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Hospital information systems designed to support the needs of health care professionals include patient data entered using both freetext and precoded storage schemes. A major disadvantage of freetext storage schemes is that data captured in this format can only be presented as is to the user for review tasks. In the view of many health care scientists, natural language understanding systems capable of identifying, extracting, and encoding information contained in freetext data may provide the necessary tools to overcome this weakness. This paper describes the development and evaluation of a such a system designed to encode freetext admission diagnoses. This system combines both semantic and syntactic linguistic analysis techniques. Evaluation results demonstrate the overall performance of this system to be reasonable, accurately encoding approximately 76% of admission diagnoses. Inefficiencies are primarily due to the inability of this system to generate encodings in roughly 15% of test cases. When encodings are produced, however, accuracy equals that of the current manual coding method. With further modification, this application can partially automate the coding process.

The authors describe their experience designing a controlled medical vocabulary server created to support the exchange of patient data and medical decision logic. The first section introduces practical and theoretical premises that guided the design of the vocabulary server. The second section describes a series of structures needed to implement the proposed server, emphasizing their conformance to the design premises. The third section introduces potential applications that provide services to end users and also a group of tools necessary for maintaining the server corpus. In the fourth section, the authors propose an implementation strategy based on a common framework and on the participation of groups from different health-related domains.

Computerized health information systems can contribute to the care received by patients in a number of ways. Not the least of these is through interactions with health care providers to modify diagnostic and therapeutic decisions. Since its beginning, developers have used the HELP hospital information system to explore computerized interventions into the medical decision making process. By their nature these interventions imply a computer-directed interaction with the physicians, nurses, and therapists involved in delivering care. In this paper we describe four different approaches to this intervention. These include: (1) processes that respond to the appearance of certain types of clinical data by issuing an alert informing caregivers of these data's presence and import, (2) programs that critique new orders and propose changes in those orders when appropriate, (3) programs that suggest new orders and procedures in response to patient data suggesting their need, and (4) applications that function by summarizing patient care data and that attempt to retrospectively assess the average or typical quality of medical decisions and therapeutic interventions made by health care providers. These approaches are illustrated with experience from the HELP system.

Few diagnostic decision-support systems are in routine clinical use, mainly because these systems typically require time-consuming manual data entry. This research investigated the feasibility of reducing manual data entry by integrating a stand-alone diagnostic expert system with an existing comprehensive hospital information system (HIS). A knowledge-based intervocabulary mapping technique was developed to map disparate vocabularies. The results of a retrospective study indicate that transferring clinical data from the HIS to the diagnostic expert system at the beginning of workup significantly reduces the manual data entry required for generating the correct diagnoses for patients.

We have developed a computer-administered history designed to directly interview hospitalized patients with pulmonary disease. A frame-based decision system is used to direct the history and to generate a one- to five-member differential diagnostic list based on this history. This system incorporates a cognitive model of question selection and a Bayesian scoring algorithm. Structures to control the choice of questions are embedded in the diagnostic frames and in a QUERY program that makes the final choice of questions. We have compared the behavior of this decision-driven approach with a history taken using a paper questionnaire. The paper-based history presents 182 questions to every patient and captured 75% of 85 pulmonary diseases in its differential lists. The decision-driven system asks 50.7 +/- 31.0 (mean +/- standard deviation) and captured 74% of 61 pulmonary diseases. Our experience suggests that the use of a computerized diagnostic knowledge base to direct the selection of pertinent questions can substantially reduce the number of questions necessary to collect a diagnostically useful patient history.