Lung cancer is the leading cause of cancer death in the United States and like any cancer, early detection is crucial to survival. Screening at-risk populations is an important part of reducing mortality, and if concerning nodules are found on imaging, further testing may be required. Today, we’ll share how Northwell Health uses Google Cloud products such as Cloud Healthcare APIs and BigQuery to increase caregiver productivity and deliver better care for patients with findings that indicate potential development of lung cancer.

Northwell is New York’s largest healthcare provider

Northwell Health is New York’s largest healthcare provider with 23 hospitals and nearly 800 outpatient facilities. Northwell’s nearly 4,000 doctors care for millions of patients each year, and at this scale, there is an immense amount of healthcare data to manage. To better manage and leverage this data, Northwell Health partnered with Google Cloud starting in 2018.

Enabling caregivers to spend more time with patients

Nic Lorenzen, the lead developer of Northwell Emerging Technology and Innovation team, has a mission to put together data for caregivers in a way that makes sense. It is no secret that inefficient electronic health records systems have a negative impact on a physician’s ability to deliver quality care. Traditional EHRs have information distributed across many tabs, which forces caregivers to spend considerable time at the computer trying to find information. Moreover, speed of care matters. If care is delayed, patients may have to spend more time in the hospital and may suffer worse health outcomes.

To solve this problem, Nic’s team focused on giving caregivers the most relevant pieces of data at the right time by developing an intelligent clinician rounding app. The data needed to derive these insights can depend on the caregiver’s role–a nurse cares about different things than a cardiologist. This system aggregates multiple data sources, and provides patient-specific insights to caregivers.

This system would not have been possible before with traditional EHRs and data warehouses that have proprietary data models and rarely sync data in real time. Now with data easily accessible through Google Cloud’s Healthcare solutions, Nic’s team can deliver the right clinical information to the right people instantly. These days, Nic says, “instead of spending 75% of our time dealing with architecting the underlying platforms, we spend 75% of our time focused on  higher value use cases for clinicians and patients. Google Cloud’s Healthcare solutions have greatly improved our developer productivity and time to value.”

Caregivers have found this new system to be a game changer.Before the implementation of this system, caregivers would spend, on average, seven to nine minutes finding the data needed to make medical decisions for one patient. Now, that aggregated information is delivered to a caregiver’s mobile device in less than a second.

Ensuring patients get the right care with the power of AI

There are a number of reasons why patients might not get the care that they need. For example, patients today can go to multiple hospitals and clinics settings, and coordinating care across multiple facilities is complex. Regional hospitals and clinics have their own siloed view of their data, so pertinent information gathered by one clinic might not be seen by another. These gaps in clinical data lead to gaps in patient care.

When a patient gets radiologic imaging, they may have findings unrelated to the reason they initially got the imaging. For example, a chest CT for a car accident might reveal an incidental lung nodule that could be cancerous. Unfortunately, research shows that a large portion of patients do not get follow up for these incidental findings because it isn’t the primary reason why the patient is seeing a doctor. Moreover, social determinants of health are a factor that affects which patients receive follow-up care. Identifying these patients and providing the necessary follow up care prevents adverse events related to delayed detection of cancer.

With Cloud Healthcare solutions, Northwell built an AI model to identify these patients so that oncologists can appropriately follow up with patients who have findings suspicious for lung cancer. The AI model detects incidental pulmonary nodules in radiology reports so that doctors can then contact the patients that need follow-up care. Nic says his team was able to build this system in a week: “Google Cloud did a lot of heavy-lifting for us and allowed us to get to the AI applications much faster. It allowed us to build a platform that just works.” 

Healthcare systems can now rapidly generate healthcare insights with one end-to-end solution, Google Cloud Healthcare Data Engine. It builds on and extends the core capabilities of the Google Cloud Healthcare API to make healthcare data more immediately useful by enabling an interoperable, longitudinal record of patient data. Northwell Health uses Google Cloud as the core of their platform, enabling their developers to create solutions to the most pressing healthcare problems.

