At the end is a quick quote on the long-running move to use cell phones as the primary mode of physician communication instead of one-way pagers. This is how my daily practice works today.

“In the old days, if you had to get in touch with house staff, the only way was to page them,” said Dr. Austrian, who is part of an NYU Langone pilot that is testing the use of cellphones as a replacement for pagers. “You had to stop what you were doing and find the nearest phone and respond to the page. That was completely distracting.”

Check out my prior post on how I use the iPad in anesthesiology.

• GoodReader — ($5) I switched from iAnnotate recently because GoodReader has better WebDAV support. I have a library of PDFs on my laptop which I sync to the iPad using the IIS WebDAV server. The screenshot here shows the Supraclavicular Block chapter from the Military Advanced Regional Anesthesia and Analgesia handbook. Note that iAnnotate has some features GoodReader lacks, such as full-text search across all PDFs in your library.



• Flipboard — Flipboard is easier to show than to explain. It aggregates RSS feeds, Facebook, LinkedIn, Twitter and other social network services into a beautiful, magazine-style interface. I use it to catch up on anesthesiology updates, medical app news, and other items of interest. Compare Page2@Anesthesiology in your web browser to the Flipboard appearance:



• Noteshelf is a beautiful note taking and drawing application for the iPad. I recommend using it with the Wacom Bamboo Stylus for iPad. Great for teaching and marking up graphs and photos. Worth the $10.
• Netter’s Anatomy Atlas — Incredibly expensive at $90, this app is a great conversion of the venerable Netter’s Anatomy Atlas into an interactive iPad application. It’s a helpful learning aid during complex surgeries to figure out what’s going on. The plates help reinforce the relationships of nerves and vessels, which are important for regional anesthesia techniques. Netter’s is not quite worth $90, but if someone offers you this app as a gift, do not turn it down. The free version is certainly worth picking up.
• The Wolters Kluwer Health apps for the journals Anesthesiology and Anesthesia & Analgesia merit a download, but I would not pay an extra fee for iPad access. There’s just not enough benefit over paper.

If you have favorite iPad or iPhone apps that you use with your work in anesthesiology, let me know in the comments.

• Amazon Web Services official blog report of the outage
• Netflix Lessons Learned – notable for how they were not affected significantly
• Adrian Cockroft (now with Netflix) on why “private” clouds are a terrible idea
• Adrian Cockroft’s Blog – his other posts share detailed experiences working with Amazon Web Services

I am looking forward to what, if anything, Amazon chooses to relay regarding the reported Amazon Cloud Drive delays on Monday.

Dr. Halamka offers a useful primer on the MDDS concept. He quotes a vendor stating that they are preparing to register their intraoperation interfaces (e.g. RIS/EDR, LIS/EDR.) It sounds to me that any sort of portable devices for displaying medical data are covered by the MDDS definition. Software that claims to display medical data for physician use is also covered according to this mobihealthnews article.

Selling a class I device requires internal quality controls and adverse event tracking. I am curious what kind of adverse events will be filed for software products after this rule comes into effect.

Decision Support Systems (DSS) model data and provide advice to assist experts in deciding if and how to react to patterns in the data. In anesthesia, DSS have been applied in the intraoperative and postoperative domains. Examples of DSS tasks include:

• Determine OR time remaining
• Correct OR locations in records
• Notify clinicians of documentation errors

Using our institution’s anesthesia information management system (AIMS) for data acquisition, we designed a network-based monitoring system that streams intraoperative demographic, drug administration, clinical event, and physiologic data into a central SQL relational database. Server-side heuristic and data analysis algorithms identify events or trends of potentially physiologic importance, and then push notifications to the anesthesia workstation. Institutional Review Board approval was obtained for our decision support system. An opt-out mechanism allows anesthesiologists to not participate in our decision support trials.

1. Initial implementation of streaming data collection required 120 man-hours of engineering work
2. Components are the Ubuntu Linux operating system, the MySQL database, and Perl for scripting.
3. The system monitors over eighty anesthetizing locations and updates the database every 30 seconds, with a latency of 1-2 minutes.
4. We are embarking on a trial of a Decision Support system to flag vital signs with low BIS and low MAP.

