Demand for real-time continuous physiological data to generate early warning scores, to alert when a patient’s condition is deteriorating, and to provide quantifiable outcome measures is driving the market for complete monitoring systems in hospital and home care settings globally.
The Sorlandet Hospital in Kristiansand, Norway, having previously implemented a manual Early Warning Scores (EWS) system was looking for a solution to automate the collection of patients’ vital signs data and calculation of the EWS. The Patient Status Engine was chosen and following agreement on integration into the hospital IT infrastructure, Isansys completed the installation within one day.
The continuous monitoring provided by the Patient Status Engine was a stark contrast to the intermittent manual observations which were the normal standard of care. Patients in general wards were having their “obs” taken at long (up to 8 hourly) intervals during which time significant negative changes could occur in their clinical status.
Deployment of the PSE in intermediate and general wards in the Sorlandet Hospital has allowed clinicians and nurses to view continuous vital sign measurements of their patients and real-time calculations of the EWS which were transmitted via the wireless bedside gateway directly to the nursing station. In this way the hospital was able to improve the accuracy and ability of the EWS system and free-up nurses’ time quickly and easily.
Not only does the PSE give patients at the Sorlandet Hospital greater freedom and security but it also provides unprecedentedly complete and accurate information to the doctors and nurses to support their care decisions.
“This is an important project to improve patient safety. Through the use of the PSE across our hospital, I believe we can both reduce mortality and save money.”
Dr Line Pedersen
Sorlandet Hospital Group, Norway
At the Sorlandet Hospital the PSE is being used to enable:
Earlier mobility for patients
unobtrusive wearable sensors allow freedom of movement and avoid confinement to bed
Accurate early warning of adverse events
through automated patient data capture and integrated calculation of Early Warning Scores
Better data quality
wireless sensors reduce motion artifact and overcome data loss due to cable detachment
through better nurse utilisation and moving from reactive to proactive care
Fewer avoidable readmissions
through better knowledge of the trajectory of a patient's status before discharge
More time to care
enabled by automatic, continuous observations and paper-free wards
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