Special thanks to Kalyan Pamarthy, Product Management Lead on Cloud Healthcare and Natural Language APIs for contributing to this blog post.

Editor’s note: Today we hear from ForgeRock, a multinational identity and access management software company with more than 1,100 enterprise customers, including a major public broadcaster. In total, customers use the ForgeRock Identity Platform to authenticate and log in over 45 million users daily, helping them manage identity, governance, and access management across all platforms, including on-premises and multicloud environments. 

Operating at that kind of scale isn’t easy. In this blog post, ForgeRock Engineering Director, Warren Strange discusses the three things that help make their developers efficient and productive, and the Google Cloud tools they use along the way. 

At ForgeRock, we’ve been an early adopter of Kubernetes, viewing it as a strategic platform. Running on Kubernetes allows us to drive multicloud support across Google Kubernetes Engine (GKE), Amazon (EKS), and Azure (AKS). So no matter which cloud our customers are running on, we are able to seamlessly integrate our products into customers’ environments. 

Making it easier for ForgeRock’s developers and operators to build, deploy and manage applications has been crucial in our ability to continually provide high quality solutions for our customers. We’re always looking for tools to improve productivity and keep our developers focused on coding instead of configuration. Google Cloud’s suite of DevOps tools have streamlined three specific practices to help keep our developers productive: 

1. Make developers productive within IDEs

Developer productivity is core to the success of any organization, including ForgeRock. Since developers spend most of their time within their IDE of choice, our goal at ForgeRock has been to make it easier for our developers to write Kubernetes applications within the IDEs they know and love. Cloud Code helps us precisely with that: it makes the process of building, deploying, scaling, and managing Kubernetes infrastructure and applications a breeze. 

In particular, working with the Kubernetes YAML syntax and schema takes time, and a lot of trial and error to master. Thanks to YAML authoring support within Cloud Code, we can easily avoid the complicated and time consuming task of writing YAML files at ForgeRock. With YAML authoring support, developers save time on every bug. Cloud Code’s inline  snippets, completions, and schema validation, a.k.a. “linting,” further streamline working with YAML files. 

The benefits of Cloud Code extend to local development as well. Iterating locally on Kubernetes applications often requires multiple manual steps, including building container images, updating Kubernetes manifests, and redeploying applications. Doing these steps over and over again can be a chore. Cloud Code supports Skaffold under the hood, which tracks changes as they come and automatically rebuilds and redeploys—reducing repetitive development tasks. 

Finally, developing for Kubernetes usually involves jumping between the IDE, documentation, samples etc. Cloud Code reduces this context switching with Kubernetes code samples. With samples, we can get new developers up and running quickly. They spend less time learning about configuration and management of the application—and spend more time on writing and evolving the code.

2. Drive end-to-end automation

To further improve developer productivity, we’ve focused on end-to-end automation: from writing code within IDEs, to automatically triggering CI/CD pipelines and running the code in production. In particular, Tekton, Cloud Build, Container Registry, and GKE have been critical to Forgerock as we streamline the flow of code, feedback and remediation through the build and deployment processes. The process looks something like this:

We begin by developing Kubernetes manifests and dockerfiles using Cloud Code. Then we use Skaffold to build containers locally, while Cloud Build helps with continuous integration (CI). The Cloud Build GitHub app allows us to automate builds and tests as part of our GitHub workflow. Cloud Build is differentiated from other continuous integration tools since it is fully serverless. It scales up and scales down in response to load, with no need for us to pre-provision servers or pay in advance for additional capacity. We pay for the exact resources we use. 

Once the image is built by Cloud Build, it is stored, managed, and secured in Google’s Container Registry. Just like Cloud Build, Container Registry is serverless, so we only pay for what we  use. Additionally, since Container Registry comes with automatic vulnerability scanning, every time we upload a new image to Container Registry, we can also scan it for vulnerabilities. 

