Cloud

Welcome and thank you for joining us today for the final installment in 2022 of the Google Cloud Security Talks series

Our quarterly Security Talks series brings together experts from Google Cloud security teams and the industry to share information on our latest security products, innovations and best practices. Below is the keynote address for our Security Talks on Dec. 7, 2022, presented by MK Palmore, director, Office of the CISO at Google Cloud. 

Diverse threats call for diverse teams, which is why one of the goals of the Google Cybersecurity Action Team (GCAT) is to foster a more diverse, equitable and inclusive cybersecurity workforce.

GCAT debuted in October 2021 as an effort to bring world class security, compliance, privacy, and engineering expertise to our customer interactions, creating a premier security advisory team within Google Cloud that supports the digital transformation efforts of our customers. GCAT’s clear and persistent focus is on the importance of cybersecurity in everything that we do. 

Building GCAT was an opportunity to combine existing talent and expertise across a number of domains at Google Cloud that we felt were important for the customer journey. By its nature, GCAT creates cross-team collaboration across the broad spectrum of security fields, including Compliance, Trust, Solutions, and Site Reliability Engineering. The core of the team brings together leadership and expertise across security, customer and solutions engineering, compliance, privacy, and trust. It’s an impressive team of experts who are helping our customers achieve their desired cybersecurity outcomes.

While the global cybersecurity workforce added 464,000 jobs over the past year, there is still a global cybersecurity employment gap of more than 3.4 million positions, according to research published by security industry nonprofit ISC2.  Yet while seats go unfilled, cybersecurity has been identified as the top area of concern for enterprise risk across the globe. The importance of securing our digital assets and infrastructure has never been more vital.  

Tackling the challenge of the talent pool requires three initiatives to happen broadly across the industry:

Hiring managers have to widen the lens they use to identify talent.

We have to do a better job at describing the skills necessary for success in written job descriptions.

We have to do a better job at understanding that each new hire may still require continuous training and development to be a fully functioning and valuable member of an overall team.   

Why does diversity in hiring matter?

Cybersecurity is a team sport, and our response to adversarial behavior should be all-encompassing. We have to avail ourselves of the best solutions possible to achieve our desired outcome of protecting business enterprise and consumers. The cybersecurity field is in need of new, fresh perspectives to meet our greatest challenges. Crucially, the stakeholders and benefactors of these changes are increasingly those within diverse communities, so we must do a better job at including these communities in building the solutions that will help us all be safer.

Some steps we’ve taken internally include the creation of a Diversity, Equity, and Inclusion council that is focused on the three pillars of hiring and representation, culture and inclusion, and progression and retention.  We understand that there needs to be heavy investment to help impact the organization across the recruiting cycle, the hiring process, and the development initiatives associated with employee growth. This is no small undertaking and is only part of the commitment we have to this overall challenge.

We also support efforts to develop cybersecurity training specific to new entrants into the field.  We recognize the value in training, and are supporting our own internal efforts to produce and develop training while also financially supporting initiatives by nonprofits in this space, to bring cybersecurity focused training to a more diverse community.

The need to make our security teams as diverse and inclusive as possible has become an imperative for cybersecurity teams all over the world. We are proud of our emphasis on this internally and in the coming year hope to share some projects we believe will make a difference in this realm. We stand ready to innovate and help change the cybersecurity industry and we are going to accomplish this with the most inclusive and diverse teams we can assemble to help bring change to our industry.  

Ultimately, creating more diverse teams is critical to our overall success.

If you want to create a Windows Server Virtual Machine (VM) in Compute Engine, it’s as quick and easy as clicking a few buttons! 

In this blog post, we’ll learn how to create a Windows Server VM in Compute Engine. If you want to follow along with this blog post, check out the documentation and in-console tutorial.

Step 1

The first step is to create a new project; we will use an existing project called “test-ctd”. If you are unable to use an existing project, you will need to create a new project. Next we’ll enable the Compute Engine API which is necessary for creating a Windows Server VM instance. If you have billing enabled and or are in your free trial mode you’ll be able to enable the necessary APIs with a button labeled “Enable Apis”.

