Revolutionizing Healthcare: Roles of AI, Machine Learning

The integration of artificial intelligence (AI) and machine learning in healthcare is poised to revolutionize the industry by enhancing efficiency, transforming medical education and augmenting the role of healthcare professionals.

The latest Private Equity Insights Report from BluWave shows strong interest in AI data analytics as well as robust activity in the healthcare industry.

LISTEN: The Window of Opportunity: Healthcare and PE Insights

Scott Becker, founder of Becker’s Healthcare and Partner at McGuireWoods, discussed these topics with BluWave CEO and Founder Sean Mooney on the Karma School of Business podcast.

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Efficiency in Healthcare Processes

Becker’s insights into the role of AI in healthcare reveal a significant shift toward efficiency, particularly in areas like revenue cycle management.

He spoke to the remarkable reduction in workforce requirements.

“You’ve got places that have 1,500 employees. They can get down to 1,000 employees with using AI,” he said.

READ MORE: Healthcare Compliance: Due Diligence Checklist

This reduction is not about diminishing the human element but rather reallocating it. By automating routine and repetitive tasks, AI allows healthcare professionals to focus their expertise on more complex and nuanced cases.

This shift is not just a matter of numbers; it represents a fundamental change in how healthcare operations are managed. Becker elaborated on the challenges faced in staffing these roles.

“A lot of those jobs are relatively lower-wage jobs where the turnover was tremendous,” he added.

This also speaks to the importance of AI in creating a more stable and focused workforce.

READ MORE: How To Extract Data from ERP Systems

Transformation of Medical Education

Becker also said that the medical education system is in dire need of an update.

“Medical school is still designed pre-internet,” he said. “A specialist isn’t out of school until they’re in their early thirties.”

This not only prolongs the training period but also imposes significant financial and mental burdens on aspiring medical professionals.

READ MORE: Professional Healthcare Recruiters: Specialized Human Capital Resources

“You’ve got this horribly inefficient medical school program and residency training program,” he added.

By leveraging artificial intelligence for educational purposes, the learning process can be significantly streamlined, reducing both time and cost for students.

Augmentation of Medical Professional Roles

There are many use cases for existing medical professionals as well. Mooney offered one example.

“They’re going to have these amazing copilots that will help do all those kind of brain sequences,” he said.

READ MORE: AI Data Analytics: Business Intelligence Tools

Mooney views AI as a tool to assist, rather than replace, human expertise. AI’s ability to process vast amounts of data and identify patterns can significantly enhance the diagnostic process, allowing medical professionals to focus on critical decision-making and patient care.

“I don’t think you could ever turn them over to the robots, at least in our visible future,” Mooney added.

The integration of AI promises to elevate the quality of healthcare, making it more precise, personalized and effective.

While the potential of AI in healthcare is immense, challenges such as resistance to change and vested interests could impede its adoption. Its future prospects, however, are promising.

The integration of AI and machine learning in healthcare represents a significant shift toward more efficient, effective and personalized care.

The invite-only Business Builders’ Network is full of AI experts who work with healthcare businesses on a regular basis.

Connect with our research and operations team, and they’ll provide a short list of tailor-made resources within 24 hours.

Listen to all the episodes from the KSOB podcast.

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Learn about the four steps our expert service providers say every business must take before implementing artificial intelligence tools:

  1. Identifying Your Use Case
  2. Working with an Advisor
  3. Evaluating Data Availability and Hygiene
  4. Implementing the Tool: Buy or Build?

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Unleashing Business Potential with AI: Beyond Open Source Tools

Artificial intelligence has evolved from a futuristic concept into a business norm. The advent of Language Learning Models (LLMs) like ChatGPT and Bard is only the tip of the iceberg.

While these consumer-focused models are noteworthy, they form only a fraction of AI’s potential. Businesses stand to gain significantly by digging deeper into the realm of AI and integrating advanced models.

Let’s dive into how AI is revolutionizing private equity firms, their portfolio companies and other private and public businesses.

