Artificial Intelligence is increasingly becoming a cornerstone in various sectors, but its impact on healthcare is particularly profound. AI technologies are facilitating breakthroughs in diagnostic processes, patient care management, and drug development, which could not have been achieved with traditional methods alone.

Enhancing Diagnostic Accuracy with AI

One of the most significant contributions of AI in healthcare is its ability to enhance diagnostic accuracy. AI systems, such as deep learning algorithms, can analyze complex medical data, including images and patient histories, to identify diseases with a level of precision often surpassing human capabilities.

AI in Radiology

In radiology, AI algorithms are used to detect anomalies in X-rays and MRI scans quickly and accurately, often identifying subtle signs of diseases like cancer much earlier than is possible through manual examination.

Personalized Treatment Plans

AI’s data-driven approach allows for the creation of personalized treatment plans tailored to individual patient needs. By analyzing data from a patient’s medical history along with broader data sets, AI can predict the most effective treatment pathways, considering potential side effects and interactions.

AI and Mental Health

AI is also making strides in mental health care by providing tools that offer emotional support and can predict when patients might experience a crisis. Chatbots and virtual health assistants powered by AI provide 24/7 support and can escalate cases to human professionals when necessary.

Improving Patient Outcomes with Predictive Analytics

Predictive analytics powered by AI is another area transforming healthcare. These systems use historical data to predict future events in a patient’s health trajectory, allowing for interventions that can prevent deterioration of the patient’s condition.

Example: Predicting Hospital Readmissions AI models are increasingly used to predict which patients are likely to be readmitted to the hospital. This information helps healthcare providers offer targeted post-discharge care to reduce readmission rates.

Streamlining Operations and Reducing Costs

AI not only improves patient care but also enhances the operational aspects of healthcare facilities. AI can streamline administrative tasks such as scheduling, billing, and compliance monitoring, significantly reducing costs and allowing medical staff to focus more on patient care.

Overcoming Challenges in AI Adoption

Despite its benefits, the adoption of AI in healthcare faces several challenges. These include issues of data privacy, the need for substantial initial investment, and the necessity for rigorous validation of AI tools to ensure they meet clinical safety standards.

Ethical Considerations

The implementation of AI in healthcare also brings up important ethical considerations. Issues such as algorithmic bias, transparency, and the impact on employment in healthcare sectors must be carefully managed to ensure equitable health outcomes.

Global Impact and Future Prospects

Globally, countries are at different stages of AI adoption in healthcare. While some are pioneering and setting benchmarks, others are just beginning to explore the possibilities. The future of AI in healthcare globally will depend on collaboration between governments, healthcare providers, and technology developers.

That’s the focus of LeanTaaS a niche firm that brings efficiency to the table for key players. Through Ai, the firm has a data-driven approach that takes out all the gaps in several areas for healthcare-related firms. 

Additionally, the vertical integrations have enabled LeanTaaS to create a different kind of AI company. One where evolution and growth are at the center of things. We had a sit down with Hugh Cassidy, Chief Data Scientist at LeanTaaS. Here’s what he had to say. 

Hugh Cassidy

Chief Data Scientist

at LeanTaaS

In the Beginning…

Hugh’s journey into the AI industry was (rather) straightforward. He indicated that his PhD program led him to LeanTaaS. 

He said, “I joined LeanTaaS straight out of my PhD program as a Senior Analyst in 2013, and my mandate was to focus on algorithmic and AI solutions for infusion operations, including level-loading infusion chairs, predicting volume and mix of infusion patients on future days and optimizing templates based on constraints caused by chair and nurse availability.” 

“Over time, that role evolved into a pure data science role, the first of its kind.”

“At the time, LeanTaaS was an early-stage startup. So, to a certain degree, I had a blank check to approach problems. This was simultaneously exhilarating and daunting. “

“After 5 fantastic years, I left to pursue other interests but returned in 2022 as the Head of Artificial Intelligence and Chief Data Scientist. To see the company, and in particular, the data science team and our AI abilities grow to a scale we are at today has been incredible!”

