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J Am Med Inform Assoc. 1999 Jan-Feb; 6(1): 6–25.

The Basis for Using the Internet to Support the Information Needs of Primary Care


Synthesizing the state of the art from the published literature, this review assesses the basis for employing the Internet to support the information needs of primary care. The authors survey what has been published about the information needs of clinical practice, including primary care, and discuss currently available information resources potentially relevant to primary care. Potential methods of linking information needs with appropriate information resources are described in the context of previous classifications of clinical information needs. Also described is the role that existing terminology mapping systems, such as the National Library of Medicine's Unified Medical Language System, may play in representing and linking information needs to answers.

Over the last two decades, studies have enumerated the information needs of physicians engaged in clinical practice, including primary care.1,2,3,4,5,6 The emergence of the Internet and widespread adoption of the World Wide Web (the Web) have improved clinician's access to information resources. The Internet and the Web provide clinicians with both a ubiquitous, standardized system interface and a variety of Web-based materials.7 This review focuses on the potential of Internet-based resources to address the information needs arising from primary care.

The Nature of Primary Care

Primary care is the “provision of integrated, accessible, health care services by clinicians who are accountable for addressing a large majority of personal health care needs, developing a sustained partnership with patients, and practicing in the context of the family and the community.”8 Noble et al.9 emphasize the broad spectrum or “constellation” of health care services comprised by primary care practice. Examples of these services include hospital services, emergency medical services, public health, counseling, and home care services.

The art of clinical practice concerns the overall management of a patient's well-being. As described by Levinson,10 this role includes the task of information management:

The physician is an information manager who acquires, processes, stores, retrieves, and applies information related to 1) individual patient history and clinical course, 2) diagnostic and therapeutic protocols, 3) disease patterns in patient populations, 4) functioning of the health care system, and 5) the vast store of published knowledge. Little occurs in the clinical encounter that is not in some way related to obtaining, processing, or applying information. Optimal performance of clinical informational tasks has for years exceeded the cognitive capability of the human mind.

Observational Studies Describing the Information Needs of Clinical Practice

When faced with clinical decisions, primary caregivers (and other clinicians) must recognize when it is important to seek additional information rather than rely on past experience, uncertain knowledge, or “tincture of time” for diagnosis or therapy. Two recent reviews of information needs11,12 concluded that many questions arise during patient care and that, while most can be answered by consulting human and print-based resources, many go unanswered. Fortunately, practitioners “are capable of reasoning with incomplete and imprecise information, and often make clinical judgments at times when they have unfulfilled information needs.”13

Gorman's review of information needs concludes that “questions about optimal patient care are frequent, with many questions occurring each day for a typical physician.”12 As part of their analysis of information needs expressed by general internists, Osheroff, Forsythe, and colleagues3,4,14 categorized questions' status as being currently satisfied (clinician recognizes question and knows answer), consciously recognized but unsatisfied (question recognized, answer unknown), and unrecognized (clinician should perceive information need exists but does not, and does not know answer). Published estimates of the incidence of information needs during clinical practice range from 1 question generated for every 15 patients in primary care settings15 to roughly 1.4 questions per patient on a given day in inpatient settings.4 It is not clear whether the observations from primary care settings covered “unrecognized” information needs.

Osheroff, Forsythe, and colleagues3 introduced a two-by-two matrix that characterizes clinical knowledge along two axes: formal to informal, and general to specific. Examples of formal knowledge include the peer-reviewed literature and databases from controlled scientific studies. Unwritten, common practices followed at a clinical site and population-based information from local, noncontrolled clinical data repositories represent examples of informal knowledge. General knowledge, whether formal (textbooks, literature) or informal (untested guidelines), is available widely and applies to categories of patients. Examples of local knowledge include the clinical findings in the chart of a patient and guidelines developed at a single clinic. Gorman12 classified the types of information used by clinicians into five categories: patient data, population statistics, biomedical knowledge, logistic information, and social influences. Thus, from a clinician's perspective, lack of desired information of any of these types may constitute an information need.

An unmet information need is an information need that has not been answered when the clinician makes a decision about the patient.16 Because of the ever-increasing size of the biomedical literature17 and the complexity of modern health care practices, clinicians could spend hours to weeks reading texts and seeking expert opinions for each patient they encounter. During a busy primary care practice, clinicians must constantly trade off providing care to more patients versus addressing information needs arising from patient encounters. Time pressures of clinical practice make it difficult to answer information needs as they arise and may explain why, after completing residency training, a physician's knowledge of medicine tends to decline over time.18,19 Other barriers to addressing information needs include limited access to resources, the cost of resources, difficulty learning or using many resources, poor organization of resources, and variable quality of information.1,2,20,21,22,23 Quantitative and qualitative analyses of unmet information needs have been performed in settings ranging from large academic institutions to small primary care offices and clinics.1,2,3,4,5,6,15,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48 Unmet information needs range from 0.1215 to 5.229 unanswered questions per half-day, depending on the practice setting and sampling methodology.20

The influence of unmet information needs on patient outcomes is unknown.11 Even though every unmet information need potentially compromises patient care, a good clinician is expected to determine whether meeting a given information need is mandatory or critical at the time of a specific patient encounter. However, Williamson et al.33 surveyed primary care practitioners in the United States and found that they require “substantial help” meeting their information needs. This finding is significant, because it showed that “... physicians face a serious problem in their effort to keep current with recent medical advances.” Bankowitz et al.49 demonstrated that diagnostic uncertainty influences resource utilization during patient evaluation.

In summary, information needs are numerous and exist in many forms, in primary care as well as other clinical settings. Studies of information needs over the last two decades underscore the persistence of the problem and imply that for whatever reasons, evolving information resources have not yet reduced primary caregivers' unmet information needs significantly.

Sources of Information Currently Available to Primary Care

Observational studies and self-reflective surveys suggest that the main resources that clinicians, including primary care providers, use to satisfy information needs include colleagues, tertiary literature (such as textbooks), primary literature (such as original research in clinical journals), and continuing medical education.24,43,44,50,51,52,53 Less widely accessible, traditional sources of information also include health science library-based and bibliographic resources, academic health care centers, and clinical consultations and referrals. Less well established but increasingly useful sources of information include clinical computer software applications, telemedicine applications, and the Web.

Health Science Libraries and Bibliographic Resources

A significant number of primary care settings are distant from facilities that provide health information.54,55,56,57,58,59,60 Many primary care practices are in underserved areas, are understaffed, and generally do not employ in-house subspecialists for consultation services.61

Health Science Libraries

Health science libraries offer a variety of services that can help primary care providers address information needs, including traditional print-based textbooks and journals, interlibrary loans, librarian-mediated literature searches, computer training on bibliographic searching software and clinical software tools, alerting and document delivery services, easy access to bibliographic citation databases (e.g., MEDLINE), and local access to electronic “full-text” journals and textbooks.43

Health science libraries often have sufficient resources to mount a representative sampling of clinical textbooks distributed in electronic (usually CD-ROM) format; few other institutions do so. Libraries or large networked office practices can make such offerings accessible via an intranet. Products utilizing electronically based reference sources provide superior searchable interfaces compared with print-based versions of the same sources. For example, the Harrison's Plus62 product contains electronic versions of Harrison's Principles of Internal Medicine and the U.S. Pharmacopeia's Drug Information for the Health Care Professional. Examples of other electronic textbooks include Scientific American Medicine63 Physician's Desk Reference (PDR),64 and Nelson's Textbook of Pediatrics, among many others.

The value of all of these library-based services to clinical practice, including primary care, has been studied in academic health care centers,24,40,65,66 urban hospitals,67 urban community health centers,60 and rural areas.2,31,40,58 Although clinicians vary by practice setting in the resources they access, there is positive support for utilization of these library-based services.24,31,43,48,68,69

Clinical Medical Librarian Programs

Over several decades, health science librarians at selected sites have served successfully as information consultants for patient care.70 Initiated by Gertrude Lamb, clinical medical librarian (CML) programs began in the early 1970s with the goal of directly increasing the relevance of the health care literature to care providers.71 In the traditional CML model, academic medical librarians attend either clinicians' morning report (when new cases admitted to the hospital are discussed) or house staff teaching rounds on a regular basis. After interacting with the health care team, they determine information needs and place relevant literature references on the patients' charts (a process called LATCH, for “Literature attached to charts”).72 This allows the librarian to provide specific, case-related information to support patient care.73 In a more recent model for CML, librarians participate as members of ward teams74 rather than simply attaching literature to charts. Overall, CML programs are well accepted among care providers,73,75,76,77,78 but they have been criticized for being labor-intensive and expensive.79

In recent years, the proliferation of end-user—friendly information resources (such as the NLM's Grateful Med80) caused a shift in biomedical librarians' role from information mediators to knowledge workers.81,82,83 This “revolution” encourages librarians to work in settings beyond the library's walls and to seek new, proactive roles for development and delivery of information products and services to the clinical practice, biomedical research, and patient communities.84,85 For example, one service extends the traditional quality filtering of the literature provided by librarian-mediated searches. Instead, in response to an information request, clinically knowledgeable librarians read, filter, and synthesize key articles and produce a concise written summary relevant to a specific clinical case. By entering the results of their consultation into a growing knowledge base, librarians can better support related inquiries in the future.74 Reusable knowledge resources created by such efforts can help support the information needs of primary care practitioners and patients themselves.

Bibliographic and Information Retrieval Software

The National Library of Medicine has developed MEDLINE-based resources such as PubMed,86 Grateful Med,80 and LOANSOME DOC that help primary caregivers benefit from biomedical library journal contents from the clinicians' practice settings.68,69 In addition, computers equipped with a CD-ROM drive or a modem can access bibliographic search software, such as Ovid,87 Aries Knowledge Finder,88 SilverPlatter,89 and PaperChase.90,91

The bibliographic citation and full-text information retrieval programs produced by developers and vendors vary with respect to domain (e.g., infectious diseases or pharmacology), content (e.g., citations or full-text articles), user interface (e.g., free-text input or codified query terms), and indexing and retrieval methods (e.g., statistical or concept-based).92,93,94,95,96,97,98,99,100,101,102,103,104 As a result, the quality of citations retrieved by various MEDLINE-based search interfaces varies considerably.105

With respect to health care providers, studies indicate that MEDLINE and its derivatives can be helpful for answering clinical questions.24,28,105,106,107,108,109,110 However, finding specific answers to questions can be time-consuming and expensive, in part because of the effort required to sift through a sometimes large set of relevant publications.17,24,28,105,111,112,113,114 In that regard, some have likened MEDLINE searching to attempting to drink water from a fire hose.

Academic Health Centers

Academic health care centers maintain a large array of resources that help clinicians keep current with biomedical advances and more effectively care for their patients.43 As noted earlier, primary caregivers lack ready access to current journals, textbooks, and multimedia resources like those held by the libraries of academic health care centers. Also, among the commonly sought-after sources of new information for primary caregivers are the continuing medical education courses offered by academic health centers.24,43,44,50,51,52,53

Academic Consultation and Referral

Primary care providers practicing outside large health care centers often lack easy access to subspecialists. They often seek their assistance in the form of referrals and consultations,115,116,117 although communication between referring physicians and consultants can also be problematic.118,119,120,121,122 Consultation and referral patterns vary considerably,117 as documented by studies in the United States118,119,123,124,125,126 and the United Kingdom.116,120,127,128,129,130,131 Factors linked to variation include availability of qualified consultants,129 diagnostic certainty,123 patient characteristics,124,125 referring-provider training115 and specialty,124 and reimbursement plan.125 It is not unusual for a referring physician to receive little or no relevant feedback related to the information request that prompted a referral.118,119

Consultation and referrals from primary caregivers to clinical specialists do not uniformly involve academic medical centers. For the purposes of this review, the authors have listed consultation and referral under academic health centers because such centers generally provide the widest range of consultative services in a region. The collective academic knowledge and pragmatic skills of the faculty and staff at academic health centers are their most valuable resource. Health centers are experimenting with a number of new approaches to replace the traditional, labor-intensive, one-on-one phone call from a primary caregiver to an academic colleague. For example, the Medical Information Services via Telephone (MIST) network at the University of Alabama in Birmingham (UAB) provides thousands of rural physicians and other health care professionals with toll-free 24 × 7 access to free consultation services with UAB Medical Center faculty and staff.132,133 The success of MIST has motivated other institutions to develop similar programs.

Academic Health Center Telemedicine Initiatives

Telemedicine is “the use of electronic information and communication technologies to provide and support health care when distance separates the participants.”134 Because they generally have sufficient resources as well as extended referral networks, academic health centers have initiated or coordinated the majority of telemedicine initiatives in civilian settings. As opposed to Web-based “generic” informational resources (discussed later in this article), telemedicine consultations are patient-specific and patient-focused.

