Optimizing Emergency Department Workflow Using ...

Proceedings of the 2017 Design of Medical Devices Conference DMD2017 April 10-13, 2017, Minneapolis, Minnesota, USA

DMD2017-3402

and especially in ED has been well recognized to save costs and improve care delivery [5]. However, the large upfront infrastructure costs, need for an integrated health information technology (HIT), advanced analytical tools for big data analysis emerging from RFID and skilled data scientists to tackle the data to derive actionable intelligence discourage many hospitals from adoption RFID technology despite its potential advantages. Our recent pilot study on the RFID data analytics demonstrated the feasibility of quantifying and analyzing two novel variables such as ‘patient alone’ time defined as the total time a patient spends alone without interaction with a health care staff in the ED and ‘provider time’ defined as the total time a patient spends interacting with any health care staff [6]. The study motivated a more comprehensive big data analytics of RFID data which can provide better insights into optimizing ED workflow which can improve the quality of care in the ED and also reduce cost. In this work, the authors attempt to describe the RFID adoption in the ED at the Saint Mary’s Hospital at Mayo Clinic, in Rochester, MN, a level one trauma center both for children and adults as a step towards optimizing ED workflow.

Optimizing Emergency Department Workflow using Radio Frequency Identification Device (RFID) Data Analytics Shivaram Poigai Arunachalam1 Mustafa Sir2 Gomathi Marisamy3 Annie Sadosty4 David Nestler4 Thomas Hellmich4 Kalyan S. Pasupathy2 Department of Radiology1, Mayo Clinic, Rochester, MN Department of Health Sciences Research2, Mayo Clinic, Rochester, MN Department of Information Technology3, Mayo Clinic, Rochester, MN Department of Emergency Medicine4, Mayo Clinic, Rochester, MN

2 Methods Radio Frequency Identification Device (RFID) in Mayo Clinic ED: Formation of ED-CELL

1 Background

Mayo Clinic’s St. Mary’s Hospital ED in Rochester, MN has 75 beds on 1.25 acres providing high quality critical care to the patients. Mayo Clinic’s ED and Clinical Engineering Learning Laboratory (CELL) collectively called as the EDCELL was formed that integrates systems engineers, care teams, ED staff and patients to advance knowledge and transform patient care. This novel integrated team works on a common mission to leverage advanced and proven ways to collect and analyze various data sources to improve the ED experience for both patients and staff. Mayo Clinic adopted RFID into ED for improving care delivery processes and quality of care. Recently more than 750 RFID sensors has been installed throughout the ED in Mayo Clinic, Rochester campus which enables real-time monitoring and tracking of physicians, nurses, allied health staff, patients, medical devices, which are all tagged using passive RFID tags. Figure 1 shows photographs of passive RFID tags on a physician staff embedded within their Mayo Clinic ID, and a wrist band passive RFID tag given to a patient upon arrival at the registration desk in ED.

Emergency Department (ED) is a complex care delivery environment in a hospital that provides time sensitive urgent and lifesaving care [1]. Emergency medicine is an unscheduled practice and therefore providers experience extreme fluctuations in their workload. ED crowding is a major concern that affects the efficacy of the ED workflow, which often is challenged by long wait times, overuse of observation units, patients either leaving without being seen by a provider and non-availability of inpatient beds to accommodate patients after diagnosis [2]. Evaluating ED workflow is a challenging task due to its chaotic nature, with some success using time-motion studies and novel capacity management tools are nowadays becoming common in ED to address workflow related issues [3]. Several studies reveal that Electronic Medical Record (EMR) adoption has not resulted in significant ED workflow improvements nor reduced the cost of ED operations. Since raw EMR data does not offer operational and clinical decision making insights, advanced EMR data analytics are often sought to derive actionable intelligence from EMR data that can provide insights to improve ED workflow. Improving ED workflow has been an important topic of research because of its great potential to optimize the urgent care needed for the patients and at the same time save time and cost.

