Source URL: https://cloud.google.com/blog/topics/customers/how-manipal-hospitals-sped-up-nurse-handoffs-across-37-hospitals/
Source: Cloud Blog
Title: Manipal Hospitals and Google Cloud partner to transform nurse handoffs with GenAI
Feedly Summary: As one of India’s largest healthcare providers, Manipal Hospitals serves nearly 7 million patients annually across 37 hospitals. To deliver clinical excellence and patient-centric care at a high standard, we are continually embracing technology. One of our most significant operational challenges we consistently face is the nurse handover process—a critical but time-consuming task. To make nurse handovers more efficient, safe and accurate, we entered a strategic partnership with the Google Cloud Consulting team to co-develop a generative AI solution, leveraging the power of Google Cloud.
Rethinking time-consuming, error-prone nurse handoffs
The process of transferring essential information about a patient’s condition and care plan from an outgoing nurse to an incoming one is crucial for ensuring continuity of care and patient safety. However, with more than 10,500 beds across our hospitals, the sheer volume of data required for a comprehensive handover meant our nurses routinely added an extra 90 minutes to their shifts for both creating and receiving these reports. This lengthy process could directly affect patient care, as it could lead to fatigue and potential mistakes, and also reduce job satisfaction for our vital nursing staff. We needed a way to make this process faster, more accurate, and less of a burden.
Building a trusted solution on Google Cloud
Our joint Manipal-Google team knew that for a clinical tool to be adopted by over 5,000 nurses, it had to be both fast and trustworthy. The primary challenge with any generative AI application in healthcare is ensuring accuracy and minimizing the risk of AI “hallucinations.”
The solution’s architecture, designed by the Google Cloud Consulting team, addresses this head-on by leveraging multiple Google Cloud components. Patient data from our TrakCare system is securely transferred in near real-time to a data lake on Google Cloud. When a nurse requests a handover summary, a serverless Cloud Run application orchestrates a multi-stage process.
Critically, instead of passing pages of raw data directly to the AI, the system first uses intelligent, time-based filters to extract only the most relevant clinical information for the specific shift. This structured, pre-processed data is then sent to Gemini on Vertex AI. This “controlled generation” approach was a key innovation; it ensures Gemini summarizes only the most pertinent facts, dramatically improving the accuracy and consistency of the final ISBAR (Identify, Situation, Background, Assessment, and Recommendation) report. The ability of Gemini to understand complex medical terminology, medication names, and clinical procedures without specialized fine-tuning was a game-changer, accelerating the entire development process.
How our partnership delivered results
By combining Manipal’s deep clinical expertise and Google Cloud Consulting’s technical leadership, our joint approach provides a blueprint for enterprise-grade AI implementation:
From ideation to production: The Google Cloud Consulting team led the engagement from the initial idea all the way to a production-ready solution now used by thousands of nurses daily. The project started with a focused Minimum Viable Product (MVP) to prove the technology’s value before scaling.
User-centric design: The solution was not built in a vacuum. The Google team conducted over eight rounds of deep discussion and evaluation sessions directly with our nurses. This ensured the final ISBAR summary format was not just technically impressive, but clinically useful from day one.
Agile and iterative rollout: The solution was piloted at one hospital initially to test its performance and safety in a real-world setting. With a successful pilot, the solution is live in 23 of Manipal hospitals, and used by more than 5,000 every day. At full scale, it is projected to help save significant nurse hours on a daily basis. This phased approach, managed jointly, has allowed us to gather feedback and ensure smooth adoption.
Ensuring better patient care
The generative AI solution we implemented has yielded impressive returns. The 70% reduction in handoff time—from 90 minutes down to 20—frees our nurses to focus more on direct patient needs and care. It also makes the process less vulnerable to errors that can arise from handwritten notes and human fatigue.
This project, delivered in partnership with Google Cloud Consulting, is a prime example of how we are pioneering the future of healthcare in India, helping us scale the delivery of quality care across the length and breadth of the country.
We’d like to give special thanks to Google Cloud Consulting team – Naveen Poosarla, Gopala Dhar, Rupjit Chakraborty, Hem Anand, Amit Dutta, Nishant Welpulwar, Preetam Dey and Shikha Saxena – for designing and developing the solution. We are grateful to the Manipal Hospitals team – Saroja Jaykumar, Sunil Bhattacharjee – in delivering this successful project.
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AI Summary and Description: Yes
**Summary:** The text discusses a collaboration between Manipal Hospitals and Google Cloud to develop a generative AI solution aimed at improving the efficiency and accuracy of nurse handovers in a healthcare setting. This strategic partnership highlights how AI and cloud technologies can enhance operational processes in the healthcare sector.
**Detailed Description:**
The document illustrates the significant operational challenges faced by Manipal Hospitals, particularly in the nurse handover process, which is critical for ensuring patient safety and the continuity of care. The partnership with Google Cloud was established to leverage generative AI technology to improve this process in several impactful ways:
– **Challenge Identification:**
– Nurse handovers involved transferring essential patient information from outgoing to incoming staff, a task that is both crucial and time-consuming.
– Nurses reported adding an extra 90 minutes to their shifts, impacting job satisfaction and increasing the risk of error.
– **Generative AI Solution Development:**
– A strategic partnership with Google Cloud Consulting was formed to create a solution that is both fast and trustworthy.
– The generative AI application aimed to minimize risks, such as inaccuracies and “hallucinations” often associated with AI-generated output.
– **Technological Architecture:**
– The architecture utilizes various Google Cloud components to accommodate secure and near-real-time data transfers from the hospital’s TrakCare system to a cloud-based data lake.
– Pre-processing using intelligent filters allows the system to provide only the most relevant clinical data needed for specific shifts, thus optimizing the information fed into the AI.
– **Controlled Generation Approach:**
– The integration of a “controlled generation” process with the Gemini model on Vertex AI helps in accurately summarizing patient information. This focused method enhances the effectiveness and consistency of clinical reports (ISBAR format).
– **Implementation Phases:**
– The project transitioned from ideation to production through a Minimum Viable Product (MVP) approach led by the Google Cloud Consulting team, ensuring its practical application through direct engagement with nursing staff.
– User-centric design and agile rollout strategies were employed, starting with pilot testing in one hospital, leading to a broader implementation across 23 locations.
– **Results Achieved:**
– The generative AI solution succeeded in reducing the handoff time from 90 minutes to just 20 minutes, allowing nurses to devote more attention to patient care while minimizing errors linked to manual notes.
– This project represents a model for future healthcare technologies, showcasing the potential for AI and cloud computing to deliver quality care more efficiently across large healthcare networks.
This collaboration serves as a benchmark for the integration of AI technology in healthcare, demonstrating substantial improvements in operational efficiency and patient care quality.