Download PDF
STATISTICA is helping the General University Hospital in Prague with clinical research of drug optimization
Technology Category
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
- Functional Applications - Remote Monitoring & Control Systems
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Business Operation
- Quality Assurance
Use Cases
- Clinical Image Analysis
- Predictive Maintenance
- Remote Patient Monitoring
Services
- Software Design & Engineering Services
- System Integration
- Training
The Challenge
In connection with an efficient and highly successful patient treatment process, the GUH performed a clinical research study. One aspect of the research was clinical pharmacology (pharmacogenetics, pharmacokinetics, pharmacodynamics) at intensive care units, whose practical use for patients entails drug dosages (doses plus intervals) individualized and optimized for specific patients. One such example would be a child with a specific pathological condition requiring an individualized treatment mode, e.g., whole-body hypothermic treatment, or extracorporeal membrane oxygenation (ECMO), or haemodiafiltration (HDF). As a practice, this facility performs individual pharmacokinetics/pharmacodynamics (PK/PD) and therapeutic drug monitoring (TDM) for patients in all cases of treatment where the effect of a drug depends on dose, plasmatic concentration, and therapeutic range. Processing of primary data and assessment of a given drug’s effect and movement within the organism for a larger group of patients—e.g., in a population of critically ill newborns and children—require not only multidisciplinary cooperation, but also suitable software. Aside from this purpose, this software is also used for post-graduate level instruction, lectures, and high-quality professional publications. In the past, the GUH addressed these needs via outsourcing, which was shown to be inefficient and unsuitable as it involved working with suppliers who do not understand this field completely.
About The Customer
The General University Hospital (GUH) in Prague is one of the largest healthcare facilities in the Czech Republic. It provides basic, specialized, and highly specialized medical treatment as well as nursing, outpatient, and diagnostic care for children and adults in all primary areas. It also provides comprehensive pharmaceutical care, including technically demanding preparation of cytostatic and sterile medical products. The hospital is known for its efficient and highly successful patient treatment processes, and it engages in clinical research studies to further enhance its medical services. The GUH is also involved in post-graduate level instruction, lectures, and professional publications, making it a significant contributor to the academic sector in healthcare.
The Solution
The GUH decided to find and implement software that can primarily help modify drug dosage, to put it simply. The goal is for patients to receive drug doses that correspond to their current ages, clinical conditions, and other factors such as genetic makeup. This requires sophisticated software that can model these variables and “recommend” drug dosage. All defined needs are met by STATISTICA Standard Cz from StatSoft CR, a regional StatSoft office in the Czech Republic. The GUH acquired STATISTICA in 2012. The software was installed quite easily during the course of one day by the hospital’s IT staff. Aside from minimum support requirements during installation, the StatSoft team also provided standard training as needed. Cooperation with the supplier was exemplary during both pre-implementation and implementation phases. The supplier’s representatives continue to be excellent partners in the area of client support and training activities, such as providing seminars in the area of statistics.
Operational Impact
Quantitative Benefit
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.