All businesses should have a business continuity plan (BCP), especially companies that rely on IT infrastructure to support or operate their business model. A business continuity plan is a predefined business process created to document and demonstrate the planning undertaken to prevent a disaster scenario occurring. The BCP also documents and demonstrates the process of how a business will recover from a declared disaster scenario. The disaster could be caused by any number of scenarios; typical causes may be user error, hardware failure or a natural disaster.
The Future of Machine Learning and AI in Healthcare Security
According to a 2017 report from ABI Research, the comprehensive healthcare damages from cybercrime will amount to greater than $1 trillion in 2018. Since healthcare firms want to avoid the huge costs of a breach (and all the ramifications related to HIPAA compliance and reputation), there is a huge incentive to adopt more intuitive and adaptive security protections.
Enter artificial intelligence – and, more specifically, machine learning.
These two concepts are very similar, so to clarify the definitions:
- Artificial intelligence – This branch of computer science is committed to create machines that are “smart”; i.e., that have the same characteristics as human intelligence. Another way to look at it comes from Stanford, which defines it as “the science of getting computers to act without being explicitly programmed.”
- Machine learning – This application of artificial intelligence, which is experiencing faster growth than any other part of AI, allows computers to pick up new information (“learn”) and refine their capabilities in response, without any code directing them to do so (per Expert System).
The future of the AI & machine learning market
To say that artificial intelligence (AI) and machine learning are growing rapidly within healthcare does not adequately capture the magnitude of the growth. A 2017 report from analyst Market Study Report forecasts that machine learning will grow to $10 billion by 2024.
One of its biggest areas will be diagnosis and medical imaging, which is expected to hit $2.5 billion (USD) by that same point. In 2016, the market size was $320 million, so to get to that total figure of $10B by 2024, the market will be skyrocketing at greater than a 38% compound annual growth rate (CAGR); during that same period, the diagnosis and imaging segment is projected to increase more than 40%.
The promise of vast machine learning expansion is even more impressive. MarketsandMarkets predicts that spending on machine learning will hit $8.81 billion in 2022, exploding from a 2017 scope of $1.41 billion, at a CAGR of 44.1%.
Beyond medical imaging and diagnostics, other primary areas of healthcare AI that will be expanding rapidly in the years to come are genomics, pharmaceutical discovery, and personal assistants.
The ABI research report noted that machine learning will not just cause ripples but large wakes; by 2021, it will have propelled the analytics, intelligence, and big data market to $96 billion.
How AI will grow
As noted by Intel senior principal engineer Ted Wilke, healthcare AI started as a method to make it easier to perform day-to-day tasks and manage big data. Now, the AI market will expand dramatically because big data is becoming ever larger, so storing and management of data must be optimally effective and efficient. Applications have become much more diverse and complex: improving treatment plans, performing genomic sequencing, and uncovering sophisticated patterns within medical images.
Furthermore, many healthcare entities are wading (or diving) into predictive clinical analytics. This branch of AI helps its users determine the percentage chance that a patient might experience a return of their illness, via analysis of patient records through increasingly strong algorithms.
Broadly speaking, applications for healthcare AI can be categorized as fulfilling needs for automated reasoning; but the demand for the less tantalizing search and classification applications is more prominent.
What is extraordinary about artificial intelligence is that it can utilize methods such as natural language processing and text analytics to make fast sense out of big data. Both medical researchers and clinicians can use these systems to find similitude between data as well as patterns. Reasoning capacity allows these platforms to advise security personnel of steps they may want to take.
How machine learning will grow
Machine learning will grow for some of the same reasons related to new innovations. The huge scope of data generation and technological progression will be key reasons businesses start to turn increasingly to machine learning, as indicated by MarketsandMarkets. Additionally, the market has strong expansion potential because more modern systems are implemented within healthcare and because the effort to make services and operations intelligent is becoming a more central point of focus.
The ABI team noted that machine learning will replace traditional AV, heuristics, and signature-based systems. As that happens, security information and event management (SIEM) systems and protocols will become more sophisticated, fostering automatic threat discovery by incorporating analytics techniques such as machine learning.
The ABI researchers expect for deep learning algorithms (a type of machine learning and further evolution of task-specific algorithms) and user and entity behavior analytics, or UEBA (automatic threat discovery, incorporating analytics techniques such as machine learning, an evolution of rule-based systems), to blossom. Given this climate, providers of security solutions are starting to transition their offerings to incorporate deep learning, UEBA, and other machine learning methods.
Today, SIEM is considered a best-practice way to approach the threat landscape in a manner that can give the infrastructure of healthcare outfits the protection they need. SIEM programs pull in data from an organization’s infrastructural components across a complete spectrum (including network security elements such as antivirus and firewalls, as well as general software and hosting platforms). After collecting this data, this software aggregates log data, giving security teams access to a full monitoring log, along with critical insights. Security analytics dynamically allow the company to identify and mitigate any threats immediately, using real-time processing.
With the advent of the machine learning era, insider error will become a less common cause of compromise because the method is unsupervised. The log-centered approach of SIEM will be joined with additional UEBA programs – both isolating them from one another but also integrating their approaches and findings.
How healthcare can prepare for the AI & machine learning world
It is not necessary to adopt Silicon Valley systems to embrace these technologies. Instead, machine learning techniques can be leveraged by organizations to build custom AI big data analytics environments.
