Reorganizing adult critical care delivery: the role of regionalization, telemedicine, and community outreach.
ABSTRACT Variation in the quality of critical care services across hospitals coupled with an emerging workforce crisis necessitates system-level change in the organization of intensive care. In this review, we evaluate three alternative organizational models that may expand access to high-quality critical care: tiered regionalization, intensive care unit telemedicine, and quality improvement through regional outreach. These models share a potential to increase survival and reduce costs. Yet there are also major barriers to implementation, including the lack of a strong evidence base and the need for significant upfront financial investment. Reorganization of intensive care will also require the support of all involved stakeholders: patients and their families, critical care practitioners, administrative and public health professionals, and policy makers. To varying degrees these models require a central authority to implement and regulate the system, as well as specific legislation, investment in information technology, and financial incentives for providers. The existing evidence does not strongly support exclusive use of a particular model, and creation of a hybrid model that integrates the three complementary approaches is a practical option. A potential framework for implementation involves triage guidelines developed by professional societies leading to demonstration projects and national legislation in support of optimal systems. Additional research is needed to determine the comparative effectiveness and cost implications of these approaches, with a goal of best matching high-quality critical care to patients' needs and professional preferences at the hospital, regional, and national level.
- SourceAvailable from: Andrea Gaggioli
[Show abstract] [Hide abstract]
- "In other models dedicated call centers or point of care act as an intermediary between hospital/heath care professional and patients. Many of the solutions available today on the market follow the above-mentioned model and call center services or point of care are used by the patients just as a complement to the hospital-centerd healthcare services    . In the more advanced Personal Health Systems      model focused on the empowerment, the ownership of the care service is fully taken by the individual. "
ABSTRACT: a b s t r a c t Developments in computational techniques including clinical decision support systems, information pro-cessing, wireless communication and data mining hold new premises in Personal Health Systems. Perva-sive Healthcare system architecture finds today an effective application and represents in perspective a real technological breakthrough promoting a paradigm shift from diagnosis and treatment of patients based on symptoms to diagnosis and treatment based on risk assessment. Such architectures must be able to collect and manage a large quantity of data supporting the physicians in their decision process through a continuous pervasive remote monitoring model aimed to enhance the understanding of the dynamic disease evolution and personal risk. In this work an automatic simple, compact, wireless, per-sonalized and cost efficient pervasive architecture for the evaluation of the stress state of individual sub-jects suitable for prolonged stress monitoring during normal activity is described. A novel integrated processing approach based on an autoregressive model, artificial neural networks and fuzzy logic mod-eling allows stress conditions to be automatically identified with a mobile setting analysing features of the electrocardiographic signals and human motion. The performances of the reported architecture were assessed in terms of classification of stress conditions.Computer Communications 06/2012; 35(11):1296-1305. DOI:10.1016/j.comcom.2011.11.015 · 1.35 Impact Factor
[Show abstract] [Hide abstract]
- "The limited and conflicting evidence for patient benefits of Tele-ICU undoubtedly contributes to its limited uptake. Additional identified barriers include depersonalization of patient care given the placement of a layer of technology between the care provider and patient, uncertain ultimate effects on human resources given the need to staff Tele-ICU facilities in addition to the ICUs supported by them, protection of patient confidentiality, implementation and operational costs, and the common needs to develop trust and shared decisionmaking models among multiple care providers . Most US implementations identify organizational and staff resistance as one of the major barriers for wider implementation  . "
ABSTRACT: This study was conducted to assess the preimplementation knowledge and perceptions of intensive care unit (ICU) clinicians regarding the ability of telemedicine in the ICU environment (Tele-ICU) to address challenges resulting from the shortages of experienced critical care human resources and the drive to improve quality of care. An online survey was administered to clinicians from a Canadian multisite critical care department. Qualitative and quantitative analyses were undertaken to identify key positive and negative themes. The overall self-rated knowledge about Tele-ICU was low, with significant uncertainty particularly related to the novelty of the technology, lack of widespread existing implementations, and insufficient education. A significant degree of skepticism was expressed regarding the ability of Tele-ICU to address the challenges of staff shortages and quality of care. Significant uncertainty and skepticism were expressed by critical care clinicians regarding the ability of Tele-ICU to address the challenges of human resource limitation and the delivery of quality care. This suggests the need for further research and education of system impact beyond patient outcomes related to this new technology.Journal of critical care 06/2011; 26(3):328.e9-15. DOI:10.1016/j.jcrc.2010.07.013 · 2.19 Impact Factor
- ". 4. The analysis module and the clinical decision support system for stress monitoring The analysis module is based on Knowledge-Based Models (KBM)     "