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Publications for CRE in Informatics and E-health: APP1032664

Gurjav, P. Jelfs, G. A. Hill-Cawthorne, B. J. Marais and V. Sintchenko. (2015). Genotype heterogeneity of Mycobacterium tuberculosis within geospatial hotspots suggests foci of imported infection in Sydney, Australia. Infect Genet Evol.

Abstract:

In recent years the State of New South Wales (NSW), Australia, has maintained a low tuberculosis incidence rate with little evidence of local transmission. Nearly 90% of notified tuberculosis cases occurred in people born in tuberculosis-endemic countries. We analyzed geographic, epidemiological and genotypic data of all culture-confirmed tuberculosis cases to identify the bacterial and demographic determinants of tuberculosis hotspot areas in NSW. Standard 24-loci mycobacterium interspersed repetitive unit-variable number tandem repeat (MIRU-24) typing was performed on all isolates recovered between 2009 and 2013. In total 1692/1841 (91.9%) cases with confirmed Mycobacterium tuberculosis infection had complete MIRU-24 and demographic data and were included in the study. Despite some year-to-year variability, spatio-temporal analysis identified four tuberculosis hotspots. The incidence rate and the relative risk of tuberculosis in these hotspots were 2- to 10-fold and 4- to 8-fold higher than the state average, respectively. MIRU-24 profiles of M. tuberculosis isolates associated with these hotspots revealed high levels of heterogeneity. This suggests that these spatio-temporal hotspots, within this low incidence setting, can represent areas of predominantly imported infection rather than clusters of cases due to local transmission. These findings provide important epidemiological insight and demonstrate the value of combining tuberculosis genotyping and spatiotemporal data to guide better-targeted public health interventions.

Fu, S. Octavia, M. M. Tanaka, V. Sintchenko and R. Lan. (2015). Defining the Core Genome of Salmonella enterica Serovar Typhimurium for Genomic Surveillance and Epidemiological Typing. J Clin Microbiol (Vol. 53, pp. 2530-8).

Abstract:

Salmonella enterica serovar Typhimurium is the most common Salmonella serovar causing foodborne infections in Australia and many other countries. Twenty-one S. Typhimurium strains from Salmonella reference collection A (SARA) were analyzed using Illumina high-throughput genome sequencing. Single nucleotide polymorphisms (SNPs) in 21 SARA strains ranged from 46 to 11,916 SNPs, with an average of 1,577 SNPs per strain. Together with 47 strains selected from publicly available S. Typhimurium genomes, the S. Typhimurium core genes (STCG) were determined. The STCG consist of 3,846 genes, a set that is much larger than that of the 2,882 Salmonella core genes (SCG) found previously. The STCG together with 1,576 core intergenic regions (IGRs) were defined as the S. Typhimurium core genome. Using 93 S. Typhimurium genomes from 13 epidemiologically confirmed community outbreaks, we demonstrated that typing based on the S. Typhimurium core genome (STCG plus core IGRs) provides superior resolution and higher discriminatory power than that based on SCG for outbreak investigation and molecular epidemiology of S. Typhimurium. STCG and STCG plus core IGR typing achieved 100% separation of all outbreaks compared to that of SCG typing, which failed to separate isolates from two outbreaks from background isolates. Defining the S. Typhimurium core genome allows standardization of genes/regions to be used for high-resolution epidemiological typing and genomic surveillance of S. Typhimurium.

Gurjav, B. Burneebaatar, E. Narmandakh, O. Tumenbayar, B. Ochirbat, G. A. Hill-Cawthorne, B. J. Marais and V. Sintchenko. (2015). Spatiotemporal evidence for cross-border spread of MDR-TB along the Trans-Siberian Railway line. Int J Tuberc Lung Dis (Vol. 19, pp. 1376-82).

Abstract:

BACKGROUND: Mongolia has the fifth highest incidence of tuberculosis (TB) in the Western Pacific Region, with high rates of multidrug-resistant TB (MDR-TB). OBJECTIVE: To examine the recent spatiotemporal dynamics of MDR-TB in Mongolia. METHODS: All MDR-TB cases diagnosed from 2004 to 2012, identified from the National Tuberculosis Control Programme database, were included in the study. Cases diagnosed from 2006 to 2012 were further examined using spatial scan statistics. RESULTS: Few MDR-TB cases (n = 29) were diagnosed before the programmatic management of MDR-TB was introduced in 2006. During 2006-2012, 1106 MDR-TB cases were detected, at an annualised rate of 5.9 cases per 100 000 population. Most (>80%) cases were identified in the 15-44 year age group; 45% were among those aged 15-29 years. Case notification rates were highest in the capital city, Ulaanbaatar, with an increasing trend over time in all locations. Three MDR-TB hotspots were identified, all in close proximity to the Trans-Siberian Railway line. The majority of the MDR-TB isolates were resistant to all first-line drugs tested. CONCLUSION: Spatiotemporal analysis indicates likely cross-border spread of MDR-TB along the Trans-Siberian Railway line, with subsequent spatial expansion across Mongolia. The frequency of MDR-TB among young patients with pan-resistance to all first-line drugs suggests ongoing MDR-TB transmission within the community.

Sintchenko and E. C. Holmes. (2015). The role of pathogen genomics in assessing disease transmission. BMJ (Vol. 350, pp. h1314).

Abstract:

Whole genome sequencing (WGS) of pathogens enables the sources and patterns of transmission to be identified during specific disease outbreaks and promises to transform epidemiological research on communicable diseases. This review discusses new insights into disease spread and transmission that have come from the use of WGS, particularly when combined with genomic scale phylogenetic analyses. These include elucidation of the mechanisms of cross species transmission, the potential modes of pathogen transmission, and which people in the population contribute most to transmission. Particular attention is paid to the ability of WGS to resolve individual patient to patient transmission events. Importantly, WGS data seem to be sufficiently discriminatory to target cases linked to community or hospital contacts and hence prevent further spread, and to investigate genetically related cases without a clear epidemiological link. Approaches to combine evidence from epidemiological with genomic sequencing observations are summarised. Ongoing genomic surveillance can identify determinants of transmission, monitor pathogen evolution and adaptation, ensure the accurate and timely diagnosis of infections with epidemic potential, and refine strategies for their control.

Zhou, Y. Wang, G. Tsafnat, E. Coiera, F. T. Bourgeois and A. G. Dunn. (2015). Citations alone were enough to predict favorable conclusions in reviews of neuraminidase inhibitors. J Clin Epidemiol (Vol. 68, pp. 87-93).

