Manipal Group: Delivering High-Quality Patient Care with Google Cloud
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One of India’s best-known healthcare brands, Manipal Group prides itself on clinical excellence and a patient-centric approach. From humble beginnings in 1953 as a single teaching hospital—the Kasturba Medical College—in a university town in Karnataka, India, Manipal Group has grown to a presence in seven cities in India and operations in Malaysia and Nigeria. “Our founder, Dr T. M. A. Pai, established Kasturba Medical College just six years after India gained independence,” says C. G. Muthana, Chief Operating Officer, Teaching Hospitals, Manipal Health Enterprises Pvt. Ltd, part of Manipal Group.
Manipal Health Enterprises Pvt. Ltd operates 11 corporate, or for-profit, hospitals and five teaching hospitals. “We have about 7,000 beds India-wide, and on that measure, we are the third-largest private-sector healthcare provider in India today,” says Muthana. The organization now employs about 6,000 people in its corporate hospitals and 5,000 people in its teaching hospitals.
Manipal Health Enterprises Pvt. Ltd acquires and operates world-class medical technologies in its teaching and corporate hospitals. However, with 75% of corporate hospital wards allocated to fee-paying private patients—compared to just 25% of the wards in teaching hospitals—the differences in information technology budgets are significant. “In the general wards that comprise most of the wards in teaching hospitals, patients are typically treated for free or at heavily subsidized rates,” explains Muthana. “So revenues and costs of delivery vary between hospitals, while employee costs remain similar.”
Rostering nurses a critical task
Rostering nurses to work shifts is one of the most important tasks at Manipal Group corporate and teaching hospitals. As at all hospitals, nurses administer medications, monitor patients, maintain records, manage intravenous lines, and work with doctors to heal patients. They can also provide advice and support to patients and loved ones, including teaching them how to administer medication outside a hospital setting. If too few nurses are rostered for a particular shift, the quality of patient care may suffer.
Google Cloud results
- Enabled the hospital to correctly size its nursing workforce
- Lowers stress on nurses by delivering more equitable rostering
- Presents opportunity to better manage nurses’ leave and other administration tasks
However, rostering at the group’s hospitals was a time-consuming exercise. Senior nurses on each ward would have to spend up to 45 minutes per day manually amending paper-based rosters to accommodate requested changes to duty shifts for personal or other circumstances. The organization began receiving complaints from patients, doctors, and other hospital staff that, on occasion, too few nurses were rostered on for certain shifts—particularly at its flagship hospital in Bangalore. “We had based our rostering calculations on the number of beds occupied by patients and, when we investigated, we found at a macro level, we were rostering on the correct number of nurses,” says Muthana. “However, on some days, on wards optimally staffed by, say, 10 nurses per shift, we might have 14 nurses rostered on for one shift and seven rostered on for another shift. Those times we had seven, we had a clear shortage.”
In 2012, Muthana asked a consultant to develop algorithms to help automate the rostering. “Unfortunately, there were so many variables, the consultant failed to solve the problem,” he says. The Chief Operating Officer’s next step was to ask the founder and Chief Executive Officer of predictive analytics business Retigence Technologies—already working with the business on a materials management project—to develop an application to manage rostering.
Removing the daily drudgery
“Our plan with the automation project was to relieve our senior nurses of the daily drudgery of amending the rosters and to deliver rosters that were as fair as possible to all our staff,” says Muthana. “We also saw an opportunity to reduce our costs by reducing the overall number of nurses needed to look after our patients.”
Retigence Technologies’ team members then worked with the group’s nurses to capture the variables and requirements for the project. For example, a minimum number of nurses with one year experience or more needs to be rostered on for each shift. Retigence Technologies then started building an application using compute resources available through Compute Engine, a Google Cloud product. “We selected Google primarily because of its pioneering work in artificial intelligence (AI) and machine learning,” says Srinibas Behera, founder and Chief Executive Officer of Retigence Technologies.
With the nurses rostering application developed, Manipal Health Enterprises Pvt. Ltd undertook several pilots to build user acceptance. “Some of our senior nurses took time to accept the fact automation removed their control over rostering assignments, but were finally convinced by the better transparency the product offered,” says Muthana. The organization deployed the application to a smaller hospital and secured user support before rolling out the application to its Bangalore flagship.
