As identified in the initial article in this series (Mastal, Matlock, & Start, 2016), and in the AAACN Industry Report (AAACN, 2016), nurse-sensitive quality measurement in ambulatory care systems is less developed than in acute care settings.
Care Nursing and the Collaborative Alliance
Diane Storer Brown Harriet Udin AronowPerspectives in Ambulatory Care
Ambulatory Care Nurse-Sensitive Indicators Series: Reaching for the Tipping Point in Measuring Nurse-Sensitive Quality in the Ambulatory Surgical and Procedure Environments
Executive Summary ► The value of the am bulatory care nurse remains
undocumented from a quality and patient safety m easurem ent perspective and the practice is at risk of being highly variable and of unknown quality.
The Am erican Academ y of Ambulatory Care Nursing and the Collaborative Alliance for Nursing Outcomes propose nurse leaders create a tipping point to meas ure the value of nursing across the continuum of nursing care, moving from inpatient to ambulatory care.
As care continues to shift into the ambulatory care environment, the quality imperative must also shift to assure highly reliable, safe, and effective health care.
A tipping point is reached when a large number of group members change behavior rapidly by widely adopting a previously rare prac tice. Malcolm Gladwell’s The Tipping Point (2000) is a best selling book that helps us under stand our nursing leadership role to introduce the concept of meas-
Diane S torer Brown uring nurse-sensitive quality indicators m ambulatory care to large numbers of people and to connect our quality and informa tion specialists with stakeholders to share the possibilities. After achieving a tipping point of inter est, as the persuaders we must translate the tipping point into a starting point for action, to help encourage adoption of this prac tice. Two organizations, Ameri can Academy of Ambulatory
Harriet Udin Aronow Care Nursing (AAACN) and the Collaborative Alliance for Nur
sing Outcomes (CALNOC), are collaboratively leading
an endeavor to reach a tipping point for ambulatory care nursing quality measurement.
As identified in the initial article in this series (Mastal, Matlock, & Start, 2016), and in the AAACN Industry Report (AAACN, 2016), nurse-sensitive quality measurement in ambulatory care systems is less developed than in acute care settings. There are no standardized measure sets that allow organiza tions to benchmark performance across settings or to understand the impact of nurse staffing on processes of care and patient outcomes. Nor are there well- understood and standardized definitions for the myr iad ambulatory settings for care. As a result, the value of the ambulatory care nurse remains undocumented from a quality and patient safety measurement per spective and the practice is at risk of being highly variable and of unknown quality.
The Quality Imperative Fundamental to high reliability in health care is
that every patient receives the right care, every single time – ensuring patient care safety, quality, effective ness, and efficiency (Agency for Healthcare Research & Quality, 2008). Lack of reliability in patient care processes contributes not only to medical errors, but also to inconsistent quality, suboptimal outcomes, and system inefficiencies. While a wide range of improvement methods are currently embraced in health care, all require comparative data to under stand performance and comparative data require stan dardized measurement and the ability to benchmark performance with internal and external comparisons (Brown, Donaldson, Burnes Bolton, & Aydin, 2010).
High reliability has been a health care topic for many years, but the predominant focus has been aimed
DIANE STORER BROWN, PhD, RN, FNAHQ, FAAN, is Executive Director, Medicare Strategy and Operations, Kaiser Permanente – Northern California Region, Oakland, CA; and Founding Member and Senior Scientist, Collaborative Alliance for Nursing Outcomes.
HARRIET UDIN ARONOW, PhD, is Research Scientist in Nursing, Cedars-Sinai Medical Center, Los Angeles, CA; and Director, Data Management Services, Collaborative Alliance for Nursing Outcomes.
