It's no secret that the entire healthcare industry keeps up with regulatory compliance, especially when it comes to technological innovation.
Given the confidentiality of medical data, any breach or discrepancy can have costly and devastating consequences, not only for patients, but also for healthcare software providers and medical institutions.
Speaking of technology and the digitization of the industry, it has been shown that compliance issues often stem from poor integration and further migration to cloud services.
Additionally, many data-driven solutions do not comply with numerous industry regulations, which in turn creates privacy concerns.
The regulatory issue in all healthcare is very complex, as there are many regulations and rules to follow that are constantly changing over time..
General regulations like the EU GDPR and specific drug laws like HIPAA in the US are very protective of patient privacy. In addition, a Medical Device Reporting Regulation (MDR) issued by the Food and Drug Administration (FDA) in 2015 requires medical device companies to report to the FDA any adverse issues related to their products and services.
Therefore, compliance with regulations presents many challenges as digitization and technological intervention permeate the healthcare industry.
Endless benefits aside, as data security issues emerged underground, people began to question the place of AI and ML in the healthcare industry.
While artificial intelligence and deep learning are a topic of concern and mistrust when it comes to regulatory compliance and data security, these technologies can help with all kinds of medical compliance, including HIPAA, MDR, and other regulations.
How machine learning and AI can help maintain compliance in healthcare
Technologies such as AI and ML can be used to ensure software compliance. In fact, 41% of healthcare executives listed AI as the technology expected to have the greatest impact on improving their organization's operations over the next three years.
As well as quality assurance and control of medical back office operations, diagnostics, patient care, and other areas where data-driven solutions can be applied, AI ensures overall compliance for medical software vendors, medical device providers and healthcare institutions.
This data-driven approach ensures consistency and accuracy in regulatory compliance. It is imperative that medical software solution providers produce legally approved and compliant software or technology products in order to reach the healthcare market faster and be successful.
Hitachi lute, AIhealth solutionscan reduce regulatory compliance time by 25%.
AI ensures greater security when handling sensitive information, risk reduction, access control, higher data accuracy, immediate response to new regulatory requirements, auto-population and comparison of information.
All of these actions ultimately result in better decision making, reduced costs, improved product liquidity, and reduced risk.
Let's take a look at some of the specific ways that AI and deep learning can ensure regulatory compliance, and in which regulatory areas they are proving most rewarding.
Data protection monitoring
As it is increasingly applied to digital platforms, data security is quickly becoming a hot topic in the healthcare industry.
According to Gartner, privacy regulations have become the number one risk factor for most organizations, including healthcare. With 41 million patient records compromised in 2019, this problem cannot be underestimated.
With sensitive patient data piling up alongside medical facility records and smart data from healthcare wearables, sensitive data in all its forms has become the target of devastating and costly cyber-attacks and security breaches.
There is no shortage of cases where an occasional data breach or systematic HIPAA breach has resulted in massive losses.
Excellus Health Inc., a US health insurance provider, was recently ordered to pay $5.1 million to the US Department of Health and Human Services (HHS) Office for Civil Rights (OCR) for a violation it disclosed confidential information from more than 9 . 3 million people.
Because there are numerous data sources, genres, and tiers, the discussion about artificial intelligence as it relates to healthcare data security can be long and varied.
As required by law under HIPAA, all hospitals, employees of medical centers, health care providers, and their affiliated contractors must comply with these regulations when providing services and products.
What kind of data does HIPAA protect?
- Information that doctors, nurses, and other health care providers add to patient records
- Health professional discussions about patient care or treatment with nurses, staff, and other professionals
- Information from the computer systems of patients' health insurance companies
- Patient billing and payment information
- Other health information held by health care providers and others covered by this law
In short, AI and deep learning algorithms are critical to protecting private data, detecting breaches, and enforcing HIPAA compliance in any given organization.
Stay up to date with upgrade policies
In addition to data security and other common issues such as unauthorized access to information, another health care compliance issue is that certain rules and regulations are constantly changing.
Without the use of AI, it is virtually impossible to timely monitor and adapt policies that change overnight. AI does a great job of detecting potential updates, allowing medical organizations and providers to respond immediately. As a result, AI significantly reduces the amount of time and resources required to comply with updated government privacy standards.
Data access management and control
One of the most critical requirements of HIPAA dictates that only authorized medical personnel can access patient information and records. Still, insider espionage is rampant in the healthcare industry.
In fact, a 2018 Verizon PHI Data Breach Report states that 56% of security breaches are due to medical workers accessing information they weren't supposed to have access to in the first place, because whether intentionally or not.
In addition to proper employee training, artificial intelligence and deep learning technologies are a great way to detect and prevent compliance violations.
Automation analysis and data protection
Due to the sensitive nature of medical data, anticipating challenges and recognizing patterns is extremely important in the fast-paced environment of the healthcare industry.