Step 2

The second step is to create the Windows Server VM instance. In the Google Cloud console navigate to Compute Engine > VM instances, this where you’ll be able to create VM instances from micro-VMs to larger instances like Debian, Windows or Linux. 

We’ll click the Create Instance button, and then fill in the requirements to create a VM instance. Starting first with the name, we’ll use the name testwindowsinstance. Make sure to remember the name because we’ll need it to find the new instance later.

Next step is to change the Boot Disk; we’ll scroll down to the section that has Boot Disk and click the button labeled CHANGE. On the Public images tab, we’ll change the operating system to Windows Server and choose Select. 

Next we’ll scroll down to the Firewall Section and select Allow HTTP Traffic, this will allow specific network traffic from the Internet. 

Finally, we’ll click the button labeled Create to generate a Windows VM instance. 

Step 3

With the Windows Server VM instance created, it’s time to connect to the VM instance. If there is a green check mark next to the Status, this indicates that the VM is running.

To set a password for the VM, use Remote Desktop Protocol. Navigate to Connect > RDP and choose Set Windows Password. This brings us to a screen where we will choose a username, then a password is automatically generated for this Windows Server VM instance. Make sure to copy the password so you can log into the VM instance. Then you’ll be able to connect to the VM instance using RDP.

Step 4

For the final step, we will delete the VM instance created to prevent incurring charges. Return to Compute Engine > VM Instance to the side of the instance there is a menu icon that will open a menu bar that allows you to delete the instance. Click DELETE to remove the instance. 

In Conclusion

There you have it! You’ve created a Windows VM Instance in Compute Engine. If you’d like to follow an interactive tutorial, check out this Create a Linux VM instance in Compute Engine tutorial. 

In today’s modern development world, a microservices-based architecture is a go-to pattern for developing applications that are independent, flexible, modular, and language-neutral, and offers significant agility benefits over a monolithic architecture. A great example of an organization that has made this transition is L.L.Bean, where its legacy on-premises system had been constraining the company’s ability to be innovative and responsive. This led to a company-wide effort to modernize with Google Kubernetes Engine (GKE) and a microservices architecture. With this approach, L.L.Bean streamlined the process of upgrading nodes and delivered cross-channel services faster by cutting feature release cycles. 

But while decoupling an application into multiple microservices can have a lot of benefits, there are complexities with this approach. In particular, it can be challenging to standardize and secure all your microservices if they need to be easily accessible and consumable by internal developers — or in some cases, by external developers. Access to these services also needs to be managed and secured to enable safe growth in service consumption. In this post we’ll summarize how to manage microservices with Google Cloud solutions and share a demo to help you get you started. 

How API management and service mesh simplify a microservices architecture

Adding a service mesh into a microservices-based application allows network and service operators to improve reliability, increase security and compliance, and spend less time managing the environment. Just as Kubernetes helps with container orchestration, a service mesh helps to standardize complex network functions within the services. A service mesh provides deep visibility into service-to-service transactions, and simplifies and manages security controls like authentication, authorization and encryption.

Moreover, organizations can extend their microservices as APIs to outside consumers such as partners, developers, web apps, mobile devices, and other business units within their organization. APIs make it easy to extend microservices to external partners and customers with security, visibility, and control. This is where an API management solution comes into play: to protect API traffic from external consumers. A full-lifecycle API management tool gives developers secure access to your microservices while providing tools to measure API consumption with analytics.

Google Cloud provides service management capabilities through Anthos Service Mesh and API management capabilities through Apigee, which are tightly integrated with one another. Here is an architecture diagram of the two solutions working together.

For a real-world example, imagine a retailer who wants to modernize their monolithic application by fragmenting it into microservices, while using cloud-native technologies. 

Our demo application uses Apigee, GKE, and Anthos Service Mesh together, extending the existing Google Cloud Boutique Shop microservices demo to illustrate how to use Apigee for exposing the internal gRPC services (within the mesh) as RESTful APIs to external consumers.

The demo assumes you are deploying all components in Google Cloud and you are using Apigee X in combination with GKE and Anthos Service Mesh. Please follow the steps provided in the GitHub Repo.

