the use of medical information retrieval systems

The use of medical information 1

The Use of Medical Information Retrieval Systems

A Review of the Literature

Christina Magnifico

LI804XS


Objectives: The literature review should help information professionals and researchers understand how to evaluate medical information retrieval systems, how medical information retrieval systems are currently utilized by clinicians, and identifies the search techniques used within medical information retrieval systems (MIRS)

Method: A preliminary search of the literature on medical information retrieval systems in both PubMed and the LIS (library information science) databases returned articles on how informationprofessionals are currently evaluating medical information retrieval systems, their general use within the clinical environment, and varied search techniques for returning relevant information.

Results: The results of this literature review show that medical information retrieval systems lack consistency and are not always efficient. Based on the way medical professionals use current medical information retrieval systems, consistency in how search queries are processed will require continued evaluation on the part of information professions and clinicians.

Search Strategies: (“Information Systems/utilization”[MeSH Terms] AND ) “Medical Staff, Hospital”[MeSH Terms]; (“Information Systems/utilization”[MeSH Terms] AND ) librar*

Key Concepts: medical information retrieval systems, information retrieval, medical librarianship, clinical librarianship, health science librarianship

Abstract: This paper contains a review of the medical and information science literature with a focus on the evaluation, usage, and search techniques within medical information retrieval systems. The review provides a detailed methodology of the search process used for the collection of the articles, so that the results can be replicated. This review shows that, although there are not currently set standards by which medical information retrieval systems are evaluated; the usage of electronic systems is on the rise, showing a need for query instruction. It also identifies several current trends within information retrieval system research and areas to be improved upon.


The Use of Medical Information Retrieval Systems

Information retrieval systems are an integral aspect of scientific research and have

increasingly been used in clinical settings. Modern health care requires medical professionals to

move beyond the hospital walls in order to incorporate evidence into standard patient care, thus

increasing the need for digital information databases that can be accessed quickly and efficiently.

The jump to evidence-based medicine now pushes researchers, clinicians, physicians and

students to the limits of their searching abilities. Gone are the days of simply searching for

citations and abstracts; now medical professionals must be able to synthesize the information

they find into their daily routine. Rounds no longer take place around a table; instead mobile

devices have allowed them to move into the clinical environment. With a vast world of

information at their fingertips, the dissemination of medical information can now have an

immediate and direct impact on the quality of care health care professionals can provide. This

digital revolution within the health science community is making the timely retrieval of medical

information of paramount import in the context of patient care.

A review of the literature on the use of medical information systems reveals that research

in this particular area follows three recurring themes: how to evaluate medical information

retrieval systems; health care professional’s use of medical information retrieval systems; and

how to properly perform search queries within medical information retrieval systems. The

research also shows that there is currently a lack of oversight in the evaluation of medical

information retrieval systems. Since there is not a set of standards by which every medical

information system are evaluated, this leads to inconsistency in both the way information

retrieval systems are used by the medical community, as well as the process of performing search

queries within individual databases.


Methodology

A preliminary search of the literature on medical information retrieval systems in both

PubMed and the LIS databases returned articles on how information professionals are currently

trying to evaluate existing medical information retrieval systems and their general use in the

clinical environment.

After briefly skimming the literature, I was able to use the cited references to find

supplemental sources that bolstered my core articles. Many of the results returned were studies

done by information professionals who were creating MIRS for use within their own academic

institution. Several publications were literature reviews, while others were overviews of studies

that fit within a certain theme. A fourth theme that came up within the search was the creation of

custom MIRS, but I did not include these articles in my review of the literature because they did

not provide information relevant to the use of MIRS in clinical settings.

I used the following search strings in my search of both PubMed and the LIS databases

through Emporia State:

(“Information Systems/utilization”[MeSH Terms]) “Medical Staff, Hospital”[MeSH

Terms]; (“Information Systems/utilization”[MeSH Terms]) librar*

The first search I did was a broad search for “medical information retrieval systems,” which

returned over 63,000 results. I quickly realized that this topic was much more diverse and

complex than I had originally anticipated, so I went back to the drawing board and used the

MeSH terms to help me narrow the search results. When I utilized the search strings mentioned

above, I returned less than 300 results, which I further filtered using the “Full text available”

filter in PubMed and the EBSCOhost (LIS) databases.