About the AI Learning Curve 

The industry has its issues, especially when trying to learn and understand the underlying precepts and technologies. 

AI classes are now a thing, then, the journey was not as straightforward. 

Hugh admitted this, saying,  “I had taken many AI classes while pursuing both my undergraduate and graduate degrees and was involved in a machine learning research group as well in graduate school.” 

“These experiences, together with a level of mathematical maturity gained through my PhD research, provided me (with) a solid enough foundation to pursue topics as interest/need arose. The AI community is very open so accessing information and materials is not much of an issue.”

We also discussed Hugh’s intellectual contributions to the field. He has two and described them for us and spoke about their impact. 

He iterated, “Yes! One patent is concerned with a machine learning method for cleansing and deduplicating large data sets. 

Data deduplication is a more complex problem than one might think, it is not just a matter of finding matches in the data – it involves identifying and removing duplicate records or entries from a dataset, which is important for maintaining data accuracy and integrity to allow for the best insights from your data.

Duplicate data can stem from various sources like mergers, lack of data entry protocols, third-party data usage, system errors, human error, and software bugs. The approach in the patent defines a system for automated data cleansing, and deduplication using fuzzy matching and machine learning.”

About AI and Jobs

Everyone thinks AI will take away jobs. While an extreme scenario may not (yet) exist, Hugh pointed out what (exactly) is going on and may (actually) happen. He said, 

“AI is more likely to change how certain jobs are done and actually create new jobs rather than take jobs away. For instance, many of the manual and repetitive aspects of certain jobs will likely be taken over by AI to allow humans to focus on other aspects. In fact, the broader adoption of AI will actually create demand for new AI-savvy workers.” 

The dual role of Chief Data Scientist and Head of AI has taken preeminence lately. Firms (now) have them in one form or another. 

Hugh said,  “The role didn’t exist when I first joined LeanTaaS in 2013. I’m so thrilled to have been with the company through its growth over the years as it evolved to solve complex operational challenges in not only the infusion space but also perioperative and inpatient areas. To have the opportunity to return and step into this new role as we continue to grow at a rapid pace is really meaningful.”

Additionally, he also described what his job is like at LeanTaas. 

He described it this way: “I’m actually really fortunate to be able to say that no two days are alike – it’s one of the great things about my position. However, there are usually aspects of leadership and management of the data science team, including coaching and project oversight (such as overseeing the team implementation and development efforts with regard to our AI initiatives and coordinating with other departments) that are part of my every day.”

He continued, “Additionally, I routinely spend a good amount of time keeping abreast of the latest developments in AI and healthcare, and how we can continue to solve the most challenging and impactful operational problems for hospitals.”

Efficiency is the Focus at LeanTaaS

LeanTaaS has an efficiency-driven focus targeted at the healthcare industry.  Future plans remain a closed secret, but we were able to pry out a few ideas. 

Hugh indicated this, saying, “While I can’t share specifics, what I can say is that we certainly are working on some exciting developments to continue to solve complex hospital operations problems! We’re always focusing on solutions that will be actionable and impactful for our nearly 200 health system customers, rather than chasing the flashy AI parlor tricks.”

As a premier healthcare-focused firm, LeanTaaS has a few competitors. But, Hugh indicated that the firm is an industry leader. 

He said,“There’s no question: we are the industry leaders. When we started out, there were no other companies trying to solve the hospital operations problems we solve. Fast-forward to today, and it’s actually flattering and validating to see large tech companies and start-ups alike following in our footsteps.”

“However, while we’re currently offering the iPhone 15, our competitors who are trying to jump on the same bandwagon after witnessing our success are only selling the iPhone 2 equivalent. But we don’t distract ourselves with what others are doing. We’re focused on continuing to solve hard problems for our customers.”

Data Duplication Has a Solution

Data in the AI sector has issues. That said, one of Dr. Cassidy’s ML models helps solve this problem.

Hugh said,  “Data duplication is a common problem across industries and is laborious and expensive to address using conventional methods. I have worked on deduplicating data sets, and it was a problem that was in clear need of automation. The scale of the problem and the difficulty of identifying matches using business rules means that it is a prime candidate for a machine learning approach.”

