Healthcare

Sparkdit stands out by bringing decision intelligence to healthcare through patient-centered decision aids. Combine expert knowledge, patient feelings, and tradeoffs to generate individualized treatment plans aligned with their care goals.

• Place the patient at the center of their treatment with interactive decision aids 

• Drive evidence-based care with the latest treatment algorithms

• Save providers time by automating testing, imaging, and referrals

• Simplify EHR documentation with upstream virtual visits

• Foster cost savings through care alignment, patient compliance, and provider collaboration

• Realize patient-centered and value-based care

Better Recommendations. Better Treatments. Better Outcomes.

Finding the next great therapeutic is always a challenge. By Augmenting AI with Decision Intelligence Spakdit can optimize the selection of the right molecule, reduce risk, and help teams collaborate by deciding what’s essential to drive research and business success.

• Molecule selection aided by trade-offs to discover the keys to therapeutic success

• Gain insights into patient and provider preferences and tradeoffs in drug selection and delivery

• Reduce costs by making better decisions and reducing steps along complex drug discovery timelines

• Find better therapeutics faster

Better Deicisions. Better Investments. Better Cures.

Sparkdit creates condition-specific virtual visits that patients can access anytime and anywhere.  Sparkdit’s decision intelligence engine helps patients and providers make and act on care decisions immediately.

• Educate patients on their conditions and treatment choices

• Enable appointment booking to the appropriate specialist based on best practices and patient preferences

• Allow patients to start care immediately with virtual resources, such as physical therapy

• Streamline treatment workflows for chronic conditions, screening tests as well as complex medical conditions

• Reduce provider burden by automating best-in-care treatment algorithms

Better Recommendations. Better Treatments. Better Outcomes.

In early detection, when predicting a health conditions based on statistical analysis, such as AI-based models, the tolerance to false positives is very low. Imagine the devastating impact of informing someone that they may have a high propensity for a certain medical condition when in fact they do not. Imagine their life, their stress in the subsequent weeks and months as they go through a battery of test and medical consultations only to discover that it was a false alarm. 

The results of Neural Network contain on average between 10% and 38% of false positives. That is simply unacceptable in healthcare.

The reason for the false positives is by and large the fact that AI acts like a black box. And thus causality is a problem. There is no explanation to a given score or classification. (other than this element meets the patterns of other elements that were labeled with the positive outcome label).

Sparkdit offers an innovative solution to validate the outcome of an AI model. Sparkdit's Inference Engine can reverse engineer the process to either justify or invalidate an AI prediction. The elements that are determined to have a reasonable explanation for the outcome and removed from the false positive list. This process significantly reduces the false positive list.

Sparkdit's Inference Engine validates the output of the AI System (typically the propensity  to contract a certain health condition) by (i) capturing expert knowledge at scale using generative AI (ii) Patient data, lifestyle, trends, history, health data, behavior and patterns.

By trying to explain what combination of factors contributed to the positive outcome of the AI model, our customer are reducing their false positives by 50%.