公司规模
Large Corporate
地区
- America
国家
- United States
产品
- COMSOL Multiphysics
- Optimization Module in COMSOL
技术栈
- COMSOL Multiphysics
- Optimization Module
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Customer Satisfaction
- Innovation Output
技术
- 分析与建模 - 预测分析
- 分析与建模 - 数字孪生/模拟
- 应用基础设施与中间件 - 数据可视化
适用行业
- 医疗保健和医院
- 生命科学
适用功能
- 产品研发
- 质量保证
用例
- 质量预测分析
- 数字孪生
- 远程病人监护
服务
- 软件设计与工程服务
- 系统集成
关于客户
Boston Scientific is a leading developer of medical devices and technologies aimed at diagnosing and treating a wide range of medical conditions. The company is known for its innovative solutions in the healthcare sector, particularly in the field of cardiovascular health. Boston Scientific's team of engineers, including Travis Schauer and Ismail Guler, are at the forefront of medical device design and development. They focus on creating advanced medical devices that improve patient outcomes and enhance the quality of healthcare. The company's recent work on drug-eluting stents is a testament to its commitment to innovation and excellence in medical technology. By leveraging computational models and simulations, Boston Scientific aims to better understand and optimize the mechanisms of drug release in stents, ultimately improving treatment for patients with cardiovascular disease.
挑战
Treating arteries in the heart that have been blocked by plaque is a common challenge for medical professionals. Known as stenosis, this condition restricts blood flow to the heart, resulting in symptoms such as shortness of breath and chest pain. It is sometimes resolved using stents, which are small, mesh-like tubular structures designed to treat blocked arteries. They are usually placed in the coronary artery and expanded with a balloon catheter to keep the artery open. While stents are successful at holding arteries open, an artery can re-narrow because of excessive tissue growth over the stent. This is called restenosis and is the body’s natural healing response, but it can actually impede recovery. Thus, drug-eluting stents were developed to deliver medicine — which acts to reduce cell proliferation and prevent the unwanted growth — into the artery tissue. These contain a coating composed of medicine and a polymer matrix designed to provide a controlled delivery; each strand of the stent mesh is surrounded by this coating. Stent designs have improved dramatically in recent years in an effort to reduce restenosis rates, but much remains unknown regarding the release process.
解决方案
Travis Schauer, Ismail Guler, and a team of engineers at Boston Scientific used COMSOL Multiphysics® to model a stent coating and investigate the release profile of the medicine. They aimed to understand the rate at which the medicine diffuses out of the coating and into the vessel tissue, as well as the factors influencing this process. Using the Optimization Module in COMSOL, they fit their simulation as closely as possible to experimental data curves. The stent coating modeled by Schauer and Guler is a microstructure with two phases: a medicine-rich, surface-connected phase and a phase with drug molecules encapsulated by a polymer. The development of this microstructure is affected by the solubility of the drug, the drug-to-polymer ratio, and the processing conditions during manufacturing. When the stent is inserted into an artery, the medicine-rich phase quickly dissolves and diffuses into the tissue, leaving behind interconnected cavities (pores) in the polymer coating. Meanwhile, the molecules encapsulated by the polymer diffuse more slowly. Schauer and Guler idealized the complex geometry of the coating microstructure in their model, which consists of a pattern of cylindrical pores filled with solid medicine surrounded by a polymer shell containing both the dissolved drug and solid drug encapsulated by the polymer. The molecules diffuse radially and axially, and the microstructure geometry only changes radially at the boundary between shell and pore. Using COMSOL allowed them to easily customize their model and focus on understanding the transport phenomena at hand. They performed simulations for two release profiles, in vitro and in vivo cases, seeking a description of the cumulative release of the medicine. They compared experimental data to the release profiles generated in their simulations to confirm their findings.
运营影响
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
相关案例.
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.