References:

1. Dexter F, Epstein RH, Lee JD, Ledolter J. Automatic updating of times remaining in surgical cases using bayesian analysis of historical case duration data and “instant messaging” updates from anesthesia providers. Anesth Analg 2009;108(3):929-40.
2. Epstein RH, Dexter F, Piotrowski E. Automated correction of room location errors in anesthesia information management systems. Anesth Analg 2008;107(3):965-71.
3. Sandberg WS, Sandberg EH, Seim AR, Anupama S, Ehrenfeld JM, Spring SF, Walsh JL. Real-time checking of electronic anesthesia records for documentation errors and automatically text messaging clinicians improves quality of documentation. Anesth Analg 2008;106(1):192,201

A 38 year-old multiparous parturient at 37 weeks gestation presented for a repeat Cesarean section. History was notable for several prior Cesareans and anemia during pregnancy. The patient was NPO for nine hours, was on liquid diet two days prior, and had an enema the evening before surgery.

Spinal anesthesia was induced with bupivicaine 0.75%, preservative-free morphine, and fentanyl. After delivery and approximately 1000 mL of blood loss a venous blood gas was checked. The pH was 7.26 and Hct was 26. A repeat arterial blood gas found similar results. The patient remained awake and hemodynamically stable.

As pregnant patients approach term, their oxygen consumption and metabolism increase steadily, leading to an increase in base excess.
The term “base excess” refers to the excess or deficit of base in the blood stream. In the same way that CO2 levels demonstrate the degree of respiratory acidosis, the base excess shows the degree of metabolic acidosis.

Possibly due to the patient’s limited diet, her body began breaking down fatty acids and producing ketone bodies, creating an acidemia. Also, the patient’s home enema treatments reduced her bicarbonate levels.

For treatment, the patient was started on D5W with 140 mEq bicarbonate at 125 mL/hr. Potassium and phosphate were repleted. An arterial line was placed to monitor ABGs and the patient was transferred to the MICU. Within 12 hours her pH normalized and she returned to the postpartum floor.

The D5W/NaHCO3 drip provides substrate for anaerobic metabolism and helps restore lost bicarbonate. It is notable that the patient’s acidemia worsened for several hours after treatment began, demonstrating the time required to correct such a severe imbalance.

References

1. Marx GF, Desai PK, Habib NS, “Detection and Differentiation of Metabolic Acidosis in Partrurients,” Anesth Analg 1980 Dec; 59(12):929-31. PMID 7192513.
2. Toth HL and Greenbaum LA, “Severe Acidosis caused by Starvation and Stress”, Am J Kid Dis 2003 Nov; 42(5):E16-19. PMID 14582074.
3. Levraut J and Grimaud D, “Treatment of metabolic acidosis”, Curr Opin Crit Care. 2003 Aug;9(4):260-5. PMID 12883279.

Wolfram|Alpha is a “computational knowledge engine” which is supposed to make a large variety of databases available through simple text query. The idea is that instead of digging through many databases, have one big database that can combine disparate results.

As with anything so ambitious, WA has collected plenty of criticism, especially because of the opaque natural language interface. Why does “life expectancy of 30 yo US woman in 2002″ work, but not “life expectancy of 30 yo US woman from 2002-2003″? Farhad Manjoo at Slate has a great article showing how straying from the example queries leads to the infamous “Wolfram|Alpha isn’t sure what to do with your input” response. SomethingAwful has put together a collection of ridiculous queries including
how much money BloodRayne made per millisecond.

The WA health examples demonstrate a number of problems which makes me not trust all of the results. Their example of “Mayo Clinic, Olmsted Medical Center” is supposed to compare two large medical centers in Rochester, Minn. However, it actually compares the Mayo Clinic satellite in Jacksonville, FL, with Rochester. Even that apples-to-oranges comparison is hampered because there is no data in WA for Mayo in Jacksonville. Try finding data on Mount Sinai Hospital in Miami — the only Mount Sinai that WA admits to knowing is in New York City (and has no affiliation with the one in Miami.)