The definition of telemedicine encompasses a wide variety of clinical applications, including telephone-based systems for voice support,132,133,135,136 relatively low bandwidth systems for sharing textual and multimedia data,23,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156 and higher bandwidth systems transmitting interactive video.157,158,159,160 While some telemedicine systems support clinical consultations across a variety of specialties,159 other telemedicine applications specialize in individual domains, such as teledermatology,138 telepsychiatry,161,162 and teleradiology.137,163,164 The use and growth of teleradiology systems have led to reimbursement by Medicare.165

A number of telemedicine systems at least partially address the information needs of primary care.166,167,168,169,170,171,172,173 Georgia's GaIN23,140,141 and West Virginia's CONSULT174 are two older, established statewide networks whose participating “spoke” sites receive MEDLINE access, e-mail, and consultative services. Both networks give community physicians the opportunity to channel information requests to trained “information gatekeepers” at remote sites, such as regional hospitals. The more recent, four-state regional IAIMS effort developed by the University of Washington implements telemedicine for education and consultation.175,176

A new, rapidly growing form of telemedicine extends support directly to patients, as an adjunct to services provided by primary caregivers. For example, Brennan et al.142,143,144,145,177,178,179,180 developed ComputerLink, an electronic network to assist family members (and others) who care for patients with Alzheimer's Disease. Participants used home computer terminals with modems to access ComputerLink. The service offered three main functions: electronic encyclopedia, e-mail, and a decision support system.180 ComputerLink enhanced caregivers' decision-making confidence143 and was useful in discovering the types of support required by patients and caregivers.142

At present, a number of unresolved issues hinder use of telemedicine in primary care settings: licensing across state lines, lack of standards, difficulty of training users and maintaining equipment, reimbursement policies, issues of patient confidentiality, and the cost of telecommunication infrastructure.181,182,183,184,185,186,187,188,189,190,191 The time required to adequately train users is substantial.177,185 Many telemedicine efforts initiated as demonstration projects assessed the technical feasibility of electronic communications to support patient care,181 but not its cost-effectiveness.184,185,186,187 Telemedicine systems based on real-time, state-of-the-art videoconferencing equipment are costly and may not be practical for most institutions.181,192 Evidence suggests that simple, less sophisticated store-and-forward methodologies may realize cost savings without compromising quality by relaying stored patient data, sound, and images to remote sites for later “asynchronous” consultation.138

Ultimately, the federal High Performance Computing and Communications program and the Next Generation Internet193,194 and Internet2195 initiatives will provide substantial support for use of the national information infrastructure by the health care community, including implementation of sophisticated telemedicine applications.196,197,198,199

Collectively, the print, electronic, and human information resources at academic health care centers can be useful to primary care. However, barriers to effective and widespread use of these resources—related to ease of access—remain.43 It is difficult for the remote practitioner to know who or what is available and how to best make use of services from a distance.43,58

Clinical Computer Software Applications

A variety of clinical computer software products have been developed to support clinical decision making. For the purposes of primary care, these applications can be broadly classified into three categories: bibliographic and full-text information retrieval systems (discussed earlier),200,201 clinical decision-support systems (CDSS),13,202 and clinical information systems (electronic medical record systems, or EMRSs). A thorough discussion of CDSS and EMRS is beyond the scope of this review92,203,204,205,206; however, a brief overview of their relevance to primary care follows below.

Clinical decision support systems have the potential to provide primary caregivers with useful information regarding diagnosis, therapy, and prognosis. Given a set of patient findings, diagnostic CDSS can, for example, compute and explain differential diagnoses, show relevant laboratory tests sorted by cost, suggest possible workup protocols, and provide links to relevant biomedical literature.207,208 Examples of broad-based diagnostic CDSSs for general internal medicine include DXplain,209 ILIAD,210,211,212 Meditel,213 and Quick Medical Reference (QMR).214 Rigorous evaluations of CDSS are difficult to conduct.13,215,216,217,218 Evaluations of DXplain,215,219,220 ILIAD,215,219,221 Meditel,215,219 and QMR49,215,219,222 indicate that no broadly based diagnostic systems perform superiorly in terms of accuracy, but many are able to suggest additional diagnoses not originally considered by users. Providing adequate training to users of such systems is an ongoing, unsolved problem.30,221 Overall, CDSSs, when properly used with an understanding of their strengths and limitations, can potentially offer useful advice to primary caregivers presented with complex clinical problems for which they might otherwise seek consultation.

Clinical information systems (EMRSs) promote more effective patient care by reliably and efficiently storing and retrieving patient data—ranging from clinician's orders to clinical textual reports (e.g., history and physical examination notes, progress notes, nursing notes, discharge summaries, radiology reports, and pathology reports), numerical laboratory results, pharmacy information, billing information, census data, and outcome data.

A number of early studies attempted to integrate microcomputer-based bibliographic citation databases, drug information databases, electronic textbooks, and decision support systems to answer clinical questions for primary care.30,35,223,224 One study compared two groups of private practice physicians, nurses, and university-based pharmacists using either BRS Colleague or Dialog Medical Connection resources for acquiring drug information.223 Users obtained less than complete information for 70 to 86 percent of their questions. Study authors found pertinent information for 59 percent of failed user searches. Improper use of search terms and failure to select all relevant databases constituted the two most significant searching errors. A separate descriptive study of eight physicians using six commercially available clinical software products during a two-week period30 found that 40 percent of the questions arising in daily practice were fully answered; 32 percent were partially answered; and no useful information was obtained for 28 percent of the questions. The study's authors described inappropriate resource selection by the physicians and were subsequently able to locate relevant information for failed searches. In a ten-month study by Hersh and Hickam,224 a computer workstation was provided for routine use in a university-based general medicine clinic. The workstation contained bibliographic search software, full-text textbook searching software, and decision support software. The authors concluded that novice searchers could retrieve large quantities of relevant information when provided with user-friendly software.

In summary, while microcomputer-based software applications contain large amounts of useful information, significant barriers to the effective retrieval and application of that information remain in primary care. Users have difficulty finding the most relevant resources, are unable to master multiple applications, and require time-consuming, out-of-the-office training. Hence, the utility of existing resources is limited.

It has been stated for decades that clinical software systems will achieve their greatest value when larger EMRSs, CDSSs, and other clinical applications are integrated seamlessly across systems and across sites. Even in academic health centers, this lofty objective has been achieved with only partial success (for example, alerting225 and critiquing226 during physician order entry227,228). Access to electronic information stored elsewhere remains as much an impediment (or possibly more, because of security issues) to primary care as is access to paper records stored elsewhere. It remains problematic to meet those information needs of primary care related to utilization of patient data stored at another site.229

The World Wide Web

While it is clear that Web-based interfaces will play essential roles in delivering easy-to-use systems to primary care providers, the role of the Web, taken as an information resource per se, in supporting and addressing primary care information needs is far less certain. Care providers can find clinically useful information on the Web,230,231,232,233,234,235,236 but the time requirements can be substantial.237,238,239,240 Indeed, critical evaluation of the representation of health information on the Web is warranted. The Web's rapid growth7,241,242,243 and lack of controls have led to numerous criticisms, including poor organization, questionable validity, and questionable reliability.244,245 These shortcomings effectively render a substantial amount of Web information unsuitable for direct clinical application.166,244,245,246,247,248,249,250,251

The best and most widely accepted strategy for use of the Web to support clinical practice involves locating and using “anchors” of known high quality. A number of U.S. government agencies, such as the National Institutes of Health (NIH)252 and the Food and Drug Administration (FDA),253 provide useful, reliable Web-based resources relevant to primary care. For example, two institutes within the NIH—The National Cancer Institute (NCI)254 and the National Library of Medicine (NLM)255—provide free, unrestricted access to a diverse set of clinically useful information resources. These include the NLM's PubMed,256 Internet Grateful Med,257 and Health Services Technology Assessment Texts (HSTAT)258—which contain clinical practice guidelines, quick reference material for clinicians, and evidence-based reports from the Agency for Health Care Policy and Research (AHCPR)— and the NCI's PDQ and Cancerlit databases—which contain information about the cause, diagnosis, prevention, and treatment of cancer.259,260,261 The FDA, in addition to other services, provides timely information on new drug products.262 Academic health care centers and professional specialty organizations are also good sources for relevant clinical information.250

Many peer-reviewed journals, such as the New England Journal of Medicine263 and the Journal of Family Practice264 have quality full-text articles on the Web. Other Web sources relevant to primary care include multimedia textbooks such as the Diagnosis of Pulmonary Embolus265 published by the Virtual Hospital266,267,268; drug review articles such as “Cardiovascular Drug Reviews”269 from the Medical Sciences Bulletin270; diagnosis and treatment information such as that published in the Merck Manual of Diagnosis and Therapy271; forums for asynchronous discussion, such as USENET newsgroups272,273,274 and listservs; Web sites of national voluntary health agencies, such the American Heart Association275; and organizations that index Web content such as Medicine in the Matrix276 and CliniWeb.277,278

No systematic comparative studies have assessed effects of Web-based information resources on the quality, accessibility, or cost of primary care. However, demonstrated utility of existing resources (e.g., MEDLINE, CDSS, printed textbooks) prior to their availability on the Web suggests that Web-accessible versions of these resources will also be useful.

A Proposed Model for Internet Use

No single genre of information resource, like those discussed in the previous section, can meet all the information needs in primary care. Primary caregivers must be empowered to utilize all relevant resources efficiently, as they are needed.

Since 1995, the authors have been developing a new model for using the Internet to support the information needs of primary care. This effort has been supported by grants from the National Library of Medicine. The model represents a commonsense synthesis of previous efforts and has been independently proposed by others with minor variations, as discussed later. The model focuses on modern academic health centers as the most logical site to integrate and distribute a wide variety of both electronic and human information resources for primary care. According to the model, academic health centers would mount a Web-based, Internet-mediated triage system to facilitate access to their electronic and human information resources. The triage system would support access to academic centers' large, well-staffed biomedical libraries and their up-to-date faculty expertise covering clinical subspecialties as well as health services and basic science research and their advanced health informatics projects, including telemedicine. The information resources mentioned earlier are well represented in most academic health care centers.

The authors' model involves three layers of triage. The first layer would use a Web-based secure interface and library-based community outreach techniques to train and remotely connect affiliated primary-care end users to academic health science libraries. Primary caregivers would then, as a first pass, have full access to the electronic resources of the health center library, such as bibliographic and health-related databases, full-text journals, and clinical software applications—nearly equivalent to being on site to address primary care information needs. As part of this process, issues of software licensing must be addressed carefully. This layer is not particularly innovative, in that a number of academic health sciences libraries have mounted significant informatics-related outreach efforts over the last two decades.

The second and third layers of the model involve academic health centers mounting, within the Web-based secure interface of layer one, a software application to field and triage individual practitioners' information requests that caregivers could not answer using first-layer resources. Users could submit queries in limited natural language format (e.g., one sentence only) or using templates. If the query were submitted in natural language, a parsing system would convert it into a structured format, using an underlying set of templates to represent the possible kinds of information requests. Alternatively, direct entry using templates could circumvent the requirement to parse and make sense of natural language queries. The second-layer triage application would be able to suggest relevant information resources (both Web-based content and dedicated application software product content) for a particular question and present the user with the choice of pursuing various resources further to the point of answer retrieval. Queries could involve general or specific and formal or informal knowledge. For example, an application program might help primary caregivers to identify qualified and available academically based subspecialty consultants and help schedule appointments for patients to see them, as well as improve the suboptimal bidirectional information flows that currently exist.

Automated or semiautomated triage of primary caregivers' requests could make use of scarce human resources more appropriate and relevant. The third layer of triage in the proposed model would become accessible when users indicated that their request for information had not been adequately answered through use of the first two layers. At this stage, the stored information request would be forwarded electronically to a knowledgeable human “gatekeeper” (e.g., librarian or clinician) for manual triage to appropriate faculty specialists (via e-mail) or to other persons with specialized access to information resources (such as a librarian with access to private institutional databases that do not have public interfaces). These specialists would respond to the primary caregiver's question via e-mail. Periodic manual audits of such sessions could be used to determine whether electronic systems might have been used to obtain similar results and, if so, why they had not been used. Issues of licensure (ability to give advice across state lines) and reimbursement for faculty and staff time would have to be addressed, as in telemedicine.

Previous Implementation of the Model

Implementing the primary care triage model requires the accomplishment of three main objectives, in a computationally tractable manner: representation of primary care information needs in a detailed, structured classification; representation of the content of electronic biomedical information resources, as well as topics of human expertise, in a detailed, structured classification; and efficient linkage of specific clinical information needs with appropriate information resources. Health care informatics researchers have already performed substantial work toward accomplishing these objectives. Implementation of the proposed model should take advantage of lessons learned from previous efforts.

Relevance of the UMLS Project to the Model

Most investigations of clinical information needs have reported results in aggregate form (as general schemes) rather than as enumerative taxonomies of what clinicians ask.1,2,3,4,5,6,15,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48 Systems designed to service information needs in primary care require a greater degree of categorization than has been reported. Critical missing resources are a standardized, validated, clinically useful classification of information needs suitable for providing Internet-based decision support and a corresponding classification of relevant information resources.

Since no detailed classification scheme for the information needs of primary care was created as the product of observational studies, it is useful to ask whether any existing classification schemes, such as clinical terminology systems or mappings developed as part of implementation projects (rather than derived from observational studies), can be used to represent the information needs of primary care.

Overall, the National Library of Medicine's Unified Medical Language System (UMLS) is potentially the essential resource related to information resources in primary care because, in essence, it combines many individually important lexical resources now in clinical use.