Abstract:

OBJECTIVES: To examine the use of supervised machine learning to identify biases in evidence selection and determine if citation information can predict favorable conclusions in reviews about neuraminidase inhibitors. STUDY DESIGN AND SETTING: Reviews of neuraminidase inhibitors published during January 2005 to May 2013 were identified by searching PubMed. In a blinded evaluation, the reviews were classified as favorable if investigators agreed that they supported the use of neuraminidase inhibitors for prophylaxis or treatment of influenza. Reference lists were used to identify all unique citations to primary articles. Three classification methods were tested for their ability to predict favorable conclusions using only citation information. RESULTS: Citations to 4,574 articles were identified in 152 reviews of neuraminidase inhibitors, and 93 (61%) of these reviews were graded as favorable. Primary articles describing drug resistance were among the citations that were underrepresented in favorable reviews. The most accurate classifier predicted favorable conclusions with 96.2% accuracy, using citations to only 24 of 4,574 articles. CONCLUSION: Favorable conclusions in reviews about neuraminidase inhibitors can be predicted using only information about the articles they cite. The approach highlights how evidence exclusion shapes conclusions in reviews and provides a method to evaluate citation practices in a corpus of reviews.

Magrabi, S. T. Liaw, D. Arachi, W. B. Runciman, E. Coiera and M. R. Kidd. (2015). Identifying patient safety problems associated with Information Technology in general practice: an analysis of incident reports. BMJ Qual Saf.

Abstract:

Objective: To identify the categories of problems with information technology (IT), which affect patient safety in general practice. Design: General practitioners (GPs) reported incidents online or by telephone between May 2012 and November 2013. Incidents were reviewed against an existing classification for problems associated with IT and the clinical process impacted. Participants and setting: 87 GPs across Australia. Main outcome measure: Types of problems, consequences and clinical processes. Results: GPs reported 90 incidents involving IT which had an observable impact on the delivery of care, including actual patient harm as well as near miss events. Practice systems and medications were the most affected clinical processes. Problems with IT disrupted clinical workflow, wasted time and caused frustration. Issues with user interfaces, routine updates to software packages and drug databases, and the migration of records from one package to another generated clinical errors that were unique to IT; some could affect many patients at once. Human factors issues gave rise to some errors that have always existed with paper records but are more likely to occur and cause harm with IT. Such errors were linked to slips in concentration, multitasking, distractions and interruptions. Problems with patient identification and hybrid records generated errors that were in principle no different to paper records. Conclusions: Problems associated with IT include perennial risks with paper records, but additional disruptions in workflow and hazards for patients unique to IT, occasionally affecting multiple patients. Surveillance for such hazards may have general utility, but particularly in the context of migrating historical records to new systems and software updates to existing systems.

Magrabi, M. Baker, I. Sinha, M. S. Ong, S. Harrison, M. R. Kidd, W. B. Runciman and E. Coiera. (2015). Clinical safety of England’s national programme for IT: a retrospective analysis of all reported safety events 2005 to 2011. Int J Med Inform (Vol. 84, pp. 198-206).

Abstract:

OBJECTIVE: To analyse patient safety events associated with England’s national programme for IT (NPfIT). METHODS: Retrospective analysis of all safety events managed by a dedicated IT safety team between September 2005 and November 2011 was undertaken. Events were reviewed against an existing classification for problems associated with IT. The proportion of reported events per problem type, consequences, source of report, resolution within 24h, time of day and day of week were examined. Sub-group analyses were undertaken for events involving patient harm and those that occurred on a large scale. RESULTS: Of the 850 events analysed, 68% (n=574) described potentially hazardous circumstances, 24% (n=205) had an observable impact on care delivery, 4% (n=36) were a near miss, and 3% (n=22) were associated with patient harm, including three deaths (0.35%). Eleven events did not have a noticeable consequence (1%) and two were complaints (<1%). Amongst the events 1606 separate contributing problems were identified. Of these 92% were predominately associated with technical rather than human factors. Problems involving human factors were four times as likely to result in patient harm than technical problems (25% versus 8%; OR 3.98, 95%CI 1.90-8.34). Large-scale events affecting 10 or more individuals or multiple IT systems accounted for 23% (n=191) of the sample and were significantly more likely to result in a near miss (6% versus 4%) or impact the delivery of care (39% versus 20%; p<0.001). CONCLUSION: Events associated with NPfIT reinforce that the use of IT does create hazardous circumstances and can lead to patient harm or death. Large-scale patient safety events have the potential to affect many patients and clinicians, and this suggests that addressing them should be a priority for all major IT implementations.

Y. S. Lau, A. Arguel, S. Dennis, S. T. Liaw and E. Coiera. (2015). “Why Didn’t it Work?” Lessons From a Randomized Controlled Trial of a Web-based Personally Controlled Health Management System for Adults with Asthma. Journal of Medical Internet Research (Vol. 17, pp. e283).

Abstract:

Background: Personally controlled health management systems (PCHMS), which may include a personal health record (PHR), health management tools, and information resources, have been advocated as a next-generation technology to improve health behaviors and outcomes. There have been successful trials of PCHMS in various health settings. However, there is mixed evidence for whether consumers will use these systems over the long term and whether they ultimately lead to improved health outcomes and behaviors. Objective: The aim was to test whether use of a PCHMS by consumers can increase the uptake or updating of a written asthma action plan (AAP) among adults with asthma. Methods: A 12-month parallel 2-group randomized controlled trial was conducted. Participants living with asthma were recruited nationally in Australia between April and August 2013, and randomized 1:1 to either the PCHMS group or control group (online static educational content). The primary outcome measure was possession of an up-to-date written AAP poststudy. Secondary measures included (1) utilizing the AAP; (2) planned or unplanned visits to a health care professional for asthma-related concerns; (3) severe asthma exacerbation, inadequately controlled asthma, or worsening of asthma that required a change in treatment; and (4) number of days lost from work or study due to asthma. Ancillary analyses examined reasons for adoption or nonadoption of the intervention. Outcome measures were collected by online questionnaire prestudy, monthly, and poststudy. Results: A total of 330 eligible participants were randomized into 1 of 2 arms (intervention: n=154; control: n=176). Access to the PCHMS was not associated with a significant difference in any of the primary or secondary outcomes. Most participants (80.5%, 124/154) did not access the intervention or accessed it only once. Conclusions: Despite the intervention being effective in other preventive care settings, system use was negligible and outcome changes were not seen as a result. Consumers must perceive the need for assistance with a task and assign priority to the task supported by the eHealth intervention. Additionally, the cost of adopting the intervention (eg, additional effort, time spent learning the new system) must be lower than the benefit. Otherwise, there is high risk consumers will not adopt the eHealth intervention.

Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12612000716864; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=362714 (Archived by WebCite® at http://www.webcitation.org/6dMV6hg4A)

C. Kwong, N. McCallum, V. Sintchenko and B. P. Howden. (2015). Whole genome sequencing in clinical and public health microbiology. Pathology (Vol. 47, pp. 199-210).

Abstract:

Genomics and whole genome sequencing (WGS) have the capacity to greatly enhance knowledge and understanding of infectious diseases and clinical microbiology. The growth and availability of bench-top WGS analysers has facilitated the feasibility of genomics in clinical and public health microbiology. Given current resource and infrastructure limitations, WGS is most applicable to use in public health laboratories, reference laboratories, and hospital infection control-affiliated laboratories. As WGS represents the pinnacle for strain characterisation and epidemiological analyses, it is likely to replace traditional typing methods, resistance gene detection and other sequence-based investigations (e.g., 16S rDNA PCR) in the near future. Although genomic technologies are rapidly evolving, widespread implementation in clinical and public health microbiology laboratories is limited by the need for effective semi-automated pipelines, standardised quality control and data interpretation, bioinformatics expertise, and infrastructure.

Hodgson and E. Coiera. (2015). Risks and benefits of speech recognition for clinical documentation: a systematic review. J Am Med Inform Assoc.

Abstract:

OBJECTIVE: To review literature assessing the impact of speech recognition (SR) on clinical documentation. METHODS: Studies published prior to December 2014 reporting clinical documentation using SR were identified by searching Scopus, Compendex and Inspect, PubMed, and Google Scholar. Outcome variables analyzed included dictation and editing time, document turnaround time (TAT), SR accuracy, error rates per document, and economic benefit. Twenty-three articles met inclusion criteria from a pool of 441. RESULTS: Most studies compared SR to dictation and transcription (DT) in radiology, and heterogeneity across studies was high. Document editing time increased using SR compared to DT in four of six studies (+1876.47% to -16.50%). Dictation time similarly increased in three of five studies (+91.60% to -25.00%). TAT consistently improved using SR compared to DT (16.41% to 82.34%); across all studies the improvement was 0.90% per year. SR accuracy was reported in ten studies (88.90% to 96.00%) and appears to improve 0.03% per year as the technology matured. Mean number of errors per report increased using SR (0.05 to 6.66) compared to DT (0.02 to 0.40). Economic benefits were poorly reported. CONCLUSIONS: SR is steadily maturing and offers some advantages for clinical documentation. However, evidence supporting the use of SR is weak, and further investigation is required to assess the impact of SR on documentation error types, rates, and clinical outcomes.

G. Dunn, J. Leask, X. Zhou, K. D. Mandl and E. Coiera. (2015). Associations Between Exposure to and Expression of Negative Opinions About Human Papillomavirus Vaccines on Social Media: An Observational Study. J Med Internet Res (Vol. 17, pp. e144).

Abstract:

BACKGROUND: Groups and individuals that seek to negatively influence public opinion about the safety and value of vaccination are active in online and social media and may influence decision making within some communities. OBJECTIVE: We sought to measure whether exposure to negative opinions about human papillomavirus (HPV) vaccines in Twitter communities is associated with the subsequent expression of negative opinions by explicitly measuring potential information exposure over the social structure of Twitter communities. METHODS: We hypothesized that prior exposure to opinions rejecting the safety or value of HPV vaccines would be associated with an increased risk of posting similar opinions and tested this hypothesis by analyzing temporal sequences of messages posted on Twitter (tweets). The study design was a retrospective analysis of tweets related to HPV vaccines and the social connections between users. Between October 2013 and April 2014, we collected 83,551 English-language tweets that included terms related to HPV vaccines and the 957,865 social connections among 30,621 users posting or reposting the tweets. Tweets were classified as expressing negative or neutral/positive opinions using a machine learning classifier previously trained on a manually labeled sample. RESULTS: During the 6-month period, 25.13% (20,994/83,551) of tweets were classified as negative; among the 30,621 users that tweeted about HPV vaccines, 9046 (29.54%) were exposed to a majority of negative tweets. The likelihood of a user posting a negative tweet after exposure to a majority of negative opinions was 37.78% (2780/7361) compared to 10.92% (1234/11,296) for users who were exposed to a majority of positive and neutral tweets corresponding to a relative risk of 3.46 (95% CI 3.25-3.67, P<.001). CONCLUSIONS: The heterogeneous community structure on Twitter appears to skew the information to which users are exposed in relation to HPV vaccines. We found that among users that tweeted about HPV vaccines, those who were more often exposed to negative opinions were more likely to subsequently post negative opinions. Although this research may be useful for identifying individuals and groups currently at risk of disproportionate exposure to misinformation about HPV vaccines, there is a clear need for studies capable of determining the factors that affect the formation and adoption of beliefs about public health interventions.

Coiera. (2015). Technology, cognition and error BMJ Qual Saf (Vol. 24, pp. 417-22)

Abstract: Not available.

Cai, O. Perez-Concha, E. Coiera, F. Martin-Sanchez, R. Day, D. Roffe and B. Gallego. (2015). Real-time prediction of mortality, readmission, and length of stay using electronic health record data. J Am Med Inform Assoc.

Abstract:

OBJECTIVE: To develop a predictive model for real-time predictions of length of stay, mortality, and readmission for hospitalized patients using electronic health records (EHRs). MATERIALS AND METHODS: A Bayesian Network model was built to estimate the probability of a hospitalized patient being “at home,” in the hospital, or dead for each of the next 7 days. The network utilizes patient-specific administrative and laboratory data and is updated each time a new pathology test result becomes available. Electronic health records from 32 634 patients admitted to a Sydney metropolitan hospital via the emergency department from July 2008 through December 2011 were used. The model was tested on 2011 data and trained on the data of earlier years. RESULTS: The model achieved an average daily accuracy of 80% and area under the receiving operating characteristic curve (AUROC) of 0.82. The model’s predictive ability was highest within 24 hours from prediction (AUROC = 0.83) and decreased slightly with time. Death was the most predictable outcome with a daily average accuracy of 93% and AUROC of 0.84. DISCUSSION: We developed the first non-disease-specific model that simultaneously predicts remaining days of hospitalization, death, and readmission as part of the same outcome. By providing a future daily probability for each outcome class, we enable the visualization of future patient trajectories. Among these, it is possible to identify trajectories indicating expected discharge, expected continuing hospitalization, expected death, and possible readmission. CONCLUSIONS: Bayesian Networks can model EHRs to provide real-time forecasts for patient outcomes, which provide richer information than traditional independent point predictions of length of stay, death, or readmission, and can thus better support decision making.

