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Biomedical subjects

F E Masarie

Publications and source records attributed to F E Masarie.

13 recordsLinked to original sources
PubMed2005

Clinical terminology support for a national ambulatory practice outcomes research network.

The Medical Quality Improvement Consortium (MQIC) is a nationwide collaboration of 74 healthcare delivery systems, consisting of 3755 clinicians, who contribute de-identified clinical data from the same commercial electronic medical record (EMR) for quality reporting, outcomes research and clinical research in public health and practice benchmarking. Despite the existence of a common, centrally-managed, shared terminology for core concepts (medications, problem lists, observation names), a substantial "back-end" information management process is required to ensure terminology and data harmonization for creating multi-facility clinically-acceptable queries and comparable results. We describe the information architecture created to support terminology harmonization across this data-sharing consortium and discuss the implications for large scale data sharing envisioned by proponents for the national adoption of ambulatory EMR systems.

Ambulatory Care Information Systems↗
PubMed2003

The use of SNOMED CT simplifies querying of a clinical data warehouse.

The usefulness of digital clinical information is limited by difficulty in accessing that information. Information in electronic medical records (EMR) must be entered and stored at the appropriate level of granularity for individual patient care. However, benefits such as outcomes research and decision support require aggregation to clinical data -- "heart disease" as opposed to "S/P MI 1997" for example. The hierarchical relationships in an external reference terminology, such as SNOMED, can facilitate aggregation. This study examines whether by leveraging the knowledge built into SNOMED's hierarchical structure, one can simplify the query process without degrading the query results.

Cardiovascular Diseases↗
PubMed2001

Clinical benchmarking enabled by the digital health record.

Office-based physicians are often ill equipped to report aggregate information about their patients and practice of medicine, since their practices have relied upon paper records for the management of clinical information. Physicians who do not have access to large-scale information technology support can now benefit from low-cost clinical documentation and reporting tools. We developed a hosted clinical data mart for users of a web-enabled charting tool, targeting the solo or small group practice. The system uses secure Java Server Pages with a dashboard-like menu to provide point-and-click access to simple reports such as case mix, medications, utilization, productivity, and patient demographics in its first release. The system automatically normalizes user-entered clinical terms to enhance the quality of structured data. Individual providers benefit from rapid patient identification for disease management, quality of care self-assessments, drug recalls, and compliance with clinical guidelines. The system provides knowledge integration by linking to trusted sources of online medical information in context. Information derived from the clinical record is clinically more accurate than billing data. Provider self-assessment and benchmarking empowers physicians, who may resent "being profiled" by external entities. In contrast to large-scale data warehouse projects, the current system delivers immediate value to individual physicians who choose an electronic clinical documentation tool.

Benchmarking↗
PubMed1998

Use of the WWW for distributed knowledge engineering for an EMR: the KnowledgeBank concept.

Developing automated tools for clinical information management requires an appreciation of user needs and capabilities. To address the reality of practice style variation, and the varying degree of comfort with computers in clinical users, we developed the KnowledgeBank concept. The KnowledgeBank concept includes an end-user authoring tool for clinical content in the EMR, and a web-based repository of content for sharing clinical content. We describe the early experiences of end-users using the KnowledgeBank.

Artificial Intelligence↗
PubMed1991

An interlingua for electronic interchange of medical information: using frames to map between clinical vocabularies.

The proliferation of medical knowledge has led to the development of extensive dictionaries for electronically accessing information resources. The task of standardizing terminology used for electronic hospital records and for knowledge bases for medical expert systems and indexing the medical literature cannot easily be met by developing a single, monolithic "official" medical vocabulary. Developing a monolithic vocabulary would require a massive effort, and its existence would not guarantee its use by third-party payors, by practicing clinicians, or by developers of electronic medical information systems. Recognizing this, the National Library of Medicine (NLM) has begun to develop the Unified Medical Language System (UMLS) as a means of promoting electronic information exchange among systems with controlled vocabularies. The authors describe a frame-based system developed as an experimental approach to mapping between controlled clinical vocabularies.

Cluster Analysis↗
PubMed1989

Evaluation issues in the development of expert systems in medicine.

