PubMed Health⌕ Search

PubMed · 7881616

Data management in the vascular laboratory.

Abstract

As the clinical workload in the vascular laboratory increases and as the demand for additional documentation by hospital oversight committees and outside agencies grow, the need for computerized data management will become obvious. Although there are no generic, broadly applicable software programs to automate the laboratory's operations, applications can easily be developed using any of the current database programs to meet the needs of most laboratories. Fortunately, the intense competition in the microcomputer industry has recently made very powerful systems that are capable of providing the necessary computing support increasingly affordable. Such systems can be very simple or incredibly complex depending on the available local expertise and each laboratory's specific needs. In addition to facilitating the laboratory's daily operations, such a system will inevitably expedite the implementation of the laboratory's quality assurance program and will maximize utilization of existing personnel. This type of cost-effective solution to the ever-increasing demand for service will become increasingly important in maintaining the laboratory's fiscal viability. Although the prospects of undertaking such a task might seem daunting, especially to the computer novice, it is important to begin. Keep the system simple, at first, and allow it to develop as local expertise and confidence develop. The only prospect more frightening that sitting down to develop a computerized system for managing the laboratory's data, is the prospect of trying to continue without one!

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R F Kempczinski. 1994. Data management in the vascular laboratory.. https://pubmed.ncbi.nlm.nih.gov/7881616/

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

KEEP EXPLORING

Related citations

Enhancement of claims data to improve risk adjustment of hospital mortality.

CONTEXT: Comparisons of risk-adjusted hospital performance often are important components of public reports, pay-for-performance programs, and quality improvement initiatives. Risk-adjustment equations used in these analyses must contain sufficient clinical detail to ensure accurate measurements of hospital quality. OBJECTIVE: To assess the effect on risk-adjusted hospital mortality rates of adding present on admission codes and numerical laboratory data to administrative claims data. DESIGN, SETTING, AND PATIENTS: Comparison of risk-adjustment equations for inpatient mortality from July 2000 through June 2003 derived by sequentially adding increasingly difficult-to-obtain clinical data to an administrative database of 188 Pennsylvania hospitals. Patients were hospitalized for acute myocardial infarction, congestive heart failure, cerebrovascular accident, gastrointestinal tract hemorrhage, or pneumonia or underwent an abdominal aortic aneurysm repair, coronary artery bypass graft surgery, or craniotomy. MAIN OUTCOME MEASURES: C statistics as a measure of the discriminatory power of alternative risk-adjustment models (administrative, present on admission, laboratory, and clinical for each of the 5 conditions and 3 procedures). RESULTS: The mean (SD) c statistic for the administrative model was 0.79 (0.02). Adding present on admission codes and numerical laboratory data collected at the time of admission resulted in substantially improved risk-adjustment equations (mean [SD] c statistic of 0.84 [0.01] and 0.86 [0.01], respectively). Modest additional improvements were obtained by adding more complex and expensive to collect clinical data such as vital signs, blood culture results, key clinical findings, and composite scores abstracted from patients' medical records (mean [SD] c statistic of 0.88 [0.01]). CONCLUSIONS: This study supports the value of adding present on admission codes and numerical laboratory values to administrative databases. Secondary abstraction of difficult-to-obtain key clinical findings adds little to the predictive power of risk-adjustment equations.

Clinical Laboratory Information Systems↗

Definition of an XML markup language for clinical laboratory procedures and comparison with generic XML markup.

BACKGROUND: Clinical laboratory procedure manuals are typically maintained as word processor files and are inefficient to store and search, require substantial effort for review and updating, and integrate poorly with other laboratory information. Electronic document management systems could improve procedure management and utility. As a first step toward building such systems, we have developed a prototype electronic format for laboratory procedures using Extensible Markup Language (XML). METHODS: Representative laboratory procedures were analyzed to identify document structure and data elements. This information was used to create a markup vocabulary, CLP-ML, expressed as an XML Document Type Definition (DTD). To determine whether this markup provided advantages over generic markup, we compared procedures structured with CLP-ML or with the vocabulary of the Health Level Seven, Inc. (HL7) Clinical Document Architecture (CDA) narrative block. RESULTS: CLP-ML includes 124 XML tags and supports a variety of procedure types across different laboratory sections. When compared with a general-purpose markup vocabulary (CDA narrative block), CLP-ML documents were easier to edit and read, less complex structurally, and simpler to traverse for searching and retrieval. CONCLUSION: In combination with appropriate software, CLP-ML is designed to support electronic authoring, reviewing, distributing, and searching of clinical laboratory procedures from a central repository, decreasing procedure maintenance effort and increasing the utility of procedure information. A standard electronic procedure format could also allow laboratories and vendors to share procedures and procedure layouts, minimizing duplicative word processor editing. Our results suggest that laboratory-specific markup such as CLP-ML will provide greater benefit for such systems than generic markup.

Clinical Laboratory Information Systems↗