Healthcare leaders are increasingly challenged to drive operational improvements throughout their facilities, and inefficiencies can cost organizations both time and money and impact patient outcomes. Additionally, staffing shortages and employee burnout remain a major concern in healthcare. Addressing these challenges can help healthcare providers improve organizational operations, patient experiences and care. However, the information that they need to solve these problems is siloed and challenging to access, buried deep in the patient record or spread across IT systems that don’t speak to one another. 

To generate a longitudinal patient view and improve patient care, healthcare providers need to reduce fragmentation, unify, and standardize data across siloed systems of records, care facilities and ultimately make it interoperable. This is the heart of Google Cloud’s Healthcare Data Engine. 

In partnership with customers, we are prioritizing a ‘use case’ approach to accelerate the adoption of data interoperability. We initially introduced these accelerators a few months ago, and the response has been overwhelmingly positive. Since then, we have been incorporating feedback from early adopters and making improvements to ensure that these accelerators deliver the best possible value to our customers.  

The accelerators are deployable use-case configured ‘starter-kits’ within Healthcare Data Engine designed to quickly surface tailored insights with the goal of enabling higher quality of care. Focused on patient flow, transition of care, and social determinants of health, the accelerators provide aggregated data and visualizations to help customers understand a broad range of facility efficiency metrics, patient insight and health equity measures across their patient populations: 

1. Patient Flow Explorer: Enables health systems to surface a range of facility efficiency metrics to identify trends and potential drivers of bottlenecks, manage patient admissions, transfers and length of stay, while optimizing departmental capacity management.
2. Transition of Care Explorer:  Provides medical professionals a consolidated view into a patient’s detail and medical history, illustrating their journeys in different settings within the health system and helps caregivers prioritize patient treatment.
3. Social Determinants of Health Explorer:  Leverages Healthcare Data Engine and social determinants of health data to identify at risk populations of patients in near-real-time and improve care in under-served communities.

The first three accelerator use cases were designed in collaboration with industry healthcare leaders including Highmark Health, Lifepoint Health, and others, helping organizations address common industry challenges.

“Interoperability is at the heart of Highmark Health’s Living Health strategy and we intend to leverage our blended structure of payor and provider to deliver a differentiated health experience for our patients and members,” said Richard Clarke, chief data and analytics officer, Highmark Health. “Healthcare Data Engine is a central component to enabling that strategy and we are excited about the new accelerators being announced today as they will speed up time-to-value for our members.”

“Google Cloud’s solution-oriented approach brings the best of technology and healthcare together to help improve quality, increase access and ensure equitable care for patients no matter where they live,” said Jessica Beegle, senior vice president and chief innovation officer of Lifepoint Health. “Instead of giving us building blocks that need to be assembled, they are delivering custom-built solutions to help us efficiently tackle key problems in our markets and provide more useful data for our clinical teams to take better care of their patients. Lifepoint is proud to partner with Google Cloud and bring the best of Silicon Valley to communities of all sizes across the United States.”

At Google Cloud, we will continue our partnership with healthcare leaders to identify the most impactful Healthcare Data Engine use cases to support, to help organizations accelerate their data transformations to bring better, more timely care to patients and communities. To learn more about Healthcare Data Engine and other solutions tailored to the healthcare and life sciences space, visit cloud.google.com/healthcare.

Editor’s note: Soundtrack Your Brand is an award-winning streaming service with the world’s largest licensed music catalog built just for businesses, backed by Spotify. Today, we hear how BigQuery has been a foundational component in helping them transform big data into music.

Soundtrack Your Brand is a music company at its heart, but big data is our soul. Playing the right music at the right time has a huge influence on the emotions a brand inspires, the overall customer experience, and sales. We have a catalog of over 58 million songs and their associated metadata from our music providers and a vast amount of user data that helps us deliver personalized recommendations, curate playlists and stations, and even generate listening schedules. As an example, through our Schedules feature our customers can set up what to play during the week. Taking that one step further, we provide suggestions on what to use in different time slots and recommend entire schedules.

