[3/1/13 Update: This blog was one of three chosen by HIMSS to highlight in the mobile and clinical & business intelligence portion of the #HIMSS13 Blog Carnival. I am honored!]
EHRs increasingly mediate patient care effectiveness, resource efficiency, and user happiness. EHR process mining is a new medical “imaging” technique, one that allows process diagnosticians to view workflow blockages, errant processes, and unused resources. Process mining promises to do for healthcare workflow what Röntgen’s invention of X-rays and radiography in 1895 did for medicine proper.
Today, EHR process mining can discover, monitor and improve evidence-based processes (not assumed processes) by extracting knowledge from event logs available in (or “generatable” from) today’s EHRs. Process mining can answer three types of questions about a hospital or clinic: What is happening inside processes (Discovery)? It can compare what is happening with what should be happening (Conformance: especially relevant to medical error and patient safety). It can suggest ways to improve healthcare process effectiveness, efficiency, and user and patient satisfaction (Enhancement).
Of particular note to anyone interested in applying process mining techniques in healthcare is the Process Mining Manifesto. It is not specifically about healthcare, though it does mention “paper-based medical records” as examples of poor event logs. The Manifesto is authoritative (co-authored by more than 75 process mining experts), timely (recent and relevant to problems facing EHR adoption), and accessible to health IT and process improvement professionals.
The credited “godfather” of process mining is Professor Wil van der Aalst, a Dutch computer scientist and mathematician. Professor van der Aalst notes that healthcare is notorious for dismayingly complex “spaghetti” processes. Nonetheless, process mining research can learn a lot from tackling the healthcare domain. On one hand there is great opportunity to learn from intuitively creative medical experts. On the other hand spaghetti processes often are the greatest process improvement opportunities.
Process Mining and Clinical & Business Intelligence
The process mining of EHR event log data is a form of clinical & business intelligence. What van der Aalst notes generally about business intelligence also applies to clinical & business intelligence:
“Business intelligence tools tend to be data-centric while providing only reporting and dashboard functionality.”
This describes many clinical & business intelligence tools.
“They can be used to monitor and analyze basic performance indicators (flow time, costs, utilization).”
These are the KPIs, or Key Performance Indicators, in clinical & business intelligence reports and dashboards.
“However, they do not allow users to look into the end-to-end process.”
If you cannot look “into the end-to-end process” you cannot, in an evidence-based way, determine what is wrong—and therefore what is to be done—for ineffective, inefficient workflows and their unhappy users.
“Moreover, despite the “I” in BI, most of the mainstream BI tools do not provide any intelligent analysis functionality.”
Again, most current clinical & business intelligence tools are reports or dashboards. Without access to detailed evidence-based representations of end-to-end processes, clinical & business intelligence reporting and dashboard systems can flag process problems, but cannot diagnose and solve them.
EHR Event Logs
An EHR event log is a record of named activities (“Check Medications”, “Patient Examination”) created as a byproduct of EHR use. Encounter, or case, ids, tie together collections of events. Events occur in an order relative to each other, usually represented by time stamps. Intervals between time stamps can be years, in long-running chronic conditions; hours or minutes, in patient encounters; or seconds or less between user clicks on a single EHR screen.
The first three columns in the following EHR event log extract—CaseID, Activity, and TimeStamp—are required for process mining to create a process map, or model, from event data. The column of “…”s to the right represents additional data not shown: UserRole (a user such as Dr. Smith or Dr. Jones, or Physician vs. Nurse), EncounterType (such as Sick vs. Well Checkup vs. Vaccination), and Facility (such as Facility 5, 7 or 9, see upcoming illustrations).
Table 1: Portion of An EHR Event Log
|| "Current Meds",
Optional additional columns, over and above case id, activity name, and time stamp, depend on what you want to compare, explain, understand, or predict about your processes. Do you want to understand processes of a poorly performing clinic or hospital relative to a better performing clinic or hospital? You need a facility column. Do you want to do the same for users? You need a user column. Or do you want to understand workflows for sick visits compared to well checkups? Add a column for that. This additional information allows you to filter an event log and ask different questions about logged processes.
The bottom row of “…”s represents the many other rows, with different CaseIDs for separate process instances, usually required to generate useful process models. Healthcare processes generate a lot of time-stamped data that can result in large event logs. Process mining will be required to understand and leverage this “Big Data.”
Evidence-Based Process Maps
Below is a relatively unannotated (for example, no frequency or performance statistics) set of process models, or process maps, generated by ProM, a free and open source process mining tool. Even a simple example, with only five or six possible EHR activities, begins to looks like the aforementioned pile of spaghetti.
The process model can be simplified using event log filtering techniques and by asking specific questions to narrow investigations. The next illustration shows process-mined process maps comparing the most common workflow from three similar medical practices.
Suppose you know some Key Performance Indicators (KPIs) for these facilities, such as patient throughput and cycle time, cost per encounter or encounter type, or perhaps even measures of user or patient satisfaction. Process mining can generate process models that you can compare to explain differences between KPIs. Traditional clinical & business intelligence report and dashboard software may tell you what the KPIs are and help benchmark them. However, to understand the likely causes of flagged KPIs, you need evidence-based process models such as process mining provides.
Summary and Conclusion
Process mining of event log data from electronic health records promises new methods to systematically improve EHR-mediated patient care processes and workflow usability. Process mining is part of a larger front of process-aware business process management (BPM) technology diffusing into the healthcare information technology industry.
Process mining can discover evidence-based process models, or maps, from time-stamped user and patient behavior; detect deviations from intended process models relevant to minimizing medical error and maximizing patient safety; and suggest ways to enhance healthcare process effectiveness, efficiency, and user and patient satisfaction.
There's a great fit between traditional clinical & business intelligence KPIs, dashboards and process mining. Process mining provides an “X-ray” of workflows that can explain clinical & business intelligence KPIs. KPI dashboards alert users to systematic problems, while process mining shows subsystem tasks and workflows driving them. Combined, clinical & business process intelligence addresses central issues of healthcare reform: identification of best practices, coordination of care among clinical staff, consistency across patient care processes, and efficient use of healthcare resources.
For more information about process mining, the best place to start is the Process Mining Manifesto. It even mentions medical records.
P.S. This blog post was submitted to the #HIMSS13 Blog Carnival.