Better together: API management + service mesh 

Both APIs and microservices are critical to application modernization. By adopting API management and service mesh solutions together, IT teams can standardize their microservices and gain visibility into their operations with full lifecycle management and performance analytics for their APIs. 

Google Cloud’s Apigee and Anthos Service Mesh work together to enable organizations to reduce microservices complexity and increase their consumption, securely extending those services to internal as well as external developers. 

Wherever you are on your application modernization journey, Google Cloud can help. To learn more about the benefits of deploying service management and API management together, read this whitepaper.

To get started with Apigee, go to the Google Cloud console or try it for free here.

Editor’s note: This post is part of an ongoing series on IT predictions from Google Cloud experts. Check out the full list of our predictions on how IT will change in the coming years.

Prediction: AI will be the primary driver for moving to a 4-day work week

Enterprise use of artificial intelligence (AI) has exploded over the past few years, touching all aspects of business. One of the greatest reasons behind this increase is its potential to increase employee productivity, especially for developers. In fact, the productivity gains that AI brings to the table portend a future where, by 2025, AI will allow developers to complete a week’s worth of work in four days — or less!

Getting AI in production is hard but can unlock many efficiencies to create new opportunities. From managing data complexity to overcoming the AI skills gap to getting prototypes into production (and scaling new use cases after) — AI projects have traditionally taken months, if not years, to get up and running. 

We’ve taken our years of experience in AI development to help companies unlock the many AI opportunities that exist, from optimizing operations to automating routine tasks to creating more compelling customer experiences. 

Google Cloud’s AI products are designed to make the path from data to AI in production as painless and streamlined as possible. And they’re already driving productivity gains, increasing the reach of AI and ML to more users and enabling organizations to make the most of their scarce data science resources. 

Out-of-the-box APIs provide a solid starting point for organizations that need to solve real-world problems but don’t have the time or resources to build AI models themselves. For instance, Contact Center AI helps call center teams manage up to 28% more conversations concurrently to increase overall wellbeing for agents. With Translation Hub, localization teams can significantly cut back on time needed to translate documents into 135 languages by getting the first pass done in a matter of seconds before their final reviews. Merchandising and ecommerce teams are driving 40% more customer conversions with Recommendations AI. 

We continue to find ways to make all our AI research, AI models, and ML toolkits available as enterprise-grade products and solutions. For instance, since its launch, Vertex AI has helped data scientists ship enterprise-grade machine learning (ML) models 5x times faster by automating routine tasks like model management, monitoring, and versions. 

We’ve also  taken this one step further with the launch of Vertex AI Vision this year, which gives developers a fully managed development environment for creating, deploying, and managing computer vision applications for inventory management, factory safety, and even monitoring traffic patterns. Our own internal research has shown that Vertex AI Vision reduces the time it takes to build and deploy apps from weeks to hours, at a fraction of the cost of alternative approaches. 

We’re excited to see how you bring AI to bear for your business — and how you choose to spend all your newfound free time! 

To learn more about how we think about operationalizing data analytics and AI, watch the session What’s next for data analysts and data scientists from Google Cloud Next ‘22.

The excitement at Next ‘22 this year was inescapable.  We celebrated a number of exciting announcements and wins that show where the cloud is heading, and what that means for our partners and customers.  As we close out 2022 and finalize our plans for 2023, I wanted to provide a perspective on the most important partner developments from the event to help you hit the ground running next year.

Google Cloud’s transformation cloud was front and center throughout our entire event. This powerful technology platform is designed to accelerate digital transformation for any organization by bringing five business-critical capabilities to our shared customers:

The ability to build open data clouds to derive insights and intelligence from data.

Open infrastructure that enables customers to run applications and store data where it makes the most sense.

A culture of collaboration built on Google Workspace that brings people together to connect and create from anywhere, enabling teams to achieve more.

The same trusted environment that Google uses to secure systems, data, apps, and customers from fraudulent activity, spam, and abuse.

And a foundational platform that uses efficient technology and innovation to drive cost savings and create a more sustainable future for everyone.

