Operational Analytics benefits and challenges

How can Operational Analytics help patients?

Operational intelligence can improve the quality of health care by using available data to help determine clinical and operational strategies. For example, operational analysis can be used to track patient bottlenecks in admission so that resources or processes can be adjusted accordingly to reduce wait times.

How can Operational Analytics help healthcare employees?

Employees can use operational analytics software to visualise data on the fly and graph trends in admissions, patient incidents, staff utilisation, and other hospital operations. Predictive analytics using machine learning enables employees to use past data to predict future patterns. This enables employees to make better, more informed decisions.

How can healthcare organisations optimize operational effectiveness?

Operational intelligence helps hospitals and healthcare organisations to streamline operations. For example, analytics can make meetings more efficient by allowing stakeholders to focus on solving problems rather than collecting data. Additionally, analytics can improve capabilities for preventing fraudulent activity, tracking payments, and dealing with improper payments.

How do I get actionable insights in the healthcare industry?

Advanced operational analysis enables you to combine data from multiple sources and use descriptive, predictive, and prescriptive analytics to help your care teams convert data into actionable insights. For example, analytics can help you ensure that you have sufficient staff to enable appropriate patient care while avoiding overstaffing. You can make your insight into hospital operations faster and more complete by using operational analysis services.


Self-service BI and analytical tools enable onsite knowledge workers to manipulate data to suit their needs and visualise it instantly to get a better understanding of past operations. They can graph trends in areas like revenue and expenses over time, number of patients treated month over month, readmission rates, or number of hospital acquired infections by department.

Diagnostic analytics tools enable you to identify meaningful correlations in data from multiple sources. By using the processing power of machine learning in the cloud, you can move from analysing the past to using predictive analytics to see where partial patterns in your data are headed.

Finally, prescriptive analytics go beyond predictive analytics to help you determine actionable operational strategies based on predictive models. By comparing patients with similar demographic characteristics and symptoms, prescriptive models can help to improve treatment results.

Operational Analytics case studies in the health industry

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