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Updated: Aug 7, 2026
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Searching for answers on a clinical information system
1Center for Clinical Computing, Beth Israel Hospital, Harvard Medical School, Boston, MA, USA.
Methods of Information in Medicine
|March 1, 1995
Summary
Clinicians primarily use information systems for diagnostic results and communication, with therapeutic information being a key need in patient care. Bibliographic retrieval and data aggregation are less frequent uses.
Area of Science:
- Clinical Informatics
- Health Information Systems
- Medical Decision Making
Background:
- Understanding clinician information needs is crucial for effective health informatics.
- Clinical computing systems are increasingly integrated into patient care workflows.
- Previous research has not fully characterized the primary drivers of clinician system use.
Purpose of the Study:
- To develop an empirical model of clinician information needs based on observational data.
- To identify the most frequent reasons clinicians interact with clinical information systems.
- To inform the design of knowledge structures that support clinical practice.
Main Methods:
- Observational study of clinical computing system usage.
- Analysis of data on clinician interactions with the system.
- Focus on general medical visits for patients with human immunodeficiency virus (HIV) infection.
Main Results:
- Clinicians most frequently use systems to review diagnostic study results.
- Communication (e.g., electronic mail) is the second most common use.
- Therapeutic information needs were prominent, especially in HIV care, suggesting a need for systems to support this.
Conclusions:
- Clinical information systems are predominantly used for immediate patient data retrieval and communication.
- The design of health information systems must prioritize access to therapeutic information to meet clinician needs.
- Further research should explore how knowledge structures can better support evidence-based therapeutic decision-making.
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