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Search the GCU Library and find two new health care articles that use quantitative research. Do not use articles from a previous assignment, or articles that appear in the Topic Materials or textbook. Complete an article analysis for each using the “Article Analysis: Part 2” template. Refer to the “Patient Preference and Satisfaction in Hospital-at-Home and Usual Hospital Care for COPD Exacerbations: Results of a Randomised Controlled Trial,” in conjunction with the “Article Analysis Example 2,” for an example of an article analysis. While APA style is not required for the body of this assignment, solid academic writing is expected, and documentation of sources should be presented using APA formatting guidelines, which can be found in the APA Style Guide, located in the Student Success Center. This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion. You are required to submit this assignment to LopesWrite Article Analysis 1 Article Citation and Permalink (APA format) Article 1 Article 2 Article 3 Point Description Description Description Broad Topic Area/Title Identify Independent and Dependent Variables and Type of Data for the Variables Population of Interest for the Study Sample Sampling Method Descriptive Statistics (Mean, Median, Mode; Standard Deviation) Identify examples of descriptive statistics in the article. Inferential Statistics Identify examples of inferential statistics in the article. © 2019. Grand Canyon University. All Rights Reserved. Article Analysis: Example 1 Article Citation Utens, C. M. A., Goossens, L. M. A., van Schayck, O. C. P., Rutten-van Mölken, M. P. M. H., van Litsenburg, W., Janssen, A., … Smeenk, F. W. J. M. (2013). Patient preference and satisfaction in hospital-at-home and usual hospital care for COPD exacerbations: Results of a randomised controlled trial. International Journal of Nursing Studies, 50, 1537–1549. doi.org/10.1016/j.ijnurstu.2013.03.006 Link: https://www.ncbi.nlm.nih.gov/pubmed/23582671 (Include permalink for articles from GCU Library.) Category Description Broad Topic Area/Title The differences in preference and satisfaction based upon hospital care location for COPD exacerbations Variables and Type of Data for the Variables Treatment Location-categorical -“home treatment” and “hospital treatment” Satisfaction – Ordinal Scale (1-5) Preference – categorical “home treatment” and “hospital treatment” Population of Interest for the Study COPD exacerbation patients from five hospitals and three home care organizations Sample 139 patients 69 from the usual hospital care group 70 from the early assisted discharge care group Sampling Method A randomized sampling method was used to select the patients who met the criteria for the study (p. 1540) Descriptive Statistics (mean, median, mode; standard deviation) Identify examples of descriptive statistics in the article. Example descriptive statistics: Usual hospital Age: Mean: 67.8 Standard deviation: 11.30 Early assisted discharge Age: Mean: 68.31 Standard deviation: 10.34 (p. 1540) Inferential Statistics Identify examples of inferential statistics in the article. Example of inferential statistics: Overall satisfaction score: Tested difference between HC and EAD p-value .863 (p. 1543) © 2019. Grand Canyon University. All Rights Reserved. 2 176 VOL. 44, NO. 2, MARCH 2017 • ONCOLOGY NURSING FORUM Systematic Review of Hospital Readmissions Among Patients With Cancer in the United States Janice F. Bell, PhD, MN, MPH, Robin L. Whitney, RN, PhD, Sarah C. Reed, MSW, MPH, Hermine Poghosyan, PhD, MPH, Rebecca S. Lash, PhD, MPP, RN, Katherine K. Kim, PhD, MPH, MBA, Andra Davis, RN, MN, PhD, Richard J. Bold, MD, and Jill G. Joseph, MD, PhD ARTICLE C ancer care has been declared a crisis in the United States because of the growing demand for services, increasing complexity of treatment, and dramatically rising costs of care (Institute of Medicine [IOM], 2013). Some 1.6 million individuals are diagnosed with cancer each year, and the number of cancer survivors is projected to increase dramatically because of the aging population and improvements in treatment (American Cancer Society [ACS], 2016; IOM, 2013). By 2020, cancer care costs are expected to reach $173 billion, reflecting a considerable increase from $72 billion in 2004 (ACS, 2014; Smith & Hillner, 2011). At the same time, national reports criticize the quality of cancer care, calling for greater patient-centered focus; improved care coordination, with management of care transitions across settings; and cost containment through the reduction of preventable healthcare use (IOM, 2013; Smith & Hillner, 2011). Programs and policies to reduce hospital readmissions are increasingly viewed as promising avenues to reduce spending and improve healthcare quality and efficiency as well as patient experiences (Naylor, Aiken, Kurtzman, Olds, & Hirschman, 2011; Robert Wood Johnson Foundation [RWJF], 2013; Schoen, Os- Purpose/Objectives: To review the existing literature on readmission rates, predictors, and reasons for readmission among adults with cancer. Data Sources: U.S.