The cognitive impact of research synopses on physicians: a prospective observational analysis of evidence-based summaries sent by email

Background Effective information transfer in primary care is becoming more difficult as the volume of medical information expands. Emailed research synopses are expected to raise awareness and thereby permit more effective information retrieval. Objective To identify key factors that influence physicians' self-reported cognitive impact of emailed research synopses. Method In this prospective observational study, research synopses sent by email between 8 September 2006 and 30 May 2007 were analysed. Seven characteristics of synopses (number of characters, research design, study setting, number of types of patient populations studied, number of comparisons, number of outcomes, and number of results) were analysed. Each synopsis was classified as either positive or negative based on physician-reported impacts. Logistic regression analysis was used to evaluate the association between a negative impact and the synopsis' characteristics. Results A total of 1960 Canadian physicians submitted 159 442 ratings on 193 synopses. Each synopsis was assessed on average by 826.1 physicians. On average there were 28.3 negative ratings per research synopsis, 146.3 neutral, and 656.2 positive. Out of the seven characteristics analysed, only the number of comparisons (odds ratio (OR) = 0.47, 95% confidence interval (CI) = 0.23_0.93) and the number of results (OR = 0.64, 95% CI = 0.44_0.93) had a statistically significant influence on physician ratings. An increase in the number of comparisons (P = 0.03) or the number of results (P = 0.02) decreased the likelihood of a negative impact. Conclusions Characteristics of the synopses appear to influence cognitive impact, and there might be lexical patterns specific to these factors. Further research is recommended in order to understand the mechanism for the influence of these characteristics

Many family physicians will not manually update PDA software: an observational study

Background In a prospective study to explore connections between clinical information delivery and information retrieval, 41 Canadian family physicians searched an electronic knowledge resource (EKR) as needed for practice. Research software, called the Information Assessment Method (IAM), prompted family physicians to report on the situational relevance, perceived cognitive impact and application of their retrieved information hits. Both the IAM and the EKR needed periodic updating to properly address our research questions. Objective To determine the frequency of software updating when manual or semi-automatic approaches are used by family physicians. Methods Each family physician received a handheld computer (PDA) that ran the Windows Mobile 6 operating system. For technical reasons, both the IAM and the EKR were accessed offline on PDA. To update the EKR and the IAM, family physicians were asked to synchronise their PDA to their PC. Updating the IAM was a manual process, whereas updating the EKR was semi-automatic. Results We found: (1) about 25% of family physicians never or rarely updated PDA software on their own, (2) a large number of software updates were never installed and (3) the semi-automatic method was associated with a small increase in the proportion of installed software updates (58.9% versus 48.6% for the manual method). Conclusions When a wireless internet connection is not used to update PDA software, sociotechnical issues complicate mobile data collection and data transfer

Mining reflective continuing medical education data for family physician learning needs

A mixed methods research (sequential explanatory design) studied the potential of mining the data from the consumers of continuing medical education (CME) programs, for the developers of CME programs. The quantitative data generated by family physicians, through applying the information assessment method to CME content, was presented to key informants from the CME planning community through a qualitative description study.The data were revealed to have many potential applications including supporting the creation of CME content, CME program planning and personal learning portfolios

Developing and user-testing Decision boxes to facilitate shared decision making in primary care - a study protocol

<p>Abstract</p> <p>Background</p> <p>Applying evidence is one of the most challenging steps of evidence-based clinical practice. Healthcare professionals have difficulty interpreting evidence and translating it to patients. Decision boxes are summaries of the most important benefits and harms of diagnostic, therapeutic, and preventive health interventions provided to healthcare professionals before they meet the patient. Our hypothesis is that Decision boxes will prepare clinicians to help patients make informed value-based decisions. By acting as primers, the boxes will enhance the application of evidence-based practices and increase shared decision making during the clinical encounter. The objectives of this study are to provide a framework for developing Decision boxes and testing their value to users.</p> <p>Methods/Design</p> <p>We will begin by developing Decision box prototypes for 10 clinical conditions or topics based on a review of the research on risk communication. We will present two prototypes to purposeful samples of 16 family physicians distributed in two focus groups, and 32 patients distributed in four focus groups. We will use the User Experience Model framework to explore users' perceptions of the content and format of each prototype. All discussions will be transcribed, and two researchers will independently perform a hybrid deductive/inductive thematic qualitative analysis of the data. The coding scheme will be developed a priori from the User Experience Model's seven themes (valuable, usable, credible, useful, desirable, accessible and findable), and will include new themes suggested by the data (inductive analysis). Key findings will be triangulated using additional publications on the design of tools to improve risk communication. All 10 Decision boxes will be modified in light of our findings.</p> <p>Discussion</p> <p>This study will produce a robust framework for developing and testing Decision boxes that will serve healthcare professionals and patients alike. It is the first step in the development and implementation of a new tool that should facilitate decision making in clinical practice.</p

Evidence summaries (decision boxes) to prepare clinicians for shared decision-making with patients: a mixed methods implementation study

Background: Decision boxes (Dboxes) provide clinicians with research evidence about management options for medical questions that have no single best answer. Dboxes fulfil a need for rapid clinical training tools to prepare clinicians for clinician-patient communication and shared decision-making. We studied the barriers and facilitators to using the Dbox information in clinical practice. Methods: We used a mixed methods study with sequential explanatory design. We recruited family physicians, residents, and nurses from six primary health-care clinics. Participants received eight Dboxes covering various questions by email (one per week). For each Dbox, they completed a web questionnaire to rate clinical relevance and cognitive impact and to assess the determinants of their intention to use what they learned from the Dbox to explain to their patients the advantages and disadvantages of the options, based on the theory of planned behaviour (TPB). Following the 8-week delivery period, we conducted focus groups with clinicians and interviews with clinic administrators to explore contextual factors influencing the use of the Dbox information. Results: One hundred clinicians completed the web surveys. In 54% of the 496 questionnaires completed, they reported that their practice would be improved after having read the Dboxes, and in 40%, they stated that they would use this information for their patients. Of those who would use the information for their patients, 89% expected it would benefit their patients, especially in that it would allow the patient to make a decision more in keeping with his/her personal circumstances, values, and preferences. They intended to use the Dboxes in practice (mean 5.6 ± 1.2, scale 1–7, with 7 being “high”), and their intention was significantly related to social norm, perceived behavioural control, and attitude according to the TPB (P < 0.0001). In focus groups, clinicians mentioned that co-interventions such as patient decision aids and training in shared decision-making would facilitate the use of the Dbox information. Some participants would have liked a clear “bottom line” statement for each Dbox and access to printed Dboxes in consultation rooms. Conclusions: Dboxes are valued by clinicians. Tailoring of Dboxes to their needs would facilitate their implementation in practic

The identification of clinically important elements within medical journal abstracts: Patient_Population_Problem, Exposure_Intervention, Comparison, Outcome, Duration and Results (PECODR)

Background Information retrieval in primary care is becoming more difficult as the volume of medical information held in electronic databases expands. The lexical structure of this information might permit automatic indexing and improved retrieval. Objective To determine the possibility of identifying the key elements of clinical studies, namely Patient_Population_Problem, Exposure_Intervention, Comparison, Outcome, Duration and Results (PECODR), from abstracts of medical journals. Methods We used a convenience sample of 20 synopses from the journal Evidence-Based Medicine (EBM) and their matching original journal article abstracts obtained from PubMed. Three independent primary care professionals identified PECODR-related extracts of text. Rules were developed to define each PECODR element and the selection process of characters, words, phrases and sentences. From the extracts of text related to PECODR elements, potential lexical patterns that might help identify those elements were proposed and assessed using NVivo software. Results A total of 835 PECODR-related text extracts containing 41 263 individual text characters were identified from 20 EBM journal synopses. There were 759 extracts in the corresponding PubMed abstracts containing 31 947 characters. PECODR elements were found in nearly all abstracts and synopses with the exception of duration. There was agreement on 86.6%of the extracts from the 20 EBM synopses and 85.0% on the corresponding PubMed abstracts. After consensus this rose to 98.4% and 96.9% respectively. We found potential text patterns in the Comparison, Outcome and Results elements of both EBM synopses and PubMed abstracts. Some phrases and words are used frequently and are specific for these elements in both synopses and abstracts. Conclusions Results suggest a PECODR-related structure exists in medical abstracts and that there might be lexical patterns specific to these elements. More sophisticated computer-assisted lexical-semantic analysis might refine these results, and pave the way to automating PECODR indexing, and improve information retrieval in primary care

Combining classifiers for robust PICO element detection

<p>Abstract</p> <p>Background</p> <p>Formulating a clinical information need in terms of the four atomic parts which are Population/Problem, Intervention, Comparison and Outcome (known as PICO elements) facilitates searching for a precise answer within a large medical citation database. However, using PICO defined items in the information retrieval process requires a search engine to be able to detect and index PICO elements in the collection in order for the system to retrieve relevant documents.</p> <p>Methods</p> <p>In this study, we tested multiple supervised classification algorithms and their combinations for detecting PICO elements within medical abstracts. Using the structural descriptors that are embedded in some medical abstracts, we have automatically gathered large training/testing data sets for each PICO element.</p> <p>Results</p> <p>Combining multiple classifiers using a weighted linear combination of their prediction scores achieves promising results with an <it>f</it>-measure score of 86.3% for P, 67% for I and 56.6% for O.</p> <p>Conclusions</p> <p>Our experiments on the identification of PICO elements showed that the task is very challenging. Nevertheless, the performance achieved by our identification method is competitive with previously published results and shows that this task can be achieved with a high accuracy for the P element but lower ones for I and O elements.</p

Association between risk factors for injurious falls and new benzodiazepine prescribing in elderly persons

<p>Abstract</p> <p>Background</p> <p>Benzodiazepines are frequently prescribed to elderly patients' despite concerns about adverse effects leading to injurious falls. Previous studies have not investigated the extent to which patients with pre-existing risk factors for falls are prescribed benzodiazepines. The objective of this study is to assess if some of the risk factors for falls are associated with new benzodiazepine prescriptions in elderly persons.</p> <p>Methods</p> <p>Using provincial administrative databases, elderly Quebec residents were screened in 1989 for benzodiazepine use and non-users were followed for up to 5 years. Logistic regression models were used to evaluate potential predictors of new benzodiazepine use among patient baseline characteristics.</p> <p>Results</p> <p>In the 252,811 elderly patients who had no benzodiazepine prescription during the baseline year (1989), 174,444 (69%) never filled a benzodiazepine prescription and 78,367 (31%) filled at least one benzodiazepine prescription. In the adjusted analysis, several risk factors for falls were associated with statistically significant increases in the risk of receiving a new benzodiazepine prescription including the number of prescribing physicians seen at baseline (OR: 1.12; 95% CI 1.11–1.13), being female (OR: 1.20; 95% CI 1.18–1.22) or a diagnosis of arthritis (OR: 1.11; 95% CI 1.09–1.14), depression (OR: 1.42; 95% CI 1.35–1.49) or alcohol abuse (OR: 1.24; 95% CI 1.05–1.46). The strongest predictor for starting a benzodiazepine was the use of other medications, particularly anti-depressants (OR: 1.85; 95% CI 1.75–1.95).</p> <p>Conclusion</p> <p>Patients with pre-existing conditions that increase the risk of injurious falls are significantly more likely to receive a new prescription for a benzodiazepine. The strength of the association between previous medication use and new benzodiazepine prescriptions highlights an important medication safety issue.</p

Effect of a web-based chronic disease management system on asthma control and health-related quality of life: study protocol for a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Asthma is a prevalent and costly disease resulting in reduced quality of life for a large proportion of individuals. Effective patient self-management is critical for improving health outcomes. However, key aspects of self-management such as self-monitoring of behaviours and symptoms, coupled with regular feedback from the health care team, are rarely addressed or integrated into ongoing care. Health information technology (HIT) provides unique opportunities to facilitate this by providing a means for two way communication and exchange of information between the patient and care team, and access to their health information, presented in personalized ways that can alert them when there is a need for action. The objective of this study is to evaluate the acceptability and efficacy of using a web-based self-management system, My Asthma Portal (MAP), linked to a case-management system on asthma control, and asthma health-related quality of life.</p> <p>Methods</p> <p>The trial is a parallel multi-centered 2-arm pilot randomized controlled trial. Participants are randomly assigned to one of two conditions: a) MAP and usual care; or b) usual care alone. Individuals will be included if they are between 18 and 70, have a confirmed asthma diagnosis, and their asthma is classified as not well controlled by their physician. Asthma control will be evaluated by calculating the amount of fast acting beta agonists recorded as dispensed in the provincial drug database, and asthma quality of life using the Mini Asthma Related Quality of Life Questionnaire. Power calculations indicated a needed total sample size of 80 subjects. Data are collected at baseline, 3, 6, and 9 months post randomization. Recruitment started in March 2010 and the inclusion of patients in the trial in June 2010.</p> <p>Discussion</p> <p>Self-management support from the care team is critical for improving chronic disease outcomes. Given the high volume of patients and time constraints during clinical visits, primary care physicians have limited time to teach and reinforce use of proven self-management strategies. HIT has the potential to provide clinicians and a large number of patients with tools to support health behaviour change.</p> <p>Trial Registration</p> <p>Current Controlled Trials <a href="http://www.controlled-trials.com/ISRCTN34326236">ISRCTN34326236</a>.</p