Showing posts with label emr. Show all posts
Showing posts with label emr. Show all posts

Tuesday, January 12, 2016

REALLY BAD IDENTIFIER METHODS

Here is a short excerpt from my book Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information


"I always wanted to be somebody, but now I realize I should have been more specific." -Lily Tomlin

Names are poor identifiers. Aside from the obvious fact that they are not unique (e.g., surnames such as Smith, Zhang, Garcia, Lo, and given names such as John and Susan), a single name can have many different representations. The sources for these variations are many. Here is a partial listing:

1. Modifiers to the surname (du Bois, DuBois, Du Bois, Dubois, Laplace, La Place, van de Wilde, Van DeWilde, etc.).

3. Accents that may or may not be transcribed onto records (e.g., acute accent, cedilla, diacritical comma, palatalized mark, hyphen, diphthong, umlaut, circumflex, and a host of obscure markings).

4. Special typographic characters (the combined "ae").

5. Multiple "middle names" for an individual, that may not be transcribed onto records. Individuals who replace their first name with their middle name for common usage, while retaining the first name for legal documents.

6. Latinized and other versions of a single name (Carl Linnaeus, Carl von Linne, Carolus Linnaeus, Carolus a Linne).

7. Hyphenated names that are confused with first and middle names (e.g., Jean-Jacques Rousseau, or Jean Jacques Rousseau; Louis-Victor-Pierre-Raymond, 7th duc de Broglie, or Louis Victor Pierre Raymond Seventh duc deBroglie).

8. Cultural variations in name order that are mistakenly re-arranged when transcribed onto records. Many cultures do not adhere to the Western European name order (e.g., Given name, middle name, surname).

9. Name changes; through legal action, aliasing, pseudonymous posing, or insouciant whim.

Aside from the obvious consequences of using names as record identifiers (e.g., corrupt database records, impossible merges between data resources, impossibility of reconciling legacy record), there are non-obvious consequences that are worth considering. Take, for example, accented characters in names. These word decorations wreak havoc on orthography and on alphabetization. Where do you put a name that contains an umlauted character? Do you pretend the umlaut isn't there, and put it in alphabetic order with the plain characters? Do you order based on the ASCII-numeric assignment for the character, in which the umlauted letter may appear nowhere near the plain-lettered words in an alphabetized list. The same problem applies to every special character.

A similar problem exists for surnames with modifiers. Do you alphabetize de Broglie under "D" or under "d" or under "B"? If you choose B, then what do you do with the concatenated form of the name, "deBroglie"?

When it comes down to it, is is impossible to satisfactorily alphabetize a list of names. This means that searches based on proximity in the alphabet will always be prone to errors.

I have had numerous conversations with intelligent professionals who are tasked with the responsibility of assigning identifiers to individuals. At some point in every conversation, they will find it necessary to explain that although an individual's name cannot serve as an identifier, the combination of name plus date of birth provides accurate identification in almost every instance. They sometimes get carried away, insisting that the combination of name plus date of birth plus social security number provides perfect identification, as no two people will share all three identifiers: same name, same date of birth, same social security number. This argument, rises to the height of folly, and completely misses the point of identification. As we will see, it is relatively easy to assign unique identifiers to individuals and to any data object, for that matter. For managers of Big Data resources, the larger problem is ensuring that each unique individual has only one identifier (i.e., denying one object multiple identifiers).

Let us see what happens when we create identifiers from the name plus birthdate. We will examine name + birthdate + social security number later in this section.

Consider this example. Mary Jessica Meagher, born June 7, 1912 decided to open a separate bank account in each of 10 different banks. Some of the banks had application forms, which she filled out accurately. Other banks registered her account through a teller, who asked her a series of questions and immediately transcribed her answers directly into a computer terminal. Ms. Meagher could not see the computer screen and could not review the entries for accuracy.

Here are the entries for her name plus date of birth:

1. Marie Jessica Meagher, June 7, 1912 (the teller mistook Marie for Mary).

2. Mary J. Meagher, June 7, 1912 (the form requested a middle initial, not name).

3. Mary Jessica Magher, June 7, 1912 (the teller misspelled the surname).

4. Mary Jessica Meagher, Jan 7, 1912 (the birth month was constrained, on the form, to three letters; Jun, entered on the form, was transcribed as Jan).

5. Mary Jessica Meagher, 6/7/12 (the form provided spaces for the final two digits of the birth year. Through the miracle of bank registration, Mary, born in 1912, was re-born a century later).

6. Mary Jessica Meagher, 7/6/2012 (the form asked for day, month, year, in that order, as is common in Europe).

7. Mary Jessica Meagher, June 1, 1912 (on the form, a 7 was mistaken for a 1).

8. Mary Jessie Meagher, June 7, 1912 (Marie, as a child, was called by the informal form of her middle name, which she provided to the teller).

9. Mary Jessie Meagher, June 7, 1912 (Marie, as a child, was called by the informal form of her middle name, which she provided to the teller, and which the teller entered as the male variant of the name).

