5 items tagged "Healthcare"

  • 4 benefits of predictive analytics improving healthcare

    4 benefits of predictive analytics improving healthcare

    There are so many wonderful ways predictive analytics will improve healthcare. Here are some of the potential benefits to consider.

    Medical care has relied on the education and expertise of doctors. Human error is common and 250,000 people per year die from medical errors. As this is the third-leading cause of death in the United States, limiting errors is a key focus in the healthcare industry.

    Big data and predictive analytics will lead to healthcare improvement.

    But how? Health IT Analytics previously published an excellent paper on some of the best use cases of predictive analytics in healthcare. We reviewed other papers on the topic and condensed the best benefits into this article.

    1. Diagnoses accuracy will improve

    Diagnoses accuracy will improve, and this will occur with the help of predictive algorithms. Surveys will be incorporated, which will ask the person that enters the emergency room with chest pain an array of questions.

    Algorithms could, potentially, use this information to determine if the patient should be sent home or if the patient is having a heart attack.

    Patients will still have insight from doctors who will use the information to assist in a diagnosis. The predictive analytics are not designed to replace a doctor’s advice.

    2. Early diagnoses and treatment options

    Big data will lead to earlier diagnoses, especially in deadly forms of cancer and disease. Annually, mesothelioma affects 2,000 to 3,000 people, but there’s a latency period that’s rarely less than 15 years and could be as long as 70 years.

    Predictive analysis will allow for doctors to put all of a person’s history into an algorithm to better determine the patient’s risk of certain diseases.

    And when a disease is found early on, treatment options are expanded. There are a variety of treatment options often available when a person is in good health. If doctors can predict a patient’s risk of cancer or certain illnesses, they can offer preventative care which may be able to slow the progression of the disease.

    Babylon Health already has raised $60 million to create a chatbot that will use an AI chatbot to help with patient diagnoses.

    3. Improve patient outcomes

    One study suggests that patient outcomes will improve by 30% to 40%, with the cost of treatment will be reduced by 50%. Medical imaging diagnosis will improve with an enhancement in care delivery, too. The introduction of predictive analytics will allow patients to live longer and have a better medical outlook as a result.

    Consumers will work with physicians in a collaborative manner to provide better overall health histories.

    Doctors will be able to create models that help predict health risks using genome analysis and family history to help.

    4. Changes for hospitals and insurance providers

    Hospitals and insurance providers will also see changes, initially bad changes. Through predictive analysis, patients will be able to seek diagnoses without going to the hospital. Wearables may be able to predict health issues that a person is likely to face.

    Revenues will initially be lost by hospitals, insurance companies and pharmacies that have fewer patients and errors sending patients to facilities.

    Hospitals and insurance companies will need to adapt to these changes or face losing profit and revenue in the process. Government funding may also increase in an effort to increase innovation in the market.

    Predictive analytics has the potential to help people live longer with better treatment options and predictive preventative care.

    Predictive analytics is the key solution to healthcare challenges

    Many healthcare challenges are still plaguing patients and healthcare providers around the United States. The good news is that new advances in predictive analytics are making it easier for healthcare providers to administer excellent care. Big data solutions will help healthcare providers lower healthcare costs and give patients excellent service that they expect and deserve.

    Author: Andrej Kovacevic

    Source: SmartDataCollective

  • Applying data science to battle childhood cancer

    Applying data science to battle childhood cancer

    Acute myeloid leukaemia in children has a poor prognosis and treatment options unchanged for decades. One collaboration is using data analytics to bring a fresh approach to tackling the disease.

    Acute myeloid leukaemia (AML) kills hundreds of children a year. It's the type of cancer that causes the most deaths in children under two, and in teenagers. It has a poor prognosis, and its treatments can be severely toxic.

    Research initiative Target Paediatric AML (tpAML) was set up to change the way that the disease is diagnosed, monitored and treated, through greater use of personalised medicine. Rather than the current one-size-fits-all approach for many diseases, personalised medicine aims to tailor an individual's treatment by looking at their unique circumstance, needs, health, and genetics.