Eliminating stress
Deploying the application has enabled Manipal Health Enterprises Pvt. Ltd to remove a buffer of about 100 nurses retained to accommodate the variations in number of nurses rostered for individual shifts. “The savings on those salaries more than paid for the cost of developing the application,” says Muthana.
The application also enabled the organization to reduce the stress on nurses—both the nurses in charge of the rosters and the nurses subject to the rosters. “Night shifts were more equitably distributed among the nurses, while we have been able to reduce the 45 minutes per day required to amend rosters to just 10 minutes,” says Muthana. “In Bangalore alone, we have 51 nurses in charge of rostering—so the combined saving there equates to nearly 30 hours per day.” This is freeing up these senior nurses to complete more important tasks.
The application was subsequently implemented at Kasturba Hospital, Manipal, again reducing the time needed to generate complete nursing rosters to less than 10 minutes.
Managing leave and training
The organization now plans to extend the application to manage leave and training for its nurses and other employees. “I would like to see every employee given an annual leave plan that is added to the roster at the start of the year,” says Muthana. “The flexibility and control afforded by the application would enable us to address challenges such as managing leave across a workforce with high attrition rates.” The organization would also be able to create a calendar to ensure nurses receive all their required training.
“We also plan to keep fine-tuning the application to deploy nurses more efficiently and continue to reduce their stress levels,” adds Muthana. “We also want to create a nursing load indicator tailored to patients’ specific circumstances. For example, a sedated patient may not require much nursing care, whereas a patient who comes in with a broken leg and may be on a ventilator may require assistance from three nurses at once.” The business plans to use Google’s AI and machine learning APIs in the future to improve the value and user experience of the product.
How Vertex Vizier’s Automated Hyperparameter Tuning Improves ML Models
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We recently launched Vertex AI to help you move machine learning (ML) from experimentation into production faster and manage your models with confidence—speeding up your ability to improve outcomes at your organization.
But we know many of you are just getting started with ML and there’s a lot to learn! In tandem with building the Vertex AI platform, our teams are dropping as much best practices content as we can to help you come up to speed. Plus, we have a dedicated event on June 10th, Applied ML Summit, with sessions on how to apply ML technology in your projects, as well as grow your skills in this field.
In the meantime, we couldn’t resist a quick lesson on hyperparameter tuning, because (a) it’s incredibly cool (b) you will impress your coworkers (c) Google Cloud has some unique battle tested tech in this area and (d) you will save time by getting better ML models into production faster. Vertex Vizier, on average, finds optimal parameters for complex functions in over 80% fewer trials than traditional methods.
So it’s incredibly cool, but what is it?
While machine learning models automatically learn from data, they still require user-defined knobs which guide the learning process. These knobs, commonly known as hyperparameters, control, for example, the tradeoff between training accuracy and generalizability. Examples of hyperparameters are the optimizer being used, its learning rate, regularization parameters, the number of hidden layers in a DNN, and their sizes.
Setting hyperparameters to their optimal values for a given dataset can make a huge difference in model quality. Typically, optimal hyperparameter values are found via grid searching a small number of combinations, or tedious manual experimentation. Hyperparameter tuning automates this work for you by searching for the best configuration of hyperparameters for optimal model performance.
Vertex Vizier enables automated hyperparameter tuning in several ways:
- “Traditional” hyperparameter tuning: by this we mean finding the optimal value of hyperparameters by measuring a single objective metric which is the output of an ML model. For example, Vizier selects the number of hidden layers and their sizes, an optimizer and its learning rate, with the goal of maximizing model accuracy.
- When hyperparameters are evaluated, models are trained and evaluated on splits of the data set. If evaluation metrics are streamed to Vizier (e.g. as a function of epoch) as the model is trained, Vizier’s early stopping algorithms can predict the final objective value, and recommend which unpromising trials should be early stopped. This conserves compute resources and speeds up convergence.
- Oftentimes, models are tuned sequentially on different data sets. Vizier’s built in transfer learning learns priors from previous hyperparameter tuning studies, and leverages them to converge faster on subsequent hyperparameter tuning studies.