A. Robinson, A. G. Dunn, G. Tsafnat and P. Glasziou. (2014). Citation networks of related trials are often disconnected: implications for bidirectional citation searches. J Clin Epidemiol (Vol. 67, pp. 793-9).

Abstract:

BACKGROUND AND OBJECTIVES: Reports of randomized controlled trials (RCTs) should set findings within the context of previous research. The resulting network of citations would also provide an alternative search method for clinicians, researchers, and systematic reviewers seeking to base decisions on all available evidence. We sought to determine the connectedness of citation networks of RCTs by examining direct (referenced trials) and indirect (through references of referenced trials, etc) citation of trials to one another. METHODS: Meta-analyses were used to create citation networks of RCTs addressing the same clinical questions. The primary measure was the proportion of networks where following citation links between RCTs identifies the complete set of RCTs, forming a single connected citation group. Other measures included the number of disconnected groups (islands) within each network, the number of citations in the network relative to the maximum possible, and the maximum number of links in the path between two connected trials (a measure of indirectness of citations). RESULTS: We included 259 meta-analyses with a total of 2,413 and a median of seven RCTs each. For 46% (118 of 259) of networks, the RCTs formed a single connected citation group-one island. For the other 54% of networks, where at least one RCT group was not cited by others, 39% had two citation islands and 4% (10 of 257) had 10 or more islands. On average, the citation networks had 38% of the possible citations to other trials (if each trial had cited all earlier trials). The number of citation islands and the maximum number of citation links increased with increasing numbers of trials in the network. CONCLUSION: Available evidence to answer a clinical question may be identified by using network citations created with a small initial corpus of eligible trials. However, the number of islands means that citation networks cannot be relied on for evidence retrieval.

Tsafnat, P. Glasziou, M. K. Choong, A. Dunn, F. Galgani and E. Coiera. (2014). Systematic review automation technologies. Syst Rev (Vol. 3, pp. 74).

Abstract:

Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects.We surveyed literature describing informatics systems that support or automate the processes of systematic review or each of the tasks of the systematic review. Several projects focus on automating, simplifying and/or streamlining specific tasks of the systematic review. Some tasks are already fully automated while others are still largely manual. In this review, we describe each task and the effect that its automation would have on the entire systematic review process, summarize the existing information system support for each task, and highlight where further research is needed for realizing automation for the task. Integration of the systems that automate systematic review tasks may lead to a revised systematic review workflow. We envisage the optimized workflow will lead to system in which each systematic review is described as a computer program that automatically retrieves relevant trials, appraises them, extracts and synthesizes data, evaluates the risk of bias, performs meta-analysis calculations, and produces a report in real time.

P. Concha, B. Gallego, K. Hillman, G. P. Delaney and E. Coiera. (2014). Do variations in hospital mortality patterns after weekend admission reflect reduced quality of care or different patient cohorts? A population-based study. BMJ Qual Saf (Vol. 23, pp. 215-22).

Abstract:

BACKGROUND: Proposed causes for increased mortality following weekend admission (the ‘weekend effect’) include poorer quality of care and sicker patients. The aim of this study was to analyse the 7 days post-admission time patterns of excess mortality following weekend admission to identify whether distinct patterns exist for patients depending upon the relative contribution of poorer quality of care (care effect) or a case selection bias for patients presenting on weekends (patient effect). METHODS: Emergency department admissions to all 501 hospitals in New South Wales, Australia, between 2000 and 2007 were linked to the Death Registry and analysed. There were a total of 3 381 962 admissions for 539 122 patients and 64 789 deaths at 1 week after admission. We computed excess mortality risk curves for weekend over weekday admissions, adjusting for age, sex, comorbidity (Charlson index) and diagnostic group. RESULTS: Weekends accounted for 27% of all admissions (917 257/3 381 962) and 28% of deaths (18 282/64 789). Sixteen of 430 diagnosis groups had a significantly increased risk of death following weekend admission. They accounted for 40% of all deaths, and demonstrated different temporal excess mortality risk patterns: early care effect (cardiac arrest); care effect washout (eg, pulmonary embolism); patient effect (eg, cancer admissions) and mixed (eg, stroke). CONCLUSIONS: The excess mortality patterns of the weekend effect vary widely for different diagnostic groups. Recognising these different patterns should help identify at-risk diagnoses where quality of care can be improved in order to minimise the excess mortality associated with weekend admission.

Coiera, Y. Wang, F. Magrabi, O. P. Concha, B. Gallego and W. Runciman. (2014). Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks. BMC Health Serv Res (Vol. 14, pp. 226).

Abstract:

BACKGROUND: Current prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models. METHOD: Patients admitted between 2000 and 2006 from 501 public and private hospitals in NSW, Australia were used for training and 2007 data for evaluation. The impact of hospital care delivered over different days of the week and or times of the day was modeled by separating hospitalization risk into 21 separate time periods (morning, day, night across the days of the week). Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/disease specific models (Charlson co-morbidity index, DRG). Combining these three generated a full model. Models were evaluated by accuracy, AUC, Akaike and Bayesian information criteria. RESULTS: There was a clear diurnal rhythm to hospital mortality in the data set, peaking in the evening, as well as the well-known ‘weekend-effect’ where mortality peaks with weekend admissions. Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively). The combined model which included time-varying risk however yielded an average AUC of 0.92. This model performed best for stays up to 7-days (93% of admissions), peaking at days 3 to 5 (AUC 0.94). CONCLUSIONS: Risks of hospitalization vary not just with the day of the week but also time of the day, and can be used to make predictions about the cumulative risk of death associated with an individual’s hospitalization. Combining disease specific models with such time varying- estimates appears to result in robust predictive performance. Such risk exposure models should find utility both in enhancing standard prognostic models as well as estimating the risk of continuation of hospitalization.

K. Choong, F. Galgani, A. G. Dunn and G. Tsafnat. (2014). Automatic evidence retrieval for systematic reviews. J Med Internet Res (Vol. 16, pp. e223).

