HealthcareTrends

The Future of AI in Healthcare  

The potential for Artificial intelligence (AI) in healthcare is enormous, and we are just scratching the surface of what is possible. We can expect to see more AI applications in patient care, such as robotic surgery, virtual nurses, and chatbots for patient support. However, it is essential to remember that AI is not a replacement for human healthcare providers but a tool that can assist those providers in delivering more effective and personalized care to patients.  

Future of AI 

Artificial intelligence (AI) has been a buzzword in many industries, and the revolution is also taking healthcare by storm. Many of us are familiar with healthcare’s apprehension around EHR adoption, so it is no surprise that similar hesitations are arising around the use of AI in patient care. Although there are still risks and challenges in using AI tools, they have the potential to revolutionize the way healthcare is delivered, leading to more accurate diagnoses, personalized treatment plans, and improved patient outcomes. Let’s explore how AI will start to affect healthcare and what the future may hold.   

Improved Diagnostic   

One of the most promising applications of AI in healthcare is improved diagnostics. AI algorithms can analyze large amounts of patient data, including medical history, test results, and imaging scans, to identify patterns and predict diagnoses. As a result, AI could lead to more accurate diagnoses, earlier detection of diseases, and more effective treatment plans.   

Personalized Treatment Plans   

Another promising application of AI in healthcare is the development of personalized treatment plans. AI algorithms can analyze patient data to identify the most effective treatment options based on medical history, genetic information, and lifestyle factors. As a result, these algorithms could lead to more personalized and effective treatment plans tailored to patients’ needs.    

Drug Development   

AI can even play a critical role in drug development. Drug development is a time-consuming and expensive process that involves testing thousands of compounds to identify potential drug candidates. AI algorithms can analyze large amounts of data to identify potential drug candidates more quickly and efficiently than traditional methods. These algorithms could lead to faster drug development and more effective patient treatments.   

Remote Patient Monitoring   

With more patients interested in healthcare at home, AI will make this possible with advanced remote patient monitoring. Remote patient monitoring involves using technology to monitor patients’ health outside a traditional healthcare setting. AI algorithms can analyze patient data in real-time to identify potential health issues before they become serious. This insight level could result in more proactive, effective healthcare delivery and better patient outcomes.   

Challenges   

While there are many promising applications of AI in healthcare, there are also several challenges and understandable fears. Some of the concerns are patient data privacy and security. Patient data is highly sensitive, and there are concerns about how AI algorithms will handle this data. In addition, concerns about whether AI complies with the Health Insurance Portability and Accountability Act (HIPAA) Privacy and Security Rules regarding healthcare data have risen. Other fears include AI becoming too powerful, underscoring the need for regulatory frameworks to ensure that AI algorithms are safe, effective, and ethical.   

In Conclusion

How Does HIPAA Plan to Address AI Data Privacy Concerns

The Health Insurance Portability and Accountability Act (HIPAA) was enacted in 1996 to protect the privacy and security of patient health information. As AI becomes more prevalent in healthcare, there are concerns about how HIPAA will address patient data privacy concerns.

HIPAA requires covered entities to implement administrative, physical, and technical safeguards to protect PHI from unauthorized access, use, and disclosure. These safeguards include access controls, audit controls, encryption, and other measures to protect PHI’s confidentiality, integrity, and availability. HIPAA also requires covered entities to conduct periodic risk assessments to identify vulnerabilities in their information systems and implement measures to address them.

Regarding AI, HIPAA applies the same privacy and security requirements to using PHI by AI algorithms as it does to using PHI by human healthcare providers. Therefore, covered entities that use AI algorithms to process PHI must ensure that the algorithms are designed and implemented to protect that information’s privacy and security. This includes implementing appropriate technical and administrative safeguards to prevent unauthorized access, use, and disclosure of PHI by AI algorithms.

HIPAA also requires covered entities to enter into business associate agreements (BAAs) with third-party vendors that handle PHI on their behalf. This includes vendors that provide AI algorithms or other services that involve using PHI. BAAs require vendors to comply with HIPAA’s privacy and security requirements and to implement appropriate safeguards to protect PHI from unauthorized access, use, and disclosure.

It is important to note that HIPAA regulations provide the federal floor of privacy and security standards. AI developers and vendors should review the mHealth App Guidelines developed by Xcertia and now managed by HIMSS to find other state and federal laws that can apply that pre-empt HIPAA – particularly concerning healthcare adjacent data – or apply to more organizations than Covered Entities and Business Associates.