- AutoML is a variant of #1, where Vertex Vizier performs both model selection, and also tunes architectures/non-architecture modifying hyperparameters. AutoML usually requires more code on top of Vertex Vizier (to ingest data etc), but Vizier is in most cases the “engine” behind the process. AutoML is implemented by defining a tree like (DAG) search space, rather than a “flat” search space (like in #1). Note that you can use DAG search spaces for any other purpose where searching over a hierarchical space makes sense.
- There are times when you may wish to optimize more than one metric. For example, we would like to optimize model accuracy, while minimizing model latency. Vizier can find the Pareto frontier, which presents tradeoffs for multiple metrics, allowing users to choose the appropriate tradeoff. Simple example: I want to make a more accurate model, but would like to minimize serving latency. I do not know ahead of time what’s the tradeoff between the two metrics. Vizier can be used to explore and plot a tradeoff curve, so users can select on the most appropriate one. For example, “a latency decrease of 200ms will only decrease accuracy by 0.5%”
Google Vizier is all yours with Vertex AI
Google published the Vizier research paper in 2017, sharing our work and use cases for black-box optimization—i.e. The process of finding the best settings for a bunch of parameters or knobs when you can’t peer inside a system to see how well the knobs are working. The paper discusses our requirements, infrastructure design, underlying algorithms, and advanced features such as transfer learning that the service provides. Vizier has been essential to our progress with machine learning at Google, which is why we are so excited to make it available to you on Vertex AI.
Vizier has already tuned millions of ML models at Google, and its algorithms are continuously improved for faster convergence and handling of real-life edge cases. Vertex Vizier’s models are very well calibrated and are self-tuning (they adapt to user data), and offer unique power features, such as hierarchical search spaces and multi-objective optimization. We believe Vertex Vizier’s set of features is a unique capability to Google Cloud, and look forward to optimizing the quality of your models by automatically tuning hyperparameters for you.
To learn more about Vertex Vizier, check out these docs and if you are interested in what’s coming in machine learning over the next five years, tune in to our Applied ML Summit on June 10th, or watch the sessions on demand in your own time.
Kohl’s Leverages Google Cloud Platform for Omnichannel Retail
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Kohl’s is an omnichannel retailer focused on driving traffic, operational efficiency and delivering seamless omnichannel customer experiences.
Ratnakar Lavu is Kohl’s Senior Executive Vice President and Chief Technology Officer. He, and Kohl’s, are at the forefront of retail technology innovation, focusing on a frictionless customer journey across digital, mobile and more than 1,150 stores.
As part of this journey to more closely unify its online and offline experiences for customers, the company was looking for supporting cloud services that would continue to drive best-in-class data center infrastructure; the ability to manage data at a very large scale; and industry leading analytics and machine learning tools to continually understand real time data streams and help personalize experiences for their customers.
Kohl’s recognized the opportunity to take on a cloud partner to help drive the improvement of the speed and reliability of their operations, while they focused on a number of innovations to deepen customer experiences.
“At the time, I was looking for an open and scalable platform to partner with our Kohl’s technology team as we transform our business by shifting to the cloud,” says Ratnakar.
“Google has great engineering talent as well as demonstrated experience solving stability and scale in its own Ads and Search business. At Kohl’s, we need to be bold and innovative in today’s retail environment, and therefore need partners who deeply understand how to manage risk.”
Kohl’s leveraged several capabilities of Google Cloud. For example:
- They built applications to automate deployment, scaling and operations.
- They used monitoring capabilities to monitor for things like response time.
- Scalable technology provided an infrastructure to elastically scale to site traffic.
- They ran their infrastructure across multiple regions for high availability.
In 2017 and 2018, record-setting numbers of customers visited Kohls.com during the Thanksgiving holiday weekend and the digital platform experienced high double-digit growth both years.
The capabilities provided by Google Cloud Platform (GCP) and Google’s data center infrastructure supported Kohl’s servers and systems during these key timeframes.
In addition, the Kohl’s team partnered together with Google’s core engineering team and services organization to optimize applications and make them more reliable.
Google Cloud’s Customer Reliability Engineers (CREs) worked with them in advance of their peak time frames to test the infrastructure for performance, scaling, and fault tolerance.
“Google CRE and services teams collaborated with us as we ran drills and exercises during each phase of preparation for peak time frames,” Ratnakar said. “As we continued to understand better how to scale, monitor, and support our applications in GCP and we are pleased that we worked with the CRE team as partners on monitoring services, alerting teams, and triaging work.”
