Multilevel reviews for coding accuracy.

S Stavely

Home/Archive/PubMed

PubMed · 11143277

Multilevel reviews for coding accuracy.

S Stavely

View original source ↗Export metadata ↓

Abstract

If coders, case managers, diagnosis-related group (DRG) coordinators, and physicians work cooperatively, then documentation and coding accuracy can be greatly improved. A five-step process is proving effective toward compliance and appropriate reimbursement in a large acute care setting.

Explore related subjects

Abstracting and Indexing Diagnosis-Related Groups Employee Performance Appraisal Forms and Records Control Humans Medical Records Medical Records Department, Hospital Quality Control Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

S Stavely. 2000. Multilevel reviews for coding accuracy.. https://pubmed.ncbi.nlm.nih.gov/11143277/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2003

PRECIS: protein reports engineered from concise information in SWISS-PROT.

MOTIVATION: There have been several endeavours to address the problem of annotating sequence data computationally, but the task is non-trivial and few tools have emerged that gather useful information on a given sequence, or set of sequences, in a simple and convenient manner. As more genome projects bear fruit, the mass of uncharacterized sequence data accumulating in public repositories grows ever larger. There is thus a pressing need for tools to support the process of automatic analysis and annotation of newly determined sequences. With this in mind, we have developed PRECIS, which automatically creates protein reports from sets of SWISS-PROT entries, collating results into structured reports, detailing known biological and medical information, literature and database cross-references, and relevant keywords.

Abstracting and Indexing↗

PubMed2003

MedScan, a natural language processing engine for MEDLINE abstracts.

MOTIVATION: The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications. RESULTS: We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.

Abstracting and Indexing↗

PubMed2003

A pilot study of contextual UMLS indexing to improve the precision of concept-based representation in XML-structured clinical radiology reports.

OBJECTIVE: Despite the advantages of structured data entry, much of the patient record is still stored as unstructured or semistructured narrative text. The issue of representing clinical document content remains problematic. The authors' prior work using an automated UMLS document indexing system has been encouraging but has been affected by the generally low indexing precision of such systems. In an effort to improve precision, the authors have developed a context-sensitive document indexing model to calculate the optimal subset of UMLS source vocabularies used to index each document section. This pilot study was performed to evaluate the utility of this indexing approach on a set of clinical radiology reports. DESIGN: A set of clinical radiology reports that had been indexed manually using UMLS concept descriptors was indexed automatically by the SAPHIRE indexing engine. Using the data generated by this process the authors developed a system that simulated indexing, at the document section level, of the same document set using many permutations of a subset of the UMLS constituent vocabularies. MEASUREMENTS: The precision and recall scores generated by simulated indexing for each permutation of two or three UMLS constituent vocabularies were determined. RESULTS: While there was considerable variation in precision and recall values across the different subtypes of radiology reports, the overall effect of this indexing strategy using the best combination of two or three UMLS constituent vocabularies was an improvement in precision without significant impact of recall. CONCLUSION: In this pilot study a contextual indexing strategy improved overall precision in a set of clinical radiology reports.

Abstracting and Indexing↗