Defining Information Retrieval Systems

An information retrieval system is “concerned with the storage, organization, and

searching of collections of information (Larson, 2009).” The term information retrieval, coined

in 1950 by Calvin Mooers (Mooers, 1950), gained popularity with the advent of machine storage

and electronic databases. Today, information retrieval systems are utilized in a variety of ways,

though they are increasingly used to store and disseminate scientific and medical information.

One of the first medical information retrieval systems, MEDLARS (MEDical Literature Analysis

and Retrieval System) was shown to have a "58% recall and 50% precision" which identified a

need for more thorough indexing and vocabulary development. Since MEDLARS, information

technologists and clinicians have pioneered new medical information retrieval systems by

working in tandem with the help of current research on the topic of medical information retrieval

(Dee, 2007, p. 421). If medical information retrieval systems are to be of continued use,

increased oversight and evaluation will be needed and many organizations, and individuals, are

stepping up to the challenge.

Evaluation of Medical Information Retrieval Systems

Medical information retrieval systems are an important aspect of scientific research, but

with new systems in development more frequently than before, each system must go through an

evaluation process in order to make sure they provide both useful and accurate information to

clinicians. In almost all cases, medical information retrieval systems must adhere to the strict

National Library of Medicine (NLM) criteria for performance, currency, and updating (Haynes,

Walker, McKibbon, Johnston, & Willan, 1994). Even though the NLM has established basic

criteria, it is important that medical information retrieval systems go through the evaluation

process in a number of ways. One of the main reasons for the evaluation of medical information


retrieval systems is that the overall complexity of health care information, systems constraints

and user and institution needs are continually changing (Deibel & Greenes, 1995, p. 765).

Several of the articles reviewed (Demiris, Folk, Mitchell, Moxley, Patrick & Tao, 2003

Grandage, Slawson & Shaughnessy, 2002 Liu & Wyatt 2011) evaluated the literature based on

the type of study returned, with a select few focusing on with systematic reviews and meta-

analyses (Demiris, Folk, Mitchell, Moxley, Patrick & Tao, 2003) or assessing the number of

randomized-controlled trials returned (Liu and Wyatt, 2011).

One image that represents the results of studies focused on the type of articles reviewed

and returned is the hierarchy of studies (Figure 1). This figure shows that Cochrane Reviews,

which contain systematic reviews as well as meta-analyses, are highly prized in the academic

community.

Figure 1 Hierarchy of Studies

Focusing solely on the number of meta-analysis returned without extensive knowledge of

research methodology, can be problematic, however. Demiris (2003), conducted a study using


Ovid MEDLINE, the articles returned were evaluated by dividing them into subsets based on the

level of effectiveness seen in their retrieval strategies. To evaluate a medical information

retrieval system based on a singular type of study is both shortsighted and time-consuming.

Circumventing the process of whittling away at meta-analyses or randomized controlled trials,

Grandage, Slawson and Shaughnessy (2002) used a simple equation in order to evaluate the

usefulness of articles returned from a medical information retrieval system:

This equation, which has the potential to mine information from multiple medical information

retrieval systems simultaneously, saves both the researcher and user time when querying

medical information retrieval systems. Mining, mentioned previously, is a valuable data

resource (Del Fiol & Haug, 2008), as it can collect usage statistics without the need to be

constantly monitored by a human being. When evaluating an information retrieval system,

Grandage (2002) places increased emphasis on the role medical librarians should take in order

to provide the most assistance to clinicians. Other articles (Elliot, Hersh, Hickam & Wolf, 1994

Masic & Milinovic, 2012 Braithwaite, Coiera & Westbrook, 2005) focus primarily on

evaluating the entire medical information retrieval system as a whole. Cimino, Ely, Lee, Sable,

Shanker and Zhu (2006) evaluated medical information retrieval systems based on the number

of documents returned after using a question answering technique, while a 2006 study by

MacCall focused on assessing information retrieval systems and clinical digital libraries in

terms of their “facilitation of timely clinical information seeking.”