Overview of the National Library of Medicine's UMLS Project

The UMLS279,280,281,282 was developed to serve as an interlingua283,284 for electronic interchange among disparate clinical and biomedical research systems. The UMLS comprises four evolving knowledge sources: the Metathesaurus, Semantic Network, Information Sources Map, and the SPECIALIST lexicon.285,286,287,288 The Metathesaurus is a database of biomedical concepts (and related information) accumulated from more than 40 important controlled vocabularies and classifications in actual use in biomedicine.286 It contains names and semantic information about 500,000 biomedical concepts. The Metathesaurus includes MEDLINE co-occurrence data, which quantify the number of times two terms listed in MeSH (the Medical Subject Headings thesaurus, one of the Metathesaurus component vocabularies) occur together in the literature as the main index terms on the same article.289 The Semantic Network lists semantic types that can be assigned to all Metathesaurus concepts and specifies the types of meaningful relationships that can occur between pairs of semantic types.286,290 The Information Sources Map contains information characterizing the scope and content of hundreds of biomedical information resources. The SPECIALIST Lexicon contains syntactic and semantic information about a subset of the Metathesaurus biomedical concepts (at the term and world levels) for use in natural language processing systems.287,288,291,292,293

Theoretically, a computer program armed with knowledge from the UMLS could be used to first recognize and then explore possible relationships among concepts in a clinical question expressed in natural language. This information could then facilitate automated or semiautomated methods for mapping the user's question into a structured representation, which in turn could facilitate determination of relevant information resources via the Information Sources Map (ISM).

Early UMLS Project Work Relevant to the Proposed Triage Model

Before and during the first five years of the UMLS project,279,280,281,282 a number of institutions participating in it attempted to address the issue of classifying and answering clinicians' information needs in a manner potentially relevant to primary care.

In 1985, researchers at the NLM reviewed 2,000 literature search request forms submitted from the National Institutes of Health and created a database of 155 representative queries for experimentation in bibliographic retrieval.294 Although not a formal classification of information needs, this resource contains carefully selected questions covering clinical research, basic science research, and health services research. The database also contains question-specific MEDLINE citations found by an expert NLM searcher and corresponding citation relevancy judgments formulated by a subject matter expert. In addition to experiments in bibliographic retrieval, this database has been successfully used to develop natural processing tools for query interpretation.287,295,296

During 1989-91, Osheroff, Forsythe, and colleagues3,4,297 developed a coding scheme (a hierarchy with 103 terminal nodes) for describing general medicine information requests at an academic teaching institution. Topics included questions about disease states (e.g., pathophysiology, specific therapies), about therapy in general (e.g., medications or surgery), and about clinical findings (e.g., differential diagnosis of a given finding). In addition to classifying the subject of each information request, the study documented anticipated sources of responses, the generality of information sought, and the nature of responses required. Of the information needs observed, 52 percent of the questions requested a fact that could have been found in a clinical record; 23 percent were potentially answerable by a library (resources such as textbook, a journal, or MEDLINE); and 25 percent required synthesis of patient information and biomedical knowledge.

From 1989 to 1992, a group at Yale University developed two knowledge-based programs designed to help clinicians find relevant literature references, one in psychiatry, called PsychTopix,298,299 and the other in hepatology, known as HepaTopix.300 The programs suggest possible topics of interest based on a scan for key words in a patient's computer-based record. Selecting a topic generated an automated MEDLINE search using MeSH logic from the program's knowledge base. For HepaTopix, two pathologists and a hepatologist created a master outline of 35 key topics. Two hundred and twenty-five subtopics were created for five main liver neoplasm topic areas. Subtopics included clinical manifestations, epidemiology, pathogenesis, signs and symptoms, diagnostic tests, and treatment. The outline format enabled further specification as needed. For example, the epidemiology of alcoholic liver disease was further specified by incidence, genetic susceptibility, and sexual susceptibility. Although these topic areas were not derived empirically, they illustrated a useful method of organizing and representing information needs in a given domain. These projects created a mechanism, in the form of hierarchic topic areas, that linked clinical questions with resources that could provide answers. A major drawback was the labor-intensive reliance on domain experts for creating topic areas. This limited application of the approach to other domains.301

From 1991 to 1996, Cimino et al.301,302 examined common syntactic and semantic patterns in a collection of clinical questions from three sources: the 1985 NLM collection of questions described above,294 a collection of questions from a cystic fibrosis research database303 for experimentation in bibliographic retrieval, and reference queries submitted to the Columbia-Presbyterian Medical Center (CPMC) health sciences library.304 The goal of Cimino et al. was to identify a set of general-purpose questions, called “generic queries,” which can be tailored to user information needs.301 They hypothesized that use of generic queries in clinical applications could facilitate determination of users' information needs and simplify selection of potentially relevant information resources. Combining manual review by expert librarians with natural language processing techniques, they derived 37 generic queries that captured the essence of all user queries in their study.302 The queries typically involved one or two clinical terms and a relation, such as “What causes X?” and “Does X cause Y?” They developed MEDLINE search strategies for each generic query and integrated this knowledge into a clinical application known as the MEDLINE Button.302 The CPMC clinical information system reviews data in a patient's computer-based record and, based on the presence of data about a particular disease or therapy, suggests possible questions for which the system may provide information. A particular strength of this effort lies in its generic approach to representing basic information needs and in tailoring the approach to a specific situation.

During 1991-93, a group at Massachusetts General Hospital and Harvard University analyzed physician-generated questions in an ambulatory care setting, in order to identify useful information resources to include in their Interactive Query Workstation (IQW).305,306,307,308,309 They determined the types of clinical questions physicians ask, how questions are generally stated, and relevant information resources for answering questions. They collected 69 questions from three physicians, whose information needs were studied by review of the clinical records of 15 of their patients. Physicians also identified the item in each record most closely related to their question. The researchers mapped these “key terms” into the following seven COSTAR310,311,312 categories: physical examination findings, problem lists, medications, nonmedication therapy, laboratory results, procedures, and administrative aspects. All key terms came from three categories: medications, laboratory results, and problem lists. Further analysis quantified the extent to which key terms, query words, and query concepts mapped to the UMLS. The authors concluded that identification of concepts using the (early versions of) UMLS was “not usually sufficient” in describing information needs and that creation of a vocabulary that can represent relationships between concepts was necessary.305

Overall, these early explorations and other investigations313,314,315 contributed valuable insights into how clinical questions may be logically organized and productively integrated into information systems. A common theme is that investigators felt it necessary to develop query representation schemes because suitable representations were not available. They had partial success in linking clinical questions with possibly relevant information resources.316,317,318,319,320 These efforts involved building integrated “front ends” for distributed information resources such as bibliographic citation databases,303,305,321,322,323,324,325 clinical textbooks,305,322,323,326 and clinical diagnostic decision support systems.305,322,323,324 Investigators either used home-grown query languages or attempted to adapt existing terminology mapping systems such as the UMLS for such purposes. It is difficult to determine whether their classifications could be extended for providing Internet-based decision support in primary care. Although no common approach yet exists to classify information needs to support computer-assisted query systems, commercial publishers have begun to exploit electronic media, combining a number of Web-based electronic bibliographic and other resources into integrated systems.327,328,329

Evaluations of the Relevance of UMLS to the Proposed Triage Model

Miller et al., at Yale University, contributed to early applications involving and evaluating the ISM.317 They reported great difficulty making practical use of the ISM, particularly in two main areas: Encoding the subject content of electronic resources was extremely difficult using current coding schemes; and achieving seamless, cross-platform access to heterogeneous resources was difficult with network communication software. Modifications of the ISM,322,330 including use of the Web324,331,332 and application of conceptual graph theory,333,334,335,336 have been explored to work around these problems.

Because the initial design of the ISM predates the dramatic rise in Internet connections and invention of the Web, its methods for description and access of available machine-readable information resources was insufficient for keeping up with the explosive growth in Web-based information.279 The dramatic advances in Internet and Web-based technology have had a “temporarily disruptive effect” on UMLS efforts to achieve its long-term goal of defining a method of describing available machine-readable information resources to support automated selection and retrieval from relevant resources.279

It is uncertain whether source vocabularies within the UMLS can collectively provide adequate coverage of clinical concepts in primary care queries.337,338,339,340,341,342,343 In 1987, Masarie and Miller337 found approximately 50 percent of the words in a medical chart mapped to MeSH. The study by Chute et al. of major clinical coding systems for representing patient information339 found that UMLS 1.3, ICD-10, SNOMED III, READ V2, ICD-9-CM and CPT—the latter two in prevalent use in the United States as well as constituting a portion of the UMLS source vocabularies—failed to capture “substantial” clinical content. Although these studies document inadequacies of individual and combined vocabularies, a study of the 1997 UMLS by the NLM found that the combination of source vocabularies represents the “majority of the terminology needed to record patient conditions.”338 It is important to remember that comparative studies of evolving lexical terminologies have a “useful” half-life of at most a few years. In general, each revision of a terminology includes enough term additions, deletions, and other modifications to effectively render previous comparisons among it and other terminologies invalid.

Several additional issues potentially hamper application of the UMLS for representing primary care information needs: the difficulty of interpreting user queries, incomplete coverage of primary care concepts, intervocabulary mapping difficulties,313,344,345,346,347 and inconsistencies within the Metathesaurus.348 Experiments in automated mapping of free-text user input into a controlled vocabulary, such as MeSH,111 demonstrate that lexical-based approaches are useful but not perfect.296,337,344,349,350,351,352,353,354,355,356,357 Thus, lexical-based methods employing the UMLS may be helpful in capture and interpretation of user queries from primary care.

Although studies suggest that individual and combined UMLS source vocabularies incompletely cover the entire primary care domain, other evidence suggests that the UMLS is a valuable resource in representing information needs. Recent work demonstrates that progress in vocabulary mapping and answer resource identification is possible but difficult and that exploration of new methods should continue. The NLM has provided an online bibliography of health informatics research projects utilizing the UMLS.358

Other Internet-based Decision Support Models in Primary Care

The emergence of the Internet and the Web has facilitated development of new models capable of providing varying levels of decision support in primary care.323,324,359 In 1995, Detmer and Shortliffe359 proposed a model of clinical query management that supports integration of biomedical information resources through a Web-based interface. The model architecture contains a Web browser, a Web server, a common-gateway-interface mediator, a representation of medical concepts (UMLS), and information resources accessible over the Internet. User queries submitted via a Web form are processed by the CGI mediator in six stages: syntactic processing, semantic analysis, selection of information resources, translation to queries, process management, and display management. They developed an application named WebMedline,360 which retrieves MEDLINE citations and integrates them with critical reviews published in the ACP Journal Club. More recently, Detmer and Shortliffe developed a system called MedWeaver,324,361 which integrates diagnostic decision support from DXplain,209 literature searching from WebMedline, and retrieval of Web sites from CliniWeb.277,278 MedWeaver employs an interface manager, query formulator, and retrieval manager to abstract the user from having to deal with separate interfaces for each resource. In a “typical” MedWeaver session, clinical findings entered by a user are processed by DXplain (through MedWeaver) to produce a differential diagnosis. From the ranked list of returned diagnoses, MedWeaver provides links for each diagnosis that show disease profile information, explain why the diagnosis appears on the list, perform literature searches, and list clinically relevant Internet sites. By providing links to individual resources, user needs are anticipated and satisfied through an “implicit model of clinician's information needs” embedded in the interface manager.324

In 1997, the Stanford Health Information Network for Education (SHINE) model outlined support for clinical decision making by unifying core health care resources in an intuitive interface over the Internet.323 A stated objective was to “integrate and deliver high-quality medical knowledge and the expertise of academic professionals to community-based primary care physicians.” The model also outlined methods for remote clinical teleconsultation and for awarding continuing-medical-education credit to users of the system. SHINE employs a client-server architecture and is accessible via a Web interface. Although implementation-level details were not published, the authors reported a “working prototype” that integrates knowledge from a biomedical textbook, bibliographic citation database, decision support system, practice guidelines, and primary care teaching modules.

Unlike the relatively broad-based general information needs addressable by systems like MedWeaver and SHINE, other groups are using the Internet to meet a narrower set of information needs. For example, researchers at the Mayo Clinic362 have used the Web to study patients with rare diseases. This approach effectively ties together otherwise isolated cases with the experts involved in highly specific areas of care. Another Web-based system, called the Physicians Research Network, provides efficient clinical trial protocol distribution and eligibility inquiries.363

In summary, these and other models364,365,366,367 have begun to explore use of the Internet as a mechanism to bridge the gap between care providers in need of information and distributed answer resources. These efforts are relevant to primary care because of the broad level of service they provide. No conclusions can be drawn about these approaches, because they have not been formally tested. However, one observation seems appropriate. Although these models may differ in terms of the resources they include or who the target users might be, they share the need for a common, organizing framework that represents the information needs they attempt to address. This concern was emphasized by the developers of MedWeaver, who suggest that “a preferred approach would be to develop an external, shareable model of information needs....”324


This review focuses on studies of the information needs of primary care (and other) clinicians, available resources, previous approaches, and barriers to use of information resources. The authors assert that modern academic health care centers may be able to satisfy many information needs in primary care by providing Internet-mediated access to their electronic and human information resources, and we propose a model for doing so. Providing Internet-based decision support in primary care will involve experimentation in at least three key areas. First, it is necessary to develop a set of core question templates representing the types of unmet information needs that occur in primary care. At present, no readily useful and comprehensive classification of information needs exists for this purpose. Although the taxonomy should ideally be derived empirically, existing classifications might serve as a good starting point for further refinement. The eventual goal is to match information needs from the taxonomy with answer resources defined by the UMLS ISM or equivalent resource.