More than just vision, the transformation cloud is delivering results today. British fashion retailer Mulberry and partner Datatonic have built data clouds to drive a 25% increase in online sales. Vodafone in EMEA is working with our partner Accenture to migrate and modernize its entire infrastructure.  Hackensack Meridian Health in New Jersey is working with partner Citrix to leverage our infrastructure and Google Workspace to modernize its systems, enable collaboration, reduce costs, bolster security, and provide better patient and practitioner experiences. Many more transformation stories are available here and in our partner directory.

For our partners, the transformation cloud is your customer satisfaction engine. It enables you to bring new capabilities to market that customers cannot get anywhere else – from overcoming challenges around organizational management, to demand forecasting, supply-chain visibility, and more – all of this is possible only with the capability of our Data, AI/ML, collaboration and security tools.

Thomas Kurian and Kevin Ichhpurani provided excellent insight and guidance for partners looking to begin, or accelerate, their journey with the transformation cloud in their Next ‘22 partner keynote. Briefly, here are the three steps partners can take now to set themselves up for success in 2023:

Customers expect you to be deeply specialized in cloud solutions and their business 

Customers have made it clear they expect to work with partners who are deeply knowledgeable about the technology solutions and foundational elements of the transformation cloud. Just as important, it’s no longer good enough for partners to offer a small group of highly trained individuals to do it all. Customers need deep cloud expertise within specific business functions and even within global regions. 

They need people who know how to leverage our cloud solutions to achieve great outcomes for finance departments, human resources, customer service, operations, and more. And more than that, customers need people who are also experts at driving these kinds of transformations within regional environments defined by unique policies, compliance requirements, and even cultural issues. This is a tall order, but it’s absolutely critical to your growth and success. 

This is why Google Cloud is investing in the tools, training, and support you need to expand your bench of trained and certified individuals.

Second, increase your focus on consumption and service delivery to land and expand opportunities.

The demand here is significant and growing. In its 2022 Global IT Market Outlook, analyst firm Canalys stated that partner-delivered IT products and services will account for more than 73% of the total global IT market this year and into next year (about even with its 2021 forecast, which suggests that services remain in high demand). This includes managed services such as cloud infrastructure and software services, managed databases, managed data warehouses, managed analytic tools, and more. These are high-margin endeavors for partners. Equally important, these kinds of services allow your customers to shift their people from managing technology to managing and growing the business.

As Thomas Kurian said during his Next ‘22 remarks, Google Cloud is not in the services business–that’s the domain of our partners. We are a product and technology company. This is why we have a partner-led service delivery commitment, and a goal of bring partners into 100% of customer engagements. 

Third, we are investing to help Google Cloud partners drive consumption and new business. 

We know you are focused on growing your customer engagements and accelerating customers’ time to value. We’re here to support you:

Our Smart Analytics platform is a key market differentiator that enables partners to tap into the fast growing Data & Analytics market, which is expected to hit $500B by 2024.1

We are investing $10 billion in cybersecurity and our recent acquisition of Mandiant extends our leadership in this area by combining offense and defense in powerful new ways.

Governments worldwide are looking for sovereign cloud solutions to meet their security, privacy, and digital sovereignty requirements. Google Cloud has a highly differentiated solution in this area, and partnerships are critical. We are driving to validate all of our ISV partner solutions through our Cloud Ready – Sovereign Solutions initiative.

We are providing increasing resources and support to help partners embed the capabilities of Google Workspace in their solutions.

We continue to allow customers to buy partner solutions and decrement their commits just like with Google Cloud products.

You’ll see more from us on all of this in kick off 2023. The opportunity to prosper – Google, partners, and customers alike – is tremendous. I’ve never been more excited about the year ahead.

1. IDC Forecast Companies to spend 342 B on AI Solutions in 2021

If you’re familiar with the Medicare/Medicaid market, you’ve probably heard of the HEDIS score: the Health Effectiveness Data and Information Set. It’s a standard created by the National Committee for Quality Assurance (NCQA) and scores the quality of Medicare and Medicaid plans by measuring the effectiveness and utilization of plan benefits, access to care, and services delivered by providers. Not only it drives reimbursement for providers and health plans, but it also impacts the attractiveness of the insurance plans to the prospective members while they enroll in the health plan. To say that it’s significant would be an understatement.

