nkrumm

To some extent there is some legislative action (particularly at the state level) that addresses this.

The term commonly used here is “Balance Billing”, though that refers to a variety of “surprise” bills.

MD here. It's "not done" because it could hurt you.

Here's the Bayesian argument:

- Let's assume a lab test (or imaging test) has a 1% false positive rate and 100% sensitivity (that would be pretty good for many tests and imaging modalities!).

- The cancer you screening for has a 1% incidence as well (low-ish, for the sake of argument).

- Therefore the chance that a "positive" scan would be due to a false positive or a true cancer would be equivalent (i.e., the Positive Predictive Value is 50%).

If we did this at population level, we'd be massively overtreating and over-biopsy-ing... not the desired outcome.

UW Laboratory Medicine | ONSITE (Seattle, WA) | Full-Time

Our department maintains all clinical testing and laboratory operations at the UW Medical Center, from sample collection to analysis and final reporting. We employ over 900 people and handle 30 million tests each year. In parallel, we research, develop and operationalize novel diagnostics in genomics, mass spectrometry, flow cytometry and automated imaging.

* Data Scientist * - Develop our data warehouse, ETL/analysis pipelines and our front-end reporting tools. - Ideal candidates will have healthcare or laboratory data experience and will have technical expertise in data science, database management, application development, etc.

* Bioinformatics Engineer * - Develop cutting-edge genomics bioinformatics pipelines for our clinical sequencing lab. As an academic lab, we pursue new and interesting diagnostic tests, including cell-free DNA testing, RNAseq, etc.

Our stack: Primarily R, python, and SQL. We run on mixed (HIPAA compliant) AWS and local infrastructure.

QUALIFIED JUNIOR APPLICANTS are welcome to apply.

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Why UW Lab Medicine?

‣ You'll impact patient care through improvements to the quality, accuracy and precision of our laboratory tests. Our department prides itself in innovation and we are highly motivated to bring on new solutions that improve our clinical service.

‣ You'll gain experience with "real world", highly complex and interesting data directly from our laboratory and clinical data streams.

‣ You'll interface with a wide range of users and stakeholders, all of who are committed to the UW's "Patients are First" aim.

-------

Feel free to contact me at nkrumm@uw.edu with [hnjob] in the subject line or see our official listings at:

https://uwhires.admin.washington.edu/eng/candidates/default.... https://uwhires.admin.washington.edu/eng/candidates/default....

GitHub: https://github.com/Piezoid/pugz.

From the readme:

"Contrary to the pigz program which does single-threaded decompression (see https://github.com/madler/pigz/blob/master/pigz.c#L232), pugz found a way to do truly parallel decompression. In a nutshell: the compressed file is splitted into consecutive sections, processed one after the other. Sections are in turn splitted into chunks (one chunk per thread) and will be decompressed in parallel. A first pass decompresses chunks and keeps track of back-references (see e.g. our paper for the definition of that term), but is unable to resolve them. Then, a quick sequential pass is done to resolve the contexts of all chunks. A final parallel pass translates all unresolved back-references and outputs the file."

UW Department of Laboratory Medicine | Data Scientist | ONSITE (Seattle, WA) | Full-Time Our department maintains all clinical testing and laboratory operations at the UW Medical Center, from sample collection to analysis and final reporting. We employ over 900 people and handle 30 million tests each year. In parallel, we research, develop and operationalize novel diagnostics in genomics, mass spectrometry, flow cytometry and automated imaging.

We are looking for a data scientist who will provide analytical and infrastructure support to our lab. You'll work with the department's faculty, laboratory staff and software programmers to develop our data warehouse, ETL/analysis pipelines and our front-end reporting tools.

Ideal candidates will have healthcare or laboratory data experience and will have technical expertise in data science, database management, application development, etc.

Our stack: Primarily R, python, and SQL. We run on mixed (HIPAA compliant) AWS and local infrastructure.

-------------

Why UW Lab Medicine?

‣ You'll impact patient care through improvements to the quality, accuracy and precision of our laboratory tests. Our department prides itself in innovation and we are highly motivated to bring on new solutions that improve our clinical service.

‣ You'll gain experience with "real world", highly complex and interesting data directly from our laboratory and clinical data streams.

‣ You'll interface with a wide range of users and stakeholders, all of who are committed to the UW's "Patients are First" aim.

