Browsing and searching e-encyclopaedias

Educational websites and electronic encyclopaedias employ many of the same design elements, such as hyperlinks, frames and search mechanisms. This paper asks to what extent recommendations from the world of web design can be applied to e-encyclopaedias, through an evaluation of users' browsing and searching behaviour in the free, web-based versions of Encyclopaedia Britannica, the Concise Columbia Encyclopaedia and Microsoft's Encarta. It is discovered that e-encyclopaedias have a unique set of design requirements, as users' expectations are inherited from the worlds of both web and print

Browsing and Searching E-encyclopaedias

Educational websites and electronic encyclopaedias employ many of the same design elements, such as hyperlinks, frames and search mechanisms. This paper asks to what extent recommendations from the world of web design can be applied to e-encyclopaedias, through an evaluation of users' browsing and searching behaviour in the free, web-based versions of Encyclopaedia Britannica, the Concise Columbia Encyclopaedia and Microsoft's Encarta. It is discovered that e-encyclopaedias have a unique set of design requirements, as users' expectations are inherited from the worlds of both web and print

Population-Based Outreach Versus Usual Care to Prevent Suicide Attempt: Study Protocol for a Randomized Clinical Trial

Background: Suicide is the 10th leading cause of death. PHQ-9 item #9 (which asks about suicidal thoughts) identifies those at risk of suicide attempt/death. Patients with scores of 2 or 3 on item 9 show a sustained increase in risk, with a cumulative hazard near 4% over 12 months. Methods: Outpatients who score a 2 or 3 on item #9 of the PHQ-9 are identified using electronic health record (EHR) data at three Mental Health Research Network sites: Group Health Cooperative, HealthPartners and Kaiser Permanente Colorado. Using a modified Zelen design, patients are automatically assigned 1:1:1 to continue in usual care (ie, no contact) or to be offered one of two population-based prevention programs meant to supplement usual care: 1) Care Management (systematic outreach to assess risk, EHR-based tools for risk-based pathways, and care management to facilitate and monitor recommended follow-up care); or 2) Skills Training (interactive online training in dialectical behavioral therapy skills supported by reminder and reinforcement messages). Randomization automatically occurs within each site’s sampling computer program, stratified by item #9 score. A computer-generated concealed allocation table provides randomly generated assignments in block sizes of either 6 or 9. The multisite interventions are embedded in the EHR. Online patient-provider secure messaging via the EHR patient portal is used for patient invitation and outreach as well as administration of suicide risk questionnaires. Secure provider-to-provider messaging is used to communicate with primary care and mental health providers. Population management and reporting tools are used to apply follow-up algorithms and deliver recommendations to care managers regarding outreach and follow-up. Nonfatal and fatal suicide attempts are identified using state vital statistics data and diagnoses of self-inflicted injury from EHR and claim records. Primary evaluation will compare risk of first suicide attempt over the 18 months following randomization. Groups will be compared according to initial treatment assignment, regardless of level of participation in either intervention. Results: To date, 4,869 outpatients out of a planned 18,000 have been randomized across the three sites. Conclusion: Our experience thus far illustrates the promise and challenges of implementing multisite clinical trial recruitment and intervention delivery in EHR systems

Among patients with unhealthy alcohol use, those with HIV are less likely than those without to receive evidence-based alcohol-related care: A national VA study.

BackgroundAlcohol use has important adverse effects on people living with HIV (PLWH). This study of patients with recognized unhealthy alcohol use estimated and compared rates of alcohol-related care received by PLWH and HIV- patients.MethodsOutpatients from the Veterans Health Administration who had one or more positive screen(s) for unhealthy alcohol use (AUDIT-C≥5) documented in their medical records 10/2009-5/2013 were eligible. Primary and secondary outcomes were brief intervention documented ≤14days after a positive alcohol screen, and a composite measure of any alcohol-related care (brief intervention, specialty addictions treatment or pharmacotherapy documented ≤365 days), respectively. Unadjusted and adjusted regression analyses compared alcohol-related care outcomes in PLWH and HIV- patients.ResultsThe sample included 830,825 outpatients (3,514 PLWH), reflecting 1,172,606 positive screens (1-5 per patient). For PLWH, 57.0% (95% confidence interval 55.4-58.5%) of positive screens were followed by brief intervention, compared to 73.8% (73.7-73.9%) for HIV- patients [relative rate: 0.77 (0.75-0.79), p<0.001]. After adjustment, comparable proportions were 61.0% (59.3-62.6%) for PLWH and 73.7% (73.6-73.8%) for HIV- patients [adjusted RR=0.83 (0.80-0.85); p<0.001]. Secondary outcome results were similar: for PLWH and HIV- patients, 67.1% (65.7-68.6%) and 77.7% (95% CI 77.7-77.8%) of positive screens, respectively, were followed by any alcohol-related care after adjustment [adjusted RR=0.86 (0.85-0.88), p<0.001].ConclusionsIn this large national sample of VA outpatients with unhealthy alcohol use, PLWH were less likely to receive alcohol-related care than HIV- patients. Special efforts may be needed to ensure alcohol-related care reaches PLWH

