The main areas of use of biotechnology today are medicine, pharmaceuticals, agriculture, and other industries, where BioTech innovations can reduce the cost of production, speed up the development of vaccines, or model the changes of genomel. But, the development in this area is very expensive, and the necessary research requires a lot of time, and all kind of resources.
The cost of BioTech solutions is also influenced by the availability of technological and human resources. Research centers, special devices, and highly qualified specialists are needed for the development, testing, and implementation of advances in biotechnology and healthcare. The more there are, the lower the cost of producing medtech products due to the ability to scale development. At the same time, it is necessary to demo the statistically representative data, which demands the repetition of experiments and very careful documentation of all the experiments’ work circles, which create additional costs.
It is necessary to increase capacity in research centers through the purchase of modern and productive equipment to reduce the cost of technology. Also, other ways are needed to modernize production facilities, which will help to speed up the Design-Build-Test-Learn cycle. It should be borne in mind that the equipment itself will not help to process and structure huge amounts of data. As one of the way to optimization, artificial intelligence integration can work nowadays. They take the burden off specialists, dismissing the routine work, and in some ways can structurize the documentation flow and research data storage.
Examples of the use of AI and ML in biotechnology
Scientists from Russia and Belarus have developed a special substrate for efficient stem cell growth. This substrate can be used to develop new materials and technologies in the field of regenerative medicine.
According to the representative of the research group, such substrates can be created based on bacterial cellulose, which is produced by the bacteria Acetobacteraceae bacteria, and which is modified with cerium oxide nanoparticles, which give it unique bioactivity and provide accelerated division of stem cells on its surface, which is the main tool of regenerative medicine.
These cells can self-renew, divide through mitosis and differentiate into specialized cells, that is, turn into cells of various organs and tissues. The researchers used special fluorescent stem cells from transgenic mice with a mutation that gives a green glow to all cells in the body. This made it possible to visualize stem cells on a substrate and analyze the process of their accelerated division.
By Amit Garg, vice president of analytics, Gramener.
The advent of the pandemic and more recent vaccination efforts means there’s never been a time where more people’s Personal Identifiable Information (PII) and Personal Health Information (PHI) in health records and medical documentation are in circulation. It’s vital to protect patients’ PII and PHI, which can include information on their age, race, or medical history, especially given that cyberthreats and fraudulent activity related to the Covid-19 vaccine are increasing.
With so much patient and clinical trial data being stored and shared at any given time, it’s becoming increasingly challenging for pharmaceutical companies to efficiently ensure that patient information is protected. Experts say that medical data is up to 50 times more valuable than credit card data.
Here’s where artificial intelligence comes into play: AI and machine learning (ML) solutions can not only automatically identify what information is classed as PII in a given record, but it can also then automatically redact or anonymize that data to make sure that no adversary can identify the patients.
Joining the dots with AI
AI algorithms use advanced methods such as entity detection, entity extraction, and entity-relationship management to handle patients’ PII and PHI from a given document. This involves identifying and categorizing key information in the text using Named Entity Recognition (NER), a form of Natural Language Processing (NLP).
Useful libraries for teams using NER include Stanford NER, spaCy’s EntityRecognizer, CliNER (a domain-specific NER tool that has been trained on clinical texts), or BioBERT (a domain-specific language representation model pre-trained on large-scale biomedical corpora).
NER works to safeguard PII in the context of healthcare by identifying different elements of a single patient’s PII across multiple health records. In one document the name and age may be present, and in another, age and race may be present but not the name, while in another religion and race, and so on.
Any hacker with access to each document and each data point would be able to join the dots to match all PII to the single person. To prevent this, an intelligent entity detection and extraction solution can identify this information across the documents and redact and anonymize the correct data to prevent reidentification of the patient.
If the solution is not able to de-identify (or redact) the information completely, it will score the subjects based on the probability of re-identification. The publication would then know the risk in advance and take the appropriate action, i.e., to assume the risk and publish or to not publish until specific confidence is reached.
Machine learning is possibly the most disruptive technology of the 21st century. Good machine learning systems will, with limited interaction from a doctor, will be able to analyze almost any kind of information so that doctors can make better decisions.
But since a machine learning system is trained on data from humans, it reflects their internal bias, and can magnify them. Hospitals need to focus not only on what advantages these systems are bringing but also the biases that they encourage. WebText, a solution used to train natural language processing to analyze new articles and documents, was trained on posts from Reddit.
Reddit is almost 70% men with more than half of the users are from the US. The majority of the users are under the age of 35. These biases in the data create machine learning solutions that reflect these biases. One criticism, for instance, when asked “A man is a doctor, as a woman is, too” responded with “nurse.”
AI systems used in hospitals have already shown to be able to do incredible things from being able to diagnose disease from a simple, but also accomplish human mistakes at the speed of a machine; for example, an AI system rejected black medical students because data it was trained on was principally white students.
To prevent these effects companies need to carefully monitor these new artificial employees and make sure they are meeting the standard of governance reflecting the values of the company and the law. This can only be accomplished through specific tools that allow you to enter the mind of these artificial employees and understand how they think.
