Tag: data

Doctors as Data Entry Clerks for the Government Health Surveillance System

As a practicing physician I have long been frustrated with the Electronic Health Record (EHR) system the federal government required health care practitioners to adopt by 2014 or face economic sanctions. This manifestation of central planning compelled many doctors to scrap electronic record systems already in place because the planners determined they were not used “meaningfully.” They were forced to buy a government-approved electronic health system and conform their decision-making and practice techniques to algorithms the central planners deem “meaningful.”  Other professions and businesses make use of technology to enhance productivity and quality. This happens organically. Electronic programs are designed to fit around the unique needs and goals of the particular enterprise. But in this instance, it works the other way around: health care practitioners need to conform to the needs and goals of the EHR. This disrupts the thinking process, slows productivity, interrupts the patient-doctor relationship, and increases the risk of error. As Twila Brase, RN, PHN ably details in “Big Brother in the Exam Room,” things go downhill from there.

With painstaking, almost overwhelming detail that makes the reader feel the enormous complexity of the administrative state, Ms. Brase, who is president and co-founder of Citizens’ Council for Health Freedom (CCHF), traces the origins and motives that led to Congress passing the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009. The goal from the outset was for the health care regulatory bureaucracy to collect the private health data of the entire population and use it to create a one-size-fits-all standardization of the way medicine is practiced. This standardization is based upon population models, not individual patients. It uses the EHR design to nudge practitioners into surrendering their judgment to the algorithms and guidelines adopted by the regulators. Along the way, the meaningfully used EHR makes practitioners spend the bulk of their time entering data into forms and clicking boxes, providing the regulators with the data needed to generate further standardization.

Brase provides wide-ranging documentation of the way this “meaningful use” of the EHR has led to medical errors and the replication of false information in patients’ health records. She shows how the planners intend to morph the Electronic Health Record into a Comprehensive Health Record (CHR), through the continual addition of new data categories, delving into the details of lifestyle choices that may arguably relate indirectly to health: from sexual proclivities, to recreational behaviors, to gun ownership, to dietary choices. In effect, a meaningfully used Electronic Health Record is nothing more than a government health surveillance system.  As the old saying goes, “He who pays the piper calls the tune.” If the third party—especially a third party with the monopoly police power of the state—is paying for health care it may demand adherence to lifestyle choices that keep costs down.

All of this data collection and use is made possible by the Orwellian-named Health Insurance Portability and Accountability Act (HIPAA) of 1996.  Most patients think of HIPAA as a guarantee that their health records will remain private and confidential. They think all those “HIPAA Privacy” forms they are signing at their doctor’s office is to insure confidentiality. But, as Brase points out very clearly, HIPAA gives numerous exemptions to confidentiality requirements for the purposes of collecting data and enforcing laws. As Brase puts it, 

 It contains the word privacy, leaving most to believe it is what it says, rather than reading it to see what it really is. A more honest title would be “Notice of Federally Authorized Disclosures for Which Patient Consent Is Not Required.”

More Information Won’t Resolve Management Problems at Border Patrol Checkpoints

A new Government Accountability Office (GAO) report claims that, among other issues, the Border Patrol is not efficiently deploying agents to maximize the interdiction of drugs and illegal immigrants at interior checkpoints. I wrote about this here. These checkpoints are typically 25 to 100 miles inside of the United States and are part of a “defense in depth” strategy that is intended to deter illegal behavior along the border. Border Patrol is making suboptimal choices with scarce resources when it comes to enforcing laws along the border. A theme throughout the GAO report is that Border Patrol does not have enough information to efficiently manage checkpoints. Contrary to the GAO’s findings, poor institutional incentives better explain Border Patrol inefficiencies, while a lack information is a result of those incentives. More information and metrics can actually worsen Border Patrol efficiency.

Inefficient Border Patrol Deployments

Border Patrol enforces laws in a large area along the border with Mexico. They divide the border into nine geographic sectors. They further divide each sector into stations that are further subdivided into zones, some of which are “border zones” that are actually along the Mexican border while the remainder are “interior zones” that are not along the border. The GAO reports that this organization allows officials on the zone level to deploy agents in response to changing border conditions and intelligence. 

The GAO states that Headquarters deploys Border Patrol agents to border sectors based on threats, intelligence, and the flow of illegal activity. The heads of each sector then allocate agents to specific stations and checkpoints based on the above factors as well as local ones such as geography, climate, and the proximity of private property. The heads of those stations and checkpoints then assign specific shifts to each agent. The time it takes for a Border Patrol agent to respond to reported activity, their proximity to urban areas where illegal immigrants can easily blend in, and road access all factor into these deployment decisions. 