Abstract:

BACKGROUND: Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing’s effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious. OBJECTIVE: Our goal was to evaluate an automatic method for citation snowballing’s capacity to identify and retrieve the full text and/or abstracts of cited articles. METHODS: Using 20 review articles that contained 949 citations to journal or conference articles, we manually searched Microsoft Academic Search (MAS) and identified 78.0% (740/949) of the cited articles that were present in the database. We compared the performance of the automatic citation snowballing method against the results of this manual search, measuring precision, recall, and F1 score. RESULTS: The automatic method was able to correctly identify 633 (as proportion of included citations: recall=66.7%, F1 score=79.3%; as proportion of citations in MAS: recall=85.5%, F1 score=91.2%) of citations with high precision (97.7%), and retrieved the full text or abstract for 490 (recall=82.9%, precision=92.1%, F1 score=87.3%) of the 633 correctly retrieved citations. CONCLUSIONS: The proposed method for automatic citation snowballing is accurate and is capable of obtaining the full texts or abstracts for a substantial proportion of the scholarly citations in review articles. By automating the process of citation snowballing, it may be possible to reduce the time and effort of common evidence surveillance tasks such as keeping trial registries up to date and conducting systematic reviews.

Tsafnat, A. Dunn, P. Glasziou and E. Coiera. (2013). The automation of systematic reviews. BMJ (Vol. 346, pp. f139)

Abstract: Not available.

S. Ong, F. Magrabi and E. Coiera. (2013). Syndromic surveillance for health information system failures: a feasibility study. J Am Med Inform Assoc (Vol. 20, pp. 506-12).

Abstract:

OBJECTIVE: To explore the applicability of a syndromic surveillance method to the early detection of health information technology (HIT) system failures. METHODS: A syndromic surveillance system was developed to monitor a laboratory information system at a tertiary hospital. Four indices were monitored: (1) total laboratory records being created; (2) total records with missing results; (3) average serum potassium results; and (4) total duplicated tests on a patient. The goal was to detect HIT system failures causing: data loss at the record level; data loss at the field level; erroneous data; and unintended duplication of data. Time-series models of the indices were constructed, and statistical process control charts were used to detect unexpected behaviors. The ability of the models to detect HIT system failures was evaluated using simulated failures, each lasting for 24 h, with error rates ranging from 1% to 35%. RESULTS: In detecting data loss at the record level, the model achieved a sensitivity of 0.26 when the simulated error rate was 1%, while maintaining a specificity of 0.98. Detection performance improved with increasing error rates, achieving a perfect sensitivity when the error rate was 35%. In the detection of missing results, erroneous serum potassium results and unintended repetition of tests, perfect sensitivity was attained when the error rate was as small as 5%. Decreasing the error rate to 1% resulted in a drop in sensitivity to 0.65-0.85. CONCLUSIONS: Syndromic surveillance methods can potentially be applied to monitor HIT systems, to facilitate the early detection of failures.

Magrabi, J. Aarts, C. Nohr, M. Baker, S. Harrison, S. Pelayo, J. Talmon, D. F. Sittig and E. Coiera. (2013). A comparative review of patient safety initiatives for national health information technology. Int J Med Inform (Vol. 82, pp. e139-48).

Abstract:

OBJECTIVE: To collect and critically review patient safety initiatives for health information technology (HIT). METHOD: Publicly promulgated set of advisories, recommendations, guidelines, or standards potentially addressing safe system design, build, implementation or use were identified by searching the websites of regional and national agencies and programmes in a non-exhaustive set of exemplar countries including England, Denmark, the Netherlands, the USA, Canada and Australia. Initiatives were categorised by type and software systems covered. RESULTS: We found 27 patient safety initiatives for HIT predominantly dealing with software systems for health professionals. Three initiatives addressed consumer systems. Seven of the initiatives specifically dealt with software for diagnosis and treatment, which are regulated as medical devices in England, Denmark and Canada. Four initiatives dealt with blood bank and image management software which is regulated in the USA. Of the 16 initiatives directed at unregulated software, 11 were aimed at increasing standardisation using guidelines and standards for safe system design, build, implementation and use. Three initiatives for unregulated software were aimed at certification in the USA, Canada and Australia. Safety is addressed alongside interoperability in the Australian certification programme but it is not explicitly addressed in the US and Canadian programmes, though conformance with specific functionality, interoperability, security and privacy requirements may lead to safer systems. England appears to have the most comprehensive safety management programme for unregulated software, incorporating safety assurance at a local healthcare organisation level based on standards for risk management and user interface design, with national incident monitoring and a response function. CONCLUSIONS: There are significant gaps in the safety initiatives for HIT systems. Current initiatives are largely focussed on software. With the exception of diagnostic, prognostic, monitoring and treatment software, which are subject to medical device regulations in some countries, the safety of the most common types of HIT systems such as EHRs and CPOE without decision support is not being explicitly addressed in most nations. Appropriate mechanisms for safety assurance are required for the full range of HIT systems for health professionals and consumers including all software and hardware throughout the system lifecycle. In addition to greater standardisation and oversight to ensure safe system design and build, appropriate implementation and use of HIT is critical to ensure patient safety.

Y. Lau, J. Proudfoot, A. Andrews, S. T. Liaw, J. Crimmins, A. Arguel and E. Coiera. (2013). Which bundles of features in a Web-based personally controlled health management system are associated with consumer help-seeking behaviors for physical and emotional well-being? J Med Internet Res (Vol. 15, pp. e79).

Abstract:

BACKGROUND: Personally controlled health management systems (PCHMS), which include a personal health record (PHR), health management tools, and consumer resources, represent the next stage in consumer eHealth systems. It is still unclear, however, what features contribute to an engaging and efficacious PCHMS. OBJECTIVE: To identify features in a Web-based PCHMS that are associated with consumer utilization of primary care and counselling services, and help-seeking rates for physical and emotional well-being concerns. METHODS: A one-group pre/posttest online prospective study was conducted on a university campus to measure use of a PCHMS for physical and emotional well-being needs during a university academic semester (July to November 2011). The PCHMS integrated an untethered personal health record (PHR) with well-being journeys, social forums, polls, diaries, and online messaging links with a health service provider, where journeys provide information for consumer participants to engage with clinicians and health services in an actionable way. 1985 students and staff aged 18 and above with access to the Internet were recruited online. Logistic regression, the Pearson product-moment correlation coefficient, and chi-square analyses were used to associate participants’ help-seeking behaviors and health service utilization with PCHMS usage among the 709 participants eligible for analysis. RESULTS: A dose-response association was detected between the number of times a user logged into the PCHMS and the number of visits to a health care professional (P=.01), to the university counselling service (P=.03), and help-seeking rates (formal or informal) for emotional well-being matters (P=.03). No significant association was detected between participant pre-study characteristics or well-being ratings at different PCHMS login frequencies. Health service utilization was strongly correlated with use of a bundle of features including: online appointment booking (primary care: OR 1.74, 95% CI 1.01-3.00; counselling: OR 6.04, 95% CI 2.30-15.85), personal health record (health care professional: OR 2.82, 95% CI 1.63-4.89), the poll (health care professional: OR 1.47, 95% CI 1.02-2.12), and diary (counselling: OR 4.92, 95% CI 1.40-17.35). Help-seeking for physical well-being matters was only correlated with use of the personal health record (OR 1.73, 95% CI 1.18-2.53). Help-seeking for emotional well-being concerns (including visits to the university counselling service) was correlated with a bundle comprising the poll (formal or informal help-seeking: OR 1.03, 95% CI 1.00-1.05), diary (counselling: OR 4.92, 95% CI 1.40-17.35), and online appointment booking (counselling: OR 6.04, 95% CI 2.30-15.85). CONCLUSIONS: Frequent usage of a PCHMS was significantly associated with increased consumer health service utilization and help-seeking rates for emotional health matters in a university sample. Different bundles of PCHMS features were associated with physical and emotional well-being matters. PCHMS appears to be a promising mechanism to engage consumers in help-seeking or health service utilization for physical and emotional well-being matters.