-------------

Feel free to contact me at nkrumm@uw.edu with [hnjob] in the subject line or see our official listing at: https://uwhires.admin.washington.edu/eng/candidates/default....

Fascinating. For some interesting US history on the topic, see: https://www.fhwa.dot.gov/infrastructure/right.cfm

In particular I found this interesting: "[...] most American cars produced before 1910 were made with right-side driver seating, although intended for right-side driving [mirroring how horse-drawn carriages had been set up]. Such vehicles remained in common use until 1915, and the 1908 Model T was the first of Ford's cars to feature a left-side driving position."

HIPAA does not explicitly call out genetic/genomic information as one of the oft-cited 18 elements of protected information [1]. However, genetic/genomic information is considered to fall under the 18th element, and HHS states that it must be treated as PHI [2,3]. In fact, HIPAA is very broad in its definition of PHI:

"The Privacy Rule protects all "individually identifiable health information" held or transmitted by a covered entity or its business associate, in any form or media, whether electronic, paper, or oral. The Privacy Rule calls this information "protected health information (PHI)." [4]

Finally, it's worth pointing out that some states (e.g, WA) have enacted their own legislation that specifically calls out genetic testing and data as PHI [5]. In addition, there is the GINA act, which provides some protections as well [6]

  [1]: https://cphs.berkeley.edu/hipaa/hipaa18.html
  [2]: https://www.hhs.gov/hipaa/for-professionals/faq/354/does-hipaa-protect-genetic-information/index.html
  [3]: https://www.law.cornell.edu/cfr/text/45/160.103
  [4]: https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html
  [5]: https://app.leg.wa.gov/billsummary?BillNumber=6284&Initiative=false&Year=1999
  [6]: https://en.wikipedia.org/wiki/Genetic_Information_Nondiscrimination_Act

UW Department of Laboratory Medicine | Data Scientist | ONSITE (Seattle, WA) | Full-Time

Our department maintains all clinical testing and laboratory operations at the UW Medical Center, from sample collection to analysis and final reporting. We employ over 900 people and handle 30 million tests each year. In parallel, we research, develop and operationalize novel diagnostics in genomics, mass spectrometry, flow cytometry and automated imaging.

We are looking for a data scientist who will provide analytical and infrastructure support to our lab. You'll work with the department's faculty, laboratory staff and software programmers to develop our data warehouse, ETL/analysis pipelines and our front-end reporting tools.

Our stack: Primarily R, python, and SQL. We run on mixed (HIPAA compliant) AWS and local infrastructure.

-------------

Why UW Lab Medicine?

‣ You'll impact patient care through improvements to the quality, accuracy and precision of our laboratory tests. Our department prides itself in innovation and we are highly motivated to bring on new solutions that improve our clinical service.

‣ You'll gain experience with "real world", highly complex and interesting data directly from our laboratory and clinical data streams.

‣ You'll interface with a wide range of users and stakeholders, all of who are committed to the UW's "Patients are First" aim.

-------------

Feel free to contact me at nkrumm@uw.edu with [hnjob] in the subject line or see our official listing at: https://uwhires.admin.washington.edu/eng/candidates/default....

Encryption is an "addressable implementation" in the final Security Rule [1]. Practically this means you are not required to encrypt your data (e.g., between LB and VM) "if the entity decides that the addressable implementation specification is not reasonable and appropriate [...]".

https://www.hhs.gov/hipaa/for-professionals/faq/2001/is-the-...

Note that these companies are not doing DNA microarrays in the sense you're describing (which is for quantifying gene expression by mRNA level). RNA-seq is (now) arguably better for this purpose as well (better dynamic range, primarily), but again this is not what any of the ancestry or personalized genetics companies are doing.

In this case "SNP microarray" is a "digital" (or binary) readout of the genomic DNA sequence; specifically the array has attached to it sequences that match both the "reference allele" (e.g., a "G") and the "alternate allele" (e.g., "A" or some other base). Note that across the world's population, most sites in the human genome are "bi-allelic" meaning that there's really only two variants at each site, though of course it is possible to have tri-allelic or even fully polymorphic (A, T, C, or G) sites. For the purposes of ancestry, bi-allelic identification of commonly polymorphic sites is adequate, though the more sites and the rarer those sites are in world populations, the more specific you can be re: ancestry. This is why the gold standard for ancestry and haplotyping is whole genome sequencing-- you simply get far more informative sites to map to other populations.