However, medical information retrieval systems contain more than just text-based

documents. Medical images are an integral part of clinical diagnosis, analyzing treatment


options and evaluating laboratory results (Demner-Fushman, 2009). Searching for medical

images within medical information retrieval systems, however, is different than searching for

articles. As stated in Demner-Fushman (2009), "Currently available information retrieval and

decision support systems rely primarily on the text of scientific publications to find evidence in

support of clinical information needs (p. 1)." Without consistent evaluations of tagging within

medical information retrieval systems, searching for images in a text-based system will be a

frustrating experience. Many medical images are out of context and a simple caption, although

searchable, does not provide the clinician with relevant information about the image (p. 2).

Timely, well-categorized images are important to clinical diagnosis, especially in certain

specialties like dermatology. Searching for those images can be inefficient due to the current

state of results returned. Based on the findings in Demner-Fushman (2009), images clearly

benefited when combined with textual features, especially clinically relevant images. However,

the overall quality of image retrieval has room to improve in the area of result reliability (pp., 8-

9).

Throughout the literature, several common ways to evaluate medical information

retrieval systems, both text-based and image-based appeared. The most common include

measuring usage frequency and measuring users' success at retrieving relevant information

(Hersh et al., 1994, p. 895). Another way to evaluate medical information retrieval systems is

by using recall-precision analysis, which is done by analyzing search logs (Hersh, 2002, p. 287)

and evaluating the results returned. Other ways of evaluating medical information retrieval

systems include frequency of use, purpose of use, user satisfaction, search failure, and searching

utility, which includes answering clinical questions, retrieval of relevant articles and relevance-

based measures in bibliographic systems (Hersh & Hickam, 1998). All of these ways of


evaluating medical information retrieval systems assist in making them usable by health care

professionals, though continuing evaluation of all systems is a requirement for continued

success.

Health Professionals Use of Medical Information Retrieval Systems

One of the most frequent themes in the review of the literature was how clinicians,

researchers and physicians use medical information retrieval systems. Many times, however,

physicians find the process of looking for articles in a database time-consuming and difficult.

They often run into obstacles that cause frustrations, which in turn push them away from using

certain electronic resources and databases (Fauqert, 2012, p. 401). A multitude of studies

(Cimino, Hunt & Koziol, 2012 Clevenger & Shelstad, 1996 Coiera, Fauquert, 2012, Gosling &

Westbrook, 2005 Grad, Meng, Pluye, Segal & Tamblyn, 2005 Grefsheim & Rankin, 2007

Haynes, 1994 Hersh & Hickam, 1998) focused on IRS usage by the medical community. A

study conducted by Haynes (1994) showed that clinicians’ use of MEDLINE had risen during

the past five years. This increase in use came from a variety of reasons including, "user-friendly

software, a proliferation of online and compact-disc formats, falling user charges, and

advertising directed at clinicians."

Though each of the studies sought to identify the way different medical professionals

were utilizing medical information retrieval systems, their methodologies were not always the

same. The environment in which the systems were utilized was an important factor in choosing

the methodology by which to study clinicians use (Cimino, 2012). Whether it be by analyzing

retrieval patterns among general surgeons (Clevenger, 1996) or family practitioners (Grad, 2005

Grefsheim, 2007 Hersh, 1998), evaluating the effects of the dissemination of information by


physicians (Fauquert, 2012), or comparing the use of pre-versus post-intervention question

answering by clinicians (Coiera, 2005), each identified the environment as important. One of

the most important uses of medical information retrieval systems was in the realm of public

health. Many public health professionals need relevant information quickly and easily. Alpi

(2005) stated, "Knowing how to quickly locate materials in multidisciplinary full-text databases

is important to public health searching." However, due to the wide variety of databases used in

medicine, especially in regards to public health, efficient searching is key to the success of

clinicians (Alpi, 2005, p. 4). When faced with literature that is difficult to find, searching

multiple databases, each with its own limitations, is time consuming and inefficient. The

amount of Websites and databases available to medical professionals is increasing

exponentially. With growth, however, come consequences; mainly in that a consistent level of

expertise is difficult to maintain with an ever-changing search landscape (Alpi, 2005, p. 5).

