Integration of Acute Stroke Care Process Measures with Healthcare Delivery System Characteristics

Enhances the Understanding of Stroke Team Effectiveness

Loveless M1, Plant T2, Wilson A3,4, Basden L2, Shah S2, Levy D5, Kamauu AWK3 1.  Oregon  Health  &  Sciences  University,  Portland,  OR.  2.  Texas  Health  Resources,  Dallas,  TX.  3.  Anolinx  LLC,  Salt  Lake  City,  Utah.  4.  University  of  Utah,    Salt  Lake  City,  Utah.  5.  Genentech,  Inc.,  South  San  Francisco,  CA.

B A C K G R O U N D ▶  Symptoms of stroke are widely known and the time of onset or the “last time normal” can often be identified by the patient or caregiver.

▶  Tissue plasminogen activator (tPA) is currently the only FDA approved thrombolytic treatment for ischemic stroke, with demonstrated efficacy if administered within 3-4.5 hours of symptom onset.1

▶  Effective therapy of acute ischemic stroke (AIS) is time dependent, and outcomes may be influenced by the organizational capacity of each healthcare delivery system to successfully implement stroke treatment protocols that include time targets.

O B J E C T I V E Analyses of both workflow process and individual stroke care team structure and function will identify critical system characteristics associated with the delivery of thrombolytic therapy according to treatment guidelines in a real-world healthcare system.

R E S U LT S  

▶  Figure 1: Network graphs representing team interdependencies for the four stroke teams.

C O N C L U S I O N ▶  Healthcare delivery systems can access basic data on stroke management and health outcomes; however, these data sources are incomplete with respect to the complex interactions of personnel and processes associated with effective acute stroke care.

▶  A mixed-method model of real-world stroke care teams provides new insights into the key system-level solutions for the rapid delivery of thrombolytic therapy including the key roles of nursing personnel.

▶  While there are interesting trends suggesting associations of the network structures and specific process and medical outcomes, further evaluation of a larger set of sites is ongoing.

M E T H O D S

▶  A mixed method analysis integrating clinical outcomes, workflow process analysis and team personnel structure/function mapping was performed in 11 acute stroke care sites within a single healthcare system.

▶  Individual facility characteristics, variation of treatment delivery, and acute stroke care critical paths were evaluated at each care site.

▶  Data were gathered on stroke care teams using a survey developed to assess interrelationships and dependency within team members and also with non-team personnel involved in acute stroke care.

▶  Formal and informal components of acute stroke care teams were mapped using network analytics and graphics software (Gephi).

▶  The results presented are from these initial four sites involved in the field validation study of the data gathering process and survey tool.

D I S C L O S U R E S This project was funded by Genentech, Inc. D. Levy is an employee of Genentech Inc. A. Kamauu is an owner and A. Wilson is an employee of Anolinx LLC which has received research funding from Genetech Inc, F. Hoffann- La Roche, ltd., and other pharma/biotech companies. M. Loveless is the owner of Alpha~R Systems LLC.

R E F E R E N C E S 1.  Jauch, E. C., Saver, J. L., Adams Jr, H. P., Bruno, A., Connors, J. J., Demaerschalk, B. M., Yonas, H. (2013). on behalf of the American Heart Association Stroke Council, Council on

Cardiovascular Nursing, Council on Peripheral Vascular Disease, and Council on Clinical Cardiology Guidelines for the Early Management of Patients With Acute Ischemic Stroke. A guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 44, 870-947.

2.  The Structure and Dynamics of Networks. Mark Newman, Albert-László Barabási, & Duncan J. Watts. The Princeton Press, 2006.

▶  Figure 2: Median process times within individual stroke teams.

Presented at the 2015 International Stroke Conference; February 11-13, 2015; Nashville, TN   Copies of this poster obtained through Quick Response (QR) Code are for personal use only and may not be reproduced without permission from APA and the authors of this poster.  

R E S U LT S  

Legend: STC – Stroke Team Coordinator Neuro – Neurologist Rdiol – Radiologist RadT – Radiology Tech Pharm – Pharmacist STN – Stroke Trained Nurse ICUN- ICU Nurse Fam – Family Member PCP – Primary Care Provider eMD – ED Physician eFD – ED Front Desk eSN – ED Staff Nurse eCN – ED Charge Nurse P/EMT – Paramed/Emerg Med Tech

CNA – Nursing Assistant MSW – Social Worker LabT – Lab Tech PTrans – Patient Transport Res – Resident/Fellow Chap – Chaplain UDir – Unit Director/Admin Red circles – Members of formal stroke team Blue circles – Non-member personnel involved in acute stroke care Green arrows – Stroke care path

Density: 0.342 Weighted Degree: 43.138 Clustering Coeff: 0.703

Density: 0.363 Weighted Degree: 25.935 Clustering Coeff: 0.538

Density: 0.352 Weighted Degree: 31.579 Clustering Coeff: 0.584 Density: 0.163

Weighted Degree: 14.319 Clustering Coeff: 0.384

▶  The survey tool performed well and was readily accepted and completed by over 90% of target personnel in all four sites.

▶  There was considerable heterogeneity in the organizational and personnel structure of the stroke teams (Figure 1). For example, all had designated STC personnel but there was variable levels of interaction between this role and other stroke care personnel. Only one of the sites did not include pharmacy personnel as a member of the stroke team. One site was without a neurologist or telestroke support; however, demonstrated a tight network structure and a very inclusive set of personnel on the stroke team.

▶  The network interactions in all sites centered around the Emergency Department; as such the network graphs accurately reflected the emphasis of the system-wide stroke care programs (Figure 1).

▶  There was considerable heterogeneity in median process times among the individual stroke teams (Figure 2).

▶  These network graphics clearly reinforce the critical roles and relationships of  nursing personnel within the structure and function of acute stroke teams and their extended networks.

A: approx. 28,000 admissions, 900 beds

B: approx. 36,000 admissions, 700 beds

C: approx. 1,500 admissions, 50 beds

D: approx. 15,700 admissions, 300 beds

A C K N O W L E D G E M E N T S The authors would like to thank Rebekah Paredes and Kaden Coil for their help in the preparation of this poster.

Network Statistics Legend2: Density is defined as the ratio of the number of actual links to the number of possible links.  Networks characterized by high density have each individual (node) linked to nearly all of the others in the network. The small dynamical networks as seen in this study generally have higher density than networks involving large numbers of individuals. The clustering coefficient of a node is the ratio of existing links connecting a node's neighbors to each other to the maximum possible number of such links. When applied to the entire network it is the average of the clustering coefficients of all the nodes. It is a quantitation of the "all-my-friends-know-each-other" property. A high clustering coefficient is typical for small professional networks. The average weighted degree is the number of links connected to each node taking into consideration the "weights" (strength) of each of those links. This is measure of the overall "bonding" of the connections in the network.