Health Monitoring

Solution Overview

Centuries ago, people died primarily due to wars, disease, and low levels of living standards and medical care. Today, chronic diseases are the main threat to longevity, and the reality of modern medicine is that there are many more patients than doctors who are able to help them. Health monitoring systems provide multiple options to change the traditional management of patients, reduce the cost of health care, and help hospitals improve their treatment processes. The global patient monitoring devices market is expected to reach over $73 billion by 2028, with significant growth attributed in part to technological advancement in patient monitoring devices.

In this example, we’ll walk through how the Decodable data service is used to transform, enrich, and aggregate real-time healthcare monitoring data. The processed data can then be used to inform healthcare staff of any changes in patient condition, alert them to issues with the monitoring devices, and enable proactive responses.

Pipeline Architecture

Below we can see a sample of raw device monitoring event data. In its current form, measurement data is nested inside an array, making it difficult to monitor the change in their values over time. By using one or more Decodable pipelines, which are streaming SQL queries that process data, we can transform the raw data into a form that is best suited for how it will be consumed.

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For this example, two separate pipelines are used in series, with the output of each one being used as the input for the next. While it is possible to perform all the desired processing in a single large, complex pipeline, it is most often desirable to split them into smaller, more manageable processing steps. This results in pipelines that are easier to test and maintain. Each stage in the sequence of pipelines is used to bring the data closer to its final desired form using SQL queries.

Decodable uses SQL to process data that should feel familiar to anyone who has used relational database systems. The primary differences you’ll notice are that:

• You activate a pipeline to start it, and deactivate a pipeline to stop it
• All pipeline queries specify a source and a sink
• Certain operations, notably JOINs and aggregations, must include windows

Unlike relational databases, all pipelines write their results into an output data stream (or sink). As a result, all pipelines are a single statement in the form INSERT INTO <sink> SELECT ... FROM <source>, where sink and source are streams you’ve defined.

Unnest Data Stream Array

For this example, each record of the raw monitoring stream contains data about the patient and device, as well as a values field, which contains an array of measurements that needs to be unnested (or demultiplexed) into multiple records to make it easier to access. To accomplish this, a cross join is performed between the device-raw data stream and the results of using the unnest function on thevalues field.

For example, if a given input record contains an array of 4 measurement values, this pipeline will transform each input record into 4 separate output records for processing by subsequent pipelines.

When the pipeline is running, the effects of unnesting the input records can be seen in the Overview tab which shows real-time data flow statistics. The input metrics will show a given number of records per second, while the output metrics will show a higher number based on how many elements are in the events array.

Pipeline: Extract And Transform Monitoring Data

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After creating a new pipeline and entering the SQL query, clicking the Run Preview button will verify its syntax and then fire up a new executable environment to process the next 10 records coming in from the source stream and display the results. Decodable handles all the heavy lifting on the backend, allowing you to focus on working directly with your data streams to ensure that you are getting the results you need.

Aggregate Data Stream

In the next stage of pipeline processing, we want to consolidate the device measurement values back into a single record with the value of each measurement in its own field. This is easily accomplished using standard SQL functions and a group by clause.

Pipeline: Consolidate Device Measurements

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Conclusion

At this point, a sink connection (one that writes a stream to an external system, such as AWS S3, Kafka, Kinesis, Postgres, Pulsar, or Redpanda) can be created to allow the results to be consumed by your own applications and services.

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As we can see from this example, a sophisticated business problem can be addressed in a very straight-forward way using Decodable pipelines. It is not necessary to create docker containers, there is no SQL server infrastructure to set up or maintain, all that is needed is a working familiarity with creating the SQL queries themselves.

You can watch demonstrations of several examples on the Decodable YouTube channel.

Additional documentation for all of Decodable’s services is available here.

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