Acknowledgments

We would like to thank Lake Erie College of Osteopathic Medicine (LECOM/LECOMT) for funding this research.

Bobbie Brotherson John Flaherty Elizabeth Gannon Jestin Carlson Melody Milliron*

Department of Emergency Medicine, Allegheny Health Network, Saint

Vincent Hospital, Erie, PA, USA

?Corresponding author at: Department of Emergency Medicine, Allegheny Health Network, Saint Vincent Hospital, 232 West 25th St,

Erie, PA 16544, USA.

E-mail address: [email protected] (M. Milliron).

25 January 2018

https://doi.org/10.1016/j.ajem.2018.04.011

References

1. Pruden CM, Kerrey BT, Mittiga M, Del Rey JG. Procedural readiness of pediatric interns: defining novice performance through simulation. J Grad Med Educ Dec 2010;2(4): 513-7.
2. Srivastava G, Roddy M, Langsam D, Agrawal D. An educational video improves tech- nique in performance of pediatric lumbar punctures. Pediatr Emerg Care Jan 2012; 28(1):12-6.
3. Nigrovic LE, Kuppermann N, Neuman MI. Risk factors for traumatic or unsuccessful lumbar punctures in children. Ann Emerg Med Jun 2007;49(6):762-71.
4. Bruccoleri RE, Chen L. Needle-entry angle for lumbar puncture in children as deter- mined by using ultrasonography. Pediatrics Apr 2011;127(4):e921-926.
5. Gleason CA, Martin RJ, Anderson JV, Carlo WA, Sanniti KJ, Fanaroff AA. Optimal posi- tion for a spinal tap in preterm infants. Pediatrics Jan 1983;71(1):31-5.
6. Weisman LE, Merenstein GB, Steenbarger JR. The effect of lumbar puncture position in sick neonates. Am J Dis Child Nov 1983;137(11):1077-9.
7. Hanson AL, Ros S, Soprano J. Analysis of infant lumbar puncture success rates: sitting flexed versus lateral flexed positions. Pediatr Emerg Care May 2014;30(5):311-4.
8. Molina A, Fons J. Factors associated with lumbar puncture success. Pediatrics Aug 2006;118(2):842-4 [author reply 844].
9. Kessler DO, Auerbach M, Pusic M, Tunik MG, Foltin JC. A randomized trial of simula- tion-based deliberate practice for infant lumbar puncture skills. Simul Healthc Aug 2011;6(4):197-203.
10. Pedscases.com and Stollery Children’s Hospital. Pediatric clinical skills: an approach to lumbar puncture. [Accessed September 1, 2016] https://youtu.be/ eEyl5g4kUm4.
11. Mellick L. Maximizing success for infant spinal taps. [Accessed September 1, 2016] https://youtu.be/jE1ZKNbwSIg.
12. Simulab Corporation. Lumbar puncture baby. [Accessed September 1, 2016] https:// youtu.be/aywaz9yzx84.

    Effectiveness of interventions to improve the efficiency of emergency department operations: An evidence map

    Numerous published studies have reported interventions attempting to improve Emergency Department (ED) efficiency, but this body of literature is limited in its real-world applicability. Studies commonly report narrowly-focused interventions, are variable in how results are

    

    Fig. 1. Evidence map: Improvement in length of stay (relative to baseline), by intervention type and resource use.

    measured, and rarely report cost data. However, decision-makers need to answer at least two simple questions that are not often answered well by the current literature: How well did this intervention work, and what did it take (e.g. resources, time) to implement it?

    An evidence map [1] can help take stock of a highly variable evi- dence base, illustrate gaps, and guide research planning in ways that will make future studies more useful for decision-makers. This approach presents results in a graphical format to quickly identify gaps and re- search needs.

    Therefore, we sought to broadly describe a range of ED efficiency im- provement studies using evidence mapping. A systematic literature scan identified studies that tested the effect of a real-world ED interven- tion on at least one utilization measure, such as length of stay (LOS), waiting-room time (WT), or Left without being seen rate (LWBS). Title/abstract review, data extraction, and full-text review were per- formed by two independent reviewers. Studies of Simulation models or of interventions limited to specific clinical conditions were excluded. For each study, utilization, resource requirements (added new re- sources, used existing resources only, or unclear), and quality (e.g. pa- tient clinical outcomes) data were abstracted, when available. Evidence maps were constructed to illustrate intervention types, re- source use, data reporting, and effect size.

    From 139 titles, 97 studies were included. Details of the full search strategy are available in the full report [2]. Seventeen intervention types were identified, with physician triage as the most common (n = 32), followed by expansion of nursing Scope of practice (SOP) (n =

    23), fast track (n = 12), and point of care testing (n = 6). Studies origi- nated from the United States (41%), Australia (19%), Canada (11%), the United Kingdom (9%), and 13 other nations (19%). Seventy three percent were academic medical centers, 93% were single-site interventions, and 37% had N10,000 patients.

