MANAGEMENT OF MEDICAL TECHNOLOGY: AN ENIGMA.
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ABSTRACT:
SUBMITTER: Matwankar S
PROVIDER: S-EPMC5530390 | biostudies-other | 1996 Jul
REPOSITORIES: biostudies-other
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Project description:New medical technology is expensive and we are introducing it in services in an unplanned manner. A significant proportion of our resources are wasted through inefficiency in the selection and production of health care strategies. We should choose new technologies which are 'need based' and 'cost effective'.
| S-EPMC5530383 | biostudies-other
Project description:IntroductionThis article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. "Smart Medical Information Technology for Healthcare (SMITH)" is one of four consortia funded by the German Medical Informatics Initiative (MI-I) to create an alliance of universities, university hospitals, research institutions and IT companies. SMITH's goals are to establish Data Integration Centers (DICs) at each SMITH partner hospital and to implement use cases which demonstrate the usefulness of the approach.ObjectivesTo give insight into architectural design issues underlying SMITH data integration and to introduce the use cases to be implemented.Governance and policiesSMITH implements a federated approach as well for its governance structure as for its information system architecture. SMITH has designed a generic concept for its data integration centers. They share identical services and functionalities to take best advantage of the interoperability architectures and of the data use and access process planned. The DICs provide access to the local hospitals' Electronic Medical Records (EMR). This is based on data trustee and privacy management services. DIC staff will curate and amend EMR data in the Health Data Storage.Methodology and architectural frameworkTo share medical and research data, SMITH's information system is based on communication and storage standards. We use the Reference Model of the Open Archival Information System and will consistently implement profiles of Integrating the Health Care Enterprise (IHE) and Health Level Seven (HL7) standards. Standard terminologies will be applied. The SMITH Market Place will be used for devising agreements on data access and distribution. 3LGM2 for enterprise architecture modeling supports a consistent development process.The DIC reference architecture determines the services, applications and the standardsbased communication links needed for efficiently supporting the ingesting, data nourishing, trustee, privacy management and data transfer tasks of the SMITH DICs. The reference architecture is adopted at the local sites. Data sharing services and the market place enable interoperability.Use casesThe methodological use case "Phenotype Pipeline" (PheP) constructs algorithms for annotations and analyses of patient-related phenotypes according to classification rules or statistical models based on structured data. Unstructured textual data will be subject to natural language processing to permit integration into the phenotyping algorithms. The clinical use case "Algorithmic Surveillance of ICU Patients" (ASIC) focusses on patients in Intensive Care Units (ICU) with the acute respiratory distress syndrome (ARDS). A model-based decision-support system will give advice for mechanical ventilation. The clinical use case HELP develops a "hospital-wide electronic medical record-based computerized decision support system to improve outcomes of patients with blood-stream infections" (HELP). ASIC and HELP use the PheP. The clinical benefit of the use cases ASIC and HELP will be demonstrated in a change of care clinical trial based on a step wedge design.DiscussionSMITH's strength is the modular, reusable IT architecture based on interoperability standards, the integration of the hospitals' information management departments and the public-private partnership. The project aims at sustainability beyond the first 4-year funding period.
| S-EPMC6193398 | biostudies-literature
Project description: Not available
| S-EPMC10808739 | biostudies-literature
Project description:BackgroundNew medical technologies are used at different rates among whites and blacks. This variation may be partially explained by racial differences in patient innovativeness-the propensity of patients to adopt unfamiliar therapies.ObjectiveTo measure how innovativeness varies among patients and how it may influence patients' attitudes toward new medical technologies.DesignCross-sectional survey.ParticipantsPrimary care patients (n=171-108 blacks, 63 whites) at an urban Veterans Affairs medical center.MeasurementsRespondents answered questions about their general innovativeness and innovativeness regarding medical technology, and they responded to a vignette describing either a hypothetical new prescription drug or implantable device.ResultsThere were no significant racial differences in general innovativeness, but whites had higher medical technology innovativeness (P=.001). Whites were also more likely to accept the new prescription drug (P=.003), but did not differ from blacks in acceptance of the new implantable device. In multivariate analyses, lower medical technology innovativeness scores among blacks were significantly associated with less favorable reactions to both the prescription drug (P<.001) and the medical device (P<.001). In contrast, although whites with lower medical technology innovativeness were similarly less inclined to accept the new implantable device (P=.02), there was no significant association between medical technology innovativeness and positive attitudes to the new prescription drug among whites.ConclusionsBlacks and whites have differing attitudes toward medical innovation. These differences are associated with significant racial differences in response to particular health care technologies. These findings suggest potentially remediable causes for racial differences in the utilization of innovative medical technologies.