A second area of investigation involves assembling and managing a set of high-quality information resources to address needs specified in the taxonomy. Availability of information resources—health science libraries, bibliographic and clinical computer software applications, Web-based information, telemedicine systems, and human consultants—will vary across institutions.

Finally, the third area of investigation involves strategies for dynamically and judiciously linking questions with answer resources. For efficiency, linkages should be established in a quasi-automated manner, with minimal human intervention required. Whether done by computer or by humans, resource selection will become more difficult as the number of available resources multiplies. Unfortunately, it is not clear how existing methods of automated resource selection will scale over time.

Each advance in technology produces new ways of sharing and using information but at the same time burdens end users with the task of staying current and knowing which resources are best suited for a given need. Equally challenging is the task of evaluating existing information resources in light of newer resources, which may be more useful but also more costly. Indeed, management of these information resources in digital form has been heralded as one of the “grand challenges” in health care informatics.368

State-of-the art technology is not ideal for addressing all information needs in primary care at this time, but the pieces of an eventual solution are coming together as continual advances in technology provide fertile ground for development of more sophisticated information systems. Triage to human resources (librarians, case managers, clinicians who screen and forward e-mail questions to subspecialists) of information requests that do not seem to map well to electronic resources may provide adequate backstopping capabilities until technology advances. The authors believe that, in the next decade, academic health care centers that leverage their resources to provide valuable information services among regional networks in primary care will probably gain a competitive advantage in the marketplace.


The authors thank the reviewers for their insightful comments and Nunzia B. Giuse for her thoughtful suggestions.


This work was supported by grants G08-LM06175-01 and G08-LM05443, and in part by grant 1-R01-LM06226-02 to RAM, all from the National Library of Medicine.