Using BigQuery, we built a data lake to empower our employees to access all this content and metadata in a structured way. Ensuring that our data is easily discoverable and accessible allows us to build any type of analytics or machine learning (ML) use case and run queries reliably and consistently across the complete data set. Today, our users are benefiting from this advanced analytics through the personalized recommendations we offer across our core features: Home, Search, Playlists, Stations, and Schedules.

Fine-tuning developer productivity

The biggest business value that comes from BigQuery is how much it speeds up our development capabilities and allows us to ship features faster. In the past 3 years, we have built more than 150 pipelines and more than 30 new APIs within our ML and data teams that total about 10 people. That is an impressive rate of a new pipeline every week and a new API every month. With everything in BigQuery, it’s easy to simply write SQL and have it be orchestrated within a CI/CD toolchain to automate our data processing pipelines. An in-house tool built as a github template, in many ways very similar to Dataform, helps us build very complex ETL processes in minutes, significantly reducing the time spent on data wrangling.

BigQuery acts as a cornerstone for our entire data ecosystem, a place to anchor all our data and be our single source of truth. This single source of truth has expanded the limits of what we can do with our data. Most of our pipelines start from a data lake, or end at a data lake, increasing re-usability of data and collaboration. For example, one of our interns built an entire churn prediction pipeline in a couple of days on top of existing tables that are produced daily. Nearly a year later, this pipeline is still running without failure largely due to its simplicity. The pipeline is BigQuery queries chained together into a BigQuery ML model running on a schedule with Kubeflow Pipelines.

Once we made BigQuery the anchor for our data operations, we discovered we could apply it to use cases that you might not expect, such as maintaining our configurations or supporting our content management system. For instance, we created a Google Sheet where our music experts are able to correct genre classification mistakes for songs by simply adding a row to a Google Sheet. Instead of hours or days to create a bespoke tool, we were able to set everything up in a few minutes.

BigQuery’s ability to consume Excel spreadsheets allows business users who play key roles in improving our recommendations engine and curating our music, such as our content managers and DJs, to contribute to the data pipeline.

Another example is our use of BigQuery as an index for some of our large Cloud Storage buckets. By using cloud functions to subscribe to read/write events for a bucket, and writing those events to partitioned tables, our pipelines can easily and in a natural way quickly search and access files, such as downloading and processing the audio of new track releases. We also make use of Log Events when a table is added to a dataset to trigger pipelines that process data on demand, such as JSON/CSV files from some of our data providers that are newly imported into BQ. Being the place for all file integration and processing, BQ allows new data to be quickly available to our entire data ecosystem in a timely and cost effective manner while allowing for data retention, ETL, ACL and easy introspection.

BigQuery makes everything simple. We can make a quick partitioned table and run queries that use thousands of CPU hours to sift through a massive volume of data in seconds — and only pay a few dollars for the service. The result? Very quick, cost-effective ETL pipelines.

In addition, centralizing all of our data in BigQuery makes it possible to easily establish connections between pipelines providing developers with a clear understanding of what specific type of data a pipeline will produce. If a developer wants a different outcome, she can copy the github template and change some settings to create a new, independent pipeline.

Another benefit is that developers don’t have to coordinate schedules or sync with each other’s pipelines: they just need to know that a table that is updated daily exists and can be relied on as a data source for an application. Each developer can progress their work independently without worrying about interfering with other developers’ use of the platform.

Making iteration our forte

Out of the box, BigQuery met and exceeded our performance expectations, but ML performance was the area that really took us by surprise. Suddenly, we found ourselves going through millions of rows in a few seconds, where the previous method might have taken an hour. This performance boost ultimately led to us improving our artist clustering workload from more than 24 hours on a job running 100 CPU workers to 10 minutes on a BigQuery pipeline running inference queries in a loop until convergence. This more than 140x performance improvement also came at 3% of the cost.

