The last few years have underscored the importance of speed in bringing new drugs and medical devices to market, while ensuring safety and efficacy. Over this time, healthcare and life sciences organizations have transformed the way they research, develop, and deliver patient care by embracing agility and innovation.

Now, the industry is set to reap the benefits of cloud technology and overcome the existing barriers to innovation.

What’s holding back innovation?

Costly clinical trials: The process of trialing and developing new drugs and devices is still long and costly, with more than 1 in 5 clinical trials failing due to a lack of funding.1 The high failure rate comes as no surprise when you consider the average clinical trial costs $19 million and takes 10-15 years (through all 3 phases) to be approved.2

Stringent security requirements: Pre-clinical R&D and clinical trials use large volumes of highly sensitive patient data – making the life sciences industry one of the top sectors targeted by hackers.3 On top of this, the FDA and other regulatory bodies have strict requirements for medical device cybersecurity.

Unpredictable supply chains: Global supply chains are becoming increasingly complex and unpredictable. This can be brought on by anything from supply shortages, to geo-political events, and even bad weather. Making things worse is the lack of visibility into medical shipment disruptions – so when disaster strikes you’re often caught off guard.

Google Cloud for life sciences

At Alphabet, we’ve made significant investments in healthcare and life sciences, helping to tackle the world’s biggest healthcare problems, from chronic disease management, to precision medicine, to protein folding.

Together with Google, you can transform your life sciences organization and deliver secure, data-driven innovation across the value chain.

• Accelerate clinical trials to deliver life-saving treatments faster and at less cost. Clinical trials require relevant and equitable patient cohorts that can produce clinically valid data. Solutions like DocAI can enable optimal patient matching for clinical trials, helping organizations optimize clinical trial selection and increase time to value. How that patient data is collected is also important. Collection in a physician’s office captures a snapshot of the participant’s data at one point in time and doesn’t necessarily account for daily lifestyle variables. Fitbit, used in more than 1,500 published studies–more than any other wearable device–can enrich clinical trial endpoints with new insights from longitudinal lifestyle data, which can help improve patient retention and compliance with study protocols. We have introduced Device Connect for Fitbit, which empowers healthcare and life sciences enterprises with accelerated analytics and insights to help people live healthier lives. We are able to empower organizations to improve clinical trials in key ways:

1. Enable clinical trial managers to quickly create and launch mobile and web RWE collection mechanism for patient reported outcomes
2. Enable privacy controls with Cloud Healthcare Consent API and, as needed, remove PHI using Cloud Healthcare De-identification API
3. Ingest RWE and data into BigQuery for analysis
4. Leverage Looker to enable quick visualization and powerful analysis of a study’s progress and results

• Ensure security and privacy for a safe, coordinated, and compliant approach to digital transformation. Google Cloud offers customers a comprehensive set of services including pioneering capabilities such as BeyondCorp Enterprise for Zero Trust and VirusTotal for malicious content and software vulnerabilities; Chronicle’s security analytics and automation coupled with services such as Security Command Center to help organizations detect and protect themselves from cyber threats; as well as expertise from Google Cloud’s Cybersecurity Action Team. Google Cloud also recently acquired Mandiant, a leader in dynamic cyber defense, threat intelligence and incident response services.

• Optimize supply chains and enhance your data to prepare for the unpredictable. With a digital supply chain platform, we can empower supply chain professionals to solve problems in real time including visibility and advanced analytics, alert-based event management, collaboration between teams and partners, and AI-driven optimization and simulation.

Ready to learn more? We’ll be taking a deep dive into each of the challenges outlined above in our life sciences video series. Stay tuned.

1. National Library of Medicine
2. How much does a clinical trial cost?
3. Life Sciences Industry Becomes Latest Arena in Hackers’ Digital Warfare

AgroStar has launched a cloud-based mobile app that is helping to boost crop yields and encourage best practices for small farmers in India. Launched as an on-premises ecommerce platform selling farm tools in 2008, the firm turned to Google Cloud Platform (GCP) to expand its offering. It now uses cloud-based analytics and is deploying ML models to provide timely advice in five languages on everything from seed optimization, crop rotation, and soil nutrition to pest control.

A 2018 survey underscored the demand for agricultural planning for Indian farmers. While farming remains a dominant sector in India, employing half of its labor force, 70 percent of small farmers – those cultivating fewer than three acres – said their crops are damaged by unforeseen weather and pests. An even higher number – 74 percent – say they lack access to farming-related information.

