Abstract
INTRODUCTION: Databases of physiological data from intensive care have facilitated the development of predictive models to improve processes and outcomes (1). Several physiological data recording systems have been implemented previously. However, their generalisability is limited since they require either in-house software development or have infrastructure requirements that rapidly increase with each additional monitored bed added to the system (2, 3). As part of a study to assess the feasibility of continuously monitoring patients throughout their post-operative hospital stay, we implemented a commercially available data recording system which overcomes these limitations. We report our experiences here.
METHODS: BedMasterEx software (version 4.1.12, Excel Medical Electronics) was used to record both waveform and numerical data between November 2012 and January 2014. The system recorded data from bedside monitors (MP70, Philips Medical Systems) on five geographically disparate critical care units. Each unit had a “central station” (Intellivue Information Centre, Philips) with Web Service functionality enabled, an essential pre-requisite. The monitors have the facility to “admit” (enter demographics) and “discharge” patients (erase demographics) from the monitor. BedMasterEx was configured to start recording when a patient was admitted to a monitor and cease on discharge. Researchers verified system functionality daily. Times which patients spent in each bed were determined from multiple hospital information systems. Recorded data was reviewed manually to ensure that transfer times had been correctly identified. This avoided incorrectly attributing data from patients subsequently or previously staying in a particular bed to a study patient. Numeric parameters that are monitored continuously (e.g. heart rate) were recorded once per second. The proportion of time for which data was recorded was calculated as the proportion of seconds in which a physiologically-plausible numeric was recorded.
RESULTS: 222 patients undergoing cardiac surgery were monitored. In total 24,839 patient hours were spent in monitored beds (critical care beds connected to BedMasterEx). The median time spent by each patient in monitored beds was 55 hours (IQR = 46 - 96). The median proportion of time in monitored beds for which data was recorded was 94% (IQR 88 - 97). All but two patients were transferred between monitored beds at least once during their stay (median transfers = 1, max = 8). Data losses were caused by delays in admitting or discharging patients from the monitors. These tasks may often be deferred in favour of clinical work. Additional data losses were caused by time-stamping errors due to network latency. Most time-stamps errors were corrected using software developed in-house. Some recordings required manual review to avoid attributing data to the wrong patient.
CONCLUSIONS: The system facilitated data recording for a high proportion of each patient’s critical care stay. Unlike previous systems the hardware requirements do not rapidly scale according to the number of monitored beds, nor does it require in-house development of complex acquisition software. However, system maintenance and fault diagnosis required significant technical expertise, and data had to be manually reviewed. Further work will investigate solutions to these limitations to facilitate fully automated recording of patient physiology at scale.
| Original language | English |
|---|---|
| Title of host publication | Journal of the Intensive Care Society |
| Subtitle of host publication | Clinical Practice, Research and Sepsis poster Presentations |
| Volume | 16 |
| DOIs | |
| Publication status | Published - 2015 |