Virtual Staff - Software Tool for Cooperative Work in a Health Care Network

In the context of knowledge management for a healthcare network, we have developed a software tool called Virtual Staff enabling a cooperative diagnosis by network actors, visualization of their collective reasoning and preparation of repository of patient records which will be reused for decision support for new patient cases. This virtual staff relies on a medical ontology and enables users to cooperative building of graphs based on the SOAP (Subjective, Objective, Assessment, Plan) model used in medical community and on the QOC (Questions, Option, Criteria) model used by CSCW community for support to design rationale and decision-making support

A telemedicine distributed system for cooperative medical diagnosis

Procceedings of: Eighteenth Annual Symposium on Computer Aplications in Medical Care, november 5-9, 1994, Washington, USA. Edited by Judy G. OzboltTelemedicine is changing the classicalform of health care delivery, dramatically increasing the number of new applications in which some type of distributed synchronous cooperation between health care professionals is required. This paper presents the design and development ofa telemedicine distributed system for cooperative medical diagnosis based on two new approaches: 1) a distributed layered architecture specially designed to add synchronous computer supported cooperative workfeatures either to new or existing medical applications; 2) the definition of a methodological procedure to design graphical user interfaces for telemedicine cooperative working scenarios. The cooperative work is supported by a collaborative toolkit that provides telepointing, window sharing, coordination and synchronization. Finally, we have implemented and installed the telemedicine system in clinical practice between two hospitals, providing teleconferencing facilities for cooperative decision support in haemodynamics studies. This specific implementation and a preliminary evaluation were accomplished under the Research Project FEST "Framework for European Services in Telemedicine" funded by the EU AIM Programme.This work is supported in part by EU - AIM FEST project no. A-201 1, and by grants CICYT TEMA TIC 92-1288-PB and TELEMEDICINA TIC 93-1279-E.Publicad

Characteristics and outcome of patients with newly diagnosed advanced or metastatic lung cancer admitted to intensive care units (ICUs)

BACKGROUND: Although patients with advanced or metastatic lung cancer have poor prognosis, admission to the ICU for management of life-threatening complications has increased over the years. Patients with newly diagnosed lung cancer appear as good candidates for ICU admission, but more robust information to assist decisions is lacking. The aim of our study was to evaluate the prognosis of newly diagnosed unresectable lung cancer patients. METHODS: A retrospective multicentric study analyzed the outcome of patients admitted to the ICU with a newly diagnosed lung cancer (diagnosis within the month) between 2010 and 2013. RESULTS: Out of the 100 patients, 30 had small cell lung cancer (SCLC) and 70 had non-small cell lung cancer. (Thirty patients had already been treated with oncologic treatments.) Mechanical ventilation (MV) was performed for 81 patients. Seventeen patients received emergency chemotherapy during their ICU stay. ICU, hospital, 3- and 6-month mortality were, respectively, 47, 60, 67 and 71%. Hospital mortality was 60% when invasive MV was used alone, 71% when MV and vasopressors were needed and 83% when MV, vasopressors and hemodialysis were required. In multivariate analysis, hospital mortality was associated with metastatic disease (OR 4.22 [1.4-12.4]; p = 0.008), need for invasive MV (OR 4.20 [1.11-16.2]; p = 0.030), while chemotherapy in ICU was associated with survival (OR 0.23, [0.07-0.81]; p = 0.020). CONCLUSION: This study shows that ICU management can be appropriate for selected newly diagnosed patients with advanced lung cancer, and chemotherapy might improve outcome for patients with SCLC admitted for cancer-related complications. Nevertheless, tumors' characteristics, numbers and types of organ dysfunction should be taken into account in the decisional process before admitting these patients in ICU.Peer reviewe

Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients: A Machine Learning Approach

