Abstract P2-10-01:onlineDeCISion.org: An interactive web-based clinical decision aid for DCIS treatment

Author(s):  
Elissa M Ozanne ◽  
Natasha K Stout ◽  
Katharine Schneider ◽  
Djøra Soeteman ◽  
Deborah Schrag ◽  
...  
2015 ◽  
Vol 154 (1) ◽  
pp. 181-190 ◽  
Author(s):  
Elissa M. Ozanne ◽  
Katharine H. Schneider ◽  
Djøra Soeteman ◽  
Natasha Stout ◽  
Deborah Schrag ◽  
...  

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 100488
Author(s):  
Rachel Gold ◽  
Mary Middendorf ◽  
John Heintzman ◽  
Joan Nelson ◽  
Patrick O'Connor ◽  
...  

2016 ◽  
Vol 16 (3) ◽  
pp. 775-791
Author(s):  
Angeline Su Lyn Lum ◽  
Thiam Kian Chiew ◽  
Chirk Jenn Ng ◽  
Yew Kong Lee ◽  
Ping Yein Lee ◽  
...  
Keyword(s):  

2018 ◽  
Author(s):  
Peter Scalia ◽  
Glyn Elwyn ◽  
Jan Kremer ◽  
Marjan Faber ◽  
Marie-Anne Durand

BACKGROUND Randomized trials of Web-based decision aids for prostate-specific antigen (PSA) testing indicate that these interventions improve knowledge and reduce decisional conflict. However, we do not know about these tools’ impact on people who spontaneously use a PSA testing patient decision aid on the internet. OBJECTIVE The objectives of this study were to (1) determine the impact of the Web-based PSA Option Grid patient decision aid on preference shift, knowledge, and decisional conflict; (2) identify which frequently asked questions (FAQs) are associated with preference shift; and (3) explore the possible relationships between these outcomes. METHODS Data were collected between January 1, 2016, and December 30, 2017. Users who accessed the Web-based, interactive PSA Option Grid were provided with 3 options: have a PSA test, no PSA test, or unsure. Users first declared their initial preference and then completed 5 knowledge questions and a 4-item (yes or no) validated decisional conflict scale (Sure of myself, Understand information, Risk-benefit ratio, Encouragement; SURE). Next, users were presented with 10 FAQs and asked to identify their preference for each question based on the information provided. At the end, users declared their final preference and completed the same knowledge and decisional conflict questions. Paired sample t tests were employed to compare before and after knowledge and decisional conflict scores. A multinomial regression analysis was performed to determine which FAQs were associated with a shift in screening preference. RESULTS Of all the people who accessed the PSA Option Grid, 39.8% (186/467) completed the interactive journey and associated surveys. After excluding 22 female users, we analyzed 164 responses. At completion, users shifted their preference to “not having the PSA test” (43/164, 26.2%, vs 117/164, 71.3%; P<.001), had higher levels of knowledge (112/164, 68.3%, vs 146/164, 89.0%; P<.001), and lower decisional conflict (94/164, 57.3%, vs 18/164, 11.0%; P<.001). There were 3 FAQs associated with preference shift: “What does the test involve?” “If my PSA level is high, what are the chances that I have prostate cancer?” and “What are the risks?” We did not find any relationship between knowledge, decisional conflict, and preference shift. CONCLUSIONS Unprompted use of the interactive PSA Option Grid leads to preference shift, increased knowledge, and reduced decisional conflict, which confirms the ability of these tools to influence decision making, even when used outside clinical encounters.


2018 ◽  
Vol 16 (1) ◽  
Author(s):  
David Benrimoh ◽  
Robert Fratila ◽  
Sonia Israel ◽  
Kelly Perlman

Globally, depression affects 300 million people and is projected be the leading cause of disability by 2030. While different patients are known to benefit from different therapies, there is no principled way for clinicians to predict individual patient responses or side effect profiles. A form of machine learning based on artificial neural networks, deep learning, might be useful for generating a predictive model that could aid in clinical decision making. Such a model’s primary outcomes would be to help clinicians select the most effective treatment plans and mitigate adverse side effects, allowing doctors to provide greater personalized care to a larger number of patients. In this commentary, we discuss the need for personalization of depression treatment and how a deep learning model might be used to construct a clinical decision aid.


CJEM ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 309-312
Author(s):  
Robert Ohle ◽  
Sarah McIsaac ◽  
Justin Yan ◽  
Krishan Yadav ◽  
Debra Eagles ◽  
...  

ABSTRACTObjectivesOne in four cases of acute aortic syndrome are missed. This national survey examined Canadian Emergency physicians’ opinion on risk stratification, the need for a clinical decision aid to risk stratify patients, and the required sensitivity of such a tool.MethodsWe surveyed 1,556 members of the Canadian Association of Emergency Physicians. We used a modified Dillman technique with a prenotification email and up to three survey attempts using electronic mail. Physicians were asked 21 questions about demographics, importance of certain high-risk features, investigation options, threshold for investigation, and if a clinical decision tool is requiredResultsWe had a response rate of 32%. Respondents were 66% male, and 49% practicing >10 years, with 59% in an academic teaching hospital. A total of 93% reported a need for a clinical decision aid to risk stratify for acute aortic syndrome. A total of 99.6% of physicians were pragmatic accepting a non-zero miss-rate, two-thirds accepting <1%, and the remaining accepting a higher miss-rate.ConclusionsOur national survey determined that emergency physicians would use a highly sensitive clinical decision aid to determine which patients are at low, medium, or high-risk for acute aortic syndrome. The majority of clinicians have a low threshold (<1%) for investigating for acute aortic syndrome, but accept that a zero miss-rate is not feasible.


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