Leveraging Technology for Research Frances R Vlasses, PhD, RN, NEA-BC Mary Malliaris, PhD, Ida Androwich,PhD, RN, FAAN Barbara Caspers, MSN, RN Mary Dominiak, PHD, MBA, RN Acknowledgment: AONE Seed Grant, 2006-07 Objectives Describe Technology solutions for multi center research partnerships Explore, using existing datasets, results to date on the relationships between the nurse professional practice environment, nurse manager preparation and selected nursesensitive patient outcomes A Natural Partnership Loyola University Chicago School of Nursing Resources to support research Mutual interest in research questions HSM faculty Catholic university Catholic Health Initiatives Striving for excellence in Evidence-based practice Mutual interest in research question Study variables are issues of concern to organization Catholic healthcare system Purpose This study explored the relationship between Staff Nurse (SN) education level and SN perception of the professional practice environment (SPPPE). Staff Perception of Professional Practice Environment Scale (SPPPE) The SPPPE is a 38 item Likert scale (Iverson et al, 2004) designed to measure the characteristics of the professional nursing practice environment The SPPPE measures 8 characteristics of PPE: autonomy; Clinician-MD Relations; control over practice; communication; teamwork; conflict management; internal work motivation; and cultural sensitivity Range of possible total score on SPPPE is 38152 CHI/Loyola University Chicago Research Collaborative Staff Nurse Perceptions of their Work Environment Williston ND u u Tacoma, Lakewood, Federal Way, Enumclaw WA u Carrington ND u Baudette MN Valley City ND u u Breckenridge MN Denville, Sussex, Boonten Township, Dover NJ u Ontario OR u Lincoln NE u u Durango CO Joplin MO u Cincinnati OH (2 sites) u u u Martin KY Louisville KY uu Lexington KY (2 sites) Berea KY u Hixson TN u Sherwood AR Morrilton AR uu Chattanooga TN u Little Rock AR Procedures General Negotiated partnership between LUC and CHI CHI senior management approval LUC IRB approval Permission to use SPPPE obtained Step by step procedures developed to guide sites Technical Survey Monkey Web-links developed for each site CNO’s completed written demographic survey Sample Recruitment Participating hospitals recruited by researchers via Webinar (3) IRB approved recruitment materials provided to each site LUC researchers worked directly with site coordinator Methods Staff Nurses: Two Options: WEBSURVEY (two week data collection) or PAPER AND PENCIL option SHORT SURVEY: questions about practice environment, organization, demographics CHI REQUEST: 5 questions added on Patient Centered care Chief Nurse Executive: BRIEF SURVEY VIA MAIL Survey Monkey Example 1. INTRODUCTION Welcome to the Job Satisfaction, Work Climate, Unit Effectiveness and Staff Nurse Retention Study. We appreciate your taking the time to complete this survey. The purpose of this study is to examine the factors that influence staff nurse job satisfaction and retention and staff nurses' perceptions of their work environment including the climate and its effectiveness. The survey should take no more than 30 minutes to complete. If you are unable to complete the survey in one setting, you will be able to save your answers and return to the site at a later time to complete the questionnaire. The survey has 3 parts. The first, the Professional Practice Environment Scale, is a 38 item list of items that will ask your opinion about your practice environment. The second consists of 5 questions related to your opinions regarding your ability to practice Patient-Centered Care. The third will ask you questions about the organization your work for, the unit you work on, and about yourself. The study team believes that there is little, if any, risk to participating in the study. All data will be kept confidential. You can not be identified from your survey. Your questionnaire will be given a study code number to be used in the analysis of the data. By participating in the study and completing the two questionnaires, you are indicating that you understand the purpose of the study and give your consent to be in the study. Benefits for Organizations Involvement in nurse run research supports Magnet standards of excellence Be among the first to forward the CHI initiative of building academic/ service partnerships Information from the study will be shared at the end of the study PROPOSED TIMELINE For June, 2008 Data Collection** Decide to join us and Contact Aimee Steadman with MBO name and # sites. DEADLINE 04/09/08 Decide on electronic or pencil and paper format and Contact Aimee Steadman. DEADLINE 04/09/08. Obtain Letter of Agreement from your organization before 04/23/08 Identify onsite study contact person (preferably CNE) for June 2-16 data collection period Loyola team contacts each site ** There is opportunity for additional dates Selected Data Mining Results Dr. Malliaris First Step Data Mining begins with a question • First the question determines appropriate techniques. The technique determines the data form. Question DM Technique The data must be cleaned so that all data the technique sees is “good” data. Data Form Cleaning the Data Cleaning: only rows where all 38 questions had been answered were used Altering: for methods requiring flag data, data responses grouped into two categories: Agree-Strongly Agree & Disagree-Strongly Disagree Techniques We ran several data mining techniques to help us find patterns in the data set. Association Analysis looks for things that occur together Decision Trees, using a specified target, displays the most important variables in reaching the value of the target Support Vector Machine also is used to model the value of a target and ranks the input variables in order of importance Association Analysis • Groups together questions that have similar response patterns • Data must be in Flag format (that is, only two responses) • Results give statements of the form If…Then… along with the likelihood of occurrence Association Analysis Example Decision Tree • This methodology requires that one variable be designated as a “target”; all other variables are used to explain the response in the target variable • Again, we used data divided into two categories of ASA and DSD • The target was statement 9: “manager who is a good manager and leader” How Good Is This Tree? Support Vector Machine Support Vector Machine (SVM): •Robust classification and regression technique •Maximizes the predictive accuracy of a model without over-fitting the training data. •Suited to analyzing data with very large numbers of predictor fields. •Requires a target variable •Generates a list of variable importance How Well Did SVM Do? Comparing Three Techniques Association Analysis •Top Rule: 1, 11, 12, 23 Decision Tree •Most Important Variables: 12, 1, 18 SVM •Most Important Variables: 12, 1, 23 Questions?