Classifying clinical decision making:interpreting nursing intuition, heuristics and medical diagnosis

This is the second of two linked papers exploring decision making in nursing. The first paper, 'Classifying clinical decision making: a unifying approach' investigated difficulties with applying a range of decision-making theories to nursing practice. This is due to the diversity of terminology and theoretical concepts used, which militate against nurses being able to compare the outcomes of decisions analysed within different frameworks. It is therefore problematic for nurses to assess how good their decisions are, and where improvements can be made. However, despite the range of nomenclature, it was argued that there are underlying similarities between all theories of decision processes and that these should be exposed through integration within a single explanatory framework. A proposed solution was to use a general model of psychological classification to clarify and compare terms, concepts and processes identified across the different theories. The unifying framework of classification was described and this paper operationalizes it to demonstrate how different approaches to clinical decision making can be re-interpreted as classification behaviour. Particular attention is focused on classification in nursing, and on re-evaluating heuristic reasoning, which has been particularly prone to theoretical and terminological confusion. Demonstrating similarities in how different disciplines make decisions should promote improved multidisciplinary collaboration and a weakening of clinical elitism, thereby enhancing organizational effectiveness in health care and nurses' professional status. This is particularly important as nurses' roles continue to expand to embrace elements of managerial, medical and therapeutic work. Analysing nurses' decisions as classification behaviour will also enhance clinical effectiveness, and assist in making nurses' expertise more visible. In addition, the classification framework explodes the myth that intuition, traditionally associated with nurses' decision making, is less rational and scientific than other approaches.

Rule induction for data exploration

This paper describes how the statistical technique of cluster analysis and the machine learning technique of rule induction can be combined to explore a database. The ways in which such an approach alleviates the problems associated with other techniques for data analysis are discussed. We report the results of experiments carried out on a database from the medical diagnosis domain. Finally we describe the future developments which we plan to carry out to build on our current work.

EDC-mediated oligonucleotide immobilization on a long period grating optical biosensor

We present the development and simplification of label-free fiber optic biosensors based on immobilization of oligonucleotides on dual-peak long period gratings (dLPGs). This improvement is the result of a simplification of biofunctionalization methodology. A one-step 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC)-mediated reaction has been developed for the straightforward immobilization of unmodified oligonucleotides on the glass fiber surface along the grating region, leading to covalent attachment of a 5´-phosphorylated probe oligonucleotide to the amino-derivatized fiber grating surface. Immobilization is achieved via a 5´phosphate-specific linkage, leaving the remainder of the oligonucleotide accessible for binding reactions. The dLPG has been tested in different external media to demonstrate its inherent ultrahigh sensitivity to the surrounding-medium refractive index (RI) achieving 50- fold improvement in RI sensitivity over the previously-published LPG sensor in media with RI’s relevant to biological assays. After functionalization, the dLPG biosensor was used to monitor the hybridization of complementary oligonucleotides showing a detectable oligonucleotide concentration of 4 nM. The proposed one-step EDC reaction approach can be further extended to develop fiber optic biosensors for disease analysis and medical diagnosis with the advances of label-free, real-time, multiplex, high sensitivity and specificity.

Evaluation of routine microbiological techniques for establishing the diagnosis of catheter-related bloodstream infection caused by coagulase-negative staphylococci

