Using Social Network Analysis as a Tool to Evaluate Medication Management in Ambulatory Care Clare Tolliver, Dr. Andrea Kjos (mentor) Drake University College of Pharmacy and Health Sciences Social Network Graphs & Data:Introduction: Objective:  The goal of this project is to describe existing systems of medication management in ambulatory care using network analyses methods.  Further, identify how network patterns may produce clues to how they link to patient outcome characteristics and patient safety.  Results from the proposed study will determine the utility of a quantitative social network analysis of organizational or ‘structural’ dynamics for evaluation of medication management in ambulatory care.  Pilot Study: first time social network analysis methods will be used to study this type of setting. Methods:  A longitudinal, roster survey will be used for collecting data in order to conduct network analysis.  Roster survey instruments are a method used to collect data in social network analysis when the research objective is to track who communicates within a given complete network.  For this study, medication management will be defined as any task, communication, or other exchange that links a minimum of two persons in the network regarding a patient’s medication therapy.  Data will be collected by extracting information from electronic medical records of a group of patients.  The network analysis will be described in terms of nodes (individual staff, providers and patients) and ties (the number of communications between them).  Network analysis will focus on the interconnectedness (density) and the prominence (centrality) of nodes in each network as is consistent with examining public health systems. Results in Progress:  Currently, three patients have returned consent forms to participate in the study.  Data is being collected from their electronic medical records.  Consent forms have been sent to twenty-five patients, and they will be added to data collection as their forms are signed and received.  Once data is collected, it will be used to create social network graphs to analyze the communication patterns. Lessons Learned from a Pilot Study:  Support from clinic collaborators and staff is important.  The dynamics of a real-world practice setting, such as a clinic environment, may cause changes in aspects during the course of study planning and data collection.  The researcher extracting data is purposefully an objective observer to remove potential for bias.  The researcher abstracting data faces a “learning curve” to understand the work environment and idiosyncrasies of electronic medical records.  The nature of public health research is often conducted in an uncontrolled environment.  Therefore, subjects become moving targets that are out of your control.  Subjects may have reasons to not want to enroll, move away or become lost to follow up. Strategies for the Future:  Difficulties arose with time spent contacting patients to participate; in the future, start earlier with the process.  Improving consent form intake process; if subjects can be contacted in person, the time spent waiting for returned forms would be decreased.  To account for “learning curve” for navigating medical records, there is a need for increased education for researchers abstracting data from the electronic system. References:  Valente T. Social Networks and Health. Oxford: Oxford University Press, Inc.;  Chang A, Schyve PM, Croteau RJ, O'Leary DS, Loeb JM. The JCAHO patient safety event taxonomy: A standardized terminology and classification schema for near misses and adverse events. International Journal for Quality in Health Care. 2005;17(2):  Knoke D, Young S. Social Network Analysis, 2nd Edition: Series for Quantitative Application in the Social Sciences. 2nd ed. SAGE Publications, Inc;  Creswick N, Westbrook JI, Braithwaite J. Understanding communication networks in the emergency department. BMC Health Services Research. 2009;9: Acknowledgements: A special thanks to the project collaborators at Penn Avenue Internal Medicine and Dr. Ginelle Schmidt. This project was funded in part by the Drake Faculty Research Awards Program.  Graphically represents patterns within a social network  Each dot represents one person in the network (node) and lines represent connections between them.  Connections can be measured, based on the variable of interest: Pilot Project Setting: Ambulatory Care Clinic in eastern Des Moines, IA  Penn Avenue Internal Medicine  22 staff members including physicians, nurses, pharmacist, and clerical.  Social networks are created from person-to- person interactions and represent the relationship between these persons.  These networks form in health care settings between physicians, nurses, patients, and other staff.  Studying the communication patterns of these clinical networks can identify the root of patient safety issues, in this case medication management.  Density - the number of people interacting for each medication management task, will provide information about network structure and cohesion  Centrality - measured in terms of in- degree and out-degree centrality scores, who within the network are doing the most communication initiation or reception, will be used to determine relative prominence of each person within the network  Path length - a network property used to calculate the distance between two persons or “nodes” in a network. Average path length can also be described as ‘betweenness centrality.’ Ongoing Considerations:  Central nodes most likely will be patient care providers.  However, it remains unknown how vital other staff are in the medication communication process.  How far are providers from each other in terms of path length?  This may vary from patient to patient and in terms of seriousness of medication related outcome/patient safety communication.