples: Emergency Response Planning Pharmaceutical R&D Portfolio Florida Case Study Ongoing San Francisco Project

20. Zozobra Crowd Dynamics

24. Roberto Clemente Bridge Open to pedestrian traffic only Fans use bridge to downtown and to closest “T” stations

28. Managing Case Flows Eli Lilly’s R&D Portfolio Planning

29. Eli Lilly R&D Portfolio Scheduling $ revenue cost time

30. Eli Lilly R&D Workflow Simulation and Portfolio Scheduling

31. Theory: Complex Systems Research Agent-Based Modeling Data Visualization Applied Examples: Emergency Response Planning Pharmaceutical R&D Portfolio Florida Case Study Ongoing San Francisco Project

32. Input is simple spreadsheet

34. Client Trajectories Number of Days in a Placement

35. Client Trajectories Number of Days in a Placement

36. Client Trajectories Distance Metric

37. Dendrogram

40. Capturing Direction of Flow

41. Capturing Direction of Flow Number of Days in a Placement

42. Capturing Direction of Flow Number of Transitions between Placements

43. Agent-based Clustering (Frey and Dueck, 2007) Science Magazine

44. Agent-based Clustering

45. Theory: Complex Systems Research Agent-Based Modeling Data Visualization Applied Examples: Emergency Response Planning Pharmaceutical R&D Portfolio Florida Case Study Ongoing San Francisco Project

46. Visualization Project Scope clients’ movement between levels of care over time. agencies client movement over time. service use by demographic and clinical characteristics, including gender, age, ethnicity, and diagnosis. Nate’s “What” client flows from Billing DataWill be integrating the “Why” from Clinical Data

49. Not all RUs are displayed

51. Not all RUs are displayed

62. Opensource or Free Toolsfor visualization and agent-based modeling Netlogohttp://ccl.northwestern.edu/netlogo/ Processinghttp://www.processing.org Nodeboxhttp://www.nodebox.net Open Frameworkshttp://www.openframeworks.cc/ HTML5 with Javascript

63. Combining Data Warehousing and Predictive Modeling to Open New Possibilities for Guiding Clinical And Management Decision Making Nathaniel Israel San Francisco Department of Public Healthnathaniel.israel@sfdph.org Stephen GuerinRedfish Group / Santa Fe Complexstephen@sfcomplex.org San FranciscoDepartment of Public Health

64. Eli Lilly R&D Portfolio Scheduling $ revenue cost time

65. Eli Lilly R&D Workflow Simulation and Portfolio Scheduling

66. Project Scope Visualize client flow through the public mental health service system based on Medicaid billing data: 1) Visualize clients’ movement (service use) between levels of care over time. 2) Visualize each agency’s clients’ movement (service use) over time. 3) Visualize clients’ service use by demographic and clinical characteristics, including gender, age, ethnicity, and diagnosis. This visualization will allow system stakeholders to understand a) how clients are moving through the system (using services at different levels of care)b) whether / how this flow varies by provider within and across levels of care and c) whether / how this flow varies by client demographic characteristics. The delivered program will also allow for the upload of new data to inform the visualizations, and for the comparison of clients’ service utilization across two distinct periods of time. A procedure document will be included with the program and this document will provide step-by-step instructions that enable users to upload new data to the program. The program will be available to users wherever it is installed and service data is uploaded.

67. Project Flow

68. Project Flow

71. Project Timeline

72. Project Timeline

74. Project Flow

75. Project Flow

76. Domain Expertise

77. Source: Juice Analytics

78. Data Issues Client Primary RUs with no mapping in provider table1380 38GS01 38AOHA 38APON 38GSOP 38APPB 3822O2 38BG2 8949RP 38ALSD” 88127 3822T5 RUs in services with no mapping in provider table:38CW2 38A6238A6338A9238A9338AMLE38APQD38CD238CD338CK238CK338CT238CT338CU238CU338CV238CV338CW338DD238DD3896538965DT897538975DT898138982289823

79. Project Flow