A

Radio Frequency Identification Device (RFID) is a wireless automatic identification and data capture technology device that has the potential for improving safety, preventing errors, saving costs, and increasing security and therefore improving overall organizational performance. RFID technology use in healthcare has opened a new space in healthcare informatics research that provides novel data to identify workflow process pitfalls and provide new directions [4]. The potential advantages of RFID adoption in healthcare

B

Figure 1: RFID passive tag on (A) a staff; (B) a patient

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Figure 2 shows RFID passive tags on various equipment’s used in the ED. The big data collected from RFID are automatically stored in a designated database, which can be retrieved for offline analysis. Real-time tracking and display system provides visual monitoring capability that staff can quickly access to locate or identify the needed resources. The raw RFID data provides time and location information of the person or object being tracked providing better insights into inefficiencies in the ED workflow and thereby improves quality of care.

RFID sensors (Fig. 3) are throughout the ED including the observation units to track objects of interest in real-time. The location-time mapping (Fig. 4) of two different patients and their interaction with a physician and nurse in real-time provides great insights into the workflow for urgent care. Such data indeed provides a novel perspective into the “total patient alone” time and “total provider time” which is believed to have a significant relationship with the ED length of stay (LOS) and quality of care delivered to the patients. For example, patient ‘1’ and ‘2’ spent different alone and staff interaction times, which could be analyzed in their care perspectives that can help to optimize the care delivery in ED.

4 Interpretation Mayo Clinic employs custom real-time dashboard that gives real-time feedback on patient flow in ED etc. which can be used to provide additional intelligent tracking based on this new information. Example, an alarm signal can be sent to the staff if a patient spends longer alone time than a desired threshold. Advanced analytics into what factors contribute and influence these variables are underway to obtain a better perspective into ED workflow issues that can be resolved and optimized. Best RFID-enabled healthcare architecture, with an overall centralized health information system that will also align well with the business needs of the hospital will be required. A major step in this direction is to identify key technical and business challenges of integrating RFID technology within the entire healthcare value chain. However, significant challenges will exist to design and develop advanced healthcare analytics framework and integrate with hospital IT systems to derive actionable intelligence from EMR and RFID big data to optimize ED workflow.

Figure 2: RFID passive tag on different equipment’s in ED

3 Results Figure 3 shows an example of the RFID reader coverage map, representing the areas covered by each RFID reader. Figure 4 shows an example of a location-time chart of patients and staff mapped by the several RFID readers in Figure 3.

References [1] King, D. L., Ben-Tovim, D. I., & Bassham, J. (2006). Redesigning emergency department patient flows: application of Lean Thinking to health care. Emergency Medicine Australasia, 18(4), 391-397. [2] Huang, YC., & Chu, CP. (2011). RFID Applications in hospitals A case study for emergency department. Journal of Communication and Computer, 8(7), 1-8. [3] McCaughey, D., Erwin, C. O., & Delli Fraine, J. L. (2015). Improving Capacity Management in the Emergency Department: A Review of the Literature, 2000-2012. Journal of Healthcare Management, 60(1). [4] Wamba, S. F., Anand, A., & Carter, L. (2013). A literature review of RFID-enabled healthcare applications and issues. International Journal of Information Management, 33(5), 875891. [5] Rousek, J. B., Pasupathy, K., Gannon, D., & Hallbeck, S. (2014). Asset management in healthcare: Evaluation of RFID. IIE Transactions on Healthcare Systems Engineering, 4(3), 144-155. [6] Arunachalam, S.P., Marisamy, G., Sir, M., Nestler, D., Hellmich, T., and Pasupathy, K. (2016). Linking Patient Alone Time and Provider Time to Staffing Levels and LOS at the Emergency Department: A RFID Based Study. In Healthcare Informatics (ICHI), 2016 IEEE International Conference on (pp. 102-105). IEEE.

Figure 3: RFID reader coverage map

Figure 4: Example of a location-time chart of patients and staff

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