Nearly every individual has a frustrating healthcare story.
They might have had challenges scheduling appointments in a timely manner. Or the medication they needed was unavailable when and where they needed it. Perhaps their test results got lost between their primary care physician and the specialist they referred them to.
These are frustrating experiences, and far too often, the root cause is suboptimal processes within healthcare organizations.
I spent over a decade in healthcare advisory roles before joining Celonis, helping providers and payors transition to value-based care. So I know that an objective, data-driven approach to process optimization doesn’t come naturally to an industry that’s uniquely focused on the very human, very subjective goal of patient care.
Further complicating the process, different teams and functions within healthcare organizations – like clinical teams, pharmacy and procurement – speak their own languages, have their own ways of working and set their own goals and performance measures, which aren’t always aligned. For example, the nursing team in a hospital might be focused on improving quality care metrics, while the inventory management team could be tasked with overseeing optimal levels of hospital supply. To deliver high-quality patient care, these teams and functions need to collaborate effectively, but even if the humans work well together, groups often have their own systems that don’t necessarily communicate with one another.
The result is a lack of visibility into complex healthcare processes, both within and across different functions. This “great disconnect” prevents operations from running smoothly and stops healthcare providers from doing the one thing they most want to do – provide care that delivers optimal patient outcomes.
Addressing the great disconnect, and making healthcare processes work better, is vital to ensure patients (as well as clinicians and support staff) have the best possible experiences. It’s also necessary for long-term sustainability of healthcare operations. Current pressures on the healthcare industry, like a shift to personalized care, volatility in supply chains, changing regulatory requirements, labor shortages and the need to do more with less, present additional challenges. But they also open up opportunities for providers that can solve the disconnect.
Process Intelligence gives healthcare providers a way to do just that, but before we look at how it helps, let’s first take a closer look at the processes requiring optimization.
The patient journey – the experience an individual has when interacting with the healthcare system – is the primary process within the healthcare industry. It has almost endless variations and can include hundreds of steps and activities like appointment scheduling, hospital admissions and discharge, diagnostics and treatment, which all involve different functions. The process for a patient who arrives at an emergency room, for instance, can be dramatically different from one who visits their doctor’s office for a routine checkup.
Whatever process path this core patient journey takes, it’s sure to overlap with other secondary processes that support healthcare operations. It’s likely to intersect with scheduling processes when appointments are arranged, with supply chain or pharmacy processes when medical supplies or medication are needed and finance processes when billing is required. All these supporting functions have their own processes and are trying to achieve core business goals, such as generating revenue or minimizing costs, at the same time as delivering optimal patient care.
As with any other industry, new processes within a healthcare organization are usually mapped out in collaboration with the process expert to determine what the optimal path looks like. But in reality, this ideal state is rarely the path these complex processes take. Instead they follow countless variations.
Process Intelligence enables healthcare providers to objectively understand how processes actually run, identify improvement opportunities and act upon them. Using industry-leading object-centric process mining (OCPM), the Celonis Process Intelligence Graph (PI Graph) ingests data from all the different systems a healthcare provider uses and creates a process digital twin (an end-to-end, system-agnostic digital representation of how processes run). This digital twin is combined with the unique business context within which the organization operates, including KPI definitions, improvement opportunities and what makes something “good” or “bad” for the organization. The PI Graph illuminates the root causes of process deviations and identifies potential bottlenecks, revealing where value is hiding and provides intelligent recommendations around opportunities to unlock that value.
The Celonis Process Intelligence platform shows the ways lab, medication and placement interact with the patient journey to provide a holistic view of how clinical care is actually delivered.
In some cases, these recommendations involve making relatively small changes to existing processes to achieve significant results and rapid value. This was the situation with an NHS Trust in the UK that used Process Intelligence to improve its use of resources and cut waiting lists.
University Hospitals Coventry and Warwickshire (UHCW) is an NHS Trust that forms part of England’s publicly-funded healthcare system. The Trust saw the potential of a process-first approach due to the sheer volume of processes within its hospitals. It began exploring high-impact use cases in outpatient care, before moving on to in-theater care.
Process Intelligence was used to visualize how the Trust communicates with outpatients and highlighted that this was directly impacting the number of Did Not Attend (DNA) appointments. The Trust was sending reminder text messages to patients four days and one day before their appointments. The result was a lot of last-minute cancellations, without time to reallocate the appointment to another patient on the waiting list.
By changing the process so that messages were sent 14 days and four days before an appointment, patients were able to either cancel earlier or rearrange their schedule so they could attend. And the Trust had more time to offer canceled appointments to other patients, making more efficient use of its resources.
The results of this small change were impressive. The initial trial saw the DNA rate fall from 10% to 4% for eligible cohorts in just four weeks – reducing the DNA appointments from a historical number of 1,800 each week, to between 900-950 and contributing to the Trust being able to see about 700 additional patients each week. This ultimately resulted in a week-on-week reduction in patient waiting lists (from 73,000 patients down to 67,000 patients, or a reduction of 5,000 patients, over an eight to 12 week period) – a rare phenomenon in the overstretched NHS. The Trust is now expanding its knowledge to other NHS Trusts to see where further efficiencies can be gained.
In addition to making recommendations for process improvements, the PI Graph gives teams the ability to take action and drive change, fast. This might mean triggering automations, not just in functions such as finance where automated invoice processing has become commonplace, but to streamline manual, repetitive tasks on the clinical side too.
The Celonis Process Intelligence platform reveals opportunities within the lab process that affect patient journey performance.
Sending automated alerts is a great example. When a long lab acceptance time is detected, an alert can be sent to the hospital laboratory automatically, ensuring samples are processed in a timely manner and speeding up time to discharge for the patient. By detecting an undesired set of activities within a clinical process in this way and enabling an automated response, Process Intelligence allows healthcare providers and support staff to focus on activities that drive the greatest value for the organization and its patients.
Process Intelligence also plays a critical role in ensuring that healthcare organizations generate value from their business operations and tech investments, especially in artificial intelligence (AI). In fact, as we like to say at Celonis, there is no enterprise AI without Process Intelligence. As I mentioned above, the Celonis PI Graph provides the unique business context in which an organization operates. This context, the connections that give raw data meaning, is necessary to enable effective enterprise AI. The PI Graph feeds process insights into an organization’s existing AI toolset. Celonis also uses this context to power its own AI offering of copilots, AI apps and custom AI solutions.
