Healthcare Identifies Cause of EMR Slowdown with NETSCOUT
Company Size
1,000+
Region
- America
Country
- United States
Product
- nGeniusONE Service Assurance platform
- InfiniStreamNG software appliance
- nGenius Visibility-as-a-Service
Tech Stack
- Citrix services
- EMR application
- SIP trunking technology
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Real Time Analytics
- Application Infrastructure & Middleware - API Integration & Management
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Maintenance
- Quality Assurance
Use Cases
- Predictive Maintenance
- Remote Asset Management
Services
- System Integration
- Testing & Certification
About The Customer
The customer is a United States healthcare organization that has seen spectacular growth in the last decade due to a greater number of patients requiring their specialized care, as well as through strategic mergers and acquisitions. Nearly 50,000 part-time and full-time employees provide wide-ranging medical services to patients, from urgent care and occupational medicine to outpatient physical rehabilitation and therapy. The healthcare organization’s enterprise network is complex, delivering treatment and applications to more than 125 hospitals and 1,500 clinics and health centers in almost all of the United States. With quality patient care as well as billions in revenue in the balance, the safe and secure delivery of patient treatment is a top priority for this healthcare organization – which means delivery of application services related to patient care is also critical.
The Challenge
The healthcare organization was facing a major challenge in determining the cause of slowdowns reported by doctors, clinicians, physical therapists, and nursing staff when trying to use their electronic medical record (EMR) application. The application is critical as it holds all the information related to their patient’s health, diagnosis, treatment history, medications, etc. The cause of the slowdown could be anywhere – the centralized data center, across the WAN links to the hospitals and clinics, the EMR itself, possibly with the Citrix services, or related due to something in the network at the hospitals or clinics or end-points. The IT staff was also struggling to establish application and network benchmark metrics for the services and communities necessary for understanding what represented normal, good-quality performance. They were also experiencing voice and unified communications (UC) issues, Session Initiation Protocol (SIP) failures, and reports from their clinics regarding poor performance from voice and other services.
The Solution
The IT staff performed a thorough evaluation of alternatives to gain visibility into the most pressing issues related to their EMR and Citrix services, voice quality and SIP trunking technology, and end-user slowdowns at remote hospitals and clinics. For comprehensive visibility, the IT team selected NETSCOUT’s nGeniusONE Service Assurance solution with InfiniStreamNG software on custom-off-the-shelf (COTS) appliances, which is delivering smart data from packet analysis with Adaptive Service Intelligence™ (ASI) technology for service assurance in their environment. In order to immediately benefit from their new visibility, the healthcare also contracted for nGenius Visibility as a Service (nVaaS), which is helping to implement their nGeniusONE platform and configure the NETSCOUT® solution for immediate troubleshooting. Leveraging the nGeniusONE solution, along with their nVaaS experts, the troubleshooting concluded that they identified and monitored the custom EMR application including the application flows and tiers, which were previously unknown, to provide rapid response to slowness issues and errors with the service.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Hospital Inventory Management
The hospital supply chain team is responsible for ensuring that the right medical supplies are readily available to clinicians when and where needed, and to do so in the most efficient manner possible. However, many of the systems and processes in use at the cancer center for supply chain management were not best suited to support these goals. Barcoding technology, a commonly used method for inventory management of medical supplies, is labor intensive, time consuming, does not provide real-time visibility into inventory levels and can be prone to error. Consequently, the lack of accurate and real-time visibility into inventory levels across multiple supply rooms in multiple hospital facilities creates additional inefficiency in the system causing over-ordering, hoarding, and wasted supplies. Other sources of waste and cost were also identified as candidates for improvement. Existing systems and processes did not provide adequate security for high-cost inventory within the hospital, which was another driver of cost. A lack of visibility into expiration dates for supplies resulted in supplies being wasted due to past expiry dates. Storage of supplies was also a key consideration given the location of the cancer center’s facilities in a dense urban setting, where space is always at a premium. In order to address the challenges outlined above, the hospital sought a solution that would provide real-time inventory information with high levels of accuracy, reduce the level of manual effort required and enable data driven decision making to ensure that the right supplies were readily available to clinicians in the right location at the right time.
Case Study
Gas Pipeline Monitoring System for Hospitals
This system integrator focuses on providing centralized gas pipeline monitoring systems for hospitals. The service they provide makes it possible for hospitals to reduce both maintenance and labor costs. Since hospitals may not have an existing network suitable for this type of system, GPRS communication provides an easy and ready-to-use solution for remote, distributed monitoring systems System Requirements - GPRS communication - Seamless connection with SCADA software - Simple, front-end control capability - Expandable I/O channels - Combine AI, DI, and DO channels
Case Study
Driving Digital Transformations for Vitro Diagnostic Medical Devices
Diagnostic devices play a vital role in helping to improve healthcare delivery. In fact, an estimated 60 percent of the world’s medical decisions are made with support from in vitrodiagnostics (IVD) solutions, such as those provided by Roche Diagnostics, an industry leader. As the demand for medical diagnostic services grows rapidly in hospitals and clinics across China, so does the market for IVD solutions. In addition, the typically high cost of these diagnostic devices means that comprehensive post-sales services are needed. Wanteed to improve three portions of thr IVD:1. Remotely monitor and manage IVD devices as fixed assets.2. Optimizing device availability with predictive maintenance.3. Recommending the best IVD solution for a customer’s needs.
Case Study
HaemoCloud Global Blood Management System
1) Deliver a connected digital product system to protect and increase the differentiated value of Haemonetics blood and plasma solutions. 2) Improve patient outcomes by increasing the efficiency of blood supply flows. 3) Navigate and satisfy a complex web of global regulatory compliance requirements. 4) Reduce costly and labor-intensive maintenance procedures.
Case Study
Harnessing real-time data to give a holistic picture of patient health
Every day, vast quantities of data are collected about patients as they pass through health service organizations—from operational data such as treatment history and medications to physiological data captured by medical devices. The insights hidden within this treasure trove of data can be used to support more personalized treatments, more accurate diagnosis and more advanced preparative care. But since the information is generated faster than most organizations can consume it, unlocking the power of this big data can be a struggle. This type of predictive approach not only improves patient care—it also helps to reduce costs, because in the healthcare industry, prevention is almost always more cost-effective than treatment. However, collecting, analyzing and presenting these data-streams in a way that clinicians can easily understand can pose a significant technical challenge.
