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Healthcare & Pharma

We at VECTOR Labs build custom software solutions for healthcare organisations, providers, and pharmaceutical manufacturers. We provide high-quality custom development services in response to the evolving healthcare industry.

We understand the challenges

According to a survey by GlobalData, 40% of pharmaceutical industry professionals in Europe and North America believe that the pandemic has accelerated digital transformation by more than five years.

We understand the complexities involved with running a pharmaceutical or healthcare organisation, where one has to balance profitability and quality patient care. Our mission is to enable those organisations to stay ahead of the competition, delivering better customer experience and improving outcomes.

Custom software solutions that adapt to the client's demands can help reduce costs, improve communication, streamline work with patient records, and deliver more accurate diagnoses.

Appointment reminders, customizable templates, electronic prescribing, patient engagement, patient portal, personal telemedicine, smart dashboard, specialty card patient, and workflow components are just a few features a healthcare provider can benefit from custom software.

End-to-end solutions

From idea and prototyping to healthcare software development and deployment, we offer a complete range of tailored technology:

We develop secure and scalable custom products for the needs of your organisation.

  • Integration

We aim to provide flawless data exchange between systems and integrate custom applications with already installed third-party systems.

Using our expertise in the latest technologies in AI, ML, and NLP helps us create winning solutions.​​​

We have created architectures covering multiple data sources, formats and types, including internal ERP data, CRM data, other downstream systems’ data, sensors stream data, and external data.

We have the expertise in all of the above to combine them into a solution that adds value to your company and transforms your business.

Healthcare & Pharma Case Studies

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NLP development for a pharmaceutical company

Challenge: The client, a multinational pharmaceutical company wanted to refactor an existing system for enquiry management and improve its performance and analytical capabilities through AI. The goal was to ensure a consistent customer experience across multiple locations. 

Industry: Healthcare  

Approach: Our team used semantic analysis with NLTK preprocessing to create different feature extraction and drug product labelling over already accumulated and classified еnquiry data. Preprocessed data were used to train Keras with the TF engine, and evaluated versus built-in SVM algorithms in SKLearn. Results were accuracy near 80% for classification. ML components were integrated into the auto-assign pipeline based on product and team recognized in free text еnquiry.

From a business perspective, the complete solution speeds up the process of enquiry management and contributes to overall customer satisfaction and increased operational efficiency. 

Tech stack: Python and Angular developers, Data Scientists, ML Engineers, Project Manager.

Results: Successful completion of refactoring tasks and introduction of the NLP module. The achieved results were near 80% accuracy for the classification of enquiries. 

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Healthcare app giving full communication between doctors and patients

Overview: Our client, a healthcare scale-up, set out to improve their telemedicine platform's capabilities, enabling patients and healthcare practitioners to connect from a distance using innovative digital-first and high-touch technology. However, the main challenge was to upgrade and expand the platform while ensuring that the tens of thousands of patients and healthcare practitioners already using it could continue to access its functionality seamlessly.

 

Team: Our team of experienced Python and ReactJS developers worked together to create a seamless and intuitive platform for healthcare practitioners and patients.

 

Solution: Our team collaborated with the client's existing team to provide support and develop new features for their healthcare app. This app facilitates seamless communication between healthcare providers and patients, as well as other important healthcare stakeholders like hospitals, pharmacies, and healthcare funds.

To ensure the highest standards of privacy and security, our team implemented a comprehensive, HIPPA-compliant workflow that creates a repeatable care plan for each patient and clinical condition. The app offers a wide range of features and interactions, empowering patients to access healthcare services, track their health, and communicate with their healthcare providers with ease.

The app integrates with multiple healthcare devices, which collect and analyze patient data and provide valuable insights to healthcare specialists. With advanced analytics capabilities, the app enables healthcare providers to detect trends and patterns in patient data, allowing them to make better-informed decisions and provide high-quality care.

Despite the project's complexity, our team successfully tackled regular refactoring and ongoing improvements to ensure a seamless user experience and optimal performance.

 

Outcomes: The project was a great success, with the team delivering new modules and functions on time and within budget. As a result, the user base grew to over 100.000 users, and the client has become a trusted partner of the management.

Key metrics: The app has been well-received by both healthcare providers and patients, with high user engagement and satisfaction. The client has reported significant improvements in operational efficiency, patient outcomes, and overall healthcare quality

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AI model development and certification for cardiovascular medicine

Overview: Our client is a European MedTech start-up focused on the detection of cardiovascular anomalies. The start-up’s objective is to challenge the market for ECG signal-based detection of heart pathologies, such as, but not limited to, atrial fibrillation, premature beats, subventricular tachycardia, etc, obtained from affordable wearable devices such as the Polar H10. The company required an ML-based model development and preparation for the Class 2A medical device certification, in order to ensure that their system is safe and effective for patients to use at home. 

Team: Our team of experts included a Data Engineer, Data Scientists, ML Engineers and a Project Manager

Approach
Discovery phase

Our Data Scientists collaborated with the client in order to understand their existing ECG signal processing system, as well as their computation-based algorithms. We created an automated testing tool that assessed the performance of the current solution against a range of internal and external medical data sets. Based on this analysis, we provided recommendations to improve the scope of detectable medical conditions and identified areas for improvement. 
We advised the client on the certification process and prepared an action plan for the  ML-related work and documentation needed for the Class 2A certification. We also provided improvement recommendations and execution support, which helped the client enhance their ECG signal processing system and algorithms. Our team completed the analysis and recommendations in less than 8 weeks, providing actionable advice, which helped the client reduce the application processing time. 

ML-based signal processing model development phase

The client provided the raw ECG signal data, including labelled data with annotations of R-peaks, pathologies, as well as ‘soft’ and ‘hard’ noise segments. This labelled data was used as inputs for the models and deemed as their training set. The quality and accuracy of pathology predictions (detection) heavily depend on the cleaness of the ECG signal. This is why it is important to have robust, well-performing models in terms of accuracy and precision, which can distinguish between the clean and noisy parts of the ECG signals. Our Data Scientists developed ML-based models for ‘soft’  and ‘hard’ noise detection. Upon successfully passing the strict model performance acceptance criteria of the client, our ML Engineers created the production pipelines for training and inference and integrated it into the client’s platform and business processes. Our collaboration continues with the development of further pathology detection ML-based algorithms. 

Results: Through our active collaboration with the client's team, we were able to complete the model certification preparation work in just 6 months, helping the client achieve their goal of obtaining a Class 2A certification for their AI-powered medical device system. This certification allows the client to offer their product to a wider range of customers, enabling them to provide early detection and prevention of heart-related medical conditions.

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