This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ http://www.biomedcentral.com/bmcmedinformdecismak/ The latest articles from BMC Medical Informatics and Decision Making (ISSN 1472-6947) published by BioMed Central Background: Access to personal health information through the electronic health record (EHR) is an innovative means to enable people to be active participants in their own health care. Currently this is not an available option for consumers of health. The absence of a key technology, the EHR, is a significant obstacle to providing patient accessible electronic records. To assess the readiness for the implementation and adoption of EHRs in Canada, a national scan was conducted to determine organizational readiness and willingness for patient accessible electronic records. Methods: A survey was conducted of Chief Executive Officers (CEOs) of Canadian public and acute care hospitals. Results: Two hundred thirteen emails were sent to CEOs of Canadian general and acute care hospitals, with a 39% response rate. Over half (54.2%) of hospitals had some sort of institutionally funded EHR, but few had a record that was predominately electronic. Financial resources were identified as the most important barrier to providing patients access to their EHR and there was a divergence in perceptions from healthcare providers and what they thought patients would want in terms of access to the EHR, with providers being less willing to provide access and patients desire for greater access to the full record. Conclusions: As the use of EHRs becomes more commonplace, organizations should explore the possibility of responding to patient needs for clinical information by providing access to their EHR. The best way to achieve this is still being debated. http://www.biomedcentral.com/1472-6947/8/33 Sara Urowitz, David Wiljer, Emma Apatu, Gunther Eysenbach, Claudette DeLenardo, Tamara Harth, Howard Pai and Kevin J Leonard BMC Medical Informatics and Decision Making 2008, 8:33 2008-07-24 doi:10.1186/1472-6947-8-33 BMC Medical Informatics and Decision Making 1472-6947 8 33 2008-07-24 Background: Text-based patient medical records are a vital resource in medical research. In order to preserve patient confidentiality, however, the U.S. Health Insurance Portability and Accountability Act (HIPAA) requires that protected health information (PHI) be removed from medical records before they can be disseminated. Manual de-identification of large medical record databases is prohibitively expensive, time-consuming and prone to error, necessitating automatic methods for large-scale, automated de-identification. Methods: We describe an automated Perl-based de-identification software package that is generally usable on most free-text medical records, e.g., nursing notes, discharge summaries, X-ray reports, etc. The software uses lexical look-up tables, regular expressions, and simple heuristics to locate both HIPAA PHI, and an extended PHI set that includes doctors' names and years of dates. To develop the de-identification approach, we assembled a gold standard corpus of re-identified nursing notes with real PHI replaced by realistic surrogate information. This corpus consists of 2,434 nursing notes containing 334,000 words and a total of 1,779 instances of PHI taken from 163 randomly selected patient records. This gold standard corpus was used to refine the algorithm and measure its sensitivity. To test the algorithm on data not used in its development, we constructed a second test corpus of 1,836 nursing notes containing 296,400 words. The algorithm's false negative rate was evaluated using this test corpus. Results: Performance evaluation of the de-identification software on the development corpus yielded an overall recall of 0.967, precision value of 0.749, and fallout value of approximately 0.002. On the test corpus, a total of 90 instances of false negatives were found, or 27 per 100,000 word count, with an estimated recall of 0.943. Only one full date and one age over 89 were missed. No patient names were missed in either corpus. Conclusions: We have developed a pattern-matching de-identification system based on dictionary look-ups, regular expressions, and heuristics. Evaluation based on two different sets of nursing notes collected from a U.S. hospital suggests that, in terms of recall, the software out-performs a single human de-identifier (0.81) and performs at least as well as a consensus of two human de-identifiers (0.94). The system is currently tuned to de-identify PHI in nursing notes and discharge summaries but is sufficiently generalized and can be customized to handle text files of any format. Although the accuracy of the algorithm is high, it is probably insufficient to be used to publicly disseminate medical data. The open-source de-identification software and the gold standard re-identified corpus of medical records have therefore been made available to researchers via the PhysioNet website to encourage improvements in the algorithm. http://www.biomedcentral.com/1472-6947/8/32 Ishna Neamatullah, Margaret M. Douglass, Li-wei H. Lehman, Andrew Reisner, Mauricio Villarroel, William J. Long, Peter Szolovits, George B. Moody, Roger G. Mark and Gari D. Clifford BMC Medical Informatics and Decision Making 2008, 8:32 2008-07-24 doi:10.1186/1472-6947-8-32 BMC Medical Informatics and Decision Making 1472-6947 8 32 2008-07-24 Background: Despite increasing recognition of the importance of involving patients in decisions on preventive healthcare interventions, little is known about how well patients understand and utilise information