It's true that these companies are not offering "sequencing" per se but rather are genotyping commonly polymorphic SNPs using an array or other genotyping method.

These SNP arrays, however, are exceedingly accurate and reproducible in their calls. A common medium-density SNP array used for human genetics is the Illumina CytoSNP-850K [1], which routinely gets >99.5% accuracy on calls. This is at least as good, or better than, whole genome sequencing.

The challenge in ancestry assignment is not determining what is in the sample, but rather in the interpretation and assignment of ancestral haplotypes.

[1] https://www.illumina.com/content/dam/illumina-marketing/docu...

The posted text is actually a series of "dot phrases" or "smart texts" (aka templates in EHR speak), _not_ actual notes. Mostly these are inserted into clinical notes to achieve a certain level of documentation; typically they are saved in a "all negative" format, and the relevant parts are edited to reflect the patient history and physical. These seem to have been derived from the Univ of Washington Emergency Medicine residency.

These might make OK material for some initial testing but they don't reflect a real clinical note. A good source of those might be the MIMIC database [1]

[1] https://mimic.physionet.org/

Clinical history, some labs, and imaging are typically used to diagnose. In adults, CT scan has the highest sensitivity and specificity (0.96 and 0.96); better than ultrasound. The difference is not as pronounced in children (smaller abdomen-- easier to visualize using ultrasound), so that is commonly used to save exposure to radiation (though note that an abdominal CT scan is actually a very low dose).

Source: https://effectivehealthcare.ahrq.gov/topics/appendicitis/res...

"The estimated effective radiation dose of abdominopelvic CT is 8 to 10 mSv with standard dose and 2 to 4 mSv with low dose techniques [40]. To put these numbers into context, the effective dose from annual background radiation is 3.1 mSv and from plain abdominal radiography is 0.7 mSv." (from https://www.uptodate.com/contents/acute-appendicitis-in-adul...)

Also ndscheduler: https://github.com/Nextdoor/ndscheduler/

The limiting factor is not the spots for medical students, but rather for residents and fellows, who are primarily funded through medicare/medicaid and the VA.

An interesting post on this is here, although it somewhat contradicts the popular notion that graduate medical education is limited by funding: https://theincidentaleconomist.com/wordpress/gme-federal-fun...

Even if they do not directly pass this on to patients, cheaper generics will benefit patients in terms of their availability. I recently was involved in a case where a flucytosine (an old, generic anti-fungal) was considered for a patient, but the cost of treatment was $7,000 (as an outpatient) and therefore not considered (despite this being the best option). For an interesting analysis of this, see [1].

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4885648/

> and the remaining third perish due to the failure of one or more other organs.

One of these organs is the heart-- the seasonal variation in influenza actually overlaps with an increase in acute myocardial infarction (AMI, or a 'heart attack'). Recent meta analysis has shown an odds ratio of ~2 for AMI in the setting of influenza.

Mechanistically, there is a suspected link between the virus and inflammatory changes in the blood vessels supplying the heart that destabilizes existing atherosclerotic plaques.

More here: http://heart.bmj.com/content/101/21/1738 http://www.sciencedirect.com/science/article/pii/S1473309909...

Edit: The article mentions (viral) myocarditis as a sequelao f the flu-- this can also happen, but is separate from AMI

Perhaps not exactly "junk dna" but you are right in your thinking about gene duplications... duplications are thought to promote "neofunctionalization" (a new function) or "subfunctionalization" (specialization).

See wikipedia [1] for an overview.

[1] https://en.wikipedia.org/wiki/Gene_duplication#As_an_evoluti...

This is an interesting study, and I highly encourage everyone to also read the accompanying editorial, which raises some important methodological shortcomings. See here: https://jamanetwork.com/journals/jama/fullarticle/2657356

The reason this study was done because there has been a very well replicated association between the plasma (not red cells) donated from previously pregnant women and the risk of TRALI, a very rare but deadly transfusion reaction. After this association was made clear, blood banks no longer collect plasma from previously pregnant ("ever pregnant") women, and the overall rate of TRALI has dramatically dropped (from about 35 fatalities in the US per year to ~15).

