AI POLICY

Health & Bioscience

Quicker diagnosis, better treatment plans and new approaches to healthcare

Gain an end-to-end view of laboratory work, improve diagnoses and forecast the spread of disease

OptimalAI supports the healthcare and life sciences value chain, from drug discovery and development, through to manufacturing, marketing, and sales. We enable quicker diagnosis, better treatment plans and enable new approaches to healthcare. Our team currently powers foundational and translational AI research for the UK's National Health Service and its AI technology suppliers.

Drug Discovery

OptimalAI assists in identifying and developing drug molecules and macromolecules that help cure illnesses and disease. Our AI systems make computer-assisted drug discovery (CADD) tools more effective by helping to predict molecular properties with high accuracy. This affect the development process in several ways, such as modeling how proteins fold and how drug candidates interact with biologically relevant proteins. Generation and validation of hypotheses can be improved by using machine-learning algorithms to uncover new structure-property relationships. Algorithms can assist in creating new types of drug-candidate libraries that are not restricted to small molecules but include peptides and antibodies. They also enable a more automated approach to drug discovery, in which a large structural library of biologically relevant targets are automatically screened against drug-like molecules via high-throughput approaches.

Real World Evidence

We help real world evidence (RWE) organizations accelerate research with pipelines to transform data sources into the OMOP Common Data Model, governance framework for data access and study approval, rapid co-horting tools for non-technical users, and sharing and management of code lists and other critical phenotype and outcome definitions.

Clinical Trial Site Selection

The key processes involved in starting a clinical trial - protocol design, site selection, and patient recruitment - require coordination between a wide array of stakeholders both within a pharmaceutical company and across its partners. OptimalAI enables data sources to be optimized, centralized and analyzed. Proprietary data on eligible HCPs, 3rd party licensed data on sites, historic trial data, and licensed patient population data.

Optimized Operations, Automated Diagnostics

OptimalAI helps improve hospital operations, staffing schedules and diagnostics. Our AI systems can sift through millions of pages of medical evidence to provide a human-in the-loop (HITL) diagnosis and treatment options in seconds. For example, glioblastomas have distinct genetic abnormalities and doctors need to treat each one based on those abnormalities. Image recognition and machine learning systems can aid radiologists in quickly and reliably identifying those abnormalities in MRI and X-ray images.  Similarly, AI-powered automation increases healthcare productivity by relieving doctors and nurses of routine activities. Chatbots equipped with deep learning algorithms can relieve emergency room personnel of tending to walk-in patients with non-emergencies like sore throats and urinary tract infections. Full AI adoption across hospital operations raises productivity of registered nurses while significantly reducing patient waiting time.

Supply Chains

Live transparency ensures life science organizations move in sync from supply chain to customer sales. For marketed therapeutics, OptimalAI can work to automatically surface supply chain disruptions to operational managers, including full visibility into the option space (alternative suppliers, routes, warehouses etc.) and possibility to simulate the consequences of alternatives.

Population Health Forecasting

OptimalAI helps accelerate the shift towards preventive medicine by predicting disease, identifying high-risk patient groups and assisting in prevention therapies. For instance, by tracking the incidence of communicable diseases, combined with personal medical records, weather data, and other information, hospital administrators can forecast spikes in admissions and accurately predict demand for inventory levels or staff resources. Using machine learning tools for population health forecasting, preventive care and the reduction of non-elective hospital admissions creates significant costs savings.

Tailored Treatments

Given the complexity of each person’s medical history and genetic makeup, standardized treatments do not work for every patient. Using AI to scan the health outcomes of millions of patients with similar symptoms, prognoses, and ages along with individual DNA and remote diagnostic devices, patients can receive personalized regimens. Adapting therapies and drug formulations to individual patients can reduce patient expenditures by 5 to 9 percent and add 0.2 to 1.3 years to average life expectancy.

Virtual Assistants

To increase hospital productivity, virtual assistants equipped with speech recognition, image recognition and machine learning are able to conduct human in the loop (HITL) consultations, diagnoses and drug prescriptions. When systems lack enough information to reach a conclusion, the virtual agents can schedule tests. OptimalAI virtual assistants can also schedule appointments and predict if the patient is likely to miss an appointment. In these instances the virtual assistant will automatically schedules a patient who is most likely to show-up.