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

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: 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

Dell 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.

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.

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

How digital transformation drives innovation at health clubs

‘Wellness’ was the most talked about topic during the entire lockdown. With nowhere to go, it took a big hit initially but soon it found solutions in digital transformation. Thriving at a faster pace, this industry accelerated the implementation of digitization, resorted to healthcare app development and replaced its offerings with digital classes, on-demand content and live streaming.

The wellness industry runs on majorly three pillars- nutrition, fitness and travel. Health clubs are part of the fitness segment of this industry which focuses on whole-body wellness, unlike gyms where the focus is devoted to physical wellness more. A health club offers a comprehensive fitness approach, providing recreational sports and exercise facilities at one place to consumers.

According to studies, health clubs globally witness an increase of 4.6% annually. Similar to other industries, health club industry also benefited from technology during the pandemic by including healthcare app development, online on-demand services, etc, in its offering.

How digital transformation is useful for health clubs?

Fitness was never this important for people as it has been over the last few years. During the lockdown, digital solutions were welcomed to reach those lockdown fitness goals. This sudden need for a shift to digitization paved the path for many interactive and innovative fitness solutions. Even though the industry started this transformation a little late, it adapted quickly to this change.

Engaging with the members

Engaging with the clients is the root driver of digitization for health clubs. Omni-channel trends were existing but engaging with clients accelerated during this period. Brands sought after pre-recorded content, live streaming for engaging with their members. They also used text experiences to connect with their clients.

Using the technology to bridge the gap of not being accessible physically, clubs who launched their apps to have a platform for their content, stood out of the crowd. The virtual offering made it easy for clients to continue their fitness routine. Many took help from the healthcare app development companies for making this transition. Pretty sure, your gym trainer was teaching you over WhatsApp and Zoom. Wasn’t he?

Making operations easy

Digitization brings with it, ease of operations. In every industry, digital transformation has proved to improve functioning and reduced costs over time by streamlining the processes. Health clubs also took advantage of the technology to reduce their operational costs and provide an easy, seamless member experience through CRM systems and easy bookings.

Bringing personalized experience

Digitization helped health clubs to bring personalized experiences for their clients. Mobile applications enabled brands to provide personal fitness plans to each of their clients based on their needs. This was earlier done face to face but with digitization, this process has become seamless, easy and virtual. Healthcare app development integrated with AI takes this to another level.

Using data

Data is of prime importance when we talk about digital transformation in healthcare. Using the data collected through IoT devices and applications, brands can develop a 360-degree view of the client’s needs. Offering a comprehensive personalized solution to their problems, it also helped in learning about the behavior and preferences by using the algorithms and executing personalized outreach.

The data can also help the brand to anticipate the fitness needs of their clients by using AI-driven methods. People need solutions to problems and data can help brands discover those problems even before their clients discover them.

Digging deeper into a fitness experience

The IoT devices help brands in expanding this digital transformation. By integrating wearable devices, applications in their health club experience, brands can elevate their member experience. They can also provide a deeper insight into their fitness regimen through analytical analysis of the data collected. There are some health clubs who are pioneering this segment.

A roadmap to how Health Clubs can leverage the benefits of digital transformation

Digital transformation in healthcare is not an easy process. Simply investing in technology will not suffice. Digital transformation in healthcare requires a stringent cultural shift. It applies to health clubs as well.

Understanding the members and the experience you want to serve based on your brand is important. This helps in ensuring that the new technology and the fitness experience you want to serve your members complement each other. They should be in sync with each other and bring an authentic fitness experience.

From the numerous ways that health clubs can leverage the benefits of digital transformation, we have a few in the list.

Smart machines: Exercise and gym equipment that can be connected to the cloud and is responsive to an individual, is a popular thing among health clubs and gyms. Some of these smart equipment are even programmed with machine learning technology that enables self-learning and improvising the fitness journey of the user.

3D body scanning: This technology can be used to create effective and accurate personal programs targeting the specific problems of the clients.