What does the HEDIS score mean for healthcare providers and payers? Increased physician collaboration and patient engagement to identify and close gaps in care. This puts tremendous focus on the need for data sharing and interoperability between the provider and the payer. Payers have responsibility to help identify gaps in care, and deliver those insights to providers, so that patients can receive the highest quality care possible. 

The catch to all of this? Patients spend limited time in exam rooms and facilities in front of physicians. In those short engagements, how well and how quickly the payer can deliver insights and opportunities to physicians, makes all the difference in terms of raising quality of patient care, and subsequently, HEDIS scores. The ability to quickly and effectively exchange, analyze and summarize data and information between payer and provider is critical.

Using data to improve quality of care

For many of us, an on-going relationship and history with our primary care physician can certainly help ensure more personalized care. When it comes to care gaps, however, it’s not just a matter of knowing a patient’s history. Care gaps are the result of comparing care and services rendered, the results of screenings and tests, and treatment recommendations based upon the patient’s historical claims data and health records. During a typical visit, finding, comparing, and summarizing all of this information quickly, and turning that into personalized care gap recommendations can be challenging. Fortunately, Google Cloud, Exafluence (EXF), and MongoDB have been working together to make identifying and closing care gaps far more efficient, effective and easier than ever before. 

Providers can enjoy a seamless user experience in SMART-on-FHIR, where clinical records turn into actionable care gaps and recommendations, helping them provide better care, in less time, with seamless and secure data exchange with payers.

With the SMART-on-FHIR application on Google Cloud, providers can safely and securely retrieve a patient’s clinical records from the Healthcare Data Engine (HDE) during patient office visits.

Providers can immediately request care gap recommendations from payers by sharing critical data and insights from patient health records with payers.

Payers can receive care gap requests from providers, and leverage an enriched data set, combining patient health records, plan benefits, and treatment pathways, to identify care gaps in real time and send those back to the provider before the patient leaves the exam room.

Providers can deliver personalized and insightful care to patients by incorporating care gap recommendations, helping both increase quality of care, and raise HEDIS scores.

Our approach to computing care gaps and how we’re making them available to the clinicians at the point of care

1. Identify data sources for computing the care gaps

When care gap recommendations are derived using historical claims data only, they are not as accurate and timely. In addition to the historical claims data, access to the patient’s current clinical data is key to computing the precise care gap recommendations. Providers and health systems use Google Cloud’s Healthcare Data Engine (HDE) to aggregate clinical data from disparate healthcare systems and create longitudinal patient records. Data from HDE is accessible in a standard compliant HL7 FHIR format through well-defined REST APIs. Many health plans have implemented MongoDB Atlas to store and manage the claims, coverage, and membership data. The EXF Care-Gap service leverages the health plan data from the MongoDB Atlas database to compute care gap recommendations based on the HDE-hosted clinical data.

2. Define and implement rules to calculate care gaps

The EXF Care-Gap service calculates the care gap recommendations in near real-time and stores them in the MongoDB Atlas database as FHIR resources. The CareGap service calculates care gap recommendations by applying configurable rules against the aggregated dataset stored in the MongoDB Atlas database and the clinical data sourced from HDE.

3. Serve care gaps through REST API and SMART-on-FHIR App

Clinical systems used at the point of care will launch a SMART-on-FHIR compliant App, which retrieves the care gap recommendations from the EXF FHIR Server and displays them to the clinicians. The App fetches the patient’s current clinical information from HDE through Apigee HealthAPIx and submits it to the EXF FHIR Server to retrieve the care gap recommendations from the Care-Gap service in FHIR format.

How Google Cloud can help compute care gap recommendations

Apigee HealthAPIx and SMART-on-FHIR

Apigee HealthAPIx is an open-source solution to accelerate interoperability in Healthcare through standards like FHIR® (Fast Healthcare Interoperability Resources) and SMART® (SMART (Substitutable Medical Applications and Reusable Technologies). It runs on Apigee Edge with Google Cloud’sHDE as a back-end. 

The primary purpose of the HealthAPIx solution is to enable the API Program in healthcare organizations and provide API-level access to healthcare data. HealthAPIx solution comes with ready-made interoperability APIs to meet theInteroperability and Patient Access final rule (CMS-9115-F) published by theCMS. It enables Payers, Providers, and technology vendors to meet the regulatory and compliance requirements defined under the21st Century Cures Act finalized by the Office of the National Coordinator for Health Information Technology (ONC), Department of Health and Human Services (HHS). 