    AML is caused by many different types of genetic mutation, alone and together. Those differences can affect how the cancer should be treated and its prognosis. To understand better how to find, track and treat the condition, tpAML researchers began building the largest dataset ever compiled around the disease. By sequencing the genomes of over 2,000 people, both alive and deceased, who had the disease, tpAML's researchers hoped to find previously unknown links between certain mutations and how a cancer could be tackled.

    Genomic data is notoriously sizeable, and tpAML's sequencing had generated over a petabyte of it. As well as difficulties thrown up by the sheer bulk of data to be analysed, tpAML's data was also hugely complex: each patient's data had 48,000 linked RNA transcripts to analyse.

    Earlier this year, Joe Depa, a father who had lost a daughter to the disease and was working with tpAML, joined with his coworkers at Accenture to work on a project to build a system that could analyse the imposing dataset.

    Linking up with tpAML's affiliated data scientists and computational working group, Depa along with data-scientist and genomic-expert colleagues hoped to help turn the data into information that researchers and clinicians could use in the fight against paediatric AML, by allowing them to correlate what was happening at a genetic level with outcomes in the disease.

    In order to turn the raw data into something that could generate insights into paediatric AML, Accenture staff created a tool that ingested the raw clinical and genomic data and cleaned it up, so analytics tools could process it more effectively. Using Alteryx and Python, the data was merged into a single file, and any incomplete or duplicate data removed. Python was used to profile the data and develop statistical summaries for the analysis – which could be used to flag genes that could be of interest to researchers, Depa says. The harmonised DataFrame was exported as a flat file for more analysis.

    "The whole idea was 'let's reduce the time for data preparation', which is a consistent issue in any area around data, but particularly in the clinical space. There's been a tonne of work already put into play for this, and now we hope we've got it in a position where hopefully the doctors can spend more time analysing the data versus having to clean up the data," says Depa, managing director at Accenture Applied Intelligence.

    Built using R, the code base that was created for the project is open source, allowing researchers and doctors with similar challenges, but working on different conditions, to reuse the group's work for their own research. While users may need a degree of technical expertise to properly manipulate the information at present, the group is working on a UI that should make it as accessible as possible for those who don't have a similar background.

    "We wanted to make sure that at the end of this analysis, any doctor in the world can access this data, leverage this data and perform their analysis on it to hopefully drive to more precision-type medicine," says Depa.

    But clinical researchers and doctors aren't always gifted data scientists, so the group has been working on ways to visualise the information, using Unity. The tools they've created allow researchers to manipulate the data in 3D, and zoom in and out on anomalies in the data to find data points that may be worthy of further exploration. One enterprising researcher has even been able to explore those datasets in virtual reality using an Oculus.

    Historically, paediatric and adult AML were treated as largely the same disease. However, according to Dr Soheil Meshinchi, professor in the Fred Hutchinson Cancer Research Center's clinical research division and lead for tpAML's computational working group, the two groups stem from different causes. In adults, the disease arises from changes to the smallest links in the DNA chain, known as single base pairs, while in children it's driven by alterations to larger chunks of their chromosomes.

    The tpAML has allowed researchers to find previously unknown alterations that cause the disease in children. "We've used the data that tpAML generated to probably make the most robust diagnostic platform that there is. We've identified genetic alterations which was not possible by conventional methods," says Meshinchi.

    Once those mutations are found, the data analysis platformcan begin identifying drugs that could potentially target them. Protocols for how to treat paediatric AML have remained largely unchanged for decades and new, more individualised treatment options are sorely needed.

    "We've tried it for 40 years of treating all AML the same and hoping for the best. That hasn't worked – you really need to take a step back and to treat each subset more appropriately based on the target that's expressed," says Meshinchi.

    The data could help by identifying drugs that have already been developed to treat other conditions but may have a role in fighting paediatric AML, and by showing the pharmaceutical companies that make those drugs there is hard evidence that starting the expensive and risky.

    Using the analytics platform to find drugs that can be repurposed in this way, rather than created from scratch, could cut the time it takes for a new paediatric AML treatment to be approved by years. One drug identified as a result has already been tested in clinical trials.

    The results generated by the team's work has begun to have an impact for paediatric AML patients. When the data was used to show a subset of children with the disease who had a particular genetic marker that were considered particularly high risk, the treatment pathway for those children was altered.

