Comparison of a commercially available clinical information system with other methods of measuring critical care outcomes data
Section snippets
Materials and methods
Given the vast amounts of data are recorded with our system the analysis was limited to a cross-section of various data fields that we thought were important. Ideally, accuracy is determined by comparing the CIS data with some gold standard and is defined by 2 elements—correctness and completeness.8 However, in many cases no other such record of that data exists with which to compare the data. In some cases where another parallel records existed, it was believed that the comparison data set was
Set 1. length of stay in ICU
Length of stay (LOS) in the ICU is often used as a measurement of outcome in critical care research. Unfortunately, in many ICUs today, the lack of bed availability outside the ICU can often cause patients to remain physically in the ICU despite being medically ready for transfer. Error can therefore be introduced into this outcome measurement by using either standard hospital census records or by the printed medical record. Nevertheless, this marker of outcome is commonly used in ICU outcomes
Discussion
In 1997, Hogan and Wagner wrote the only comprehensive review of the literature on the accuracy of computer-based records.8 Their analysis showed that good accuracy data is clearly lacking. They reviewed the data from 20 previous studies that sought to measure, in some way, the accuracy of a CIS, which they defined as including both correctness and completeness of information. Unfortunately, the systems studied and methodology of the studies were so different that no clear conclusions could be
Conclusions
An important question that needs to be addressed is the degree to which these data from a single ICU with a specific CIS can be extrapolated to other care units with other systems. To some degree, most of these data can not be automatically extrapolated to other systems in other units. One of the queries in our study (set 3/DOV) was specifically programmed into our system by the vendor and is not automatically available even to those with our same system. This lack of generalizability has
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Impact of computerized information systems on workload in operating room and intensive care unit
2009, Best Practice and Research: Clinical AnaesthesiologyCitation Excerpt :For instance, most ICU's are required to collect information on the severity of illness and intensity of nursing activity of their patients using scoring systems like the TISS, APACHE or SOFA score.12,34–37 The raw data for these scoring systems can be extracted automatically from the CIS, not only reducing the workload, but probably improving the quality of the data.9,13,38–46 Another aspect is billing: manual collection of chargeable items is no longer necessary, the accumulated items per patient can be sent automatically to the financial department of the hospital.16,21,46,47
Validation of Surgical Intensive Care-Infection Registry: A Medical Informatics System for Intensive Care Unit Research, Quality of Care Improvement, and Daily Patient Care
2008, Journal of the American College of SurgeonsTowards automated classification of intensive care nursing narratives
2007, International Journal of Medical InformaticsImplementing Single Source: The STARBRITE Proof-of-Concept Study
2007, Journal of the American Medical Informatics AssociationCitation Excerpt :Thus, data that may already be available electronically are keyed twice in the healthcare setting, transcribed to a paper case report form (CRF), and then keyed twice in the research setting (Figure 1). Large-scale direct use of healthcare data for research, although advocated by many,1–18 has thus far eluded researchers.9 Successful implementations described in the literature6,26 cite workflow incompatibility, additional research data requirements, and regulatory differences as challenges.7
The accuracy of clinical information systems
2004, Journal of Critical CareComputers in the intensive care unit
2004, Journal of Critical Care