provided on the relative benefits from these interventions. The aim of this study was to explore whether lay people can discriminate between preventive interventions when effectiveness is presented in terms of relative risk reduction (RRR), and whether such discrimination is influenced by presentation of baseline risk. Methods: The study was a randomised cross-sectional interview survey of a representative sample (n=1,519) of lay people with mean age 59 (range 40-98) years in Denmark. In addition to demographic information, respondents were asked to consider a hypothetical drug treatment to prevent heart attack. Its effectiveness was randomly presented as RRR of 10, 20, 30, 40, 50 or 60 percent, and half of the respondents were presented with the baseline risk of heart attack. The respondents were also asked whether they were suffering from hypercholesterolemia or had experienced a heart attack. Results: In total, 873 (58 %) of the respondents consented to the hypothetical treatment. While 49% accepted the treatment when RRR=10%, the acceptance rate was 58-60% for RRR>10. There was no significant difference in acceptance rates across respondents irrespective of whether they had been presented with information on baseline risk or not. Conclusion: In this study, lay peoples decisions about therapy were only slightly influenced by the magnitude of the effect when it was presented in terms of RRR. The results may indicate that lay people have difficulties in discriminating between levels of effectiveness when they are presented in terms of RRR. http://www.biomedcentral.com/1472-6947/8/31 Lene Sorensen, Dorte Gyrd-Hansen, Ivar S Kristiansen, Jorgen Nexoe and Jesper B Nielsen BMC Medical Informatics and Decision Making 2008, 8:31 2008-07-17 doi:10.1186/1472-6947-8-31 BMC Medical Informatics and Decision Making 1472-6947 8 31 2008-07-17 Background: Strokes are a leading cause of morbidity and the first cause of adult disability in the United States. Currently, no biomarkers are being used clinically to diagnose acute ischemic stroke. A diagnostic test using a blood sample from a patient would potentially be beneficial in treating the disease. Results: A classification approach is described for differentiating between proteomic samples of stroke patients and controls, and a second novel predictive model is developed for predicting the severity of stroke as measured by the NIH Stroke Scale. The models were constructed by applying the Logical Analysis of Data (LAD) methodology to the mass peak profiles of 48 stroke patients and 32 controls. The classification model was shown to have an accuracy of 75% when tested on an independent validation set of 35 stroke patients and 25 controls, while the predictive model exhibited superior performance when compared to alternative algorithms. In spite of their high accuracy, both models are extremely simple and were developed using a common set consisting of only 3 peaks. Conclusions: We have successfully identified 3 biomarkers that can detect ischemic stroke with an accuracy of 75%. The performance of the classification model on the validation set and on cross-validation does not deteriorate significantly when compared to that on the training set, indicating the robustness of the model. As in the case of the LAD classification model, the results of the predictive model validate the function constructed on our support-set for approximating the severity scores of stroke patients. The correlation and root mean absolute error of the LAD predictive model are consistently superior to those of the other algorithms used (Support vector machines, C4.5 decision trees, Logistic regression and Multilayer perceptron). http://www.biomedcentral.com/1472-6947/8/30 Anupama Reddy, Honghui Wang, Hua Yu, Tiberius O Bonates, Vimla Gulabani, Joseph Azok, Gerard Hoehn, Peter L Hammer, Alison E Baird and King C Li BMC Medical Informatics and Decision Making 2008, 8:30 2008-07-10 doi:10.1186/1472-6947-8-30 BMC Medical Informatics and Decision Making 1472-6947 8 30 2008-07-10 Background: A dengue fever outbreak occured in French Guiana in 2006. The objectives were to study the value of a syndromic surveillance system set up within the armed forces, compared to the traditional clinical surveillance system during this outbreak, to highlight issues involved in comparing military and civilian surveillance systems and to discuss the interest of syndromic surveillance for public health response. Methods: Military syndromic surveillance allows the surveillance of suspected dengue fever cases among the 3,000 armed forces personnel. Within the same population, clinical surveillance uses several definition criteria for dengue fever cases, depending on the epidemiological situation. Civilian laboratory surveillance allows the surveillance of biologically confirmed cases, within the 200,000 inhabitants. Results: It was shown that syndromic surveillance detected the dengue fever outbreak several weeks before clinical surveillance, allowing quick and effective enhancement of vector control within the armed forces. Syndromic surveillance was also found to have detected the outbreak before civilian laboratory surveillance. Conclusion: Military syndromic surveillance allowed an early warning for this outbreak to be issued, enabling a quicker public health response by the armed forces. Civilian surveillance system has since introduced syndromic surveillance as part of its surveillance strategy. This should enable quicker public health responses in the future. http://www.biomedcentral.com/1472-6947/8/29 Jean-Baptiste Meynard, Hervé