Big Data Tool For Trump’s Big Government Immigration Plans

During his campaign President Trump made it clear that his administration would strictly enforce immigration law while also seeking to limit immigration. Trump’s executive orders so far are consistent with his campaign rhetoric, including a revitalization of the controversial 287(g) program, threats to withdraw grants from so-called “Sanctuary Cities,” the construction of a wall on the southern border, a temporary ban on immigration from six Muslim-majority countries, and the hiring of 10,000 more Immigration and Customs Enforcement (ICE) agents. Recent reporting reveals that these agents, tasked with implementing significant parts of Trump’s immigration policy agenda, will have access to an intelligence system that should concern all Americans who value civil liberties.

Earlier this month The Intercept reported on Investigative Case Management (ICM), designed by Palantir Technologies. ICE awarded Palantir a $41 million contract in 2014 to build ICM. ICM is scheduled to be fully operational by September of this year.

Here is The Intercept’s breakdown of how ICM works:

ICM funding documents analyzed by The Intercept make clear that the system is far from a passive administrator of ICE’s case flow. ICM allows ICE agents to access a vast “ecosystem” of data to facilitate immigration officials in both discovering targets and then creating and administering cases against them. The system provides its users access to intelligence platforms maintained by the Drug Enforcement Administration, the Bureau of Alcohol, Tobacco, Firearms and Explosives, the Federal Bureau of Investigation, and an array of other federal and private law enforcement entities. It can provide ICE agents access to information on a subject’s schooling, family relationships, employment information, phone records, immigration history, foreign exchange program status, personal connections, biometric traits, criminal records, and home and work addresses.

Better Data, More Light on Congress

There’s an old joke about a drunk looking for his keys under a lamp post. A police officer comes along and helps with the search for a while, then asks if it’s certain that the keys were lost in that area.

“Oh no,” the drunk says. “I lost them on the other side of the road.”

“Why are we looking here?!”

“Because the light is better!”

In a way, the joke captures the situation with public oversight of politics and public policy. The field overall is poorly illuminated, but the best light shines on campaign finance. There’s more data there, so we hear a lot about how legislators get into office. We don’t keep especially close tabs on what elected officials do once they’re in office, even though that’s what matters most.

(That’s my opinion, anyway, animated by the vision of an informed populace keeping tabs on legislation and government spending as closely as they track, y’know, baseball, the stock market, and the weather.)

Our Deepbills project just might help improve things. As I announced in late August, we recently achieved the milestone of marking up every version of every bill in the 113th Congress with semantically rich XML. That means that computers can automatically discover references in federal legislation to existing laws in every citation format, to agencies and bureaus, and to budget authorities (both authorizations of appropriations and appropriations).

Is There No “Hiatus” in Global Warming After All?

A new paper posted today on ScienceXpress (from Science magazine), by Thomas Karl, Director of NOAA’s Climate Data Center, and several co-authors[1], that seeks to disprove the “hiatus” in global warming prompts many serious scientific questions.

The main claim[2] by the authors that they have uncovered a significant recent warming trend is dubious. The significance level they report on their findings (.10) is hardly normative, and the use of it should prompt members of the scientific community to question the reasoning behind the use of such a lax standard.

In addition, the authors’ treatment of buoy sea-surface temperature (SST) data was guaranteed to create a warming trend. The data were adjusted upward by 0.12°C to make them “homogeneous” with the longer-running temperature records taken from engine intake channels in marine vessels. 

As has been acknowledged by numerous scientists, the engine intake data are clearly contaminated by heat conduction from the engine itself, and as such, never intended for scientific use. On the other hand, environmental monitoring is the specific purpose of the buoys. Adjusting good data upward to match bad data seems questionable, and the fact that the buoy network becomes increasingly dense in the last two decades means that this adjustment must put a warming trend in the data.

The extension of high-latitude arctic land data over the Arctic Ocean is also questionable. Much of the Arctic Ocean is ice-covered even in high summer, meaning the surface temperature must remain near freezing. Extending land data out into the ocean will obviously induce substantially exaggerated temperatures.

Additionally, there exist multiple measures of bulk lower atmosphere temperature independent from surface measurements which indicate the existence of a “hiatus”[3]. If the Karl et al., result were in fact robust, it could only mean that the disparity between surface and mid-tropospheric temperatures is even larger that previously noted. 

Getting the vertical distribution of temperature wrong invalidates virtually every forecast of sensible weather made by a climate model, as much of that weather (including rainfall) is determined in large part by the vertical structure of the atmosphere.