1. Covell DG, Uman GC, Manning PR. Information needs in office practice: are they being met? Ann Intern Med. 1985; 103:596-9. [PubMed]
2. Lundeen GW, Tenopir C, Wermager P. Information needs of rural health care practitioners in Hawaii. Bull Med Libr Assoc. 1994;82(2):197-205. [PMC free article] [PubMed]
3. Forsythe DE, Buchanan BG, Osheroff JA, Miller RA. Expanding the concept of medical information: an observational study of physicians' information needs. Comput Biomed Res. 1992;25:181-200. [PubMed]
4. Osheroff JA, Forsythe DE, Buchanan BG, Bankowitz RA, Blumenfeld BH, Miller RA. Physicians' information needs: analysis of questions posed during clinical teaching. Ann Intern Med. 1991;114(7):576-81. [PubMed]
5. Dee C, Blazek R. Information needs of the rural physician: a descriptive study. Bull Med Libr Assoc. 1993;81(3):259-64. [PMC free article] [PubMed]
6. Stinson, ER, Mueller DA. Survey of health professional information habits and needs conducted through personal interviews. JAMA. 1980;243(2):140-3. [PubMed]
7. Lowe HJ, Lomax EC, Polonkey SE. The World Wide Web: a review of an emerging Internet-based technology for the distribution of biomedical information. J Am Med Inform Assoc. 1996;3(1)1-14. [PMC free article] [PubMed]
8. Institute of Medicine. Primary care: America's health in a new era. 1996. National Academy of Sciences Web site. Available at: Accessed Apr 17, 1998.
9. Noble J, DeFriese GH, Pickard FD, Meyers AR. Concepts of health and illness. In: Noble J (ed): Textbook of General Medicine and Primary Care. Boston, Mass.: Little, Brown & Co, 1987:3-12.
10. Levinson DJ. Information, computers, and clinical practice [commentary]. JAMA. 1983;249(5):607-9. [PubMed]
11. Smith R. What clinical information do doctors need? BMJ. 1996;313:1062-8. [PMC free article] [PubMed]
12. Gorman PN. Information needs of physicians. J Am Soc Inform Sci. 1995;46(10):729-36.
13. Miller, RA. Medical diagnostic decision support systems—past, present, and future: a threaded bibliography and brief commentary. J Am Med Inform Assoc. 1994;1:8-27. [PMC free article] [PubMed]
14. Forsythe DE. Using ethnography to investigate life scientists' information needs. Bull Med Libr Assoc. 1998;86(3):402-9. [PMC free article] [PubMed]
15. Ely JW, Burch RJ, Vinson DC. The information needs of family physicians: case-specific clinical questions. J Fam Pract. 1992;35(3):265-9. [PubMed]
16. Wagner MM. Decision-theoretic Reminder Systems That Learn from Feedback [PhD thesis]. Pittsburgh, Pa.: University of Pittsburgh, 1995;3.
17. Haynes RB, Sackett DL, Tugwell P. Problems in the handling of clinical research evidence by medical practitioners. Arch Intern Med. 1983;143:1971-5. [PubMed]
18. Leigh TM, Young PR, Haley JV. Performances of family practice diplomates on successive mandatory recertification examinations. Acad Med. 1993;68(12):912-8. [PubMed]
19. Ramsey, PG, Carline JD, Inui TS, et al. Changes over time in the knowledge base of practicing internists. JAMA. 1991;266(8):1103-7. [PubMed]
20. Elson RB, Faughnan JG, Connelly DP. An industrial process view of information delivery to support clinical decision making: implications for systems design and process measures. J Am Med Inform Assoc. 1997;4:266-78. [PMC free article] [PubMed]
21. Ludwig L, Mixter JK, Emanuelle MA. User attitudes toward end-user literature searching. Bull Med Libr Assoc. 1988;76(1):7-13. [PMC free article] [PubMed]
22. Greenes RA. Medical education and decision support using network-based multimedia information resources. Ann N Y Acad Sci. 1992;670:244-56. [PubMed]
23. Rankin, JA. GaIN: online delivery of medical information to physicians and hospitals in Georgia. Ann N Y Acad Sci. 1992;670:171-9. [PubMed]
24. Chambliss ML, Conley J. Answering clinical questions. J Fam Pract. 1996;43(2):140-4. [PubMed]
25. Woolf SH, Benson DA. The medical information needs of internists and pediatricians at an academic medical center. Bull Med Libr Assoc. 1989;77(4):372-80. [PMC free article] [PubMed]
26. Jennett PA, Kishinevsky M, Parboosingh IT, Lockyer JM, Maes WR. Responses to nonemergency questions in rural medicine: their usefulness to practice decisions. Med Educ. 1991;25(3):238-42. [PubMed]
27. Wildemuth BM, de Bliek R, Friedman CP, Miya TS. Information-seeking behaviors of medical students: a classification of questions asked of librarians and physicians. Bull Med Libr Assoc. 1994;82(3):295-304. [PMC free article] [PubMed]
28. Gorman P. Does the medical literature contain the evidence to answer the questions of primary care physicians? Preliminary findings of a study. Proc 18th Annu Symp Comput Appl Med Care. 1994:571-5. [PMC free article] [PubMed]
29. Gorman PN, Helfand M. Information seeking in primary care: how physicians choose which clinical questions to pursue and which to leave unanswered. Med Decis Making. 1995;15(2):113-9. [PubMed]
30. Osheroff JA, Bankowitz RA. Physicians' use of computer software in answering clinical questions. Bull Med Libr Assoc. 1993;81(1):11-9. [PMC free article] [PubMed]
31. Dorsch JL, Landwirth TK. Document needs in a rural GRATEFUL MED outreach project. Bull Med Libr Assoc. 1994;82(4):357-62. [PMC free article] [PubMed]
32. Gorman PN, Ash J, Wykoff L. Can primary care physicians' questions be answered using the medical journal literature? Bull Med Libr Assoc. 1994;82(2):140-6. [PMC free article] [PubMed]
33. Williamson JW, German PS, Weiss R, Skinner EA, Bowes F. Health science information management and continuing education of physicians: a survey of U.S. primary care practitioners and their opinion leaders. Ann Intern Med. 1989;110(2):151-60. [PubMed]
34. Strasser TC. The information needs of practicing physicians in northeastern New York State. Bull Med Libr Assoc. 1978;66(2):200-9. [PMC free article] [PubMed]
35. Giuse NB, Huber JT, Giuse DA, Brown CW, Bankowitz RA, Hunt S. Information needs of health care professionals in an AIDS outpatient clinic as determined by chart review. J Am Med Inform Assoc. 1994;1:395-403. [PMC free article] [PubMed]
36. Jennett PA, Lockyer JM, Parboosingh JP, Maes WR. Practice-generated questions: a method of formulating true learning needs of family physicians. Can Fam Physician. 1989;35:497-500. [PMC free article] [PubMed]
37. Timpka T, Arborelius E. The GP's dilemmas: a study of knowledge need and use during health care consultations. Methods Inf Med. 1990;29(1):23-9. [PubMed]
38. Griffin EM, Vidgen GA, Hepworth JB. Information use, information perceptions and information flows in primary care medical practice. Comput Methods Programs Biomed. 1994;43:207-11. [PubMed]
39. Barrie AR, Ward AM. Questioning behaviour in general practice: a pragmatic study. BMJ. 1997;315:1512-5. [PMC free article] [PubMed]
40. Bowden VM, Kromer ME, Tobia RC. Assessment of physicians' information needs in five Texas counties. Bull Med Libr Assoc. 1994;82(2):189-96. [PMC free article] [PubMed]
41. Tang PC, Jaworski MA, Fellencer CA, et al. Methods for assessing information needs of clinicians in ambulatory care. Proc 19th Annu Symp Comput Appl Med Care. 1995:630-4. [PMC free article] [PubMed]
42. Tang PC, Jaworski MA, Fellencer CA, Kreider N, LaRosa MP, Marquardt WC. Clinical information activities in diverse ambulatory care practices. Proc AMIA Annu Fall Symp. 1996:12-6. [PMC free article] [PubMed]
43. Gruppen LD. Physician information seeking: improving relevance through research. Bull Med Libr Assoc. 1990;78(2):165-72. [PMC free article] [PubMed]
44. Gruppen LD, Wolf FM, Van Voorhees C, Stross JK. Information-seeking strategies and differences among primary care physicians. Mobius. 1987;7(3):18-26. [PubMed]
45. Dorsch JL, Pifalo V. Information needs of rural health professionals: a retrospective use study. Bull Med Libr Assoc. 1997;85(4):341-7. [PMC free article] [PubMed]
46. Cullen R. The medical specialist: information gateway or gatekeeper for the family practitioner. Bull Med Libr Assoc. 1997;85(4):348-55. [PMC free article] [PubMed]
47. Florance V. Medical knowledge for clinical problem solving: a structural analysis of clinical questions. Bull Med Libr Assoc. 1992;80(2):140-9. [PMC free article] [PubMed]
48. Jennett PA, Parsboosingh IJT, Lockyer JM, Maes WA, Paul CA. A pilot study of a medical information system for family physicians in practice. J Med Educ. 1988;63(3):193-5. [PubMed]
49. Bankowitz RA, McNeil MA, Challinor SM, Parker RC, Kapoor WN, Miller RA. A computer-assisted medical diagnostic consultation service: implementation and prospective evaluation of a prototype. Ann Intern Med. 1989;110(10):824-32. [PubMed]
50. Hersh WR. Health information. In: Information Retrieval: A Health Care Perspective. New York: Springer-Verlag, 1996:13-34.
51. Wyatt J. Use and sources of medical knowledge. Lancet. 1991;338:1368-73. [PubMed]
52. Verhoeven AHH, Boerma EJ, Meyboom-de Jong B. Use of information sources by family physicians: a literature survey. Bull Med Libr Assoc. 1995;83(1):85-90. [PMC free article] [PubMed]
53. Connelly DP, Rich EC, Curley SP, Kelly JT. Knowledge resource preferences of family physicians. J Fam Pract. 1990;30(3):353-9. [PubMed]
54. Rambo N, Fuller S. From bench to bedside: research and testing of Internet resources and connections in community hospital libraries. Proc 18th Annu Symp Comput Appl Med Care. 1994;549-53. [PMC free article] [PubMed]
55. Califano, JA. Rural health: a crisis of our time: Biosci Commun. 1978;4:5-8.
56. Bashshur RL. Public acceptance of telemedicine in a rural community. Biosci Commun. 1978;4:17-38.
57. Smego RA, Brick JE. Medical informatics and the rural physician. W V Med J. 1993;89:534-6. [PubMed]
58. Hulkonen DA, Mack BR. Physician's perceptions of library services in a rural state. Bull Med Libr Assoc. 1986;74(3):205-9. [PMC free article] [PubMed]
59. Bashshur RL. Telemedicine effects: cost, quality, and access. J Med Syst. 1995;19(2):81-91. [PubMed]
60. Martin ER, McDaniels C, Crespo J, Lanier D. Delivering health information services and technologies to urban community health centers: the Chicago AIDS Outreach Project. Bull Med Libr Assoc. 1997;85(4):356-61. [PMC free article] [PubMed]
61. Bridgers WF, Kronenfeld J, Charles ED, Goodson L, Klapper MS. Health education in rural Alabama. Biosci Commun. 1978;4:51-8.
62. Harrison's Plus. Available at: Accessed Jul 4, 1998.
63. Scientific American Medicine. Available at: Accessed Jul 4, 1998.
64. Physician's Desk Reference. Available at: Accessed Jan 9, 1998.
65. Curtis KL, Weller AC, Hurd JM. Information-seeking behavior of health sciences faculty: the impact of new information technologies. Bull Med Libr Assoc. 1997;85(4):402-10. [PMC free article] [PubMed]
66. Bellamy LM, Silver JT, Givens MK. Remote access to electronic library services through a campus network. Bull Med Libr Assoc. 1991;79(1):53-62. [PMC free article] [PubMed]
67. Marshall JG. The impact of the hospital library on clinical decision making: the Rochester study. Bull Med Libr Assoc. 1992;80(2):169-78. [PMC free article] [PubMed]
68. Lovas I. A look at LOANSOME DOC service. Bull Med Libr Assoc. 1994;82(2):176-80. [PMC free article] [PubMed]
69. Robishaw SM, Roth BG. Grateful med—loansome doc outreach project in central Pennsylvania. Bull Med Libr Assoc. 1994;82(2):206-13. [PMC free article] [PubMed]
70. Cimpl K. Clinical medical librarianship: a review of the literature. Bull Med Libr Assoc. 1985;73(1):21-8. [PMC free article] [PubMed]
71. Algermissen V. Biomedical librarians in a patient care setting at the University of Missouri—Kansas City School of Medicine. Bull Med Libr Assoc. 1974;62(4):354-8. [PMC free article] [PubMed]
72. Sowell SL. LATCH at the Washington Hospital Center, 1967-1975. Bull Med Libr Assoc. 1978;66(2):218-22. [PMC free article] [PubMed]
73. Veenstra RJ. Clinical medical librarian impact on patient care: a one-year analysis. Bull Med Libr Assoc. 1992;80(1):19-22. [PMC free article] [PubMed]
74. Giuse NB, Kafantaris SR, Miller MD, et al. Clinical medical librarianship: the Vanderbilt experience. Bull Med Libr Assoc. 1998;86(3):412-6. [PMC free article] [PubMed]
75. Royal M, Grizzle WE, Algermissen V, Mowery RW. The success of a clinical librarian program in an academic autopsy pathology service. Am J Clin Pathol. 1993;99(5):576-81. [PubMed]
76. Barbour GL, Young MN. Morning report: role of the clinical librarian. JAMA. 1986;255:1921-2. [PubMed]
77. Byrd GD, Arnold L. Medical school graduates' retrospective evaluation of a clinical medical librarian program. Bull Med Libr Assoc. 1979;67(3):308-12. [PMC free article] [PubMed]
78. Greenberg B, Battison S, Kolisch M, Leredu M. Evaluation of a clinical medical librarian program at the Yale Medical Library. Bull Med Libr Assoc. 1978;66(3):319-26. [PMC free article] [PubMed]
79. Demas JM, Ludwig LT. Clinical medical librarian: the last unicorn? Bull Med Libr Assoc. 1991;79(1):17-27. [PMC free article] [PubMed]
80. Internet Grateful Med. Available at: Accessed Apr 14, 1998.
81. Funk CJ. Evolving roles of life and health sciences librarians for the 21st century. Bull Med Libr Assoc. 1998;86(3):380-4. [PMC free article] [PubMed]
82. Giuse NB. Advancing the practice of clinical medical librarianship [editorial]. Bull Med Libr Assoc. 1997;85(4):437-8. [PMC free article] [PubMed]
83. Erhardt-Domino K, Pletcher T, Wilson W, Atkins D, Panko WB. The Internet: will this highway serve the digital library? Bull Med Libr Assoc. 1994;82(4):426-33. [PMC free article] [PubMed]
84. Giuse NB, Huber JT, Giuse DA, Kafantaris SR, Stead WW. Integrating health sciences librarians into biomedicine. Bull Med Libr Assoc. 1996;84(4):534-40. [PMC free article] [PubMed]
85. Giuse NB, Huber JT, Katantaris SR, et al. Preparing librarians to meet the challenges of today's health care environment. J Am Med Inform Assoc. 1997;4:57-67. [PMC free article] [PubMed]
86. PubMed. Available at: http//www.ncbi.nlm. Accessed Jul 2, 1998.
87. Ovid. Available at: Accessed Aug 29, 1998.
88. Aries Knowledge Finder. Available at: Accessed Aug 29, 1998.
89. Silver Platter. Available at: Accessed Aug 29, 1998.
90. Horowitz GL, Jackson JD, Bleich HL. PaperChase, JAMA. 1983;250(18):2494-9. [PubMed]
91. PaperChase. Available at: Accessed Aug 29, 1998.
92. Hersh WR. Information Retrieval: A Health Care Perspective. New York: Springer-Verlag, 1996.