With Apigee HealthAPIx, healthcare providers and health systems can:

Manage, secure, and scale APIs with an enterprise-grade platform that is FHIR-server agnostic

Quickly close innovation gaps and keep up with digital advances

Easily ingest healthcare data from internal, external, or open-source FHIR-ready partners

Google Cloud Healthcare Data Engine

HDE aggregates clinical data from disparate health systems to create an interoperable, longitudinal patient record. HDE can ingest and map data from HL7v2 messages and CSV and FHIR formatted flat files. It supports data ingestion in batches and near real-time. HDE harmonizes and reconciles the ingested data into FHIR R4 format, flattens it, and stores it in BigQuery. Patients’ longitudinal health records in HDE are accessible through REST API with FHIR formatted payload for transactional processing. The same data is also accessible from BigQuery through SQL-friendly interfaces for analytical processing. HDE incorporates a cloud configuration for healthcare that extends Google Cloud’s security and privacy, with support for HIPAA and HITRUST best practices. HDE leverages Google Cloud’s highly scalable and secure HIPAA-compliant managed services like Google’s Cloud Healthcare API and BigQuery. 

EXF FHIR with EXF proxy in Apigee X

When it comes to managing, securing and scaling your FHIR API, an enterprise-grade platform that is FHIR-agnostic is more than recommended, it’s essential. 

A FHIR server, in its most primitive format, handles inbound requests, and processes, retrieves and exchanges data over REST-based API’s, using standardized FHIR Resources, to and from underlying data sources. They have the responsibility of doing the most low-level data processing for data exchange and data format standardization, and keep us compliant with FHIR mandates.

When it comes to delivering your FHIR API’s to the outside world, security, scale and performance are mandatory. An API gateway is essential, and sits in-between your backend FHIR API, and the external clients that interact with them. Google’s Apigee X makes it easy for healthcare IT delivery teams to safely, securely, and efficiently deploy and manage your FHIR API, helping you go to market faster, with production-grade solutions and API-based digital services.

The EXF FHIR server offers unparalleled flexibility, with the ability of giving you the choice of pairing your FHIR APIs with new or existing MongoDB deployments. All flavors of MongoDB are supported, from Community-Server, Enterprise Advanced, and MongoDB Atlas. Having the flexibility to deploy in the cloud, or on-prem, or in hybrid-mode, also provides configuration for whatever requirements your organization must meet. By leveraging new or existing MongoDB deployments, the EXF FHIR server helps you reduce complexity, and more quickly integrate with existing organization datasets and sources.

EXF Care-Gap Service

Combining, enriching, and aggregating data from the provider’s FHIR resources, along with the existing plan, benefit, and treatment data, the EXF Care-Gap service can deliver actionable care gap insights in real time. It can also map and transform existing data into FHIR-native format, helping healthcare organizations bring FHIR-based, modern applications to market from existing legacy sources.

The Exafluence and MongoDB teams can work with the healthcare organizations to define, plan and execute the implementation of the Care-Gap service. 

MongoDB for FHIR

MongoDB is a Document-based database, which implements the JSON-based Document Model, and as such, is a natural fit to store all of your FHIR Resources. Your FHIR Resources can be stored in native format within MongoDB, with no modification needed to the schema definitions. This helps accelerate application delivery, simplifies your overall data architecture strategy, and allows FHIR to serve as not only the data model supporting your API’s, but the new, canonical model that your organization can migrate to. The extensibility and flexibility of the Document Model also allows you to extend your FHIR Resources, allowing you to leverage native FHIR, and add the information needed to support your organization’s unique requirements.

