The Future of Healthcare: Embracing Generative AI for Product Management
The Future of Healthcare: Embracing Generative AI for Product Management
Healthcare is on the precipice of transformative change. While Artificial Intelligence (AI) has been making headway for some time now, a particularly promising subfield - Generative AI, also known as Large Language Models (LLMs), such as OpenAI's GPT-3 or its latest GPT-4, is beginning to leave a profound mark with deep implications for Healthcare. Product Management teams operating in healthcare can leverage these technologies to advance the industry.Healthcare product management is a subset of product management. And as such, there are general principles that guide useful application of Generative AI. Here we will review those first. Then hone in on healthcare more specifically.
How should Product Management leverage AI in Healthcare?
Healthcare is on the precipice of transformative change. While Artificial Intelligence (AI) has been making headway for some time now, a particularly promising subfield - Generative AI, also known as Large Language Models (LLMs), such as OpenAI's GPT-3 or its latest GPT-4, is beginning to leave a profound mark with deep implications for Healthcare. Product Management teams operating in healthcare can leverage these technologies to advance the industry.Healthcare product management is a subset of product management. And as such, there are general principles that guide useful application of Generative AI. Here we will review those first. Then hone in on healthcare more specifically.
Where is Generative AI useful for product management in general?
It’s tempting to apply Generative AI or LLMs everywhere, but there are limitations. LLMs and humans will both be inherently better at different areas because we operate differently.
LLM vs.
Human
Replicate
Understand
General
Contextual
Historical learning
Real time learning
Therefore, Product managers using LLMs should seek to do so in areas where critical thinking and scientific or domain knowledge are of lesser importance.
For example, LLMs are valuable in conducting market research, developing product positioning and messaging, and product marketing. LLMs can also assist in data analysis and synthesis when conducting market research.
However, where human interpretation and decision-making are key components, then LLMs are not as useful.
For example, tasks like creating a go-to-market strategy, conducting user analysis, and identifying user needs and pain points are not good for LLMs because they rely heavily on critical thinking and domain knowledge.
Where are LLMs useful for product management in healthcare?
Let’s take the conversation back to healthcare now. Keep in mind that LLMs are great in areas that do not require critical thinking and domain knowledge. We would like to highlight 3 areas in healthcare that LLMs are well positioned to transform.
1: LLMs will increase Information Accessibility đź§
LLMs are built on a foundation of natural language processing (NLP), capable of generating, and translating human-like text. This feature has far-reaching implications for the base-level functions in healthcare, as it could enhance the ability of health systems to collect, interpret, and communicate medical information.
From the patient's perspective, an AI-powered system leveraging a Language Model (LLM) could enhance accuracy and provide near real-time support in delivering health information, answering queries, and addressing doubts. For practitioners, these models could aid in interpreting complex medical reports, streamlining consultations, and improving patient-doctor communication.
A noteworthy example comes from Epic, who recently made an exciting announcement.
Epic has integrated ChatGPT-4 with Nuance's Dragon Ambient eXperience Express (DAX Express), with the goal of empowering the automation of electronic health record (EHR) notes. (Source:Â EHR Intelligence)
This is huge!
Clinicians using the DAX ambient solution have reported saving an average of seven minutes per encounter and experiencing reduced burnout.
That translates to savings of two hours a day!
Now, with the addition of ChatGPT-4 capabilities provided by Microsoft Azure OpenAI Service, it is anticipated that these benefits will be further enhanced, leading to a remarkable improvement in overall efficiency and well-being of clinicians.
Generative AI could play a significant role in decision support and diagnostics. Combining the vast data-processing abilities of AI with human-like language understanding, these models can sift through heaps of patient data, medical literature, and case studies. Consequently, they can aid in diagnosing diseases, predicting outcomes, and recommending treatments.
For PMs, these capabilities can translate into decision-support tools that aid healthcare professionals. It can translate to risk-assessment applications that inform patients about potential health risks. It can translate to cross-check tools that compare diagnoses and treatments with unstructured electronic health record (EHR) notes and patient data.
In instances involving complex patients, hundreds of pages of information needs reviewing, thus the integration of Generative AI is essential. By incorporating Generative AI, we can anticipate an improved quality of clinical decisions, leading to enhanced patient outcomes, as well as reduced healthcare costs.