According to The HIPAA Journal, “…it is the responsibility of each Covered Entity and Business Associate to determine what health information is PHI, what health information is adjacent, and how each should be managed. It is also the responsibility of each Covered Entity and Business Associate to conduct due diligence on any AI technologies that are implemented to improve efficiency and the patient experience to make sure that they are compliant with the HIPAA Rules, especially with respect to disclosures of PHI.”

10 Healthcare IT Trends to Consider in 2023

Healthcare has gone through a lot of change in the last few years, and with Chat GPT and other AI tools making a big splash in just the last few months, information technology and tools will continue to snowball in the healthcare industry. Of course, COVID threw the industry for a loop, accelerating the adoption of tools, technologies, and resources to manage the pandemic. Still, it also introduced the need for that change and innovation to stick around. As a result, we compiled a list of the top 10 emerging trends in healthcare IT in 2023.

1. The evolution of IT in healthcare

As technology aids and enhances everything in our world, healthcare is no longer the exception. As a result, CIOs and IT teams will need to evolve their strategies to keep up. Risk management, virtual care, remote work, telehealth, blockchain, artificial intelligence (AI), machine learning, augmented reality (AR), wearable technologies, and all the security to operate these technologies safely are essential innovations we will be seeing more broadly adopted in hospitals this year.

2. Healthcare at home

Not only did COVID generate fear in patients, keeping them from entering healthcare facilities, but it also proved that some healthcare can be done at home. Not only can it be done, but now this convenience is a prerequisite for some patients seeking care. According to a Harris Poll conducted on behalf of Tegria, a convenient healthcare experience is more important than having a designated healthcare provider for 59 percent of consumers. Telehealth, remote patient monitoring, and even wearable devices are just a few of the technologies making this possible.

3. Opening the digital front door 

The digital front door is becoming a more common term in healthcare IT. The proverbial front door describes the patient engagement technologies needed to access care virtually and in person quickly. These efforts improve the consumer experience and help empower patients.

4. Clinical support from artificial intelligence (AI) and machine learning

AI and machine learning are being introduced into the clinical setting and can potentially improve productivity for clinical staff and care for patients by using predictive tools and natural language processing alert clinicians of secondary issues that may need follow-up. Clinical workflows and augmenting or automating clinical staff work enable them to be even better at their jobs. AI and machine learning have untapped potential that could lead to enormous value-adds for healthcare facilities.

5. Friendly robots 

Over 200,000 healthcare workers left the industry in 2021 amid COVID-19, physician burnout, and staff shortages. The industry is still trying to recoup those losses, making it even more difficult for the remaining staff. Although movies like iRobot and The Terminator have made us apprehensive about the idea, robots may revolutionize healthcare in 2023, filling in some gaps for teams already spread thin by performing routine and mundane tasks.

6. EHR Evolution

EHRs have finally become commonplace in most healthcare facilities, but they must continue to grow wiser. They will evolve to become powerful technology with new tools and updates to help them truly be able to talk to other systems, eliminate the need for manual entry, catch and fix human and system errors, integrate with telehealth systems, the cloud, and more.

7. Big data management 

Centralized, efficient, accurate data management is the cornerstone of decision-making in all industries, especially when dealing with valuable patient data. There is so much to learn from data. Still, with vast amounts of it, extensive data management cleanses and interprets the data for broad business areas, enabling enhanced insight, decision-making, and process automation.

8. Predictive analytics 

Through bid data management, predictive analytics provide the technical capabilities to practice precision medicine. For example, Analytics can help identify patient populations with certain risk factors or health issues, informing providers so they can take preventative measures or create personalized treatments and care plans based on a patient’s characteristics.

9. A new age of cybersecurity

Each new technology brings with it more risk of cybersecurity attacks. According to a recent survey, there was a rise from 14 million total victims of healthcare attacks in 2018 to 45 million in 2021, and there is no sign of this letting up. Therefore, healthcare organizations must prioritize cybersecurity to avoid financial catastrophes and improve patient outcomes.

10. Rise of unlikely competitors

Providers are challenged to keep up with all these trends to compete with other providers, in addition to the rise of unlikely competitors breaking into the healthcare space like Amazon, CVS, Walgreens, and Walmart. These retail competitors are striking up deals left and right to expand their primary and in-home care offerings, which could cause traditional providers to lose their younger and healthier patient population.