PubMed · 16334017
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Beckie Kelly Schuerenberg. 2005. PDA software keeps plugging along.. https://pubmed.ncbi.nlm.nih.gov/16334017/
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Hospitalised patients, who suffer cardiac arrest and require unanticipated intensive care unit (ICU) admission or die, often exhibit premonitory abnormalities in vital signs. Sometimes, the deterioration is well documented, though there is little discernable evidence of intervention. In other cases, monitoring and recording of vital signs is infrequent or incomplete. Healthcare providers have introduced "track and trigger" systems to allow early identification of patients with physiological abnormalities, and rapid response teams to facilitate rapid and appropriate management. However, even when "track and trigger" systems are used, the recording of vital signs, patient chart completion and team activation remain sub-optimal. We have developed a system for collecting routine vital signs data at the bedside using standard personal digital assistants (PDA). The PDAs act as "thin clients" linked by a wireless local area network (W-LAN) to the hospital's intranet system, where raw and derived data are integrated with other patient information, e.g., name, hospital number, laboratory results. It is possible for raw physiology data, early warning scores (EWS), vital signs charts and oxygen therapy records to be made instantaneously available to any member of the hospital healthcare team via the W-LAN or hospital intranet. Early and direct contact with members of the patient's primary clinical team or rapid response team can be made through an automated alerting system, triggered by the EWS data. The ability to capture physiological data at the bedside, and to make these available to anyone with appropriate access rights at any time and in any place, should provide previously unattainable, clinical and administrative benefits. Analysis of the raw physiological data and patient outcomes will also make it possible to validate existing and future "track and trigger" systems.
To assist in the early detection of critical illness, many hospitals now use a "track and trigger" system that allocates points to routine vital signs measurements on the basis of their derangement from an arbitrarily agreed "normal" range. These points are summed to provide an early warning score (EWS). Little is known about the accuracy with which EWS are calculated and charted. We compared the speed and accuracy of charting the weighted value attributed to each vital sign, and of calculating the EWS, using the traditional pen and paper method with that using a specially programmed, personal digital assistant (VitalPAC). Incorrect entries or omissions occurred in 24 (29%) of 84 EWS computed using pen/paper compared to 8 (10%) computed using the VitalPAC method. Fewer incorrect clinical actions were indicated using EWS derived via the VitalPAC method (4/84, 5%) than from those calculated using pen/paper (12/84, 14%). The mean time (+/-S.D.) taken for participants to calculate and chart a set of weighted values and EWS using the pen/paper method was 67.6+/-35.3 s (n=84). The corresponding time taken to enter a set of physiological data using the VitalPAC was 43.0+/-23.5 s (n=84). By comparison with the conventional pen/paper method, the use of VitalPAC was on average 1.6-times faster. The use of a device such as VitalPAC offers significant advantages both in speed and accuracy of recording of EWS.
BACKGROUND: Personal digital assistants (PDAs) allow healthcare professionals to check for potential drug-drug interactions (DDIs) at the point of care, reducing the need to consult traditional references. However, PDAs can only be as effective as the software programs they use. OBJECTIVE: To examine the ability of DDI software programs manufactured for Palm OS-compatible PDAs in detecting clinically important DDIs. METHODS: Eight PDA software programs were assessed for sensitivity, specificity, and positive and negative predictive values for 16 well-documented DDIs contained within 6 simulated patient profiles. RESULTS: Sensitivity of the software programs ranged from 0.81 to 1.0, specificity ranged from 0.52 to 1.0, positive predictive values ranged from 0.62 to 1.0, and negative predictive values ranged from 0.88 to 1.0. Five programs scored perfect sensitivity scores: DrugIx, ePocrates Rx, ePocrates Rx Pro, Lexi-Interact, and the Tarascon pocket Pharmacopoeia. Of these, the ePocrates programs scored the highest in specificity (0.9), while Lexi-Interact and the Tarascon pocket Pharmacopoeia scored considerably lower (0.52). MosbyIx was the only program to score a 1.0 in specificity; however, its sensitivity was just 0.81. CONCLUSIONS: ePocrates Rx and ePocrates Rx Pro scored greater than or equal to 90% in regard to both sensitivity and specificity, making them the most reliable in detecting the clinically relevant interactions studied without the distraction of detecting those of no clinical significance. In addition, ePocrates Rx is updated regularly and is easily accessible on the Internet at no cost.