Y. Lau, A. G. Dunn, N. Mortimer, A. Gallagher, J. Proudfoot, A. Andrews, S. T. Liaw, J. Crimmins, A. Arguel and E. Coiera. (2013). Social and self-reflective use of a Web-based personally controlled health management system. J Med Internet Res (Vol. 15, pp. e211).

Abstract:

BACKGROUND: Personally controlled health management systems (PCHMSs) contain a bundle of features to help patients and consumers manage their health. However, it is unclear how consumers actually use a PCHMS in their everyday settings. OBJECTIVE: To conduct an empirical analysis of how consumers used the social (forum and poll) and self-reflective (diary and personal health record [PHR]) features of a Web-based PCHMS designed to support their physical and emotional well-being. METHODS: A single-group pre/post-test online prospective study was conducted to measure use of a Web-based PCHMS for physical and emotional well-being needs during a university academic semester. The PCHMS integrated an untethered PHR with social forums, polls, a diary, and online messaging links with a health service provider. Well-being journeys additionally provided information to encourage engagement with clinicians and health services. A total of 1985 students and staff aged 18 and above with access to the Internet were recruited online, of which 709 were eligible for analysis. Participants’ self-reported well-being, health status, health service utilization, and help-seeking behaviors were compared using chi-square, McNemar’s test, and Student’s t test. Social networks were constructed to examine the online forum communication patterns among consumers and clinicians. RESULTS: The two PCHMS features that were used most frequently and considered most useful and engaging were the social features (ie, the poll and forum). More than 30% (213/709) of participants who sought well-being assistance during the study indicated that other people had influenced their decision to seek help (54.4%, 386/709 sought assistance for physical well-being; 31.7%, 225/709 for emotional well-being). Although the prevalence of using a self-reflective feature (diary or PHR) was not as high (diary: 8.6%, 61/709; PHR: 15.0%, 106/709), the proportion of participants who visited a health care professional during the study was more than 20% greater in the group that did use a self-reflective feature (diary: P=.03; PHR: P<.001). CONCLUSIONS: There was variation in the degree to which consumers used social and self-reflective PCHMS features but both were significantly associated with increased help-seeking behaviors and health service utilization. A PCHMS should combine both self-reflective as well as socially driven components to most effectively influence consumers’ help-seeking behaviors.

Fernandez-Luque, A. Y. S. Lau, C. S. Bond, K. Denecke and F. Martin-Sanchez. (2013). New Trends in Health Social Media: Hype or Evidence-based medicine 14th World Congress on Medical and Health Informatics (Medinfo 2013). Copenhagen, Denmark IMIA, Copenhagen, Denmark

Abstract: Not available.

E. Chai, S. Anthony, E. Coiera and F. Magrabi. (2013). Using statistical text classification to identify health information technology incidents. J Am Med Inform Assoc (Vol. 20, pp. 980-5).

Abstract:

OBJECTIVE: To examine the feasibility of using statistical text classification to automatically identify health information technology (HIT) incidents in the USA Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database. DESIGN: We used a subset of 570 272 incidents including 1534 HIT incidents reported to MAUDE between 1 January 2008 and 1 July 2010. Text classifiers using regularized logistic regression were evaluated with both ‘balanced’ (50% HIT) and ‘stratified’ (0.297% HIT) datasets for training, validation, and testing. Dataset preparation, feature extraction, feature selection, cross-validation, classification, performance evaluation, and error analysis were performed iteratively to further improve the classifiers. Feature-selection techniques such as removing short words and stop words, stemming, lemmatization, and principal component analysis were examined. MEASUREMENTS: kappa statistic, F1 score, precision and recall. RESULTS: Classification performance was similar on both the stratified (0.954 F1 score) and balanced (0.995 F1 score) datasets. Stemming was the most effective technique, reducing the feature set size to 79% while maintaining comparable performance. Training with balanced datasets improved recall (0.989) but reduced precision (0.165). CONCLUSIONS: Statistical text classification appears to be a feasible method for identifying HIT reports within large databases of incidents. Automated identification should enable more HIT problems to be detected, analyzed, and addressed in a timely manner. Semi-supervised learning may be necessary when applying machine learning to big data analysis of patient safety incidents and requires further investigation.

Bowden and E. Coiera. (2013). Comparing New Zealand’s ‘Middle Out’ health information technology strategy with other OECD nations. Int J Med Inform (Vol. 82, pp. e87-95).

Abstract:

Implementation of efficient, universally applied, computer to computer communications is a high priority for many national health systems. As a consequence, much effort has been channelled into finding ways in which a patient’s previous medical history can be made accessible when needed. A number of countries have attempted to share patients’ records, with varying degrees of success. While most efforts to create record-sharing architectures have relied upon government-provided strategy and funding, New Zealand has taken a different approach. Like most British Commonwealth nations, New Zealand has a ‘hybrid’ publicly/privately funded health system. However its information technology infrastructure and automation has largely been developed by the private sector, working closely with regional and central government agencies. Currently the sector is focused on finding ways in which patient records can be shared amongst providers across three different regions. New Zealand’s healthcare IT model combines government contributed funding, core infrastructure, facilitation and leadership with private sector investment and skills and is being delivered via a set of controlled experiments. The net result is a ‘Middle Out’ approach to healthcare automation. ‘Middle Out’ relies upon having a clear, well-articulated health-reform strategy and a determination by both public and private sector organisations to implement useful healthcare IT solutions by working closely together.

S. Ong, F. Magrabi and E. Coiera. (2012). Automated identification of extreme-risk events in clinical incident reports. J Am Med Inform Assoc (Vol. 19, pp. e110-8).

