Biomedical subjects

Samson Tu

Publications and source records attributed to Samson Tu.

3 recordsLinked to original sources

PubMed2002

The helpful patient record system: problem oriented and knowledge based.

In contrast to existing computerized patient record systems, which merely offer static functionality for storage and presentation, a helpful patient record system is a problem-oriented, knowledge-based system which provides the clinician with situation-specific information from the patient record, relevant to the activity within the patient care process. We suggest extending the data model of current patient record systems with (1) knowledge for recognizing and interpreting care situations, (2) knowledge of how clinicians work and what information they need, and (3) means to rank information according to its relevance in a given situation. We present a framework that enables representation of three prerequisite features for a future helpful patient record system: the primary care workflow process, the problem-oriented information model, and means to identify relevant information to the care process and medical decisions.

Artificial Intelligence↗

PubMed2002

Support for guideline development through error classification and constraint checking.

Clinical guidelines aim to eliminate clinician errors, reduce practice variation, and promote best medical practices. Computer-interpretable guidelines (CIGs) can deliver patient-specific advice during clinical encounters, which makes them more likely to affect clinician behavior than narrative guidelines. To reduce the number of errors that are introduced while developing narrative guidelines and CIGs, we studied the process used by the ACP-ASIM to develop clinical algorithms from narrative guidelines. We analyzed how changes progressed between subsequent versions of an algorithm and between a narrative guideline and its derived clinical algorithm. We recommend procedures that could limit the number of errors produced when generating clinical algorithms. In addition, we developed a tool for authoring CIGs in GLIF3 format and validating their syntax, data type matches, cardinality constraints, and structural integrity constraints. We used this tool to author guidelines and to check them for errors.

Algorithms↗

PubMedDate not supplied

Comparing computer-interpretable guideline models: a case-study approach.

OBJECTIVES: Many groups are developing computer-interpretable clinical guidelines (CIGs) for use during clinical encounters. CIGs use "Task-Network Models" for representation but differ in their approaches to addressing particular modeling challenges. We have studied similarities and differences between CIGs in order to identify issues that must be resolved before a consensus on a set of common components can be developed. DESIGN: We compared six models: Asbru, EON, GLIF, GUIDE, PRODIGY, and PROforma. Collaborators from groups that created these models represented, in their own formalisms, portions of two guidelines: American College of Chest Physicians cough guidelines [correction] and the Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. MEASUREMENTS: We compared the models according to eight components that capture the structure of CIGs. The components enable modelers to encode guidelines as plans that organize decision and action tasks in networks. They also enable the encoded guidelines to be linked with patient data-a key requirement for enabling patient-specific decision support. RESULTS: We found consensus on many components, including plan organization, expression language, conceptual medical record model, medical concept model, and data abstractions. Differences were most apparent in underlying decision models, goal representation, use of scenarios, and structured medical actions. CONCLUSION: We identified guideline components that the CIG community could adopt as standards. Some of the participants are pursuing standardization of these components under the auspices of HL7.

Decision Support Systems, Clinical↗