This paper examines two interconnected aspects of clinical decision support systems (CDSS). The first section addresses the importance, challenges, and barriers of terminologic systems in CDSS, focusing on how inconsistent medical vocabulary across disciplines, institutions, and nations impedes data integration and patient safety. The Unified Medical Language System (UMLS) is discussed as a unifying framework. The second section evaluates ONCOCIN, an early AI-driven oncology CDSS developed in 1979, whose treatment recommendations were approved by experts in only 79% of cases. The paper assesses whether this performance is adequate, proposes safeguards for responsible system use, and argues that CDSS tools function best as decision aids supplementing — rather than replacing — trained clinical judgment.
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A clinical decision support system (CDSS) is a type of software designed to aid clinicians in their decision-making by matching individual patient characteristics and knowledge about a patient's disease to computerized databases "for the purpose of generating patient-specific assessments or recommendations" (CDSS, 2010, Glossary). However, implementation of CDSS has been inconsistent at best. Today, despite the influx of technology into most spheres of modern life, information regarding drug treatment and other vital aspects of patient care is often paper-based in many clinical environments. Even when information is present in the form of electronic documents on the web or in clinical information systems, "doctors are not realistically given adequate time to search for the information specific to a particular patient sitting in front of them" (Park 2009, p. 293).
Consistent and comprehensive integration of patient data into easily retrievable systems is essential to preserving patient safety. In one Korean hospital, medication error rates fell 81% following CDSS implementation — from 142 per 1,000 patients to 26.6 per 1,000 patients after the system was fully mobilized (Park 2009, p. 293). However, while the shift to computer-based systems that provide individualized guidance for physicians is gradually becoming more accepted, inconsistent medical terminology remains a significant barrier between different hospitals — and even between different nations — in sharing medically related information.
One important response to the challenge of terminological inconsistency is the Unified Medical Language System (UMLS). As Campbell et al. (1998) describe it, "the approach taken by the UMLS, in which disparate terminology systems are integrated, has allowed construction of an electronic thesaurus (the Metathesaurus) that avoids imposing any restrictions upon the content, structure, or semantics of the source terminologies. As such, the UMLS has served as a unifying paradigm by providing appropriate links among equivalent entities that are used in different contexts or for different purposes. It accordingly provides a vehicle through which possibly orthogonal semantic models can co-exist within a single framework. This framework provides a model for the collaborative evolution of biomedical terminology and allows a synergistic relationship between the UMLS and its source terminology systems."
Medical terminologies tend to be broad, sprawling, and complex in scope, and are often quite regional or discipline-specific. The goal is for CDSS systems to translate a variety of meanings into the same treatment protocols. The barriers to this are partly technological — errors in translating similar yet distinct terminologies can occur, as can more conventional errors such as misreading descriptions of drugs from physicians' handwriting or working from incomplete patient histories. There is also a human factor: terms can change quickly in practice, particularly the specialized vocabulary used by physicians in subspecialties. As a result, there may be incorrect labeling of cases when physicians use the database, and the information itself may be mistranslated and poorly integrated.
It is unlikely that a perfectly universal system of terminology will emerge among practitioners in a cross-cultural, cross-disciplinary fashion, and equally unlikely that any computerized system, however flexible, will be able to create a seamless web of translation for CDSS users. However, a lack of perfection is no excuse not to try, given the substantial benefits that can be derived for patient care through the use of CDSS.
One of the unfortunate aspects of oncology is that the drugs used to treat cancer are toxic to the body in a broad sense: they harm the body's beneficial functions as well as the cancer cells. When administering chemotherapy, the aim is to find the correct balance between delivering a high enough dose to destroy cancer cells while still managing treatment side effects (Musen et al. 1986, p. 44). ONCOCIN, a computerized clinical decision support system developed in 1979, was designed to use artificial intelligence to offer advice to physicians on medicines, dosages, and testing relating to oncology. It united medical record keeping with decision support (ONCOCIN, 2010, Discovery Media).
ONCOCIN was used to suggest potential treatments based upon two databases of oncology protocols — one procedural and one inferential. Procedural knowledge defined a patient's progression through the various states of the treatment plan. After consulting ONCOCIN for the general treatment plan, the system used its inferential knowledge base component to "refine" the plan based upon the patient's individual needs and medical history (Musen et al. 1986, p. 44).
Before ONCOCIN was developed, computer-based treatment programs were subject to frequent criticism. Computers must not simply store data; they must be able to select and analyze pertinent data. Until ONCOCIN, most computerized support systems were merely data warehouses. ONCOCIN was considered an improvement because it provided information about a patient's past as well as his or her immediate health status, giving the system a more intuitive and responsive character (Kahn et al. 1985, p. 172). By weighing two foundational aspects of patient care — treatment history and current medical status — ONCOCIN was hoped to revolutionize oncology. For example, it could determine drug doses on the basis of time schedule, toxicity, and blood counts, drawing on clinical and patient data, chemotherapy protocol guidelines, and the recommendations of oncologists who had participated in building the advice system (ONCOCIN, 2010, Discovery Media).
However, there were a number of problems with ONCOCIN's implementation. The first was that it was not clear what type of training was required to intelligently review ONCOCIN's results. Typical users were new residents and even non-physician clinical assistants rather than experienced doctors (ONCOCIN, 2010, Discovery Media). The justification for this was that in oncology, so much knowledge regarding toxicity had been formally documented that the system was considered fairly straightforward to use. Nevertheless, regardless of the statistical level of confidence in the results, it is difficult to imagine any patient feeling comfortable receiving treatment from a new resident relying on a computerized program to generate a prefabricated treatment plan.
"Assessing whether 79% approval is clinically adequate"
"Proposed safeguards for responsible CDSS use"
"CDSS as aide, not replacement, for clinical judgment"
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