    Reporting of utilization metrics varied widely (LOS 69%, WT 38%, LWBS 35%). With respect to resource requirements, 45% of studies added new resources, 19% reallocated existing resources, and 36% were unclear. Twenty percent of studies reported actual implementa- tion costs. With respect to quality, 13% reported patient clinical out- comes, 13% reported unplannED revisit rates, and 8% reported incidence of clinical harms.

    Only 3 of 97 studies (3%) reported utilization data, resource require- ments, input costs, and quality outcomes. All three were single-site stud- ies, and all required the addition of new resources [3-5]. As an example, an Australian study of emergency nurse practitioners in an academic setting [3] added 3 FTE NPs, and yielded a 3% improvement in LOS (207 vs 213 min, p b 0.001), a 36% improvement in WT (38 vs 60 min, p b 0.001), and a 44% reduction in LWBS (8.1% vs 4.5%, p b 0.0001). Of 5248 patients seen by NPs, there were two missed diagnoses (appendicitis and a non-displaced hand fracture) but no subsequent adverse outcomes. When possible, we plotted results by intervention type and resource requirements. Effects on LOS (n = 67 studies) were typically an improvement of 5% to 20% relative to baseline (Fig. 1). Fast track and nurse SOP interventions had the highest number of studies with

    improvements N30%.

    

    Fig. 2. Evidence map: Improvement in wait time (relative to baseline), by intervention type and resource use.

    

    Fig. 3. Evidence map: Improvement in Left without being seen rate (relative to baseline), by intervention type and resource use.

    Effects on waiting-room time (n = 37 studies) were typically an im- provement of 10% to 50% relative to baseline (Fig. 2). Physician triage and nurse SOP had the highest number of studies with improvements N60%.

    Effects on LWBS (n = 34 studies) were typically an improvement of 0 to 5 absolute percentage points. When compared to baseline rate, these represented relative improvements ranging from 0% to 65% (Fig. 3).

    These evidence maps illustrate several gaps in the evidence base for interventions improving ED efficiency. First, very few studies reported utilization, cost, and quality of care outcomes together. Two- thirds of studies reported data for LOS, with less than half reporting data for WT or LWBS. Only a small fraction reported on Patient harms or Medical errors. This limits the ability to apply the findings of an improvement study when authors do not provide a full accounting of an intervention’s effects [6]. Second, only a minority of studies quantified the resources required to implement an intervention. One- third were unclear as to whether additional resources were required. As ED leaders and decision-makers are often faced with resource constraints, more accurate reporting of resource requirements is imperative. Future research should emphasize consistent reporting of utilization, resource requirements, cost and quality impact data, and how to achieve efficiency improvements without investing new resources. Filling these gaps will make ED efficiency studies more useful to decision-makers.

    Conflicts of interest

    No investigators have any affiliations or financial involvement (e.g. employment, consultancies, honoraria, stock ownership or options, ex- pert testimony, grants or patents received or pending, or royalties) that conflict with material presented in the report.

    Financial support

    This report is based on research conducted by the Evidence-based Synthesis Program (ESP) Center located at the West Los Angeles VA Medical Center, Los Angeles, CA, funded by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Develop- ment, Quality Enhancement Research Initiative. The findings and con- clusions in this document are those of the author(s) who are responsible for its contents; the findings and conclusions do not neces- sarily represent the views of the Department of Veterans Affairs or the United States government. Therefore, no statement in this article should be construed as an official position of the Department of Veterans Affairs.

    Meetings

    This work was presented at the “Toward a VA Emergency Medicine Research Agenda: Setting Priorities to Improve the Health of Veterans

    Seeking Emergency Care Conference” in Nashville, TN on February 9, 2017.

    Acknowledgements

    This topic was developed in response to a nomination by Dr. Michael Ward on behalf of Dr. Chad Kessler, MD, National Director of the VA Emergency Medicine Field Advisory Committee. The scope was further developed with input from the topic nominators, the ESP coordinating center, the review team, and the technical expert panel (TEP). The tech- nical expert panel (TEP) for the project included: Chad S. Kessler, MD, National Program Director, VA Emergency; Michael Ward, MD, Depart- ment of Emergency Medicine, Vanderbilt University Medical Center, VA; Kristina Cordasco, MD, Core Investigator, VA Greater Los Angeles Center for the Study of Healthcare Innovation, Policy and Practice. Dr. O’Neill was supported by the VA Office of Academic Affiliations through the Robert Wood Johnson Foundation Clinical Scholars Program and the UCLA Gerald R. Levey Surgical Resident Research Award. The contents do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.

    Sean M. O’Neill, MD PhD Isomi Miake-Lye, PhD*

    Evidence-based Synthesis Program (ESP) Center, 11301 Wilshire Blvd. (111G), West Los Angeles, CA 90073, United States

    ?Corresponding author at: 11301 Wilshire Blvd, Building 206, Los

    Angeles, CA 90073, United States.

    E-mail address: [email protected] (S.M. O’Neill).

