Personalizing Healthcare with Generative AI

Introduction

Healthcare is changing. More patients expect personalized care. Healthcare providers are focusing on patient experience as well as quality of care. Also, more physicians and medical practitioners are facing burnout due to increased work pressure. The American Osteopathic Association reveals nearly 50% of physicians experienced burnout in 2024. A recent survey found that 50% of the physician’s time is spent on administrative work like updating electronic health records (EHRs). Even during patient engagement, physicians spend 52.9% of their time in EHR-related activities.

With the emergence of Generative AI, healthcare professionals are wondering whether they can personalize patient care – without increasing their workload. Thanks to its ability to tap into massive healthcare data, can Generative AI enable personalized care

How can physicians benefit from Generative AI-based healthcare software? Here are some clear use cases:

1. Patient Diagnosis

Generative AI can analyze data points from any patient’s medical history or health records to deliver an accurate diagnosis.

Additionally, Generative AI can help physicians diagnose medical images more accurately. For instance, using the image segmentation technique, Generative AI algorithms can automatically segment medical images (for example, MRI or CT scans) into various regions of interest. This is more effective for diagnosis of tumors or lesions than manual segmentation.

IBM’s Watson Health technology applies AI and data analytics to analyze patient records including their:

• Medical history
• Genetics
• Symptoms

Another published study on Watson Health’s AI-based decision support system recorded a 93% concordance rate with treatment recommendations from an expert panel of doctors.

2. Administrative Work

As mentioned earlier, healthcare practitioners spend a lot of time in administrative work such as documenting medical records and scheduling appointments for patients. By adopting Generative AI, healthcare software solutions can automate administrative work so that physicians can focus on delivering patient care. By using AI technology for dictations and medical scribes, physicians can now spend more time with patients, thus enabling personalized care.

AI-powered tools can generate clinical notes from doctor-patient consultations and manage the billing process. One example is that of the AI-enabled Zocdoc platform used for booking doctor’s appointments.

3. Medical Research

Among other use cases, Generative AI has the potential to advance medical research and innovations. For instance, medical researchers can leverage Generative AI to generate synthetic data based on patient cohorts. This enables them to simulate various scenarios for clinical trials and evaluate the efficacy levels of their treatment.

Besides, AI-powered tools can help research work by preparing interview scripts and research briefs for medical teams. With AI-powered transcription during user sessions, researchers can focus on the user’s non-verbal communications and reactions to make accurate decisions.

4. Predictive Medicine

With the use of Generative AI, physicians can also identify individuals at maximum risk from diseases or chronic conditions. Through predictive medicine, they can personalize the disease prevention plan for each patient, thus delivering an early-stage intervention to stop the onset of the health problem.

Further, Generative AI tools can analyze vital health indicators from personal wearables. This includes indicators like heart rate, stress levels, and blood glucose levels. Generative AI algorithms can identify data patterns from patient records and accurately predict the trajectory of diseases.

How physicians can adopt Generative AI

What’s the best way for healthcare professionals to create Generative AI models for various use cases? There are 3 possibilities:

● In-house Development

This option is feasible if the healthcare company has the necessary technical expertise to build AI models. Through this option, they can also customize the AI model to suit their applications and use cases.

On the plus side, in-house development enables companies to have complete control over their development process. Internal teams also have a better understanding of the project requirements and can easily collaborate with other stakeholders. On the flip side, in-house development is expensive due to high hiring and training costs.

● Buy

This is a feasible option for generic or industry-specific use cases. These AI-powered solutions are cost-effective and built with the vendor’s industry expertise.

Among its advantages, industry-specific solutions are backed by industry experts. Hence, these solutions often meet industry-specific needs and standards. Additionally, companies incur a lower upfront cost when buying these solutions. Among the disadvantages, these solutions cannot be customized to specific business requirements—or can incur high customization costs. Additionally, they may include a host of features (or functionalities) that are not useful to the purchasing company.

● Outsourced Development

Healthcare professionals or companies can outsource their AI development to external AI experts. This provides them access to customized AI solutions tailored to their needs and processes in a quick time.

As compared to in-house development, outsourcing is more cost-effective as it allows companies access to technical knowledge and expertise without any hiring process. On the flip side, healthcare companies have lower control over the development process. Besides, external solution providers may not fully comprehend business objectives or may not have the necessary industry experience to undertake this project.

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