Using SMS and texting services: Apart from providing only an exercise routine, a health club can also leverage the use of SMS or text services to motivate their members and give health reminders. This can also be used to send out awareness messages on mental health and other issues concerning the members. This will help in creating a more connected engagement channel.

Wearable 3rd party devices: Health clubs can provide members with 3rd party wearable devices to track their progress and vitals inside and outside the gym for providing in-depth analysis.

Digital transformation in healthcare has proved to be of supreme advantage. Adapting to advanced technology in the health club industry has and will prove to be groundbreaking. Not only does it bring efficiency in operations, but it also gives a competitive advantage. 

Author: Robert Jackson

Source: Datafloq

Natural Language Processing is the New Revolution in Healthcare AI

After a late start, Artificial Intelligence (AI) has made massive inroads in penetrating the pharmaceutical and healthcare industries. Nearly all aspects of the industry have felt impacts from the increasing uptake of the technologies – from visual and image diagnostics, to Machine Learning and Neural Networks for drug discovery, data analysis and other models. One of the more recently ascendant forms of AI in healthcare has been Natural Language Processing (NLP) – which promises to revolutionize digitization, communication and analyzing human-generated data. We explore the foundations of the technology and its current and future applications in the industry.

What is Natural Language Processing?

NLP refers to the practice of using computer methods to process language in the form generated by humans – often disordered, flexible and adaptable. The phenomenon is not limited to AI technology; it first originated in the 1950s with symbolic NLP. This was a rudimentary concept which was originally intended for machine translation applications – first demonstrated by the 1954 Georgetown Experiment. Symbolic NLP was largely founded on complex, manually defined rulesets: it soon became apparent that the unpredictability and fluidity of human language could not truly be defined by concise rules. 

With the exponential growth in computing power, symbolic NLP soon gave rise to statistical NLP – largely pioneered by IBM and their Alignment Models for machine translation. IBM developed six such models, which enabled a more flexible approach to machine-based translation. Other companies soon followed, and statistical NLP evolved into what we know today as self-learning NLP, underpinned by machine learning and neural networks. The developments in its ability to recognize and process language have put it to use in fields far more diverse than translation – although it continues to make improvements there too. 

While symbolic NLP is still often employed when datasets are small, statistical NLP has been largely replaced by neural network-enabled NLP. This is due to how neural networks can simplify the process of constructing functional models. The trade-off lies in the opacity of how they operate – while statistical methods will always be fully transparent and the path to how they obtain their results will be fully visible, neural network models are often more of a “black box”. Their power in interpreting human language is not to be underestimated however – from speech recognition, including the smart assistants we have come to rely on, to translations and text analytics, NLP promises to bridge many gaps.

Current Models in Healthcare

One of the most obvious applications of NLP in the healthcare industry is processing written text – whether that be analog or digital. A leading source of data heterogeneity, which often prevents downstream analysis models from directly utilizing datasets, is the different terminology and communication used by healthcare practitioners. Neural-enabled NLPs can condense such unstructured data into directly comparable terms suitable for use in data analysis. This can be seen in models inferring International Classification of Diseases (ICD) codes based on records and medical texts.

Medical records present rich datasets that can be harnessed for a plethora of applications with the appropriate NLP models. Medical text classification using NLPs can assist in classifying disease based on common symptoms – such as identifying the underlying conditions causing obesity in a patient. Models such as this can then be later used to predict disease in patients with similar symptoms. This could prove particularly revolutionary in diseases such as sepsis – which has early symptoms that are very common across a number of conditions. Recurrent Neural Network NLP models using patient records to predict sepsis showed a high accuracy with lower false alarm rates than current benchmarks. 

These implementations are also critical in clinical development. But clinical operations also generate another source of unstructured data: adverse event reports, which form the basis of pharmacovigilance and drug safety. We already explored the applications of AI models in pharmacovigilance in a different article – but their introduction to that field particularly highlights the need for increased cooperation with regulatory authorities to ensure all stakeholders remain in lockstep as we increasingly adopt AI. 

