[Neuropsychiatric disorders and technological solutions]. / Troubles neuropsychiatriques et solutions technologiques.

Innovative new tools today allow better clinical evaluation. Indeed, new information and communication technology is particularly interesting for the screening, monitoring and management of neuropsychiatric disorders of the elderly. A personalised approach to patients can further enhance their adherence and involvement.

Piloting electronic self report symptom assessment - Cancer (ESRA-C) in Hong Kong: a mixed method approach.

PURPOSE: The benefits of utilizing computerized assessment in clinical settings have been reported in studies over the last three decades and more recently in the oncology settings. This study aimed to assess the feasibility and acceptability of using an electronic self report symptom assessment tool among Chinese oncology patients. METHODS AND SAMPLE: The tool, developed by The University of Washington, Distributed Health Assessment and Intervention Research (DHAIR) group was translated into Chinese. The adapted web-based survey platform, the Electronic Self Report Assessment - Cancer (ESRA-C), was tested in a local cancer resource center in Hong Kong. Participants' perceptions of the acceptability and feasibility of the symptom assessment process were assessed using the Acceptability E-scale as well as observation and qualitative interview data. Demographic data were also collected through the touch screen computer system. A convenience sample of 30 (11 male and 19 female) oncology patients was recruited to use the touch-screen computer to assess participants' symptoms using ESRA-C. RESULTS: The acceptability scale indicated moderately high acceptability in each subscale (mean score of 3.32-4.71). On average, participants took 17.5 min (SD: 8.9) to complete the ESRA-C. The qualitative interview data revealed that the majority (25 participants) asserted that the ESRA-C was useful and effective in reporting personal health conditions. CONCLUSIONS: The study suggested that electronic assessment can offer a feasible, attractive, and viable means of implementing regular and comprehensive symptom assessment, which can lead to better symptom management in cancer patients.

Factors influencing the implementation of a point-of-care screening tool for delirium.

Given the ample evidence delirium in hospitalized older adults is underdiagnosed and not recognized by nurses up to 85% of the time, interventions are needed at the point of care to assist nurses in recognizing delirium. In this qualitative study, factors that might influence the implementation of a point-of-care screening tool for delirium were examined.

ST analysis as an adjunct to electronic fetal monitoring: an overview.

Conventional electronic fetal monitoring in the United States has value as a screening tool but is extremely limited as a diagnostic tool. ST analysis was developed as an adjunctive technology, able to measure changes in the ST segment of the fetal electrocardiogram during periods of hypoxia, improving the identification of the fetus at risk for metabolic acidemia at birth. Currently used only in a handful of hospitals in the United States, studies abroad have demonstrated that an integrated approach utilizing electronic fetal monitoring, ST analysis, and standardized guidelines in a selected patient population can improve neonatal outcome, decrease acidemia at birth, and decrease obstetric operative delivery. Research is needed to determine whether similar results are possible in the US population.

Mental health diagnosis by nurses using the Global Mental Health Assessment Tool: a validity and feasibility study.

BACKGROUND: The Global Mental Health Assessment Tool-Primary Care Version (GMHAT/PC) has been developed to assist health professionals to make a quick and comprehensive standardised mental health assessment. It has proved to be a reliable and valid tool in a previous study involving GPs. Its use by other health professionals may help in detecting and managing mental disorders in primary care and general health settings. AIM: To assess the feasibility of using a computer-assisted diagnostic interview by nurses and to examine the level of agreement between the GMHAT/PC diagnosis and psychiatrists' clinical diagnosis. DESIGN OF STUDY: Cross-sectional validation study. SETTING: Primary care, general healthcare (cardiac rehabilitation clinic), and community mental healthcare settings. METHOD: A total of 215 patients between the ages of 16 and 75 years were assessed by nurses and psychiatrists in various settings: primary care centre (n = 54), cardiac rehabilitation centre (n = 98), and community mental health clinic (n = 63). The time taken for the interview, and feedback from patients and interviewers were indicators of feasibility, and the kappa coefficient (kappa), sensitivity, and specificity of the GMHAT/PC diagnosis were measures of validity. RESULTS: Mean duration of interview was under 15 minutes. The agreement between nurses' GMHAT/PC interview-based diagnosis and psychiatrists' International Classification of Diseases (ICD)-10 criteria-based clinical diagnosis was 80% (kappa = 0.76, sensitivity = 0.84, specificity = 0.92). CONCLUSION: The GMHAT/PC can assist nurses to make accurate mental health assessment and diagnosis in various healthcare settings and it is acceptable to patients.

Using a genetic algorithm to predict evaluation of acute coronary syndromes.

BACKGROUND: Because rapid therapy can improve the unfavorable prognosis of individuals with acute coronary syndromes (ACSs), it is critical that nurses accurately associate the cues of ACS and quickly and aggressively initiate interventional strategies that reduce mortality. OBJECTIVES: To determine if genetic algorithms (GAs) can be used to decipher the prediction rules that emergency department (ED) nurses use to triage persons suspected of having ACS and to determine whether these rules differ based on patient gender. METHODS: A nonexperimental, descriptive study was conducted. Three thousand ED nurses were selected randomly to receive a mailed clinical vignette questionnaire, and 840 questionnaires were returned. Data analysis included binary logistic regression (BLR), development of GA, sensitivity, specificity, receiver-operator characteristic curves, and Monte Carlo simulations. RESULTS: Nurses use different prediction rules for triaging male and female vignette patients with possible ACS. Accuracies were similar between BLR and GA. Both suffered a loss of predictive accuracy when algorithms or equations developed on one sex were tested on the other sex. Monte Carlo simulations showed that similar cues were used in triaging both men and women but they were combined differently in producing GA. DISCUSSION: Using GA was as accurate as results found by BLR and can be used to predict nurses triage decisions for ACS. The GA presented as flow charts may be user-friendly.

The contribution of nursing data to the development of a predictive model for the detection of acute pancreatitis.

The increasing use of information system has resulted in the accumulation of a large volume of nursing data in electronic medical records. These data have great potential for supporting the various clinical decisions made by physicians, nurses, and managers. However, how to re-use of nursing data remains largely an issue of informatics. The aim of this study was to demonstrate how these nursing data can be used and how much they could contribute to developing a predictive model for an expert system for early detection of acute pancreatitis. We employed a probability-based model consisting of a Bayesian network and trained this model with the patient data retrospectively retrieved from the enterprise data warehouse of a tertiary hospital. The performance of the predictive model was measured based on the error rate and the area under receiver operating characteristics curve, which were 13.89 % and 0.93, respectively. The sensitivity of the acute pancreatitis to the findings from each nursing data was measured using a test of sensitivity. The results showed that the role of nursing data is as important as laboratory data in formulating a model for an expert system.

Walking through gait analysis.

As medical technology advances, the assessment of complex tasks such as walking is becoming easier. Today's clinical gait analysis laboratory provides the essential tools for the quantification of pathologic gait patterns. Dynamic joint motion, muscle activity, and the forces acting on the body are documented via complex computer and camera systems. The information gained from the gait analysis is essential to the understanding of each individual's unique gait pattern. Gait analysis provides both the patient and the physician with valuable information that can be used to plan better treatment regimens.