Daniel Cane is co-CEO and cofounder of South Florida-based ModMed®, a healthcare IT company that’s transforming healthcare through specialty-specific, intelligent platforms to extend practice efficiency and improve patient outcomes.
Founded in February 2010, ModMed has grown to over 1,200 employees and has raised over $332 million in total investment. Known for its progressive growth as a medical technology company, ModMed is continuously recognized each nationally and regionally for its achievements under Daniel’s leadership. In 2020, the corporate was named considered one of the Best Workplaces within the Country by Inc. magazine. Between 2016 and 2018, the corporate was named considered one of the fastest-growing corporations in North America on the Deloitte Technology Fast 500™ list. Starting in 2015, the corporate has been named annually to the exclusive Inc. 5000 list, a prestigious compilation of the fastest-growing private corporations within the country.
Are you able to share some insights into your background and the way it has influenced your work at ModMed?
My journey into tech began during my undergraduate years at Cornell once I co-founded Blackboard. We transformed education by digitizing class notes and making a platform that gave students and college unprecedented flexibility and interaction. For me, Blackboard’s success culminated in 2004 with its IPO, and while our solutions were game-changing in edTech, I couldn’t help but keep an eye fixed out for brand new challenges.
One such challenge presented itself once I went for a routine checkup with my dermatologist. We had an incredible talk in regards to the struggles of using outdated paper-based systems and ways to repair them. Realizing the bridge between his medical expertise and my technical know-how, we decided to team up and create ModMed together with our first electronic health record (EHR) platform.
On the time, some EHRs already existed, but unfortunately, studies often cited them as considered one of the leading causes of physician burnout. We took a distinct approach and designed our EHR to adapt the user experience to the particular workflows of a medical specialty. Our flagship cloud-based EHR, EMA, is and continues to be designed by doctors, for doctors, which has set us apart and defines our secret sauce available in the market. Through the years, we’ve expanded our product offerings to incorporate a full suite of solutions that help medical providers simplify and streamline their practice operations and expedite the delivery of care.
How do you see the battle for effective AI in healthcare being won or lost with data?
We’re beginning to see an increase within the adoption of AI technology inside practices to streamline workflows and maximize efficiency. As we move into an era of using AI to do more sophisticated tasks – comparable to suggesting treatment or other clinical-support recommendations – it’s paramount to have the proper data and AI training strategy in place. AI has the chance to significantly improve the experience for patients and providers and create systemic change that can truly improve healthcare, but making this a reality will depend on large amounts of high-quality data used to coach the models.
Why is data so critical for AI development within the healthcare industry?
Data is the lifeblood of AI, and poor data quality will impair an AI’s performance, resulting in suboptimal outcomes. This may have dire consequences in a healthcare setting as patient lives could also be at stake. But a more likely scenario is that these negative experiences could undermine each patients’ and providers’ trust in AI, slowing down progress and the positive impact this revolutionary technology can have on healthcare.
For instance, within the exam room, AI-enabled ambient listening tools are designed to suggest content for clinical notes for the provider to review and approve. Ideally, this could reduce the period of time a provider spends documenting throughout the EHR and permit for more quality time with the patient. Nonetheless, poor data sourcing and ill-trained AI tools could have the alternative effect, leaving providers to as an alternative spend an inordinate period of time fixing errors and re-writing notes.
Moreover, bias is a major risk related to AI algorithms, and quality data can play a key role in mitigating healthcare disparities. AI models can learn patterns that effectively treat one patient population preferentially in comparison with other populations, including legally protected groups. By monitoring the information inputs and training on robust and representative data, AI outputs will be more inclusive and accurate.
Are you able to elaborate on the varieties of data ModMed uses to coach its AI models and the way this data is sourced and managed?
At ModMed, we use comprehensive specialty-specific data to assist train our AI models with precision. Over the past 14 years, we’ve created specialty-specific, de-identified structured data sets consistent with privacy laws and at the moment are leveraging this in-house data to coach our AI models. For instance, our ambient listening tool ModMed Scribe has been trained for dermatology, our first specialty launch, on hundreds of thousands of structured parameters from de-identified patient records sampled from a group of 500 million patient encounters.
How does ModMed define “ethical AI” within the context of healthcare?
The potential for AI to have biases or provide inaccurate information in the shape of “hallucinations” or omissions can impact patient lives. For that reason, ethical AI in healthcare is about setting a high standard for accuracy and precision. It means developing algorithms fastidiously and responsibly and using high-quality and diverse data to assist enable more accurate predictions for each user.
