5 Ways Small Language Models (SLMs) Are Transforming Indian Healthcare Workflows

Administrative tasks in healthcare are a growing burden for clinicians and organisations. Studies show that clinicians spend up to 40% of their time on paperwork—leaving less time for what truly matters: patient care.

This is where Small Language Models (SLMs) come in. Trained on large volumes of text data, SLMs understand and generate human language. Unlike Large Language Models (LLMs), they are more focused and efficient, making them ideal for specific healthcare tasks.

So, how are SLMs transforming healthcare workflows? Let’s explore five key areas:

1. Automating Administrative Tasks:

It would be great if repetitive tasks could save time for clinicians. SLMs can easily handle these chores, freeing up doctors and nurses to focus on what they do best: patient interaction and diagnosis. Here’s a breakdown of some administrative tasks SLMs can automate:

a. Appointment Scheduling SLMs can manage appointment scheduling by:

• Integrating with clinic calendars to identify available slots.

• Allowing patients to book appointments online or through chatbots.

• Sending automated appointment confirmations and reminders.

b. Insurance Verification:

The complex world of insurance can be a hassle for both patients and providers. SLMs can streamline this process by:

• Verifying patient insurance eligibility in real-time.

• Handling pre-authorisation requests for procedures.

• Generating accurate insurance claims.

c.Report Generation: Clinicians spend a significant amount of time writing reports. SLMs can automate this process by:

• Populating reports with patient data from electronic health records (EHRs).

• Generating customised reports based on specific templates.

• Summarising key findings and recommendations.

By automating these tasks, SLMs can significantly reduce administrative burdens, allowing clinicians to dedicate more time to patient care and improve overall clinic efficiency.

2. Enhancing Patient Communication

SLM-powered chatbots can give patients 24/7 access to information, ensuring clarity and responsiveness.

• Always Available: Answering health queries anytime, day or night.

• Simplifying Language: Translating complex medical terms into easy-to-understand explanations.

• Multilingual Support: Bridging language gaps for diverse patient populations.
  

This empowers patients to actively participate in their care while boosting satisfaction.

3. Supporting Clinical Decision-Making (5 Ways SLMs Improve Healthcare in India)

With medical data spread across records, research, and trials, clinicians face information overload. SLMs act as intelligent assistants by:

• Analysing Data: Spotting patterns and correlations across vast datasets.

• Suggesting Diagnoses: Providing data-driven insights as a starting point.

• Predictive Analytics: Forecasting complications or adverse drug reactions.
  

SLMs assist, not replace clinicians, strengthening their decision-making capabilities.

4. Streamlining Research and Development

Medical discovery is time-intensive. SLMs can accelerate progress by:

• Literature Review: Processing research papers, trial data, and journals to highlight promising areas.

• Pattern Recognition: Finding hidden links that may lead to breakthroughs.

• Grant Writing: Assisting researchers in drafting compelling funding proposals.
  

This helps speed up the path to new treatments and cures.

5. Personalising Patient Care

The future of telemedicine is personalised. SLMs can tailor care by analysing medical history, lifestyle, and genetic data.

• Risk Assessment: Identifying patients at risk of disease for early intervention.

• Customised Treatment Plans: Recommending tailored therapies for better outcomes.

• Preventative Care: Suggesting lifestyle and genetic-specific measures to promote wellness.
  

With such deep insights, strong data privacy and ethical safeguards are essential.

Conclusion

SLMs are reshaping healthcare by reducing administrative work, enhancing communication, supporting clinical decisions, and driving research. Imagine a future where doctors spend less time on paperwork, patients have 24/7 access to information, and researchers uncover breakthroughs faster. That future is closer than ever.

Of course, challenges like bias, security, and explainability remain. But with collaboration between researchers, developers, and healthcare professionals, SLMs can be applied ethically and effectively.

At Achala Healthcare Services, we are passionate about using AI to transform healthcare. We believe SLMs have the potential to benefit clinicians, patients, and researchers alike. If you would like to explore how SLMs can revolutionise your healthcare practice, contact Achala Health today. Together, we can design a customised SLM solution for your needs.