Top 10 Healthtech advances to watch in 2016

Writing for Healthcare IT News, Jessica Davis describes the following 10 advances that may define 2016. The original article goes into each in some detail:

1. Mobile stroke units,
2. Medical device cybersecurity, 
3. Wireless wearable sensors,
4. Miniature leadless pacemakers, 
5. Blue-violet LED light fixtures,
6. New high-cost cardiovascular drugs,
7. Changing landscape of robotic surgery,
8. Spectral computed tomography,
9. Injected bioabsorbable hydrogels, 
10. Warm donor organ perfusion systems.

What, no genomics?!  :-)

Internet connected inhalers: Technology to watch

From Reuters:

Novartis wants every puff of its emphysema drug Onbrez to go into the cloud.
The Swiss drugmaker has teamed up with U.S. technology firm Qualcomm to develop an internet-connected inhaler that can send information about how often it is used to remote computer servers known as the cloud.

This kind of new medical technology is designed to allow patients to keep track of their drug usage on their smartphones or tablets and for their doctors to instantly access the data over the web to monitor their condition.

It also creates a host of "Big Data" opportunities for the companies involved - with huge amounts of information about a medical condition and the efficacy of a drug or device being wirelessly transmitted to a database from potentially thousands, even millions, of patients.

This technology has amazing potential. If you have an idea regarding how much this device would cost, send me a message on Twitter.

Presumably a more pricey inhaler wouldn't be disposable like the current plastic devices; It would be a reusable device that simply accepts replacement Onbrez cartridges as new prescriptions are filled.
In this case, the inhaler cost becomes less relevant as it's amortized over the life of the patient's disease (long) versus the life of the patient's prescription fill (short).

Since it's internet connected, presumably it would be easy to add features like a reminder at the next dose (i.e. sound or LED), automatic prescription refills, etc.

All in all, very nice!

There may be a link between immunosuppressants and cancer

The Toronto Star reports that some transplant recipients have a 3 times higher risk of dying from cancer, related to the general population:

The increased cancer mortality may be due to the immunosuppressant drugs that allow the patient not to reject the organ. The suppressed immune system may not be able to fight the cancer from developing and may allow the malignancy to be more aggressive, says [Nancy] Baxter, a senior scientist at ICES. Transplant recipients are often on the drugs for life. 

Once cancer develops, a transplant recipient may receive less aggressive treatment because of other existing health problems and the fear of possible transplant rejection, according to the study.

A follow up commentary to the study notes that while the role of wholesale cancer screening for the general population is being re-thought, this study argues that screening those that are solid transplant recipients might be worthwhile.

Whether these people are high enough risk to justify the cost of providing screening services is a question left for health economists to answer.

The original study can be found here.

Personalized Medicine has Two Sides

Andre Picard, in the National Post, writes:

The cost of sequencing is falling rapidly. It cost in excess of $3-billion (U.S.) to decode the first human genome, but now the $1,000 test is imminent, putting the technology within the reach of many. Practically, this means we are moving to an era in which medical treatments, and drugs in particular, are tailored to individuals based on their genetic makeup.

These advances, however, bring with them a host of ethical and economic challenges – in part, whether the new technologies and the benefits that flow from them, will be available equitably, to those most in need and not just those who can afford them.

One of the big potential benefits of personalized medicine is not giving expensive treatments to those who can afford them, if they won't work because of their (or their tumor's) genetics. For example, a $250 ALK mutation that's negative rules out a $90,000/year run of Xalkori, an ALK inhibitor,

Maternal kissing study is clearly a hoax

There's an entertaining hoax article in the Journal of Evaluation in Clinical Practice, “Maternal Kisses Are Not Effective in Alleviating Minor Childhood Injuries (Boo-Boos): A Randomized, Controlled, and Blinded Study,” authored by the Study of Maternal and Child Kissing (SMACK) Working Group.

Their conclusions clearly find that maternal kissing has no effect on making children feel better, suggesting that the practice is probably a waste of everyone's time.

Maternal kissing of boo-boos confers no benefit on children with minor traumatic injuries compared to both no intervention and sham kissing. In fact, children in the maternal kissing group were significantly more distressed at 5 minutes than were children in the no intervention group. The practice of maternal kissing of boo-boos is not supported by the evidence and we recommend a moratorium on the practice.

Not only is kissing boo-boos not beneficial, it could actual be harmful! So what should mothers do instead?

