Qlikview Gives Allina Health the Tools to Deliver High Quality, Affordable Care and Operate As A Pioneer ACO
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- QlikView
Tech Stack
- Microsoft SQL Server
- HP Hardware
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Business Operation
Use Cases
- Predictive Maintenance
Services
- Data Science Services
About The Customer
Allina Health is a not-for-profit healthcare system that is dedicated to the prevention and treatment of illness and enhancing the greater health of individuals, families, and communities throughout Minnesota and western Wisconsin. The organization provides care for patients from beginning to end-of-life through its 90+ clinics, 15 pharmacies, and specialty medical services, including hospice care, oxygen and home medical equipment, and emergency medical transportation. Allina Health has nearly 24,000 employees, 5,000 associated and employed physicians, and 2,500 volunteers who share a common mission– to deliver exceptional health care and support services to the people in their communities– putting the patient first in everything they do. The Allina Health electronic medical record (EMR) system, Epic, is one of the most comprehensive in the nation. The company’s Epic EMR project, Excellian®, plays a key factor in the success of Allina Health.
The Challenge
Allina Health, a not-for-profit healthcare system, is dedicated to the prevention and treatment of illness and enhancing the health of individuals, families, and communities throughout Minnesota and western Wisconsin. The organization has a comprehensive electronic medical record (EMR) system, Epic, which plays a key role in its success. However, Allina Health is also part of the Center for Medicare & Medicaid (CMS) Innovation’s Pioneer Accountable Care Organization (ACO) model, which requires a strong information infrastructure to manage and visualize the enormous amount of data that will eventually be reported to the government. The goal of this infrastructure is to bring all medical and patient information together, enabling Allina to evaluate its performance, identify areas for improvement, and discover ways for clinicians and care teams to deliver better care to patients.
The Solution
Allina Health uses QlikView, QlikTech’s Business Discovery– user driven Business Intelligence (BI)– platform to help manage and visualize the enormous amount of data that will eventually be reported to the government as a part of the ACO requirements. The information is pulled from a data warehouse that has an estimated 2 terabyte worth of data and then put into various dashboards. The ability to take this raw data and get it into the hands of people who can use it effectively has provided great benefits to the organization. QlikView was rolled out at Allina Health to help manage data in five areas. As a result, individual dashboards were created within each of those areas that allow users of any kind– not only the IT department to see, understand and analyze data critical to the organization’s continued success. Each of the five dashboards was rolled out in 2012 and received high praise from users. Users now rely on QlikView to manage and analyze data in areas of Patient Census, Patient Experience Dashboard (HCAHPS), Ambulatory Quality Measures, Patient Experience (CGCAHPS), and Potentially Preventable Readmissions.
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.