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Enabling Futuristic Healthcare Delivery

From EHRs and wearable health devices to connected hospitals with real-time location systems (RTLS), futuristic healthcare institutions are implementing digital strategies that change the way medical care is delivered. We enable hospitals to leverage the potential of Internet of Things (IoT), big data analytics, and artificial intelligence (AI) to drive improvement in efficiency and patient care.

Connected Hospitals

The healthcare industry is one of the most advanced in IoT implementation with around 60% of hospitals having adopted the technology in some form.

As data from IoT sensors get integrated into the hospital information system (HIS), staff can have a more comprehensive, real-time view of the establishment. This leads to manifold improvements in operational efficiency and care delivery.

Be it summoning medical staff in emergencies or continuous monitoring of in-patients, IoT integration in hospitals ushers in a lot of efficiencies. Utilizing sensor technologies that cause the least interference with medical equipment, we ensure safe IoT implementation in hospitals.

RTLS Powered by SeeMyMachines

Connect all hospital resources including equipment, patients, and medical staff to the SeeMyMachines IoT platform to access information in real time and improve care response.

Continually track the location of movable assets and medical equipment to recover them when a need arises.
Track down and summon medical staff from anywhere in the hospital premises.
Monitor patients and visitors, and warn staff of movement beyond designated rooms or safe zones.
Regulate asset utilization to reduce idle times and resource crunch.
Guide outpatients and visitors through the hospital complex to different departments.

RTLS-Based Solutions

Artificial Intelligence in Medical Diagnosis

Healthcare data can be structured and unstructured requiring different AI approaches. Machine learning (ML) techniques are useful in analyzing imaging and genetic data while natural language processing (NLP) methods can be used to extract information from clinical notes, which can then be analyzed by ML techniques.

AI diagnostic systems that use deep learning support doctors in identifying diseases by improving the accuracy and speed of diagnosis.

For instance, a pair of AI systems were developed to augment the accuracy of lung cancer and heart disease diagnosis at a hospital in Oxford. Facial recognition software supplemented with machine learning is being used to analyze patient photos and diagnose certain rare genetic diseases.

Patients may also make a verbal report of their symptoms. Utilizing artificial neural networks (ANN) for speech processing, chatbots can be built to identify patterns in patient symptoms and form a diagnosis or suggest a course of action.

AI Powered by IBM Watson

Use the Watson AI platform to build cognitive systems that can perform tasks like annotation of medical records. Watson has been trained with industry-specific data such as healthcare data. Our data engineers will customize the natural language processing model to answer queries according to your business rules and use case. The intelligent system can be embedded in a variety of applications such as virtual assistants.

Custom AI Solutions

Healthcare Analytics

The growing adoption of digital technology in healthcare industry has created opportunities for improving patient outcomes while managing costs through data-driven decisions.

Predictive Healthcare

Advanced analytics on data available through Electronic Health Records (EHR), remote monitors, and wearable devices allows doctors to predict illness and prescribe prophylaxis. Predictive analytics also allows clinicians to prevent deterioration of patient’s condition in chronic illness.

Preventing Claim Denials

Analyzing medical claim data can determine trends in denials allowing practices to understand the reason for rejections and write-offs that result in revenue loss. Organizations can develop claim denial key performance indicators (KPIs) for hard and soft denials. With analytical insight, practices can focus on reducing contributing factors such as missing data or errors in coding through suitable process automation.

Insights Through Big Data Analytics

Data analysis allows healthcare organization to deliver better patient care. However, extracting actionable insights from medical data presents challenges owing to the enormity and complexity of the heterogeneous dataset.

Our data science team helps clients apply machine learning algorithms such as neural networks as well as linear models and decision trees in the context of healthcare applications. Intelligent decision support systems are developed by integrating complex data analysis and visualization techniques with HIS.

Machine Learning

Patient Self-Enablement

Acknowledging the role patients play in their own healthcare, organizations are utilizing digital technologies to facilitate self-care while improving care delivery.

Integrating data analytics, machine learning, and cloud technologies with mobility, healthcare providers can offer patient services even remotely.

Patient-centric solutions can include educational medical content and personalized health tips delivered via smartphones, wellness tracker apps, appointment booking/reminder apps, self-service kiosks, and ambulatory monitoring solutions.

Healthcare Apps

As a software solutions provider experienced in the healthcare domain, QBurst has been successful in delivering self-enablement solutions for patients.

Rather than take a current website and squeeze it to fit the mobile platform, we help clients adopt mobile health technology meaningfully. We can integrate mHealth with customer experience platforms and CRM solutions to deliver personalized care at patients’ finger tips.

Mobile App Development

Clients

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That’s been a really exciting time, really fast-paced execution and QBurst have really been flexible all the way along the way to meet the needs of EMIS and their development challenges.

David Weir

Development Director

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