    Christopher P. Childers, MD Evidence-based Synthesis Program (ESP) Center, 11301 Wilshire Blvd. (111G), West Los Angeles, CA 90073, United States

    Division of General Surgery, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA 90095, United States

    Selene Mak, MPH Jessica M. Beroes, BS

    Evidence-based Synthesis Program (ESP) Center, 11301 Wilshire Blvd. (111G), West Los Angeles, CA 90073, United States

    Melinda Maggard-Gibbons, MD, MSHS Evidence-based Synthesis Program (ESP) Center, 11301 Wilshire Blvd. (111G), West Los Angeles, CA 90073, United States

    Division of General Surgery, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA 90095, United States

    Paul G. Shekelle, MD, PhD Evidence-based Synthesis Program (ESP) Center, 11301 Wilshire Blvd. (111G), West Los Angeles, CA 90073, United States

    18 January 2018

    https://doi.org/10.1016/j.ajem.2018.04.012

    References

    [1] Miake-Lye IM, Hempel S, Shanman R, Shekelle PG. What is an evidence map? A sys- tematic review of published evidence maps and their definitions, methods, and prod-

    ucts. Syst Rev Feb 10 2016;5:28.	Total ED patient volume	-	-	118,239
    [2] Miake-Lye IM, O’Neill S, Childers C, Gibbons M, Mak S, Shanman R, et al. Effectiveness	Total ED inpatient boarders	-	18.6%	21,942
    of interventions to improve emergency department efficiency: an evidence map.	Total inpatient boarders with exam	29,384	61.5%	13,497
    Washington DC: Department of Veterans Affairs (US); Sep 2017.	Orders after boarder start time	3422	12%	2284
    [3] Fry M, Jones K. The clinical initiative nurse: extending the role of the emergency	Exam type: CT	20,841	50%	10,971
    nurse, who benefits? Australas Emerg Nurs J 2005;8(1):9-12.	Exam type: MRI	4241	9.0%	1964
    [4] Ieraci S, Digiusto E, Sonntag P, Dann L, Fox D. Streaming by case complexity: evalua-	Exam type: ultrasound	3487	14.6%	3197

    [5] Inokuchi R, Sato H, Iwagami M, Komaru Y, Iwai S, Gunshin M, et al. Impact of a new medical record system for emergency departments designed to accelerate clinical documentation: a crossover study. Medicine Jul 2015;94(26):e856.
    [6] Doupe M, Chateau D, Derksen S, Sarkar J, de Faria RL, Strome T, et al. Factors affecting emergency department waiting room times in Winnipeg. Manitoba Centre for Health Policy; 2017.

    Quantifying the operational impact of boarding inpatients on emergency department radiology services

    Emergency Department (ED) crowding continues to be a pervasive national problem, with myriad and intransigent negative effects on care efficiency, quality, and cost [1-11]. Furthermore, this demand- capacity mismatch is often due to hospital capacity constraints, manifested as prolonged patient ED length-of-stay while awaiting inpatient admission [12,13]. Termed “ED inPatient boarding“, patients often spend many hours awaiting an inpatient bed, and fre- quently undergo additional testing and treatment during that time [14]. As an important example, radiology testing is a frequently utilized process, highly subject to capacity constraint, and with significant ef- fects on ED Patient throughput [15-19]. Given a large subset of admitted inpatients receive ED imaging, we sought to quantify radiology utiliza- tion in boarding inpatients in our ED.

    This investigation was a retrospective record review of 12 months of radiology data (8/1/2016-8/1/2017) from a large academic ED with an annual census of approximately 112,000 visits, approved by the Institu- tional Review Board. Patient flow in the ED follows a relatively standard course of diagnosis and disposition, with an inpatient admission rate of approximately 25%. If an admitted patient remains in the ED 2 hours after their bed request, they are defined as a boarding inpatient by the Massachusetts Department of Public Health. Radiology studies are ordered at any point during the patient visit, and virtually all (N98%) studies ordered in the ED are performed using ED radiology imaging and interpretation resources.

    The primary outcomes included the total volume and rate of radiol- ogy examinations ordered for boarding inpatients in the ED; secondary outcomes included study distribution by patient status and exam type. Data were extracted from the RIS (Radiology Information System, Boston, MA) and ED EMR (EPIC, Verona, WI) on September 1st, 2017. Data elements included patient level demographics and disposition, process timestamps, boarder status, and image type. No data were ex- cluded from the analysis, conducted using Microsoft Excel.

    A total of 29,384 radiological exams were ordered on boarding inpatients during the study period. Of 21,942 boarding inpatients, 13,497 (61.5%) had at least one exam ordered. Of these, 3422 exams (12%) were ordered after the patient became a boarding inpatient. Regarding exam type, 20,841 exams (71%) were CTs, 4241 (14%) were

    MRIs, 3487 (12%) were ultrasounds, and 333 (1%) were plain films

    (See Table 1).

    In this single center investigation, a large volume of ED radiology exams were ordered on boarding inpatients awaiting admission. While it has been known for years that the practice of inpatient boarding

    Table 1

    Results

    Metric Exam volume Rate Patient volume

    tion of a model for emergency department fast track. Emerg Med Australas Jun 2008; 20(3):241-9.

    Exam type: plain films 482 2% 436