Imagine you are working as a nurse at the intensive care department in a hospital. Statistics show you will hear 150 to 200 hospital alarms a day, of which approximately 90% is false. This creates a lot of unnecessary and avoidable amount of stress. Moreover, there might be a risk that legit hospital alarms would not stand out among the false hospital alarms. This also leads to stressful situations for patients and their families. Hospital alarms can also disrupt the development of premature babies. Although it is hard to imagine modern health care without these hospital alarms, they might be destructive in the situations as described above. Existing hospital alarms no longer provide sufficient safety.

One of the challenges is the amount of data in a electronic health record (EHR). Traditional models choose a limited number of variables, resulting in – either negative or positive – false hospital alarms. An effective alarm merely goes off when a serious complication evolves, a doctor recognizes the alarm as symptomatic for the said issue and the required expertise to address the issue at hand exists.

The Máxima Medisch Centrum and the Technical University in Eindhoven, the Netherland, are cooperating to tackle these problems. Algorithms can help ringing more useful hospital alarms. Data of sensors of someone’s breathing and heartbeat can be combined, which results in an alarm that rings 20 seconds earlier when detecting an irregularity of the breathing and the heartbeat. Through Machine Learning, relations and patterns can be recognized which would not be recognized by humans. For example, mathematical models are able to detect potentially dangerous movements of the premature babies in their incubators by linking these movements to previous situations. This type of Artificial Intelligence is able to learn “baselines” and consolidates real-time trends to process alarms. As a result, currently disintegrated hospital alarms could transform into a more undivided, patient-centered analytic monitoring model. In this modern approach, the core transfers from individual hospital alarms to an integrated set of data from all patient-related equipment.

Some of these solutions can already be implemented, while others would take years before they become beneficial. This can be caused by strict regulations for medical equipment. However, Machine Learning could really help to prevent the negative consequences of a hospitalization.

Sources:

Rajkomar, A., Oren, E., Chen, K., Dai, A., Hajaj, N., Hardt, M., Liu, P., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G., Irvine, J., Le, Q., Litsch, K., Mossin, A., Tansuwan, J., Wang, D., Wexler, J., Wilson, J., Ludwig, D., Volchenboum, S., Chou, K., Pearson, M., Madabushi, S., Shah, N., Butte, A., Howell, M., Cui, C., Corrado, G. and Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1(1).

Chopra, V. and McMahon, L. (2014). Redesigning Hospital Alarms for Patient Safety. JAMA, 311(12), p.1199.

Nederlandse Omroep Stichting. Slimme algoritmes moeten ‘alarmstress’ in ziekenhuis voorkomen. Derived from:
https://nos.nl/artikel/2298745-slimme-algoritmes-moeten-alarmstress-in-ziekenhuis-voorkomen.html