“We believe consumer health technologies — apps, wearables, self-diagnosis tools — have the potential to strengthen the patient-physician connection and improve health outcomes,” said Dr. Glen Stream, Chairman of Family Medicine for America’s Health. It is this sentiment that will perhaps shape tech adoption in healthcare through 2021 and beyond, the keywords being accessibility and connectivity.
As the world reels from the effects of the COVID-19 pandemic, we look to medical advances that will shape the future of healthcare. Emergent healthcare tech must connect a socially-distanced world to offer greater healthcare solutions for a greater portion of the population. Through cloud data and artificial intelligence, these solutions are increasingly possible.
Consumer-focused, accessible technologies are transforming the healthcare industry, with impacts likely to be felt as we move into 2021. COVID-19 quickened this transformation, and now healthcare professionals and patients alike look to benefit from the connective devices and technologies of the future.
Entering the Matrix through Digital Healthcare
A few years ago it might have seemed absurd to entertain the notion of increasingly virtual healthcare. The coronavirus changed that. Now, state and local governments are breaking down barriers to allow for novel, digital treatment plans that can take place over a smartphone video call. This has been a groundbreaking shift in terms of healthcare accessibility.
Telehealth innovations are emerging that offer everything from cardiology to infectious disease treatments all through virtual platforms. Care providers are even cutting costs through tele-paramedicine, which allows emergency patients to speak with specialists before they even make it on an ambulance. In turn, unnecessary transportation can be avoided for cost savings for patients and providers alike.
Throughout 2021, we will likely see vertical growth of telemedicine as more and better data analytics, paired with smart software, build a matrix for remote healthcare possibilities.
Alexa, Track My Medical Records
The digitization of healthcare is trending into smart home systems. These hubs of living room convenience are making waves in at-home healthcare, offering care coordination for chronic disease management. In the landscape of COVID-19 concerns, such innovations offer the kind of safety and accessibility needed for vulnerable patients.
Programs designed with Amazon’s Alexa in mind have made possible the tracking of diabetic information, blood pressure, medication compliance, and more for the benefit of at-home users. The online nature of these devices offers physicians the ability to experience real-time metric tracking alongside wearables to better monitor patient health.
Over the past several years, we’ve heard a lot of predictions about new and innovative ways artificial intelligence (AI) will dramatically impact healthcare. For example: robot doctors, drug discovery and clinical diagnosis. While there is – and should be – excitement around what innovation may come, we need to look at what technologies exist today, and more importantly, what providers need to deliver higher quality and more coordinated care at a lower cost.
In 2020, we’ve watched a pandemic upend the healthcare industry as ICUs became overwhelmed, clinics had to close their doors and patients avoided care. Physician burnout that was 42% in 2018 is soaring as COVID-19 cases surge and disruptions continue.
With this backdrop, it’s time for us all to agree that the biggest need in healthcare right now is to help providers do their job more effectively and remove burdens that both stand in the way of the delivery of care and lead to that unacceptable rate of burnout.
In 2019, the U.S. is predicted to have spent more than $3 trillion on healthcare, over 95% of which was dispersed through an insurance company, where every dollar must be codified. The current international standard for those codes is ICD-10, which contains more than 70,000 codes for diagnoses. With that many codes, it comes as no shock that estimates for annual medical coding errors are around 30%, with billing errors reaching as high as 80% in many cases.
With such a complex coding system, we are adding to a provider’s administrative burden which, for many, is already at a breaking point. Today, a physician spends an average of 16 minutes on administration, which adds up to several hours every single day.
By Dan Schulte, MBA, CHFP, senior vice president, provider operations, HGS.
As outbreaks of COVID-19 continue to crop up around the country, the ongoing public health crisis is just one facet of the situation; economic disruption is another grim reality, including for the healthcare industry itself. The American Hospital Association estimates COVID-19 will result in losses of $202.6 billion for the country’s hospitals and health systems due to factors such as the cancellation of nonemergency procedures; the high cost of treating a patient with COVID-19; and the millions of Americans who could become suddenly uninsured due to the economic implications of the virus.
Providers must improve cash flow to remain stable, which will require new revenue cycle management strategies supported by technology. Artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) together can provide an effective automation strategy that will help healthcare systems recover and retain more of their revenue — while boosting patient satisfaction — as they navigate this costly crisis.
Nine revenue cycle functions ripe for automation include:
Prior authorizations: With manual prior authorizations requiring an average of 21 minutes and as much as 45 minutes per transaction, the opportunity to drive cost savings through automation is significant. Because of well-defined business rules in this area and structured data that systems exchange in conducting prior authorizations, RPA can significantly improve this process: Implementing a “bot” that can perform the same tasks repetitively and without variation can help reduce error rates, so patients can get the authorization they need quickly, and lower the likelihood of claim denials.
Eligibility and benefit verification: While fully electronic transactions account for more than 84% of all eligibility and benefit verification transactions — a positive development — more can be done to reduce wasteful spending in this part of the revenue cycle. As the starting point for care delivery, this function represents a significant potential for improvement via intelligent automation. The focused manager will ensure that the EDI tools bring the right data across to the patient accounting system (timely, accurate and complete data), and will have the necessary add-ons to find the last 15% of data from screen scraping and outsourcing to a reliable service provider.
