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August 11, 2023 | Premangsu Bhattacharya
Blog / Piloting Generative AI in Healthcare: How to Start Small and Achieve Quick Wins
Nobody questioned their business systems until Generative AI came into the picture. It was almost like a silent agreement among companies to sit on piles of data, looking for problems elsewhere. And quite rightly so; turning that internal data into something useful meant diving into the chaos of data strategy—a mess that everyone knew about but no one dared to tackle.
Suddenly, the shift happened. Data models like Google Cloud’s Cortex Framework and specialized data platforms transformed the chaos into a foundation layer. Companies went from lamenting “my data is all over the place” to demanding “I need instant insights on my inventory” in a matter of minutes. Record-keeping systems were no longer enough; decision intelligence capabilities became the new gold standard for ERP systems.
Except in healthcare.
Here, the complexity ran deeper, the challenge more intricate. Compliance, safety standards, fragmented information – it all seemed too much.
That was until the unexpected breakthrough of Google Cloud’s enterprise search for Mayo Clinic. On the surface, it might seem like a small step. But when you consider the complexities involved, the uniqueness of healthcare data, and the need to align it all while maintaining stringent standards, it’s nothing short of revolutionary.
Now, healthcare companies are not only talking about data but actively building strategies around it. The unspoken mess has become an opportunity. And healthcare is no longer sitting silently on the sidelines; it’s stepping into the game, ready to explore the possibilities of Generative AI in healthcare.
In this blog, we’ll dive into specific use cases for Generative AI in Healthcare and explore how it is redefining the healthcare industry, unlocking value, efficiency, and innovation.
We have meticulously arranged the Generative AI use cases based on key segments in the industry, each serving distinct roles and facing unique challenges. These segments are:
Enhancing Patient Care Through Personalized Treatment Plans
Generative AI is revamping how patient care is approached. By tapping into comprehensive data such as medical history, genetics, and lifestyle factors, Generative AI systems can tailor treatment recommendations for each patient. This not only ensures personalized care but also equips physicians with AI-assisted insights, enhancing the accuracy and efficiency of the diagnosis process.
Enhanced Medical Record Search
The complexity of health records often makes it challenging to retrieve critical information swiftly. With Generative AI-enabled search capabilities, healthcare providers can quickly access and synthesize vital data from vast databases. These tools can bridge the gaps between dispersed documents and databases, making it easier to find relevant data that can aid in precise diagnosis and treatment.
The traditional drug discovery process is labor-intensive and time-consuming. Generative AI offers a solution by generating potential drug candidates based on certain criteria. By analyzing the properties of known drugs, these systems can propose novel compounds, possibly leading to safer and more effective medications. Moreover, it streamlines the prediction of a drug’s efficacy and safety, potentially revolutionizing pharmaceutical research.
Clinical Trial Optimization
Clinical trials are pivotal yet notoriously challenging to execute efficiently. Generative AI can assist in designing optimal trial structures and in identifying the best-suited participants. This proactive approach not only reduces the trial’s duration but also increases its overall efficacy and accuracy, ensuring more robust results and reduced costs.
Claims Processing Automation
Processing insurance claims can be a drawn-out procedure. With Generative AI, the verification and calculation of claims can be automated, leading to faster processing times and reduced overhead costs. It ensures that claims are processed accurately, benefiting both the insurer and the insured.
Personalized Insurance Plans
One-size-fits-all is becoming a thing of the past in the insurance sector. With Generative AI-driven insights, insurance providers can craft policies tailored to individual needs and health profiles. This personalization ensures that individuals are adequately covered while also allowing insurance companies to optimize risk management.
Wearable Device Insights
The data from wearable devices is vast and often underutilized. Generative AI can interpret this data, offering predictive health insights that can be crucial for monitoring chronic conditions or even anticipating health issues before they become severe. This proactive approach promotes better health outcomes and more informed patient-doctor interactions.
Robotic Assistance in Surgery
Surgical procedures demand precision. Generative AI can play a transformative role by enhancing robotic surgical tools, allowing for more accurate and less invasive surgeries. These advancements can lead to quicker recovery times and reduced hospital stays, benefiting both the medical establishment and patients.
Hospital Administration Efficiency
Hospital operations often suffer from disconnected data across departments like purchasing, HR, finance, and patient care. Generative AI brings this information together, providing a unified view that helps in informed decision-making. It can automate tedious tasks such as invoice matching, payroll processing, and resource allocation, freeing up administrative staff for more critical responsibilities.
Supply Chain Optimization
The healthcare supply chain is a complex web involving procurement, manufacturing, distribution, and delivery. Generative AI can play a vital role in unraveling this complexity. By assimilating information from various sources, it can forecast demand, optimize inventory levels, and manage relationships with suppliers.
The result is a more responsive and efficient supply chain that minimizes waste, prevents stock-outs of essential medical supplies and ensures that the right products reach the right places at the right time, ultimately benefiting patients and healthcare providers alike.
Epidemic Prediction and Control
Public health threats require a vigilant and proactive approach. Generative AI offers tools for early detection by analyzing vast amounts of data, including historical epidemics, weather patterns, population movement, and social media trends. This analysis can pinpoint potential hotspots and patterns of disease spread, allowing agencies to deploy resources strategically. This timely response can mitigate the impact of outbreaks and possibly prevent them from escalating into pandemics.
Health Policy Formation
Formulating effective public health policies is a multifaceted task that demands an understanding of current health trends and the ability to anticipate future challenges. Generative AI facilitates this by mining data from various sources, such as hospital records, census data, academic studies, and global health reports.
It can uncover underlying patterns, such as correlations between socioeconomic factors and health outcomes or the effectiveness of previous policy interventions. These insights empower policymakers to craft policies that are more targeted, evidence-based, and adaptable to emerging health scenarios.
Generative AI is a powerful tool, not a standalone strategy. Healthcare organizations must align it with an overarching data strategy. Building a solid Data Foundation is critical. Without it, even the most advanced AI applications cannot function optimally.
AI models, especially in their evolving stages, can generate false results. Transparent models that are open to human review and validation help in overcoming this challenge. Educating users about this “hallucination” phenomenon ensures realistic expectations and safe usage.
The risk of over-dependence on Generative AI-generated insights is real. Clear communication that emphasizes these insights as recommendations, not mandates, balances the reliance on technology with human judgment. Training and guidelines can help professionals use AI responsibly.
Generative AI relies on vast data sets. Ensuring that this data is coherent with the data used in existing systems is paramount. Data interoperability, where information can flow seamlessly across different platforms, must be achieved to realize the full potential of AI integration.
When embarking on the Generative AI journey in healthcare, a critical decision lies in choosing the right partner and determining whether to build or buy the solution. Let’s break down the paths and the advantages of going the build route with Pluto7’s unique offerings.
While buying an off-the-shelf solution might seem like a quick fix, the build route offers a degree of customization and alignment with organizational goals that a ready-made product often can’t match. Here’s why the build approach often stands out:
Pluto7’s Planning in a Box, developed in deep collaboration with Google Cloud, is designed to streamline this build process. It provides a framework that simplifies decision-making and prioritizes high-impact use cases. Here’s what it brings to the table:
Ready to take the plunge? Starting with a tailored workshop can pave the way for a smooth journey. It can clarify the vision, align stakeholders, and set the stage for a successful implementation of Generative AI in healthcare.