Next, a Tekton pipeline is triggered, which deploys the docker images stored in Container Registry and Kubernetes manifests to a running GKE cluster. Along with Cloud Build, Tekton is a critical part of our CI/CD process at ForgeRock. Most importantly, since Tekton comes with standardized Kubernetes-native primitives, we can create continuous delivery workflows very quickly.

After deployment, Tekton triggers a functional test suite to ensure that the applications we deploy perform as expected. The test results are posted to our team Slack channel so all developers have instant access and insights about each cluster. From there, we are able to provide our customers with their finished product request.

3.  Leverage multicloud patterns and practices

The industry has seen a shift towards multicloud. Organizations have adopted multicloud strategies to minimize vendor lock-in, take advantage of best-in-class solutions, improve cost-efficiencies, and increase flexibility through choice. 

At ForgeRock, we’re big proponents of multicloud. Part of that comes from the fact that our identity and access management product works across Google Cloud, AWS, and Azure. Developing products using open-source technologies such as Kubernetes has been particularly helpful in driving this interoperability. 

Tekton has been another critical project that has allowed us to prevent vendor lock-in. Thanks to Tekton, our continuous delivery pipelines can deploy across any Kubernetes cluster. Most importantly, since Tekton pipelines run on Kubernetes, these pipelines can be decoupled from the runtime. Like Tekton and Kubernetes, both Cloud Build and Container Registry are based on open technologies. Community-contributed builders and official builder images allow us to connect to a variety of tools as a part of the build process. And finally, with support for open technologies like Google Cloud buildpacks within Cloud Build, we can build containers without even knowing Docker. 

Making it easier for developers and operators to build, deploy and manage applications is critical for the success of any organization. Driving developer productivity within IDEs, leveraging end-to-end automation, and support for multi-cloud patterns and practices are just some of the ways we are trying to achieve this at ForgeRock. To learn more about ForgeRock, and to deploy the ForgeRock Identity Platform into your Kubernetes cluster, check out our open-source ForgeOps repository on GitHub.

Cloud infrastructure offers so many advantages: on-demand scalability, built-in security, and a bevy of tooling to scale your business at the speed of the Internet.

It enables companies to pursue “blitzscaling,” as Reid Hoffman calls it. Capital expenses that take years or decades to pay off are no longer required to establish global networking, compute capacity, storage resources, and application enablement tooling. By renting these assets from cloud providers, you can translate capital costs to operational costs, help your company to manage resources efficiently, keep expenses aligned with growth trajectory, and develop a portfolio of business-driving technology assets faster than would have previously been possible.

Certainly, this is the message from many founders and venture capitalists: move to the cloud, build a ton of software, scale like crazy, and win. Sounds great, right?

But history has shown us that the process is not quite this simple. Scale can be great and is a prerequisite to many of today’s most exciting business opportunities — but scale also frequently produces unintended consequences. You need to be able not only to achieve scale but also to manage it.

When scale produces bloat

To understand how unforeseen impacts can ripple out from a rapidly-scaled technology, consider the first mass-produced vehicle, the Model T. The first production model was produced in 1908, and less than two decades later, Ford had produced 15 million. This rapid growth profoundly affected urban living for decades.

Thanks to cars, fewer workers needed to live near cities or along major public transportation lines. The ease-of-access to personal transportation led to suburban population centers and, ultimately, urban sprawl. “Sub-cities” extricated homeowners from the density of urban population centers but also created a complex web of unintended consequences: new and often duplicative administrative bodies, new taxes, new zoning laws, and more intricate infrastructure projects. We are arguably still dealing with this fallout today as communities grapple with antiquated zoning laws and, in their attempts to find ways forward, often produce only more sprawl.

If your technology is in the cloud, you may be challenged with similar issues. For example, when developers build software in the cloud, many of the barriers to building software are removed. As a result, developers build a lot of software — but often without a lot of intentional design. This in turn results in companies building a large number of disparate systems and overwhelming app sprawl.

The cloud can help you proliferate technologies so quickly, in other words, that effective management and re-use of resources becomes incredibly tough.