At Electronic Health Reporter, we take innovations from healthcare companies very seriously. For nearly a decade, we’ve featured their work, products, news and thought leaders in an effort to bring our readers the best, most in-depth insight about the organizations powering healthcare. That mission lies at the heart of all we do, for the benefit of our audience.
For the first time, we are officially naming some of the most progressive companies in healthcare technology, in our inaugural class of the best, most innovative brands serving health systems and medical groups. Our call for nominations for this “award” series received hundreds of submissions. From these, we selected the best companies from that class. The work these organizations are doing is forward-thinking; award-worthy, we think. We think you’ll agree with all of our choices.
In each of the profiles to come in this series, we’re share their stories — from their own perspective, through their own responses to our questions about what makes them remarkable. Some of the names featured here you’ll recognize, some you won’t. But we believe you’ll agree – all those profiled are doing innovative, groundbreaking work! That said, here’s a member of our inaugural class:
What is the single-most innovative technology you are currently delivering to health systems or medical groups?
We use AI-backed systems to help hospitals resolve avoidable variation, harm, and mortality with typical monitoring and reporting systems that currently are only able to detect 10% of what our systems can detect. Meaning, through our systems, we can see substantially more information then what current hospital systems are providing executives. Using the world’s largest patient dataset (140 million records from 46 countries) and built around the work of the developer of the world’s most commonly used patient safety system, POSSUM, we have built predictive applications that save lives, prevent harm and help hospital systems improve margins.
There’s never been a more exciting era in the healthcare IT space than now. The intersection of disruptive technological innovation and a more tech-savvy generation of customers provides endless opportunities across a wide range of medical applications.
The healthcare industry has traditionally been reluctant to embrace tech. Given the strict regulations, the sensitivity of healthcare, and the potentially deadly consequences if something does go wrong, this reluctance is understandable. Slowly but surely though, healthcare is embracing IT, thereby unleashing new levels of efficiency and customer satisfaction.
Here’s a look at some of the key tech trends that are fast establishing a foothold in healthcare.
Technology has changed a dramatic amount over the last ten years alone, and digital health is now ever-present. From telemedicine and health-related wearables to online medical providers and health resources, digital health is growing faster than ever.
Consumers are using digital resources to better manage their health levels, and medical facilities are using digital technology to track, manage, and improve the health of their patients. Now, patients do not even have to meet in person to get the treatments or advice they need.
Putting power back into the hands of the patient while giving doctors and medical professionals access to the tools and data they need; the rise of digital healthcare is something that cannot be ignored.
Digital Healthcare for the Individual
Consumers have access to more technology than ever before, and that’s good news for those in the healthcare sector. Now that consumers can easily buy a wide range of wearable technologies, they can monitor their health levels from anywhere, and provide their doctors with detailed information. Going further than external wearables like fitness trackers, we have also seen pacemakers with their own dedicated monitoring apps.
This unprecedented level of data gathering is proving vital for catching early signs of health issues. The health industry is being forced to keep up the pace of tech innovation simply because of the wide range of benefits that those new technologies bring.
More than 50 organizations – from major tech giants to startups and healthcare industry leaders – convened by the Consumer Technology Association (CTA) have developed the first-ever ANSI-accredited standard for the use of artificial intelligence in healthcare. This standard, part of CTA’s new initiative on AI, is the first in a series that will set a foundation for implementing medical and healthcare solutions built on AI.
“This standard creates a firm base for the growing use of AI in our healthcare—technology that will better diagnose diseases, monitor patients’ recoveries and help us all live healthier lives,” said Gary Shapiro, president and CEO, CTA. “This is a major first step – convening some of the biggest players in the digital health world – to help create a more efficient healthcare system and offer value-based healthcare to Americans.”
AI-related terms are used in different ways, leading to confusion – especially in the healthcare industry, including telehealth and remote patient monitoring. To address this problem, CTA announced the working group with 30 members less than a year ago, which now includes a wide range of decision makers from 52 organizations and member companies to develop a standard built on consensus.
The standard – 11 definitions and characteristics – provides a framework for better understanding AI technologies and common terminology so consumers, tech companies and care providers can better communicate, develop and use AI-based healthcare technologies.
A broader AI committee at CTA also published an ANSI-accredited standard that addresses the pervasiveness of AI-enabled technology across the entire consumer technology industry. The standard defines over 30 terms including machine learning, model bias, artificial neural network and trustworthiness.
“So far, common terminology has defined the intent of use — and that’s one of the most significant challenges in developing standard application of AI,” said Rene Quashie, vice president of policy and regulatory affairs, digital health, CTA. “As health systems and providers use AI tools such as machine learning to diagnose, treat and manage disease, there’s an urgent need to understand and agree on AI concepts for consistent use. This standard does exactly that.”
As the healthcare system deals with clinician shortages, an aging population and the persistence of chronic diseases in the US, technologically driven solutions, such as AI, will increasingly be used to meet clinician and patient needs, the group notes.