Studies in health technology and informatics
-
Stud Health Technol Inform · Jan 2004
Randomized Controlled Trial Clinical TrialA new model for home care for COPD.
A new model for home care of COPD patients is investigated, as a part of a coordinated provision model across levels of care. In the Spanish pilot of the e-Vital project, relevant vital signs for COPD are closely monitored and used for early detection of deterioration in the state of the patient and all prompt treatment. This can also reduce the need for in-person check-ups and re-admission to hospital. ⋯ Results so far are encouraging. In the previous phase, a similar set-up without monitoring facilities at the patient's home showed improvements in several clinical indicators (ER visits, SGRQ, Quality of life, LOS and costs) for a home hospitalisation program and in a prevention of exacerbation program. The current set-up aims at increasing such benefits and further extending the target population.
-
Stud Health Technol Inform · Jan 2004
ReviewOptimizing workflow and knowledge in healthcare through innovation.
People's desire is to stay healthy during the entire course of their live. Innovations in medicine in care and technology have always contributed significantly to meet this desire as close as possible. Today, healthcare systems are faced with huge additional challenges. ⋯ But is this debate target-oriented and does it support the struggle for further enhancing the quality of care? The implementation of IT assisted workflow and knowledge supporting tools throughout the entire healthcare process--prevention to cure--leads to care which would be much more focused on people's needs and efficiency. The information gained from monitoring and wearable devices has to be included to these tools for delivering comprehensive patient information to the point of care. Then the puzzle of the different components in healthcare linked by IT will be complete, and the care process could be continuously optimized in an efficient way.
-
Stud Health Technol Inform · Jan 2004
Predicting survival causes after out of hospital cardiac arrest using data mining method.
The prognosis of life for patients with heart failure remains poor. By using data mining methods, the purpose of this study was to evaluate the most important criteria for predicting patient survival and to profile patients to estimate their survival chances together with the most appropriate technique for health care. ⋯ Data mining methods could help clinicians to predict the survival of patients and then adapt their practices accordingly. This work could be carried out for each medical procedure or medical problem and it would become possible to build a decision tree rapidly with the data of a service or a physician. The comparison between classic analysis and data mining analysis showed us the contribution of the data mining method for sorting variables and quickly conclude on the importance or the impact of the data and variables on the criterion of the study. The main limit of the method is knowledge acquisition and the necessity to gather sufficient data to produce a relevant model.
-
While guideline-based decision support is safety-critical and typically requires human interaction, offline analysis of guideline compliance can be performed to large extent automatically. We examine the possibility of automatic detection of potential non-compliance followed up with (statistical) association mining. Only frequent associations of non-compliance patterns with various patient data are submitted to medical expert for interpretation. The initial experiment was carried out in the domain of hypertension management.
-
The MET (Mobile Emergency Triage) system is an m-health application that supports emergency triage of various types of acute pain at the point of care. The system is designed for use in the Emergency Department (ED) of a hospital and to aid physicians in disposition decisions. Given patient's condition, MET recommends a triage by consulting decision rules stored in the system's knowledge base. ⋯ The system facilitates patient-centered service and timely, high quality patient management. It provides recommendations using a limited amount of clinical data, normally available at the point of care. Furthermore, it provides a possibility for the structured evaluation of this data by an attending physician.