Integration of a handheld based anaesthesia rounding system into an anaesthesia information management system.

BACKGROUND: At the University Hospital Giessen, an anesthesia information management system (AIMS) is used for online record keeping of perioperative patient care, but preoperative anaesthesia assessments were still being recorded on paper and subsequently entered into the AIMS. Personal digital assistants (PDAs) seem to be useful instruments to establish a seamless digital anesthesiological documentation. OBJECTIVES: We decided to implement a solution for direct integration of data gathered during the preoperative assessment into the existing data management infrastructure. Parallel to the development of the system, we surveyed the future users to match their wishes and needs as far as possible. SYSTEM DESCRIPTION: A C program embedding the preoperative AIMS' data fields was developed. Data alignment with the Hospital information system (HIS) is controlled by a Java desktop software. The anaesthesiologist completes the available fields at the patient's bedside following the same algorithm and integrity check as the PC version. STATUS REPORT: Overall, 68% of the surveyed physicians supported the implementation of the system. The PDA solution has been available since May 2002. Data replication into the handheld and integration of mobile collected data into the AIMS generally work without problems. The HIS interconnection software converts the PDA file into the AIMS format for further processing. DISCUSSION: The preoperative anaesthetic assessment is a standardised task well suitable for conversion to an electronic data storage medium. Changing from redundant data entry in the OR to direct electronic recording at the patient's bedside seems simply logical. Handheld computers are inexpensive, flexible gadgets to realize this.

Automatic calculation of the nine equivalents of nursing manpower use score (NEMS) using a patient data management system.

OBJECTIVE: The most recent approach to estimate nursing resources consumption has led to the generation of the Nine Equivalents of Nursing Manpower use Score (NEMS). The objective of this prospective study was to establish a completely automatically generated calculation of the NEMS using a patient data management system (PDMS) database and to validate this approach by comparing the results with those of the conventional manual method. DESIGN: Prospective study. SETTING: Operative intensive care unit of a university hospital. PATIENTS: Patients admitted to the ICU between 24 July 2002 and 22 August 2002. Patients under the age of 16 years, and patients undergoing cardiovascular surgery or with burn injuries were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The NEMS of all patients was calculated automatically with a PDMS and manually by a physician in parallel. The results of the two methods were compared using the Bland and Altman approach, the interclass correlation coefficient (ICC), and the kappa-statistic. On 20 consecutive working days, the NEMS was calculated in 204 cases. The Bland Altman analysis did not show significant differences in NEMS scoring between the two methods. The ICC (95% confidence intervals) 0.87 (0.84-0.90) revealed a high inter-rater agreement between the PDMS and the physician. The kappa-statistic showed good results (kappa>0.55) for all NEMS items apart from the item "supplementary ventilatory care". CONCLUSION: This study demonstrates that automatical calculation of the NEMS is possible with high accuracy by means of a PDMS. This may lead to a decrease in consumption of nursing resources.

A data model for managing drug therapy within a patient data management system for intensive care units.

The major intent of this article was to describe the design principles of the drug-therapy documentation module of the Patient Data Management System (PDMS) ICUData, in routine use at the intensive care unit (ICU) of the Department of Anesthesiology and Intensive Care Medicine at the University Hospital of Giessen, Germany, since February 1999. The new drug management system has been in routine use since March 2000. Until 8 January 2001, 1140 patients have been documented using this approach. It could be demonstrated that it was possible to transform the formerly unstructured text-based documentation into a detailed and structured model. The mediated benefit resulted in the automatic calculation of fluid balance. Further, detailed statistical analyses of therapeutic behavior in drug administration are now possible.

Automatic calculation of a modified APACHE II score using a patient data management system (PDMS).

OBJECTIVE: To investigate a fully automated and modified APACHE II score calculation exclusively based on routine data supplied by patient data management system, the ICUData, and to assess the predictive performance of this score using analysis of discrimination and calibration at an operative ICU. METHOD: SQL scripts (calculation programs) were developed to calculate the scores of 524 patients who stayed at the ICU between April 1st, 1999 and March 31st, 2000. The calculation programs considered unavailable data as 'not pathological'. The main outcome measure was survival status at ICU discharge. The discriminative power on mortality of this modified APACHE II score was checked with a receiver operating characteristic (ROC) curve. Calibration was tested using the Hosmer-Lemeshow goodness-of-fit test. RESULTS: The 459 survivors had an average APACHE score of 17.8+/-5.3. The score of the 65 deceased patients averaged 22.7+/-4.6. The area under the ROC curve of 0.790 was significantly >0.5 (P<0.01) and had a 95% confidence interval (CI) of 0.712-0.825. The goodness-of-fit test showed a good calibration (H=4.89, P=0.70, dof 7, C=6.96, P=0.541, dof 8). CONCLUSION: A prediction model based on completely automatically calculated 'modified APACHE II scores' can be constructed using data collected with PDMS. However, due to differences in the patient collective and methods used, the results need validation and can only be partially compared to results from other studies.

Discriminative power on mortality of a modified Sequential Organ Failure Assessment score for complete automatic computation in an operative intensive care unit.

OBJECTIVE: To evaluate the discriminative power on mortality of a modified Sequential Organ Failure Assessment (SOFA) score and derived measures (maximum SOFA, total maximum SOFA, and delta SOFA) for complete automatic computation in an operative intensive care unit (ICU). DESIGN: Retrospective study. SETTING: Operative ICU of the Department of Anesthesiology and Intensive Care Medicine. PATIENTS: Patients admitted to the ICU from April 1, 1999, to March 31, 2000 (n = 524). Data from patients under the age of 18 yrs and patients who stayed <24 hrs were excluded. In the case of patient readmittance, only data from the patient's last stay was included in the study. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The main outcome measure was survival status at ICU discharge. Based on Structured Query Language (SQL) scripts, a modified SOFA score for all patients who stayed in the ICU in 1 yr was calculated for each day in the ICU. Only routine data were used, which were supplied by the patient data management system. Score evaluation was modified in registering unavailable data as being not pathologic and in using a surrogate of the Glasgow Coma Scale. During the first 24 hrs, 459 survivors had an average SOFA score of 4.5 +/- 2.1, whereas the 65 deceased patients averaged 7.6 +/- 2.9 points. The area under the receiver operating characteristic (ROC) curve was 0.799 and significantly >0.5 (p <.01). A confidence interval (CI) of 95% covers the area (0.739-0.858). The maximum SOFA presented an area under the ROC of 0.922 (CI: 0.879-0.966), the total maximum SOFA of 0.921 (CI: 0.882-0.960), and the delta SOFA of 0.828 (CI: 0.763-0.893). CONCLUSION: Despite a number of differences between completely automated data sampling of SOFA score values and manual evaluation, the technique used in this study seems to be suitable for prognosis of the mortality rate during a patient's stay at an operative ICU.