NOTE: This column is written by members of the American Academy of Ambulatory Care Nursing (AAACN) and edited by Kitty Shulman, MSN, RN-BC. For more information about the organiza tion, contact: AAACN, East Holly Avenue/Box 56, Pitman, NJ 08071-0056; (856) 256-2300; (800) AMB-NURS; FAX (856) 589- 7463; Email: aaacn@ajj.com; Website: http://AAACN.org
NURSING ECO NO M IC$/M ay-June 2016/Vol. 34/No. 3
mailto:aaacn@ajj.com
http://AAACN.org
at acute care in hospital settings. Approximately 1.5 million registered nurses (RNs) work in acute hospi tals, while roughly one-third as many work in ambu latory care (Bureau of Labor Statistics 2014). That, still, is almost one-half million RNs supporting a rap idly growing ambulatory care industry. Nurses prac ticing in ambulatory care deserve to have the data they need to evaluate and improve their services. Two recent publications by national reputable authorities build the case that now is the time to strive for a tip ping point in ambulatory care nurse-sensitive meas urement. The National Patient Safety Foundation (2015) assessed the state of the patient safety field 15 years after the Institute of Medicine report To Err is Human was published. The report highlights that much of the work to date addresses hospital care, yet, today most care is provided outside hospitals. The report concludes patient safety concerns are still a serious public health issue. The report’s new recom mendations for achieving total system safety include the need to address safety across the entire care con tinuum, in addition to the creation of a common set of safety metrics that reflect meaningful outcomes and support the health care workforce.
Concurrently, an assessment of nursing advance ment in the past 5 years was published as a follow up to the 2010 report, The Future o f Nursing (National Academies of Sciences, Engineering and Medicine, 2015). This report recommended an emphasis for the next 5 years on ensuring nurses are prepared to fill the need for quality health care in a rapidly changing health care delivery system, since nurses are the largest group of health care professionals and posi tioned to lead and partner in teams that provide serv ices across the continuum.
The Case for Outpatient Surgery One method to implement this recommendation
is to promote collaboration among organizations and associations to create more robust datasets, and to encourage expansion of data collection activities to better measure and monitor the roles of nurses. To that end, the AAACN and CALNOC entered into a collaboration to leverage the strength of both organi zations to expedite the development of nurse-sensi tive measures in the ambulatory care environment. Building on the expertise and 20-year experience of CALNOC in measurement development for hospitals, the initial work has focused on ambulatory surgery and procedure centers.
Ambulatory surgery centers (ASC) and hospital outpatient procedure units are a good transitional platform from which to move hospital nursing quality indicators into the ambulatory care arena. Staffing and dependence on nursing care are critical and the volume is high and growing. In 2006 there were 34.7 million patient visits for outpatient surgery, with 19.9 million occurring in hospital outpatient procedure
units and 14.9 million in freestanding ambulatory surgery centers (Cullen, Hall, & Golosinskiy, 2009). The number of ambulatory surgery centers has con tinued to grow with a 15% increase between 2007 and 2014 (MedPac, 2015).
Benchmarking Quality in Outpatient Surgery – The Basics
The ultimate goal of quality measurement is the ability to benchmark performance against other simi lar units or organizations. Measuring the performance of an individual unit or units within an organization or system allows nurses to understand performance trends (Brown, Aydin, & Donaldson, 2008). However, without external comparison groups it is not possible to understand an individual unit’s performance in ref erence to benchmarks and to understand performance within the context of the industry. For example, per formance may be improving gradually – a very good outcome of performance measurement. However, the performance may be an outlier and far from where the rest of the industry performs in either a good or bad way. When performance is very good compared to the industry, prioritization of improvement efforts could be shifted to other areas of practice. Without external benchmarking, an organization only has anecdotal information to put performance in context.
To benchmark with “like” organizations or unit types, descriptive or demographic data are required to stratify by types of units. The initial CALNOC stratifi cation measures selected were based on affiliation with hospitals and the predominant age group served by ambulatory surgery centers or procedure units. The first stratification, hospital affiliation, describes hospital-based clinics, ambulatory surgery and proce dure centers with direct access to emergency response teams (not a 911 call) and higher levels of care. Freestanding units would not be under the license or billing of a hospital, thus provider-based billing differentiates a center that is free standing, ver sus hospital billing for a hospital-based center. A free standing facility would not be on the hospital tax ID and would have billing associated with the practice that owns and operates the site. The second stratifica tion addresses the predominant age group served with adult units having a predominate population over the age of 18, pediatric units having a predomi nate population under 18 years, and mixed units including both pediatric and adult.
Ambulatory care settings vary widely in size and the volume of patients that are served on a given day. Measures are summarized over a standardized period of time such as a shift, a day, a week, a month, or a year, but to compare in a meaningful way across settings, the data must also be standardized by creating rates using volume denominators. For the initial CALNOC meas ures for ambulatory surgery centers and procedure cen ters, the following volumes were selected:
148 NURSING ECONOMIC$/May-June 2016/Vol. 34/No. 3
• Total patient visits. Patient visits may be called by different names (e.g., registrations, admissions, encounters). Our general definition for one visit is a bundled patient encounter: The patient crosses the threshold, registers, several things may hap pen to him/her while in the visit (including being sent to lab or x-ray, having one or more proce- dure/surgery), and then he/she is discharged and leaves the unit/center. This is one visit.