Unlike many COTS FHIR solutions, leveraging MongoDB as your underlying datastore also allows you to reduce the number of databases needed in your tech-stack. Typically, a FHIR server will ship with an underlying database, and a relational schema that differs from the FHIR schema. These COTS solutions often don’t allow you to change the underlying database platform, and even worse, do not give you access to the underlying database directly. At MongoDB, we believe healthcare organizations should be able to do more with FHIR collections, than simply serving the external FHIR API. With direct access to the underlying database, and the MongoDB Document Model, organizations can build a new Operational Data Layer that helps modernize to Domain Driven Design principles, serve FHIR API needs, and helps build any new applications or services needed from one common data layer. To learn more about using MongoDB with FHIR, and as an Operational Data Layer, click here to read more about implementing an Operational Data Layer.

Building a more seamless, interoperable future 

The approach to make care gaps accessible to clinicians at the point of care is an example of how Health Plans and Providers can exchange important healthcare information in real-time using interoperability standards like FHIR and SMART-on-FHIR regardless of the underlying technology used to implement the specifications. In this instance, the providers and health systems leverage Google’s FHIR implementation in HDE and HealthAPIx, while the Health Plans leverage FHIR implementation from EXF and MongoDB for exchanging key healthcare information in real time.

The solution outlined in this blog post helps providers and health plans to receive better scores and star ratings from the government, with significant medicare financial incentives, and it enables providers to deliver personalized and contextual care for better patient outcomes. 

We look forward to partnering with healthcare organizations to build a standards-compliant and interoperable foundation for seamless, prompt, and secure information exchange between providers and payers.

References

Building FHIR Applications with MongoDB Atlas

Learn more about FHIR on MongoDB

Using Apigee X with the Cloud Healthcare API | Cloud Architecture Center

GitHub – cqframework/cql-execution: A JavaScript framework for executing CQL

GitHub – cqframework/cql-exec-fhir: A FHIR data source for the JavaScript CQL Execution project

Care Gaps – AmeriHealth Caritas Delaware

HEDIS Medicare Health Outcomes Survey – NCQA

Introducing: NCQA on FHIR

Beneficiary Claims Data API

GitHub – google/medical_claims_tools: Examples, libraries, and tools for working with bulk FHIR data (particularly FHIR BCDA Claims at the moment)

2023 Medicare Advantage and Part D Star Ratings

If you’re self-managing relational databases such as MySQL, PostgreSQL or SQL Server, you may be thinking about the pros and cons of cloud-based database services. Regardless of whether you’re running your databases on premises or in the cloud, self-managed databases can be inefficient and expensive, requiring significant effort around patching, hardware maintenance, backups, and tuning. Are managed database services a better option?

To answer this question, Google Cloud sponsored a business value white paper by IDC, based on the real-life experiences of eight Cloud SQL customers. Cloud SQL is an easy-to-use, fully-managed database service for running MySQL, PostgreSQL and SQL Server workloads. More than 90% of the top 100 Google Cloud customers use Cloud SQL.

The study found that migration to Cloud SQL unlocked significant efficiencies and cost reductions for these customers. Let’s take a look at the key benefits in this infographic.

A deeper dive into Cloud SQL benefits

To read the full IDC white paper, you can download it here: The Business Value of Cloud SQL: Google Cloud’s Relational Database Service for MySQL, PostgreSQL, and SQL Server, by Carl W. Olofson, Research Vice President, Data Management Software, IDC and Matthew Marden, Research Vice President, Business Value Strategy Practice, IDC.

Looking for commentary from IDC? Listen to the on-demand webinar, How Enterprises Have Achieved Greater Efficiency and Improved Business Performance using Google Cloud SQL, where Carl Olofson discusses the downsides of self-managed databases and the benefits of managed services like Cloud SQL, including the cost savings and improved business performance realized by the customers interviewed in the study.

Getting started

You can use our Database Migration Service for an easy, secure migration to Cloud SQL. Since Cloud SQL supports the same database versions, extensions and configuration flags as your existing MySQL, PostgreSQL and SQL Server instances, a simple lift-and-shift migration is usually all you need. So let Google Cloud take routine database administration tasks off your hands, and enjoy the scalability, reliability and openness that the cloud has to offer.

Start your journey with a Cloud SQL free trial.

While great strides have been made toward providing communities greater access to information technology, a digital accessibility gap still remains. In Los Angeles County, it is estimated that 416,000 households lack home internet service and 265,000 households are without a home computer. Notably, low-income neighborhoods and populations that are predominantly Black and Latinx are amongst some of the most impacted areas, with over 20% of households lacking a broadband connection in some areas.