As the practice of evidence-based medicine continues to increase, more clinicians will

begin to integrate their clinical expertise with the most relevant clinical evidence culled from

medical databases (Chung, 2009, p. 1). It was noted in a 2005 study by Grad that "the

tremendous volume of clinical information makes it difficult for doctors to rapidly access what

they need (p. 582)." They also stated that, "an information need is recognized when a doctor

reflects on their practice, and asks a question. For example, ‘does the patient have acute

sinusitis?’ The framework proposes that doctors may recognize their information needs, pursue

and satisfy them, and subsequently implement information in their decision making (as cited in

Ebell, 2003, p. S53). It is important to identify the needs of physicians and clinicians, as “the

information needs of practicing clinicians are distinct from the needs of students, researchers, or

nonclinical personnel. Clinicians seek information to stay current with new relevant medical


developments and to find answers to patient-specific questions (Grandage, 2002, p. 298)."

"Clinicians [also] generate highly specific patient-related questions at a rate of about one to

three questions for every three patient visits. Of every ten questions posed, they only look up the

answers to four and only find the answers to three. Of those they do not look up, they estimate

[that] at least half are important. Thus, clinicians are guessing at seven of ten questions, due in

large part to the amount of work it takes to find valid and reliable information that applies to

their patients (Grandage, 2002, p. 299).” Help is on the way, however.

Darmoni (2012) posited that, "it can be expected that users’ search experiences in

MEDLINE [as well as other medical information retrieval systems] will be enhanced by

techniques whereby both database and search engine developers make full use of the MeSH

structure" (p. 181). Regular search engines do not have the complexity to return relevant results

when queried with complex scientific requests, thus their use by clinicians falls short in the area

of scientific literature searching. Unlike computer-based medical information retrieval systems,

regular search engines cannot filter relevant results efficiently, making the extraction process

frustrating and difficult (Earl, 2010, p. 1).

In order to begin assessing the search situation, mining studies have been conducted. A

French study of Entréz, an NCBI integrated search system that handles more than 3 million

searches daily, [showed that] 70% of the searches are done as simple queries, in which terms are

entered without field specifiers, Boolean queries, or other advanced search techniques that can

be applied to achieve more meaningful search results. Just 21% of the searches use Boolean

operators, 13% use field specifiers, and 1% each use wildcard, range searching, and the History

function (Geer, 2006, p. 290)." Without the proper training, health care professionals’ use of

medical information retrieval systems falls short of their initial expectations. This could cause a


decline in the use of medical information retrieval systems by the scientific community as

whole, if instructional measures are not put into place.

Performing Search Queries in Medical Information Retrieval Systems

Many clinicians feel that they are adequately able to search within medical information

retrieval systems. Haynes (1994) demonstrated that clinicians who had experience with database

searching retrieved relevant citations, but their citations were not nearly as precise as librarians

were. However, with search training, clinicians "acquired comparable proficiency to that of

librarians [after several searches] (p. 293)." By using controlled vocabularies or Medical Subject

Headings (MeSH Terms), clinicians are able to replicate searches performs by information

professionals. However, Alpi (2005) discussed the shortfalls of controlled vocabularies,

particularly Medical Subject Headings (MeSH): Controlled vocabularies in health databases do

not capture the importance of place and partners well. For example, in Medical Subject Headings

(MeSH), the terms ‘‘neighborhood,’’ ‘‘community,’’ ‘‘place of birth,’’ ‘‘living arrangements,’’

and ‘‘domicile’’ are all entry terms mapping to the MeSH term ‘‘Residence Characteristics,’’

which is defined as ‘‘Elements of residence that characterize a population. (p. 2). Since MeSH

Terms are not consistent between all medical information retrieval systems, many physicians

tend to stick with a single system instead of exploring other options. In order to assist physicians

in their searching, one study (Bekhuis, Demner-Fushman and Crowley, 2013) provided an

extensive crosswalk of MeSH headings and Emtree matches (Table 1) that could be of use to

librarians and researchers. The crosswalk identified major differences between the two

databases, which could be cause for confusion and frustration to novice searchers. However,

consistency in search terminology is not the only shortfall within medical information retrieval

systems.


