Healthcare & Life Sciences (HCLS)

Healthcare and life sciences (HCLS) organizations focus on protecting human lives, but they also need to defend the digital lives of patients and customers. Doing so depends on protecting data, devices, and networks while also complying with complex healthcare regulations. Trend Micro understands the unique challenges of the healthcare industry and provides layered solutions that integrate with existing infrastructure and have the flexibility to grow and change as the needs of HCLS organizations and their patients and customers evolve.

Trend Micro security solutions include products targeted at user protection, hybrid cloud security, and network defense. Trend Micro Deep Security, powered by XGen, provides a comprehensive suite of cloud security capabilities from a single agent, so organizations can secure patient data in less time and at lower cost. The solution provides a consolidated view into all areas of security, automating repetitive tasks, immediately securing new AWS instances, and producing reports for auditors. The ease of deployment for Trend Micro Deep Security on AWS frees healthcare organizations to focus on growing the business and providing better patient and customer care.

Service interruptions and physical disasters are two of the many risks involved in healthcare IT. Business continuity (BC) and disaster recovery (DR) ensures data resilience in the event of physical disasters, network outages, and malicious attacks. Healthcare organizations need to ensure that their cloud workloads are always available and include a reliable, easy-to-use backup solution that allows for instant recovery. Assurance that patient records are protected and not lost if the accessing hardware is destroyed, is key.

NetApp® Cloud Volumes ONTAP® solves this by providing a scalable, affordable way to protect data and ensure it is recoverable and accessible in case of disaster. NetApp® Cloud Volumes ONTAP® can be easily integrated with your AWS infrastructure to backup data from your on-premises and/or cloud environments to AWS storage targets. With near-instant online recovery capabilities that do not require a secondary disaster recovery location, this solution provides flexibility and speed you cannot get from tape backup. NetApp Cloud Volumes ONTAP is a data storage management platform that enables stakeholders across the health IT spectrum to reliably and cost-effectively leverage the benefits of the cloud while streamlining data management and improving patient care.

Both patients’ wellbeing and healthcare providers’ businesses depend on cost-efficient and compliant disaster recovery solutions that can keep systems up and running when disasters strike. This is an increasing challenge for healthcare and life sciences (HCLS) organizations with growing IT infrastructures, many different systems operating independently, and limited capital to add and manage new servers.

Software-as-a-Service (SaaS) disaster recovery solutions, such as CloudEndure Disaster Recovery, deliver key advantages to HCLS organizations. CloudEndure Disaster Recovery is a one-touch disaster recovery solution that ensures a near-zero Recovery Point Objective (RPO) and Recovery Time Objective (RTO) for all applications, while reducing many traditional disaster recovery expenses. CloudEndure offers both Tier 1 and Tier 2 disaster recovery, with each tier providing a different level of functionality at a different price, so organizations can choose the most cost-effective solution based on workload criticality.

Many healthcare and life sciences (HCLS) organizations struggle to store and quickly analyze data from new data sources like log files, data from click-streams, social media, and Internet-connected devices. Existing Extract-Transform-Load (ETL) and other storage solutions can get overwhelmed, slowing down innovation and projects that are critical to the bottom line, and can ultimately affect the quality of patient care. However, using a data lake along with complementary tools and architecture—such as Amazon Redshift, Amazon S3, and Matillion ETL—can alleviate these challenges to improve patient care.

With data integration by Matillion, HCLS organizations can easily load their data to Amazon S3 using pre-built connectors. They can then use Matillion to trigger transformation jobs to make data relatable, analyze it in a clean state, and send it to visualization tools. This enables HCLS organizations to harness more data from new sources in less time, leading to faster, better decision-making to improve everything from clinical processes and care delivery to long-term patient outcomes.

Manufacturing pharmaceuticals at commercial scale involves an array of complex variables and dependencies, including many different processes, types of equipment, systems, raw materials, and human activities. All of this must be carefully orchestrated to develop and manufacture pharmaceutical products of high quality, adhere to stringent regulations for ‘good manufacturing practices’ (GMP) and FDA validation, and maintain sustainable production costs. By collecting and storing real-time data from sensors, equipment, and operational systems such as alarms, manufacturers gain trustworthy visibility into operations. It also provides the basis for creating predictive models to identify improvements and root causes before adverse events occur.

Bigfinite Inc. is an advanced analytics and manufacturing intelligence platform that provides pharmaceutical and biotech manufacturers a way to gain the understanding and insights needed to improve efficiencies and ensure product quality. The Bigfinite platform collects and stores real-time data from production equipment, processes, and operational systems into a GMP-compliant big data environment using IIoT technologies. The data can then be used to build and train artificial intelligence/machine learning models to identify improvements that are then deployed with targeted solutions, helping to make manufacturers’ processes more robust and their operations more profitable.

Healthcare and life sciences organizations need to focus on existing challenges, but they also need to focus on the future. Being able to predict what’s coming next—disease development in individual patients or population-based trends, for instance—is essential to improving the quality of care and running more efficient, cost-effective businesses. To perform fast, accurate predictive analytics, organizations need proven machine learning (ML) solutions.

TIBCO Data Science, a collaborative platform for operationalizing data science, is at the forefront of predictive healthcare. The solution applies ML to big data and automates analytical models, so organizations can identify trends and patterns and make informed predictions about future medical events.

TIBCO offers ML models that use patient medical records to predict hospital readmissions, a costly issue that directly affects patient quality of life. The TIBCO platform streamlines collaboration and makes it easy to combine data, such as patient demographics and hospital treatment plans, to make accurate predictions that can transform healthcare treatments and follow-through.

By leveraging machine learning models to analyze a patient’s medical history, it is possible to calculate their risk of being diagnosed with multiple diseases within the next year. Organizations that can predict the onset of serious diseases can provide better care management for entire communities and save individual lives through early detection.

Perception Health provides proprietary CARE algorithms powered by Amazon SageMaker, a fully-managed platform that is designed to enable developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale—without exposing HIPAA-protected information. Perception Health’s algorithm can be licensed by users on a per-patient basis and run on AWS. There are more than 14 billion medical claims and 6.5 million more added daily. With at-a-glance visuals, a spectrum of tools, and personalized expertise from data scientists, healthcare and life sciences organizations can validate strategies and optimize workflows that benefit providers, payers, and patients across the entire system.

Machine learning (ML) models can be used to address many problems and inefficiencies in the healthcare and life sciences industry. For instance, ML models can be used to provide early detection of diseases like dementia, but also to predict when patients are likely to fail to show up for appointments, a problem that accounts for billions of wasted dollars every year.

Cloudwick’s robust ML capabilities can meet the analytics needs of organizations across healthcare. By automating the entire ML workflow, business users and developers can spend less time extracting and processing data and more time using forecast models to drive results. Cloudwick solutions on AWS include Medical No Show Prediction, which uses a machine learning model to predict the probability that a customer will not show up for an appointment.

Cloudwick’s Medical No Show Prediction model uses a patient’s medical background and appointment-related information to make its prediction, while protecting patient privacy. The results are just one example of how machine learning models can help providers improve the quality of care and the care experience, while at the same time reducing costs.