Studies in health technology and informatics
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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.
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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.
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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.
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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.
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Stud Health Technol Inform · Jan 2004
Distributed interactive virtual environments for collaborative experiential learning and training independent of distance over Internet2.
Medical knowledge and skills essential for tomorrow's healthcare professionals continue to change faster than ever before creating new demands in medical education. Project TOUCH (Telehealth Outreach for Unified Community Health) has been developing methods to enhance learning by coupling innovations in medical education with advanced technology in high performance computing and next generation Internet2 embedded in virtual reality environments (VRE), artificial intelligence and experiential active learning. Simulations have been used in education and training to allow learners to make mistakes safely in lieu of real-life situations, learn from those mistakes and ultimately improve performance by subsequent avoidance of those mistakes. ⋯ The ability to make mistakes in a safe environment is well received by students and has a positive impact on their understanding, as well as memory of the principles involved in correcting those mistakes. Bringing people together as virtual teams for interactive experiential learning and collaborative training, independent of distance, provides a platform for distributed "just-in-time" training, performance assessment and credentialing. Further validation is necessary to determine the potential value of the distributed VRE in knowledge transfer, improved future performance and should entail training participants to competence in using these tools.