    "This data will not only have an impact ongoing but is already having an impact right now," says Julie Guillot, co-founder of tpAML.

    "One cure for leukaemia or one cure for AML is very much unlikely. But we are searching for tailored treatments for specific groups of kids… when [Meshinchi] and his peers are able to find that Achilles heel for a specific cluster of patients, the results are dramatic. These kids go from a very low percentage of cure to, for example, a group that went to 95%. This approach can actually work."

    Author: Jo Best

    Source: ZDNet

  • Healthcare analytics and the opportunities to improve patient care

    Healthcare analytics and the opportunities to improve patient care

    Healthcare: everyone needs it, it’s a rapidly technologizing industry, and it produces immense amounts of data every day.

    To get a sense of where analytics fit into this vital market, Sisense interviewed Hamza Jap-Tjong, CEO and Co-Founder of GeriMedica Inzicht, a GeriMedica subsidiary. GeriMedica is a multi-disciplinary electronic medical record (EMR) company servicing the elderly care market and as such, their SaaS platform is filled with data of all kinds. Recently, they rolled out analytics that practitioners could use to improve the quality of care (versus the prior main use case in healthcare analytics, which was done by the billing and finance departments). This helps keep practitioners focused on helping patients instead of spending (wasting) hours in a software product. Hamza opened up about the state of healthcare analytics, how it can improve care for patients, and where the industry is going.

    The state of healthcare analytics

    As previously mentioned, the healthcare industry creates tons of data every day from a wide array of sources.

    'I think tons of data might be an understatement', says Hamza, citing a Stamford study. 'They were talking about data on the scale of exabytes (an exabyte equals a billion gigabytes). Where does all that data come from? Fitbits, iPhones, fitness devices on your person… healthcare data is scattered everywhere: not only treatment plans and records created by practitioners, but also stored in machines (X-rays, photographs, etc.)'.

    Data is the new oil, but without the right tools, the insights locked in the data can’t help anyone. At present, few healthcare organizations (let alone frontline practitioners) are taking advantage of the data at their disposal to improve patient care. Moreover, these teams are dealing with amounts of information so vast that they are impossible to make sense of without help (like from a BI or analytics platform). They can’t combine these datasets to gain a complete picture without help, either. Current software offerings, even if they have some analytical capabilities for the data that they capture, often can’t mash it up with other datasets.

    'In my opinion, we could really improve the data gathering', says Hamza. 'As well as the way we use that data to improve patient care. What we know is that when you look at doctors, nurses, physical therapists, everybody close to care processes and patients, is hankering for data and insights and analytics and we see that at the moment there isn’t a tool that is good enough or easy enough for them to use to gain the insights that they are looking for'.

    Additionally, the current generation of medical software has a high barrier to entry/learning curve when it comes to getting useful insights out. All these obstacles prevent caregivers from helping clients as much as they might be able to with analytics that are easier to use.

    Improving patient care (and improving analytics for practitioners)

    Analytics and insight-mining systems have huge potential to improve patient care. Again, healthcare data is too massive for humans to handle unaided. However, there is hope: Hamza mentioned that AI systems were already being used in medical settings to aggregate research and present an array of options to practitioners without them having to dig through numerous sources themselves.

    'Doctors or nurses usually don't work nine-to-five. They work long shifts and their whole mindset is focused on solving mysteries and helping the patients. They don't have time to scour through all kinds of tables and numbers. They want an easy-to-understand dashboard that tells a story from A to Z in one glance and answers their question'.

    This is a huge opportunity for software and analytics companies to help improve patient care and user experience. Integrating easy-to-understand dashboards and analytics tools within medical software lowers the barrier to entry and serves up insights that practitioners can use to make better decisions. The next step is also giving clinicians the right tools to build their own dashboards to answer their own questions.

    The future of healthcare analytics

    Many healthcare providers might not know how much analytics could be improving their work and the care they give their patients. But they certainly know that they’re spending a lot of time gathering information and putting it into systems (and, again, that they have a ton of data). This is slowly changing today and will only accelerate as time goes on. The realization of how much a powerful analytics and BI system could help them with data gathering, insight harvesting, and providing better care will drive more organizations to start using a software’s analytics capabilities as a factor in their future buying decisions.