Currently we have more than 100 Neural Network ML models being trained and run regularly in batch in BQML. This setup has become our favorite method for both fast prototyping and creating production ready models. Not only is it fast and easy to hypertune in BQML, but our benchmarks show comparable performance metrics to using our own Tensorflow code. We now use Tensorflow sparingly. Differences in input data can have an even greater impact on the experience of the end user than individual tweaks to the models.

BigQuery’s performance makes it easy to iterate with the domain experts who help shape our recommendations engine or who are concerned about churn, as we are able to show them the outcome on our recommendations from changes to input data in real-time. One of our favorite things to do is to build a Data Studio report that has the ML.predict query as part of its data source query. This report shows examples of good/bad predictions in the report along with bias/variance summaries and a series of drop-downs, thresholds and toggles to control the input features and the output threshold. We give that report to our team of domain experts to help manually tune the models, putting the model tuning right in the hands of the domain experts. Having humans in the loop has become trivial for our team. In addition to fast iteration, the BigQuery ML approach is also very low maintenance. You don’t need to write a lot of Python or Scala code or maintain and update multiple frameworks—everything can be written as SQL queries run against the data store.

Helping brands to beat the band—and the competition

BigQuery has allowed us to establish a single source of truth for our company that our developers and domain experts can build on to create new and innovative applications that help our customers find the sound that fits their brand.

Instead of cobbling together data from arbitrary sources, our developers now always start with a data set from BigQuery and build forward. This guarantees the stability of our data pipeline and makes it possible to build outward into new applications with confidence. Moreover, the performance of BigQuery means domain experts can interact with the analytics and applications that developers create more easily and see the results of their recommended improvements to ML models or data inputs quickly. This rapid iteration drives better business results, keeps our developers and domain experts aligned, and ensures Soundtrack Your Brand keeps delivering sound that stands out from the crowd.

The app economy has enabled a huge range of unique business models to flourish. One such model is online food ordering and delivery services, in which apps leverage geo-location data to aggregate local food choices and offer personalized options to consumers.

A leading company in this space is Just Eat. Launched in the UK in 2001 with a vision of ‘serving the world’s greatest menu. Brilliantly.’ The company has capitalized on the popularity of online food delivery and grown its presence across 12 markets. 

Just Eat acts as an intermediary between take-out food outlets and hungry customers, giving local restaurants access to a broader base of potential diners, while providing consumers with an easy and secure way to order and pay for food from their favourite restaurants.

Today the company helps 27 million customers find food from more than 112,000 restaurants—everything from homemade Italian pasta, to Chinese noodle bowls, to fish-and-chips. 

Data is the fuel of Just Eat’s rapid growth, but it wasn’t always looked at that way. In its early days, Just Eat struggled with the deluge of information and faced fragmentation across its systems. In fact, the company realized its legacy data vendor wasn’t capable of ingesting 90 percent of the data produced by its food platform. This was incredibly frustrating for Just Eat’s analysts and data scientists, who had to waste time cleaning up sources instead of leveraging the data to create a better user experience. 

Just Eat turned to Google Cloud, and now uses machine learning (ML) to power sophisticated consumer recommendations on both its app and website. It also makes heavy use of features offered by Google Cloud Platform, including BigQuery for running analytics on its customer data set and Cloud Pub/Sub for messaging app users with relevant offers in real-time. 

Having all of Just Eat’s data in one platform has translated into real value for its customers. With Google Cloud tools, Just Eat has created its own proprietary Customer Ontology framework, which today contains 5.5 billion features that better understand consumers’ behavior and food habits, and provides insights into previous visits.

Just Eat recently created an “Adventurous Index” to map its customers according to their ordering habits, enabling them to tailor their marketing and user experiences. For example, mid-adventurous customers are shown a choice of restaurants that serve their most ordered cuisine, while adventurous customers can choose from restaurants that serve a wider variety. This not only has prompted consumers to be more adventurous with their choices, but also has led to more business at a more diverse set of restaurants.

