GenAI in Healthcare: Build vs. Buy

GenAI in healthcare operations opens opportunities for innovation in patient care, diagnostics, and administrative processes. For healthcare leaders, a pressing question is whether to build a custom Generative AI solution in-house or adopt enterprise-ready tools like healthcare chatbots. This choice is not just about immediate expenses—it affects scalability, the extent of customization, and how much control the organization retains over AI integration into existing healthcare systems.

Evaluating these options requires an understanding of the organization’s long-term goals and priorities. The right decision can align Generative AI implementation with improving patient outcomes, streamlining workflows, and navigating the complexities of adopting advanced healthcare technologies.

A recent KPMG survey revealed that 50% of organizations buy Generative AI tools, 29% adopt a mixed approach, and 12% opt to build their solutions in-house. They consider cost, customization needs, intellectual property ownership, and data privacy concerns related to Generative AI implementation. For many, the balance lies in understanding these factors and aligning them with their business objectives. Let’s understand more about GenAI in healthcare below:

Super Smart AI: Game Changer for Healthcare

Key Insights You Will Gain Through This Whitepaper :

▪️AI in Diagnostics and Treatment

▪️Operational Efficiency

▪️Patient Engagement

▪️Data Management and Security

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➡️ Strategic Value of GenAI in Healthcare

A McKinsey report reveals the immense potential of Generative AI, projecting it could add $2.6 trillion to $4.4 trillion to the global economy by improving clinical operations, personalizing care, and speeding up drug discovery. Let’s understand the strategic value of GenAI in healthcare:

🔹 Business Impact in Healthcare

Generative AI is impacting industries, with healthcare being a standout example. In healthcare, it aids advanced diagnostics by analyzing extensive datasets to generate actionable insights, improving patient care and treatment outcomes. GenAI in healthcare supports operational efficiency by automating repetitive processes and enhancing providers’ decision-making accuracy.

In other sectors like finance, it detects fraudulent transactions, streamlines risk assessments, and delivers personalized advisory services, while manufacturing utilizes AI to boost efficiency, anticipate maintenance needs, and innovate product design. These applications showcase how GenAI addresses unique challenges in healthcare and beyond, helping industries adapt to evolving demands and unlock new opportunities.

🔹 High-Value Use Cases of GenAI in Healthcare

GenAI in healthcare opens up new opportunities by addressing critical challenges and improving operational efficiency. Predictive modeling stands as a transformative application, enabling healthcare providers to anticipate patient outcomes, identify potential risks, and make well-informed, data-driven decisions. This proactive approach improves care and optimizes resource allocation across the organization.

Automation is another key area where Generative AI delivers value. Streamlining repetitive tasks such as patient documentation and billing frees up healthcare professionals to focus on quality care delivery. Personalization enables customized treatment recommendations and enhances patient engagement by addressing individual health needs. These use cases highlight how Generative AI is becoming an indispensable tool for advancing healthcare practices and meeting clinical and operational goals.

Related read: Use Cases of Healthcare Chatbots: A Detailed Guide

➡️ Key Factors in the Build vs. Buy Decision for GenAI in Healthcare

Deciding whether to build or buy a Generative AI solution for healthcare involves carefully evaluating multiple factors. Each option has its advantages, challenges, and long-term implications. Here are some critical aspects to consider when making this decision.

1️⃣ Customization Needs

Building a GenAI solution allows for greater control over the features and functionality, which is ideal for organizations with unique requirements. On the other hand, buying a pre-built system may offer the essential features needs to be started quickly, albeit with limited customization options.

2️⃣ Budget Constraints

Developing an in-house GenAI system typically requires a significant upfront investment of infrastructure, talent, and time. Purchased solutions may involve lower initial costs but might include recurring expenses like subscriptions, upgrades, and licensing fees.

3️⃣ Time to Market

For organizations looking to deploy GenAI quickly, buying a ready-made solution is often the faster route. Building a system from scratch can take months or even years, especially when dealing with healthcare data and compliance complexities.

4️⃣ Expertise and Resources

Building a GenAI solution requires a dedicated team of data scientists, engineers, and domain experts. If your organization lacks in-house expertise, buying a system can reduce the technical burden while ensuring reliable performance.

5️⃣ Compliance and Security

Healthcare involves strict regulations and sensitive patient data. A pre-built GenAI solution often comes with pre-validated compliance measures. Building in-house, however, requires rigorous testing to ensure adherence to standards like HIPAA and GDPR.

6️⃣ Scalability and Long-Term Goals

Organizations aiming for long-term scalability might find that building an in-house solution provides more control over future updates and expansions. Buying a solution, while initially convenient, may limit scalability depending on vendor offerings and licensing agreements.