Chaudet, Gaetan Texier, Vanessa Ardillon, Françoise Ravachol, Xavier Deparis, Henry Jefferson, Philippe Dussart, Jacques Morvan and Jean-Paul Boutin BMC Medical Informatics and Decision Making 2008, 8:29 2008-07-02 doi:10.1186/1472-6947-8-29 BMC Medical Informatics and Decision Making 1472-6947 8 29 2008-07-02 Background: Few researchers have examined the perceptions of physicians referring cases for angiography regarding the degree to which collaboration occurs during percutaneous coronary intervention (PCI) decision-making. We sought to determine perceptions of physicians concerning their involvement in PCI decisions in cases they had referred to the cardiac catheterization laboratory at a major academic medical center. Methods: An anonymous survey was mailed to internal medicine faculty members at a major academic medical center. The survey elicited whether responders perceived that they were included in decision-making regarding PCI, and whether they considered such collaboration to be the best process of decision-making. Results: Of the 378 surveys mailed, 35% (133) were returned. Among responding non-cardiologists, 89% indicated that in most cases, PCI decisions were made solely by the interventionalist at the time of the angiogram. Among cardiologists, 92% indicated that they discussed the findings with the interventionalist prior to any PCI decisions. When asked what they considered the best process by which PCI decisions are made, 66% of non-cardiologists answered that they would prefer collaboration between either themselves or a non-interventional cardiologist and the interventionalist. Among cardiologists, 95% agreed that a collaborative approach is best. Conclusion: Both non-cardiologists and cardiologists felt that involving another decision-maker, either the referring physician or a non-interventional cardiologist, would be the best way to make PCI decisions. Among cardiologists, there was more concordance between what they believed was the best process for making decisions regarding PCI and what they perceived to be the actual process. http://www.biomedcentral.com/1472-6947/8/28 Catherine R Rahilly-Tierney and Ira S Nash BMC Medical Informatics and Decision Making 2008, 8:28 2008-06-25 doi:10.1186/1472-6947-8-28 BMC Medical Informatics and Decision Making 1472-6947 8 28 2008-06-25 Background: Treatments for coronary heart disease (CHD) have evolved rapidly over the last 15 years with considerable change in the number and effectiveness of both medical and surgical treatments. This period has seen the rapid development and uptake of statin drugs and coronary artery revascularization procedures (CARPs) that include Coronary Artery Bypass Graft procedures (CABGs) and Percutaneous Coronary Interventions (PCIs). It is difficult in an era of such rapid change to accurately forecast requirements for treatment services such as CARPs. In a previous paper we have described and outlined the use of a Markov Monte Carlo simulation model for analyzing and predicting the requirements for CARPs for the population of Western Australia (Mannan et al, 2007). In this paper, we expand on the use of this model for forecasting CARPs in Western Australia with a focus on the lack of adequate performance of the (standard) model for forecasting CARPs in a period during the mid 1990s when there were considerable changes to CARP technology and implementation policy and an exploration and demonstration of how the standard model may be adapted to achieve better performance. Methods: Selected key CARP event model probabilities are modified based on information relating to changes in the effectiveness of CARPs from clinical trial evidence and an awareness of trends in policy and practice of CARPs. These modified model probabilities and the ones obtained by standard methods are used as inputs in our Markov simulation model. Results: The projected numbers of CARPs in the population of Western Australia over 1995–99 only improve marginally when modifications to model probabilities are made to incorporate an increase in effectiveness of PCI procedures. However, the projected numbers improve substantially when, in addition, further modifications are incorporated that relate to the increased probability of a PCI procedure and the reduced probability of a CABG procedure stemming from changed CARP preference following the introduction of PCI operations involving stents. Conclusion: There is often knowledge and sometimes quantitative evidence of the expected impacts of changes in surgical practice and procedure effectiveness and these may be used to improve forecasts of future requirements for CARPs in a population. http://www.biomedcentral.com/1472-6947/8/27 Haider R Mannan, Matthew Knuiman and Michael Hobbs BMC Medical Informatics and Decision Making 2008, 8:27 2008-06-25 doi:10.1186/1472-6947-8-27 BMC Medical Informatics and Decision Making 1472-6947 8 27 2008-06-25 Background: The evolving concepts of pervasive computing, ubiquitous computing and ambient intelligence are increasingly influencing health care and medicine. Summarizing published research, this literature review provides an overview of recent developments and implementations of pervasive computing systems in health care. It also highlights some of the experiences reported in deployment processes. Methods: There is no clear definition of pervasive computing in the current literature. Thus specific inclusion criteria for selecting articles about relevant systems were developed. Searches were conducted in four scientific databases alongside manual journal searches for the period of 