93. Gauch S. Intelligent information retrieval: an introduction. J Am Soc Inform Sci. 1992;43(2):175-82.
94. Fidel R. Toward expert systems for the selection of search keys. J Am Soc Inform Sci. 1986;37:37-44.
95. Salton G, Buckley C, Fox EA. Automatic query formulations in information retrieval. J Am Soc Inform Sci. 1983;34:262-80. [PubMed]
96. Hersh WR, Hickam D. Information retrieval in medicine: the SAPHIRE experience. J Am Soc Inform Sci. 1995;46(10):743-7.
97. Srinivasan P. Retrieval feedback in MEDLINE. J Am Med Inform Assoc. 1996;3(2):157-67. [PMC free article] [PubMed]
98. Srinivasan P. Optimal document-indexing vocabulary for MEDLINE. Inf Processing Manage. 1996;32(5):503-14.
99. Frants VI, Shapiro J. Algorithm for automatic construction of query formulations in Boolean form. J Am Soc Inform Sci. 1991;42(1):16-26.
100. Purcell GP, Mar DD. SCOUT:information retrieval from full-text medical literature. Proc 16th Annu Symp Comput Appl Med Care. 1992:91-5. [PMC free article] [PubMed]
101. Merz RB, Cimino C, Barnett GO, et al. Q & A: a query formulation assistant. Proc 16th Annu Symp Comput Appl Med Care. 1992:498-502. [PMC free article] [PubMed]
102. Biswas G, Bezdek JC, Subramanian V, Marques M. Knowledge-assisted document retrieval, part II: the retrieval process. J Am Soc Inform Sci. 1987;38:97-110.
103. Aigrain P, Longueville V. A model for the evaluation of expansion techniques in information retrieval systems. J Am Soc Inform Sci. 1994;45(4):225-34.
104. Harter SP, Cheng Y. Colinked descriptors: improving vocabulary selection for end-user searching. J Am Soc Inform Sci. 1996;47(4):311-25.
105. Haynes RB, Walker CJ, McKibbon KA, Johnston ME, Willan AR. Performances of 27 MEDLINE systems tested by searches with clinical questions. J Am Med Inform Assoc. 1994;1(3):285-95. [PMC free article] [PubMed]
106. Lindberg DAB, Siegel ER, Rapp BA, Wallingford KT, Wilson SR. Use of MEDLINE by physicians for clinical problem solving. JAMA. 1993;269(24):3124-9. [PubMed]
107. Haynes RB, McKibbon KA, Walker CJ, Ryan N, Fitzgerald D, Ramsden MF. Online access to MEDLINE in clinical settings: a study of use and usefulness. Ann Intern Med. 1990;112(1):78-84. [PubMed]
108. McKibbon KA, Wilczynski N, Hayward RS, Walker-Dilks CJ, Haynes RB. The medical literature as a resource for health care practice. J Am Soc Inform Sci. 1995;46(10):737-42.
109. Walker CJ, McKibbon KA, Ryan NC, Ramsden MF, Fitzgerald D, Haynes RB. Methods for accessing the competence of physicians' use of MEDLINE with GRATEFUL MED. Proc 13th Annu Symp Comput Appl Med Care. 1988: 441-4.
110. Haynes RB, McKibbon KA, Fitzgerald D, Guyatt GH, Walker CJ, Sackett DL. How to keep up with the medical literature, part V: access by personal computer to the medical literature. Ann Intern Med. 1986;105(5):810-24. [PubMed]
111. Lowe HJ, Barnett GO. Understanding and using the Medical Subject Headings (MeSH) vocabulary to perform literature searches. JAMA. 1994;271:1103-8. [PubMed]
112. McKibbon KA, Walker CJ, Ryan NC, Fitzgerald D, Haynes RB. A study of MEDLINE in clinical settings: design and preliminary results. Proc 12th Annu Symp Comput Appl Med Care. 1988:526-9.
113. Haynes RB, Hayward RSA, Lomas J. Bridges between health care research evidence and clinical practice. J Am Med Inform Assoc. 1995;2:342-50. [PMC free article] [PubMed]
114. Haynes RB. Loose connections between peer-reviewed clinical journals and clinical practice. Ann Intern Med. 1990;113(9):724-8. [PubMed]
115. Brock C. Consultation and referral patterns of family physicians. J Fam Pract. 1977;4(6):1129-34. [PubMed]
116. Coulter A, Noone A, Goldacre M. General practitioners' referrals to specialist outpatient clinics. BMJ. 1989;299:304-8. [PMC free article] [PubMed]
117. Nutting PA, Franks P, Clancy CM. Referral and consultation in primary care: do we understand what we're doing? [editorial]. J Fam Pract. 1992;35(1):21-3. [PubMed]
118. McPhee SJ, Lo B, Saika GY, Meltzer R. How good is communication between primary care physicians and subspecialty consultants? Arch Intern Med. 1984;144:1265-8. [PubMed]
119. Hansen JP, Brown SE, Sullivan RJ, Muhlbaier LH. Factors related to an effective referral and consultation process. J Fam Pract. 1982;15(4):651-6. [PubMed]
120. de Marco P, Dain C, Lockwood T, Roland M. How valuable is feedback of information on hospital referral patterns? BMJ. 1993;307:1465-6. [PMC free article] [PubMed]
121. Helliwell PS, Wright V. Referrals to rheumatology: a better communication should prevent waste of resources [editorial]. BMJ. 1991;302(6772):304-5. [PMC free article] [PubMed]
122. Branger PJ, Duisterhout JS. Communications in health care [review paper]. In: van Bemmel JH, McCray AT (eds): Yearbook of Medical Informatics: Advanced Communications in Health Care. Geneva, Switzerland: International Medical Informatics Association & Schattauer, 1994:69-77.
123. Calman NS, Hyman RB, Licht W. Variability in consultation rates and practitioner level of diagnostic certainty. J Fam Pract. 1992;35(1):31-8. [PubMed]
124. Rothert ML, Rovner DR, Elstein AS, Holzman GB, Holmes MM, Ravitch MM. Differences in medical referral decisions for obesity among family practitioners, general internists, and gynecologists. Med Care. 1984;22(1):42-53. [PubMed]
125. Penchansky R, Fox D. Frequency of referral and patient characteristics in group practice. Med Care. 1970;8(5):368-85. [PubMed]
126. Holtgrave DR, Lawler F, Spann SJ. Physicians' risk attitudes, laboratory usage, and referral decisions: the case of an academic family practice center. Med Decis Making. 1991;11:125-30. [PubMed]
127. Coulter A, Bradlow J. Effect of NHS reforms on general practitioners' referral patterns. BMJ. 1993;306:433-7. [PMC free article] [PubMed]
128. Roland MO, Morrell DC, McDermott A, Paul E. Understanding hospital referral rates: a user's guide. BMJ. 1990;301:98-102. [PMC free article] [PubMed]
129. Roland M, Morris R. Are referrals by general practitioners influenced by the availability of consultants? BMJ. 1988;297:599-600. [PMC free article] [PubMed]
130. Armstrong D, Britten N, Grace J. Measuring general practitioner referrals: patient, workload and list size effects. J R Coll Gen Pract. 1988;38:494-7. [PMC free article] [PubMed]
131. Fertig A, Roland M, King H, Moore T. Understanding variation in rates of referral among general practitioners: are inappropriate referrals important and would guidelines help to reduce rates? BMJ. 1993;307:1467-70. [PMC free article] [PubMed]
132. Klapper MS, Harper IB, Bridgers WF. MIST: an aide in the delivery of health care. Biosci Commun. 1978;4:67-73.
133. Holt N, Crawford MA. Medical information service via telephone. Ann N Y Acad Sci. 1992;670:155-62. [PubMed]
134. Institute of Medicine. Introduction and background. In: Field MJ (ed): Telemedicine: A Guide to Assessing Telecommunications in Health Care. Washington, D.C.: National Academy Press, 1996:16-33.
135. Nobili A, Gebru F, Rossetti A, et al. Doctorline: a private toll-free telephone medical information service. Five years of activity: old problems and new perspectives. Ann Pharmacother. 1998;32:120-5. [PubMed]
136. Martin C, Fruin M. Telephone consultation for a “managed care” population. J Emerg Nurs. 1995;21(2):155-6. [PubMed]
137. Lear JL, Manco-Johnson M, Feyerabend A, Anderson G, Robinson D. Ultra-high-speed teleradiology with ISDN technology. Radiology. 1989;171(3):862-3. [PubMed]
138. Perednia DA, Brown NA. Teledermatology: one application of telemedicine. Bull Med Libr Assoc. 1995;83(1):42-7. [PMC free article] [PubMed]
139. Morris TA, Guard JR, Marine SA, et al. Approaching equity in consumer health information delivery. J Am Med Inform Assoc. 1997;4:6-13. [PMC free article] [PubMed]
140. Rankin JA, Williams JC, Mishelevich DJ. Information system linking a medical school with practitioners and hospitals. J Med Educ. 1987;62(4):336-43. [PubMed]
141. Bartlett O, Rankin JA, Statom ST. GaIN: a network of physicians and hospitals in Georgia. J Med Assoc Ga. 1988;77:632-7. [PubMed]
142. Brennan PF, Moore SM, Smyth KA. ComputerLink: electronic support for the home caregiver. Adv Nurs Sci. 1991;13(4):14-27. [PubMed]
143. Brennan PF, Moore SM, Smyth KA. The effects of a special computer network on caregivers of persons with Alzheimer's Disease. Nurs Res. 1995;44(3):166-72. [PubMed]
144. Brennan PF, Ripich S. Use of a home-care computer network by persons with AIDS. Int J Technol Assess Health Care. 1994;10(2):258-72. [PubMed]
145. Brennan PF. Characterizing the use of health care services delivered via computer networks. J Am Med Inform Assoc. 1995;2(3):160-8. [PMC free article] [PubMed]
146. Dayhoff RE, Kuzmak PM, Frank SA, Kirin G, Saddler C. Extending the multimedia patient record across the wide area network. Pro AMIA Annu Fall Symp. 1996:653-7. [PMC free article] [PubMed]
147. Reddy R, Jagannathan V, Srinivas K, et al. Artemis: a collaborative framework for health care. Proc 18th Annu Symp Comput Appl Med Care. 1994:559-63. [PMC free article] [PubMed]
148. Reddy S, Niewiadomska-Bugaj M, Reddy YV, et al. Experiences with ARTEMIS: an Internet-based telemedicine system. Proc AMIA Annu Fall Symp. 1997:759-63. [PMC free article] [PubMed]
149. London JW, Morton DE, Marinucci D, Catalano R, Comis RL. The implementation of telemedicine within a community cancer network. J Am Med Inform Assoc. 1997;4:18-24. [PMC free article] [PubMed]
150. Overhage JM, Tierney WM, McDonald CJ. Design and implementation of the Indianapolis Network for Patient Care and Research. Bull Med Libr Assoc. 1995;83(1):48-56. [PMC free article] [PubMed]
151. Chang GY. Babinski's bulletin board system: a computerized message BBS for neurologists. Ann N Y Acad Sci. 1992;670:298-300. [PubMed]
152. Alden K, Dellinger J, Glasgow A, et al. Nursetalk: The latest addition to the information highway. Proc 18th Annu Symp Comput Appl Med Care. 1994:1018. [PMC free article] [PubMed]
153. Friedman RH, Strollerman JE, Mahoney DM, Rozenblyum L. The virtual visit: using telecommunications technology to take care of patients. J Am Med Inform Assoc. 1997;4:413-25. [PMC free article] [PubMed]
154. Lisse EW. An overview of networking for physicians and problems of network consulting in remote areas. Ann N Y Acad Sci. 1992;670:19-28. [PubMed]
155. Schultz EK, Bauman A, Hayward M, Holtzman R. Improved care of patients with diabetes through telecommunications. Ann N Y Acad Sci. 1992;670:141-5. [PubMed]
156. Robinson TN. Community health behavior change through computer network health promotion: preliminary findings from Stanford Health-Net. Comput Methods Programs Biomed. 1989;30:137-44. [PubMed]
157. Grigsby B, Allen A. Fourth annual telemedicine program review, part 2: United States. Telemed Today. Aug 1997:30-42. [PubMed]
158. Lindberg CCS. Implementation of in-home telemedicine in rural Kansas: answering an elderly patient's needs. J Am Med Inform Assoc. 1997;4:14-7. [PMC free article] [PubMed]
159. Balch DC, Tichenor JM. Telemedicine expanding the scope of health care information. J Am Med Inform Assoc. 1997;4:1-5. [PMC free article] [PubMed]
160. Hassol A, Gaumer G, Irvin C, Grigsby J, Mintzer C, Puksin D. Rural telemedicine data/image transfer methods and purposes of interactive video sessions. J Am Med Inform Assoc. 1997;4:36-7. [PMC free article] [PubMed]
161. Baer L, Elford DR, Cukor P. Telepsychiatry at forty: what have we learned? Harv Rev Psychiatry. 1997;5(1):7-17. [PubMed]
162. Trott P, Blignault I. Cost evaluation of telepsychiatry service in northern Queensland. J Telemed Telecare. 1998;4(suppl 1):66-8. [PubMed]
163. Franken EA Jr, Harkens KL, Berbaum KS. Teleradiology consultation for a rural hospital: patterns of use. Acad Radiol. 1997;4(7):492-6. [PubMed]
164. Franken EA Jr, Berbaum KS, Brandser EA, D'Alessandro MP, Schweiger GD, Smith WL. Pediatric radiology at a rural hospital: value of teleradiology and subspecialty consultation. AJR Am J Roentgenol. 1997; 168(5):1349-52. [PubMed]
165. Institute of Medicine. Introduction and background. In: Field MJ (ed): Telemedicine: a guide to assessing telecommunications in health care. Washington, D.C.: National Academy Press, 1996:42.
166. Rauch S, Holt MC, Horner M, Rambo N. Community hospitals and the Internet: lessons from pilot connections. Bull Med Libr Assoc. 1994;82(4):401-6. [PMC free article] [PubMed]
167. Fuller SS. Internet connectivity for hospitals and hospital libraries: strategies. Bull Med Libr Assoc. 1995;83(1):32-6. [PMC free article] [PubMed]
168. Parsons DF. Progress and problems of interhospital consulting by computer networking. Ann N Y Acad Sci. 1992;670:1-11. [PubMed]
169. Meter RK. The Synapse health information network: Linking Nebraska and the Midwest. Ann N Y Acad Sci. 1992;670:98-100. [PubMed]
170. Cohen BJ, Goldberg HS, Pauker SG. DecisionNET: Database/network support for clinical decision analysis. Ann N Y Acad Sci. 1992;670:127-32. [PubMed]
171. Chute CG. Clinical data retrieval and analysis: I've seen a case like that before. Ann N Y Acad Sci. 1992;670:133-40. [PubMed]
172. Greenes RA. Medical education and decision support using network-based multimedia information resources. Ann N Y Acad Sci. 1992;670:244-56. [PubMed]
173. Ten Haken JD, Calhoun JG, Ellison J, Miotto MP. M NET: a statewide referring physician computer network. Proc 16th Annu Symp Comput Appl Med Care. 1992:814-5. [PMC free article] [PubMed]
174. Jacknowitz L. West Virginia CONSULT: enhanced information access for health care practitioners in a rural environment. Ann N Y Acad Sci. 1992;670:163-70. [PubMed]
175. Tarczy-Hornoch P, Kwan-Gett TS, Fouche L, et al. Meeting clinician information needs by integrating access to the medical record and knowledge resources via the Web. Proc AMIA Annu Fall Symp. 1997:809-13. [PMC free article] [PubMed]
176. Fuller S. Regional health information systems: applying the IAIMS Model. J Am Med Inform Assoc. Mar-Apr 1997;4(suppl):S47-51. [PMC free article] [PubMed]
177. Brennan PF, Moore SM, Smyth KA. Alzheimer's disease caregivers' uses of a computer network. West J Nurs Res. 1992;14(5):662-73. [PubMed]
178. Brennan PF. Computer networks promote caregiving collaboration: the ComputerLink Project. Proc 17th Annu Symp Comput Appl Med Care. 1993:156-60. [PMC free article] [PubMed]
