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Hospital Data Analytics

Data is a precious commodity - but are you using its full potential?

Data is a precious commodity - this insight has become so prevalent in recent years that it almost seems proverbial to us. But what applies to all raw materials, is also true here: To exploit their value, it is crucial to process them specifically. Only when they are prepared along the needs of their users, data becomes relevant information. And only through a professional interpretation of information it becomes superior knowledge.


About Data Analytics in clinical applications

Analytics tools can leverage the power of data in clinical applications to increase efficiency and drive health outcomes in hospitals. Data analytics provides valuable insights for adjusting processes and improving clinical results aligned with the quadruple aim (improving clinical outcomes, managing costs of care, enhancing patient experience, ensuring staff satisfaction).

Utilizing Analytics to reduce the overwhelming amount of alarms at the point-of-care

"Alarm Fatigue", the decreasing perception of alarms due to their enormous number, is one of the biggest threats in the hospital for patient safety. Research has shown that on average, up to 350 alarms a day can occur at an intensive care bed. Of these alarms, up to 95% are clinically irrelevant and the remaining clinical alarms are only noticed properly by a rate of 50%.1 Missing alarms of medical devices is listed in the Top 10 Health Technology Hazards of 2019.

The stress for patients and caregivers is negatively affecting therapy outcomes, important clinical work-flows are unnecessarily interrupted, and an alarm desensitization of the nursing staff can be observed. 

Data can create transparency by providing an analysis of all the alarms that have occurred in a hospital. On this basis, systematic process flows, alarm settings and personnel planning can be optimized to increase the alarm management efficiency and ultimately reduce the stress for clinical staff and patients.

Identify measures to reduce alarms in Critcal Care!

The Alarm History Analytics dashboard provides insights on alarms that have occurred in a critical care environment from supported Infinity patient monitors. Analyze alarms in the unit to optimize systematic process flows and staff planning. Increase alarm management efficiency and aim to enable reduction of alarms.

  • Optimization of the departmental staff allocation for managing alarms
  • Review the impact of measures on reduction of alarm fatigue
  • Ensuring SOP compliance with regards to the alarm reaction times

Alarm History Analytics: Enabling to reduce alarms at the Point-of-Care

How Alarm History Analytics can support your workflows

Watch the video to discover why Alarm History Analytics is an important tool to optimize your hospital alarm management.  


Alarm History Analytics Dashboard in Dräger Connect

Click on the link below to view the Alarm History Analytics dashboard in full screen size. Discover which application windows are available to analyze the alarms from Infinity patient monitors in your department.

View dashboard


Case Study Avera Heart Hospital, USA

With a data-driven approach, Dräger helped the Avera Heart Hospital to reduce alarm rates by more than 30% while continuing to keep patients safe.

Learn more


Data security and privacy

Over 75% of clinicians expect that securely transmitted health information and data can improve the quality of care that is being delivered.10

Our Data Analytics solutions are designed with security and data privacy in mind. Find more detailed information on the security concept in the following infographic. 

Security and Data Privacy Concept

Alarm History Analytics

Alarm History Analytics

​The Alarm History Analytics application provides insights on alarms from connected Infinity patient monitors in a critical care environment. The application enables both the analysis of alarms in a care unit to optimize systematic process flows and staff planning as well as improving alarm management efficiency. By identifying the sources of ...

Product details

Gas Consumption Page

Gas Consumption Page

​Reduce the consumption of volatile anesthetics with Gas Consumption Analytics. The application helps you to derive clinical and economical insights from the agent consumption of your anesthesia devices and creates transparency on consumption uptake, efficiency, cost, and applied fresh gas flows.

Product details

Contact us to learn more about our Hospital Data Analytics solutions

Types of Data Analytics applications

“Data Analytics is the aggregation, analysis, and use of clinical, financial, operational, and nontraditional data captured inside and out of the healthcare setting to directly inform decision-making.”11


Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question "What happened?", characterized by traditional business intelligence (BI) and visualizations such as pie charts, bar charts, line graphs, tables or generated narrative.


Diagnostic Analytics is a form of advanced analytics to answer the question "Why did it happen?", and is characterized by techniques such as drill-down, data discovery, data mining and correlations.


Predictive Analytics is a form of advanced analytics to answer the question "What is likely to happen?", and is characterized by techniques such as regression analysis, forecasting, multivariate statistics, pattern matching, predictive modeling, and forecasting.


Prescriptive Analytics is a form of advanced analytics to answer the question "What should be done?", and is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.


1. Jones, K. (2014). Alarm fatigue a top patient safety hazard. Canadian Medical Association Journal, 186(3), 178

2. ECRI Institute (2018). 2019 Top 10 Health Technology Hazards: Executive Brief

3. Canet et al. (2010). Prediction of postoperative pulmonary complications in a population-based surgical cohort. Anesthesiology, 113(6), 1338-1350

4. Branson et al. (1999). Humidification for Patients with Artificial Airways. Respiratory Care, 44(6), 630-642

5. Bilgi et al. (2011). Comparison of the effects of low-flow and high-flow inhalational anaesthesia with nitrous oxide and desflurane on mucociliary activity and pulmonary function tests. Eur. J. Anesthesiol., 28(4), 279-283.

6. Branson et al. (1998). Anaesthesia circuits, humidity output, and mucociliary structure and function. Anesthesia Intensive Care, 26(2), 178-183.

7. Kilgour et al. (2004). Mucociliary function deteriorates in the clinical range of inspired air temperature and humidity. Intensive Care Med., 30(7), 1491-1494

8. Hönemann C. & Mierke B. (2015). Low-Flow, Minimal-Flow und Metabolic-Flow Anaesthesia. Lübeck: Drägerwerk AG & Co. KGaA.

9. Yasny, J. S., & White, J. (2012). Environmental implications of anesthetic gases. Anesthesia progress, 59(4), 154–158.

10. Bain & Company (2018). Front line of healthcare report 2018.

11. HiMSS (2019). HiMSS20 Conference Education Topics.

Get in Touch With Your Specialist

Contact us Hospital

Draeger, Inc. – Medical

3135 Quarry Road
Telford, PA 18969