Clinician Search Behaviors and Search Engine Design

The article “Clinician Search Behaviors” studies how users are able to search for specific medical terms and how their method of searching is dependent on the design of the search engine and how its usability directly affects their ability to successfully search for the proper types of records they are seeking (Coiera et al, 1). In terms of overall content, the lecture is somewhat sparse, consisting of only a few pages. It aims to directly inform through information collected rather than use overly elaborate literature in order to back up its claims.

While it does reference several sources, the overall length of the study itself lacks any apparent in-depth research that most academic studies should have. The study itself consists mostly of an examination of data gathered through a trial test of 75 clinicians. Its short length can be justified as being a method of numerical data examination rather than an apparent in-depth research article.

In terms of the overall time limit utilized in the study, namely 80 minutes of testing, there seems to be no issue with it since this particular study is examining the rate by which a trained medical professional is able to search for data and as such does not require a significant investment of time to accomplish. In terms of overall form, the length, the statistical data available and the direct implication of result show an adequate representation of what a numerical examination based on the study of performance should be.


  • Width Aspect Ratio = 2620 Pixels
  • Height Aspect Ratio = 3300 Pixels
  • Current Size: 78.6kb
  • Uncompressed: 116kb

Change at 256 colors: At a color ratio of 256 colors, the estimated compression rate would make the photo reach 132kb in total file size however the overall quality rate in terms of photo depth, clarity and detail distinction would of course go up as a result of the change.

There are two distinct reasons as to why using social security numbers as patient identifiers is a bad idea: first there is the possibility of overlap with certain numbers resulting in some individuals being placed under the wrong type of medication due to the possibility of clerical error. The second reason is the possibility of identity theft where social security numbers will be used in order to commit medical fraud (FDCPA Update: When Will They Ever Learn, 7).

While it may be true that there is no possibility for overlap when the entire social security number is utilized in the identification process, at times patients within hospitals are categorized using the last four digits of their social security number. The inherent problem with using such a system is the fact that the last set of social security numbers do have a degree of overlap especially in cases where people come from the same general area. While patients are usually called using their last name or full name hospital charts and pharmacies still utilize the last four digits of identification as a means of assigning the proper prescriptions.

Here is a possible scenario that may occur: patient 2121 is set for a sex change operation on Monday as indicated by hospital records. On the other hand patient 1212 is also set for an operation where his kidney will be examined by the doctor. While the numbers are similar the operations both of them will undergo is vastly different. If the person who is set to get both patients were to mix up the charts by mistake and send patient 1212 to have a sex change while patient 2121 goes for a liver operation this represents a serious problem for both patients! While it may seem funny now, incidences like this have happened in various hospitals in the U.S. and as such identification using social security numbers does have an inherent defect. Lastly the use of social security numbers in the U.S is inextricably linked to many services within the country.

From a clerical standpoint this serves to make the process of service utilization faster. Unfortunately, it does however open the floodgates for medical malpractice where social securities numbers accumulated by various hospitals and clinics wind up being charged for operations and services that the patient had never even undergone (Boerner, 29). The 2010 MediCare (Medical Care) fraud investigation by the FBI showed exactly how security numbers could be used in such a way by the unscrupulous in order to make a dishonest profit (Boerner, 29).

Overall the process model did adequately represent all the possible factors involved with the proper facilitation of prescription chart filling using EMR (Electronic Medical Records) as a way of reducing fiscal saving. I, however, do not agree with the process of scanning as a better way of facilitating the delivery of prescription forms. While I do agree that their use is far better than the original system utilized, a more efficient system would be to use an online prescription application that connects hospitals and pharmacies.

An online form can simply be filled out by a doctor or nurse and the prescription be sent immediately through the pharmacy using that method. One possible way of doing so would be to use the latest pop culture device, the iPad 2, as a means for doctors and nurses to easily input and send information while they are with the patient. This would result in faster prescription deliveries and a more efficient system. It must be noted though that switching over to an electronic system involves two distinct factors namely: a large capital investment to develop an appropriate system and subsequent training schedules to help nurses and doctors know of the new arrangement.