    Additionally, just serving up insights won’t be enough. As analytics become more mainstreamed, users will want the power to dig into data themselves, perform ad hoc analyses, and design their own dashboards. With the right tools and training, even frontline users like doctors and nurses can be empowered to create their own dashboards to answer the questions that matter most to them.

    'We have doctors who are designers', says Hamza. 'They are designing their own dashboards using our entire dataset, combining millions of rows and records to get the answers that they are looking for'.

    Builders are everywhere. Just as the healthcare space is shifting away from only using analytics in financial departments and putting insights into the hands of frontline practitioners, the right tools democratize the ability to create new dashboards and even interactive analytics widgets and empower anyone within an organization to get the answers and build the tools they need. Such as many other industries, healthcare has to go through a technological transformation.

    Creating better experiences

    When it comes to the true purpose of healthcare analytics, Hamza summed it up perfectly:

    'In the end, it’s all about helping end users create a better experience'.

    The staggering volume of data that the healthcare industry creates presents a huge opportunity for analytics to find patterns and insights and improve the lives of patients. As datasets become more massive and the analytical questions become more challenging, healthcare teams will rely more and more on the analytics embedded within their EMR systems and other software. This will lead them to start using the presence (or lack thereof) and quality of those analytics when making decisions. Software companies that understand this will build solutions that answer questions and save lives, the ones that don’t might end up flatlining.

    Author: Jack Cieslak

    Source: Sisense

  • How big data is having a 'mind-blowing' impact on medicine

    istock000016682100doubleDell Services chief medical officer Dr. Nick van Terheyden explains the 'mind blowing' impact big data is having on the healthcare sector in both developing and developed countries.

    For a long time, doctors have been able to diagnose people with diabetes—one of the world's fastest growing chronic diseases—by testing a patient's insulin levels and looking at other common symptoms, as well as laboratory results.

    While there has been great accuracy in their diagnoses in the past, the real opportunity in healthcare at the moment, according to Dell Services chief medical officer Dr. Nick van Terheyden, is the role big data can play in taking the accuracy of that diagnosis a step further by examining a person's microbiome, which changes as people develop diabetes.

    "We can come up with a definitive diagnosis and say you have it based on these criteria. But now, interestingly, that starts to open up opportunities to say 'could you treat that?'" Terheyden said.

    He described these new advancements as "mind-blowing."

    "So, there is now the potential to say 'I happen to know you're developing diabetes, but I'm going to give you therapy that changes your biome and reverses that process, and to me that's just mind-blowing as I continue to see these examples," Terheyden said.

    He pinned a major contributor to the "explosion" of data to genomics, saying having additional data will increase the opportunity for clinicians to identify correlations that have previously been poorly understood or gone unnoticed, and improve the development and understanding of causation.

    "When the first human was sequenced back in the early 2000s, it was billions of dollars, and many years and multiple peoples' work and effort. We're now down to sequencing people in under 24 hours and for essentially less than US$1,000. That creates this enormous block of data that we can now look at," he said.

    Increasingly, Terheyden believes the healthcare sector will see the entry of data experts, who will be there to help and support clinicians with the growing influx of the need to analyse data.

    When asked about the impact technology has had on healthcare in developing countries, Terheyden said he believes medical advances will overtake the pace of developed countries, much like how the uptake of telephonic communication has "leapfrogged" in those countries.

    He said despite the lack of resources in Africa, for instance, the uptake of mobile devices is strong and networks are everywhere, which he says is having a knock-on effect on the medical sector as it is helping those living in remote areas gain access to clinicians through telehealth.

    Research by Ericsson predicted that, while currently only 27% of the population in Africa has access to the internet, data traffic is already predicted to increase 20-fold by 2019—double the growth rate compared to the rest of the world.

    Terheyden explained while infrastructure may be rather basic in places such as Africa, and some improvements still need to be made around issues such as bandwidth, telehealth has already begun to open up new opportunities, so much so that when compared to the way medicine is practiced in developed countries, it appears archaic.

    "I know there are still some challenges with bandwidth...but that to me is a very short term problem," he said. "I think we've started to see some of the infrastructure that people are advocating that would completely blow that out of the water.