Widening that gap is the relative lack of access to new, higher yield seeds and improved soil analyses for small farmers, who must otherwise rely on traditional methods. “It could take a few years for innovative information to trickle down from universities to small, grassroots farmers,” says Pritesh Gudge, AgroStar Software Engineer. “Today, just by clicking through our Android application, farmers learn about new, effective farming practices and receive advice customized to their crop and soil.”

Connecting a million farmers in the cloud

Operating in the Indian states of Gujarat, Maharashtra, Rajasthan, Orissa, Bihar, and Karnataka, AgroStar is closing the knowledge gap with a full-service, cloud-based SaaS solution – the only one of its kind in India. It combines agronomy, data science, and analytics to help farmers by providing a variety of resources.

AgroStar has reached over a million farmers through its Android app, the AgroStar Agri-Doctor. The mobile client is available as a web-based or full-featured native app. Both provide access to the firm’s knowledge base hosted on GCP, a Q&A forum that connects farmers to each other to help understand and better solve problems and to learn about innovative practices and products. Farmers can also click through to follow local and national market trends that help forecast crop prices.

In addition to the self-service knowledge base, AgroStar provides access to agronomy experts who use cloud-based analytics tools and historical data to provide season-and locale-specific advice to each farmer. “We are now tracking thousands of calls in 5 languages each day,” says Pritesh.

The AgroStar app also provides links to purchase and then track the delivery of farm tools and supplies such as cultivators and fertilizers. An in-house platform manages fulfillment centers and a doorstep delivery network simplifies the supply chain while giving farmers what they need, when they need it. By procuring directly from the manufacturers and primary distributors of farm supplies, Agrostar is achieving cost savings, which it passes on to farmers.

Build fast, pivot faster

From the start, the human and environmental variables of farming in India, not to mention the volume of AgroStar’s few hundred thousand monthly active users, made a highly scalable cloud-based solution inevitable. Farmers rely on the firm’s Agri-Doctor app to provide advice in multiple languages on topics that range widely throughout three growing seasons, each with distinct crop nutrition and rotation cycles and farm implementation requirements.

“For farmers, the focus keeps changing every month, and every season,” says Pritesh. “To serve our growing community, we needed a platform that could process images at high volume, fulfill tools and seed orders across thousands of miles, and respond to multilingual queries. We quickly moved away from spreadsheets and server-based solutions – we needed to build fast and pivot faster.”

Ending late-night deployments

The firm’s first cloud experience was with an AWS solution. At the time, AWS was the only cloud provider in India, but AgroStar wanted to find a solution that was easier to use and offered better integration with Android devices. “Deployment and processing costs were very high, and the developer tools and documentation were not as intuitive as we needed,” says Pritesh.

When GCP service arrived in India in October 2017, AgroStar embarked on a platform re-implementation that made possible dramatic changes in the way it developed and deployed its solution. Using Google Kubernetes Engine (GKE) for crop advice management and Compute Engine for its production application services, the firm built the backend for the Agri-Doctor discussion forum in only three weeks. The platform’s microservice architecture is implemented in Python and Golang and deployed on GCP.

AgroStar began to realize significant efficiencies in its build, deploy, and test cycles. “We previously needed to work overnight to deploy to production,” says Pritesh. “Now using Google for Kubernetes containers and a rolling update strategy, we can deploy during the day without any problems or interruptions to service.”

The move to GCP streamlined AgroStar’s stack. “We were running 12 independent instances on AWS,” says Pritesh. “With Google Kubernetes Engine, we are deployed on a single cluster at a cost savings of $1,300 per month and growing.”

Improving customer response times by 85 percent

With a managed deployment capability, AgroStar can devote more time and resources to executing on its platform and Agri-Doctor app development plan. A strategic goal was managing customer response times as the firm grew its base. GCP has helped the firm meet that goal, achieving an 85 percent improvement in customer response times even as traffic grew significantly.

“With our on-premises solution, we could handle around 100 customers daily, which took 30 to 50 minutes for each customer,” says Pritesh. “We now handle thousands of customers daily, taking only 4 to 5 minutes for each one.”

AgroStar used Firebase to implement its Agri-Doctor app. A real-time cloud database, Firebase provides an API that enables the Agri-Doctor advice forum to be synchronized across all its far-flung mobile clients, effectively sharing knowledge base updates with one million users in near real time.