Beyond having broadband access, students – our next generation of workers – need to build digital literacy skills through exposure to emerging technology. To address this, the National Defense Industrial Association (NDIA) launched Digital Inclusion Week to bring momentum to digital equity movements across the country, and promote actions at both the national and local levels. 

Students participated in hands-on training and demonstrations from over 20 vendors including global tech companies and local non-profits, experienced AR/VR technology and robotics demonstrations, interactive coding workshops, and were offered private tours of the football field, home to the Los Angeles Rams and Chargers.

At the Google Cloud booth alongside Carahsoft and SpringML, students built an LED circuit to power a lightbulb, learned about AI models through a drawing game, and interacted with YouTube creators who promote digital equity and STEM technologies including Extra Credits, 3SB Games, and GoldieBlox.

Selwyn Hollins, director of LA County, explained the impact of the event saying “Tech Empowerment Day was a tremendous success. Students as well as educators benefitted from experiences with so many different technologies. We are appreciative to Google for its ongoing partnership and commitment to helping deliver resources that support digital equity in Los Angeles County.” 

We’re proud to support Los Angeles County in this critical mission enabling pathways to personal development and economic growth through greater technology, and help “Delete the Divide.” Beyond K-12, Google Cloud continues to support state and local organizations on their missions to help prepare faculty and higher education learners for in-demand technology careers, with Google Cloud skills programs. While technology alone will not improve education, students and teachers can thrive when they have access to the best tools and resources. Google.org also donated $10 Million to the NDIA to help remove digital divide barriers for rural and tribal communities at a national level in February 2022.

To learn more about Google’s initiatives to help reduce the digital divide through workforce development, or to get started today check out our Cloud Skills Boost, and Cloud for Faculty Programs.

Read more: Helping the state of Ohio build a digitally skilled workforce

If you want to create a serverless service, you’ll want to check out Cloud Build. 

Cloud Build is a popular Google Cloud service that allows you to execute your builds on Google Cloud. Cloud Build can import source code from various repositories or cloud storage spaces, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives. Cloud Build is serverless, which means it scales up and down with no infrastructure to set up, upgrade, or scale. In this blog post, we’ll learn how to build and push a Docker image with Cloud Build. If you want to follow along with this blog post, check out this tutorial.

Step 1

The first step is to create a new project, we’ll use an existing project called “test-ctd” and enable Google Cloud APIs like Cloud Build API, Artifact Registry API. If you have billing enabled and or are in your free trial mode you’ll be able to enable the necessary APIs. 

Step 2

The second step is to prepare the source files. We’ll open the Cloud Shell, where we’ll be able to create a directory called “quickstart-docker” with the commands in the image below

Running this command brings us to the Cloud Shell Editor, where we’ll create a file named quickstart.sh in the quickstart-docker folder we previously created. The file will hold a small command, which you’ll see in the image below. 

Make sure to save the file, by clickingFile > Save in the top navigation bar.

Next we’ll create the Dockerfile, which Cloud build will execute. In the dockerfile we’ll add a small script to run quickstart.sh. Make sure to save this file as well. 

We’ll return to the terminal where we’ll run the command in the image below, to make quickstart.sh executable. 

Step 3

We’ll move on to the third step, which is to create a new Docker repository in the repository in Artifact Registry.

We’ll start by creating a new Docker repo named quickstart-docker-repo in the location us-west2 with the description “Docker repository”. The command to run this action is depicted below

Next we’ll verify that the repository was created by running the command below. After running the command we see quickstart-docker repo is in the Artifact Registry. 

Step 4

Next we’ll navigate to Cloud Build > History to see the build we executed. If you click on the particular build you’ll be able to see the details of the build summary like the build log, execution details, and build artifacts. 

Step 5

For the final step, we will delete the resources created to prevent incurring charges. Go back to the navigation bar and visit Artifact Registry Next we’ll select our recently created quickstart-docker-repo and click DELETE.

In Conclusion

There you have it! Now you’ve learned how easy it is to build and push a Docker image with Cloud Build. If you’d like to follow another Google Cloud tutorial visit this blog post.