    "So, now you remove that barrier and suddenly instead of saying, 'hey you need go to a hospital and see a doctor to have a test', we're saying, 'why would you?'"

    Despite the benefits, Terheyden expects clinicians, particularly in the western world, will be faced with the challenge of coping with how their roles are changing. He pointed out that they are increasingly becoming more of a "guide, an orchestrator, and conductor," versus the person that previously "played all the instruments, as well as being the conductor."

    He highlighted given how much medical information is out there, believing it doubles every 18-24 months, it would require clinicians to be reading 80-90 hours per week to keep up to date.

    "There's this change in behaviour to longer be the expert," he said. "You're not the Wizard of Oz. People don't come to you and you dispense knowledge; you're there as the guide."

    Source: Techrepublic.com


  • SAS: 4 real-world artificial intelligence applications

    SAS: 4 real-world artificial intelligence applications

    Everyone is talking about AI (artificial intelligence). Unfortunately, a lot of what you hear about AI in movies and on the TV is sensationalized for entertainment.

    Indeed, AI is overhyped. But AI is also real and powerful.

    Consider this: engineers worked for years on hand-crafted models for object detection, facial recognition and natural language translation. Despite honing those algorithms by the best of our species, their performance does not come close to what data-driven approaches can accomplish today. When we let algorithms discover patterns from data, they outperform human coded logic for many tasks, that involve sensing the natural world.

    The powerful message of AI is not that machines are taking over the world. It is that we can guide machines to generate tremendous value by unlocking the information, patterns and behaviors that are captured in data.

    Today I want to share four real-world applications of SAS AI and introduce you to five SAS employees who are working to put this technology into the hands of decision makers, from caseworkers and clinicians to police officers and college administrators.

    Augmenting health care with medical image analysis

    Fijoy Vadakkumpadan, a Senior Staff Scientist on the SAS Computer Vision team, is no stranger to the importance of medical image analysis. He credits ultrasound technology with helping to ensure a safe delivery of his twin daughters four years ago. Today, he is excited that his work at SAS could make a similar impact on someone else’s life.

    Recently, Fijoy’s team has extended the SAS Platform to analyze medical images. The technology uses an artificial neural network to recognize objects on medical images and thus improve healthcare.

    Designing AI algorithms you can trust

    Xin Hunt, a Senior Machine Learning Developer at SAS, hopes to have a big impact on the future of machine learning. She is focused on interpretability and explainability of machine learning models, saying, 'In order for society to accept it, they have to understand it'.

    Interpretability uses a mathematical understanding of the outputs of a machine learning model. You can use interpretability methods to show how the model reacts to changes in the inputs, for example.

    Explainability goes further than that. It offers full verbal explanations of how a model functions, what parts of the model logic were derived automatically, what parts were modified in post-processing, how the model meets regulations, and so forth.

    Making machine learning accessible to everyone

    From exploring and transforming data to selecting features and comparing algorithms, there are multiple steps to building a machine learning model. What if you could apply all those steps with the click of a button?

    That’s what the development teams of Susan Haller and Dragos Coles have done. Susan is the Director of Advanced Analytics R&D and Dragos is a Senior Machine Learning Developer at SAS. They are showing a powerful tool that offers an API for a dynamic, automated model building. The model is completely transparent, so you examine and modify it after it is built.

    Deploying AI models in the field

    You can do everything right when building and refining a machine learning model, but if you do not deploy it where decisions are made it will not do any good.

    Seb Charrot, a Senior Manager in the Scottish R&D Team, enjoys deploying analytics to solve real problems for real people. He and his team build SAS Mobile Investigator, an application that allows caseworkers, investigators and officers in the field to receive tasks, be notified of risks and concerns regarding their caseload or coverage area, and raise reports on the go.

    Moving AI into the real world

    When you move past the science project phase of analytics and build solutions for the real world, you will find that you can enable everyone, not just those with data science degrees, to make decisions based on data. As a result, everyone’s jobs become easier and more productive. Plus, increased access to analytics leads to faster and more reliable decisions. Technology is unstoppable, it is who we are, it is what we do. Not just at SAS, but as a species.

    Author: Oliver Schabenberger

    Source: SAS

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