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Beyond Data Availability and Hygiene

AI models are adept at analyzing and interpreting massive datasets, providing businesses with valuable insights that drive decision-making. With data being produced at an unprecedented rate, AI’s role in sifting through this sea of information and drawing actionable conclusions is invaluable.

READ MORE: The Road to AI Implementation: Precursor Activities

Ken McLaren, partner at Frazier Healthcare Partners, spoke to this on a recent AI-focused webinar hosted by BluWave.

“We do a lot of prototyping on desktops,” McLaren said. “As we prove the value and the use cases, we then start getting ready for production. But don’t build in production first. Get the proof value with your customer market in place before you start building.”

He elaborated on the importance of not just having clean data, but that it’s also production-ready, which means having a quality data lake infrastructure.

“Having your data pipes with things like Azure Data Factory, having good storage…or using Databricks Delta Lake on top of that, having a production-ready data environment is important,” he said. “Once you’ve got your models ready…you can plug in a lot of open source tools. So there’s really no one platform to rule at all.”

Protecting Your Sensitive Data

With AI tools new and old evolving so rapidly, there’s also concern from business leaders that the data they share with these same tools is not safe.

“If you’ve got history turned on, then it becomes part of that AI system,” said Keith Thomas, National Practice Lead, Cybersecurity Operations, at AT&T. “It gets built into the models, and there’s the ability for the model to use that data.”

Since McLaren’s firm exclusively works with healthcare companies, they err on the side of holding back data from open tools that could otherwise compromise privacy.

“We still guide our portfolio companies for sensitive business data, customer data – keep it out of any open tool,” McLaren said.

BluWave CEO and founder Sean Mooney also cautioned about sharing propriety data that gives your company an edge:

“If that’s something that’s competitively sensitive or advantageous your business,” he said of adding it to an open-source tool, “you’ve just given it to the world.”

Beyond Open-Source AI Tools

Tech stacks at innovative businesses are changing faster than ever. Not only are the tools themselves changing, but they’re also becoming easier to use for team members who aren’t as technically skilled.

“In software development in general, there’s this movement more and more toward no-code, low-code solutions,” said Alex Castrounis, Why of AI founder and CEO. “Part of the benefit of those things is, one, accessibility and making it easier for people in organizations to sort of build software, or in this case, train models, iterate on models, tune them, optimize them, deploy them and so on.”

He added that the future of AI could look more like J.A.R.V.I.S. in Ironman than simply getting help summarizing large sets of data or writing an email.

He describes this potential technology as an “interface that becomes sort of an information-retrieval system or a question-answering system on top of your data. …It solves a lot of those issues that I know a lot of organizations are wondering when it comes to proprietary data and confidential data.”

Other tools like LangChang – used in conjunction with other tools – can help users make templates out of their existing prompts and iterate them for future inputs. These can then be set up with outside sources such as Wikipedia, as well as databases and APIs.

These, however, are just a small sample of the growing list of possibilities.

While OpenAI, Microsoft and Google continue to grab the lion’s share of attention when it comes to new AI tools, there are countless others being developed and improved every day.

Business leaders must strike the delicate balance between experimenting and staying ahead of the curve against protecting proprietary, and even sensitive data. Miscalculating could not only compromise competitive advantages, but also user safety.

The Business Builders’ Network is full of expert, trustworthy service providers who are on the leading edge of artificial intelligence technology. When you’re ready to connect with an industry-specific resource for your business, contact our research and operations team to set up a call.

The Road to AI Implementation: Strategic Planning, Data Management, Cybersecurity

What’s worse than not implementing artificial intelligence tools into your business?

Implementing them without a plan.

While it might feel like you’re falling further behind competitors every day you’re not adding AI to your tech stack, you’re better off waiting a little longer to get it right. Rushing out a half-baked product will only cause you more harm in the long run.

Let’s dive in to some of the key precursor activities for implementing artificial intelligence into your business.

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Aligning AI with Business Strategy

As you choose your AI use cases, it’s essential to align them with your broader digital and business strategies.

Nik Kapauan, principal at Access Holdings, recently talked about this on a BluWave-hosted webinar, Activating AI.