➡️ Pros and Cons of Adopting Generative AI for Healthcare Innovation

Unless you know about the pros and cons of GenAI in healthcare, you cannot decide for your organization. What to choose? And what will benefit you? Let us understand below the pros and cons of applying GenAI in healthcare:

🟢 Pros of Applying GenAI in Healthcare Solutions

1️⃣ Improved Diagnosis and Treatment Planning

GenAI algorithms can analyze large datasets, identify patterns, and assist in early diagnosis or treatment recommendations, leading to better patient outcomes.

2️⃣ Personalized Patient Care

The technology enables tailored care plans based on patient history, genetic information, and real-time health data, improving treatment effectiveness.

3️⃣ Administrative Efficiency

Automating routine tasks like documentation, appointment scheduling, and insurance claims allows healthcare providers to focus more on patient care.

4️⃣ Enhanced Medical Research

GenAI accelerates research by analyzing complex datasets and identifying potential breakthroughs in drug discovery and treatment options.

5️⃣ Patient Engagement

AI-powered chatbots and virtual assistants improve communication, answer patient queries, and provide education, making healthcare more accessible.

6️⃣ Cost Optimization

By streamlining workflows and reducing human error, GenAI helps optimize operational costs while maintaining high-quality care delivery.

🔴 Cons of Applying GenAI in Healthcare Solutions

1️⃣ Data Privacy Concerns

The use of GenAI in healthcare raises significant privacy issues. Sensitive patient data must be handled carefully to avoid breaches, which could result in legal and ethical challenges.

2️⃣ High Development Costs

Building or integrating GenAI solutions can be expensive. From data preparation to infrastructure setup, the costs can strain resources, especially for smaller healthcare providers.

3️⃣ Complex Implementation

The integration of GenAI with existing healthcare systems often requires significant customization and testing. This complexity can lead to delays and increased costs during implementation.

4️⃣ Bias and Accuracy Issues

GenAI systems rely heavily on the quality of the training data. Poor or biased datasets can lead to inaccurate results, affecting patient care and decision-making.

5️⃣ Dependence on Technology

Relying on GenAI might reduce human oversight in critical decision-making processes. This dependence could lead to errors if the system malfunctions or produces inaccurate outputs.

➡️ Deciding Between Buying or Building GenAI Solutions in Healthcare

✔️ When Buying GenAI in Healthcare Makes Sense

1️⃣ Reducing Administrative Burden

Healthcare professionals face heavy administrative workloads that can slow down operations. GenAI can automate tasks like report generation, paperwork processing, and scheduling. This allows staff to focus more on patient care, reducing burnout and increasing efficiency. Investing in GenAI makes sense when administrative tasks overwhelm the healthcare team, improving overall productivity.

2️⃣ Improving Patient Outcomes with Personalized Treatment Plans

GenAI can analyze patient data to suggest personalized treatment options, improving clinical decision-making. By using AI to offer recommendations based on data, healthcare providers can create more effective care plans. When your organization prioritizes individualized care for complex cases, GenAI can enhance treatment outcomes and provide better patient care.

3️⃣ Streamlining Medical Imaging Analysis

GenAI can assist healthcare providers in analyzing medical images faster and more accurately. With AI’s help, radiologists can identify issues more efficiently, speeding up diagnoses and reducing human error. If your healthcare facility struggles with a high volume of imaging studies, GenAI can support clinicians, making the process faster and more reliable.

4️⃣ Enhancing Drug Discovery and Research

In drug discovery, GenAI can accelerate the process by analyzing medical research and predicting the effectiveness of new drug compounds. This can reduce the time and cost of developing new treatments. For organizations focused on pharmaceutical research, using GenAI can help streamline drug development, improving efficiency and innovation in the industry.

5️⃣ Addressing Staff Shortages

Healthcare organizations often face staffing challenges, especially in critical areas. GenAI can help by automating repetitive tasks, allowing staff to focus on more essential functions. If your facility faces staffing shortages, GenAI can support your team, improving productivity and ensuring that high-quality care is delivered while managing labor constraints.

6️⃣ When You Have the Data Infrastructure to Support It

GenAI relies on large datasets to provide accurate predictions and recommendations. If your healthcare organization already has a solid data management system, integrating GenAI becomes easier and more effective. The right data infrastructure is a must for AI to work properly, so ensure your organization is prepared before adopting this technology.

7️⃣ Scaling Services in a Cost-Effective Manner

As healthcare organizations grow, scaling operations without increasing costs can be challenging. GenAI can help by automating tasks and supporting remote consultations, which reduces the pressure on staff. For organizations looking to scale efficiently, GenAI offers a cost-effective solution to expand services while maintaining quality care and minimizing overhead costs.