| S-EPMC1924614 | biostudies-literature
Project description:BackgroundWeb-based medical service (WBMS), a cooperative relationship between medical service and Internet technology, has been called one of the most innovative services of the 21st century. However, its business promotion and implementation in the medical industry have neither been expected nor executed. Few studies have explored this phenomenon from the viewpoint of inexperienced patients.ObjectiveThe primary goal of this study was to explore whether technology attractiveness, medical creditability, and diversified medical information sources could increase users' behavior intention.MethodsThis study explored the effectiveness of web-based medical service by using three situations to manipulate sources of medical information. A total of 150 questionnaires were collected from people who had never used WBMS before. Hierarchical regression was used to examine the mediation and moderated-mediation effects.ResultsPerceived ease of use (P=.002) and perceived usefulness (P=.001) significantly enhance behavior intentions. Medical credibility is a mediator (P=.03), but the relationship does not significantly differ under diverse manipulative information channels (P=.39).ConclusionsMedical credibility could explain the extra variation between technology attractiveness and behavior intention, but not significant under different moderating effect of medical information sources.
| S-EPMC5559646 | biostudies-literature
Project description:The medical management of bronchiectasis includes confirming the diagnosis of the disease, evaluating the patient for possible underlying etiologies and then properly assessing the patient for appropriate therapies. Patients with bronchiectasis are heterogeneous and a personalized approach to each patient is needed in order to properly formulate an optimal management plan.
| S-EPMC6218363 | biostudies-other
Project description:The development of brain metastases occurs in 10-20% of all patients with cancer. Brain metastases portend poor survival and contribute to increased cancer mortality and morbidity. Despite multimodal treatment options, which include surgery, radiotherapy, and chemotherapy, 5-year survival remains low. Besides, our current treatment modalities can have significant neurological comorbidities, which result in neurocognitive decline and a decrease in a patient's quality of life. However, innovations in technology, improved understanding of tumor biology, and new therapeutic options have led to improved patient care. Novel approaches in radiotherapy are minimizing the neurocognitive decline while providing the same therapeutic benefit. In addition, advances in targeted therapies and immune checkpoint inhibitors are redefining the management of lung and melanoma brain metastases. Similar approaches to brain metastases from other primary tumors promise to lead to new and effective therapies. We are beginning to understand the appropriate combination of these novel approaches with our traditional treatment options. As advances in basic and translational science and innovative technologies enter clinical practice, the prognosis of patients with brain metastases will continue to improve.
| S-EPMC7415253 | biostudies-literature
Project description:This article provides and describes a database containing three different variables for medical technology. To capture the various dimensions of medical technology, the set of variables covers not only drugs and devices but also general advances in medical knowledge. Information was extracted and processed from the Drug Canada Product Database, and the National Institute of Health. Variables are extracted from 1997 to 2017 and they represent global proxies. The provided data is relevant for healthcare research in various fields of study.
| S-EPMC7182663 | biostudies-literature
Project description:Medical image classification is a novel technology that presents a new challenge. It is essential that pathological images are automatically and correctly classified to enable doctors to provide precise treatment. Convolutional neural networks have demonstrated their effectiveness in classifying images in deep learning, which may have dozens or hundreds of layers, to illustrate the relationship between them in terms of their different neural network features. Convolutional layers consisting of small kernels take weights as input and guide them through an activation function as output. The main advantage of using convolutional neural networks (CNNs) instead of traditional neural networks is that they reduce the model parameters for greater accuracy. However, many studies have simply been focused on finding the best CNN model and classification results from a single medical image classification. Therefore, we applied a common deep learning network model in an attempt to identify the best model framework by training and validating different model parameters to classify medical images. After conducting experiments on six publicly available databases of pathological images, including colorectal cancer tissue, chest X-rays, common skin lesions, diabetic retinopathy, pediatric chest X-ray, and breast ultrasound image datasets, we were able to confirm that the recognition accuracy of the Inception V3 method was significantly better than that of other existing deep learning models.
| S-EPMC9600639 | biostudies-literature