“Your strategy for using AI obviously needs to tie to your broader digital strategy, which needs to tie to your broader business strategy as a firm,” Kapauan said. “I’d also bifurcate it because when we say AI, it’s a broad spectrum of things. You have your traditional analytics, which is descriptive analytics, just getting stuff on a screen and reporting. And then you have your more predictive analytics for predicting the future.”

In either case, Kapauan reiterated the importance of aligning with your overall goals, noting that predictive analytics allow for more flexibility.

“The way you’d approach that strategy is a bit more iterative, a bit more experimental,” he said, “trying to get use cases and experimenting as soon as you can to figure out where the value is.”

Tackling Data Challenges

Data is at the heart of any AI initiative. The service providers in our network say the number one hurdle businesses face to adding artificial intelligence tools is not having a good sense of data availability or hygiene, respectively.

“A lot of people want to jump to the model or the technology. ‘What if we could do this with customers?’ I think it’s really important to start with, ‘What is the space of data that we have at our disposal?’” Michael Woods*, the CEO of an AI consulting firm BluWave works with regularly, said in an interview. “Then just as importantly, ‘Do we have any sense of the inaccuracies or things that could really lead us astray in that data?’

On the AI webinar, Kapauan said that handling data is often the most significant part of large analytics projects.

“That centralization of data, the cleaning of data, the ongoing maintenance of data, is the lion’s share of the effort,” he said.

BluWave CEO & Founder Sean Mooney said the effort, however, is worth it.

“You’ve got to do the unglamorous data cleanliness part… the only thing worse than no data is bad data,” he said. “Keep [the data] good because it’s like a piece of equipment that’s gotta be maintained. Anytime there’s rotation and force in anything, it wants to lose calibration.”

READ MORE: AI Data Analytics: BI Tools

Change Management: A Key Component

Kapauan emphasized the need for a high-level leader to drive the change internally when significant changes are being made to the way a business operates.

“I think one of the biggest predictors of success is a champion inside the organization that could really own the vision and drive the opportunity. And often that’s the CEO or someone the CEO directly holds accountable for the digital agenda,” Kapauan said. “Having that leadership voice to set the vision and drive the organization and mobilize change is critical to success for analytics and any other kind of major digital transformation.”

Mooney added that this is a key part of change management.

“AI’s going be part of your strategy,” he said. “It’s a tactic, it’s not your strategy.”

Securing Your Data Assets

Finally, as businesses build up their data assets, it is vital to safeguard them.

“We want to make sure that we protect [our resources] from theft, making sure that if someone gets into our organization that they can’t pull that model out and take it with them to use somewhere else,” said Keith Thomas, the Cybersecurity Operations National Practice Lead at AT&T. “There are some ways that we protect using different security tools, and different security capabilities support the idea of a [data] model theft by attackers.”

Thomas also emphasized the importance of having a robust disaster recovery plan. If an AI system goes down, the team must be prepared to mitigate any negative impact on data and analytics.

“Even if it is to go to a manual approach, that’s OK. Having the plan is the most important part of that,” Thomas said.

Mooney pointed out that various resources are available to help businesses of all sizes protect their most critical asset: their data.

“Once again, we’re seeing this theme of, ‘failing to prepare is preparing to fail,'” Mooney said. “You’ve gotta do the work in advance. Not just even on the data and the analytics side, but also in protecting your data.”

BluWave has seen a rapid uptick in demand for AI-related services recently. What many firms lack, though, is the necessary foundation to get started.

Aligning your AI tactics with your overall business strategy, preparing your data, identifying an internal champion and protecting your data assets are crucial precursors to implementing these powerful new tools.

Whether you’re at a private equity firm, portfolio company or private or public organization, BluWave’s Business Builders’ Network is full of expert third-party AI resources. These highly vetted service providers can not only help you with the aforementioned preparations, but will also work with you to implement these tools.

Contact our research and operations team to learn more, and we’ll connect you with an industry specific expert to assist your digital transformation using artificial intelligence.

*Privacy is important to us. While the source and company name have been changed, these are real quotations from a real service provider in the BluWave Business Builders’ Network.