2002 to 2006. Articles included present prototypes, case studies and pilot studies, clinical trials and systems that are already in routine use. Results: The searches identified 69 articles describing 67 different systems. In a quantitative analysis, these systems were categorized into project status, health care settings, user groups, improvement aims, and systems features (i.e., component types, data gathering, data transmission, systems functions). The focus is on the types of systems implemented, their frequency of occurrence and their characteristics. Qualitative analyses were performed of deployment issues, such as organizational and personnel issues, privacy and security issues, and financial issues. This paper provides a comprehensive access to the literature of the emerging field by addressing specific topics of application settings, systems features, and deployment experiences. Conclusion: Both an overview and an analysis of the literature on a broad and heterogeneous range of systems are provided. Most systems are described in their prototype stages. Deployment issues, such as implications on organization or personnel, privacy concerns, or financial issues are mentioned rarely, though their solution is regarded as decisive in transferring promising systems to a stage of regular operation. There is a need for further research on the deployment of pervasive computing systems, including clinical studies, economic and social analyses, user studies, etc. http://www.biomedcentral.com/1472-6947/8/26 Carsten Orwat, Andreas Graefe and Timm Faulwasser BMC Medical Informatics and Decision Making 2008, 8:26 2008-06-19 doi:10.1186/1472-6947-8-26 BMC Medical Informatics and Decision Making 1472-6947 8 26 2008-06-19 Background: There is uncertainty about how GPs should convey information about treatment effectiveness to their patients in the context of cardiovascular disease. Hence we study the concordance of decisions based on one of four single information formats for treatment effectiveness with subsequent decisions based on all four formats combined with a pictorial representation. Methods: A randomized study comprising 1,169 subjects aged 40–59 in Odense, Denmark. Subjects were randomized to receive information in terms of absolute risk reduction (ARR), relative risk reduction (RRR), number needed to treat (NNT), or prolongation of life (POL) without heart attack, and were asked whether they would consent to treatment. Subsequently the same information was conveyed with all four formats jointly accompanied by a pictorial presentation of treatment effectiveness. Again, subjects should consider consent to treatment. Results: After being informed about all four formats, 52%–79% of the respondents consented to treatment, depending on level of effectiveness and initial information format. Overall, ARR gave highest concordance, 94% (95% confidence interval (91%; 97%)) between initial and final decision, but ARR was not statistically superior to the other formats. Conclusion: Decisions based on ARR had the best concordance with decisions based on all four formats and pictorial representation, but the difference in concordance between the four formats was small, and it is unclear whether respondents fully understood the information they received. http://www.biomedcentral.com/1472-6947/8/25 Henrik Stovring, Dorte Gyrd-Hansen, Ivar S Kristiansen, Jorgen Nexoe and Jesper B Nielsen BMC Medical Informatics and Decision Making 2008, 8:25 2008-06-19 doi:10.1186/1472-6947-8-25 BMC Medical Informatics and Decision Making 1472-6947 8 25 2008-06-19 Background: Predicted patient life expectancy (LE) and survival probability (SP), based on a patient's medical history, are important components of surgical decision-making and informed consent. The objective of this study was to assess patients' interpretation of and desire to know information relating to LE, in addition to establishing the most effective format for discussion. Methods: A cross sectional survey of 120 patients (mean age = 68.7 years, range 50–90 years), recruited from general urological and surgical outpatient clinics in one District General and one Teaching hospital in Southwest England (UK) was conducted. Patients were included irrespective of their current diagnosis or associated comorbidity. Hypothetical patient case scenarios were used to assess patients' desire to know LE and SP, in addition to their preferred presentation format. Results: 58% of patients expressed a desire to know their LE and SP, if it were possible to calculate, with 36% not wishing to know either. Patients preferred a combination of numerical and pictorial formats in discussing LE and SP, with numerical, verbal and pictorial formats alone least preferred. 71% patients ranked the survival curve as either their first or second most preferred graph, with 76% rating facial figures their least preferred. No statistically significant difference was noted between sexes or educational backgrounds. Conclusion: A proportion of patients seem unwilling to discuss their LE and SP. This may relate to their current diagnosis, level of associated comorbidity or degree of understanding. However it is feasible that by providing this information in a range of presentation formats, greater engagement in the shared decision-making process can be encouraged. http://www.biomedcentral.com/1472-6947/8/24 Michael G Clarke, Katherine P Kennedy and Ruaraidh P MacDonagh BMC Medical Informatics and Decision Making 2008, 8:24 2008-06-15 doi:10.1186/1472-6947-8-24 BMC Medical Informatics and Decision Making 1472-6947 8 24 2008-06-15