Patient Journey Analytics using Process Intelligence

The first enterprise-ready process intelligence platform to deliver advanced process insights for the healthcare industry is helping healthcare providers better understand the patient experience. 

Read more – ABBYY Timeline Launches the First Enterprise-Ready Process Intelligence Platform to Deliver Advanced Process Insights for the Healthcare Industry

ABBYY Timeline is in the business of giving healthcare leaders real-time visibility into how processes are actually working for the healthcare system, confirming whether they are being executed as prescribed, and, most importantly, predicting potential critical risk factors in processes.

Our healthcare clients tell us they are dealing with critical process evaluation issues such as:

  • Avoiding human error when evaluating processes performance
  • Rapidly identifying and correcting process defects
  • Identifying best practices that can be shared across departments and facilities
  • Drilling down into process instances and identify root cause of issues
  • Optimizing asset utilization in key areas such as the ED and OR
  • Predicting and planning for future process failures
  • Positively impacting Lengths of Stay
  • Identifying additional improvements that impact the Revenue Cycle

Using our revolutionary Process Intelligence Platform, Healthcare providers can finally create an accurate digital reconstruction of how processes are performing against planned performance. This is now made possible by being able to combine and simultaneously analyze data from multiple existing systems.

  • Can you track each patient interaction with your healthcare professionals?
  • Are your patient journeys clearly defined and mapped?
  • Do analytics reveal lost revenue due to improper patient routing or record keeping?

Many healthcare organizations are challenged to answer these questions. They have no method to see patient journeys or to explore how their processes are executed within one treatment silo, much less across the entire organization.

Simple Procedures for Actionable Insights

ABBYY Timeline reveals a complete picture of all patient interactions with your healthcare organization – across systems – in sequence – over time – using three data fields from one or more of your systems:

  • A common patient ID – most likely a “patient identification number”.
  • A description of the interaction or event between the patient and your healthcare organization, i.e. medical code.
  • A time stamp of the interaction or event.

ABBYY Timeline accepts data from any source: your legacy programs and data bases, all EHR’s, including EPIC, Cerner, Meditech, and McKesson, and all other systems, such as LIMS, RIS, PACS and RCM.

Patient Journeys Explained

Using this data, ABBYY Timeline recreates the patient journey as an interactive model – sometimes this is referred to as the Process Digital Twin – for each individual patient. This represents their interaction and journey through their healthcare experience as it is. ABBYY Timeline then organizes all the patient journeys into groups representing similar paths. As you look these paths, you will distinguish patterns of patient experiences– in a comprehensive way – maybe for the first time. The standard, expected paths will be there, along with unexpected, surprising outlier paths.

By adding patient demographics, diagnoses, and payment histories, additional patterns will emerge to guide policy and process development. Overlaying your organizational data such as treatment location, equipment and staff members involved in patient interactions will provide other insights for management follow-up.

Learn More

Watch this video to see just how easy it is to get started with TimelinePI.

Using data from a real life Emergency Room, this demo shows the benefits of understanding the variety of patient journeys.

So…if you are running Epic, Cerner, Infor, and a variety of other critical systems, ABBYY Timeline would like to show you what you and your team are missing.

To learn more, visit our healthcare page at

November 26, 2018