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[theory-seminar] Friday March 25th, 1pm: quantitative models in a fully integrated healthcare system

Omer Reingold reingold at stanford.edu
Fri Mar 25 09:17:18 PDT 2022


This is happening today at 1pm. It is a unique perspective on the
challenges and opportunities of causality and predictive models in
healthcare. Many of the examples are from some of the highest impact
scientific research on public health (e.g., effectiveness of vaccination)
during the COVID pandemic. (A tiny but personally exciting part describes
the way multicalibration (born at Stanford) was deployed in this context.)

Highly recommended,
Omer

On Tue, Mar 8, 2022 at 6:57 PM Omer Reingold <reingold at stanford.edu> wrote:

> Noa and Noam (bios below) will give a talk on Friday March 25th at 1pm in
> the Fujitsu conference room (on the 4th floor in Gates). Details on the
> talk are below and (based on their past talks) I highly recommend
> attending. The speakers are experts in public health care with a very
> wide and deep education and a tremendous openness to adopting advanced CS
> research in real-life health care systems (through one of the largest
> health-care providers in the world). They have been involved in some of the
> highest profile COVID research publications (e.g., on the effectiveness of
> vaccines) and I was lucky to have them apply some of our
> algorithmic-fairness work. Feel free to discriminate this invitation to
> anybody who may be interested.
>
> Best wishes
> Omer
>
> *Title*: Opportunities for the application of quantitative models in a
> fully integrated healthcare system
>
> *Abstract*
>
> Health insurance in Israel is mandatory, comprehensive in its list of
> services, and provided by four
> integrated payer-provider organizations. Clalit Health Services is the
> largest of these organizations –
> responsible for the care of over half of the Israeli population. Most of
> this care (outpatient and
> inpatient) is directly provided by Clalit, and the rest is purchased by
> Clalit. All services provided or
> purchased are stored in a single comprehensive analytic data warehouse.
> Our talk will focus on the
> opportunities that such an integrated system and its data offers in using
> quantitative models for
> state-of-the-art research and digital healthcare interventions.
> We will discuss the two main quantitative tools used for digital
> healthcare – causal inference and
> prediction models. We will show how the depth and immediacy of the data
> allowed the conduct of
> causal research that provided necessary and timely information regarding
> the effectiveness and
> safety of mRNA Covid-19 vaccines. We will also show how such unique data
> enabled us to study an
> often-overlooked aspect of the vaccination – indirect protective effects.
> We will also demonstrate
> how this data can be used for promoting predictive, proactive, and
> personalized care. We will
> demonstrate how prediction models are created and how they are integrated
> into the point of care.
>
> Noam Barda holds an MD from Tel-Aviv University, a PhD co-advised in
> public health and computer
> science from Ben-Gurion University and a BSc in computer science from the
> Open University. He
> completed his post-doctorate in the Department of Biomedical Informatics
> (DBMI) at Harvard
> Medical School. He is the head of the Real-World Evidence Research and
> Innovation Lab at Tel-
> HaShomer medical center, Israel’s largest hospital, and co-heads the
> Digital Healthcare Laboratory in
> the department of Software and Information Systems Engineering at
> Ben-Gurion University.
> Noa Dagan holds an MD and an MPH from the Hebrew University, and a PhD in
> Computer Science
> from Ben-Gurion University. She completed her post-doctorate in the
> Department of Biomedical
> Informatics (DBMI) at Harvard Medical School. Dr. Dagan is currently the
> director of the AI-driven
> Medicine Department in Clalit Innovation and the Clalit Research
> Institute, and co-heads the Digital
> Healthcare Laboratory in the department of Software and Information
> Systems Engineering at Ben-
> Gurion University.
>
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