Some would likely argue that, given that maternal kisses did not clearly harm children, the practice is innocuous. ... [Since] maternal resources are very limited, and time spent on delivering ineffective kisses to boo-boos means that maternal attention is not devoted to other activities that have clearly been shown to be beneficial to toddlers. ... Most importantly, reliance on ineffective therapies may delay or prevent the delivery of proven and appropriate medical care, such as Bac-Be-Gone® antibacterial ointment and Steri-Aids® self-adhesive bandages.

If you weren't convinced that the paper is a joke, you have to read the fine print. Always read the conflicts or funding sections of papers.

Acknowledgement
Funding provided by The Initiative to Improve Childhood Health.¹
Footnote
¹ A fully owned subsidiary of Proctor and Johnson, Inc., manufacturers of Bac-Be-Gone ointment and Steri-Aids self-adhesive bandages.

Right. Not only is there a clear conflict of interest, the conflict is due to funding from a company that's obviously a completely fake!

Well played!

10 Things Scientists Should Do to Start 2016

1. Don't start doing research right away. Think.
2. Figure out which 2015 projects are past their prime and finish/publish/kill them. Quickly.
3. Identify projects worth developing/growing in 2016 and make a plan to do so.
4. Think about the best way your work creates value. Are you working towards getting grants, making intellectual property, or within some business model? Focus on whatever fits.
5. Spend some time reconnecting with co-workers that you've lost touch with, because you were so busy in 2015.
6. Find a few good review articles of interest to you and read them.  It will help with #3.
7. Recycle that pile of papers you printed pre-2015 and never got around to reading. Admit it, this pile exists.
8. Identify a few good bloggers/tweeters and follow them. They often turn up hard to notice papers or science news.
9. Develop a plan to improve your communication and/or presentation skills. Focus on the style used in good writing, TED talks, etc. If you think you're already good here, there's always room for improvement.
10. Think about what things were a poor use of your time in 2015 and find a way to stop doing them. Usually this means delegation or outsourcing.

Why academia has a data sharing problem

Martin Bobrow, Chair of the Wellcome Trust's advisory group on data access, submitted an enlightening summary of data sharing problems in Nature, where he asked:

Most research-funding agencies, and most scientists, now agree that research data should be shared — provided that those who donate their data and samples are protected. This approach is strongly advocated by organizations such as the Global Alliance for Genomics and Health. But data sharing will work well only when it is streamlined, efficient and fair. How can more scientists be encouraged and helped to make their data available, without adding an undue administrative burden?

I think the burden he's addressing is actually split into at least two parts:

1. The burden of actually sharing data. This is what usually comes to mind when people think of data sharing being difficult, and it involves hammering down infrastructure and data formats to enable sharing.

2. The burden created by actually making data available. Being the 'owner' of data brings both the opportunity for first crack at investigating that data and also the responsibility to share it. There's a real cost that sharing imposes, both in serving people that want access to data and the cost of storing it (though both are continually falling).

Thinking realistically, there's an actual disincentive to share academically generated data. Sharing data essentially gives potential competitors 'your data' at no cost, which may vaporize whatever competitive scientific advantage you may have gained.

Further on in the article, Bobrow offers this explanation:

It is reasonable for scientists to impose certain conditions or restrictions on the use of their hard-earned data sets, but these should be proportionate and kept to a minimum. Justifiable conditions can range from requiring secondary users to acknowledge the source of the data in publications, to stipulating a fair embargo time on the use of new data releases. Whatever the conditions imposed, they need to be presented clearly to data users.

Criteria used to judge academic careers still focus heavily on individual publication records and provide little incentive for wider data sharing. Scientists who let others use their data deserve reward too.

So yes, the issue with academic data sharing is incentive.

People who put together well designed data sets should be rewarded for their expertise and talents in doing so. Good data isn't as simple as sending a box of samples to a [insert your favourite high-throughput technology] production center; it requires knowledge of what constitutes 'normal' samples, experimental design, not to mention actually handling the logistics of obtaining the right samples in the first place.

Why wouldn't someone deserve credit for that?

The Bias against Downgrading: Why too many PhDs graduate

Last month, my friend and fellow blogger David Kent posted a few ideas about restructuring PhD programs at The Black Hole, highlighting three main points:

1. Collect and provide data on PhD outcomes,
2. Modernize the PhD degree, and
3. Cut the number of PhDs.

Though I tend to agree with David's assessments points #1 and #2 in his article, I'm going to take the opposite position on cutting the number of PhD positions. Here's what he said about #3:

I often struggle with this one (and maybe I’m part of the problem for this reason), but to me it seems that as long as we have big unanswered questions in medicine, biotechnology, etc., we need people to educate themselves in the life sciences. Should they all become academics? God no. Should they all move into life sciences related industry positions – again, no. But should they acquire skills and knowledge to critically assess these areas – absolutely. ...