Abstract:

OBJECTIVES: To explore the feasibility of using statistical text classification to automatically detect extreme-risk events in clinical incident reports. METHODS: Statistical text classifiers based on Naive Bayes and Support Vector Machine (SVM) algorithms were trained and tested on clinical incident reports to automatically detect extreme-risk events, defined by incidents that satisfy the criteria of Severity Assessment Code (SAC) level 1. For this purpose, incident reports submitted to the Advanced Incident Management System by public hospitals from one Australian region were used. The classifiers were evaluated on two datasets: (1) a set of reports with diverse incident types (n=120); (2) a set of reports associated with patient misidentification (n=166). Results were assessed using accuracy, precision, recall, F-measure, and area under the curve (AUC) of receiver operating characteristic curves. RESULTS: The classifiers performed well on both datasets. In the multi-type dataset, SVM with a linear kernel performed best, identifying 85.8% of SAC level 1 incidents (precision=0.88, recall=0.83, F-measure=0.86, AUC=0.92). In the patient misidentification dataset, 96.4% of SAC level 1 incidents were detected when SVM with linear, polynomial or radial-basis function kernel was used (precision=0.99, recall=0.94, F-measure=0.96, AUC=0.98). Naive Bayes showed reasonable performance, detecting 80.8% of SAC level 1 incidents in the multi-type dataset and 89.8% of SAC level 1 patient misidentification incidents. Overall, higher prediction accuracy was attained on the specialized dataset, compared with the multi-type dataset. CONCLUSION: Text classification techniques can be applied effectively to automate the detection of extreme-risk events in clinical incident reports.

Magrabi, M. S. Ong, W. Runciman and E. Coiera. (2012). Using FDA reports to inform a classification for health information technology safety problems. J Am Med Inform Assoc (Vol. 19, pp. 45-53).

Abstract:

OBJECTIVE: To expand an emerging classification for problems with health information technology (HIT) using reports submitted to the US Food and Drug Administration Manufacturer and User Facility Device Experience (MAUDE) database. DESIGN: HIT events submitted to MAUDE were retrieved using a standardized search strategy. Using an emerging classification with 32 categories of HIT problems, a subset of relevant events were iteratively analyzed to identify new categories. Two coders then independently classified the remaining events into one or more categories. Free-text descriptions were analyzed to identify the consequences of events. MEASUREMENTS: Descriptive statistics by number of reported problems per category and by consequence; inter-rater reliability analysis using the kappa statistic for the major categories and consequences. RESULTS: A search of 899 768 reports from January 2008 to July 2010 yielded 1100 reports about HIT. After removing duplicate and unrelated reports, 678 reports describing 436 events remained. The authors identified four new categories to describe problems with software functionality, system configuration, interface with devices, and network configuration; the authors’ classification with 32 categories of HIT problems was expanded by the addition of these four categories. Examination of the 436 events revealed 712 problems, 96% were machine-related, and 4% were problems at the human-computer interface. Almost half (46%) of the events related to hazardous circumstances. Of the 46 events (11%) associated with patient harm, four deaths were linked to HIT problems (0.9% of 436 events). CONCLUSIONS: Only 0.1% of the MAUDE reports searched were related to HIT. Nevertheless, Food and Drug Administration reports did prove to be a useful new source of information about the nature of software problems and their safety implications with potential to inform strategies for safe design and implementation.

Y. S. Lau, A. Parker, J. Early, G. Sacks, F. Anvari and E. Coiera. (2012). Comparative usage of a web-based personally controlled health management system and normal support: A case study in IVF. eJHI-The Electronic Journal of Health Informatics (Vol. 7, pp. e16).

Abstract:

Background: Research into the impact of personal health record-enabled consumer systems is still in its infancy.  Little is known about effective designs of web-based personally controlled health management system (PCHMS), how people use these systems in their real-life settings, nor how usage relates to concurrent support from other sources. Objective: To inform PCHMS design and feature development by assessing how patients undergoing in-vitro fertilization (IVF) use a web-based PCHMS in their real-life setting, and how their usage compares to concurrent support from other sources. Methods: An in-depth formative evaluation study was conducted with 17 women undergoing IVF, who were invited to use a web-based PCHMS called Healthy.me, providing targeted IVF program information over an eight-week treatment. Online interactions were recorded on computer logs. Participants were interviewed weekly by telephone throughout their cycle, and were specifically questioned about their feedback of PCHMS and concurrent sources of support beyond that provided by the system. Interview data was coded and analysed using two-way repeated ANOVA. Results: 62 interviews were collected from 14 participants who completed the study.  Twelve of 14 participants accessed all features in Healthy.me, which included i) accessing information about the next steps of their treatment (i.e. journey), and ii) viewing or updating details in their pillbox, schedule, test results and team members during their IVF treatment. Healthy.me alerted 21% (3/14) participants to seek advice from clinic staff on issues that could affect treatment outcome of which they were previously unaware (e.g. sexual practices, frequency and order of ultrasound screenings after a sequence of blood tests).  Patients additionally sought support from people, information and organisational tools outside Healthy.me to help decide IVF and manage different stages of their treatment. There was a significant interaction between IVF stage and sources of support (F(10,50)=2.54, p=.015, ηp2=.34).  Suggestions are presented on ways to tailor support at different stages of the IVF cycle using a PCHMS. Conclusions: IVF patients seek support from the web-based PCHMS, and concurrently do so with other sources of support.  More research is needed to inform PCHMS design that effectively tailors support for patients at different stages of their health journey.

Y. Lau, V. Sintchenko, J. Crimmins, F. Magrabi, B. Gallego and E. Coiera. (2012). Protocol for a randomised controlled trial examining the impact of a web-based personally controlled health management system on the uptake of influenza vaccination rates. BMC Health Serv Res (Vol. 12, pp. 86).