OBJECTIVE: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) risk predictors for cancer outpatients that might be of advantage over the currently recommended model (Khorana score). DESIGN: Multiple kernel learning (MKL) based on support vector machines and random optimization (RO) models were used to produce VTE risk predictors (referred to as machine learning [ML]-RO) yielding the best classification performance over a training (3-fold cross-validation) and testing set. RESULTS: Attributes of the patient data set ( n = 1179) were clustered into 9 groups according to clinical significance. Our analysis produced 6 ML-RO models in the training set, which yielded better likelihood ratios (LRs) than baseline models. Of interest, the most significant LRs were observed in 2 ML-RO approaches not including the Khorana score (ML-RO-2: positive likelihood ratio [+LR] = 1.68, negative likelihood ratio [-LR] = 0.24; ML-RO-3: +LR = 1.64, -LR = 0.37). The enhanced performance of ML-RO approaches over the Khorana score was further confirmed by the analysis of the areas under the Precision-Recall curve (AUCPR), and the approaches were superior in the ML-RO approaches (best performances: ML-RO-2: AUCPR = 0.212; ML-RO-3-K: AUCPR = 0.146) compared with the Khorana score (AUCPR = 0.096). Of interest, the best-fitting model was ML-RO-2, in which blood lipids and body mass index/performance status retained the strongest weights, with a weaker association with tumor site/stage and drugs. CONCLUSIONS: Although the monocentric validation of the presented predictors might represent a limitation, these results demonstrate that a model based on MKL and RO may represent a novel methodological approach to derive VTE risk classifiers. Moreover, this study highlights the advantages of optimizing the relative importance of groups of clinical attributes in the selection of VTE risk predictors

Disease surveillance and patient care in remote regions: an exploratory study of collaboration among healthcare professionals in Amazonia

The development and deployment of information technology, particularly mobile tools, to support collaboration between different groups of healthcare professionals has been viewed as a promising way to improve disease surveillance and patient care in remote regions. The effects of global climate change combined with rapid changes to land cover and use in Amazonia are believed to be contributing to the spread of vector-borne emerging and neglected diseases. This makes empowering and providing support for local healthcare providers all the more important. We investigate the use of information technology in this context to support professionals whose activities range from diagnosing diseases and monitoring their spread to developing policies to deal with outbreaks. An analysis of stakeholders, their roles and requirements, is presented which encompasses results of fieldwork and of a process of design and prototyping complemented by questionnaires and targeted interviews. Findings are analysed with respect to the tasks of diagnosis, training of local healthcare professionals, and gathering, sharing and visualisation of data for purposes of epidemiological research and disease surveillance. Methodological issues regarding the elicitation of cooperation and collaboration requirements are discussed and implications are drawn with respect to the use of technology in tackling emerging and neglected diseases

Medical social work in the clinic and ward settings of a general hospital : a study of the medical social problems and the related social services in twenty-five medical clinic cases as compared with twenty-five medical ward cases at the Beth Israel Hospital /

Thesis (M.S.)--Boston University Includes bibliographical references (leaves 98-99)

AAPT Diagnostic Criteria for Chronic Sickle Cell Disease Pain

Pain in sickle cell disease (SCD) is associated with increased morbidity, mortality, and high health care costs. Although episodic acute pain is the hallmark of this disorder, there is an increasing awareness that chronic pain is part of the pain experience of many older adolescents and adults. A common set of criteria for classifying chronic pain associated with SCD would enhance SCD pain research efforts in epidemiology, pain mechanisms, and clinical trials of pain management interventions, and ultimately improve clinical assessment and management. As part of the collaborative effort between the Analgesic, Anesthetic, and Addiction Clinical Trial Translations Innovations Opportunities and Networks public-private partnership with the U.S. Food and Drug Administration and the American Pain Society, the Analgesic, Anesthetic, and Addiction Clinical Trial Translations Innovations Opportunities and Networks-American Pain Society Pain Taxonomy initiative developed the outline of an optimal diagnostic system for chronic pain conditions. Subsequently, a working group of experts in SCD pain was convened to generate core diagnostic criteria for chronic pain associated with SCD. The working group synthesized available literature to provide evidence for the dimensions of this disease-specific pain taxonomy. A single pain condition labeled chronic SCD pain was derived with 3 modifiers reflecting different clinical features. Future systematic research is needed to evaluate the feasibility, validity, and reliability of these criteria. Perspective: An evidence-based classification system for chronic SCD pain was constructed for the Analgesic, Anesthetic, and Addiction Clinical Trial Translations Innovations Opportunities and Networks-American Pain Society Pain Taxonomy initiative. Applying this taxonomy may improve assessment and management of SCD pain and accelerate research on epidemiology, mechanisms, and treatments for chronic SCD pain