• No shows /cancellations. Patients who cancel vis- its/appointments at the last minute or do not show up for their scheduled appointment, not permitting replacement, are totaled per month. This measure may be used as a benchmark by facilities interested in overall efficiency of cen- ter/unit operations.
• Total surgeries/procedures. This measure is an “unbundled” count of procedures/surgeries per formed in the center/unit for the entire month. There may be more than one procedure in a patient visit.
• Total operating room (OR)/procedure room min utes for surgeries/procedures. This measure tracks the total amount of time patients spent in the OR/procedure room over the entire month. It is the summed amount of time in minutes the patients were actually in the OR or procedure suite – using the elapsed time between the times recorded for patient “in room” and “out of room.” The next step in understanding performance mea
sures is to organize the measures into a framework. The dominant framework for assessing the quality of health care is attributed to the work of Avis Donabedian (1988) using a structure, process, and outcome model. This framework was used in 2004 for the initial set of endorsed nurse-sensitive quality measures (National Quality Forum [NQF], 2004).
The NQF defined nurse-sensitive performance measures as processes and outcomes and structural proxies for these processes and outcomes such as skill mix and nurse staffing hours that are affected, provid ed, and/or influenced by nursing personnel but for which nursing is not exclusively responsible. The ini tial set of ambulatory measures in CALNOC includes structure measures and outcome measures.
Structure measures describe the staff who pro vide the care in the ASC or procedure unit. By des cribing the hours of care by each type of staff, the mix of skills can be compared when using the volume denominators, which quantify the amount of care provided. It is important to measure musing hours but also support staff hours and other licensed hours to understand how the structure of care impacts both processes and outcomes of care. For example, if the industry has concerns there are not adequate numbers of RNs involved in care, the measurement of the skill mix can be used to understand if processes are not delivered and outcomes not consistently achieved to
provide safe, quality care. Without these data, anec dotal evidence is all that is available to form the argu ment for additional resources or a different mix of resources.
The staffing structural measures included in the CALNOC initial measure set include direct patient care hours. Direct hours are those related to providing face-to-face patient care (back office). Indirect hours are those related to front office employees (registra tion, surgery scheduling, or billing). Front office staff, managers, unit billing and registration clerks, medical records staff, and others with no direct patient care responsibilities are excluded. Included for bench marking are the following: • RN nursing care hours. Total number of productive hours worked by all registered RNs with direct patient care responsibilities.
• Licensed vocational nurse (LVN) nursing care hours. Total number of productive horns worked by all LVNs (known in some states as licensed practical nurses) with direct patient care respon sibilities.
• Non RN/LVN caregiver care hours. Total number of productive hours worked by other unlicensed assistants (e.g., certified medical assistant, patient care technician, nurse’s aide). This measure excludes unit clerks and others with no direct patient care responsibilities.
• Advanced practice RN (APRN) care hours. Total number of productive hours worked by advanced practice nurses employed by the unit/center. This staff category includes certified nurse anes thetists, clinical nurse specialists, nurse mid wives, and nurse practitioners, but excludes APRNs who work for the medical staff as physi cian extenders.
• Other licensed professional hours. Total number of productive hours worked by other licensed professionals employed by the unit/center. Ex amples of other licensed professionals include physical/occupational therapists, neuropsycholo gists, physician assistants, licensed radiologic tech nologists, registered dieticians, medical social workers, and licensed clinical social workers. The outcome measures selected for inclusion are
measures that have been developed by the Ambula tory Surgery Center Quality Collaboration and endorsed by the NQF in 2007 and 2008 (Ambulatory Surgery Center Quality Collaboration, 2015). This cooperative effort was formed as a collective of organ izations interested in ensuring data are measured and reported in a meaningful way. By seeking endorse ment by the NQF, these measures are available for use in the public domain and therefore desirable in our endeavor to reach a tipping point to standardize measurement for nurse-sensitive care. The measures below list the ASC-developed measure and the NQF- endorsed measure number. Using the NQF (2004) def-
NURSING ECONOMIC$/May-June 2016/Vol. 34/No. 3 149
inition of nurse-sensitive care, the selected measures are influenced by nursing personnel but for which nursing may not be exclusively responsible.