Managing scale

Software assets are often seen as comprising the “brains” of a company — but to effectively grow, you should consider not only brains but also the digital nervous system. You need systems that connect the brains to all of the other important limbs that have to coordinate in order for your company to drive value.

One way of creating this nervous system and managing this complexity is to leverage the facade design pattern: applying an API layer that abstracts the underlying complexity of multiple systems into an elegant, reliable interface that encourages discovery and re-use of applications, functions, and other technology artifacts such as build and deployment pipelines.

For example, too many enterprises build a new digital “road” for each application that needs to authenticate or authorize users. Instead, you can leverage a facade pattern to help establish one “main road” for these purposes, encourage reuse of the road for new projects, and — with API management — monitor and control all traffic along the road. For tasks such as aligning risk and compliance operations or unifying developer onboarding functions, the distinction between reusing elegant roads and continually building new complicated ones could not be more important.

API management tools mean you can establish a single-pane-of-glass view into your network of digital roads and destinations or, if you prefer the biology metaphor, into the nervous system routes connecting your company’s software brains. This view becomes a point at which suspicious API usage patterns can be detected, reported, and handled and through which business-driving insights from legitimate traffic can be gleaned. Rather than dealing with IT sprawl, you can maintain visibility over your assets, control how they are used, roll out experimental digital products and get immediate feedback on adoption, and generate analytics to help you effectively divest from and invest in opportunities as the market demands.

More is not always better

More is not always better, and, in fact, it is sometimes worse.

It’s a time-worn sales axiom that would-be customers are more likely to make a choice when presented with two or three options rather than fifty, for example, and virtually all of us can relate to moments of “analysis paralysis” triggered by too many choices. These dynamics of choice are such that the diminishing marginal utility of each choice can detract from each option: with each choice comes a little stress, and as these stresses accumulate, customer satisfaction suffers. IT systems are no different; if developers building new connected experiences are left to their own designs, rather than encouraged with standardized resources and best practices, their work may add to complexity and customer dissatisfaction.

One need look only at the various open air markets around the world to see this point in action. They may offer many things to see but the experience is anything but efficient. The multitude of shops selling similar and duplicate items, the zigzag layout, and the expected friction from bargaining with each vendor all mean concepts such as market-wide product discovery and product inventory go out the window. If your developer experience mirrors these marketplaces, your efforts to scale are more likely to tangle up your operations than to satisfy customers.

Contrast this experience with luxury retail experiences where product areas are clearly demarcated in different retail spaces, product explanations accompany showcase items, inventory is available locally or ready to ship, clear pricing is readily available, and similar stores are intentionally anchored to strategic physical areas to encourage customer flow among them. The developer programs that cloud efforts are often meant to enable require a similar focus on luxurious experiences.

If your growth strategy creates bloat, internal developers will not use resources efficiently and your ability to share resources with external partners will likely be hamstrung. Applying API facades helps to ensure that your growing software portfolio is not a complicated maze to be navigated but rather a series of technology products for developers to leverage.

Indeed, you should think of the API itself as a product, not just a way of surfacing or connecting technology. The better the product, the more easily it can be managed, the better the experience developers will have using it, and the more control you’ll have harnessing the cloud to extend your business’s footprint.

It has been seven years since Marc Andreesen’s famous article “Why Software is Eating the World” was published in the Wall Street Journal, and given the slow pace of change in many companies, some executives still may not be taking the threat of being “eaten” seriously enough. 

Software, and platform business models based on software, have the potential to deliver powerful economic forces into virtually any company or industry. Every company has valuable assets—such as data, expertise or access to certain services or user bases—and most of these assets can be delivered via software. Once an asset is expressed as software in a modern way—that is, as an application programming interface (API)—it can be combined with other software to create new applications and digital experiences.

Benefits of this approach, just to name a few, include near-zero marginal cost to scale up APIs for new users or use cases; global reach for both partners using APIs and end users consuming the digital experiences those APIs power; and network effects triggered as more partners use a given company’s digital assets and spread its services into new markets and use cases.