-based empirical studies reporting readmission rates from January 2005 to December 2015 were identified using four online library databases—PubMed, CINAHL®, EconLit, and the online bibliography of the National Cancer Institute’s Surveillance Epide- miology and End Results Program. Some articles were identified by the authors outside the database and bibliography searches. Data Synthesis: Of the 1,219 abstracts and 271 full-text articles screened, 56 studies met inclusion criteria. The highest readmission rates were observed in patients with blad- der, pancreatic, ovarian, or liver cancer. Significant predictors of readmission included comorbidities, older age, advanced disease, and index length of hospital stay. Common reasons for readmission included gastrointestinal and surgical complications, infection, and dehydration. Conclusions: Clinical efforts to reduce the substantial readmission rates among adults with cancer may target high-rate conditions, infection prevention, proactive management of nausea and vomiting, and nurse-led care coordination interventions for older adult patients with multiple comorbid conditions and advanced cancer. Implications for Nursing: Commonly reported reasons for readmission were nursing-sensitive patient outcomes (NSPOs), amenable to nursing intervention in oncology settings. These findings underscore the important role oncology nurses play in readmission prevention by implementing evidence-based interventions to address NSPOs and testing their impact in future research. Bell is an associate professor in the Betty Irene Moore School of Nursing at the Uni- versity of California (UC) Davis; Whitney is an associate professor in the Department of Internal Medicine at UC San Francisco in Fresno; Reed is a doctoral candidate in the Betty Irene Moore School of Nursing at UC Davis; Poghosyan is an assistant professor in the School of Nursing in the Bouvé College of Health Sciences at Northeastern University in Boston, MA; Lash is a nursing publications manager in the Department of Nursing Practice, Research, and Education at UCLA Health System; Kim is an assistant professor in the Betty Irene Moore School of Nursing at UC Davis; Davis is an assistant professor in the College of Nursing at Washington State University in Vancouver; Bold is a physician at the UC Davis Comprehensive Cancer Center in Sacramento; and Joseph is an associate dean for research and a professor in the Betty Irene Moore School of Nursing at UC Davis. Bell, Poghosyan, Lash, Kim, Bold, and Joseph contributed to the conceptualiza- tion and design. Bell, Whitney, Reed, Poghosyan, and Lash completed the data collection. Bell and Poghosyan provided statistical support. Bell, Whitney, Reed, Poghosyan, Lash, Kim, and Joseph provid- ed the analysis. Bell, Whitney, Reed, Lash, Kim, Davis, Bold, and Joseph contributed to the manuscript preparation. Bell can be reached at jfbell@ucdavis.edu, with copy to editor at ONFEditor@ons.org. Submitted May 2016. Accepted for publi- cation July 12, 2016. Keywords: clinical practice; nursing re- search quantitative; outcomes research ONF, 44(2), 176–191. doi: 10.1011/17.ONF.176-191 ONCOLOGY NURSING FORUM • VOL. 44, NO. 2, MARCH 2017 177 born, How, Doty, & Peugh, 2009). Hospital stays are stressful and inconvenient for patients and their fam- ilies, and substantially contribute to out-of-pocket healthcare costs. One aim of the Patient Protection and Affordable Care Act is to reduce healthcare spending through improved outpatient management of chronic disease and reduced hospital readmis- sions (Carroll & Frakt, 2013; Kocher & Adashi, 2011). Likewise, the Center for Medicaid and Medicare Innovation instituted a five-year Community Care Transitions Program to test models for improving patient transitions from hospitals to other settings and avoiding unnecessary readmissions (Agency for Healthcare Research and Quality [AHRQ], 2014; Kocher & Adashi, 2011). Such initiatives are built on the assumption that some readmissions are prevent- able; the validity of readmission rates as indicators of healthcare quality depends on this premise (Gold- field et al., 2008). Oncology nurses play important roles in prevent- ing readmission from the moment patients are ad- mitted to hospitals by identifying and addressing complications and adverse inpatient events that may increase readmission risk, assessing patient and family knowledge, providing education throughout the hospital stay and in preparation for discharge, assisting with medication management, support- ing advanced care planning, and coordinating care transitions between inpatient and community-based providers and services (Feigenbaum et al., 2012; Naylor et al., 2011). Indeed, a growing body of evi- dence suggests that multicomponent interventions focused on care transitions and incorporations of strategies—such as comprehensive discharge plan- ning and instructions with follow-up, home visits, individualized care planning, clinical management, education, and behavioral support—may be effec- tive in reducing readmission rates (Coleman, Parry, Chalmers, & Min, 2006; Epstein, Jha, & Orav, 2011; Feigenbaum et al., 2012; Hansen, Young, Hinami, Leung, & Williams, 2011; Hari & Rosenzweig, 2012; Jack et al., 2009; Naylor et al., 