Matt Cresswell, Director of Customer Platforms at Just Eat said that Google Cloud has become integral to its product delivery: “Consumer food choice is a hugely nuanced topic. We know that individuals have their own unique journeys when they use Just Eat. We’ve sought to create a truly one-to-one relationship with every customer. The changes we’ve made to the platform mean they can access the dishes they enjoy at the touch of a fingertip, and find inspiration to discover new dishes they’ll love. We’re grateful to Google Cloud for helping us support our customers on their culinary explorations.”

There are multiple reasons a growing number of database administrators, enterprise architects, application developers and other technology practitioners are moving to Google Cloud’s various database services.

Some are being driven by missing features in offering from other providers such as AWS. In Gartner’s Magic Quadrant for Operational Database Management Systems, the research and advisory firm points out that, “AWS’s surveyed reference customers scored its overall product capabilities one standard deviation (STD) below the mean. Their responses identified missing features such as multiregion writes and autosharding.”

Others are moving to database services on Google Cloud Platform driven by a few benefits. According to Gartner, “Reference customers repeatedly commented on Google’s ease of use and implementation, reliability and integration (with other services and other systems). Reference customers scored Google a full STD above the mean for satisfaction with GCP’s pricing; it received the second-highest satisfaction score of any vendor in this Magic Quadrant.

If you are looking to leverage the power of Google Cloud database offerings—but were unsure of which database services comes closest to the service you are currently using, here’s a handy map to find your way.

We’re pleased to announce that Gartner has named Google Cloud a Leader in its 2019 Magic Quadrant report for Operational Database Management Systems (OPDBMS). This news reflects what we hear from our customers: that Google Cloud databases are flexible, open, and easy to use.

These include our fully compatible managed services for popular database engines like MySQL, PostgreSQL, SQL Server and Redis, and scalable cloud-native relational and non-relational databases like Cloud Spanner, Cloud Bigtable, and Cloud Firestore, plus fully managed partner services like MongoDB Atlas, Elastic, and Redis Enterprise. You can also run proprietary database workloads on Google Compute Engine or via our Bare Metal Solution.

Enterprises databases in production
We’ve heard great stories from our customers about their use of Google Cloud databases to run their businesses with ease and flexibility. Our database products meet varying needs for scalability and power.

Gaming company Bandai Namco Entertainment needed fast scalability, a global network, and real-time analytics to serve users its Dragon Ball Legends game. They were initially considering sharded MySQL to handle the scale, but opted for Cloud Spanner. Because it’s strongly consistent, fully managed, and scales seamlessly, Cloud Spanner supported the game’s rollout and allowed millions of worldwide players to compete without downtime. 

And media leader The New York Times found our Cloud Firestore database service as they built a truly real-time collaboration tool that lets multiple writers and editors make changes in docs at the same time, keeping track of what’s newest. Cloud Firestore is designed for just this type of task, since it supports offline and real-time sync. 

E-commerce brand analytics and protection company 3PM Solutions empowers global brands to manage, protect, and grow revenue by using Google Cloud Platform services such as Cloud Bigtable. Using Google Cloud, they’ve been able to analyze 160 million customer reviews of more than 2 million sellers in less than four hours. 

Leanplum built its cloud-native marketing platform on Google Cloud to help marketers personalize and orchestrate cross-channel customer engagement campaigns at scale. “

We were looking for cost-effective cloud services that can reduce engineers’ overhead associated with managing infrastructure,” says Athena Koutsonikolos, Leanplum’s VP of marketing. “Using fully managed services like Cloud SQL, developers can now focus on building what matters to our customers.”

Gartner 2019 Magic Quadrant for Operational Database Management Systems – November 25, 2019, Merv Adrian, Donald Feinberg, Henry Cook. 

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.