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Healthcare Data Intelligent Synthesis
You can take big data in healthcare—from lab results to X-rays and Fitbit stats—and, with the help of our data science service, turn it into actionable insights, like figuring out the best personal treatment, not the average person with such a condition. Generative AI cuts through bureaucracy, speeding work up and freeing doctors and nurses to focus on caring for patients.
AI and Data Analytics in Healthcare
The common thread for our services is using data and AI to make things faster, wiser, and more efficient in healthcare and pharma. It’s turning data into actionable insights to solve business challenges, improve care, and streamline processes.
Healthcare Data Analytics Consulting
It helps companies tap into the power of AI and data to improve their operations, deliver better care, and advance innovation.
Generative AI Creates Care
Gen AI speeds up drug development, facilitates personalized treatments, and improves diagnostics, all while helping things run more smoothly.
Predictive Analytics as a Service
In healthcare and pharma, data and machine learning predict future trends, outcomes, and needs for data-driven choices that improve care.
Healthcare Data Science
Data science extracts insights from data, and in healthcare and pharma, it analyzes medical records, clinical trials, and genetic data to improve patient outcomes and drug development.
Clinical Data Integration
Centralize data from multiple EHR systems, integrate and standardize diverse healthcare data sources, improving accuracy in research and diagnostics.
DataOps Management
It makes it easier for healthcare and pharma companies to manage their data, ensuring everything is secure, accurate, and always ready to use.
Business Benefits of Our Services in Healthcare and Pharma
By combining data science and Generative AI, healthcare and pharma companies get competitive advantages, improving patient outcomes.
01
Better care: Pinpointing at-risk patients, customizing treatment plans, and predicting illnesses.
02
Cost-cutting: Streamlining operations, optimizing resources, and stopping
fraud.
fraud.
03
Smarter decisions: Using data to plan strategically and allocate resources effectively.
04
Faster drug discovery: Creating drugs, optimizing their structures, and predicting their properties.
05
Personalization: Tailoring treatments to individuals based on their genes
and medical history.
and medical history.
06
Improved image analysis: Making and studying medical images more
accurate and efficient.
accurate and efficient.
Tired of the same old healthcare routine?
Our data-driven solutions will shake things up.
Reporting & Analysis Automation with AI Chatbots
The client, a water operation system, aimed to automate analysis and reporting for its application users. We developed a cutting-edge AI tool that spots upward and downward trends in water sample results. It’s smart enough to identify worrisome trends and notify users with actionable insights. Plus, it can even auto-generate inspection tasks! This tool seamlessly integrates into the client’s water compliance app, allowing users to easily inquire about water metrics and trends, eliminating the need for manual analysis.
Automating Reporting and Analysis with Intelligent AI Chatbots
Gen AI Hairstyle Try-On Solution
Dataforest developed a top-on-the-market Gen AI hairstyles solution for US clients. It consists of the technology for the main product and the free trial widget. The solution generates hairstyle try-ons using the user's selfie. We had two primary objectives. The first was to ensure high accuracy in preserving the user's facial features. The second one was to create hairstyles that showcase the most natural hair texture. Our vast experience in Gen AI and Data science helped us achieve 94% model accuracy. It guarantees high-quality user face resemblance and natural hair in the generated photos. And it results in much higher user satisfaction, making it #1 on the market.
Gen AI Hairstyle Try-On Solution
All Success Stories
Healthcare Super Boost
Data science and Gen AI give the whole healthcare system a significant upgrade. It makes things work better for patients, doctors, and researchers. That means healthier people and exciting new medical breakthroughs.
Data analytics in healthcare help doctors make better decisions faster.
Data science in healthcare saves money and time.
It speeds up the process of finding new medicines.
Healthcare data security makes medicine more personal.
Big data in healthcare helps predict problems before they get serious.
It helps make sense of healthcare big data.
Data in healthcare makes life easier for patients.
Don't wait any longer!