Wrong site, wrong side, wrong patient, wrong pro cedure, wrong implant (ASC 3, NQF 0267). Measure is calculated as a rate per 1,000 visits where patients experience a wrong site, wrong side, wrong patient, wrong procedure, or wrong implant event. Wrong is defined as not in accordance with intended site, side, patient, procedure, or implant and reflects adherence to universal protocols.
Patient burns (ASC 1, NQF 0263). Rate of patients experiencing a burn prior to discharge per 1,000 patient visits. Burn is defined as an unintended tissue injury caused by any of the six recognized mecha nisms: scalds, contact, fire, chemical, electrical, or radiation (e.g., warming devices, prep solutions, elec- trosurgical unit or laser).
Patient falls (ASC 2, NQF 0266). Rate of patients experiencing one or more falls per 1,000 visits. A fall within the surgery center or procedure unit is defined as a sudden, uncontrolled, unintentional, downward displacement of the body to the ground or other object, excluding falls resulting from violent blows or other purposeful actions.
Total number of falls with any injury. Rate of injury falls per 1,000 patient visits. This additional measure was adapted from the hospital environment NQF-endorsed measure set. Understanding the care team may mitigate the risk of fall injury with preven tive measures, the count of all falls that occurred in the center/unit that resulted in an injury level of “minor” or greater was added to the measure set.
All-cause hospital transfer/admission (ASC 4, NQF 0265). Rate per 1,000 patient visits of ASC/pro- cedure unit admissions or visits requiring a hospital transfer or hospital admission upon discharge.
Emergency Department Nurse-Sensitive Measures We would be remiss to not include a reminder
that emergency department (ED) ambulatory meas ures have been part of the CALNOC registry for sever al years. These measures use the same framework for measurement as referenced previously. Structure measures include nurse staffing measures as well as encounter volumes for denominators to create rate- based data. Process measures include the Centers for Medicare & Medicaid Services (CMS, 2016) NQF- endorsed throughput of patients from both the arrival of the patient in the ED and the decision to admit. Outcome measures relate to the ability of providers to assess and complete patient treatments. Stratification of ED measures for benchmarking is done by type of ED (standby, basic, or comprehensive), trauma status, and by volumes. The ED ambulatory measures are described briefly below.
ED encounters. Used as volume metric to stratify and calculate denominators for measures per 100
encounters. ED encounters include patients who had a face-to-face contact with the provider. Excluded from encounters are those who left without being seen where the patient did not have a face-to-face encounter with the provider for the medical screening exam (MSE), and direct admissions into the hospital through the ED.
Admission encounters. Total count of encounters resulting in an admission at this hospital.
Boarded patients. Rate (per 100 encounters) of patients held in the ED or a temporary location for 4 hours or more after the decision to admit or transfer has been made.
Decision to admission time (CMS eMeasure 111; NQF 0497). Median time in minutes from admit deci sion time to time of departure from the ED for ED patients admitted to inpatient status.
Arrival to admission time (CMS eMeasure 55; NQF 0495). Median time in minutes from ED arrival to time of departure from the ED for patients admitted to the facility from the ED.
Left without being seen (LWBS). Rate of patients who leave before initiation of the MSE. Patient pres ents and registers for an ED visit but leaves prior to the MSE completion.
Left before treatment complete (LBTC). Rate of patients who leave after MSE but before the provider treatment is complete. Patient presents and registers for an ED visit, the MSE is done, but patient leaves before the practitioner completes treatment.
Left against medical advice (AMA). Rate of patients recognized by the institution and left after interaction with the ED staff, but before the ED encounter is officially ended. This differs from LBTC in that it includes documentation of patient compe tence, discussion about risks and benefits, and com pletion or refusal to complete document confirming the intent to leave against the recommendation of medical care staff.
Conclusion Armed with these measures, CALNOC and
AAACN are encouraging ambulatory care nurse lead ers to become persuaders with a starting point for action to encourage adoption in practice and begin the nurse-sensitive quality journey. As care continues to shift into the ambulatory care environment, the quality imperative must also shift to assure highly reliable, safe, and effective health care. The challenge is ours. Can we reach the tipping point? $
REFERENCES Agency for Healthcare Research and Quality. (2008). Becoming a
high reliability organization: Operational advice for hospital leaders. Rockville, MD: The Lewin Group.
