Huntsville Hospital Health System leverages Watson Health to ease its transition to value-based care
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- IBM Watson Health
- IBM Phytel Outreach
- IBM Phytel Insight
- IBM Phytel Coordinate
- IBM Phytel Remind
Tech Stack
- Cloud Computing
- Data Analytics
- Electronic Medical Records (EMR)
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Big Data Analytics
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Quality Assurance
- Business Operation
Use Cases
- Predictive Maintenance
- Remote Patient Monitoring
Services
- Data Science Services
- System Integration
About The Customer
The Huntsville Hospital Health System is located in Huntsville, Alabama. Originally founded in 1895, today the health system is the third-largest publicly owned hospital system in the nation with more than 1,800 beds and 12,000 employees. It is also a teaching facility for the University of Alabama at Birmingham’s (UAB) Family Practice and Internal Medicine Residency Programs. The Huntsville Hospital Physician Network is an important component of this health system. The network comprises five primary care practices and 21 PCPs. While the Huntsville Hospital Physician Network does not currently have any value-based contracts in place, executives there recognize such contracts are an important consideration for the future. As a result, the organization has been taking steps to earn patient-centered medical home (PCMH) recognition from the National Committee for Quality Assurance (NCQA) in the near future.
The Challenge
Huntsville Hospital Health System, located in Huntsville, Alabama, realized it needed to change the way technology was being used across its five primary care practices and 21 PCPs that comprise the Huntsville Hospital Physician Network. The organization was preparing for value-based contracts and aiming to earn patient-centered medical home (PCMH) recognition from the National Committee for Quality Assurance (NCQA). However, they faced challenges in incorporating population health management (PHM) principles, which required reviewing the information contained in electronic medical records (EMRs) and verifying documentation from both internal and external providers is accurate and up-to-date. Another issue was the way patient data was being entered into the EMR, which was not standardized and led to inaccuracies in measurements. These issues needed to be resolved to set the organization on the right course for value-based contracts and PCMH recognition.
The Solution
Huntsville Hospital Health System implemented the Watson Health platform to enable provider-led, scalable PHM. The use of the Watson Health platform accelerated after Huntsville secured a Center for Medicare and Medicaid Improvement (CMMI) grant and added two clinician advocates to the team. The platform was used to spot and solve data issues within the EMR, remap the data, and educate clinicians on precisely where data should be entered. This improved the accuracy of the measurements and put Huntsville in a better position to take on value-based contracts. The Watson Health platform was also used to generate reports identifying care gaps for patients, which led to more conversations about closing those gaps. The platform was used to create printed summaries of patient information for office visits, showing previous vital signs and test results, and identifying care gaps. It also helped identify and solve documentation issues on the part of the Huntsville team, which helped the percentage of physicians meeting internal goals on the quality scorecards to increase significantly.
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.