Disruption by software-powered business models

In the last two decades we’ve seen individual companies and entire industries upended by these kinds of software-powered business models. Examples abound: Amazon and the retail industry, Netflix and movie rentals, Uber and ride hailing, Airbnb and hotels, etc. We’ve reached the point that these companies’ names have become verbs synonymous with being “eaten” by software (e.g., “Amazoned” or “Netflixed”).

The most famous examples of digital disruption involve digital natives, of course, but legacy businesses are leveraging software to evolve too. Brazilian retailer Magazine Luiza—a company I’ve worked with through my employer, Google Cloud’s Apigee team—has enjoyed enormous revenue growth and seen its stock soar, for example, as it has built out its digital platform capabilities and transitioned from a primarily brick-and-mortar model to an omnichannel one. The point is, whether a company has been in business five decades, five years or five months, software remains ravenous and is always looking for new companies and industries to “eat.”

Change in the face of serious threats

Facing this threat, how should corporate leadership respond? There are some excellent examples of CEOs who have galvanized their companies and led them through the massive, gut-wrenching change required to pivot in the face of a serious threat. A few of the biggest examples include: 

• In 1995, it became apparent to Microsoft co-founder and then-CEO Bill Gates that the internet was “the most important single development to come along since the IBM PC,” and, if not embraced in haste, a threat to many of Microsoft’s businesses. In May of that year he published the “The Internet Tidal Wave” memo and focused all of Microsoft on adopting and building for the internet. Almost 20 years later, current Microsoft CEO Satya Nadella similarly made the bold decision to redirect the company for a cloud-first world. 

• Facebook went public at $38 per share in May of 2012 but within months, stocks could be had for a little over half that. The concern? Facebook was a desktop-optimized website without a polished mobile presence, and by 2012, consumer attention had begun to accelerate towards mobile at a much higher rate than many initially predicted.  Facebook CEO Mark Zuckerberg reacted by not only proclaiming Facebook a mobile-first company, but also backing up that proclamation with action.  

• Turning to another company I’ve worked with via Apigee, T-Mobile launched its highly visible “Uncarrier” campaign—which offers streamlined, customer-friendly plans and services—while also investing in and executing a new IT vision dedicated to ongoing digital transformation. T-Mobile execs have credited the technology effort, spearheaded by CEO John Legere, with helping the company to introduce new services and better service customers. T-Mobile’s market cap has more than doubled since Legere took over in 2012.  

Keeping pace with changing customer needs

In the face of an existential threat, strong executive leadership is required to pivot the company to safety, as these examples attest. Digital transformationisn’t about deploying new technologies just to make an existing approach more efficient or to add a few new apps or features to the status quo; digital transformation is about keeping pace with changing customer needs by leveraging software platforms to continuously evolve how the business operates. This can be akin to turning an enormous ship—and a ship can’t turn very well without her captain, first mate, and other leaders showing the way.  

Research supports this. A recent Deloitte survey, for example, found that over “80 percent of respondents from digitally maturing organizations say their leaders have sufficient knowledge and ability to lead the company’s digital strategy,” compared to only “22 percent of early-stage business respondents [who] have the same belief.” Similarly, Gartner research finds that CEOs are seeking a “deeper understanding of digital business” as they shift their focus from growth in general to how technology helps them attain it.  

More recently, the onslaught of software devouring the world has been further accelerated by machine learning making everything smarter, voice interfaces changing how people interact with devices, and more. To keep pace, corporate leaders need to galvanize their companies to build and deploy software faster, make systems and data easily accessible inside and outside their companies, and improve digital experiences through not only machine learning but also constant data-driven iteration. 

Seven years after Andreesen’s editorial, the pace of digital disruption is still increasing, and so is the need for strong leadership to pivot fully into digital. Over half of the Fortune 500 has been acquired, merged or declared bankruptcy since 2000—and the companies that survive in coming years won’t be those whose leaders treat technology as an IT concern rather than a core part of the business.