Ambulatory Surgery Center Quality Collaboration. (2015). ASC quality measures: Implementation guide. Retrieved from http://ascquality.org/documents/ASC% 20QC% 20tmplementa tion%20Guide%203.2%200ctober%202015.pdf
150 NURSING ECONOMIC$/May-June 2016/Vol. 34/No. 3
http://ascquality.org/documents/ASC%25
American Academy of Ambulatory Care Nursing (AAACN). (2016). Ambulatory nurse-sensitive indicator industry report: Meaningful measurement of nursing in the ambulato ry patient care environment. Pitman NJ: Author.
Brown, D.S., Aydin, C., & Donaldson, N. (2008). Quartile dash boards: Translating large data sets into performance im provement priorities. Journal for Healthcare Quality, 30(6), 18-30.
Brown, D.S., Donaldson, N., Burnes Bolton, L., & Aydin, C.E. (2010). Nursing-sensitive benchmarks for hospitals to gauge high-reliability performance. Journal of Healthcare Quality, 32(6), 9-17. doi:10.1111/j,1945-1474.2010.00083.x.
Bureau of Labor Statistics. (2014). Occupational employment and wages, May 2014: 29-1141 registered nurses. Retrieved from http://www.bls.gOv/oes/current/oes291141.htm#ind
Centers for Medicare & Medicaid Services (CMS). (2016). Clinical quality measures finalized for eligible hospitals and critical access hospitals beginning with FY 2014. Retrieved from h ttp s ://w w w .cm s.gov /reg u la tio n s-an d -g u i dance/ legislation/ehrincentiveprograms/downloads/2014_cqm_ eh_finalrule.pdf
Cullen, K.A., Hall, M.J., & Golosinskiy, A. (2009). Ambulatory sur gery in the United States, 2006. National Health Statistics Reports, 11. Revised. Hyattsville, MD: National Center for Health Statistics.
Donabedian, A. (1988). The quality of care: How can it be assessed? JAMA, 221(11) 1145-1150.
Gladwell, M. (2000). The tipping point: How little things can make a big difference. New York, NY: Little, Brown and Company.
Mastal, M., Matlock, A.M., & Start, R. (2016). Capturing the role of nursing in ambulatory care: The case for meaningful nurse-sensitive measurement. Nursing Economics, 34(2), 92- 97, 76.
MedPac. (2015). June 2015 data book: Health care spending and the Medicare program. Retrieved from http://www.medpac. gov/-documents-/data-book
National Academies of Sciences, Engineering and Medicine. (2015). Assessing progress on the Institute of Medicine Report The Future of Nursing. Washington, DC. The National Academies Press.
National Patient Safety Foundation. (2015). Free from harm: Accelerating patient safety improvement fifteen years after To Err Is Human. Retrieved from http://c.ymcdn.com/sites/ www.npsf.org/resource/resmgr/PDF/Free_from_Harm.pdf
National Quality Forum (NQF). (2004). National voluntary con sensus standards for nursing sensitive care: An initial per formance measure set. Retrieved from http://www.qualityfo rum.org/Publications/2004/10/National_Voluntary_Consens us_Standards_for_Nursing-Sensitive_Care_An_Initial_ Performance_Measure_Set.aspx
Nursing Economics Manuscript Review Panel Kathleen Abrahamson, PhD, RN Kathleen Bobay, PhD, RN, NEA-BC Marjorie J. Bott, PhD, RN Barbara B. Brewer, PhD, MALS, MBA, RN Carol S. Brewer, PhD, RN Kameka L. Brown, PhD, MBA, MS, FNP Kathleen Burke, PhD, RN Mary D. Mites Campbell, PhD, MSN-Adm, RN,
