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'Market research' for a potentially cool community activity for our academic partners (and others)

Today, @noemie, @chunhua and I were brainstorming a big idea, in part prompted by a good suggestion by @gowtham_rao: Imagine there was a graduate-level (masters and PhD) course offered in ‘healthcare data sciences’, which taught students from either the clinical sciences, public health, computer science, or statistics the applied skills to take clinical questions, translate into an observational study design, and execute the study to generate reliable evidence that can meaningfully inform medical decision making. Students would learn about healthcare data (claims, ehr, registries), would learn data wrangling (including database basics with sql) and would cover 3 flavors of analyses (descriptive predictive, and causal inference) with specific skills in r or python. We discussed the various modalities of such a course: you can do traditional on-campus lecture/lab format, you can do a mooc, you can offer video tutorials on youtibe, etc, each has their own merits and limitations. Imagine a different model, where universities partnered together to each offer the same course during the same calendar time/semester. There would be shared lectures/mooc-style lectures which taught principles and basics of healthcare data science using some common datasets (think synpuf, mimic, nhanes, hcup nis), but then on each campus, universities would also conduct labs on their campus-specific datasets to answer real clinical quesrions, either initiated by the students or prompted by the faculty. During the semester, students from across campuses could learn from each other via some common platforms, like a slack channel and github. Students’ final project would be to complete a study on their local data (hopefully resulting in a publication) but would also be provided the opportunity to submit their project across entire ohdsi network (ideally resulting in another publication).

So our questions to the community, before going too much further down this flight of fancy:

for our academic partners:

  1. do you have some course on your campus that already covers this content? if yes, what is it? if not, any reason why?
  2. would you or a colleague on your campus have interest in leading such a course within your university?
  3. do you have a omop cdm instance and associated computing infrastructure (database server and analysis server) which could be made accessible to students for coursework with the appropriate legwork? if yes, what’s your data? if not, what barriers do you have to establishing such an environment?
  4. how willing would your institution be in collaborating in a cross-institutional educational opportunity like this? would the novelty and impact of such an idea make it fly, or would the suggestion of collaboration create insurmountable administrative hurdles?
  5. on a scale from 1-10, how excited are you by this idea…1=i’ll never do it no matter what you say, to 10= awesome, sign me up for the first cohort of universities, I’m all in!

for our non-academic partners:

  1. if a network of academic centers decided to band together to offer a course in ‘healthcare data sciences’, how would you like to be involved? could you provide data to sites that need that? provide good clinical questions that impact public health and require reliable evidence? be a guest lecturer in the shared videos or on campus labs? provide funding for the effort required by faculty to make this vision a reality?
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Thank you @patrick_ryan, regarding course work. I think what is needed is:

The content of the course should definitely be advanced for a college graduate, but within the reach (not easily) of somebody in the first or second semester of a Master’s program in related field. It should be refresher course for someone with a PhD or an exceptional candidate with several years of active healthcare Analytics experience. Health care/clinical background should be suggested but not required (Wikipedia level), but mathematical or statistical background should be required. Degree seeking vs certificate of completion - degree seeking is optional. The content should welcome non-academic students like actuaries, medical professional, quality analysts, business executives in healthcare, public health professionals, population health experts - who are interested in framing a question, developing a study, and finding answer. This course is probably not designed for an academic scientist pursuing a career in funded research.

This will get non academic centers more engaged, offer better framed questions, etc.

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+1 on this! FINALLY a training to help us generate more RWE unicorns.

As a non-academic partner in this ecosystem, I see extremely high value in enabling these kinds of trainings and coursework. I could see us non-academics contributing as a mentor network throughout the research process helping provide insight on the use cases and marketplace perspectives on how this kind of real world evidence is helping improve patient outcomes. After all Roche just acquired Flatiron for $1.9B… so we know that the landscape of how these data sources exist is going to be rapidly changing and may not even stay the same in one semester!

I highly suggest seeking some support perhaps through existing corporate engagement models (e.g. academic partnerships, dedicating recruiting teams at specific schools, etc). The value is clear: this training helps bring talent who would otherwise have to learn on the job. It’s a win-win.

A MOOC version of this content (think a freemium model) could be a really cool way to help bridge some of the gaps from the amazing existing state tutorials to the world of a million questions people ask once they’re activated.

Also, I think SynPUF doesn’t get enough credit for how great it is! You can do a lot without having access to live data.

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Dear all,

Just a quick note for now, but what is being described here is strikingly
similar to my thoughts on what we are trying to create here at Case Western
Reserve University. Our team of analyst/programmers and research
scientists are fairly new to OHDSI, however, we are currently sold on the
power of the OMOP CDM model and that of the entire OHDSI collaborative.
I’d like to see this integrated into the curriculum of our recently
launched Health Informatics certificate programs as well as our pending
M.S. and Ph.D. programs. Next week we will be presenting OHSDI to our ICB
Data Club, which is open to faculty, research staff, students, and anyone
else in the greater Cleveland area interested in health data and big data.
In March and April, OHDSI and tools will be featured at grand rounds talks
at two of our Cleveland area hospitals.