Beyond Healthcare

But NLP can also be applied beyond human language. Exploratory studies have also shown its potential in understanding artificial languages, such as protein coding. Proteins, strings of the same 20 amino acids presenting in variable order, share many similarities with human language. Research has shown that language generation models trained on proteins can be used for many diverse applications, such as predicting protein structures that can evade antibodies. 

There are other sources of unstructured, heterogeneous data that are simply too big to pore over with human eyes in cost-efficient manners. Science literature can be one of these – with countless journal articles floating around libraries and the web. NLP models have previously been employed by companies such as Linguamatics to capture valuable information from throughout the corpus of scientific literature to prioritize drug discovery efforts.

Quantum computing also represents a major growing technology, and firms are already seeking to combine it with NLP. One example is Quantinuum’s λambeq library, which can convert natural language sentences to quantum circuits for use in advanced computing applications. Future endeavors in the area promise massive advancements in text mining, bioinformatics and all the other applications of NLP.

Research conducted by Gradient Flow has shown that Natural Language Processing is the leading trend in AI technologies for healthcare and pharma. This is for good reason – AI can prove useful in a cornucopia of different implementations, but NLP is what facilitates the use of heterogeneous, fragmented datasets in the same model. Integrating existing and historical datasets, or new datasets generated in inherently unstructured manners – articles, records and medical observations, will remain crucial in the progress of other AI technologies. NLP is what enables that – and future advancements are likely to see its prominence rise on its own, as well.

Author: Nick Zoukas

Source: PharmaFeatures

Powering the Future of Healthcare: Microsoft BI Takes the Lead

New modifications are important to be done in the distribution methods of healthcare all over the globe. They need to reduce their operating costs, improve the management of their human resources, update and strengthen their internal procedures, and work toward providing better care for their patients. These businesses need to exercise greater guidance if they are to continue adding value to society in light of the rapid pace at which government regulations are updated and the growing expectations of the general public. 

Introduction to Microsoft Power BI

The development of healthcare technology has made it possible to provide greater treatment for patients, and healthcare analytics is no exception to this trend. Microsoft Power BI is a robust application that has fundamentally altered the process of data interpretation and analysis in the healthcare industry. Insights that were previously unattainable for healthcare practitioners have become available as a result of advances in data collection, analysis, and sharing capabilities that operate in real-time.

You can facilitate (BI) for healthcare by putting into place an up-to-date business solution. Power BI Developers assist businesses in extracting additional worth from the data they have collected. Unlock your data and display it in a framework that makes it simple for everyone to identify patterns and outliers. Implementing a comprehensive solution for managing the revenue cycle will allow you to obtain additional information regarding your claims, customers, or suppliers.

The current Healthcare Industry is facing several Challenges

1. Huge data

A large number of medical data presents a challenge for those working in the healthcare sector. It presents an important obstacle in terms of organizing and evaluating the data, although it makes it possible for professionals in the healthcare industry to access more information than ever before. When there is such a massive quantity of data, it can be challenging to recognize patterns. For the industry to effectively control this expansion, it requires advanced data analytics solutions that can manage enormous amounts of data.

2. Errors in medical reports

The difficulty of preventing healthcare errors caused by a shortage of data is a significant problem that faces those who work in the medical field. Healthcare professionals can’t make rational decisions about a patient’s treatment if they do not have access to reliable data. This can result in errors, incorrect diagnoses, and even mortality in some cases. The absence of data may be attributable to a variety of factors, including insufficient instruction, inadequate recordkeeping, and outmoded technology.

3. Data privacy and security

It is impossible to overstate the significance of data in healthcare. The complications of healthcare data can be difficult to navigate, which can be a huge challenge. The data that pertains to healthcare is frequently disorganized, stored in silos, and difficult to access. The delivery of healthcare presents its own unique set of challenges, not the least of which is ensuring that statistics are accurate and comprehensive. Because patient information is considered to be extremely confidential and is subject to numerous stringent regulation requirements, data privacy, and security are also significant concerns in the healthcare business.