Contact us today to learn how data science and AI solutions will transform your healthcare organization.
DATAFOREST Values for Healthcare and Pharma
In our client work, we strictly adhere to the company's three philosophical values: flexibility, cooperation, and focus on results. Here is how this is reflected in the healthcare and pharmaceutical industries.
Flexibility
- Shape-shifting AI: Models that adapt to different medical specialties or drug development stages
- Regulation-ready: Quick pivots to meet ever-changing healthcare laws and pharma guidelines
- Scalable from clinic to hospital chain: Solutions that grow with the client, big or small
Cooperation
- Doc-and-dev dream teams: Pairing AI experts with healthcare pros to build spot-on solutions
- Open-book AI: Transparent about how the AI thinks, so healthcare teams trust its suggestions
- Cross-pollination: Sharing insights between healthcare and pharma (within legal bounds)
Results
- Health outcomes that wow: Measurable improvements in patient care or drug discovery timelines
- Show me the saved lives (and money): Clear metrics on both health impacts and cost benefits.
- Continuous health check-ups: AI models fine-tuning based on real-world medical feedback
What Data Science technologies do we use?
Pandas
SciPy
TensorFlow
Numpy
ADTK
DBscan
G. AutoML
Keras
MLFlow
Natural L. AI
NLTK
OpenCV
Pillow
PyOD
PyTorch
FB Prophet
SageMaker
Scikit-learn
SpaCy
XGBoost
YOLO
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Still have questions?
How to set up and maintain data security in healthcare?
Implement robust encryption for all patient data at rest and in transit, and establish strict access controls with multi-factor authentication for all users. Regularly conduct security audits and vulnerability assessments, keeping all systems updated with the latest security patches. Develop and maintain a comprehensive incident response plan, including regular backups and disaster recovery procedures, while ensuring compliance with healthcare regulations like HIPAA through ongoing documentation.
What are healthcare datasets, and why do they matter?
Datasets in healthcare are collections of medical information ranging from patient records and clinical trial results to genomic data and medical imaging. They matter because they're the lifeblood of modern healthcare, enabling everything from personalized treatments and disease prediction to drug discovery and health system optimization. When analyzed with advanced tools like AI, these datasets can reveal patterns and insights that improve patient care, streamline operations, and drive medical breakthroughs.
What services do healthcare data analytics companies usually provide?
Healthcare data analytics companies typically provide services that help healthcare vendors extract insights from data. This includes data cleaning, data integration, and data visualization. These companies often develop predictive models to forecast trends and outcomes in healthcare.
Where does big data in pharma come from?
Big data in pharma comes from various sources, including electronic health records (EHRs), clinical trials, patient registries, genomic data, and social media. EHRs capture detailed information about patient health, while clinical trials generate data on drug efficacy and safety. Patient registries collect data on specific patient populations, and genomic data provides insights into genetic variations. Social media can be used to monitor public sentiment and identify emerging trends in healthcare.
Describe big data analytics for pharma development.
Big data analytics in pharma development involves analyzing massive datasets from various sources, such as clinical trials, patient records, and genomic data. This analysis helps identify patterns and trends that would be difficult to detect with smaller datasets, accelerating drug discovery and development. Pharma big data analytics optimize clinical trial design, reduce costs, and ensure regulatory compliance.
How can pharma data analytics companies address business pain points?
Pharma data analytics companies can address business pain points by providing insights that help pharmaceutical companies make more informed decisions. This includes identifying new drug targets, optimizing clinical trials, improving patient outcomes, and reducing costs. By leveraging data analytics, pharmaceutical companies gain a competitive advantage and accelerate drug development.
How do we use big data and analytics for pharma?
Using big data in pharma is analyzing vast amounts of data from various sources to gain insights into disease mechanisms, identify potential drug candidates, and evaluate the effectiveness of treatments. Data analytics can be used to predict adverse drug reactions, personalize treatment plans, and improve drug safety. It’s the way of big data forecasting in pharma.
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