What Are India’s Biggest Companies Doing on Google Cloud?
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In the last year, there’s been an upward trend in cloud adoption in India. In fact, NASSCOM finds that cloud spending in India is estimated to grow at 30% per annum to cross the US$7 billion mark by 2022.
At Google, in our conversations with customers, discussions have evolved beyond cost savings and efficiencies. While those are still very relevant reasons for adopting cloud technologies, Indian enterprises are looking to Google Cloud to help them drive digital transformation, identify new revenue generating business models, reach previously untapped consumer markets, and build customer loyalty through greater insight and personalization.
Here are some companies and their stories.
Tata Steel: Mining data and maximizing its power
Tata Steel is a great example of an established enterprise from a traditional industry that is modernizing and embracing cloud computing. With an ambition to be a leader in manufacturing in India and a digital-first organization by 2022, Tata Steel believes smart analytics is key to enhancing operational efficiency and gaining business advantage.
To organize data from siloed systems across the organization and make it easily accessible to all employees, Tata Steel is using Cloud Search and plans to scale it to more than one million documents and 28 disparate enterprise content sources including enterprise resource planning (ERP) and SharePoint. In fact, Tata Steel is one of the first Indian enterprises to harness the power of Cloud Search to meet some of the most aggressive ingestion demands, with indexing durations reduced from weeks to seconds.
They are also leveraging Google Cloud Platform (GCP) services like Google Cloud Storage and BigQuery to build their data lake and enterprise data warehouse so they can take advantage of advanced analytics and machine learning. Managed services such as AI Platform further enable Tata Steel to manage end-to-end AI/ML workflows within the GCP console. This complements their existing on-premise reporting and analytics tools, and brings data management to the forefront of everything they do—from forecasting market demand to predictive equipment maintenance.
“Digital is not just a goal, it’s become a way of life. We are digitizing everything from the deployment of factory vehicles to improving material throughput to marketing and sales. As a result, we have petabytes of structured and unstructured data that is not only waiting to be mined, but that we can generate intelligence from to create opportunities across our multiple lines of business using GCP,” said Sarajit Jha, Chief Business Transformation & Digital Solutions at Tata Steel.
Helping L&T Financial Services reach customers in rural communities
In rural communities, quick access to financial services can make a tremendous difference to livelihoods. L&T Financial Services provides farm-equipment finance, micro loans and two-wheeler finance to consumers across rural India backed by a strong digital and analytics platform. Their digital-loan approval app, which runs on GCP, makes it significantly faster and easier for people to apply for financial assistance to purchase important things such as farming equipment and two-wheelers. It also helps rural women entrepreneurs get quicker access to funds for their businesses through micro loans.
L&T Financial found G Suite to be a far better collaborative tool to help staff work together efficiently. Employees can interact with each other in real time using Hangouts Meet, and the task of information sharing is more seamless and secure through Drive. BigQuery also helps L&T Financial Services generate behavior scorecards to track credit quality of its micro-loan customers.
“Cloud is the technology that enables us to achieve scale and reach. Today there are countless data points available about rural consumers which enable us to personalize our products to serve them better. With access to faster compute power, we can also on-board consumers more efficiently. Our rural businesses have clocked a disbursement CAGR of 60% over the past three years.” said Sunil Prabhune, Chief Executive-Rural Finance, and Group Head-Digital, IT and Analytics, L&T Financial Services.
Creating conversational connections for Digitate’s customers
Digitate, a venture of TCS (Tata Consultancy Services), has integrated Dialogflow into its flagship brand ignio, an award-winning artificial intelligence platform for driving IT operations, workload operations and ERP operations for diverse enterprises. This integration is the next step in ignio’s product development journey, and will enable users to chat or talk with ignio to detect issues, triage problems, resolve them and even predict system behavior.
“ignio combines its unique self-healing AIOps capabilities for enterprise IT and business operations with Dialogflow’s AI/ML-based, easy to use, natural and rich conversational capabilities to create an unparalleled, intuitive and feature-rich experience for our customers,” says Akhilesh Tripathi, Head of Digitate.
