Healthcare
Transform Health Outcomes, Business Outcomes, Patient Satisfaction
OUTCOME DRIVEN TECHNOLOGY
VALUE-BASED CARE
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.
FALSE POSITIVES PREDICTION
When predicting a health conditions based on statistical analysis, such as AI-based models, the tolerance to false positives is very low. Imagine the devatating 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 often contain false positives. A prediction rate of 50% in eCommerce may be considered a great achievement. That same prediction rate is unacceptable in healthcare.
The reason for eh falso positives is by enlarge 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 validate and justify 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.
BIOPHARMA
Drug selection assistant for the physician and patient
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
Why choose one treatment over another?
Better understanding of patient/physician preferences
Indications
Efficacy
Cost
Method of administration
Dosing interval
Side effects
Research possibilities for biopharma
Molecule selection
More granular research insights (Physician and patient preferences in clinical trials)
CLINICAL DECISION AUGMENTATION
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
Standard treatment options create a foundation of care
Physician diagnostic layered in
Patient values, preferences and tradeoffscaptured
Collaborative Decision Intelligence combining all stakeholders
MED. DECISION CO-PILOT
Integrated into the EHR
On-demand second opinion
Populate essential factors (demographics, co-morbidities, current medication regimen) from the EHR
Personalized care as patient-specific factors are drawn from the medical record
Standard treatment options create a foundation of care
Physician input
Patient values/preferences
Precision and personalized decision intelligence in care plan
Incorporate ambient AI
HOSPITAL PURCHASING
Hospitals are focused on acquiring best value, not cheapest. And best value is reached by trading off several
Hospital contracting for supplies can be augmented to combine cost-effectiveness and provider preferences
Implant Evaluation
Efficacy
Cost
Physician preference
Quality
Past Performance
Volume
OTHER USE CASES
> Molecular Target Early Discovery
> Physician Selection by Patient
> Pharma Sales Operation Optimization
> Insurance Plan Recommendation