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pharmaceutical and healthcare
Be more human, by using machines.
From medical records, hospital records and physical examinations, to the stream of data generated by devices: the healthcare sector collects a sizable quantity of data.
It’s clear that pharmaceutical and medical industries are perfect territories for the automation, categorisation and predictive might of machine powered tools.
Our highly specialised partners have dedicated their businesses to solving these complicated challenges.
Custom software solutions work in with your existing technical environments, without requiring your team to change how they work.
Every lab, every research project, every problem is different. Generic, off-the-shelf solutions weren't made for your specialist requirements. Specialist consultancies, on the other hand, are built to solve your challenges specifically.
Consider how much of your day to day workload involves menial tasks that could otherwise be automated. Machine automations mean you can do more of the human suff.
Really smart researchers spend days and days doing very repetitive work. This grunt work is a necessary part of the process. Machine learning solutions perform this work almost instantly – something that used to take two weeks can take five minutes.
Why Machine Learning?
Applications of Artificial Intelligence, Applied to Healthcare
How AI and machine learning can help across the healthcare landscape
What’s your challenge?
Exceptional challenges in machine learning enhanced medical research
Medical practitioners have been understandably slow to accept machine learning as a technology in a discipline with such high stakes.
This is despite the clear speed and precision gains that have surfaced in machine learning research. Machine learning must take a unique approach to healthcare because the burden of accuracy is far greater. This is in comparison to, for example, marketing or business intelligence.
The past few years of adoption have therefore not been one of capability acceptance, but rather a demonstration of responsible caution.
How are decisions made?
What steps have there been to remove bias?
What points of safety checks are built into deployed machine learning processes?
Get in touch to find out how state of the art approaches address these questions.
“So, we’re using drones and neural network tech to take images of crops, using AI to make accurate estimations on the quality of produce, disease recognition, helping the farmer make price estimations.”
“In the kitchen, we have the knife. You can perform various operations with it, but you still need to know what operations to perform. You need to learn to read the data. What type of tools to use, what type of insights you can get.”
From the blog
Drug discovery and clinical trials
Machine learning can be applied at every stage of drug discovery, including target validation, prognostic biomarker isolation and in the clinical trial analysis of digital pathology data.
Speed up Drug Discovery, Save Lives
Development speed matters.
In the case of drug development, reducing manual workflows and speeding up analysis literally translates to saving lives.
Get to publication stages faster with our partners, who have direct experience building software and writing code documentation to FDA-approval standard.
Research has shown machine learning to be particularly effective in the outlined areas across these four key steps in discovery.
1) Target identification and validation
Identify and prioritise targets based on gene/disease correlations; predict druggability; identify alternative targets.
2) Compound screening and lead discovery
Synthesise, screen and design compounds for desirable properties based on gene expression, proteomic and transcriptomic data.
3) Preclinical development
Biomarker identification and prediction of biomarkers of clinical end points.
4) Clinical development
Cellular phenotyping in oncology to determine drug response; precise measurements of tumour microenvironment.
Select Better Participants
Predict clinical trial outcomes to predict futile efforts before wasting time and investment
The expectation of success is the key variable in valuing drugs in a drug development pipeline.
Machine learning algorithms - often a combination of multiple algorithms - learn from past cases and ingest all data related to the trial and participants in order to forecast the success rate of a given trial.
These predictions have been shown to improve on the more heuristic methods currently employed.
Better detection, better decision
Successful treatment is only possible if it's targeting the right condition and it's caught early. Machine learning approaches recognise patterns faster and more accurately than humans can, so you can better decide how to treat.
Cut through noise and reveal underlying patterns across multiple data sources
Proteomics, biomarkers, genomics, primary care indicators, etc. Each data stream may provide a single indicator for a disease, but low signal to noise ratio help diseases go unnoticed.
Different data sources represent different aspects of a complex system and, taken together, can magnify biologically relevant signals and reveal the underlying pattern of a disease.
Traditional statistical methods struggle with the level of data integration required to robustly model complex biochemical systems, whereas the flexibility of deep learning methods cater to multi-modal analysis and help you generate meaningful inferences.
Automate the analysis of datasets
Automate diagnostic solutions
Proactive detection and live monitoring
Computer vision can be trained to see what field experts see, but can process at super human speed
Breakthroughs in computer vision image processing occur almost weekly and can be used to flag potential risks to practitioners.
Inattentional blindness leads humans to miss things they're not looking for, whereas computational systems suffer no such bias. People have a tendency to search for what they are already looking for, missing what they don't expect to see.
Detect disease through image processing
Automate early diagnosis accurately
Alert professionals to examine red flags
Two categories of robotic process automation
More efficient patient engagement
Introduce automation at every patient touchpoint to attract the right patients and ensure their treatment experience is accurate and streamlined.
Intelligently generate patient care plans with software the makes suggestions based on patient behaviour and history.
Automate check ups with email and scheduling integrations.
Explore our business intelligence vertical for more.
Design patient care plans
Streamline administrative processes
Reduce administrative workloads of highly qualified medical professionals and streamline administrative staff workflows.
Optimise hospital admin
Digitise customer data with computer vision and scanning technology
Manage bed inventory more efficiently; automatically trigger supply orders
Optimise resource utilization to save rental costs and streamline staff rosters through predictive demand forecasts
Natural language processing (NLP) can process medical documents, update medical records and schedule patient visits
Prioritise patients in real-time and reduce waiting times
Automatically update the patient’s Admission-Discharge-Transfer status and simultaneously trigger staff direction
Automate menial processes
Employ robotic process automation (RPA) to assist administrative workflows
Automate insurance claim handling, ensuring regulatory compliance and avoid manual input errors whilst increasing claim speed
Streamline account settling with data merging, consolidation and intelligent calculation, automatically sending invoices or customer alerts
Generate and send discharge instructions, informing patients of guidelines and checking in on patients at various stages after/during treatment, with prescription reminders , upcoming appointment notification and tests
Real Client Examples From the Collective
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