Using cloud tools to manage and monitor

Cloud Pub/Sub, Kafka, and Cloud Dataflow manage data ingestion and queueing of event and transaction data to the analytics layer. BigQuery fetches and persists data to Cloud Storage. Cloud SQL and dashboards powered by Tableau deliver farmer crop and soil profiles within minutes.

Cloud IAM helps AgroStar control access to all its cloud resources. And Stackdriver, the integrated logging aggregation capability for GCP, helps monitor and speed debugging on every tier of the AgroStar solution.

Machine learning to enhance yields

AgroStar is developing a variety of ML components to improve responsiveness and extend its platform offerings.

To speed up the diagnosis of and treatment for crop blight, AgroStar is building a deep learning pipeline using TensorFlow. The pipeline relies on GoogLeNet models that use multi-layered convolutional visual pattern recognition. It will assess uploaded images to support a disease-detection capability on the mobile app. Based on the commercially successful AI algorithms that automated postal code processing, GoogLeNet offers improved performance and computational efficiencies by using a creative layering technique that distinguishes them from older, sequential recognition engines.

To improve its customer search experience, AgroStar is developing an ML pipeline that shrinks fetch times by suggesting tags mapped to stored data. Processed using TPUs, Cloud Natural Language and Video AI, the tags provide a metadata layer that supports queries in any of the ten natural languages that AgroStar farmers can use.

The AgroStar search pipeline consists of Long Short-Term Memory (LSTM) models of Recurrent Neural Networks. Recurrent networks exhibit “memory” through iterative processing and are distinguished from feedforward networks by a feedback loop connected to their past decisions, ingesting their own outputs moment after moment as input.

Implementing a recommendation engine

The firm is also adapting the Random Forests TensorFlow AI model to develop a crop and product recommendation engine. The model is trained by consuming numerical (rainfall, humidity, water availability per acre) and categorical (soil type, water sources) parameters to suggest appropriate products by season, region, and locale.

To simplify the product suggestion experience, AgroStar developers are testing Cloud Dialogflow, the Google Cloud conversational interface, to build a chatbot capability into its mobile app. The bot will track a farmer’s crop schedules and answer simple questions by linking to the recommendation engine.

AgroStar is also extending its analytics platform with AI-powered sales planning and forecasting. Using linear regression models implemented in TensorFlow and powered by Cloud ML Engine, the capability will enhance supply chain logistics as the company scales its operations across India.

To provide a credit on-demand offering for a range of seed-to-harvest cycle products, AgroStar is attempting to use Vision API to create an AI model that will convert uploaded photos of customer application records into standard data formats. The firm’s credit policy features a grace period in which farmers begin paying back loans after harvested crops go to market.

A versatile and friendly development ecosystem

AgroStar credits the convivial tools and documentation that GCP offers and its incremental, pay-as-you-go pricing model for both the firm’s success and its ability to manage growth.

“What Google Cloud offers is extremely good documentation and extremely simple-to-use tools and interfaces across all services,” says Pritesh. “It helped us initially deploy our platform and at every scale that we have required since then, and its cost effectiveness enabled us to staff up to meet new feature milestones.”

Editor’s note: Today’s post highlights five takeaways from our session at the first ever Google Cloud Public Sector Summit. To watch the full session, check out All the Right Moves: Prioritizing Investments in Technology.

Now more than ever, government agencies need to invest in digital services to fulfill their missions and better serve communities. Yet modernization isn’t a one-and-done approach; it’s a sustained effort, with multi-year implications, and requires careful consideration of how to integrate existing investments to optimize costs. Digital transformation requires coordination between different programs and agencies, including all of their many competing considerations. In short, maximizing technology investments requires careful planning, strategic thinking, and industry partners that can provide flexibility and security and meet agencies where they are. 

I sat down with Suzette Kent, former U.S. federal chief information officer, and Dominic Sale, assistant commissioner for Technology Transformation Services at the General Services Administration (GSA) for a conversation to unpack this important topic and discuss industry best practices. 

The panelists had five tips for government employees who are making technology purchasing decisions for their agency.

1.   Put the agency’s mission first.Avoid getting distracted by exciting new trends and focus on long-term goals that can impact which procurement strategies or funds could be used. The discussion started with how government agencies could cut through the noise about technology and prioritize which technology is best for their needs. Sale and Kent agreed that an agency should focus on its core mission outcomes and let its technology needs flow from that. Sale also emphasized the importance of having technology design respond to humans’ needs, which has historically been a challenge for government agencies.