Evaluation of medical decision support software (MDSS)--computer programs to assist health professionals with diagnostic and/or therapeutic decisions--has not kept pace with the development of such programs. This article describes the following formative evaluation issues that must be addressed by developers of MDSS to evaluate these programs properly: (1) How can systematic feedback be obtained about an evolving program? (2) How can enough data to evaluate the program be obtained? (3) How much instruction is necessary? (4) What are the most important aspects for users to evaluate? and (5) How can the appropriate use of a developing MDSS be assured? Data from an ongoing evaluation of an existing MDSS, Quick Medical Reference, are used to illustrate the issues and to suggest recommendations for addressing them.

Data Collection↗
PubMed1989

Use of the Quick Medical Reference (QMR) program as a tool for medical education.

The original goal of the INTERNIST-1 project, as formulated in the early 1970s, was to develop an expert consultant program for diagnosis in general internal medicine. By the early 1980s, it was recognized that the most valuable product of the project was its medical knowledge base (KB). The INTERNIST-1/QMR KB comprehensively summarizes information contained in the medical literature regarding diagnosis of disorders seen in internal medicine. The QMR program was developed to enable its users to exploit the contents of the INTERNIST-1/QMR KB in educationally, clinically, and computationally useful ways. Utilizing commonly available microcomputers, the program operates at three levels--as an electronic textbook, as an intermediate level spreadsheet for the combination and exploration of simple diagnostic concepts, and as an expert consultant program. The electronic textbook contains an average of 85 findings and 8 associated disorders relevant to the diagnosis of approximately 600 disorders in internal medicine. Inverting the disease profiles creates extensive differential diagnosis lists for the over 4250 patient findings known to the system. Unlike a standard printed medical textbook, the QMR knowledge base can be manipulated "on the fly" to format displays that match the information needs of users. Preliminary use of the program for education of medical students and medical house officers at several sites has met with an enthusiastic response.

Computer-Assisted Instruction↗
PubMed1987

Medical Subject Headings and medical terminology: an analysis of terminology used in hospital charts.

Terminology used by health professionals in everyday written discourse was compared with terminology in a standardized medical vocabulary, the Medical Subject Headings (MeSH). Fifty written hospital charts were selected at random and analyzed by a computer program that identified MeSH terms in the charts. The charts were analyzed against two related MeSH vocabularies--one containing MeSH terms and one containing both MeSH terms and backwards cross-reference terms. When small words such as articles and prepositions were disregarded, approximately 50% of the words in a medical chart were found to be MeSH-related terminology. In addition, about 40% of MeSH-related words in the charts were either MeSH terms or backwards cross-reference terms.

Hospital Records↗
PubMed1986

The INTERNIST-1/QUICK MEDICAL REFERENCE project--status report.

INTERNIST-1 and its successor, QUICK MEDICAL REFERENCE (QMR), are computer programs designed to provide health care professionals with diagnostic assistance in general internal medicine. Both programs rely on the INTERNIST-1 computerized knowledge base, which comprehensively describes 570 diseases in internal medicine. The philosophies behind the development of each program differ. Whereas INTERNIST-1 functions solely as a high-powered diagnostic consultant program, the QMR program acts more as an information tool, providing users with multiple ways of reviewing and manipulating the diagnostic information in the program's knowledge base. At the lowest level, the program can be viewed as an electronic textbook of medicine. In addition, the QMR program has the ability to assist users with generating hypotheses in complex patient cases. The QMR program has not been evaluated formally as an information tool for practicing physicians. A preliminary study indicates that QMR's case-analysis capabilities are of potential benefit in most patients in internal medicine admitted for diagnostic evaluation.

Diagnosis, Computer-Assisted↗
PubMed1985

INTERNIST-I properties: representing common sense and good medical practice in a computerized medical knowledge base.

INTERNIST-I is an experimental diagnostic consultant program for use on clinically challenging cases in internal medicine. Properties are a part of the INTERNIST-I knowledge representation scheme which embody essential information about the medical facts contained in the knowledge base. Properties provide the INTERNIST-I program with a measure of common sense and encourage the program to follow good medical practice. Properties substantially influence the behavior of the INTERNIST-I diagnostic program during case analyses. This paper reviews the implementation and significance of properties.

Artificial Intelligence↗