Biomedical subjects
Publications and source records attributed to P Ford-Carleton.
A microcomputer-based system designed to provide nurses and physicians access to expert synthesized knowledge in the area of pulmonary arterial waveform troubleshooting has been developed and implemented. Evaluation using triangulation methods show that there was a substantive increase in knowledge in both nurses and physicians.¿.
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In a busy clinical environment, access to knowledge must be rapid and specific to the clinical query at hand. This requires indices which support easy navigation within a knowledge source. We have developed a computer-based tool for trouble-shooting pulmonary artery waveforms using a graphical index. Preliminary results of domain knowledge tests for a group of clinicians exposed to the system (N = 33) show a mean improvement on a 30-point test of 5.33 (p < 0.001) compared to a control group (N = 19) improvement of 0.47 (p = 0.61). Survey of the experimental group (N = 25) showed 84% (p = 0.001) found the system easy to use. We discuss lessons learned in indexing this domain area to computer-based indexing of guidelines for pressure ulcer prevention.
Existing bedside cardiovascular monitors often inaccurately measure arterial blood pressure during intra-aortic balloon pump (IABP) assist. We have developed an algorithm that correctly identifies features of arterial pressure waveforms in the presence of IABP. The algorithm is adaptive, functions in real-time, and uses information from the electrocardiographic (ECG) and arterial blood pressure signals to extract features and numeric values from the arterial blood pressure waveform. In its current form, it requires reliable ECG beat detection and was not intended to operate under conditions of extremely poor balloon timing. The algorithm was evaluated by an expert (P.F-C.) on a limited data set, which consisted of 12 1-minute epochs of data recorded from 6 intensive care unit patients. A criterion for selection of patients was that the ECG beat detector could detect ECG beats correctly from the waveforms. The overall sensitivity and positive predictivity for beat detection were 94.04% and 100%, respectively. For feature identification, the overall sensitivity was greater than 89%, positive predictivity was 100%, and the false-positive rate was 0%. The performance measures may be biased by the criteria for patient selection. This approach to identifying waveform features during IABP improves the accuracy of measurements. The utility of using 2 sources of information to improve measurement accuracy has been demonstrated and should be applicable to other physiologic signal-processing applications.
This paper describes a microcomputer system for providing computer-based access to expert knowledge in the area of troubleshooting pulmonary artery (PA) catheter waveforms. The system is used by both nurses and physicians in an 18-bed medical intensive care unit. Its dominant features are 1) problem-focused access to knowledge, and 2) heavy use of graphics and images to explicate knowledge. The system is used by both nurses and physicians in an 18-bed medical intensive care unit. An evaluation protocol is in place to examine the impact of the system on clinicians' knowledge, their decision-making skills, their satisfaction with the system, and costs of orientation related to PA waveform troubleshooting.