Cutting PhD numbers by making stiffer entrance requirements is a reasonable thought, but as pointed out in the article, these requirements will be difficult to establish. I shudder at the thought of having medical school style requirements for PhDs since this will almost certainly serve to cut off those who cannot “work the system” in the same way as others in more fortunate positions. 

I'd argue that the current system already favours people who can work the system (and social access is a problem recognized in the original Nature article The Black Hole's post is based on). Entrance requirements to PhD programs are already competitive and tend to have a decent weighting on marks, requiring a B+ average at a minimum.  However, good marks accrue to undergraduates who can spend more time studying, which coincidentally include those spending less time working to pay tuition bills. Enter with your favourite socioeconomic argument.

Another way that graduate school candidates can look like prime candidates is by working summers in laboratories, partly to gain "lab experience" and partly to get decent reference letters. I don't mean to make research labs seem unique in this arena, as experience and references are valued in almost any industry I know of. Again, not working for pay in those precious undergraduate summers helps to make research time available.

That said, working the admissions system isn't what I'm concerned with.

Both articles look at the flow of PhD entrants into programs and don't examine how to reduce the number of PhDs exiting training programs. Here's what I think the major reason that once students are in a PhD program, they're committed:

Reclassifying from a PhD to an MSc program isn't seen often enough.

At least, I'm personally only aware of two people that did so; one to accept a job and another to enter medical school. But I digress.

I think one main reason reclassifying this way (i.e. PhD-to-MSc) is discouraged is that every PhD student goes through some period of self-doubt where Impostor Syndrome runs wild, so with the best intention people try to help the poor PhD student through this difficult time. Grad school isn't a game of Texas Hold'em (okay, sometimes it is) and I don't think anyone wants people to fold their hand on a degree because of a period of stress.

On the other hand, there are several possible behavioural and organizational reasons why the "Too many PhDs" problem exists:

1. Students don't want to be perceived as failures. I've sometimes heard that failing PhD students can be "encouraged" to graduate with a Masters, so PhD students are reluctant to fold their cards and take the MSc. I've never seen this. Similarly, some people may have the perception that by not reclassifying to a PhD, a MSc student is admitting that they can't cut it.
2. Scientists don't want to be perceived as unable to train PhDs. Here's a more powerful incentive to keep that PhD student around for 6, 7, or 8 years. It may really be that finishing that PhD thesis isn't motivating the student anymore and it's best that they capitalize their experience as an MSc, but the question would remain: "Why couldn't Professor So-and-so help that student finish their PhD?"  There might also be a culture of only graduating PhDs in that department, faculty or university. 
3. There's a incentive to have PhD students over MSc students. Disregarding differences in individual talents, more complex projects are performed by people with more experience (presumably with the same lab).  Therefore lab heads don't want to let employees (ahem) students leave after two years of experience; they'd rather have productive people around for a few more years.
4. Finally: No one wants to 'downgrade' from a PhD to a MSc. This problem is caused by academic jargon. The two degrees are for different people, different purposes, and different career paths. You could probably find many talented people where reclassifying to a PhD was, in retrospect, a 'downgrade' for their true career potential. Eliminate 'downgrading' from the lexicon, now.

If you have more ideas about what influences the too many PhDs problem, tweet them to @CheckmateSci and if they're good, I'll update this post (with credit, of course).

A Potential Solution: Replace 'Masters' and 'Doctoral' student classifications and call everyone a graduate student.

In doing so, the relationship between the two streams can be eliminated and the whole problem with 'upgrading' and 'downgrading' becomes moot.

I would like to see a system where the only point of differentiation between graduate students is when they petition for graduation. For instance, such a system could be as simple as an achievement checklist at an exit interview:

• Completed your posters? Check.  
• Done your comprehensive exam? Check. 
• Do you fulfil all the requirements for a Masters?  Check.
• Published some number/quality of papers needed for a PhD? No.

In this case, why wouldn't you award this person a Masters and let them reach for whatever they had on their mind? No 'downgrading' required. They're not a failure; they just decided to follow another career opportunity and didn't feel working in academia for a few more years was going to be worth it.

They gained scientific knowledge and (hopefully) contributed to some important research direction. They learned to think about problems in a particular way, to follow the scientific method, and now they're going to apply it to something other than basic science.

And besides, I think that's what many research funders expect in return for their dollars.