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The Trifecta: Empowering People through the Power of AI

  • Augment Intelligence

    • Bringing data, algorithms, and computation to every decision
    • Using AI to make BI contextual, personalized, and real-time
    • Finding new signals in new data
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    Together, AI and IA power innovations in healthcare:

    • Healthcare service providers will collaboratively and proactively manage population health, monitor disease status, provide at home acute care treatment, and maintain continuous communications with patients and caregivers.
    • Patients and clinicians will work together to achieve improved health outcomes through coordination of care, health literacy, sharing of pertinent data and records, and continuous tracking of patient health indicators.
    • Life sciences companies will enhance decision-making to improve product safety. FDA records of adverse drug reactions can be mined to identify pairs of medications that seem to cause problems when taken together.
  • Automate and Learn

    It’s time ‘Clinical Decision Support’ was replaced by ‘Intelligent Clinical Decision Automation’ that combines AI with workflow automation to transform healthcare delivery.

    That means:

    • Building learning systems
    • Leveraging unsupervised learning
    • "Never sending a human to do a machine's job"
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    80% of doctor’s current activities will be replaced in the future by intelligent systems:

    • Diagnostic tests and procedures will be automatically ordered, medications will be prescribed, digital therapeutics will be recommended, and prior authorizations will be instantly approved.
  • Incorporate Human Behavior

    At the heart of all of this is people: patients, employees, and physicians. It’s about using AI to improve customer engagement.

    That means:

    • Reducing information overload
    • Accounting for human biases
    • "Nudging" the right behaviors
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    Healthcare patients will receive better healthcare management when behavioral economics are applied to understand their needs.

    Healthcare payers will understand human bias by integrating data science with behavioral science for high impact interventions to induce desired behaviors, such as adhering to medication regimens, enrolling on care management programs or avoiding unnecessary ER visits.

  • Together, AI and IA power innovations in healthcare:

    • Healthcare service providers will collaboratively and proactively manage population health, monitor disease status, provide at home acute care treatment, and maintain continuous communications with patients and caregivers.
    • Patients and clinicians will work together to achieve improved health outcomes through coordination of care, health literacy, sharing of pertinent data and records, and continuous tracking of patient health indicators.
    • Life sciences companies will enhance decision-making to improve product safety. FDA records of adverse drug reactions can be mined to identify pairs of medications that seem to cause problems when taken together.
  • 80% of doctor’s current activities will be replaced in the future by intelligent systems:

    • Diagnostic tests and procedures will be automatically ordered, medications will be prescribed, digital therapeutics will be recommended, and prior authorizations will be instantly approved.
  • Healthcare patients will receive better healthcare management when behavioral economics are applied to understand their needs.

    Healthcare payers will understand human bias by integrating data science with behavioral science for high impact interventions to induce desired behaviors, such as adhering to medication regimens, enrolling on care management programs or avoiding unnecessary ER visits.

Featured Solutions

External data rating modifier for a payer

With an innovative analytical framework applied on external data, payers can enhance the performance of existing rating modifiers to inform additional risk. Health insurers can leverage new signals from consumer data to predict claims experience to deliver an improved risk assessment process. These new signals include factors indicating buying behavior patterns, socio economic and financial statuses and health interests.

The enhanced rating modifier can help payers create an R-squared lift of up to 15%.

This lift is over and above claims experience typically informed by internal risk factors that payers already assess—such as age, gender, and location.

A few external indicators of additional risk informed by claims experience can include data on economic stability, usage of mobile phones, prime time television usage, smoking, community engagements, derogatory records, and dwelling status.

Claims anomaly detection for a payer

It’s time for payers to experience a better way to identify anomalous claims. The common process to identify anomalies is business-rules-driven, manual intensive, applied in a post-pay scenario and focuses mainly on known patterns, thus solving the problem partially.

Applying advanced analytics and looking for opportunities beyond overpaid dollars, such as better utilization management, plan design changes and network optimization, helps detect hundreds of unknown anomalies.

Payers can use the new analytical framework to drive up to $50M additional impact within the first year of operationalization — by increasingly focusing on unknown anomalies.

Get better results by:

  • Applying predictive analytics and AI to better prioritize claims for SIU review
  • Automating the entire anomaly identification process
  • Creating a visual solution suite to help identify anomalies, track alerts, and measure the impact of interventions
  • Delivering significant impact in both post-pay and pre-pay scenarios

 

The significant ROI in initial year/s enables payers to self-fund a suite of more advanced AI-driven scalable solutions to keep identifying and tracking anomalies, and improving recoveries from the flagged claims.

Case Studies

  • 3 min. read

    Reduce high costs of care associated with avoidable ER visits

    A payer identifies opportunities to redirect avoidable ER visits, saving $10MM in cost reductions

    The Big Picture The high cost of maintenance and limited availability of Emergency Rooms (ER) facilities are under intense scrutiny by payers, the government, providers and employers. According to the Centers for...

  • 3 min. read

    Improve collectability of the self-pay portion of medical expenses

    Analytics helps a leading provider network identify patient segments to collect $20MM from unpaid invoices

    The Big Picture Escalating healthcare costs have forced employees with employer-provided insurance to bear a higher portion of self-payment costs such as co-pay, coinsurance, deductible and out-of-pocket expenses....

  • 3 min. read

    Develop prediction framework to address high attrition

    A pharmacy distributor identifies $45MM opportunity to boost customer retention by predicting attrition

    The Big Picture Wholesale drug distributors have experienced strong competition and consolidation leaving only a few surviving entities to service most of the US market. A leading distributor of drugs to long-term care...

  • 2 min. read

    Improve medication adherence to lower health costs and improve patient outcomes

    Better problem solving and machine-learning helps healthcare payer save $5MM from reducing medication non-adherence

    The Big Picture Non-adherence of medication is one of the most critical problems when treating patients with chronic conditions. Patients who do not follow the prescribed drug regimen are more likely to suffer poor...

  • 2 min. read

    Leverage external data to improve pricing and underwriting decisions

    A payer better predicts claims experience with external data, supplemental to internal claims data.

    The Big Picture A leading health insurer believed it could better predict claims experiences using external data, as supplement to internal claims data. The organization wanted to leverage the predicted claims...

  • 3 min. read

    Improve claims anomaly identification and tracking

    A payer identifies $5M+ in potential savings in first year of powering claims anomaly identification with analytics

    The Big Picture A top 5 US payer wanted to improve its ability to identify and track claims anomalies. Its existing process was business rules-driven, significantly manual, applied only in a post pay scenario and lacked...

Our People

  • Jessica Macknight

    Client Partner

    • SME on clinical analytics, customer experience, sales & marketing functions & day-to-day operational activities
    • Adept at helping clients capitalize & monetize their data assets
    • SME on clinical analytics, customer experience, sales & marketing functions & day-to-day operational activities
    • Adept at helping clients capitalize & monetize their data assets
  • Pooja Rao

    Principal Data Scientist, Qure.ai

    • Physician-scientist
    • Machine learning expertise across healthcare and life sciences
    • Bioinformatics and genomics
    • Physician-scientist
    • Machine learning expertise across healthcare and life sciences
    • Bioinformatics and genomics