4. Employee data administration

Because there is a growing volume of employee data that needs to be saved, handled, and evaluated, it can be challenging to ensure that the data is accurate and that it is secure. If documents on the number of physicians, nurses, and other staff are not readily available, it may be difficult to prepare for emergencies, designate responsibilities, and ensure that operations are carried out effectively. For this purpose, they need a combination of specialized knowledge, cutting-edge technology, and data analytics to guarantee the precision of employee records.

How Power BI can revolutionize healthcare analytics

The field of healthcare produces a significant amount of data. There is a plethora of information that is readily accessible, ranging from electronic health records to invoicing data, that can assist medical professionals in making decisions regarding patient treatment and business operations that are more educated. Having said that, the difficulty lies in being able to evaluate and understand these facts accurately. This is where Microsoft Power BI comes in.

1. Identify patterns and trends

You may be able to recognize trends and patterns in your data with the support of Power BI. The capability of creating data models within Power BI is one of the program’s most useful features because it enables users to conduct various types of analyses on their data. You can quickly connect to multiple data sources and integrate them into a singular perspective thanks to the data connections that are built right in.

2. Study of patient comments

The ability of providers to recognize areas that could use development and implement the necessary changes to improve the experience of patients can be gained through the analysis of remarks received from patients. Obtaining information regarding customer grievances, suggestions, and assessments of satisfaction is made simple for healthcare organizations who use Power BI. By using this tool healthcare practitioners can gain a deeper understanding of the requirements of their patients and more successfully modify their services to satisfy those requirements.

3. Proper data modeling

Data modeling is the method of building a graphic representation of data and the interactions between that data to gain a better understanding of how that data can be used to support healthcare targets. Power BI comes equipped with robust data modeling capabilities, which make it possible for medical professionals to construct intricate data models that can then be put to use in advanced analytical procedures.

4. Epidemic trend breakdown

The evaluation of outbreak trends with Power BI is a vital tool that is used by health specialists to monitor the spread of illnesses and discover prospective breakouts before they develop into significant disasters. Researchers can determine potential areas and take precautionary measures to mitigate the spread of disease by evaluating data on historical pandemics as well as patterns that are occurring currently in the world. Tracking a variety of variables, such as the number of cases, dissemination pathways, and demography of those who are afflicted is a vital part of the process of analyzing the pattern of an outbreak.

5. Improved communication

Power BI is consistently updated with new features by Microsoft, which contributes to the program’s robustness and versatility. The power BI solution allows for every person involved to have access to pertinent reports, assessments, documents, folders, etc., and it is also beneficial to collaboration between physicians, surgeons, and other medical professionals. All of that is accomplished while accurately, thoroughly, and controllably representing high-level data.

Bottom Line

The field of healthcare is one in which Power BI Developers play a particularly significant part. These software developers can be of assistance to healthcare organizations by developing interfaces and reports that offer information about improvements for patients, the financial performance of the organization, and other important measures.

In general, using Power BI for healthcare statistics opens up a world of unimaginable opportunities. Healthcare organizations can remain ahead of the curve if they adopt the latest trends and advancements in the industry and embrace them.

Author: Daniel Jacob

Source: Datafloq

Recent study unveils lack of sound data infrastructure in healthcare organizations

In the race to unearth enterprise insights, the modern health system is like a prospector whose land contains precious metals deep beneath the surface. Until the organization extracts and refines those resources, their value is all but theoretical. In much the same way, only after harmonizing its data can a health system run analytics to inform stronger decision making and realize its full potential.

In a survey commissioned by InterSystems, Sage Growth Partners found that most health system executives prioritize analytics as a fundamental step toward their broader goals. But they don’t have the tools to get there — at least not yet.