There have been efforts to create a more user-friendly search interface within MIRS. In

PubMed a study was undertaken to address the way rankings are evaluated and how query

strategies affect the PubMed search engine (Darmoni et al., 2012). In order to give a better

overview of the current set-up of many medical information retrieval systems, particularly the

Unified Medical Language System (UMLS) used within them, Darmoni (2012) described the

2011 version of the MeSH Thesaurus, which "contains 26,142 Descriptors, 83 Qualifiers, 25,801

Entry Terms, 200,676 Supplementary Concepts, and 317,554 Concepts." A structure this

complex can make searching the system an extremely convoluted and inefficient process. The

use of filters can help alleviate some of the burden of search inefficiencies.

In evaluating the literature, many of the articles (Abernethy, Currow, Fazekas, Sladek &

Tieman 2006 Carlson, Cvitan, Krieger, Lavin, McNary, Meyer, Perry, Reese & Spasser, 2005

Meyer & Sarin, 2005 Stoddart & Workman, 2012) described studies on the usage of filters in

medical information retrieval system searching. Search filters are developed to “improve the

efficiency and effectiveness of searching and are typically created by identifying and combining

search terms to retrieve records with a common feature (Glanville, 2008, p. 356). Each article

that focused on search filters, used a different set of filters: palliative care search filters

(Abernathy, 2006), evidence-based search filters (Carlson, 2005), anesthesia-related search

filters (Meyer & Sarin, 2005), and natural language filters (Stoddart & Workman, 2012). One set

of filters, a “semi-manually constructed Boolean [query] of MeSH terms, publication type and

MEDLINE record,” which was designed by Haynes (1994) are the most “widely available

method for identifying high quality articles through PubMed (Fu, 2011).”

The ability to perform efficient, relevant searches within medical information retrieval systems

is important to clinical practices and evidence based medicine. "In 1996, the National Library of


Medicine addressed the need for clinicians to refine their MEDLINE search retrieval in PubMed

by applying proven clinical filters. Clinical Queries provide a way to limit search retrieval to

articles about the four types of clinical research: diagnosis, etiology, therapy, and prognosis, as

well as options to direct the emphasis of the search to be more sensitive or more specific

(Grandage, 2002, p. 300).” Though there have been many studies which have taken up the task

of improving the search function within medical information retieveal systems, few have gone so

far as to implement the results. One suggestion by Haynes (1994) was to “develop a potential

method for improving the detection of studies of high quality for clinical practice from

MEDLINE. A way to implement this is to include search terms that select studies that are at the

most advanced stages of testing for clinical application (p.457).” As advances in natural

language processing, machine learning, and metadata tagging continue to improve, the way

clinicians search for information within medical information retrieval systems will surely evolve.

Conclusion

As medical information retrieval systems become increasingly prevalent within clinical

practice, improved oversight and better evaluation is needed. A definitive set of standards by

which to judge new and existing systems needs to be set in place, especially as access to medical

information retrieval systems increases. Studies into the use of medical information retrieval

systems will continue to be integral to the evaluation of the systems as a whole. Since user

experience is such an important aspect of designing information retrieval systems, the feedback

from the medical community and the data mined from their usage will be vital to those tasked

with designing information retrieval systems for the next generation of users. Not only does this

include superior interface design, but enhanced search processing and more efficient use of

existing search processing.


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Appendix

Table 1. An example of the spreadsheet in which the authors compiled the crosswalk

the use of medical information retrieval systems

Documents

information professionals

relevant information

information professions

medical librarianship

medical staff

digital information

way medical professionals

vast world of information