Many healthcare companies are approaching the AI field through incorporation of semantic data lakes, explained Albert Einstein College of Medicine professor Parsa Mirhaji, MD, PhD. The semantic approach is an upgrade from relational databases, which have very exact structural requirements. The relational method makes it necessary to have a pre-established, organized schematic strategy. Isolated data silos result.
Data lakes are collections of data in a wide variety of formats. Insights are derived by bringing together data without any known relationship by applying unique standardized identifiers to each. This capability is permitted by the incorporation of natural language processing within data lakes.
HIPAA-compliant AI infrastructure
Is your organization planning to implement more robust AI and machine learning systems? As always, healthcare systems must go beyond stopping data breaches to meet the standards and regulations of federal law. At Atlantic.Net, we are not only HIPAA and HITECH audited, but also SSAE 18 SOC 1 and SOC 2 certified. See our HIPAA Compliant Hosting Solutions.
Internet of Things vs. Internet of Medical Things
As processors have declined in price and wireless networks have become more prevalent (to the point of near ubiquity), the Internet of Things (IoT) has become not just possible, but inevitable. The IoT is a collection of many devices worldwide, billions of them, all assimilating and sharing data through the Internet. The IoT is made up of many different types of items, ranging from airplanes to refrigerators, from thermostats to pills. One of the chief reasons the IoT is so exciting to people is that it allows, through the connection to the Web, each of these devices or endpoints a level of autonomous “smartness” that would otherwise not be possible. The increase in those endpoints’ ability to intelligently process and analyze data allows them to interact without any manual intervention. It also means that the Internet is being more comprehensively integrated into the physical environment, at a global scale.
Top 5 Biggest DDoS Attacks, and What Hosting Companies Learned
For businesses that rely on their websites for revenue or as their customers’ main gateway to information, there’s nothing more frightening than the threat of a distributed denial of service (DDoS) attack. Revenue lost, service interrupted, brand identity marginalized, trade secrets or customer data stolen; it really does sound like the worst-case scenario. Some companies hope that they are too small to be noticed by hackers, others that their size alone implies they must have ample security to survive a DDoS attack. Both ways of thinking are horribly misguided, as has been proven time and again.
Physiology of Fear Driving Healthcare IT – or Is It Real?
The healthcare cybersecurity market is expanding at an incredible rate. An April 2016 Grand View Research report projected that the scope of the industry would reach $10.85 billion by 2022. That may sound high, but it now looks like it was an underestimate: a February 2018 analysis released by Market Research Future predicts that health information technology (HIT) security will rise at a compound annual growth rate (CAGR) of 22% through 2022, ballooning from $4.8 billion to $15.82 billion.
Blockchain Security & Compliance: Is This the Future?
The technology that pushed Bitcoin and other cryptocurrencies into the public eye is a distributed ledger, better known as blockchain. This approach gives any entity that uses it an ongoing and dynamic transaction log. Over time, data is shared and compared to various other nodes, all of which continually have an up-to-date database copy. Auditing controls the adding of transactions to the database. By distributing transactions in this manner, you are able to remove single points of failure (SPOFs); prevent the controlling of the ledger from a single position; and verify all your transactions, with no need to utilize an independent service. All transactions are public, making it much less likely that the ledger is gamed to meet the needs of a nefarious party.
Why Do Medical Billing and Coding Companies Need HIPAA Hosting?
Medical billing providers and healthcare programmers cannot simply be satisfied with standard hosting, but instead should implement HIPAA-compliant hosting whenever sensitive health data is transferred server-side. This is because organizations like medical billing companies and healthcare programmers are business associates that have direct responsibility for compliance; because the expenses for breaches (which HIPAA hosting is intended to prevent) extend far beyond the fines; and because the definition of a breach is broader in recent years than it was prior to 2013.
What Can You Do with a VPS and Cloud Server?
Choosing the right direction with hosting gets a little complex. For instance, you might think that there are strong points to be made for using virtual private server (VPS) hosting just as there are for using cloud hosting. Actually, these two options are not contrary to one another but can be complementary: you can get a VPS within a cloud infrastructure. That cloud VPS hosting option means you can benefit from both of these incredibly popular and useful technological approaches for your backend.
How to Prevent a HIPAA Data Breach
In early October, Henry Ford Health System announced that it had been hacked and that the records of 18,470 patients had been stolen. On July 25-26, Arkansas Oral Facial Surgery Center was infiltrated by a virus that blocked the practice from being able to access images, files, and notes related to 128,000 patients. In September, Augusta University Medical Center announced that fewer than 1 percent of its patients’ records were stolen during a breach; however, this attack was the second phishing effort to work against the healthcare provider in just 5 months. These are just three of the most notable healthcare data breaches that occurred in 2017.
Who Uses DDoS Attacks and Why?
If your business has ever suffered through a Distributed Denial of Services (DDoS) attack, it’s was likely experience you haven’t forgotten, as much as you might like to. For your website, a DDoS attack is like being paralyzed – your website becomes unable to respond to the simplest request, your online store is completely unusable by paying customers, everything that makes your business go suddenly grinds to a halt like you’re stuck in the world’s worst traffic jam.
