Information Management

Information technology in quality management in anesthesiology and intensive care

Aarno Kari, M.D., Ph.D., Managing director
Intensium Oy
PO BOX 1188
FIN-70211 Kuopio, Finland
Phone +358 17 264 9670

Email: aarno.kari@intensium.fi

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Introduction

Quality management and applications of information technology have a long tradition in anesthesiology, and especially in intensive care. The key success factor in quality management has been the early acceptance of standard methods for measuring risk and severity of illness (ASA classification, APACHE-II, –III, and SAPS-II scores (1-3)) and the intensity of treatment (4).

Point-of-care information management systems have been successfully used in these areas for more than two decades. Although the penetration rates of these systems are not high, several studies have shown their advantages (5 – 9).

Taking into account the long history of the point-of-care clinical information systems (CIMS), it is surprising how little we know about their advantages in quality management. Here, I will review the problems and possibilities of CIMS as tools for quality management.

Quality of data

Charting

Manual charting has been considered "a golden standard" for the quality of data recorded during the process of intensive care or anesthesia. The methods for automated charting are not sophisticated enough to identify and reject the measurement artifacts. However, manual charting is not free of errors either. Hammond et al. showed that manual charting results in a significant number of errors even if failures to detect short-lasting changes were excluded (10).

The superiority of CIMS in charting become apparent if we reconsider the purpose of the charting process: it is not only to document the patient’s state and response to treatment, but also to document the quality of monitoring. Measurement artifacts should be documented instead of being filtered out. Their detection and exclusion become important issues when data are used for specific purposes afterwards (like severity score calculations; see next chapters).

The CIMS is better than a nurse in detecting short-lasting disturbances in physiological variables. This is obvious during the induction of anesthesia when the required interventions distract the nurse’s attention from monitoring and charting (5). In intensive care settings, the nurse has to take care of several simultaneous tasks , perhaps for more than one patient, which makes it impossible to chart monitoring data accurately enough (Figure 1.).

Figure 1. Automated (red) and manual (black) recording of systemic blood pressure trend in intensive care. In the automated recording, a ten minutes median filtering was used. This included only the changes lasting longer than five minutes.

Severity scores

Both APACHE and SAPS scores are based on the most deviated values of physiological parameters over the first 24 hours of intensive care. There are no definitions about the required duration of these deviations. This results in problems with automatically calculating the scores from the CIMS data.

Blood pressures and heart rate are recorded with a high sampling rate in the CIMS’s database. Should even very short duration disturbances be included (which the nurse may also eventually detect) (Figure 2.), or should we include only longer lasting changes? This question has been answered differently in different CIMS. As a result, the severity scores from different CIMS are not comparable with each other or with manually charted scores (11). When the CIMS data were used for the calculations, the severity scores were higher than when manual charting was used (12, 13). The difference in APACHE-II and SAPS-II were 7.8 % and 11.5 %, respectively (13).

Figure 2. Short lasting disturbances of systemic arterial pressure and heart rate in a CIMS recording in intensive care.

Despite these issues, the CIMS should be used for automated calculation of the severity scores. We have measured the manual abstraction of APACHE-II score takes on average 11 and 17 minutes from the experienced research nurses and from the bedside nurses respectively. If the calculations can be automated without deterioration of the data quality, a significant amount of manpower can be reallocated for more productive tasks.

We have developed a validation workstation (IDExpo software, Intensium Oy) which can be easily interfaced to any CIMS using a SQL database. The workstation retrieves data for a predefined quality dataset in 24 hours periods starting from the admission. It automatically suggests maximum and minimum values for each variable, but allows the user to validate and change them, if necessary. The source data with high sampling rate (blood pressures, heart rate, temperatures etc.) can be displayed with the highest possible resolution.

The software automatically identifies maximum and minimum values when the user has decided the method of filtration (mean vs. median) and the time window (how long the deviation should last before being detected) (Figures 3., 4., 5.). The user can override the automated detection if the filtration fails to avoid an artifact.

The validation with IDExpo makes data comparable regardless of the type of CIMS. The method of filtration should be standardized among the ICUs collecting data to a common database. In the long run, it is obvious that all severity scores should be updated taking into account the durations of the disturbances of the physiological variables.

Figure 3. A display of the validation workstation (IDExpo). Systolic blood pressure trend is displayed with the maximum resolution of the source system (deioCliniSoft, Deio Corp.) and the maximum and minimum values are automatically detected.

Figure 4. Ten minutes median filtering has been applied (blue line) and the most deviated values have been detected automatically.

Figure 5. The maximum and minimum values change according to the selected window width of filtering.

References

You can find the references at the end of this article: "Information management and quality systems – benchmarking and the Internet".

Last updated: 1 January 2002Created
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