Just 16% of integrated delivery networks rate their clinical data quality as excellent, 55% consider their supply chain data poor or average, and 87% say their claims data is poor, average, or good. All told, only 20% of organizations fully trust their data. Yet providers recognize the urgent need for healthy data to power analytics, as evidenced by the 80% who say creating and sharing high-quality data is a top priority for the next year.

These data challenges have real consequences. Poor, untimely decisions and the inability to identify gaps in care translate to severe financial impacts for the enterprise and less desirable outcomes for patients. But while the precious metals remain underground, health systems have the opportunity to start digging today.

Barriers to Healthcare Insights

Now 12 years after the HITECH Act accelerated the move to electronic data, healthcare has yet to address bedrock issues such as the lack of a centralized database, challenges integrating multiple data sources, low-quality information, and the failure to create standardized reports. Sage’s findings revealed a harsh truth: Health systems cannot use analytics to generate actionable insights until they overcome these obstacles.

More than half of surveyed executives acknowledge that poor data impedes enterprise decision making and their ability to identify gaps in care. What’s more, 51% point to data integration and interoperability as the most significant barriers to leveraging analytics for the good of the organization.

On the ground, the disconnect has meant that health systems are strapped with huge data latency and duplication challenges, despite massive investments in data warehouses. Although many organizations designed dashboards and predictive or prescriptive models, most of these tools either fail to reach production or scale past the walls of a single department due to workflow integration issues. Clinical, claims, and other data, meanwhile, remain siloed.

Health systems simply haven’t built the infrastructure to produce accurate, real-time, high-quality data.

Healthy Data and Analytics: Healthcare’s Future

COVID-19 forced C-suites to make big decisions more often and more quickly, from managing overworked staff to allocating resources among sick and dying patients. Even tracking health outcomes morphed into a tall task. The whiplash of the pandemic led the industry to an inflection point: 85% of executives told Sage that real-time, harmonized data is vital for leaders to make informed operational decisions.

To make the right moves at the right time, health systems need the most reliable information. That requires strong data technology from start to finish, encompassing pipeline capabilities, aggregation, normalization, standardization, a robust data model, and consistent access.

If any element of that equation is missing, health system decision making will continue to lag. But success can transform the enterprise.

Imagine a group of executives — each trusting their data — receiving timely, standardized reports about their health system. Knowing the underlying data is healthy, they would all be confident in the veracity of the insights and ready to draw conclusions. One InterSystems customer, for example, can see in real time and retrospectively how many patients are within a given department, empowering informed staffing decisions and lowering costs with the click of a button.

Clinical departments stand to gain similar benefits. Interoperability enables them to see previously hidden correlations, improving patient care and outcomes. At InterSystems, we saw how a precise understanding of data enabled a health system client to set effective data governance protocols, which steered clinicians to take quick, knowledgeable action when it mattered most.

And at a time when artificial intelligence and machine learning models promise to optimize patient care, it’s all the more important that clinicians trust the data driving those insights. Otherwise, these advances will struggle to deliver anything beyond hype.

Bridge the Healthcare Insights Gap

Most health systems recognize that it’s time to harmonize their data in pursuit of analytics-driven insights. Organizations that don’t act quickly can bet that their competitors will. When everyone is sitting on precious metals, the only reasonable option is to invest in the technologies that are proven to sift soil and rock from the gold.

Author: Fred S. Azar

Source: InterSystems

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

Trendsetting Applications of AI in Healthcare

We live in a digital age, so it’s not surprising that the healthcare industry follows suit. From the data-driven insights of wearables to mobile apps that help manage chronic conditions, it’s clear that technology is changing healthcare forever.

But what exactly are people getting their hands on? As a long-time public health physician and leader on digital transformation in healthcare, here are some of the most exciting trends in digital health that I see shaping the industry right now.

Consumer AI

Many health systems have already chosen to incorporate artificial intelligence (AI) into their operations — and it’s not just for cost-saving purposes. With the help of AI, healthcare organizations can better tackle complex challenges related to population health management like declines in patient satisfaction, rates of readmission and rising costs of care.

