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SYMPOSIUM: DATA ANALYSIS AND TREND IDENTIFICATION IN THE INTENSIVE CARE UNIT
Year : 2017  |  Volume : 3  |  Issue : 3  |  Page : 129-141

Republication: Financial analysis techniques in clinical practice: From “Micro” to “Macro”


Department of Research and Innovation, St. Luke's University Health Network; OPUS 12 Foundation, Bethlehem, PA, USA

Correspondence Address:
Stanislaw P Stawicki
Department of Research and Innovation, St. Luke's University Health Network, EW2 Research Administration, 801 Ostrum Street, Bethlehem, PA 18015
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/IJAM.IJAM_36_17

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Improved methods of clinical data collection, storage, and processing are changing the way critical care practitioners examine and utilize the wealth of patient-related information available in the modern Intensive Care Unit (ICU). The author of this report previously outlined general methods and principles of a proposed new biomedical parameter analysis system based on well-established financial analysis (FA) methods. The new system helps to define, confirm, and potentially predict trends, and trend reversals in a number of traditional biomedical variables (vital signs, laboratory parameters, various catheter-derived pressure measurements, oximetry, temperature, and input/output variables). To examine the behavior of FA parameters and indicators across different temporal scales (i.e., minutes, hours, days, weeks), the author analyzed trends in various biomedical parameters using different time scales and a standardized set of FA techniques, including: (1) the stochastic oscillator, (2) the relative strength index (RSI) tool, (3) moving average, (4) moving average convergence-divergence, and (5) price envelope analysis. Randomly chosen and anonymized laboratory, urinary output, hemodynamic, and intracranial pressure datasets of patients who spent at least 21 days in the ICU were retrospectively examined. Variables were entered into specialized FA software (MetaStock, Equis International, Salt Lake City, Utah, USA) and subjected to computerized processing methods. Analyses of the above clinical parameters demonstrate that laboratory, input/output, oximetry, and transduced pressure-based data can be successfully “trended” using simple FA techniques. The data were easy to examine when displayed in FA fashion. In addition, some trends that were not easily apparent on the examination of raw numeric data or basic non-FA graphs became more apparent after the application of FA methods. These findings suggest that biomedical parameters can be subjected to the same manipulations as financial market data. In addition, FA tools appear to provide the interpreting physician with means to examine biomedical parameters across different temporal scales and resolutions (from minutes to weeks per each measurement epoch). In fact, biomedical parameters tend to display similar trends regardless of whether the data are collected on the scale of minutes, hours, days, weeks, or months. The following core competencies are addressed in this article: Medical knowledge, Patient Care, Practice based learning and improvement, Systems based practice. Republished with permission from: Stawicki SP. Financial analysis techniques in clinical practice: From 'micro' to 'macro'. OPUS 12 Scientist 2008;2(3):3-9.


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