10. Marie Jesse Mahrer, 1/1/12 (an underzealous clerk combined all of the mistakes on the form and the computer transcript, and added a new orthographic variant of the surname).

For each of these ten examples, a unique individual (Mary Jessica Meagher) would be assigned a different identifier at each of 10 banks. Had Mary re-registered at one bank, ten times, the results may have been 10 different registration identifiers, for one person.

If you toss the social security number into the mix (name + birth date + social security number) the problem is compounded. The social security number for an individual is anything but unique. Few of us carry our original social security cards. Our number changes due to false memory ("You mean I've been wrong all these years?"), data entry errors ("Character trasnpositoins, I mean transpositions, are very common"), intention to deceive ("I don't want to give those people my real number), or desperation ("I don't have a number, so I'll invent one"), or impersonation ("I don't have health insurance, so I'll use my friend's social security number"). Efforts to reduce errors by requiring patients to produce their social security cards have not been entirely beneficial.

Beginning in the late 1930s, the E. H. Ferree Company, a manufacturer of wallets, promoted their product's card pocket by including a sample social security card with each wallet sold. The display card had the social security number of one of their employees. Many people found it convenient to use the card as their own social security number. Over time, the wallet display number was claimed by over 40,000 people. Today, few institutions require individuals to prove their identity by showing their original social security card. Doing so puts an unreasonable burden on the honest patient (who does not happen to carry his/her card) and provides an advantage to criminals (who can easily forge a card).

Entities that compel individuals to provide a social security number have dubious legal standing. The social security number was originally intended as a device for validating a person's standing in the social security system. More recently, the purpose of the social security number has been expanded to track taxable transactions (i.e., bank accounts, salaries). Other uses of the social security number are not protected by law. The Social Security Act (Section 208 of Title 42 U.S. Code 408) prohibits most entities from compelling anyone to divulge his/her social security number.

Considering the unreliability of social security numbers in most transactional settings, and considering the tenuous legitimacy of requiring individuals to divulge their social security numbers, a prudently designed medical identifier system will limit its reliance on these numbers. The thought of combining the social security number with name and date of birth will virtually guarantee that the identifier system will violate the strict one-to-a-customer rule.

-Jules Berman (Copyrighted material)

key words: big data, complex data, data identification, data identifiers, jules berman, jules j. berman, informatics, ehr, emr, precision data, electronic health record, electronic medical record

Wednesday, February 20, 2008

JCAHO policy on abbreviations

Effective January 1, 2004, hospitals accredited by the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) were required to exclude certain types of abbreviations from hand-written medical records.

As yet (to the best of my knowledge), there is no equivalent ruling for the realm of electronic medical records. Electronic records provide enormous opportunity for the creation and propagation of miscommunications that can lead to medical errors.

Trailing and leading zeros, micrograms (mcg, not µg) and units (units, not U) are issues that can be easily solved in an electronic record. Abbreviations with alternate expansions (discussed in a prior blog , are due for a remedy.

Despite advances in text processing software, no computational algorithms now exist that can accurately expand polysemous abbreviations from their sentence context. Polysemous abbreviations (abbreviations with alternate expansions) must be accompanied by their correct expansions in order to be understood correctly. Text markup languages (HTML, XML RDF) all support this kind of annotation. In HTML, there is evan a designated tag just for abbreviations:

http://www.w3schools.com/tags/tag_abbr.asp


Reports can be viewed to "show tags" or "hide tags" for the convenience of readers.

These kinds of solutions should be easy to implement in EMRs (Electronic Medical Records).

-Jules Berman
Science is not a collection of facts. Science is what facts teach us; what we can learn about our universe, and ourselves, by deductive thinking. From observations of the night sky, made without the aid of telescopes, we can deduce that the universe is expanding, that the universe is not infinitely old, and why black holes exist. Without resorting to experimentation or mathematical analysis, we can deduce that gravity is a curvature in space-time, that the particles that compose light have no mass, that there is a theoretical limit to the number of different elements in the universe, and that the earth is billions of years old. Likewise, simple observations on animals tell us much about the migration of continents, the evolutionary relationships among classes of animals, why the nuclei of cells contain our genetic material, why certain animals are long-lived, why the gestation period of humans is 9 months, and why some diseases are rare and other diseases are common. In “Armchair Science”, the reader is confronted with 129 scientific mysteries, in cosmology, particle physics, chemistry, biology, and medicine. Beginning with simple observations, step-by-step analyses guide the reader toward solutions that are sometimes startling, and always entertaining. “Armchair Science” is written for general readers who are curious about science, and who want to sharpen their deductive skills.

Friday, January 11, 2008

Does the EHR really help?

Yesterday, I posted a blog that discussed a white paper indicating that the most important causes of hospital errors (leading to death) were: Failure to Rescue, Decubitus Ulcer, and Post-operative Sepsis. Most of the causes of medical error described in the article seemed to have very little to do with hopital information systems or the electronic health record (EHR).