Source: MD/lab medicine resident

It's worth pointing out that that the Deutsche Bahn (German Rail) has been providing this sort of service for at least a decade (I remember seeing them parked outside of train stations in 2008):

https://www.callabike-interaktiv.de/de/registrieren

The underlying biology of spermatogenesis is also involved. As males age, the germ cells (~stem cells) that produce sperm are continuously dividing. One of the daughter cells from the division becomes sperm, the other continues on as a germ cell [0]. Over time and with aging, the germ cells accumulate new mutations (primarily single nucleotide changes), which are then also passed on in the newly created sperm. While it is also possible that a mother's eggs carries a new mutation, these do not increase in frequency with aging because her eggs are created in utero and do not undergo the lifetime of regeneration that the father's sperm do.

These "de novo" mutations can sometimes affect or "break" genes important for normal development, thus resulting in autism. They can be studied by sequencing father/mother/child families and comparing the genetic sequence of each. The de novo mutations show up in the child only. We can differentiate if they arose in the father's sperm or the mother's eggs by examining the "haplotype" around each of these mutations (basically, looking for other nearby mutations that were inherited by the child from either mom or dad).

For more on this, see this paper: https://www.ncbi.nlm.nih.gov/pubmed/22495309

[0]: https://en.wikipedia.org/wiki/Spermatogenesis#/media/File:Sp...

The US does vaccinate more than other countries, but it is not 4-fold higher. See this very interesting infographic:

http://www.bmj.com/content/351/bmj.h5966/infographic

By age 6, the standard vaccine schedule would result in 49 vaccinations in the US, 44 in Germany.

Hard to tell exactly what this entails, but there is a very functional low-cost solution already on the market called the Pocket Talker (https://www.amazon.com/Pocket-Talker-Ultra-System-Headphone/...)

Yes-- the inheritance of mtDNA is maternal, but interestingly not 100% of her children will inherit the disease. This is because the mother will have a mix of mutated (diseased) mtDNA and wild-type (healthy) mtDNA in each of her cells, including her eggs. Some of the eggs will have close to all mtDNA mutated, but many will have a relatively low mutational burden. (This variation amongst the mother's cells and eggs is called heteroplasmy). Most mitochondrial genetic diseases have a heteroplasmy threshold (varying by gene/pathway, between 30-90% for many); when the overall burden in a fertilized egg is above this threshold, the disease is manifested in the offspring. Thus, another way to circumvent the transmission of mtDNA diseases is by using regular in vitro fertilization, and checking one of the cells at the 16 or 32 cell stage for their heteroplasmic content.

Here are some references:

Lightowlers, R. N., Taylor, R. W., & Turnbull, D. M. (2015). Mutations causing mitochondrial disease: What is new and what challenges remain? Science, 349(6255), 1494–1499. http://doi.org/10.1126/science.aac7516

Taylor, R. W., & Turnbull, D. M. (2005). Mitochondrial DNA mutations in human disease. Nature Reviews Genetics, 6(5), 389–402. http://doi.org/10.1038/nrg1606

Smeets, H. J. M., Sallevelt, S. C. E. H., Dreesen, J. C. F. M., Die Smulders, C. E. M., & Coo, I. F. M. (2015). Preventing the transmission of mitochondrial DNA disorders using prenatal or preimplantation genetic diagnosis. Annals of the New York Academy of Sciences, 1350(1), 29–36. http://doi.org/10.1111/nyas.12866

MRSA is Staph Aureus, a gram-positive organism, and colistin is only (to my knowledge) used in treating gram negative infections (such as e coli, which can carry mcr-1).

MRSA can be treated with a number of different antibiotics, including vancomycin, daptomycin, linezolid, Bactrim, and clindamycin to name a few. Teixobactin is not yet on the market or approved for use in humans.

If you are curious as to what this might look like, here is the University of Washington 'antibiogram' for 2015:

http://depts.washington.edu/labweb/Divisions/Micro/antibiogr...

Are there any efforts do implement his "immediate feedback" ideas in the context of data science/scientific computing?

I could see some amazing iPython/jupyter demos, where the user can import some data, filter/rearrange data, and create a few plots-- but then adjust and see the plots change dynamically by adjusting values throughout the notebook.

Not just the quality of the busses but also the quality of the roadway, which is terrible in Seattle...

Agreed-- especially in the early years of education. The Montessori model [0] is based on this. I remember being taught by older students, and then teaching/helping younger students with the same concepts. In addition, mixed-age models lets each student advance at his/her own pace for each subject area.

[0] https://en.wikipedia.org/wiki/Montessori_education

This would make a great IPython notebook style as well...