Machine Learning Prediction of Suicide Risk Does Not Identify Patients Without Traditional Risk Factors

Objective: To determine whether predictions of suicide risk from machine learning models identify unexpected patients or patients without medical record documentation of traditional risk factors. Methods: The study sample included 27,091,382 outpatient mental health (MH) specialty or general medical visits with a MH diagnosis for patients aged 11 years or older from January 1, 2009, to September 30, 2017. We used predicted risk scores of suicide attempt and suicide death, separately, within 90 days of visits to classify visits into risk score percentile strata. For each stratum, we calculated counts and percentages of visits with traditional risk factors, including prior self-harm diagnoses and emergency department visits or hospitalizations with MH diagnoses, in the last 3, 12, and 60 months. Results: Risk-factor percentages increased with predicted risk scores. Among MH specialty visits, 66%, 88%, and 99% of visits with suicide attempt risk scores in the top 3 strata (respectively, 90th-95th, 95th-98th, and ≥ 98th percentiles) and 60%, 77%, and 93% of visits with suicide risk scores in the top 3 strata represented patients who had at least one traditional risk factor documented in the prior 12 months. Among general medical visits, 52%, 66%, and 90% of visits with suicide attempt risk scores in the top 3 strata and 45%, 66%, and 79% of visits with suicide risk scores in the top 3 strata represented patients who had a history of traditional risk factors in the last 12 months. Conclusions: Suicide risk alerts based on these machine learning models coincide with patients traditionally thought of as high-risk at their high-risk visits

Accuracy of ICD-10-CM encounter diagnoses from health records for identifying self-harm events

OBJECTIVE: Assess the accuracy of ICD-10-CM coding of self-harm injuries and poisonings to identify self-harm events. MATERIALS AND METHODS: In 7 integrated health systems, records data identified patients reporting frequent suicidal ideation. Records then identified subsequent ICD-10-CM injury and poisoning codes indicating self-harm as well as selected codes in 3 categories where uncoded self-harm events might be found: injuries and poisonings coded as undetermined intent, those coded accidental, and injuries with no coding of intent. For injury and poisoning encounters with diagnoses in those 4 groups, relevant clinical text was extracted from records and assessed by a blinded panel regarding documentation of self-harm intent. RESULTS: Diagnostic codes selected for review include all codes for self-harm, 43 codes for undetermined intent, 26 codes for accidental intent, and 46 codes for injuries without coding of intent. Clinical text was available for review for 285 events originally coded as self-harm, 85 coded as undetermined intent, 302 coded as accidents, and 438 injury events with no coding of intent. Blinded review of full-text clinical records found documentation of self-harm intent in 254 (89.1%) of those originally coded as self-harm, 24 (28.2%) of those coded as undetermined, 24 (7.9%) of those coded as accidental, and 48 (11.0%) of those without coding of intent. CONCLUSIONS: Among patients at high risk, nearly 90% of injuries and poisonings with ICD-10-CM coding of self-harm have documentation of self-harm intent. Reliance on ICD-10-CM coding of intent to identify self-harm would fail to include a small proportion of true self-harm events

A review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures

Background: Retaining participants in cohort studies with multiple follow-up waves is difficult. Commonly, researchers are faced with the problem of missing data, which may introduce biased results as well as a loss of statistical power and precision. The STROBE guidelines von Elm et al. (Lancet, 370:1453-1457, 2007); Vandenbroucke et al. (PLoS Med, 4:e297, 2007) and the guidelines proposed by Sterne et al. (BMJ, 338:b2393, 2009) recommend that cohort studies report on the amount of missing data, the reasons for non-participation and non-response, and the method used to handle missing data in the analyses. We have conducted a review of publications from cohort studies in order to document the reporting of missing data for exposure measures and to describe the statistical methods used to account for the missing data. Methods: A systematic search of English language papers published from January 2000 to December 2009 was carried out in PubMed. Prospective cohort studies with a sample size greater than 1,000 that analysed data using repeated measures of exposure were included. Results: Among the 82 papers meeting the inclusion criteria, only 35 (43%) reported the amount of missing data according to the suggested guidelines. Sixty-eight papers (83%) described how they dealt with missing data in the analysis. Most of the papers excluded participants with missing data and performed a complete-case analysis (n = 54, 66%). Other papers used more sophisticated methods including multiple imputation (n = 5) or fully Bayesian modeling (n = 1). Methods known to produce biased results were also used, for example, Last Observation Carried Forward (n = 7), the missing indicator method (n = 1), and mean value substitution (n = 3). For the remaining 14 papers, the method used to handle missing data in the analysis was not stated. Conclusions: This review highlights the inconsistent reporting of missing data in cohort studies and the continuing use of inappropriate methods to handle missing data in the analysis. Epidemiological journals should invoke the STROBE guidelines as a framework for authors so that the amount of missing data and how this was accounted for in the analysis is transparent in the reporting of cohort studies. © 2012 Karahalios et al.; licensee BioMed Central Ltd