Apart from this, consumer AI also has a significant role in improving the current state of healthcare. At home, for instance, AI can help patients better understand their symptoms and treatments by making personalized recommendations generated from an individual’s own unique biological data.

After all, personalization is key to providing better healthcare outcomes. What is also helping push consumer AI further into healthcare is its use as a popular application for wearables. Apps such as Google Fit and Apple HealthKit provide individuals with an in-depth picture of their health and wellness through tracking data, which can potentially help them better understand their conditions.

Challenges in working on improving the regulatory framework will persist. In what some experts describe as “digital trust,” these issues of privacy in health data will need to be addressed as innovations rapidly emerge in response to the needs for patient-centered care.

Big Health Data

The proliferation of wearable technology also helps contribute to the rise of big health data, an ever-increasing trove of information that can offer businesses and healthcare providers useful insights about patient care.

For instance, big health data can be used to better predict the onset of chronic conditions among patients who are predisposed to them. It can also create more efficient and effective clinical pathways and improve hospital management operations. This aligns with recent calls to take the emphasis off people’s medical records and on overall health plans, as well as utilizing data to deliver information over simply supporting transactions. 

There is also now a greater focus on developing big health data initiatives that enable secure exchange between healthcare providers and their patients, especially when it comes to cloud-based technologies that offer real-time tracking of patient health data.

Cloud Data

The adoption of cloud computing technologies is also helping to usher in a new era of digital transformation in healthcare. Cloud computing enables rapid data access and processing, which can help healthcare providers make more informed, real-time decisions.

Healthcare organizations are also starting to use cloud-based technologies for better information management. This includes adopting solutions such as electronic health records (EHRs) since they allow healthcare providers to store, manage and share data more easily.

Cloud networks are also paving the way for better telehealth solutions like remote medical monitoring and mobile health services. In the future, I see virtual healthcare services as becoming an increasingly viable option for patients who want to stay at home.

Drug Discovery With Machine Learning

With the rise of big health data comes an increased emphasis on machine learning (ML) in healthcare. ML refers to predictive analytics to sift through huge amounts of medical data and identify patterns that can be used to improve patient outcomes.

In the coming years, I predict we’ll see a bigger focus on applying ML technologies to drug discovery, drug development and pharmaceutical industry processes. An example of this is the use of ML to predict patient drug responses that can help identify which patients will benefit the most from a certain treatment.

This type of predictive analytics works well with genetic data and offers clues about how an individual will react in specific situations. Using predictive analytics in this way enables healthcare providers to deliver targeted care plans that are based on individual patient needs.

Personalized Genetic Testing

Genetic testing is another area where predictive analytics will play a major role in the future of consumer AI and healthcare. With genetic testing, healthcare providers can analyze an individual’s DNA to create a model that predicts how they are likely to respond to certain drugs or treatments.

Using this type of advanced predictive analytics enables drug developers to develop personalized treatment plans that could potentially improve the lives of patients with certain conditions. For example, pharmacogenetic testing has recently been used to treat chronic pain in children, and the process can potentially save billions of dollars that go to ineffective drug therapies.

With the proliferation of this technology and service, consumers will have more options for genetic testing, and these tests can give healthcare providers access to even greater amounts of data in order to improve patient outcomes and treat patients more effectively. However, more research is needed to determine the benefit of this testing broadly in diverse populations.

Bottom Line

Digital health transformation will continue to gain momentum over the next few years, and healthcare providers are increasingly looking toward digital technologies for ways to improve patient care while determining the best practices in a regulatory framework.

As more people worldwide get access to smart technology in their homes, health-related apps and services, including telehealth solutions, will continue to become an increasingly viable option for patients who want to stay home but still get quality healthcare. And as innovation continues to move ahead, global policies and regulations will have to determine how best to use these technologies to ensure safety and efficacy.

Author: Anita Gupta

Source: Forbes

Why market intelligence has never been more important in healthcare