As with the implementation of any new system it does come with the need to invest a certain amount of money into fleshing out the necessary concepts needed for it to run efficiently. Not only that there is also the necessary investment of time by the various doctors and nurses so that they will be able to better understand what the proper procedures are in utilizing the new system itself.

5The inherent problems in high quality mapping and conversion between SNOMED (Systematized Nomenclature of Medicine) and ICD-10 (International Statistical Classification of Diseases and Related Health Problems 10th Revision) is the fact that there is a distinct lack of agreement by the coders involved due to the inherent structural and content factor that can be seen with SNOMED when compared to ICD-10 (Vikströmet al, 17).

The reasoning behind this lies with difficulties in actually being able to interpret the various meanings of the categories in ICD 10 versus the concepts in SNOMED, many of which are related (Vikströmet al, 18). The different structural and content mechanisms of both systems are the reason why there are so many problems during the conversion phase. While conversion is possible it is highly unlikely for it to be conceived as simple process due to the base differences between the two classification styles. Classification systems such as the current ICD-10 system is used to categorize similar diseases, their necessary procedures and other types of inter-related information in way that can be retrieved easily (Vikströmet al, 17).

Vikstromet states “due to their usage in external reporting requirements and measuring the quality care such systems are classified as output rather than input systems and as such cannot be considered and are not inherently designed to be the primary method of resource documentation when it comes to clinical care” (Vikströmet al, 17). On the other hand SNOMED systems can be used as a system for documentation due to their design as an input system however they also suffer from certain setbacks such as their considerably immense size and rather complex hierarchies which makes them an inadequate platform for serving the secondary purposes ascribed to the current ICD 10 classification systems (Vikströmet al, 19).

In such a case the best method would be utilize both ICD 10 and SNOMED on a case by case basis but a much more efficient system of classification and data input would be to combine the characteristics of both systems into a single input/output system that can be easily used.

Accumulated versus specialized

An examination of the GED (General Educational Development), ACT (American College Testing) and NCLEX (National Council Licensure Examination) test questions shows a distinction between general accumulated knowledge and specialized knowledge that comes from studying a certain profession. For example, the various questions shown in the GED and ACT sample test questions involved general mathematics, grammar, history and various science related questions meant to test the level a person is currently at after graduating from high school and is about to enter college.

They are meant to gauge a person’s academic level rather than their specific knowledge on a certain type of subject. On the other hand the NCLEX test questions revolved almost entirely on subjects devoted to nursing. In terms of the various strategies and techniques presented in lecture eight the questions show how testing methods change depending on what is actually being examined. In cases where specialized knowledge on a particular subject matter is utilized it is rare to even find a single general question that is not in direct relation to the specialized subject matter.

Works Cited

Boerner, C. “60 Minutes Story on Medicare Fraud.” Journal of Health Care Compliance 12.1 (2010): 29-65. Business Source Premier. EBSCO. Web.

Coiera, E. Compton, P. Lau, A. Zrimec, T. (2010). Clinician Search Behaviors May Be Influenced by Search Engine Design. Journal of Medical Internet Research, 12(2): e25. Web.

“FDCPA Update: When Will They Ever Learn?.” Health Care Collector: The Monthly Newsletter for Health Care Collectors 15.2 (2001): 7. Business Source Premier. EBSCO. Web.

Vikström, A, Nyström, M, Ă…hlfeldt, H, Strender, L, & Nilsson, G 2010, ‘Views of diagnosis distribution in primary care in 2.5 million encounters in Stockholm: a comparison between ICD-10 and SNOMED CT’, Informatics in Primary Care, 18, 1, pp. 17-29, Academic Search Premier, EBSCOhost.

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NursingBird. (2022, August 29). Clinician Search Behaviors and Search Engine Design.

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"Clinician Search Behaviors and Search Engine Design." NursingBird, 29 Aug. 2022,


NursingBird. (2022) 'Clinician Search Behaviors and Search Engine Design'. 29 August.


NursingBird. 2022. "Clinician Search Behaviors and Search Engine Design." August 29, 2022.

1. NursingBird. "Clinician Search Behaviors and Search Engine Design." August 29, 2022.


NursingBird. "Clinician Search Behaviors and Search Engine Design." August 29, 2022.