Ethical AI can also be about ensuring that humans remain within the equation. An AI shouldn’t “out doctor the doctor” but as an alternative reduce the executive burden physicians and their staff experience in order that they can focus more on helping patients.
What measures are in place at ModMed to permit AI technologies to be developed and deployed ethically?
Our structured data approach—curating high-quality, representative training data sets—helps us make responsible AI a reality. Relevant and de-identified data collected from our EHR systems from a wide selection of practices provides us with a various set of coaching data that reflects different patient populations.
Moreover, our development team embraces data cleansing to facilitate collecting and utilizing high-quality data. This process allows our teams to discover, rectify, and take away inconsistencies, errors, and missing values from the information set. Through this regular maintenance, we will consistently update the AI based on performance data, especially clinical data, where patient outcomes will be impacted.
Are you able to discuss the importance of transparency and accountability in AI development, especially in healthcare?
Transparency makes accountability possible, which is why it’s such a vital underpinning to any AI solution in healthcare. Physicians’ top priorities are patient care and safety, so it’s no surprise that 80% of physicians need to know the characteristics and features of the design, development, and deployment of AI tools.
Moreover, not all data is created equal. It is important to know where and the way data is stored and sourced and the way frequently it’s updated. We’re fortunate that since ModMed’s inception, we have now been committed to an information strategy that prioritizes transparency and accuracy. Now we have an intensive understanding of our data’s sources and quality and are confident that our AI integrations will deliver considerable value to our clients.
How is AI being integrated into ModMed’s specialty-specific EHR systems like EMA and gGastro?
Across our portfolio, we have now been utilizing machine learning for a while and strengthening our investment in advanced and generative AI to simplify the business of drugs and expedite quality care. We’re constructing out a whole AI-powered practice experience that starts before a patient walks within the door, extends through the exam room, right through to the billing department.
Within the clinical setting, we’re in the ultimate stages of our AI ambient listening pilot program for EMA, which we imagine can be a game-changer for its downstream functionality and suggested structured content. Our AI-powered documentation solution is designed to streamline the care process beyond just transcription or drafting a SOAP note. Utilizing vast amounts of structured data, we’re training our AI models to capture essential information from doctor-patient conversations and, working alongside our EHR, to suggest relevant content for visit notes, including ICD-10 codes, surgical codes, and prescriptions. This protects physicians precious time and allows them to spend more quality time with their patients.
What specific advantages do specialty-specific AI solutions provide to healthcare providers and patients?
No two medical specialties are alike. They vary widely with the patients they see, the conditions they treat, and the medical codes used for reimbursements. AI solutions should be tailored to accommodate these variations to be effective in any truly meaningful way.
For instance, ModMed’s EHRs and AI ambient listening tools are tailored explicitly to every medical specialty, providing highly relevant and precise support to clinicians. Each specialty’s documentation process requires different components throughout the structured data note, including unique medical codes and terminology. This specialization allows the AI to higher understand and anticipate the unique needs and workflows of various specialty practices, which we imagine will lead to more efficient implementation, faster adoption, and greater overall effectiveness in improving operational efficiency.
Where do you see probably the most significant opportunities for AI in healthcare over the following five to 10 years?
In the long run, AI will undoubtedly permeate nearly every aspect of healthcare in ways we will’t imagine. Already, AI is being harnessed for administrative tasks, and within the near term, this trend will likely surge as AI’s value becomes more apparent.
I also see a future when AI is seamlessly integrated throughout doctor-patient interactions, where the ‘user interface’ or UI is virtually invisible. As an alternative of today’s screen-based interactions, AI could offer a mix of reality and augmented reality. This future state AI could potentially analyze health records to discover critical insights, predicting a patient’s risk for various diseases. The vast amount of knowledge in medical records presents a possibility for AI to anticipate future care needs and create and help manage preventive care treatment plans.
This experience could extend beyond the practice setting and develop into integral to a patient’s day by day life. AI-powered wearables could provide personalized support, answer questions, and schedule appointments amongst other things. AI could also monitor vital signs remotely, detecting and alerting providers to potential health issues. Personalized treatment plans, tailored to individual patients based on data and preferences, could develop into the norm.
This is actually an exciting time for healthcare. The subsequent five to 10 years are ripe with opportunities to further transform the industry and improve the patient experience.