AI-enabled technology can not only reduce risk and increase privacy, but also help analyze and predict potential threats through deep learning.
Pharmacy monitoring and medication diversion
The ever-growing drug and pharmaceutical industry is a critical branch of the healthcare industry, bound together by many regulations and policies, including FDA regulations.
Problems related to inaccurate prescriptions, theft or substance abuse, drug diversion, and other events related to the pharmaceutical industry are common in the industry and often result in serious policy violations and fines.
In 2019, 148 million doses of medicines worth $183 million were lost to theft or misuse, 77% of which were directly related to healthcare workers.
To reduce non-compliance in this area of the healthcare industry, artificial intelligence can be used to enforce regulatory standards, help identify deviations and unwanted use cases, and monitor transactions to ultimately increase security of the patient.
Audit of pharmaceutical transactions
AI-enabled solutions ensure that all operations and transactions follow established workflows and immediately notify professionals if deviations from predetermined processes are detected.
These algorithms successfully process payments and audits and ensure that pharmaceutical inventory numbers are correct.
Reduce the number of redirect incidents
Artificial intelligence and algorithms not only recognize and react to security warnings, but also analyze behavior patterns and learn from them.
In this way, healthcare technology companies using AI solutions can expect a reduced number of non-compliance incidents, whether they involve medication misuse, theft, or other policy violations.
Improve patient safety
Ultimately, AI and ML lead to increased patient safety as they ensure correct dosing and prescription.
AI in pharmacovigilance
Pharmacovigilance is another large area where AI can be used successfully to monitor and analyze whether a particular pre-approved treatment is effective for a patient.
As you can imagine, this area is also subject to strict regulation and enforcement. With deep learning and analytics, tracking the effects of drugs and substances becomes more effective. It also ensures synchronization with newly approved drugs that are constantly being added to the ever-growing list of approved drugs.
The complexity here lies in the different data sources and formats that need to be processed and analysed. Traditionally, pharmacovigilance has been carried out by large teams of doctors who compile reports on the effects of treatments and the responses of patients to them.
Any discrepancy in reporting on data processing was a potential compliance risk. Therefore, the manual process was time consuming, tedious and risky.
With AI, data collection, analysis, and processing are simplified and automated, greatly reducing the potential for human error.
Therefore, regulators have less scope to question the validity of the pharmacovigilance process in a particular health facility. finally there isTwo key ways AI and ML can helpCompanies with consistent and compliant pharmacovigilance:
- Receive and process structured and unstructured data formats and extract the required regulatory data from the given information source
- Recognize patterns and make predictions for better decision making about doses, prescriptions, alternative treatments, etc.
Why AI is necessary for healthcare compliance
While AI is nothing new in the healthcare industry, its impact on regulatory compliance is often underestimated. Machine learning algorithms and techniques are often recognized for their ability to improve quality and diagnostic capability, as well as their widespread use across healthcare technology.
However, compliance and government policies remain the mainstays of the medical industry. Despite this, AI is often the subject of debate over data security concerns, although the technology itself can be widely used to prevent data breaches, keep users safe, and ensure regulatory compliance.
It is especially important that healthcare technology providers recognize that compliance must play a leading role in deciding a solution.
First of all, non-compliant software and applications won't even make it to market. Furthermore, having a consistent and reliable mechanism to have healthcare solutions in line with up-to-date guidelines and government standards is a key factor in succeeding in the saturated healthcare technology market.
The stakes are high in this space when it comes to compromising sensitive patient data.
With this in mind, it is imperative to use the most suitable technology for data management and compliance enforcement. Artificial intelligence and machine learning algorithms have shown promising results when it comes to helping healthcare providers comply with government regulations.
In short, in any area of healthcare where there are strict regulatory guidelines, whether it is patient privacy assessment, drug inventory assessment, medical device notification, or pharmacovigilance, AI-enabled technology helps collect , monitor, process and analyze data to automate and refine.
Algorithms and data-driven solutions ensure authorized access control, instant detection of new regulatory standards and policies, model automated responses to detected threats, and create advanced analytics.
Ultimately, these preventative measures not only save medical companies millions of dollars in non-compliance penalties, but also ensure what matters most: patient trust.
As the world shifts to a more customer-centric approach, the medical industry is just as concerned about patient experiences. Patients, in turn, are more concerned about their rights when they receive medical advice and treatment.
More and more people have expressed a desire to become more involved in their treatment process, including the pros and cons of the system. Therefore, when it comes to choosing a healthcare provider or solution, safety and reliability are the first things patients will look for.
For these reasons, it's important to be 100 percent sure that a health care provider is compliant with state regulations and HIPAA regulations. It's a fact: AI and ML are the most tried and trusted options for healthcare technology providers.
Experienced CTO with proven experience in the information technology and service industries. Knowledge of Scrum, embedded software, object-oriented design, SQL, and medical devices.
