Patient flow data registration: A key barrier to the data-driven and proactive management of an emergency department

Editorial Patient flow data registration: A key barrier to the data-driven and proactive management of an emergency department Emergency department (ED) crowding is a global issue, and one of the most researched operational challenges in healthcare [1-4]. Several adverse outcomes are linked to crowding, including reduced patient satisfaction, increasing patient mortality rates, and rising stress levels among ED staff [2,5]. Besides input-related aspects (e.g., the influx of low-acuity patients), crowding often originates from issues including inadequate staffing or inpatient boarding. Such issues impede smooth patient flows and, hence, contribute to crowding [1,2]. A wide range of studies have been conducted to investigate measures to improve ED operations (e.g., crowding scales, team triage, fast track, escalation protocols, …) [1,2]. It has been suggested that these approaches are predominantly reactive, influenced by a paradigm focused on predicting and controlling crowding-related issues [6]. In the same paper, a paradigm of analysing and managing is proposed. In the view of the authors, this latter paradigm has several advantages as it allows for a proactive approach to management, instead of an approach focused on solving crowding-related issues. Data-driven decision-making A wide range of studies has been conducted to investigate measures to improve ED operations and decision-making [1,2]. While it has been argued that clinical data can be leveraged in this respect [7,8], the same holds for patient flow data which is recorded in the ED's information system. Patient flow data relates to data recordings which enable the reconstruction of the patient's trajectory throughout the ED, ranging from his/her registration, triage and examination, to discharge from the ED or admission into the hospital. As this information is recorded for each patient, it enables an ED to take a bird's eye view of a set of patients flow data instead of focusing on an individual patient. While patient flow data is increasingly being recorded by electronic information systems, paper-based systems are still frequently used in EDs around the world. The potential of electronically recorded patient flow data has been demonstrated in digital tools such as dashboarding and process mining. A dashboard uses patient flow data to provide real-time insights into the current situation of the ED by visualising some key metrics such as the door-to-doctor time or the length-of-stay [9,10]. In this way, a dashboard constitutes a key instrument to proactively manage an ED as it can, for instance, inform ED staff about problems that are likely to occur in the near future. While dashboarding is typically centred around key performance metrics, process mining provides a set of algorithms to discover the end-to-end process of patients from patient flow data. In other words, patient flow data is used to retrieve a process model which visualises the real patient flow at the ED. Process mining algorithms serving a wide range of other goals have been developed, including to automatically check the compliance between the actual patient flow and a normative model (e.g., originating from clinical guidelines) [11,12]. By providing insights into end-to-end processes based on data, process mining results can support evidence-based process improvement and decision-making in EDs. Difficulties in gathering adequate data Despite the enormous potential of data-driven tools and techniques to proactively manage an ED and assist with evidence-based process improvement, many EDs worldwide are unable to make use of this due to issues with patient flow data registration. An ED can, for instance, be confronted with a low information system maturity, in which essential parts of a (or the whole) patient's trajectory are still recorded in paper files. While this makes real-time patient flow analysis almost impossible , it also makes systematic post-hoc analysis (e.g., using a process mining algorithm) complicated due to the efforts required to create a sufficiently large patient flow dataset. This would require the digitisa-tion of the relevant data points from the paper files of a sufficiently large number of patients, which might not be feasible in practice given the high work pressure of an ED. Moreover, paper files tend to focus on clinical data and not on providing information about a patient's tra-jectory with associated timestamps. For instance: files might contain the clinical parameters observed during a clinical examination, but not the exact times at which the examination started and ended. Some steps in the process might not even be recorded at all. While paper-based systems still exist in many EDs, an increasing number of EDs possess state-of-the-art integrated electronic information systems to support all of their operations. However, the fact that data is recorded in an integrated electronic system does not guarantee that patient flow data will be of good quality. This can be attributed to the fact that data registration still depends on a manual act by ED staff. For example: to analyse patient flow processes, it is key that activities are recorded at the moment at which they are executed. In practice, it is often seen that ED staff perform a series of activities on several patients only to record them at a later point in time, potentially even in a random order [13]. Such recording behaviour can be, at least partly, explained by the fact that ED patients often require immediate clinical action and, hence, timely data recording is of secondary importance. Moreover, when the average level of data and process literacy tends to be low in an ED, the potential of the patient flow data might not be recognised. Attention to these topics in medical and nursing education tends to be limited, entailing the risk that ED staff do not appreciate the added value of accurate and timely patient flow data registration.

Publication year:2020

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