[reureu]

I wrote a longer response to another comment with examples of some of the experiments and learnings. But, yes, I think there are effective alternatives, but I think it starts with being really clear on what your success measures are. Do you want to maximize patient outcomes? Do you want providers to feel engaged (or, perhaps, actually engage) with data? Do you want to minimize provider burnout? I was always surprised by how few clinical and tech leaders could actually articulate what the goals were-- it'd often just be "we need providers to have more data!", which I suspect isn't actually what your goal is.

The tldr was that telling providers directly what you want, generally in an emailed newsfeed-style format was the most effective at improving actual outcomes. No slicing and dicing. No graphs. No comparisons. Just "hey, look at these 6 uncontrolled hypertensive patients, and follow-up with any that need follow-up."

Also, to caveat: I'm talking about how to engage the worker-bee providers. Not clinical leadership. Not the quality team. Not the data science/analyst team. Providers who are super busy with patient care, but also expected to manage patients between visits. Basically every experiment we ran favored the most direct, no frills, least effort approach to look at data. Which, coincidentally, was the exact opposite of what the engineering teams wanted to build :-/

[troelsSteegin]

With respect to the tldr - so, on one arm, so to speak, is an interface where clinicians could use different combinations of measures to identify patients who might need some kind of intervention, like follow up attention. Then the other side is an analytic system that uses a specific set of measures, etc, and then messages clinicians with a recommended set of actions?

In the first case, the clinicians have to do analytical work (slice, dice) towards understanding the population of patients. That sounds more like epidemiology... In the latter case, how is it that clinicians will trust the recommender? Is it understood that there is a clinical rationale or authority behind the algorithm? It sounds like "uncontrolled" in this case is based on a measure that clinicians trust.

I think of dashboards as potentially good for monitoring outcomes against expectations, EDA as potentially good for focusing attention on subpopulations, and recommenders as potentially good for efficiently allocating action. In a broad way what you described is a monitoring system that pushes recommended actions out to doers. I'd venture that with busy clinicians that that needs to be pretty accurate, too, and/or that recommendations need both explicit justification and a link to collateral information.

[reureu]

Quality measures are generally well-defined by external authorities, so questions like "what defines uncontrolled" are generally answered. Even when providers personally disagree with this (I worked with a provider who didn't believe in pre-diabetes), they still acknowledge that health care organizations are being judged on these measures, and that the measures are not just arbitrarily defined. How you improve your quality measures becomes where the question turns.

Your comment about epidemiology/EDA/etc really hit the nail on the head. If you sit in on population health meetings at your average hospital/clinic system, you'll see that many people really don't get this. Further, people often conflate their needs/desires with that of others-- so, the data-driven administrator is quick to say "we just need doctors to be able to slice and dice their data, and then we'll have better quality scores." But they're talking about what their needs are, and it's completely not what the doctors actually need (well, and from monitoring usage of dashboards for those types, I'd argue it's also not what they need either, but that's a different issue). And, the reason I keep saying "slice and dice" is because I've heard that phrase used by every vendor I've evaluated, and in practically every strategy meeting regarding population health at multiple institutions.

I'd personally shy away from describing this issue in terms of a recommender, since that has a pretty connotation in the ML world, and it doesn't really line up well (e.g., there's not a well-defined objective function or a clear feedback loop to train a recommendation system on). However, getting away from that specific concept, I think it's reasonable to say that there are needs for multiple distinct but ideally-related systems in the population health world: one for analysis to be used by quality and data people, and one specifically for the clinicians doing the work.