How is AI and machine learning used in healthcare? ›
Machine learning in healthcare can be used to develop better diagnostic tools to analyze medical images. For example, a machine learning algorithm can be used in medical imaging (such as X-rays or MRI scans) using pattern recognition to look for patterns that indicate a particular disease.How artificial intelligence play an important role in health care sector? ›
It helps in accurate decision making - There are many times when a patient requires to be operated on or given medications instantly. At such times, AI will prove to be life-saving. Where the doctors need to go through previous records of the patient, AI can quickly analyze the records and suggest actions instantly.What is AI in healthcare Why is this important today in healthcare? ›
AI has the ability to analyze big data sets – pulling together patient insights and leading to predictive analysis. Quickly obtaining patient insights helps the healthcare ecosystem discover key areas of patient care that require improvement.What is Artificial Intelligence AI and how is it utilized in healthcare? ›
In the simplest sense, AI is when computers and other machines mimic human cognition, and are capable of learning, thinking, and making decisions or taking actions. AI in healthcare, then, is the use of machines to analyze and act on medical data, usually with the goal of predicting a particular outcome.What are the four uses of AI in healthcare? ›
AI in healthcare can be used for a variety of applications, including claims processing, clinical documentation, revenue cycle management and medical records management.How does AI improve quality of healthcare? ›
AI technology can help healthcare professionals diagnose patients by analyzing symptoms, suggesting personalized treatments, and predicting risk. It can also detect abnormal results.What are examples of Artificial Intelligence in healthcare? ›
Artificial Intelligence In Medical Assistance
Sensely is one such example of a virtual nurse that implements Natural Language Processing, speech recognition, Machine Learning and wireless integration with medical devices such as blood pressure cuffs to provide medical assistance to patients.
Administrative workflow automation
Doctors, nurses, and other care providers all have to spend a lot of time doing paperwork and dealing with administrative tasks that burn a lot of their valuable time. AI can assist them in these arduous tasks: an example of such support may be computer-assisted documentation.
When new and known technology is presented to people it can create hesitations. Patient reluctance is another major challenge in implementing AI in healthcare. For example, at the beginning of the Covid-19 pandemic, patients were not comfortable with online checkups. However, now many people prefer it.How artificial intelligence is changing the healthcare industry? ›
AI is changing the health industry by improving diagnostics, treatment recommendations, drug development, clinical trials, and population health management. The future of AI in healthcare is very promising and we are excited to see what the next few years hold!
How AI in healthcare is making hospitals smarter? ›
It effectively leverages AI and machine learning to not only learn from the data, but also act on the data by building automation around it. “A smart hospital takes in information from sensors, processes it in the data center, and then triggers a result,” said Dr.Which of the following application of AI is used in healthcare industry? ›
Machine learning (ML), large and often unstructured datasets, advanced sensors, natural language processing (NLP) and robotics are all being used in a growing number of healthcare sectors.Which is the best application of AI in healthcare? ›
- Accurate Cancer Diagnosis.
- Early Diagnosis of Fatal Blood Diseases.
- Customer Service Chatbots.
- Virtual Health Assistants.
- Treatment of Rare Diseases.
- Targeted Treatment.
- Automation of Redundant Healthcare Tasks.
- Management of Medical Records.
Beyond scanning health records to help providers identify chronically ill individuals who may be at risk of an adverse episode, AI can help clinicians take a more comprehensive approach for disease management, better coordinate care plans and help patients to better manage and comply with their long-term treatment ...What are the 3 major AI issues? ›
AI presents three major areas of ethical concern for society: privacy and surveillance, bias and discrimination, and perhaps the deepest, most difficult philosophical question of the era, the role of human judgment, said Sandel, who teaches a course in the moral, social, and political implications of new technologies.Is AI currently being used in healthcare? ›
AI in healthcare provides a many benefits, including automating tasks and analyzing big patient data sets to deliver better healthcare faster, and at a lower cost. According to Insider Intelligence, 30% of healthcare costs are associated with administrative tasks.What are the 4 applications of AI? ›
- Personalized Shopping. ...
- AI-powered Assistants. ...
- Fraud Prevention. ...
- Administrative Tasks Automated to Aid Educators. ...
- Creating Smart Content. ...
- Voice Assistants. ...
- Personalized Learning. ...
- Autonomous Vehicles.
As such, the five basic components of artificial intelligence include learning, reasoning, problem-solving, perception, and language understanding.What are main types of AI and its applications in healthcare? ›
The primary aim of health-related AI applications is to analyze relationships between clinical techniques and patient outcomes. AI programs are applied to practices such as diagnostics, treatment protocol development, drug development, personalized medicine, and patient monitoring and care.How are AI robots used in healthcare? ›
The AI detects patterns that are leading the patient towards various health conditions. It determines the patient's current state by analyzing and studying the healthcare records and data. Tests so far have concluded that AI is capable of accurately diagnosing diseases in 87% of the cases.