2011; Peikes, Chen, Schore, & Brown, 2009; VanSuch, Naessens, Stroebel, Huddleston, & Williams, 2006). Successful nursing interventions to reduce re- admission depend on identifying groups at risk for preventable readmission; however, the burden of readmissions for patients with cancer is not well described in extant literature, nor is the extent to which readmissions are preventable in this popula- tion. To date, cancer-specific readmission rates are not publicly reported, and the Centers for Medicare and Medicaid Services (CMS) penalties for readmis- sions do not apply to cancer hospitals (Horwitz et al., 2012). In a predictive model of avoidable read- missions developed at a large academic medical center (Donzé, Aujesky, Williams, & Schnipper, 2013), discharge from an oncology service was a significant risk factor, even when excluding planned readmis- sions for chemotherapy. Similarly, a Canadian study (Ji, Abushomar, Chen, Qian, & Gerson, 2012) found that the all-cause readmission rates of patients with cancer were higher than the rates of patients with other conditions. Whether these findings are relevant to the unique U.S. clinical, payment, and healthcare policy environment is unknown. Studies of readmissions among patients with cancer in the United States are needed to ascertain the extent of this population’s risk for readmission, to identify subgroups that might benefit from interventions to reduce readmissions, and to provide benchmarks against which to measure the success of such interventions. Accordingly, this systematic literature review had three related aims focused on patients with cancer: (a) to examine the proportion of patients with cancer who are readmitted to the hospital within 30 days of discharge, (b) to enumerate the reasons for and predictors of readmissions, and (c) to assess whether and how current studies identify potentially preventable readmissions. Methods Following the Preferred Reporting Items for System- atic Reviews and Meta-Analyses (PRISMA) guidelines (Moher, Liberati, Tetzlaff, & Altman, 2009), the authors of the current study searched three electronic library databases (PubMed, CINAHL®, EconLit) and the online bibliography of the National Cancer Institute’s Surveil- lance Epidemiology and End Results (SEER) Program. The Medical Subject Heading (MeSH) terms patient readmission and neoplasms or neoplasm metastasis or carcinoma were employed in the PubMed search. The keywords readmission(s) or rehospitalization(s) were used in the EconLit search, which was limited to publications in analysis of healthcare markets, health, government policy, regulation, public health, and health production. The subject headings readmis- sion and neoplasms were employed in the CINAHL search. The SEER bibliography search focused on the keywords readmission(s) or rehospitalization(s) in abstracts and titles. In addition, the authors identified relevant articles outside the database and bibliogra- phy searches. Inclusion and Exclusion Criteria The inclusion criteria included (a) peer-reviewed empirical studies conducted in the United States, (b) articles published from January 1, 2005, to Decem- ber 31, 2015, (c) articles with sample sizes of 50 or 178 VOL. 44, NO. 2, MARCH 2017 • ONCOLOGY NURSING FORUM more, and (d) studies that identified the proportion of readmissions among patients with cancer aged 18 years or older. Articles were excluded if they were (a) reports of a literature review, meta-analysis, commentary, or case study; (b) focused solely on health service use at the end of life, given higher expected rates of readmissions attributed to con- founding by progression of disease; or (c) presented readmission rates that were not exclusive to patients with cancer. Screening Process All citations were managed in EndNote X7, and duplicates were discarded. A two-stage screening process was applied to assess whether articles met inclusion criteria, with all articles screened by the lead author and at least one other investigator. In the first stage, the authors searched all EndNote fields, including titles and abstracts, for the keywords readmission(s) or rehospitalization(s). Articles were retrieved and the full text examined if they could not be included or excluded based on the EndNote keyword search, as in the case of scanned papers. In the second stage of the review, the full text of all included papers from the first stage was obtained and examined against the inclusion and exclusion criteria independently by at least two investigators. All the references of the included articles, meta-analyses, and review papers identified during the review were iteratively examined. Data Abstraction Included studies were sorted into one of two groups according to their focus on a single institution (hospital or medical center) or multiple institutions. A stan- dardized abstraction form was developed to systematically collect and summarize key data elements from each article. The authors of the current study calculated 30-day readmission rates for articles pre- senting readmission rates in time frames other than 30 days, assuming a constant rate of readmission over time. This ap- proach yielded conservative 30-day read- mission estimates