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Background: The Acute Pain Service Information Management System (APSIMS), as we coined, is the utilization of a portable computer to register the data of the patients who need acute pain management during anesthesiologist's ward round. Initially, the data of the daily acute pain assessment at the ward are recorded on a sheet of paper by the rounding anesthesiologist, which are subsequently entered into the hospital main frame computer by an anesthetic nurse. In order to save manpower in data entry, we planned to introduce the personal digital assistant (PDA) into acute pain assessment. The anesthesiologist can record a patient's data directly into the PDA device at the bedside. After acute pain assessment is finished, we can directly up load the data from the PDA to the hospital mainframe computer without the need of further manpower for doing data input. This study was to evaluate the use of PDA for acute pain assessment and compare the PDA-based method with that of the current paper-transcription method in work efficiency., Methods: Two computer applications were developed: the APS Mobile Assistant and the Data Transformation Wizard (DTW). The APS Mobile Assistant is a PDA application running on a portable computer with Windows Mobile 2003 operation system. The anesthesiologist can use this application to perform APS assessment at the bedside. The Data Transformation Wizard is a PC application which can transfer data from the PDA device to the hospital mainframe computer, by which the data in the PDA system can be integrated into the hospital information system. The evaluation included the reckoning of the timings of two periods i.e. the time spent by the physician to perform acute pain assessment at the bedside and the time required for data management by the nurse. To compare the paper-transcription method with the PDA-based technique, the Student's t test was performed to assess the data of time of each category collected. A P value less than 0.05 was considered to be significant., Results: When the time required for assessment of acute pain was determined, no statistically significant difference was observed between the use of the paper-transcription-based system and the PDA system (P = 0.258). In comparison the PDA system was clearly shown to facilitate faster management of data (Paper-transcription method: 1.57 +/- 0.08 min per patient compared with PDA-based method: 0.24 +/- 0.01 min per patient, P < 0.0001)., Conclusions: Implementation of PDA device during APS assessment can provide the anesthesiologists with more time to acquire information during APS visits. Using the PDA technology in clinical settings can increase work efficiency. We can save manpower and are convinced that data collection is more complete with the use of a PDA system.

Personal digital assistants (PDA) are increasingly being used in many medical fields. The object of this study is to introduce how the hospital computerization and the utilization of PDA can help to reduce the time of data processing for Acute Pain Service. After this study completed, we found that the use of PDA can dramatically reduce the time of data management and improve the quality of medical records.

Abstract Background Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment. Methods The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction. Results The prediction accuracies of total analgesic consumption (continuous dose and PCA dose) and PCA analgesic requirement (PCA dose only) by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naïve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works. Conclusion This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA demands over time. We analyzed PCA patient data and conducted several experiments to evaluate the potential of applying machine-learning algorithms to assist anesthesiologists in PCA administration. Results demonstrate the feasibility of the proposed ensemble approach to postoperative pain management.