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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.
The healthcare industry is one of the most advanced in IoT implementation with around 60% of hospitals having adopted the technology in some form.
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.
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.
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.
The growing adoption of digital technology in healthcare industry has created opportunities for improving patient outcomes while managing costs through data-driven decisions.
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.
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.
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.
Acknowledging the role patients play in their own healthcare, organizations are utilizing digital technologies to facilitate self-care while improving care delivery.
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.
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.
Development Director