The healthcare sector has witnessed many transformations and investments over the years, and you can only expect this trend to continue, especially after the difficult lessons from the coronavirus pandemic. Amid the global health crises, the healthcare sector responded by adopting various techniques both in practice and technology. If these encouraging signs are anything to go by, 2022 may usher the American healthcare sector into a new era of healthcare. That said, here are some key trends that are currently transforming the healthcare sector in 2022 and beyond.
One of the first trends you can expect to see more of is the use of artificial technology (AI) in healthcare delivery and general medical practice. While the use of AI isn’t necessarily a novelty in the healthcare sector, you can expect to see it take more center stage in the way healthcare is delivered. One of the main results of AI’s impact on healthcare is the availability of various mobile apps and websites that help people to self-diagnose right in their homes.
AI is now also being used in the form of augmented intelligence to enhance the intelligence of clinicians and improve the quality of service they deliver. AI also plays a crucial role in the use of medical technology like hearing aids. For example, it’s used in a hearing aid evaluation to help predict the best possible hearing or sound settings based on data obtained from the user.
Another huge trend taking the US healthcare sector by storm is telehealth services. You probably already know how popular video calling apps have become since the pandemic. Now the healthcare sector is taking advantage of telehealth to deliver healthcare services remotely. Patients who cannot visit their hospitals physically can now see their doctors literally while at home.
The healthcare industry is under intense pressure to improve its efficiency. However, interoperability between technology and various integrated systems presents many challenges that are hindering health facilities from being fully connected and productive.
We have known for years that healthcare needs solutions that artificial intelligence can provide. But the initial proofs of concept have taken too long to materialize. Without clear boundaries and use cases showing how AI in healthcare can work, leadership teams are unable to horizontally collaborate with each other.
How AI in Healthcare Could Solve Interoperability Problems
Technology has the potential to transform the way healthcare works for patients, but right now, interoperability is difficult to attain. Despite industry guides such as the Fast Healthcare Interoperability Resources, data is still a messy business. Data is stored in different ways and in different silos — and not every facility has the ability to read and understand the information contained within the respective silos and make it actionable.
This has a heavy impact on how practitioners work with technology. A radiologist reading film and a doctor making a diagnosis for a chronic pain patient only have access to their siloed expertise. With AI solutions in healthcare, data can be drawn from different disciplines and diagnosis can become faster and smarter.
When used in conjunction with AI, blockchain technology has the power to help practitioners and organizations work together without security risks. Because the blockchain represents a transparent, single source of information that cannot be changed, it can store data from multiple sources and create a harmonized picture of truth that different users can access without bias. In addition, limits can be put in place as to who has access to the data.
This helps healthcare experts form a central hub where the very best knowledge, therapies, and drug research can be pooled, therefore helping target diseases more effectively while keeping patient and research data absolutely secure and private.
It’s clear that leaders at healthcare organizations need to remove the siloed approach and develop an atmosphere of increased collaboration. But how, exactly?
How Blockchain, AI, and Healthcare Can Work Together
Blockchain technology in healthcare helps fulfill all four kinds of interoperability defined by the Healthcare Information and Management Systems Society: foundational, structural, semantic, and organizational. Blockchain’s uses in healthcare create a basis — a structure — where data can live safely and transparently. Then, blockchain can enable a rendering that helps different kinds of readers see and understand the data.
Two aspects of blockchain technology that are especially interesting to the healthcare industry are permissioned blockchains and smart contracts. A permissioned blockchain maintains the privacy of data, knows all the stakeholders, and makes data viewable by actors on the network who are authorized to see it. Smart contracts are “instructions” on the blockchain that are executed automatically once all necessary conditions or events are met. This means decisions can be made available automatically without human intervention. That’s where the power of AI’s uses in healthcare really materialize. This harmonized dataset — coupled with safe and secure automation — means that AI can be used to make faster, better, and more predictive decisions.
Data is the engine behind AI, but it’s also becoming the engine behind healthcare systems and how doctors diagnose and treat patients. If we can aggregate and translate vast amounts of data into streamlined workflows, AI can be used to efficiently diagnose and monitor patients, detect illness, accelerate drug development, and seamlessly run clinical trials.
The ingredients for interoperability are all there, but it’s now up to operators and developers to find ways to work together. The benefits of AI in healthcare are massively transformative — as long as we can find ways to solve problematic perceptions of blockchain and data privacy and get human beings to open up their silos.
No one technology will save the future of healthcare interoperability. It will take collaboration between developers, operators, academics, drug researchers, and an interwoven stack of technologies to bring together a universe of data and put it to good use.
By Joachim Roski, Principal, Booz Allen Hamilton and Kevin Vigilante, Executive Vice President and Chief Medical Officer, Booz Allen Hamilton.
As artificial intelligence (AI) continues to transform our world, the healthcare sector stands to substantially gain from AI. The ability to compute a massive amount of information quickly has promising implications for delivering better health outcomes, improved healthcare operations, or expanded research capabilities. However, AI is not immune to the risks that accompany the adoption of any new technological advancement – risks that may cause healthcare professionals to doubt its reliability or trustworthiness.
Fortunately, there are steps available that can help build a strong AI foundation, improving the quality of life for patients and healthcare professionals alike. If you’re looking to implement AI in your healthcare organization, here are a few things to keep in mind:
Understand your Data
If a data is not of high quality or is not representative of the intended group of study, the conclusions based on that data will likely be flawed. To accurately anticipate how a flu outbreak will impact hospital visits among a community population, it’s essential to account for variables that can affect all or part of the relevant population. For example, if a significant subset of the population is more susceptible to serious flu symptoms (e.g., based on pre-existing conditions), this needs to be taken into account by the hospital(s) in question. Given that algorithmic models are often designed to grow and expand with compounding data sets, any previous mistakes can snowball and lead to significant long-term bias or inaccuracy in the results.
As such, it’s critically important to build an AI foundation on a robust and comprehensive data acquisition and management operating system, complete with careful and consistent oversight of AI algorithms. Such a system should be supported by compliance and monitoring protocols to ensure that data is securely flowing. Having a clear understanding of where relevant data comes from and how it was collected is critical to ensure AI algorithms create meaningful output.
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.
Amit Garg
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.
Medical coding
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.
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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: