Background

Decision Boxes are summaries of the most important benefits and harms of health interventions provided to clinicians before they meet the patient, to prepare them to help patients make informed and value-based decisions. Our objective is to explore the barriers and facilitators to using Decision Boxes in clinical practice, more precisely factors stemming from (1) the Decision Boxes themselves, (2) the primary healthcare team (PHT), and (3) the primary care practice environment.

Methods/design

A two-phase mixed methods study will be conducted. Eight Decision Boxes relevant to primary care, and written in both English and in French, will be hosted on a website together with a tutorial to introduce the Decision Box. The Decision Boxes will be delivered as weekly emails over a span of eight weeks to clinicians of PHTs (family physicians, residents and nurses) in five primary care clinics located across two Canadian provinces. Using a web-questionnaire, clinicians will rate each Decision Box with the Information Assessment Method (cognitive impacts, relevance, usefulness, expected benefits) and with a questionnaire based on the Theory of Planned Behavior to study the determinants of clinicians’ intention to use what they learned from that Decision Box in their patient encounter (attitude, social norm, perceived behavioral control). Web-log data will be used to monitor clinicians’ access to the website. Following the 8-week intervention, we will conduct semi-structured group interviews with clinicians and individual interviews with clinic administrators to explore contextual factors influencing the use of the Decision Boxes. Data collected from questionnaires, focus groups and individual interviews will be combined to identify factors potentially influencing implementation of Decision Boxes in clinical practice by clinicians of PHTs.

Conclusions

This project will allow tailoring of Decision Boxes and their delivery to overcome the specific barriers identified by clinicians of PHTs to improve the implementation of shared decision making in this setting.

Background

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.

Methods/Design

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.

Discussion

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.

Background

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.

Methods

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.

Results

Combining multiple classifiers using a weighted linear combination of their prediction scores achieves promising results with an f-measure score of 86.3% for P, 67% for I and 56.6% for O.

Conclusions

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.