Design of a novel multifunction decision support/alerting system for in-patient acute care, ICU and floor (AlertWatch AC)

Abstract

Background Multifunction surveillance alerting systems have been found to be beneficial for the operating room and labor and delivery. This paper describes a similar system developed for in-hospital acute care environments, AlertWatch Acute Care (AWAC). Results A decision support surveillance system has been developed which extracts comprehensive electronic health record (EHR) data including live data from physiologic monitors and ventilators and incorporates them into an integrated organ icon-based patient display. Live data retrieved from the hospitals network are processed by presenting scrolling median values to reduce artifacts. A total of 48 possible alerts are generated covering a broad range of critical patient care concerns. Notification is achieved by paging or texting the appropriated member of the critical care team. Alerts range from simple out of range values to more complex programing of impending Ventilator Associated Events, SOFA, qSOFA, SIRS scores and process of care reminders for the management of glucose and sepsis. As with similar systems developed for the operating room and labor and delivery, there are green, yellow, and red configurable ranges for all parameters. A census view allows surveillance of an entire unit with flashing or text to voice alerting and enables detailed information by windowing into an individual patient view including live physiologic waveforms. The system runs via web interface on desktop as well as mobile devices, with iOS native app available, for ease of communication from any location. The goal is to improve safety and adherence to standard management protocols. Conclusions AWAC is designed to provide a high level surveillance view for multi-bed hospital units with varying acuity from standard floor patients to complex ICU care. Alerts are generated by algorithms running in the background and automatically notify the selected member of the patients care team. Its value has been demonstrated for low acuity patients, further study is required to determine its effectiveness in high acuity patients.

Development and Validation of a Meta-Instrument for the Assessment of Functional Capacity, the Risk of Falls and Pressure Injuries in Adult Hospitalization Units (VALENF Instrument) (Part II)

“…In fact, the VALENF Instrument is currently being implemented in the electronic clinical records of the hospital where it was developed. Due to the reliability results, interventions based on previous studies are being carried out, such as including help text on how to assess and interpret the items in the programming [ 38 ], setting up a team of assistants that will help in the implementation of the VALENF Instrument [ 39 ], carry out specific training [ 40 ] or schedule reassessment alerts [ 41 ]. Future studies should explore the opinion of nurses on the usefulness and applicability of the VALENF Instrument and other similar tools that may begin to be developed.…”

The Internet, Apps, and the Anesthesiologist

“…In addition to being a guide to the general management of a condition, point-of-care apps can also utilize patient-level data to guide decision making. This can vary in complexity from simple apps displaying an algorithm to follow, all the way through to continually transmitting patient-worn devices that provide AI-generated clinical advice [26]. Another anesthetic example is a visual reaction time app that assesses the depth of propofol sedation to alert to impending oversedation [8].…”

Systems Anesthesiology: Integrating Insights From Diverse Disciplines to Improve Perioperative Care

“…There is increasing interest in developing early warning systems to recognize deteriorating patients in general care wards who are becoming critically ill and need a higher level of care. 36 An ML algorithm developed using a retrospective cohort in a UK hospital system could detect 42% of cardiac arrests or unplanned ICU admissions up to 48 hours in advance, 37 while a remote surveillance program monitoring vital sign and laboratory abnormalities in otolaryngology and ophthalmology general care ward patients generated actionable alarms at a rate unlikely to cause alarm fatigue. 38 Another group used an automated warning system for early detection of severe postpartum hemorrhage in an obstetric population.…”