Sparkdit is an innovative and collaborative platform designed to revolutionize the healthcare landscape by augmenting the latest treatment algorithms with the nuanced decision-making process inherent in human cognition. Sparkdit harnesses the power of the latest advances in artificial intelligence (AI) and machine learning algorithms to augment an expert-driven decision-making process, ensuring that patient needs, preferences, and tradeoffs are considered at every stage of care. Healthcare should be personalized, and the system can provide insights into the most suitable treatment options accommodating patient constraints, preferences, and trade-offs in collaboration with physicians using the latest treatment algorithms. This platform acts as a sophisticated decision assistant, augmenting AI with the expertise of healthcare providers and enhancing patient outcomes.

By leveraging (i) the vast amounts of patient data, including medical history, genetic profiles, lifestyle factors, and treatment responses, this platform employs the outcome of AI combined with (ii) the medical expert knowledge gathered at scale with generative AI it empowers  a stats-of-the-ast decision intelligence solution to generate personalized recommendations tailored to each patient's unique circumstances, constraints, preferences and tradeoffs. Through continuous learning and adaptation, it refines its decision-making capabilities over time, ensuring that recommendations remain relevant and aligned with evolving patient needs and preferences. Moreover, by fostering seamless communication and collaboration between patients, providers, and payers, this platform promotes a holistic approach to healthcare delivery, where decisions are made collaboratively with full consideration of clinical efficacy, patient values, and cost-effectiveness. Doing so not only empowers patients to take an active role in their healthcare journey but also enhances the efficiency and effectiveness of healthcare delivery, ultimately improving patient outcomes and satisfaction.

What sets Sparkdit apart is its unique ability to deal with tradeoffs. Sparkdit is the ONLY solution that is based on Tradeoffs. Yet, every treatment selection, and in general every healthcare decision is fundamentally based on tradeoffs. Tradeoffs are not easy to characterize or express in a way that rationalizes the intrinsic biases. The Sparkdit platform is the ONLY solution that focuses on tradeoffs to drive health  outcomes, business outcomes and patient satisfaction.

Sparkdit puts back the patient and physician in the driving seat and leverages AI outcome and the latest advances in Decision Intelligence to help them reach the optimal outcome. 

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 reduces their false positives. 

1. Drug selection assistant for the physician and patient

1. • Eli Lily Zepound vs. Novo Nordisk Ozempic/Wegovy vs. Viking

   • Moderna vs. Pfizer vs. Johnson & Johnson vs. Novavax vs. Sinopharm vs. CoronaVac vs. Sputnik V

2. Why choose one treatment over another?

3. Better understanding of patient/physician preferences

   • Indications

   • Efficacy

   • Cost

   • Method of administration

   • Dosing interval

   • Side effects

4. Research possibilities for biopharma

• Molecule selection

• More granular research insights (Physician and patient preferences in clinical trials)

1. Personalization of a treatment algorithm for medical conditions

   • Cancer care

   • Surgical shared decision-making (elective or urgent)

   • Critical care decision-making

   • End-of-life care

   • Appropriate medication selection

2. Standard treatment options create a foundation of care

3. Physician diagnostic layered in

4. Patient values, preferences and tradeoffscaptured

5. Collaborative Decision Intelligence combining all stakeholders