Here are three example companies that work on extracting insights from unstructured EHR data using NLP and AI:
Augmedix: focused on optimizing patient and financial outcomes through predictive analytics, startup recently announced the launch of “Chart prep” which leverages technology to prepare a patient note structure for the physician based on the patient’s previous medical records and the unique visit type. This allows physicians to have an efficient review of relevant key points prior to seeing patients. https://medicalfuturist.com/top-artificial-intelligence-companies-in-healthcare/
3: Engaging patients in preventive healthcare rather than sick-care 🚴🏼‍♂️
Healthcare providers face a significant challenge in engaging patients on their healthcare journey. The sheer number of patients, coupled with their unique needs, makes this task complex.
Nevertheless, Generative AI offers a promising solution by enabling the delivery of personalized health advice, reminders, and recommendations in a more effective manner. Moreover, the effectiveness of these communication protocols can be automatically measured. So best practices tailored to each patient's can be more quickly identified. A world of personalized and efficient healthcare experience is already in the works. With generative AI, this trend improved personalized preventative care will accelerate.
Here are three examples of companies that are tackling preventative care, with the opportunity to leverage Generative AI and LLM heavily to deliver their solutions.
Omada Health: This company offers digital health programs to help people change their habits and reduce the risk of chronic disease. The programs involve personalized coaching, interactive learning, peer support, and connected devices like scales and step trackers.
Livongo: Acquired by Teladoc in 2020, Livongo offers a whole-person platform that empowers people with chronic conditions to live better and healthier lives. They have devices and coaching for managing diabetes, hypertension, weight management, and more.
Noom: Noom is a wellness company that provides behavior change programs and a platform for weight loss, exercise, and diet tracking. Their programs are based on cognitive behavioral therapy and are customized to each user.
Here’s the “But” 🚧
The introduction of Generative AI, especially LLMs, in healthcare comes with a unique set of ethical and regulatory challenges. These include issues related to data privacy, consent, accountability, and the potential for bias in AI-driven decisions.
For PMs at a strategic level, understanding these implications is critical. Formulating guidelines and mechanisms to handle these ethical considerations is an integral part of managing healthcare products powered by Generative AI and LLMs.
The voyage into the domain of LLMs is not without challenges. Still, the potential opportunities it presents are extraordinary. As we move towards a future where Generative AI plays an increasingly pivotal role in healthcare, product managers who can strategically leverage these advancements will lead the charge in transforming patient care.
As the technology advances and as the industry grows accustomed to its impacts, the agile product manager must not just adapt, but stay ahead, guiding the transformation instead of just reacting to it. With a clear understanding of the potential opportunities and challenges, product managers can turn the advent of LLMs into a strategic advantage, crafting the future of healthcare.
As promising as Generative AI like GPT-3 or GPT-4 are for product management, they also have limitations that users should be aware of:
Understanding vs. Replicating: While LLMs can generate human-like text based on a provided context, they don't actually understand the content in the way humans do. They don't have beliefs, opinions, or consciousness. They generate responses based on patterns identified in the data they were trained on. This limitation can lead to outputs that may be contextually inappropriate or factually incorrect.
Ethics and Bias: LLMs can unintentionally generate biased or offensive content, as they're trained on large-scale internet text that may contain inherent biases. Addressing these biases is a crucial challenge, especially in sensitive industries like healthcare.
Data Privacy: In product management, protecting user data is paramount. When using LLMs, there's a risk of exposing sensitive data if conversations are not handled securely. It’s crucial to ensure that any data used is anonymized and that the model does not inadvertently generate sensitive or private information.
Lack of Personalization: While LLMs can generate human-like text, they may lack the personalized touch that comes with human interaction. This can be a limitation in customer-facing roles where personal interaction is important for building relationships.
Dependence on Quality of Training Data: The efficiency of LLMs is heavily reliant on the quality of the training data. Inaccurate, incomplete, or unrepresentative data can lead to subpar or misleading outputs. Ensuring access to high-quality, diverse data for training these models can be a challenging task.
Difficulty with Real-Time Learning: LLMs do not learn or adapt from each interaction in real time. This lack of dynamic learning limits their ability to improve or refine their performance based on real-time feedback.
In conclusion, while Generative AI and LLMs offer powerful capabilities that can transform product management, it's important to be aware of these limitations to effectively mitigate potential risks and challenges.
The future is bright for Generative AI in healthcare
The Healthcare industry stands at the precipice of transformative change. Generative AI will unlock so much potential, and Product Managers are well positioned to create solutions to save lives and make us all healthier.
Excited for the future ahead!
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