Abstract:

BACKGROUND: Online social networking and personally controlled health management systems (PCHMS) offer a new opportunity for developing innovative interventions to prevent diseases of public health concern (e.g., influenza) but there are few comparative studies about patterns of use and impact of these systems. METHODS/DESIGN: A 2010 CONSORT-compliant randomised controlled trial with a two-group parallel design will assess the efficacy of a web-based PCHMS called Healthy.me in facilitating the uptake of influenza vaccine amongst university students and staff. Eligible participants are randomised either to obtain access to Healthy.me or a 6-month waitlist. Participants complete pre-study, post-study and monthly surveys about their health and utilisation of health services. A post-study clinical audit will be conducted to validate self-reports about influenza vaccination and visits to the university health service due to influenza-like illness (ILI) amongst a subset of participants. 600 participants older than 18 years with monthly access to the Internet and email will be recruited. Participants who (i) discontinue the online registration process; (ii) report obtaining an influenza vaccination in 2010 before the commencement of the study; or (iii) report being influenced by other participants to undertake influenza vaccination will be excluded from analysis. The primary outcome measure is the number of participants obtaining influenza vaccination during the study. Secondary outcome measures include: number of participants (i) experiencing ILI symptoms, (ii) absent from or experiencing impairment in work or study due to ILI symptoms, (iii) using health services or medications due to ILI symptoms; (iv) expressing positive or negative attitudes or experiences towards influenza vaccination, via their reasons of receiving (or not receiving) influenza vaccine; and (v) their patterns of usage of Healthy.me (e.g., frequency and timing of hits, duration of access, uptake of specific functions). DISCUSSION: This study will provide new insights about the utility of online social networking and PCHMS for public health and health promotion. It will help to assess whether a web-based PCHMS, with connectivity to a health service provider, containing information and self-management tools, can improve the uptake of preventive health services amongst university students and staff. TRIAL REGISTRATION: ACTRN12610000386033 (Australian New Zealand Clinical Trials Registry).

Y. Lau, V. Sintchenko, J. Crimmins, F. Magrabi, B. Gallego and E. Coiera. (2012). Impact of a web-based personally controlled health management system on influenza vaccination and health services utilization rates: a randomized controlled trial. J Am Med Inform Assoc (Vol. 19, pp. 719-27).

Abstract:

OBJECTIVE: To assess the impact of a web-based personally controlled health management system (PCHMS) on the uptake of seasonal influenza vaccine and primary care service utilization among university students and staff. MATERIALS AND METHODS: A PCHMS called Healthy.me was developed and evaluated in a 2010 CONSORT-compliant two-group (6-month waitlist vs PCHMS) parallel randomized controlled trial (RCT) (allocation ratio 1:1). The PCHMS integrated an untethered personal health record with consumer care pathways, social forums, and messaging links with a health service provider. RESULTS: 742 university students and staff met inclusion criteria and were randomized to a 6-month waitlist (n=372) or the PCHMS (n=370). Amongst the 470 participants eligible for primary analysis, PCHMS users were 6.7% (95% CI: 1.46 to 12.30) more likely than the waitlist to receive an influenza vaccine (waitlist: 4.9% (12/246, 95% CI 2.8 to 8.3) vs PCHMS: 11.6% (26/224, 95% CI 8.0 to 16.5); chi(2)=7.1, p=0.008). PCHMS participants were also 11.6% (95% CI 3.6 to 19.5) more likely to visit the health service provider (waitlist: 17.9% (44/246, 95% CI 13.6 to 23.2) vs PCHMS: 29.5% (66/224, 95% CI: 23.9 to 35.7); chi(2)=8.8, p=0.003). A dose-response effect was detected, where greater use of the PCHMS was associated with higher rates of vaccination (p=0.001) and health service provider visits (p=0.003). DISCUSSION: PCHMS can significantly increase consumer participation in preventive health activities, such as influenza vaccination. CONCLUSIONS: Integrating a PCHMS into routine health service delivery systems appears to be an effective mechanism for enhancing consumer engagement in preventive health measures. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12610000386033. http://www.anzctr.org.au/trial_view.aspx?id=335463.

M. D. Jensen, M. M. Jensen, A. S. Korsager, M. S. Ong, F. Magrabi and E. Coiera. (2012). Using virtual worlds to train healthcare workers – a case study using Second Life to improve the safety of inpatient transfers. eJHI-The Electronic Journal of Health Informatics (Vol. 7, pp. e7).

Abstract:

Virtual worlds such as Second Life may offer a new environment to deliver simulation-based safety training to clinicians. The objective of this study was to design and implement a simulation of inpatient transfers in the virtual world of Second Life, and to undertake a preliminary evaluation of its usability as an educational tool. A simulation of inpatient transfer was developed using the Linden Scripting Language in Second Life. A virtual hospital was built and four scenarios of inpatient transfer varying in mode of transport (bed, trolley or wheelchair) and infection control precautions (no-infection, droplet, contact or airborne infection) were implemented.  System usability was assessed using a “think aloud” protocol in combination with surveys and interviews with 15 participants who found the simulation environment easy to use, and fit for purpose. The novelty of using a virtual world was regarded as an advantage over other training methods, as was the opportunity to learn and practice inpatient transfers while receiving instant feedback during the process. Participants agreed that simulation has potential to improve awareness about hand hygiene and prevent errors. Second Life was able to support the development of a virtual environment for patient safety training. Results from preliminary usability tests indicate acceptance of the simulation environment. Further investigation is required to evaluate usability with a representative group and determine if training porters in a virtual world will reduce errors in the real world.

Coiera, J. Aarts and C. Kulikowski. (2012). The dangerous decade. J Am Med Inform Assoc (Vol. 19, pp. 2-5).

Abstract:

Over the next 10 years, more information and communication technology (ICT) will be deployed in the health system than in its entire previous history. Systems will be larger in scope, more complex, and move from regional to national and supranational scale. Yet we are at roughly the same place the aviation industry was in the 1950s with respect to system safety. Even if ICT harm rates do not increase, increased ICT use will increase the absolute number of ICT related harms. Factors that could diminish ICT harm include adoption of common standards, technology maturity, better system development, testing, implementation and end user training. Factors that will increase harm rates include complexity and heterogeneity of systems and their interfaces, rapid implementation and poor training of users. Mitigating these harms will not be easy, as organizational inertia is likely to generate a hysteresis-like lag, where the paths to increase and decrease harm are not identical.

Coiera. (2012). The true meaning of personalized medicine. Yearb Med Inform (Vol. 7, pp. 4-6)

Abstract: Not available.

Magrabi, M. S. Ong, W. Runciman and E. Coiera. (2011). Patient safety problems associated with heathcare information technology: an analysis of adverse events reported to the US Food and Drug Administration. AMIA Annu Symp Proc (Vol. 2011, pp. 853-7).

Abstract:

The objective of this paper is to analyze healthcare information technology (HIT) events associated with patient harm submitted to the US Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database. We examined the problems in 46 relevant events submitted to MAUDE from January 2008 to July 2010 to identify natural categories of problems from a clinical perspective. CPOE and PACS were involved in 93% of the events. Adverse events were associated with medications in 41%, clinical processes in 33%, radiation in 15% and surgery in 11%. There were four deaths. Strategies to improve the safety of HIT should focus on designing safe user interfaces, integrated checks of key identifiers and decision support, and engineering safer clinical processes.