Another interesting article, recently published, is:

Electronic Health Record Use and the Quality of Ambulatory Care in the United States. Jeffrey A. Linder, MD, MPH; Jun Ma, MD, RD, PhD; David W. Bates, MD, MSc; Blackford Middleton, MD, MPH, MSc; Randall S. Stafford, MD, PhD. Arch Intern Med. 2007;167:1400-1405.

In this article , the authors found that "Electronic health records were used in 18% (95% confidence interval [CI], 15%-22%) of the estimated 1.8 billion ambulatory visits (95% CI, 1.7-2.0 billion) in the United States in 2003 and 2004. For 14 of the 17 quality indicators [that the authors examined], there was no significant difference in performance between visits with vs without EHR use."

In the rush to adopt the EHRs in American medical care, it might be worthwhile to think about those conditions for which the EHR may not be a particular benefit, and those conditions for which EHRs may be a disruptive technology carrying extra risk, and those conditions for which rapid adoption of EHRs is greatly beneficial (if not crucial).

In the next few days, I'll try to write additional blogs on this subject.

- Jules Berman
My book, Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information was published in 2013 by Morgan Kaufmann.



I urge you to explore my book. Google books has prepared a generous preview of the book contents.

tags: big data, metadata, data preparation, data analytics, data repurposing, datamining, data mining, disruptive technology, ehr, emr, laboratory information system

Saturday, March 24, 2007

Data specification is rewarded with funding opportunities

This blog is devoted to the topic of data specification (including data organization, data description, data retrieval and data sharing) in the life sciences and in medicine. You might wonder why anyone would think that data specification is sufficiently important for anyone to host a blog on the topic. Well, there are many reasons that I hope to describe in future posts. For today, just consider an RFA (request for applications) announced yesterday by the NIH.

The RFA is entitled: Genome-Wide Studies in Biorepositories with Electronic Medical Record Data.

Release Date: March 23, 2007
Letters of Intent Receipt Date(s): April 17, 2007
Application Receipt Date(s): May 17, 2007
Peer Review Date(s): July/August 2007
Council Review Date(s): August 2007
Earliest Anticipated Start Date(s): September 30, 2007
Additional Information To Be Available Date (Url Activation Date): N/A
Expiration Date: May 18, 2007

It's a very good RFA. Basically, if you have a tissue repository and the tissues are linked to a hospital EMR (Electronic Medical Record), the NHGRI (National Human Genome Research Institute) is interested in receiving a grant application from you.

The NHGRI will perform or pay for genomic studies on collected tissues [provided by awardees] that can be integrated with clinical data in the EMR.

This approach rewards institutions that have made serious efforts in biorepository science, EMR data organization and genomic testing (basically, the bread-and-butter of biomedical data specification).

Why is data specification (for biorepository data and EMR data) so important? Why can't the NHGRI just figure everything out by experiments conducted on cells in a culture dish?

Several years ago there was a lot of hype written about the imminent impact of pharmacogenomics on medical care. Everyone would get drugs specifically tailored to their own genome. Well, that was six or seven years ago, and with very few exceptions, people are prescribing drugs the old-fashioned way (one drug/dose fits all, unless there are bad side-effects or a poor response, in which case, try another drug/dose).

Before you can get much benefit from pharmacogenomics, you need to collect a lot of phenotypic (treatment, outcome, clinical, historical, physical) data on a lot of patients and match these with genotypic data. There's no substitute for clinical correlation with millions of patients. To integrate genotypic and phenotypic data, you need to have large amounts of organized and specified data.

It will take years and years before we have rich collections of well-annotated medical data sets on large numbers of patients. Smart data specification is one of the hurdles that we, as a society, must cross. Yesterday's RFA announcement is a step in the right direction.

-Jules Berman tags: ehr, electronic medical record, emr, funding, medical research, nhgri, nih, rfa, specifications, science
Science is not a collection of facts. Science is what facts teach us; what we can learn about our universe, and ourselves, by deductive thinking. From observations of the night sky, made without the aid of telescopes, we can deduce that the universe is expanding, that the universe is not infinitely old, and why black holes exist. Without resorting to experimentation or mathematical analysis, we can deduce that gravity is a curvature in space-time, that the particles that compose light have no mass, that there is a theoretical limit to the number of different elements in the universe, and that the earth is billions of years old. Likewise, simple observations on animals tell us much about the migration of continents, the evolutionary relationships among classes of animals, why the nuclei of cells contain our genetic material, why certain animals are long-lived, why the gestation period of humans is 9 months, and why some diseases are rare and other diseases are common. In “Armchair Science”, the reader is confronted with 129 scientific mysteries, in cosmology, particle physics, chemistry, biology, and medicine. Beginning with simple observations, step-by-step analyses guide the reader toward solutions that are sometimes startling, and always entertaining. “Armchair Science” is written for general readers who are curious about science, and who want to sharpen their deductive skills.