Tag Archives: ACEhp

Outcomes curriculum at the Alliance 2020 Annual Meeting


If outcomes are the “cowbell” of CME, then the upcoming Alliance Annual Meeting is, well…going to have a lot of cowbell. Sorry, I ran out of steam with this intro.

So…more quickly to the matter at hand: the Alliance has organized a five-part outcomes curriculum within the regular sessions at the upcoming Annual Meeting in San Francisco. That means there is no additional charge to participate, but attendance will be limited-ish (trying to keep each session to around 30 participants to facilitate a more skills-based, workshop style). Just “favorite” the sessions via the conference app to make sure you are on the list.

As far as the curriculum components, it kicks-off with a 60-minute session (Thursday, January 9 @ 2:30) led by Jack Kues regarding outcome study design (yes, there is more out there than pre/post). On the following day (Friday, January 10), the Alliance digs into data…a 90-minute workshop on qualitative data (guided by Wendy Turell @ 10:15), a 60-minute quantitative analysis primer (steered by Karyn Ruiz-Cordell @ 1:15) and a 60-minute discussion of data ethics (yes, it’s a thing) helmed by Gary Bird @ 4:15. On the final day (Saturday, January 11), Karen Roy will direct a 90-minute workshop on reporting.

The intention of this curriculum is to empower learners in all phases of outcome study – not just highlight one particular area. Sure, you can cherry-pick sessions in the curriculum, but be aware that good outcomes speak to each of these five areas – focusing on just a few affects the credibility of whatever conclusions you eventually draw from your data.

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Filed under ACEhp, Outcomes, Uncategorized

Bringing boring back

I want to play guitar. I want to play loud, fast and funky.  But right now, I’m wrestling basic open chords.  And my fingers hurt.  And I keep forgetting to breathe when I play.  And my daughter gets annoyed listening to the same three songs over and over.  But so is the way.

When my daughter “plays”.  She cranks up a song on Pandora, jumps on and off the furniture, and windmills through the strings like Pete Townshend.  She’d light the thing on fire if I didn’t hide the matches.  Guess who’s more fun to watch.  But take away the adorable face and the hard rock attitude and what do you have?  Yeah…a really bad guitar player.

I was reminded of this juxtaposition while perusing the ACEhp 2015 Annual Conference schedule.  I know inserting “patient outcomes”  into an abstract title is a rock star move.  But on what foundation is this claim built?  What limitations are we overlooking?  Have we truly put in the work to ensure we’re measuring what we claim?

My interests tend to be boring.  Was the assessment tool validated?  How do you ensure a representative sample?  How best to control for confounding factors?  What’s the appropriate statistical test?  Blah, blah, blah…  I like to know I have a sturdy home before I think about where to put the entertainment system.

So imagine how excited I was to find this title: Competence Assessments: To Pair or Not to Pair, That Is the Question (scheduled for Thursday, January 15 at 1:15).  Under the assumption that interesting-sounding title and informational value are inversely proportional, I had to find out more.  Here’s a excerpt:

While not ideal, providers are often left with unpaired outcomes data due to factors such as anonymity of data, and low overall participation. Despite the common use of unpaired results, literature on the use of unpaired assessments as a surrogate for paired data in the CME setting is limited.

Yes, that is a common problem.  I very frequently have data for which I cannot match a respondent’s pre- and post-activity responses.  I assume the same respondents are in both groups, but I can’t make a direct link (i.e., I have “upaired” data).  Statistically speaking, paired data is better.  The practical question the presenters of this research intend to answer is how unpaired data may affect conclusions about competence-level outcomes.  Yes, that may sound boring, but it is incredibly practical because it happens all the time in CME – and I bet very few people even knew it might be an issue.

So thank you Allison Heintz and Dr. Fagerlie.  I’ll definitely be in attendance.

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Filed under ACEhp, Alliance for CME, CME, Methodology, paired data, Statistical tests of significance, Statistics, unpaired data