Indian enterprises going G Suite
The base of Indian enterprises that are making the switch to G Suite to streamline their productivity and collaboration also continues to grow. Sharechat, BookMyShow, Hero MotorCorp, DB Corp and Royal Enfield are now able to move faster within their organizations, using intelligent, cloud-based apps to transform the way they work.
A hybrid and multi-cloud future in India
IDC predicts that by 2023, 55% of India 500 organizations will have a multi-cloud management strategy that includes integrated tools across public and private clouds. (IDC FutureScape: Worldwide Cloud 2019 Predictions — India Implications (# AP43922319). We look forward to sharing more success stories of Indian enterprises that have taken the next step in their digital transformation journey.
IBM Spectrum LSF and Google Collab: Leverage Google Cloud’s Scalability and Compute Engine Infrastructure
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High Performance Computing (HPC) is prevalent today across many industries, including financial services, life sciences, higher education research, manufacturing, and energy. More and more businesses are deploying HPC workloads in the cloud to take advantage of its elasticity, scalability, and availability. Job schedulers are critical for HPC applications given the nature of these workloads that create, process, and tear down thousands, and sometimes millions, of vCPU and network resources with TB to PB of storage capacity. Job scheduling tools lead to improved operational efficiency and a degree of certainty that a particular HPC job, which can run for hours to weeks, will complete successfully.
IBM Spectrum LSF is used extensively in the manufacturing and semiconductor industry to manage Electronic Design Automation (EDA) workloads. Dynamically running workloads on-premises and in the cloud, also known as cloud bursting, is becoming a more common practice to address capacity and provisioning time constraints within data centers and enable enterprises to take advantage of virtually unlimited resources.
However, the challenge with cloud bursting is integrating and maintaining operational consistency across on-premises and cloud environments. IBM Spectrum LSF, in combination with Google Cloud, addresses this problem head on.
Google Cloud is excited to announce, in collaboration with IBM, enhanced capabilities to IBM Spectrum LSF that enables organizations to integrate their on-premises job scheduling scripts with resources deployed in Google Cloud. Customers are now able to fully leverage Google Cloud’s highly scalable and secure Compute Engine, networking and storage infrastructure.
The LSF-Google Cloud resource connector patch supports key Google Cloud differentiators including Local SSDs, GCE instance templates, Preemptible VMs, and more:
- Bulk API support– Deploy large fleets of VM instances in a matter of seconds.
- Instance Templates – Simplify VM configuration by creating reusable templates.
- All Machine Types – Supports all GCE VM families and machine types, including Custom Machine Types.
- GPUs – Attach up to 16 GPUs per instance, including the largest A2 instances with up to 16 NVIDIA A100 GPUs
- Preemptible VMs – Preemptible VMs are provisioned from excess Compute Engine capacity and is a significant way to save money on GCE resources.
- Local SSD – Attach up to 9TB of NVMe SSD per instance
- Hyperthreading – Supports the “threads-per-core” option in GCE, which allows per-VM hypervisor level Hyperthread configuration (when supported by Instance Templates)
- Images – Supports custom disk images, including full support for Windows, for all attached Persistent Disks
- Placement Policies – Control where the instances are physically located relative to each other within a zone for improved low-latency performance
- Labels – Supports GCE Labels, which can be used for management of firewall rules, tracking billing, etc
- Minimum CPU Platform – Supports the ability to specify a minimum CPU Platform for your Virtual Machines.
Getting Started
This improvement to the IBM LSF Resource Connector was developed by IBM in coordination with Google. Find out more about the new supported features and their operation in the official IBM Spectrum LSF Resource Connector Documentation. You can also find additional documentation and download the software in the IBM Spectrum Computing Community. If you have further questions, you can contact IBM, or Google Cloud Sales.
Special thanks to Annie Ma-Weaver, Mark Mims, and Wyatt Gorman for their contributions.
Cloud Migration and Modernization is ‘Easier Done than Said’ with Google Cloud RAMP!
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The reliance on cloud compute, storage and network has accelerated with the growing usage of digital products and services. To keep up with the customer demands and deliver digital solutions, many companies are struggling through their cloud migration and modernization journeys. But not anymore, says Google Cloud with the Rapid Assessment & Migration Program (RAMP)!
Download the infographic to have succinct view of what cloud migration and modernization would look like by RAMPing up! Bid Adieu to delays and budget overspend with the tools, resources, partners and fundings with RAMP.
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