Authored by Matt Azarva
Across numerous industries, in the news, and even at the dinner table, everyone is buzzing about the introduction of artificial intelligence (AI) into our daily lives. In healthcare and life sciences, AI is not a new concept at all, but rather, a vital tool that’s been utilized for years now. That said, we’re just beginning to comprehend its full potential, and many organizations that have been slow on the uptake are realizing if they’re not yet fully onboard, they’re missing the boat.
For quite some time, hospitals have utilized AI to help lower patient readmission rates, and in the development of predictive models to prevent hospitalized patients from contracting sepsis and other infections.
More recently, AI is garnering a lot of attention in life sciences, particularly in the area of drug discovery. This conversation has intensified with the awareness that AI and machine learning will significantly reduce the length of time, as well as the funding required, to identify and develop critically needed treatments for patients with numerous medical conditions and chronic diseases.
AI and Drug Development
We’re already seeing how technology is changing the drug development space, where you have a mix of biologists and chemists working toward new discoveries, but you also have a dry lab with computational biologists and computational chemists as well. It’s become more of a combined effort between the wet lab and the dry lab, so to speak.
When it comes to drug development, and even more immediately, drug repurposing, AI is a complete game-changer. The estimated time to develop a new drug is 10 to 12 years. The ability of AI to quickly comb through and analyze massive amounts of data opens a huge door to successful drug repurposing―the ability to quickly and effectively repurpose existing medications approved for certain conditions to also treat other conditions for which there are no known treatments. This approach will prove to be life-changing, and lifesaving, for millions of patients around the world.
I think the COVID-19 pandemic bolstered the general public’s understanding of the critical importance of drug repurposing and the role advanced technology can play in drug development. COVID shined the spotlight on companies that were brilliantly using technology to come to the rescue with answers. And the speed at which we all had access to effective vaccines was nothing short of extraordinary.
AI and the Physician Experience
Beyond the obvious advantages of AI in drug development, we’re also hearing from several clients about how technology is being used to combat physician burnout. For many physicians, time is more valuable than any currency. And most, if not all, will tell you they have to spend far too much time on administrative tasks. The impact of AI right off the bat is the time it saves physicians by using natural language processing to automate visit notes and quickly summarize medical records.
AI can also automate the cumbersome process of obtaining prior authorizations. An April 2022 study conducted by McKinsey & Company found that AI-enabled preauthorization can automate 50 to 75 percent of manual tasks, leading to boosted efficiency and reduced costs, and freeing up clinicians at both payers and providers to focus more on the delivery of care and the management of complex cases.
AI and the Patient Experience
For patients, AI will completely transform how we interact with our healthcare providers. Technology has already given us telehealth―an essential alternative to in-person visits during the pandemic. Our engagement is already shifting, and the evolution will be rapid.
Long gone will be the days of holding on the phone for what feels like forever to schedule or change an appointment with your healthcare provider, or trading multiple voicemails before finally connecting live. Instead, we’ll be engaging with chatbots to complete such tasks in less than 60 seconds.
AI-powered virtual assistants will also be able to help patients manage their medications and provide reminders when it’s time to take them. This is a huge step forward for medication adherence.
Most exciting is the ability of AI to improve patient outcomes by analyzing massive amounts of data to help guide healthcare providers in making clinical decisions and delivering care. AI can improve diagnosis accuracy and reduce medical errors, analyze medical images and detect abnormalities the human eye may not see, identify patterns in patient data that can help doctors make more accurate diagnoses, and so much more.
Is There a Downside?
When we consider the potential drawbacks, among the greatest concerns is the fact that the algorithms created through AI are only as good as the data that’s being collected. Is the data that’s being entered accurate? Are the findings potentially biased based on the patient population included, or not included?
Data integrity is now a key area of focus for many organizations, as well as for the candidates we engage with. When we talk with potential candidates in this space about interviewing for a particular role, the first question they ask us is, where is their data coming from? Data is king. Because data without integrity yields inaccurate results.
The other hot-button issues include data privacy and data breaches. In an age when breaches abound, and hospital data can actually be held for ransom, what policies and procedures are being put in place to protect patient information?
And of course, regulation is a big question mark. There is no governing body as of yet―whether it be the government or the healthcare industry―regulating how AI is utilized in healthcare and life sciences, or in any other vertical for that matter.
Such concerns are what led Elon Musk, Steve Wozniak, and more than 1,000 other technology leaders to recently sign an open letter calling for an immediate and minimum six-month pause in the development of more powerful AI systems, and making the case that the development of these systems “should be planned for and managed with commensurate care and resources.”
What’s Next?
There is increasing convergence between healthcare and technology companies as these two sectors explore how they can work together to benefit patient care. And more technology companies will be entering into the healthcare space, as tech learns what it can bring to healthcare, and healthcare identifies the multitude of ways it can utilize technology to become smarter, faster, and more efficient.
Despite the current concerns, which are commonplace at the start of anything this big and transformative, the benefits are undeniable. We’ll certainly experience some growing pains and perhaps even a little bit of chaos for a time. But there’s no doubt in anyone’s mind that AI and machine learning have officially arrived and the growth in this space will be swift.
Recruiting for Top Talent
One of the trends we’re seeing as we recruit top-level analytics executives, as well as data science, data management, data strategy, biostatistics and bioinformatics professionals, is that these positions are becoming peer roles to an organization’s CTO or CIO, whereas previously, they may have reported into senior leadership.
And we’re seeing companies creating new leadership roles that focus solely on AI technology, where the remit is to begin developing proprietary machine learning models. There’s a new level of urgency to create these capabilities in-house versus utilizing external models. And experts in data and analytics are now taking on an increasingly greater role. Organizations that were on the fence about AI before are now looking to hire these individuals.
An Eye on the Future
I’ve been in this practice for 10 years now, and in the past three or four years, I’ve watched as analytics and data science have become increasingly more important―not only because of the tremendous technological advances being made, but because there is a social acceptance of technology in the healthcare space that wasn’t there five or six years ago.
We’re witnessing what is the mainstream moment for AI across all verticals. There are hundreds of ways AI and machine learning can and will be utilized in healthcare and life sciences―from drug discovery to hospital readmissions to customer service to operations to human capital and countless others―and the potential is limitless. We’re just beginning to scratch the surface.
About the Author
Matt Azarva is the founder and leader of the Data Practice at Klein Hersh. In this role, Matt partners with organizations across digital health, payers, and pharma/biotech to recruit and hire top analytics executives, as well as data science, data management, data strategy, biostatistics, and bioinformatics professionals. Prior to joining Klein Hersh, Matt earned his B.S. in Media Studies and Broadcasting from Northeastern University and spent eight years as a Sports Producer for Comcast, where he was honored with two Emmy Awards. Since joining Klein Hersh in 2013, he has helped numerous organizations successfully build out their analytics groups.