Have to put my head back down now, but more later.

Best,
Mark

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I am interested in this. As I mention in the panel at the symposium, I think one of the most important next steps for OHDSI is education. We need to train medical students, but also the current epidemiologist on all the points described in your email Patrick. There is still a lot off misunderstanding about the CDM and Vocabs we now need to get off the table for good.

Training of stakeholders will be high on the agenda of projects we will be leading out of Erasmus in the upcoming 5 years so this is a nice link.

We do already have a Clinical Technology MSc track for students that are partly located at the medical center and partly at the technical university. However, this is still not covering all the important parts i think (i do give a lecture and practical on machine learning).

I have also been experimenting with DataCamp and have implemented a first rough version of CDM + Vocab training including queries against SYNPUF. What is nice in their tool is that you could have the vocab for example accessible in SQL box on the page, you can run R without installing anything etc etc. I had a call with DataCamp (located in Belgium and Boston) and they were interested in this idea and would be willing to help out in creating the courses if we have clear outline etc etc. There is a nice business model behind these courses as well at DataCamp.

I know ErasmusMC will be moving more and more into the data science domain in the upcoming 5 years and we will be actively involved in this new vision in 2018. Therefore, their may be interest in the thing you propose…

There is experience in courses that are given across different institutes for example through EU2P (https://www.eu2p.org). However, I am not really involved in that myself anymore (others in our Department still are).

So yes, would be nice to think this through in more depth. If there is a working group created around this I am happy to participate.

Peter

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Hi All,

  1. do you have some course on your campus that already covers this content? if yes, what is it? if not, any reason why?

REPLY: No. As a biomedical informatics department, we teach most of the various parts of this but not in the coherent manner described.

  1. would you or a colleague on your campus have interest in leading such a course within your university?

REPLY: Absolutely. We are in the School of Medicine and attract a number of computationally minded students who want to apply their skills to clinical research problems. Colleagues and students in public health, pharmacy, and various medicine departments who would find this course of interest.

  1. do you have a omop cdm instance and associated computing infrastructure (database server and analysis server) which could be made accessible to students for coursework with the appropriate legwork? if yes, what’s your data?

REPLY: Yes. We have a relatively large amount of hospital data and a relatively small amount of nursing home data in CDM.

  1. how willing would your institution be in collaborating in a cross-institutional educational opportunity like this?

REPLY: I don’t the institution as a whole (e.g., at the higher levels) would be particularly excited about collaboration unless there was funding. The U depends on soft money and indirects. Education is valued but not generally given priority. In our department, some excitement would come from the Center for Clinical Research and from various other departments. There would be some barriers to collaboration but none that should be insurmountable b.c. we already are involved in multiple research networks.

  1. on a scale from 1-10, how excited are you by this idea

REPLY: 8 - 9 : very excited, and it is good timing to consider this b/c we are redesigning our curriculum, the main limitation that I am spread thin.
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Would love to be a part of this! At Stanford, there’s interest in a hands-on population health data science course for epidemiology, biomedical data science and CS students. Since the course would cover healthcare topics, but also things like health disparities, “deaths of despair” and teach students to also use datasets like IPUMS/CDC Wonder, we felt “healthcare” was too limiting and was going to call the course, “population health data science”.

I’m glad to see that many other institutions have similar ideas too! Before they dive into research, Stanford students have told me that they want more experience working with data, learning about the strengths and limitations of different data sets relative to each other, and thinking about how to set-up, execute and troubleshoot traditional and more nuanced (e.g. ML) experiments with health-related data. Departments seem very supportive of this type of courses too, but it’s much more work to put together than something that’s lecture-based – that’s been the main hold up here.

We don’t have it all together at the moment, but have an application in to AHRQ to start a Learning Health System training program that includes both online topic material and an annual hands-on workshop. Fingers crossed . . .

We were lucky enough to get interest across several universities, though as with all things academic, funding is an important step. :smile:

We have the PEDSnet core instance, which we’d plan to use as the “lab” for the training program.

Well, we got them on board once . . .

We’d be an easy 10 in the group here, of course. It’s a bit harder to sell to the institution at large, as people tend to be focussed on whatever data architecture is closest to their project. But there’s a growing awareness of the benefits of collaborative infrastructure, and the more we teach up-and-coming investigators, the better.

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I got this email today:

And immediately thought about this forum post. I could imagine a world where we could support this kind of curriculum. I would even have applied to get this kind of degree if it existed when I was in school (you know, back when we were afraid of Y2K).

Even though it’s still blizzarding around some of us, let’s keep this topic warm! I’m developing a curriculum internally for my younger staff to get more attuned to these types of analytical challenges. This is very important for our community! Let’s use some of our upcoming face-to-face meetings to keep the momentum.

t