Please use the following outline to guide your submission for the academic competition. The submission should include a no more than 25 slide PowerPoint presentation containing your investigative plan, coupled with the algorithms you used and an optional bibliography of references used to support your work.

Submission Outline

• Team contact information including team name, university name, captain name and email.
• List of team members, and the academic advisor(s).
• Title of the investigative recommendation plan.
• 25 slide investigative recommendation plan to include information on each of the following, if applicable.
  • Share your hypotheses: What fraud schemes, issues of concern, or provider types were prioritized in your analytics and why?
    • Describe where you successfully found outliers and schemes
    • Evaluate the scope of all the schemes found by their frequency and impacts.
    • For priority schemes identified, provide scheme complexity, opportunity for collaboration, patient harm, financial exposure
  • Share your decision-making process: What influenced your decision to include certain features in your recommendations? Examples could be: amount of financial exposure, background research, potential for patient harm, known FWA issues, media reports, or a combination of factors.
    • How did you prioritize your recommendations and why?
    • What influenced your decision to select the #1 priority?
    • What is your allegation of fraud, and why does the data support this allegation?
    • Describe for investigators using this report what makes the data actionable (what does the data prove, what laws may have been broken, what additional investigation needs to occur to confirm your hypothesis)
  • Share your analytics process: Describe your process for analytic tool(s) choice and the strengths of your algorithms and analyses.
    • Proposed design including overall scope, scale, concept of operations, and description of applicability across programs and eligibility criteria.
    • Describe the specific techniques used such as utilization-based metrics, geospatial analysis, link analysis, time-based pattern analysis, or community-based analysis
    • Include information regarding the strengths of your algorithms and analyses and if there are any factors that should be further explored. (Examples: efficiency of targeting and degree of complexity)
    • Describe the analytics tools you used, and why they were selected.
    • Demonstrate the creativity of your approach