Patient Flight Path - Diabetes


Product Overview

Patient Flight Path uses disease-specific metrics, costs, analytics, simulation, and predicted outcomes… to engage both the patient and clinician in more efficient diabetes care. Predicting an optimized patient “flight path” allows clinicians and patients a glimpse ahead into near-term risk and how to avoid complications and associated high costs. Predicts near-term patient risk, complications and cost. In-Development

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  • Diabetes disease specific metrics, labs, demographics, complications and associated costs.
  • Population view for clinicians to summarize all patients under their care.
  • Patient View with projected costs and next likely complications to avoid.
  • Recommended treatments in various categories (lifestyle, medications, checkups, etc.)Ability to view population risk and patient risk over time


Get a broad overview of risk models

  • Explore average risk scores for specific conditions
  • View population risk over time
  • Become familiar with various risk models

Dive deep into population and patient risk

  • View patient risk variables over time
  • View patient coverage within each risk model



  • Patient ID, Patient Name, Birthdate, Age, Gender, Ethnicity, etc.
  • Diabetes Type (1 or 2), HemA1c, LDL, BMI, Alcohol and Tobacco use, Family history, etc.
  • Primary Care Provider, Most frequent clinic location, avg total charges


  • Number of encounters by patient type, clinical location and primary care provider
  • History of Charlson co-morbidity risk and diabetic complications

Comparison by

  • Provider Name, Clinic Location
  • Avg # of complications
  • Avg charges per year, forecasted out-of-pocket costs

See Sample Screenshots of Patient Flight Path - Diabetes

Data Sources

  • EMR and Financials
  • Diabetes SAM
  • General Flight Path SAM
  • Risk SAM

Patient Flight Path: A Deeper View


We are leveraging the ‘flight path’ concept from military and airlines industry, to data mine a historical cohort of disease specific treatments, outcomes and costs. New patients can then compared to this historical cohort to help optimize their recommended treatments and avoid or delay disease complications. This approach also leads to the most efficient path of care at the most appropriate cost.

What types of problems does ACO Explorer address?

Most healthcare organizations wonder how to leverage the capabilities of our analytics platform to better engage and activate patients. Can existing data be used interactively to help modify patient behavior? The goal of our ‘flight path’ approach is to motivate patients and improve outcomes by avoiding next most likely complications and decreasing cost.

Use Cases

  • A primary care provider wants to better understand what percent of patients currently being seen are diabetic and what treatments may be most appropriate to recommend.
  • As a patient in the diabetes care clinic, I’d like to know how I compare to others close to my same age and gender and historically, what treatments will have the most impact to keep my blood glucose in control.
  • A hospital finance group is looking to gauge patient volume and forecast total costs for diabetes patients for the coming year in three new clinic locations.

Anticipated Improvements

The Health Catalyst patient flight path models:

  • Historical cohort trends
  • Forecasted patient outcomes (costs and complications)
  • Treatment recommendations optimized/ranked for greatest impact

Success Measures

There are 3 types of success measures:

  • Gain patient understanding of the life choices and things within their control that can impact their potential clinical outcomes
  • Show measurable improvement in patient engagement and clinical outcomes
  • Inform future application development