179. Brennan PF. Computer use and nursing research. West J Nurs Res. 1992;14(2):239-40. [PubMed]
180. Brennan PF. Differential use of computer network services. Proc 18th Annu Symp Comput Appl Med Care. 1994:27-31. [PMC free article] [PubMed]
181. Grigsby J, Sanders JH. Telemedicine: where it is and where its going. Ann Intern Med. 1998;129:123-7. [PubMed]
182. Thrall JH, Boland G. Telemedicine in practice. Semin Nucl Med. 1998;28(2):145-57. [PubMed]
183. Gardiner Jones M. Telemedicine and the national information infrastructure: are the realities of health care being ignored? J Am Med Inform Assoc. 1997;4:399-412. [PMC free article] [PubMed]
184. Perednia DA, Allen A. Telemedicine technology and clinical applications. JAMA. 1995;273(6):483-8. [PubMed]
185. Puskin DS, Sanders JH. Telemedicine infrastructure development. J Med Syst. 1995;19(2):125-9. [PubMed]
186. Bashshur RL. Telemedicine effects: cost, quality, and access. J Med Syst. 1995;19(2):81. [PubMed]
187. Perednia DA. Telemedicine system evaluation and a collaborative model for multi-centered research. J Med Syst. 1995;19(3):287-94. [PubMed]
188. Guttman-McCabe C. Telemedicine's imperiled future? Funding, reimbursement, licensing and privacy hurdles face a developing technology. J Contemp Health Law Policy. 1997;14(1):161-86. [PubMed]
189. Stanberry B. The legal and ethical aspects of telemedicine. J Telemed Telecare. 1998;4(suppl 1):95-7. [PubMed]
190. Jerrold L. Litigation, legislation, and ethics: the problem, electronic data transmission and the law. Am J Orthod Dentofacial Orthop. 1998;113(4):478-9. [PubMed]
191. Young HJ, Waters RJ. Licensure barriers to the interstate use of telemedicine. Telemed Today. Mar-Apr 1996:10-34. [PubMed]
192. Flaherty RJ. Electronic bulletin board systems extend the advantages of telemedicine. Comput Nurs. 1995;13(1):8-10. [PubMed]
193. Next Generation Internet (NGI). Available at: Accessed Jun 27, 1998.
194. Shortliffe EH. Health care and the Next Generation Internet. Ann Intern Med. 1998;129(2):138-40. [PubMed]
195. Internet2. Available at: Accessed Jun 27, 1998.
196. Lindberg DAB. HPCC and the national information infrastructure: an overview. Bull Med Libr Assoc. 1995;83(1):29-31. [PMC free article] [PubMed]
197. Lindberg DAB. Humphreys BL. The High-Performance and Communications Program, the national information infrastructure, and health care. J Am Med Inform Assoc. 1995;2(3):156-9. [PMC free article] [PubMed]
198. Lindberg DAB, Humphreys BL. High-performance computing and communications and the national information infrastructure: new opportunities and challenges [editorial]. J Am Med Inform Assoc. 1995;2(3):197-6. [PMC free article] [PubMed]
199. Corn M, Johnson FE. Connecting the health sciences community to the Internet: the NLM/NSF grant program. Bull Med Libr Assoc. 1994;82(4):392-5. [PMC free article] [PubMed]
200. Hersh WR, Greenes RA. Information retrieval in medicine: state of the art. MD Comput. 1990;7(5):302-11. [PubMed]
201. Wiesman F, Hasman A, van den Herik HJ. Information retrieval: an overview of system characteristics. Int J Med Inform. 1997;47(1-2):5-26. [PubMed]
202. Wyatt J. Computer-based knowledge systems. Lancet. 1991;338:1431-6. [PubMed]
203. Blum BI. Clinical information systems. New York: Springer-Verlag, 1986.
204. van Bemmel JH, Musen MA (eds). Handbook of Medical Informatics. Heidelberg, Germany: Springer-Verlag, 1997.
205. Shortliffe EH, Perreault LE, Wiederhold G, Fagan LM (eds). Medical Informatics: Computer Applications in Health Care. Reading, Mass.: Addison-Wesley, 1990.
206. Collen MF. A History of Medical Informatics in the United States, 1950-1990. Washington, D.C.: American Medical Informatics Association, 1995.
207. Miller RA, Giuse NB. Medical knowledge bases. Acad Med. 1991;66(1):15-7. [PubMed]
208. Gorry A. Strategies for computer-aided diagnosis. Math Biosci. 1968;2:293-319.
209. Barnett GO, Cimino JJ, Hupp JA, Hoffer EP. DXplain: an evolving diagnosis decision-support system. JAMA. 1987;258(1):67-74. [PubMed]
210. Warner HR, Haug P, Bouhaddou O, et al. ILIAD as an expert consultant to teach differential diagnosis. Proc 12th Annu Symp Comput Appl Med Care. 1988:371-6.
211. Warner HR Jr. ILIAD: moving medical decision-making into new frontiers. Methods Inform Med. 1989;28(4):370-2. [PubMed]
212. ILIAD. Available at: Accessed Jul 4, 1998.
213. Meditel. Computer-assisted Diagnosis. Devon, Pa.: Meditel, 1991.
214. Miller RA, Masarie FE, Myers JD. Quick Medical Reference (QMR) for diagnostic assistance. MD Comput. 1986;3:34-48. [PubMed]
215. Berner ES, Jackson JR, Algina J. Relationships among performance scores of four diagnostic decision support systems. J Am Med Inform Assoc. 1996;3:208-15. [PMC free article] [PubMed]
216. Miller RA. Evaluating evaluations of medical diagnostic systems [editorial]. J Am Med Inform Assoc. 1996;3:429-31. [PMC free article] [PubMed]
217. Johnston ME, Langton KB, Haynes RB, Mathieu A. Effects of computer-based clinical decision support systems on clinician performance and patient outcome: a critical appraisal of research. Ann Intern Med. 1994;120(2):135-42. [PubMed]
218. Stead WW, Haynes RB, Fuller S, et al. Designing medical informatics research and library: resource projects to increase what is learned. J Am Med Inform Assoc. 1994;1(1):28-33. [PMC free article] [PubMed]
219. Berner ES, Webster GD, Shugerman AA, et al. Performance of four computer-assisted diagnostic systems. N Engl J Med. 1994;330(25):1792-6. [PubMed]
220. Feldman MJ, Barnett GO. An approach to evaluating the accuracy of DXplain. Comput Methods Programs Biomed. 1991;35:261-6. [PubMed]
221. Elstein AS, Friedman CP, Wolf FM, et al. Effects of a decision support system on the diagnostic accuracy of users: a preliminary report. J Am Med Inform Assoc. 1996;3:422-8. [PMC free article] [PubMed]
222. Berner ES, Maisiak RS. Physician use of interactive functions in diagnostic decision support systems. Proc AMIA Annu Fall Symp. 1997:842.
223. Abate MA, Jacknowitz AI, Shumway JM. Characterization of end-user computer searching by private practice physicians, pharmacists, and nurses. Proc 14th Annu Symp Comput Appl Med Care. 1990:375-9.
224. Hersh WR, Hickam D. Use of a multi-application computer workstation in a clinical setting. Bull Med Libr Assoc. 1994;82(4):382-9. [PMC free article] [PubMed]
225. Jha AK, Kuperman GJ, Teich JM, et al. Identifying adverse drug events: development of a computer-based monitor and comparison with chart review and stimulated voluntary report. J Am Med Inform Assoc. 1998;5:305-14. [PMC free article] [PubMed]
226. Harpole LH, Khorasani R, Fiskio J, Kuperman GJ, Bates DW. Automated evidence-based critiquing of orders for abdominal radiographs: impact on utilization and appropriateness. J Am Med Inform Assoc. 1997;511-21. [PMC free article] [PubMed]
227. Sittig DF, Stead WW. Computer-based physician order entry: the state of the art. J Am Med Inform Assoc. 1994;1:108-23. [PMC free article] [PubMed]
228. Zielstorff RD. Online practice guidelines: issues, obstacles, and future prospects. J Am Med Inform Assoc. 1998;5:227-36. [PMC free article] [PubMed]
229. Kittredge R, Rabbani U, Melanson F, Barnett GO. Experiences in deployment of a Web-based CIS for referring physicians. Proc AMIA Annu Fall Symp. 1997:320-4. [PMC free article] [PubMed]
230. Kastin S, Wexler J. Bioinformatics: searching the Net. Semin Nucl Med. 1998;28(2):177-87. [PubMed]
231. Lacroix E, Backus JEB, Lyon BJ. Service providers and users discover the Internet. Bull Med Libr Assoc. 1994;82(4):412-8. [PMC free article] [PubMed]
232. McKinney WP, Wagner JM, Bunton G, Kirk LM. A guide to Mosaic and the World Wide Web for physicians. MD Comput. 1995;12(2):109-41. [PubMed]
233. Hollander SM, Lanier D. Orientation to the Internet for primary care health professionals. Bull Med Libr Assoc. 1995;83(1):96-8. [PMC free article] [PubMed]
234. Peters R, Sikorski R. Digital dialogue: sharing information and interests on the Internet. JAMA. 1997;277(15):1258-60. [PubMed]
235. Glowniak JV. Medical resources on the Internet. Ann Intern Med. 1995;123(2):123-31. [PubMed]
236. Glowniak JV, Bushway MK. Computer networks as a medical resource: accessing and using the Internet. JAMA. 1994;271(24):1934-9. [PubMed]
237. Kibbe DC, Smith PP, LaVallee R, Bailey D, Bard M. A guide to finding and evaluating best practices health care information on the Internet: the truth is out there? Jt Comm J Qual Improv. 1997;23(12):678-89. [PubMed]
238. Morgan C. The search is on. Windows Mag. Nov 1996:212-30.
239. Powsner SM, Roderer NK. Navigating the Internet. Bull Med Libr Assoc. 1994;82(4):419-25. [PMC free article] [PubMed]
240. Dolin R. Medical applications on the Internet [letter, comment]. MD Comput. 1995;12(3):162-5. [PubMed]
241. Glowniak JV. History, structure, and function of the Internet. Semin Nucl Med. 1998;28(2):135-44. [PubMed]
242. Zelingher J. Exploring the Internet. MD Comput. 1995;12(2):100-44. [PubMed]
243. CyberStrategy Project. Available at: Accessed May 5, 1998.
244. Criteria for Assessing the Quality of Health Information on the Internet. Available at: Accessed Apr 17, 1998.
245. Jadad AR, Gagliardi A. Rating health information on the Internet: navigating to knowledge or to Babel? JAMA. 1998;279(8):611-4. [PubMed]
246. Silberg WM, Lundberg GD, Musacchio RA. Assessing, controlling, and assuring the quality of medical information on the Internet. JAMA. 1997;277(15):1244-5. [PubMed]
247. Wyatt JC. Commentary: measuring quality and impact of the World Wide Web. BMJ. 1997;314:1879-81. [PMC free article] [PubMed]
248. FTC warns against online health claims. USA Today. Nov 5, 1997. Available at: Accessed Apr 17, 1998.
249. Rogers G. Nurses and the Internet. J Emerg Nurs. 1995;21(2):160-2. [PubMed]
250. Wootton JC. The quality of information on women's health on the Internet. J Womens Health. 1997;6(5):575-81. [PubMed]
251. Dvorak JC. Web, schmeb. PC Comput. Mar 1996:61.
252. National Institutes of Health Web site. Available at: Accessed Jun 25, 1998.
253. Food and Drug Administration Web site. Available at: Accessed Jun 25, 1998.
254. National Cancer Institute Web site. Available at: Accessed Apr 17, 1998.
255. National Library of Medicine Web site. Available at: Accessed Jun 25, 1998.
256. PubMed. Available at: http://www.ncbi.nlm. Accessed Jun 25, 1998.
257. Internet Grateful Med. Available at: Accessed Jun 25, 1998.
258. Health Services Technology Assessment Texts (HSTAT). Available at: Accessed Jun 25, 1998.
259. Physicians Data Query/Cancer Net. Available at: gopher:// Accessed Nov 17, 1997.
260. Angier JJ, Beck SL, Eyre HJ. Use of the PDQ system in a clinical setting. Bull Med Libr Assoc. 1990;78(1):15-22. [PMC free article] [PubMed]
261. Shaw DJ, Czaja RF. User interactions with the PDQ cancer information system. Bull Med Libr Assoc. 1992;80(1):29-35. [PMC free article] [PubMed]
262. Food and Drug Administration: Drug Information. Available at: Accessed Jun 25, 1998.
263. New England Journal of Medicine. Available at: Accessed Apr 17, 1998.
264. Journal of Family Practice. Available at: Accessed Nov 1, 1997.
265. Diagnosis of Pulmonary Embolus. Available at: Accessed Jun 25, 1998.
266. Virtual Hospital home page. Available at: Accessed Apr 17, 1998.
267. Galvin JR, D'Alessandro MP, Erkonen WE, Knutson TA, Lacey DL. The virtual hospital: a new paradigm for life-long learning in radiology. RadioGraphics. 1994;14(4):875-9. [PubMed]
268. Zelingher J. The Virtual Hospital [news]. MD Comput. 1995;12(3):166-71. [PubMed]
269. Cardiovascular Drug Reviews. Available at: Accessed Jul 31, 1998.
270. Medical Sciences Bulletin. Available at: Accessed Apr 14, 1998.
271. Merck Manual, 1992 ed. Available at:!!vFxfY32cSvFxfY32cS/pubs/mmanual/. Accessed Jul 31, 1998.
272. Stoddard MJ, Siebert JL. Biomedical resources on Usenet. Bull Med Libr Assoc. 1994;82(4):379-81. [PMC free article] [PubMed]
273. Zakaria AM, Sitting DF. Medical informatics on the Internet: creating the newsgroup. J Am Med Inform Assoc. 1995;2(4):215-9. [PMC free article] [PubMed]
274. Kramer JM, Cath A. Medical resources and the Internet: making the connection. Arch Intern Med. 1996;156:833-42. [PubMed]
275. American Heart Association Web site. Available at: Accessed May 5, 1998.
276. Medicine in the Matrix. Available at Accessed Jun 25, 1998.
277. CliniWeb. Available at: Accessed May 5, 1998.
278. Hersh WR, Brown KE, Donohoe LC, Campbell EM, Horacek AE. CliniWeb: managing clinical information on the World Wide Web. J Am Med Inform Assoc. 1996;3:273-80. [PMC free article] [PubMed]
279. Humphreys BL, Lindberg DAB, Schoolman HM, Barnett GO. The Unified Medical Language System: an informatics research collaboration. J Am Med Inform Assoc. 1998;5:1-11. [PMC free article] [PubMed]
280. Lindberg, DAB, Humphreys BL, McCray AT. The Unified Medical Language System. Methods Inform Med. 1993;32:281-91. [PubMed]
281. Humphreys, BL, Lindberg DAB. The UMLS project: making the conceptual connection between users and the information they need. Bull Med Libr Assoc. 1993;81(2):170-7. [PMC free article] [PubMed]
282. Unified Medical Language System (UMLS). Available at: Accessed May 5, 1998.
283. Masarie FEJ, Miller RA, Bouhaddou O, Giuse NB, Warner HR. An interlingua for electronic interchange of medical information: using frames to map between clinical vocabularies. Comput Biomed Res. 1991;24:379-400. [PubMed]
284. Rassinoux AM, Miller RA, Baud RH, Scherrer JR. Modeling just the important and relevant concepts in medicine for medical language understanding: a survey of the issues. Proc IMIA Working Group 6 Conference on National Language and Medical Concept Representation. Jan 19-22, 1997:53-68.
285. Schuyler PL, Hole WT, Tuttle MS, Sheretz DD. The UMLS Metathesaurus: representing different views of biomedical concepts. Bull Med Libr Assoc. 1993;81(2):217-22. [PMC free article] [PubMed]
286. National Library of Medicine. UMLS Knowledge Sources: Documentation. 9th ed. Bethesda, Md.: Department of Health and Human Services, 1998:1-155.
287. McCray AT. Extending a natural language parser with UMLS knowledge. Proc 15th Annu Symp Comput Appl Med Care. 1991:194-8. [PMC free article] [PubMed]
