Big Data and Patient Journey Analytics for Precision Medicine & Value-Based Contracting

Big Data and Cognitive Computing for Precision Medicine & Value-Based Contracting

Precision Medicine is transforming health care by shifting from “one-size-fits-all” to considering the variability of genes, environment factors and lifestyle preferences of patients in designing highly individual treatment and prevention plans.

AccelarPath is designed as an adaptable, flexible and scalable Cognitive Computing Platform leveraging Big Data, Predictive Analytics and AI to power the transformation of Oncology care delivery models through personalized medicine – with the right treatment, at the right time and in the right dose and set of services.

AccelarPath enhances the traditional provider performance analysis by leveraging vast amount of health data and applying advanced AI/machine learning models and real-time analytics:

Identify Complex Narrow Patient Cohorts using Advanced Analytics and AI to quantify relationships between outcomes and very specific patient characteristics:

  • Genomic Information
  • Clinical Information including diagnoses, medical history, treatment plans (surgeries, chemo-, radiation and drug therapies) etc.
  • Lab Results such as LDH levels, calcium serum, hemoglobin levels, platelet counts, white cell counts, examination of lymph nodes, erythrocyte sedimentation rates,
  • Lifestyle data such as BMI, history of smoking etc.
  • Various scores (Karnofsky score, ECOG score, Gleason grading), AJCC staging information etc.
  • Survival rates

The Machine Learning algorithms can identify patient cohorts with similar characteristics and the specific combination of medications and treatments which are linked to better outcomes and higher survival rates.

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Define Value-Based goals for Quality, Cost and Utilization and monitor performance tailored to the specifics of the identified narrow patient cohorts

  • Aggregate clinical, quality and financial data into one frame
  • Identify feasible cost and quality thresholds using the AccelarPath “Best Practices” Dashboards and Reports and actionable insights into cost, quality and utilization across the continuum of care for the patient cohorts and across the enterprise business lines.
  • Tracking of key financial, quality and utilization metrics against the defined targets – on a monthly, quarterly and yearly basis
  • Actionable insights into cost, quality and utilization, unwarranted variations of services compared to selected benchmarks with a high level of granularity across the enterprise service lines, geographic areas, facilities, provider groups and individual providers
  • Identify Virtual Provider Teams and Individual Providers that deliver most effective and efficient care for the identified patient cohorts

Identify Opportunity for Improvement:

  • Tailor treatment plans to the specific needs of patients based on the identified best practices
  • Identify most effective treatments and medication doses and eliminate wasteful use of services and resources not related to improved outcomes
  • Reduce complications and adverse reactions to drugs
  • Referral of patients to providers with “best practices” outcomes, cost and utilization pattern for specific conditions/treatments
  • Negotiate mutually beneficial value based contracts between payers and providers that reflect the specific complex patient cohorts

Related – value pathways in patient journeys:

    1. The Challenge: examples of staggering wasteful spending and lost revenue opportunities (0:00)
    2. The Solution: Identify Value Pathways (01:42)
    3. Why MSIDE Solutions (02:40)

Want to learn more about how to utilize Cognitive Computing, Big Data and Predictive Analytics to deliver Personalized Treatments in Value Based Care Models?

Give us a call at 
(610) 783-3601

Or simply:

Review our resources to see showcases:

Patient Journey Insights in Cancer

Patient Journey Insights in Autoimmune Diseases

Learn how you can augment your Big Data and AI ecosystem with insights from pre-built patient journey data models, national claims data and medical literature:

Data as an Infrastructure – cloud or on prem