Microbiological diagnosis of catheter-related bloodstream infection (CR-BSI) is often based on isolation of indistinguishable micro-organisms from an explanted catheter tip and blood culture, confirmed by antibiograms. Whether phenotypic identification of coagulase-negative staphylococci (CoNS) allows an accurate diagnosis of CR-BSI to be established was evaluated. Eight patients with a diagnosis of CR-BSI had CoNS isolated from pure blood cultures and explanted catheter tips which were considered as indistinguishable strains by routine microbiological methods. For each patient, an additional three colonies of CoNS isolated from the blood and five from the catheter tip were subcultured and further characterized by antibiogram profiles, analytical profile index (API) biotyping and PFGE. PFGE distinguished more strains of CoNS compared to API biotyping or antibiograms (17, 10 and 11, respectively). By PFGE, indistinguishable micro-organisms were only isolated from pure blood and catheter tip cultures in four out of eight (50%) patients thus supporting the diagnosis of CR-BSI. In another patient, indistinguishable micro-organisms were identified in both cultures; however, other strains of CoNS were also present. The remaining three patients had multiple strains of CoNS, none of which were indistinguishable in the tip and blood cultures, thus questioning the diagnosis of CR-BSI. Phenotypic characterization of CoNS lacked discriminatory power. Current routine methods of characterizing a limited number of pooled colonies may generate misleading results as multiple strains may be present in the cultures. Multiple colonies should be studied using a rapid genotypic characterization method to confirm or refute the diagnosis of CR-BSI. © 2007 SGM.

Use of Google as a tool to aid diagnosis by optometry students

Background: Previous work has shown that medical problems can be diagnosed by practitioners using Google. The aim of this study was to determine whether optometry students would benefit from using Google when diagnosing eye diseases. Methods: Participants were given symptoms and signs and instructed to list three key words and use them to search Aston University e-Library and Google UK. Results: Aston University e-Library only search resulted in correct diagnosis in 16 of 60 simulated cases. Aston e-Library plus Google search resulted in correct diagnosis in 31 of 60 simulated cases. Conclusion: Google is a useful aid to help optometry students improve their success rate when diagnosing eye conditions.

Psychotic symptoms are associated with physical health problems independently of a mental disorder diagnosis:results from the WHO World Health Survey

This study explored whether physical health problems are related to psychotic symptoms independently of a mental disorder diagnosis. A total of 224,254 subjects recruited for the World Health Organization World Health Survey were subdivided into those with both a lifetime diagnosis of psychosis and at least one psychotic symptom in the 12 months prior to the evaluation, those with at least one psychotic symptom in the past 12 months but no lifetime diagnosis of psychosis, and those without psychotic symptoms in the past 12 months and without a lifetime diagnosis of psychosis. The three groups were compared for the presence of medical conditions, health problems, and access to health care. Medical conditions and health problems (angina, asthma, arthritis, tuberculosis, vision or hearing problems, mouth/teeth problems, alcohol consumption, smoking, and accidents), medication consumption, and hospital admissions (but not regular health care visits) were more frequent in individuals with psychotic symptoms but no psychosis diagnosis, compared to those with no symptoms and no diagnosis. The number of medical conditions increased with the number of psychotic symptoms. Given the sample analyzed, this trend seems to be independent from the socio-economic development of the country or the specific health care system.

Regularity analysis of nocturnal oximetry recordings to assist in the diagnosis of sleep apnoea syndrome

The relationship between sleep apnoea–hypopnoea syndrome (SAHS) severity and the regularity of nocturnal oxygen saturation (SaO2) recordings was analysed. Three different methods were proposed to quantify regularity: approximate entropy (AEn), sample entropy (SEn) and kernel entropy (KEn). A total of 240 subjects suspected of suffering from SAHS took part in the study. They were randomly divided into a training set (96 subjects) and a test set (144 subjects) for the adjustment and assessment of the proposed methods, respectively. According to the measurements provided by AEn, SEn and KEn, higher irregularity of oximetry signals is associated with SAHS-positive patients. Receiver operating characteristic (ROC) and Pearson correlation analyses showed that KEn was the most reliable predictor of SAHS. It provided an area under the ROC curve of 0.91 in two-class classification of subjects as SAHS-negative or SAHS-positive. Moreover, KEn measurements from oximetry data exhibited a linear dependence on the apnoea–hypopnoea index, as shown by a correlation coefficient of 0.87. Therefore, these measurements could be used for the development of simplified diagnostic techniques in order to reduce the demand for polysomnographies. Furthermore, KEn represents a convincing alternative to AEn and SEn for the diagnostic analysis of noisy biomedical signals.