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Huddle by Byte IQ

Using our proprietary tools, Huddle seamlessly connects to your practice management software and presents your complex data into powerful tools to help you optimize your business.

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DeepWater By Byte IQ

DeepWater is our bespoke data lake solution built specifically for the healthcare sector. With DeepWater, you get the technology, pre-built integrations & resources to help your organisation make the most of its data.

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Latest insights from our blog


Amazon Web Services (AWS): Innovating Healthcare Technologies

Organisations large and small, regardless of location or industry are highly likely to use Amazon Web Services (AWS), given their global reach as a provider of cloud solutions. The AWS Marketplace is a digital catalogue with a dizzying range of solutions designed to improve business operations. Categorised by industry, we are only concerned with their solutions in the ‘Healthcare and Life Sciences’ category. By using AWS, healthcare organisations can ensure business continuity, optimise processes, provide tools to enhance clinical care, diagnostics and treatment and use new analytics capabilities and machine learning technologies. This is achieved while meeting both regional and global security and privacy requirements. Broadly speaking, AWS healthcare solutions are broken down into the following categories: Core operations and business continuity Care Coordination Health Analytics Patient Engagement Clinical Information Systems Storage and Archiving Compliance Each category is important, of course, but it’s important to remember that there is no one-size-fits-all solution for your organisation. After all, there are more than 1600 software vendors in the AWS Marketplace, covering everything from analytics and telehealth solutions to real-time robotic assistants in surgical training scenarios. For brainstorming purposes, why not read up on some of the available use cases that could be applied to your situation? Note that there is some expertise required to set up, configure and maintain AWS solutions, the majority of which are pay-as-you-go. However, with a trusted partner, you do not need a high level of technical expertise, even to incorporate healthcare data analytics. The extraction of useful insights from medical data is complicated by the fact that much of it is free-form text (such as handwritten doctor’s notes, clinical trial results and medical records), none of which are easily catalogued by traditional means. However, with AWS Comprehend Medical, all this data is easily manipulated for maximum results, compiling a viewable set of data by treatment, medication dosages and any other variables you’d like defined. Such information is key to population health analytics, clinical research and pharmacological analysis and the extracted data can then be linked to electronic health records if necessary. AND, all of this is achieved without hiring data scientists to refine algorithms or create new ones. In conclusion, we highly recommend AWS to our clients and utilise some of their solutions when rolling out innovative and fully-compliant healthcare solutions for the Australian market. Byte IQ is proud to be an AWS partner as well as an AWS client. Our technologies including Healthcare data lakes, AI/ML, and security tools are all built on AWS cloud technologies.


What is a Predictive Algorithm and Can it Predict Who Will be Sick and When?

In the computing world, an algorithm is a set of instructions, or steps, required to get from one situation to another. It could be as simple as providing directions to exit a building from a fixed starting point to a defined fire exit. Your GPS uses algorithms to take you to your destination using the shortest viable route, for example. But what would make the same GPS algorithms predictive? A predictive algorithm would provide a different outcome, based on previously stored data. In the GPS analogy, this stored data could consist of breaking news bulletins, social media alerts and historical traffic information. In order words, based on all available data, your GPS could offer a different route because: A pattern of traffic jams has been established for the time you are traveling. A news bulletin or social media alert indicates that there is a traffic jam due to an accident or natural disaster. The normal route is closed for repairs. Clearly, even with GPS, predictive algorithms offer benefits, but there is one key element necessary before use. Historical or even updated real-time data is necessary before predictions are possible, given that data scientists are not psychic and all predictions are based on extracted patterns. Therefore, in a healthcare environment, data sets are necessary, as these will allow actionable insights that are not apparent using traditional means. Data analytics and predictive algorithms go hand in hand. You cannot have one without the other. AND predictive algorithms evolve, given their link to machine learning and artificial intelligence (AI). Let’s assume your healthcare organisation has all the necessary data (even if currently free-form and/or unstructured). With the right tools, insights are extracted to highlight patterns that are not obvious, even to the keenest researcher. The potential for preventative care is limitless as risk factors/ adverse reactions for patients taking multiple medications become apparent. In a unified environment, threats to population health are identified as infections increase in what are now recognisable patterns across jurisdictions, hospitals and clinics. In a world now recovering from a global pandemic, can we really afford not to invest in healthcare analytics?


What is Big Data & Will it Solve Our Healthcare Problems?

In its simplest form, Big Data refers to large volumes of data that is impossible to extract insights from manually or using standard computing techniques. Volume data can be structured (in a database, for example) or unstructured (composed of everything from Individual text files, marketing material or even social media messages and internet voice calls). Big Data Is Not ENOUGH Additional techniques are necessary to make sense of all the data and this where data scientists and machine learning experts come into the picture. Having Big Data is one thing but without analytics (where data scientists create algorithms to sort and extract actionable insights from previously nonsensical information) it’s pointless. Trained professionals create unique algorithms for the non-technical to visualise their data or tools to allow automated searches useful to your healthcare organisation. In addition to standard analytics, it’s also possible to generate predictions based on historical data, both of which aid clinicians in preventative care by age, gender, family history or other risk factors. We’re Medical Professionals Not Technologists Clearly, Big Data has the potential to solve many healthcare problems but is not a cure-all. Clinicians are concerned with diagnosis and medical care. They are not technology experts and don’t need to be. However, advanced tools and resources are needed, as mentioned previously. Implemented correctly, a healthcare big data solution can improve efficiency by identifying bottlenecks, reduce fraud such as identity theft, can improve preventative care by analysing risk factors and can improve research capabilities by identifying data that would have been missing using standard methods. To take a grand real-world example, how about population health, whether your community or the country as a whole? If everyone’s health information was centralised and anonymised correctly (patient privacy is a major consideration when implementing systems of this nature), it makes it easy to identify patterns using analytics. Let’s Improve Would Covid-19 have caused so much turmoil around the world if each country had a comprehensive healthcare data solution to compare a surge or similar symptoms in their patients, regardless of location? I don’t believe so. In the meantime, individual healthcare organisations can embrace technology and use analytics to extract valuable operational and indeed medical insights from data they already have inhouse. As the drive towards digitisation and value-based care (including telehealth) continues, why not take advantage of the benefits?

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