WA says that for a 50 year old with resting HR 60, the maximum HR is 180 and the target exercise HR is 132-156. However, the AHA says that target rate is 50%-80% of the maximum, and that a 50 year old’s maximum is 170. Maybe WA’s numbers are better, but it’s tough to tell without references or justification.

WA says it calculates heart disease risk based on the Framingham study, but I get different results. (Assuming LDL 111, HDL 54, BP 120/80, nonsmoker, not diabetic.) Using the male score sheet from Wilson, Prediction of Coronary Heart Disease Using Risk Factor Categories. Circulation 1998 97 (18): 1837-1847., I get 6%, versus WA’s 4.6%.

As the score sheets just return whole numbers, WA is likely using the Framingham model which is discussed in the paper. However, even using that I get 5.4%, a solid 0.8% more than WA’s result. (for sticklers, my work is after the “more”.)

I would encourage anyone who uses Wolfram|Alpha for medical decisions to run the numbers on their own. I would like WA to be more explicit about where the data is coming from and how results are derived.

See Appendix 1 of the Circulation paper for background.

Assuming LDL 111, HDL 54, BP 120/80, nonsmoker, not diabetic:

L_LDL = 0.04808 * 50 + (-0.0471) (for the HDL of 54). (No additions for normal BP and LDL.) So L_LDL = 2.3569.
G_LDL is 3.00069 (this is the "Linear predictor at risk factor means.")
A = L_LDL- G_LDL = -0.64379
B = exp(A) = 0.525298
P = 1-s(t)^B; s(t) = 0.90017 for LDL male, so P = 0.053748 or 5.4%.


I cancelled my Kindle subscription to the New England Journal today. I am still considering an online subscription.

What really annoyed me this week was realizing that the Kindle doesn’t get Early Release articles until they show up in the print magazine. As soon as this content is on the website, it should be available for the Kindle too.

Journals on the Kindle are apparently just electronic versions of the paper editions, without any fancy additions. No interactive component, no updates to corrected content, none of the things that a constant Internet connection offers. (After all, the 3G connection on my Kindle is faster than my iPhone’s connection.)

The price certainly wasn’t great either; a Kindle subscription from Amazon runs $9*12 = $108 per year. The print/online subscription from NEJM is $159, and the online-only subscription is $99. It’s cheaper if you are a resident or medical student.

Finally, note that the $108/year to Amazon does not include subscriber access to the NEJM website.

Recently I received a Kindle 2 (not the same thing as a Kindle DX.) The E-Ink display is really cool. The screen looks great when read in bright light. It doesn’t need any current to maintain the screen image (like an Etch-A-Sketch) so battery life is very long. Kindle runs the Linux operating system with Java for the UI.

Kindle is great for reading novels and long-form articles. I read A Study in Scarlet in about two hours and forgot I wasn’t reading from a book halfway through.

I got a trial subscription for the New England Journal of Medicine and found that the experience was better than I expected, but not great. Reading the Perspective and review articles was better than reading them on a web browser, but research articles were difficult, especially because any tables or figures are very difficult to read. You can zoom into a figure slightly with several arduous clicks using the 5-way tool, but the result is not worth the trouble. The image at the top of this article shows what NEJM looks like on a Kindle, and a screenshot dump appears to the right.

After experimentation with some medical and non-medical works, I have to say that Kindle is terrible for reference works because:

• Substantial latency to user input, particularly cursor movements. Next and previous page are the fastest commands.
• It’s difficult to select links from a long list (such as a table of contents or search result) using the 5-way cursor device.
• There’s no good interface for magnifying pictures and tables. I kept touching the screen before I remembered that there is no touchscreen on a Kindle.

Jakob Nielsen talks about some of these points in his Kindle 2 usability review.

I was surprised that the web browser works well with mobile feeds of newspapers and Google Reader, which I use for RSS. The image to the right shows the Google Operating System blog in Reader.

I loaded my Kindle with a variety of classics from various free e-book sources.

While Kindle 2 does not read PDFs natively, you can translate any PDF to Amazon’s Kindle format for free.

I added the ability to search for a physician’s NPI. Enjoy.