For agencies to stay focused on their mission, final decisions about technology need to be made by the program manager who best understands each program’s mission. The government CIO’s role is to be the enabler for the technology and leverage it at the enterprise level, particularly when it comes to sharing infrastructure.  The takeaway: enable mission programs by empowering your teams and providing access to authorized, compliant, innovative data platforms that programs can move confidently and quickly with.

2.   Invest in interoperability. Agency employees often struggle to balance the need for a positive return on investment (ROI) with requirements for meeting mission objectives. While the panelists agreed that the total cost of ownership was important, they also emphasized taking an expanded view of ROI, including future-proofing and investing in functionality that may not realize its return for many years based on the initiative. Saving money isn’t particularly valuable if the solution doesn’t meet an agency’s needs. When choosing a technology partner, government employees should understand its long-term vision to ensure that the partner fits agency priorities. Partners’ technologies should also integrate seamlessly with existing systems so agencies don’t duplicate investment costs.

For example, Google Anthos extends Google Cloud services and engineering practices into an organization’s existing environment, establishing operational consistency across apps and modernization. With Anthos, agencies can simply and securely build and deploy applications anywhere, integrating cloud services across platforms. This allows them to enjoy a consistent DevOps experience for hybrid and multi-cloud environments and enables new innovation. Most importantly, this enables an enterprise data platform, one of the largest catalysts for mission transformation and applied AI.

3.   Take advantage of artificial intelligence (AI) benefits. Over the course of the pandemic, the rapid application of AI has improved government productivity, efficiency, and the ability to deliver critical new services to the public at scale. This has further cemented AI’s role as an essential government technology for the present and future. In fact, Nextgov reports that “46% of government IT specialists plan to use AI and machine learning (ML) for embedded systems in the near future.”

As we’ve seen over the course of the pandemic, government programs can start small with AI  pilots before moving into broad deployment. This can help agencies understand AI’s potential before moving to full production. People always supervise AI technologies, and the possibilities are endless. Google Cloud’s Contact Center AI (CCAI) has helped government agencies improve the customer experience, by allowing citizens to schedule vaccine appointments via a platform of their choice with up to 28 languages and dialects, and manage vaccine deployment. The U.S. Navy spends billions annually to fight rust and corrosion on its ships. Inspections of ships, aircraft and vehicles are a time-consuming and critical part of keeping the U.S. Navy at top performance so Google Cloud and Simple Technology Solutions (STS) rapidly built an AI-based corrosion-detection and analysis system. The system detected and analyzed corrosion on vessels with 90% accuracy and will eventually be used to automate inspections of vessels, aircraft, and vehicles—saving billions of dollars. Document AI helps a variety of government agencies scale their document processing, reducing the time it typically takes to process enormous amounts of data and related citizen claims.

Successful adoption of AI also depends on the quality of the data. Ultimately, agencies need high-quality enterprise data pipes so that employees and the community trust the system and public sector agencies. Sale described a GSA project that used AI bots to read legal contracts and look for particular phrases that would indicate a specific use case. Previously, an employee would have had to read through the contracts and search for the information. In this way, AI is saving the government both money and time.

4.   Creating better experiences for the public. Sale observed that, “trust is the government’s currency and profit motive.” And trust comes when the public can be served with the same modern tools and technology they’re used to – in real-time and with transparency in mind. For example, agencies can provide transparency in public-facing dashboards for programs and supply services that deliver information in real-time through solutions like CCAI.

Trust also requires that constituents feel that government agencies will keep their data safe and secure. The need for a globally secure infrastructure with systems that are up-to-date and designed with security at every level, underpinned by zero-trust enterprise-wide remains paramount – particularly after the series of recent cyberattacks targeting government IT infrastructure.

5.   Finding the right technology partner. Government leaders need technology partners who  provide a flexible and interoperable platform to integrate existing investments and maximize technical value. Historically, public sector agencies have largely been forced to adopt private clouds, which has reduced their access to richer features, and hindered their ability to adopt a full range of security and product capabilities. The right partner won’t require government leaders to compromise on functionality or service availability to achieve compliance. The right partner can harness the power of emerging technology to make it Government-ready and the true promise of cloud– the access and integration of open data– to make missions more powerful and impactful for the constituencies they serve.