CTTS, CCHP Cynthia Caroselli, PhD, RN JoEllen Carpenter, DNP, MBA, RN, NEA-BC, CENP Teri Chenot, EdD, RN Rebecca C. Clark, PhD, RN Joanne T. Clavelle, DNP, RN, NEA-BC Catherine Crawford Cohen Cathleen Colleran-Santos, DNP, RN Lynne M. Connelly, PhD, RN Lisa Diane Cowan, NP, DNPc, NEA-BC Melanie J. Cozad, PhD, MBA Betty C. Crandall, MS, RN Mary Lind Crowe, PhD, RN Christine Cutugno, PhD, RN, NEA-BC Cathleen M. Deckers, EdD, RN, MSN, CHSE Robert L. Dent, DNP, MBA, RN, NEA-BC, CENP,
FACHE Joseph T. DeRanieri, DM, MSN, RN, BCECR Maja Djukic, PhD, RN Michelle Dunwoody, MS, BSN, WHNP-BC Jeanne Dzurenko, MPH, BSN, RN Suellyn Ellerbe, MN, RN, NEA-BC Linda T. Ely, DNP, CNS-BC Mary L. Fisher, PhD, RN, CNAA-BC Therese A. Fitzpatrick, PhD, RN
Kimberly S. Glassman, PhD, RN, CNAA-BC Joyce A. Hahn, PhD, APRN, NEA-BC Diana Halter, MSN, RN Susan B. Hamner, MSN, RN Debra A. Harrison, DNP, RN, NEA-BC Jeffrey P. Harrison, PhD, MBA, MHA Jane Hokanson Hawks, PhD, RN, BC Kristin Jaye Henderson, MSN, RN Lanis Hicks, PhD Susan M. Hinck, PhD, RN Debra R. Honey, MHA, BSN, RN, CPHQ, FACHE,
CENP E. Mary Johnson, BSN. RN-C, NE-BC Faith M. Jones, MSN, RN Beatrice J. Kalisch, PhD, RN Karen Kelly, EdD, RN, CNAA-BC Michelle D. Kelly, DNP, FNP, RN Donna L. Kistler, MS, RN Christine Kovner, PhD, RN, FAAN Judith Kutzleb, DNP, RN, CCRN, CCA, APN-C Candia Baker Laughlin, MS, RN-BC Patricia Lavin, MS, RN Lisa C. Lindley, PhD, RN Barbara Mark, PhD, RN, FAAN Margaret Fisk Mastal, PhD, MSN, RN Deborah Maust Martin, PhD, MSN, RN Cheryl McKay, PhD, RN Jennifer S. Mensik, PhD, RN, NEA-BC, FACHE Donna J. Middaugh, PhD, RN Kelly S. Miles, MSN, RN, NEA-BC Martha J. Mills, MSN, RN, CCRN Mary Ellen Murray, PhD, RN Karen M. Myrick, DNP, APRN, FNP-BC, ANP-BC
Brenda M. Nevidjon, MSN, RN, FAAN Barbara I. O’Brien, MSN, RN Karen M. Ott, MSN, RN Mary Val Palumbo, DNP, APRN, GNP-BC Miguel A. Villegas Pantoja, BN, MNSc, DNSc Sharon Pappas, PhD, RN, NEA-BC, FAAN Virginia Plummer, PhD, MSc, RN, RM, GCHPE E. Carol Polifroni, EdD, NEA-BC, CNE, RN Terry Tavella Quell, PhD, RN Sharon Radcliffe, MN, MBA, RN Rose Rivers, PhD, RN, CNAA Marie-Claire Rosenberg Roberts, PhD, RN Marcella M. Rutherford, PhD, MBA, MSN Joanne G. Samuels, PhD, RN, CNL Constance M. Savage, PhD Maria Shirey, PhD, MBA, RN, NEA-BC, FACHE Lisa Smith, MSN, RN-BC Allison Squires, PhD, RN, FAAN Leslie Edwards Tarlo, BSN, RN, CMSRN Sylvain Trepanier, DNP, RN, CENP Carol A. Tuttas, MSN, RN, NE-BC Huey-Ming Tzeng, PhD, RN, FAAN Connie Vance, EdD, RN, FAAN Susan C. Vonderheid, PhD, RN Susan H. Weaver, PhD, RN, CRNI, NEA-BC Carol Toussie Weingarten, PhD, RN, ANEF Martha Whetsell, PhD, ARNP, RN K. Lynn Wieck, PhD, RN, FAAN Tracey E. Williams, DNP, RN Meredeth Wood, MSN, RN Patricia S. Yoder-Wise, EdD, RN, NEA-BC, FAAN
Revised 5-1-2016
NURSING ECONOMIC$/May-June 2016/Vol. 34/No. 3 151
http://www.bls.gOv/oes/current/oes291141.htm%23ind
https://www.cms.gov/regulations-and-gui
http://www.medpac
http://c.ymcdn.com/sites/
Click to access Free_from_Harm.pdf
http://www.qualityfo
Copyright of Nursing Economic$ is the property of Jannetti Publications, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email articles for individual use.
PLACE THIS ORDER OR A SIMILAR ORDER WITH NURSING TERM PAPERS TODAY AND GET AN AMAZING DISCOUNT