✔️  When Building GenAI in Healthcare Makes Sense

1️⃣ Improving Operational Efficiency in Healthcare

Building GenAI in healthcare can streamline repetitive tasks, such as appointment scheduling, medical record sorting, and administrative work. By automating these processes, healthcare providers can save valuable time, reduce human error, and improve overall efficiency. This allows medical staff to focus more on patient care and less on non-clinical tasks, ultimately optimizing the healthcare experience.

2️⃣ Personalizing Patient Care

Generative AI can support personalized treatment plans by analyzing patient data and providing insights that help healthcare professionals make more informed decisions. With AI’s ability to process vast amounts of medical information, healthcare providers can tailor treatment options to each individual’s specific needs, improving outcomes and ensuring patients receive the most effective care for their conditions.

3️⃣ Supporting Decision-Making in Complex Medical Cases

In challenging medical cases, healthcare providers must sift through large volumes of data to identify potential treatment options. GenAI can assist by quickly processing medical literature, patient records, and treatment histories to suggest possible diagnoses and treatment approaches. This reduces decision fatigue, accelerates the diagnostic process, and ensures critical decisions are based on comprehensive insights.

4️⃣ Enhancing Diagnostic Accuracy

Diagnostic accuracy is crucial in healthcare, and GenAI can improve it. By analyzing medical imaging, lab results, and patient symptoms, AI can help detect patterns that might be overlooked by human eyes. This results in more accurate and timely diagnoses, leading to better patient outcomes and fewer errors in treatment planning.

5️⃣ Managing Healthcare Data Efficiently

Everyday healthcare generates an enormous amount of data, making it difficult to organize and interpret manually. GenAI processes and organizes this data, enabling healthcare providers to quickly access relevant information when needed. This leads to better decision-making and a more streamlined workflow, improving the overall quality of patient care.

6️⃣ Enabling Predictive Analytics for Preventive Care

By analyzing patient histories, lifestyle factors, and genetic data, GenAI can identify patterns that predict potential health issues before they occur. This predictive ability supports preventive care strategies by alerting providers to at-risk patients, allowing for early interventions that prevent more serious health problems. This leads to improved patient health and reduced healthcare costs over time.

➡️ Steps to Implement GenAI in Healthcare: Buy or Build Strategy

When deciding between buying an enterprise Generative AI tool or building your own AI models, a structured approach can make the process smoother. Here’s a breakdown of key steps for each option:

✅ Assessing Organizational Needs

The first step in implementing GenAI in healthcare is evaluating your organization’s specific needs. Understand your goals, whether it’s improving patient care, streamlining administrative tasks, or automating processes. This will help you decide if building a custom solution or buying an off-the-shelf product is more suitable for your requirements.

✅ Analyzing the Build Option

Building a GenAI solution internally gives you complete control over the features and design. If your organization has unique needs or processes, creating a custom system may provide more flexibility. However, this requires skilled talent, time, and resources, making it a more expensive and lengthy option than buying.

✅ Exploring the Buy Option

Opting for a pre-built GenAI solution offers the advantage of quicker setup and cost savings. These solutions often come with ready-to-use features designed to tackle common healthcare needs, such as analyzing patient data or supporting predictive care. While they can get you up and running faster, they might not align perfectly with your organization’s unique requirements, and the ability to customize them could be limited.

✅ Budget Considerations

When deciding between building or buying a GenAI solution, consider your budget. Developing a custom system can be costly, especially when it comes to development time, testing, and deployment. On the other hand, purchasing an existing solution can be more cost-effective upfront but may have ongoing subscription or licensing fees.

✅ Integration with Existing Systems

Evaluate how either option will integrate with your existing healthcare infrastructure. A custom-built solution allows for deeper integration, while pre-built solutions may require additional configuration or third-party tools. Ensure the chosen system works well with your EHR and other healthcare technologies.

✅ Long-Term Maintenance and Support

Consider the long-term costs associated with maintaining your GenAI system. A custom-built solution may require ongoing development, updates, and technical support. With a pre-built solution, the vendor usually handles maintenance and updates, but you may face challenges in dealing with service limitations or upgrades outside your control.

✅ Scalability and Future Growth

As your organization grows, so will your need for GenAI in healthcare. A custom-built solution can be developed to scale with your organization’s future needs, whereas a pre-built solution may offer limited scalability or flexibility. Think about how each option will handle future demands for data processing, new features, or broader patient engagement.

✅ Compliance and Security

GenAI in healthcare requires strict adherence to regulations like HIPAA and data privacy laws. Custom-built solutions give you more control over security measures and compliance, while pre-built solutions from trusted vendors often come with built-in compliance features. Ensure that any solution you choose can meet healthcare industry standards for data security.