Instead, it would seem more logical to seriously question the Karl et al. result in light of the fact that, compared to those bulk temperatures, it is an outlier, showing a recent warming trend that is not in line with these other global records.

And finally, even presuming all the adjustments applied by the authors ultimately prove to be accurate, the temperature trend reported during the “hiatus” period (1998-2014), remains significantly below (using Karl et al.’s measure of significance) the mean trend projected by the collection of climate models used in the most recent report from the United Nation’s Intergovernmental Panel on Climate Change (IPCC). 

It is important to recognize that the central issue of human-caused climate change is not a question of whether it is warming or not, but rather a question of how much. And to this relevant question, the answer has been, and remains, that the warming is taking place at a much slower rate than is being projected.

The distribution of trends of the projected global average surface temperature for the period 1998-2014 from 108 climate model runs used in the latest report of the U.N.’s Intergovernmental Panel on Climate Change (IPCC)(blue bars). The models were run with historical climate forcings through 2005 and extended to 2014 with the RCP4.5 emissions scenario. The surface temperature trend over the same period, as reported by Karl et al. (2015, is included in red. It falls at the 2.4th percentile of the model distribution and indicates a value that is (statistically) significantly below the model mean projection.

The distribution of trends of the projected global average surface temperature for the period 1998-2014 from 108 climate model runs used in the latest report of the U.N.’s Intergovernmental Panel on Climate Change (IPCC)(blue bars). The models were run with historical climate forcings through 2005 and extended to 2014 with the RCP4.5 emissions scenario. The surface temperature trend over the same period, as reported by Karl et al. (2015, is included in red. It falls at the 2.4th percentile of the model distribution and indicates a value that is (statistically) significantly below the model mean projection.


[1] Karl, T. R., et al., Possible artifacts of data biases in the recent global surface warming hiatus. Scienceexpress, embargoed until 1400 EDT June 4, 2015.

[2] “It is also noteworthy that the new global trends are statistically significant and positive at the 0.10 significance level for 1998-2012…”

[3] Both the UAH and RSS satellite records are now in their 21st year without a significant trend, for example

In Holding NSA Spying Illegal, the Second Circuit Treats Data as Property

The U.S. Court of Appeals for the Second Circuit has ruled that section 215 of the USA-PATRIOT Act never authorized the National Security Agency’s collection of all Americans’ phone calling records. It’s pleasing to see the opinion parallel arguments that Randy Barnett and I put forward over the last couple of years.

Two points from different parts of the opinion can help structure our thinking about constitutional protection for communications data and other digital information. Data is property, which can be unconstitutionally seized.

As cases like this often do, the decision spends much time on niceties like standing to sue. In that discussion—finding that the ACLU indeed has legal standing to challenge government collection of its calling data—the court parried the government’s argument that the ACLU suffers no offense until its data is searched.

“The Fourth Amendment protects against unreasonable searches and seizures,” the court emphasized. Data is a thing that can be owned, and when the government takes someone’s data, it is seized.

In this situation, the data is owned jointly by telecommunications companies and their customers. The companies hold it subject to obligations they owe their customers limiting what they can do with it. Think of covenants that run with land. These covenants run with data for the benefit of the customer.

Will the Administration Make a Run at Transparency?

Last fall, I reported that the Obama administration lagged the House of Representatives on transparency. The conclusion was driven by a study of the quality of data publication regarding key elements of budgeting, appropriating, spending, and the legislative process. (Along with monitoring progress in these area, we’ve been producing data to show that it can be done, to produce a cadre of users, and to simply deliver government transparency at a less plodding pace.)

There are signs that the administration may make a run at improving its transparency record. Buried deep in the FY 2014 budget justification for the Treasury Department’s Bureau of the Fiscal Service, it says that funds will support “government-wide data standardization efforts to increase accuracy and transparency of Federal financial reporting.” That means the public may get better access to where the money goes – outlays – in formats that permit computer-aided oversight.

In parallel, a Performance.gov effort called the Federal Program Inventory says that, in May of 2014, it will publish a Unique Federal Program Inventory number (pg. 4-5) for each federal program, along with agency IDs and bureau IDs. This may be the machine-readable federal government organization chart whose non-existence I have lamented for some time.

If this sounds jargon-y, you’re normal. Think of federal spending as happening on a remote jungle island, where all the inhabitants speak their own language. On Federal Spending Island, no visitor from the U.S. mainland can understand where things are there, or who is saying what to whom.

True machine-readable data will turn Federal Spending Island into a place where English is spoken, or at least a some kind of Federal Spending-English dialect that makes the movement of our tax dollars easier to track.

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