288. McCray AT, Aronson AR, Browne AC, Rindflesh TC, Razi A, Srinivasan S. UMLS knowledge for biomedical language processing. Bull Med Libr Assoc. 1993;81(2):184-94. [PMC free article] [PubMed]
289. Nelson SJ, Tuttle MS, Cole WG, et al. From meaning to term: semantic locality in the UMLS Metathesaurus. Proc 15th Annu Symp Comput Appl Med Care. 1991:209-13. [PMC free article] [PubMed]
290. McCray AT, Nelson SJ. The representation of meaning in the UMLS. Methods Inform Med. 1995;34:193-201. [PubMed]
291. McCray AT, Srinivasan S, Browne AC. Lexical methods for managing variation in biomedical terminologies. Proc Annu Symp Comput Appl Med Care 1994:235-9. [PMC free article] [PubMed]
292. Wehrli E, Clark R. Natural language processing, lexicon and semantics. Methods Inform Med. 1995;34:68-74. [PubMed]
293. Spyns P. Natural language processing in medicine: an overview. Methods Inform Med. 1996;35:285-301. [PubMed]
294. Schuyler PL, McCray AT, Schoolman HM. A test collection for experimentation in bibliographic retrieval. In: Barber B, Cao D, Qin D, Wagner G (eds). Medinfo. 1989:910-2.
295. Aronson AR. The effect of textual variation on concept based information retrieval. Proc AMIA Annu Fall Symp. 1996:373-7. [PMC free article] [PubMed]
296. Rindflesch TC, Aronson AR. Ambiguity resolution while mapping free text to the UMLS Metathesaurus. Proc 18th Annu Symp Comput Appl Med Care. 1994:240-4. [PMC free article] [PubMed]
297. Forsythe DE. Using ethnography to investigate life scientists' information needs. Bull Med Libr Assoc. 1998;86(3):402-9. [PMC free article] [PubMed]
298. Powsner SM, Miller PL. Automated online transition from the medical record to the psychiatric literature. Methods Inform Med. 1992;31:169-74. [PubMed]
299. Powsner SM, Miller PL. Linking bibliographic retrieval to clinical reports. PsychTopix. Proc 13th Annu Symp Comput Appl Med Care. 1989:431-5.
300. Powsner SM, Riely CA, Barwick KW, Morrow JS, Miller PL. Automated bibliographic retrieval based on current topics in hepatology: HepaTopix. Comput Biomed Res. 1989;22:552-64. [PubMed]
301. Cimino JJ. Linking patient information systems to bibliographic resources. Methods Inform Med. 1996;35:122-6. [PubMed]
302. Cimino JJ, Johnson SB, Aguirre A, Roderer N, Clayton PD. The MEDLINE button. Proc 16th Annu Symp Comput Appl Med Care. 1992:81-5. [PMC free article] [PubMed]
303. Shaw WM, Wood JB, Wood RE, Tibbo HR. The Cystic Fibrosis Database: content and research opportunities. Libr Inform Sci Res. 1991;13:347-66.
304. Cimino JJ, Aguirre A, Johnson SB, Peng P. Generic queries for meeting clinical information needs. Bull Med Libr Assoc. 1993;81(2):195-206. [PMC free article] [PubMed]
305. Cimino C, Barnett GO, Hassan L, Blewett DR, Piggins JL. Interactive query workstation: standardizing access to computer-based medical resources. Comput Methods Programs Biomed. 1991;35:293-9. [PubMed]
306. Cimino C, Barnett GO. Analysis of physician questions in an ambulatory care setting. Comput Biomed Res. 1992;25:366-73. [PubMed]
307. Cimino C, Barnett GO. Analysis of physician questions in an ambulatory care setting. Proc 16th Annu Symp Comput Appl Med Care. 1992:995-9. [PMC free article] [PubMed]
308. Cimino C, Barnett GO, Blewett DR, et al. Interactive query workstation: a demonstration of the practical use of UMLS Knowledge Sources. Proc 16th Annu Symp Comput Appl Med Care. 1993:823-4. [PMC free article] [PubMed]
309. Cimino C, Barnett GO. Standardizing access to computer-based medical resources. Proc 14th Annu Symp Comput Appl Med Care. 1990:33-7.
310. Barnett GO, Zielstorff RD, Piggins J, et al. COSTAR: a comprehensive medical information system for ambulatory care. Proc 7th Annu Symp Comput Appl Med Care. 1982:8-18.
311. Barnett GO. The application of computer-based medical record systems in ambulatory practice. N Engl J Med. 1984; 310(25):1643-50. [PubMed]
312. Collen MF. A History of Medical Informatics in the United States, 1950-1990. Washington, D.C.: American Medical Informatics Association, 1995:400-3.
313. Miller RA, Jamnback L, Giuse NB, Masarie FEJ. Extending the capabilities of diagnostic decision support programs through links to bibliographic searching: addition of “canned MeSH logic” to the Quick Medical Reference (QMR) Program for use with Grateful Med. Proc 15th Annu Symp Comput Appl Med Care. 1991:150-5. [PMC free article] [PubMed]
314. Hammond JE, Hammond WE, Stead WW. Information management through integration of distributed resources: the TMR-NLM connection. Proc 14th Annu Symp Comput Appl Med Care. 1990:719-23.
315. Fan C, Lincoln MJ, Haug PJ, Turner CW, Warner HR. Odyssey: a program to access medical knowledge. Proc 11th Annu Symp Comput Appl Med Care. 1987:488-91.
316. Loonsk JW, Lively R, Tinhan E, Litt H. Implementing the medical desktop: tools for the integration of independent information resources. Proc 15th Annu Symp Comput Appl Med Care. 1992:574-7. [PMC free article] [PubMed]
317. Miller PL, Frawley SJ, Wright L, Roderer NK, Powsner SM. Lessons learned from a pilot implementation of the UMLS Information Sources Map. J Am Med Inform Assoc. 1995;2:102-15. [PMC free article] [PubMed]
318. Zeng Q, Cimino JJ. Linking a clinical information system to heterogeneous information resources. Proc AMIA Annu Fall Symp. 1997:553-7. [PMC free article] [PubMed]
319. Bright MW, Hurson AR, Pakzad SH. A taxonomy and current issues in multidatabase systems. Computer IEEE. Mar 1992:50-9.
320. Seth AP, Larson JA. Federated database systems for managing distributed, heterogeneous, and autonomous data-bases. ACM Comput Surv. 1990;22(3):183-236.
321. Ketchell DS, Freedman MM, Jordan WE, Lightfoot EM, Heyano S, Libbey PA. Willow: a uniform search interface. J Am Med Inform Assoc. 1996;3(1):27-37. [PMC free article] [PubMed]
322. Clyman JI, Powsner SM, Paton JA, Miller PL. Using a network menu and the UMLS Information Sources Map to facilitate access to online reference materials. Bull Med Libr Assoc. 1993;81(2):207-16. [PMC free article] [PubMed]
323. Hubbs PR, Tsai M, Dev P, et al. The Stanford Health Information Network for education: integrated information for decision making and learning. Proc AMIA Annu Fall Symp. 1997:505-8. [PMC free article] [PubMed]
324. Detmer WM, Barnett GO, Hersh WR. MedWeaver: integrating decision support, literature searching, and Web exploration using the UMLS Metathesaurus. Proc AMIA Annu Fall Symp. 1997:490-4. [PMC free article] [PubMed]
325. Barber S, Fowler J, Brook Long K, Dargahi R, Meyer B. Integrating the UMLS into VNS Retriever. Proc 16th Annu Symp Comput Appl Med Care. 1992:273-7. [PMC free article] [PubMed]
326. Brandt CA, Fernandes LA, Powsner SM, Ruskin KJ, Miller PL. Using coded semantic relationships to retrieve and structure clinical reference information. Proc AMIA Annu Fall Symp. 1997:846.
327. MD Consult. Available at: Accessed Aug 29, 1998.
328. Ovid. Available at: Accessed Aug 29, 1998.
329. Silver Platter. Available at: Accessed Aug 29, 1998.
330. Silverstein SM, Miller PL, Cullen MR. An information sources map for occupational and environmental medicine: guidance to network-based information through domain-specific indexing. Proc Annu Symp Comput Appl Med Care. 1994:616-25. [PMC free article] [PubMed]
331. Rodgers RPC. Automated retrieval from multiple disparate information sources: the World Wide Web and NLM's Sourcerer Project. J Am Soc Inform Sci. 1995;46(10):755-64.
332. Rodgers RPC, Srinivasan S, Fullton J. Sourcerer: thesaurus-assisted source identification for the World Wide Web. Available at: Accessed Apr 14, 1998.
333. Joubert M, Fieschi M, Robert JJ, Volot F, Fieschi D. UMLS-based conceptual queries to biomedical information data-bases: an overview of the project ARIANE. J Am Med Inform Assoc. 1998;5:52-61. [PMC free article] [PubMed]
334. Volot F, Joubert M, Fieschi M. Review of biomedical knowledge and data representation with conceptual graphs. Methods Inform Med. 1998;37:86-96. [PubMed]
335. Johnson SB, Aguirre A, Peng P, Cimino JJ. Interpreting natural language queries using the UMLS. Proc 17th Annu Symp Comput Appl Med Care. 1994:294-8. [PMC free article] [PubMed]
336. Peng P, Aguirre A, Johnson SB, Cimino JJ. Generating MEDLINE search strategies using a librarian knowledge-based system. Proc 17th Annu Symp Comput Appl Med Care. 1984:596-600. [PMC free article] [PubMed]
337. Masarie FE, Miller RA. Medical subject headings and medical terminology: an analysis of terminology used in hospital charts. Bull Med Libr Assoc. 1987;75(2):89-94. [PMC free article] [PubMed]
338. Humphreys BL, McCray AT, Cheh ML. Evaluating the coverage on controlled health data terminologies: report on the results of the NLM/AHCPR Large Scale Vocabulary Test. J Am Med Inform Assoc. 1997;4:484-500. [PMC free article] [PubMed]
339. Chute CG, Cohn SP, Campbell KE, Oliver DE, Campbell JR. The content coverage of clinical classifications. J Am Med Inform Assoc. 1996;3:224-33. [PMC free article] [PubMed]
340. Campbell JR, Kallenberg GA, Sherrick RC. The clinical utility of META: an analysis for hypertension. Proc 16th Annu Symp Comput Appl Med Care. 1992:397-401. [PMC free article] [PubMed]
341. Friedman C. The UMLS coverage of clinical radiology. Proc 16th Annu Symp Comput Appl Med Care. 1992:309-13. [PMC free article] [PubMed]
342. Campbell JR, Carpenter P, Sneiderman C, Cohn S, Chute CG, Warren J. Phase II evaluation of clinical coding schemes: completeness, taxonomy, mapping, definitions, and clarity. J Am Med Inform Assoc. 1997;4:238-51. [PMC free article] [PubMed]
343. Cimino JJ. Review paper: coding systems in health care. Methods Inform Med. 1996;35:273-84. [PubMed]
344. Sherertz DD, Tuttle MS, Blois MS, Erlbaum MS. Intervocabulary mapping within the UMLS: the role of lexical matching. Proc 12th Annu Symp Comput Appl Med Care. 1988:201-6.
345. Cimino JJ, Johnson SB, Peng P, Aguirre A. From ICD-9-CM to MeSH using the UMLS: a how-to-guide. Proc 18th Annu Symp Comput Appl Med Care. 1994:730-4. [PMC free article] [PubMed]
346. Bicknell EJ, Sneiderman CA, Rada RF. Computer-assisted merging and mapping of medical knowledge bases. Proc 12th Annu Symp Comput Appl Med Care. 1988:158-64.
347. Cimino JJ. Formal descriptions and adaptive mechanisms for changes in controlled medical vocabularies. Methods Inform Med. 1996;35:202-10. [PubMed]
348. Cimino JJ. Auditing the Unified Medical Language System with semantic methods. J Am Med Inform Assoc. 1998;5:41-51. [PMC free article] [PubMed]
349. Haug PJ, Christensen L, Gundersen M, Clemons B, Koehler S, Bauer K. A natural language parsing system for encoding admitting diagnoses. Proc AMIA Annu Fall Symp. 1997:814-8. [PMC free article] [PubMed]
350. Powsner SM, Miller PL. From patient reports to bibliographic retrieval: a Meta-1 front end. Proc 15th Annu Symp Comput Appl Med Care. 1992:526-30. [PMC free article] [PubMed]
351. Kanter SL, Miller RA, Tan M, Schwartz J. Using POSTDOC to recognize biomedical concepts in medical school curricular documents. Bull Med Libr Assoc. 1994;82(3):283-7. [PMC free article] [PubMed]
352. Miller RA, Gieszczykiewicz FM, Vries JK, Cooper GF. Chartline: providing bibliographic references relevant to patient charts using the UMLS Metathesaurus Knowledge Sources. Proc 16th Annu Symp Comput Appl Med Care. 1993:86-90. [PMC free article] [PubMed]
353. Cooper GF, Miller RA. An experiment comparing lexical methods and statistical methods for extracting MeSH terms from clinical free text. J Am Med Inform Assoc. 1998;5:62-75. [PMC free article] [PubMed]
354. Elkin PL, Cimino JJ, Lowe HJ, et al. Mapping to MeSH: the art of trapping MeSH equivalence from within narrative text. Proc 12th Annu Symp Comput Appl Med Care. 1988:185-90.
355. Lowe HJ, Barnett GO. MicroMeSH: a microcomputer system for searching and exploring the National Library of Medicine's Medical Subject Headings (MeSH) Vocabulary. Proc 11th Annu Symp Comput Appl Med Care. 1987:717-20.
356. Lowe HJ, Barnett GO, Scott J, Eccles R, Foster E, Piggins J. Remote access MicroMesh: a microcomputer system for searching the MEDLINE database. Proc 12th Annu Symp Comput Appl Med Care. 1988:535-9.
357. Lowe HJ, Barnett GO, Scott J, Mallon L, Blewett DR. Remote access MicroMeSH: evaluation of a microcomputer system for searching the MEDLINE database. Proc Annu Symp Comput Appl Med Care. 1989:445-7.
358. Unified Medical Language System: Current Bibliographies in Medicine. Available at: Accessed May 5, 1998.
359. Detmer WM, Shortliffe EH. A model of clinical query management that supports integration of biomedical information over the World Wide Web. Proc 19th Annu Symp Comput Appl Med Care. 1995:898-902. [PMC free article] [PubMed]
360. Detmer WM, Shortliffe EH. WebMedline: transforming Medline into a hypertext environment with links to full-text documents. Proc AMIA Annu Fall Symp. 1996:933.
361. Detmer WM, Shortliffe EH. Using the Internet to improve knowledge diffusion in medicine. Commun ACM. 1997;40(8):101-8.
362. de Groen PC, Barry JA, Schaller WJ. Applying World Wide Web technology to the study of patients with rare diseases. Ann Intern Med. 1998;129:107-13. [PubMed]
363. Afrin LB, Kuppuswamy V, Slater B, Stuart RK. Electronic clinical trial protocol distribution via the World Wide Web: a prototype for reducing costs and errors, improving accrual, and saving trees. J Am Med Inform Assoc. 1997;4:25-35. [PMC free article] [PubMed]
364. Kannery J, Du Pont J, Krol M. Data-Mail: a design for electronic consultation. Proc AMIA Annu Fall Symp. 1996:838.
365. Vanzyl AJ, Cesnik B. A model for connecting doctors to university based medical resources through the Internet. Proc 19th Annu Symp Comput Appl Med Care. 1995:517-21. [PMC free article] [PubMed]
366. Canfield K, Ramesh V, Quirolgico S, Silva M. An intelligent information systems architecture for clinical decision support on the Internet. Proc AMIA Annu Fall Symp. 1996:175-8. [PMC free article] [PubMed]
367. Cimino JJ, Socratous SA, Clayton PD. Internet as clinical information system: application development using the World Wide Web. J Am Med Inform Assoc. 1995;2(5):273-83. [PMC free article] [PubMed]
368. Matheson NW. Things to come: postmodern digital knowledge management and medical informatics. J Am Med Inform Assoc. 1995;2(2):73-8. [PMC free article] [PubMed]

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