On the issue of patents, Dr. Cassidy indicated that they practice what they preach at  LeanTaaS. 

He said, “We use our patents regularly in our products, but we have not licensed them out to other companies.”

A while back, AI was (relatively) unpopular.  Even academics (generally) avoided the field. 

Hugh reminisced about this, saying, “The popularity of a topic has not ever really affected my research interests. My interest in AI has always been driven by practical applications, and I’ve been fortunate in my career to never have been short of such problems to solve.”

Additionally, he also pointed (out) the factor that prompted him to go into AI. 

He said, “AI has always been interesting to me, even before college. My PhD research did not focus on AI directly, but the problems I have worked on at LeanTaaS gave me the opportunity to go deep into research and (to) develop practical AI solutions.”

Dr. Cassidy also talked about the impact of government regulation on AI and healthcare. 

He spoke about LeanTaaS’s focus, saying, “It’s true that healthcare is a sensitive industry, and for a good reason – patient care is at the forefront of everything our customers do. However, we’re focused on the operational and business side of health systems and not clinical care delivery.”

He continued, “As a result, we have not run into any issues with government compliance. Additionally, we always stay ahead of the curve when it comes to issues such as data privacy, which is the most likely place companies run into regulatory challenges.”

He concluded, “As we are still (at) the early stages of widespread AI adoption, it is clear that more regulations will need to be put in place. A measured and mindful approach will need to be taken when introducing new regulations to avoid strangling innovation. Broad stakeholder involvement will be key, including technical experts, lawmakers, and industry experts among others.”

The Dangers of AI

All industries have (hidden) dangers. Especially the emerging ones. A lot has been said about the dangers of AI. We (may) not be there yet, but there are issues that need to be addressed. 

Hugh alluded to this, iterating that, “AI-powered tools can be used to generate and spread misinformation or manipulate public opinion, as seen in deep fakes and social media bots. In the same sense, they can also be used to power certain scams.“

He continued, “This can be mitigated by developing detection tools and establishing regulations and standards for responsible content creation and distribution. Additionally, AI applications are powered by data, users should be aware of how their data is being used and how the vendor is securing their data from malicious attacks.”

“AI developers need to implement robust security measures, regular security audits, and invest in research to develop more secure AI models that comply with security protocols where appropriate.”

Dr. Cassidy also spoke about the critical challenge AI tech startups face. Being a niche sector, the healthcare industry has more issues than most.

He said, “While media coverage has been positive in spreading awareness of AI, it has also highlighted many of the risks involved. Establishing trust with new customers is key as they are usually concerned with many of the issues that have gained attention including bias, data security, and inaccuracies.”

He continued, “However, LeanTaaS has best-in-class security, a robust MLOps pipeline for training and retraining AI models, and monitoring quality.” 

Data is Critical 

On the data end, Hugh indicated that LeanTaaS has a prime commitment to security. 

He iterated, “Data privacy is always a top concern of any customer. LeanTaaS demonstrably has the highest data security standards in the industry.”

All data models need training. Dr. Cassidy let us in on how they were able to do it. 

He said, “As part of our discovery and model-building processes, we get an understanding of what data is broadly available in hospitals that might be useful in the particular problem we are solving. This ensures that our solutions are scalable and compatible with the vast majority of health systems.”

He continued, “As a part of on-boarding any new customer, we work with IT teams to make sure our data requirements are met. This ensures our models can ingest the historical data and provide high quality predictions when we go live. From there, we ingest data as we go.”

On the “AI vs. humanity” question, Dr. Cassidy indicated that a terminator-type scenario may not occur on one condition. He said,  “I think in the distant future we will get to a point where AI is at least comparable to human intelligence, but if progress is made at a measured pace with the right controls, hopefully, we can avoid any kind of Skynet-type situation.”

On the “peak AI” question, Dr. Cassidy was direct. 

He indicated, “It doesn’t appear as if we’ll reach a peak in the near future. The main limiting factor is likely compute power – however, quantum computing has been developing at an accelerated clip and is likely to push that boundary out significantly.”