because most readmis- sions occur within the first 30 days and decline afterward (Benbassat & Taragin, 2000). Most studies using alternative time frames reported readmissions within time frames longer than 30 days. Significant predictors of readmission from the results of multivariate regression models were re- corded, as were the most common reasons for readmission, if specified in the articles. Finally, the authors examined the studies to ascertain whether the readmissions were classified as potentially preventable and, if so, they recorded the definition. At least 90% agreement was reached in each stage of the review, with discrepancies resolved by the consensus of all participating authors. Results After duplicates were discarded, a total of 1,219 articles were collected from the combined searches of PubMed, EconLit, CINAHL, the SEER bibliographic database, and studies found outside the search crite- ria by the authors (see Figure 1). Of these, 948 studies were excluded based on a review of the abstracts, titles, and keywords. The full text of the remaining 271 articles was reviewed, and 215 were excluded, primar- ily because they did not measure readmission, the readmission data were not specific to patients with cancer, or they were not based in the United States. In total, 56 studies met the inclusion criteria, including 24 single-institution and 32 multiple-institution stud- ies (see Table 1). Characteristics of the Studies Almost all the studies examined readmissions fol- lowing surgical (n = 53) rather than medical index ad- missions. Most used retrospective cohort designs (n = 52), with the remainder using prospective consecutive cohort designs. Most single-institution studies relied on a review of medical records, while cancer registry FIGURE 1. Selection of Studies Examining Hospital Readmissions Records identified through database searching (n = 1,864) Additional records identified through other sources (n = 74) Records screened after duplicates were removed (n = 1,219) Full-text articles assessed for eligibility (n = 271) Included in review (N = 56) Excluded (n = 948) Based on review of abstract, titles, and keywords Excluded (n = 215) • Readmission proportion not reported (n = 93) • Readmission proportion not cancer-specific (n = 53) • Not based in the United States (n = 46) • Did not meet other inclu- sion criteria (n = 23) ONCOLOGY NURSING FORUM • VOL. 44, NO. 2, MARCH 2017 179 TABLE 1. Studies of Readmissions Among Patients With Cancer (N = 56) Readmission Study Samplea Data Source Definition Rateb Single Institution (n = 24) Ahmad et al., 2014 419 patients with gastric cancer, 49% at an advanced stage, with a median age of 68 years. Patients underwent surgery related to their cancer; about 50% reported comorbidities. Hospital database, medical records 30 days 15% AlHilli et al., 2015 538 patients with ovarian cancer, 77% at an advanced stage, with a mean age of 63 years. Patients underwent surgery related to their cancer; about 58% reported comorbidities. Hospital database, medical records 30 days 19% Clark et al., 2013 460 patients with ovarian cancer, 87% at an advanced stage, with a median age of 61 years. Patients underwent surgery related to their cancer; 65% reported comorbidities. Medical records 30 days 12% Dedania et al., 2013 70 patients with pancreatic cancer, 54% at an advanced stage, with a mean age of 66 years. Patients underwent surgery related to their cancer. Hospital database, medical records 30 days 29% Dickinson et al., 2015c 362 patients with brain cancer, with a median age of 63 years. Patients underwent surgery related to their cancer. Hospital database, medical records 30 days 8% Doll et al., 2014 152 patients with gynecologic cancer, 30 at an advanced stage, with a median age of 59 years. About 64% reported comorbidities. CRPR, hospital data- base, medical records 30 days 12% Fauci et al., 2011 207 patients with ovarian cancer, 84% at an advanced stage, with a mean age of 64 years. Patients underwent surgery related to their cancer. Hospital database 30 days 16% Glasgow et al., 2014 53 patients with gynecologic cancer, 90% at an advanced stage, with a median age of 63 years. Patients underwent surgery related to their cancer; about 42% reported comor- bidities. Medical records 30 days 34% Grant et al., 2005 100 patients with hematologic cancer, with a mean age of 45 years. Patients underwent a medical procedure related to their cancer; 34% reported comorbidities. Medical records 180 days 8% Gustafson et al., 2012c 76 patients with hepatic cancer, with a mean age of 57 years. Patients underwent surgery related to their cancer. CRPR, research database 30 days 15% Hari & Rosen- zweig, 2012 62 patients with pancreatic cancer underwent surgery related to their cancer. Medical records, research database 90 days 9% Kastenberg et al., 2013 257 patients with pancreatic cancer, with a mean age of 65 years. Patients underwent surgery related to their cancer. Medical records 30 days 18% Kimbrough et al., 2014 245 patients with hepatic cancer, with a median age of 59 years. Patients underwent surgery related to their cancer; about 41% reported comorbidities. Medical records 30, 60, and 90 days 11% Klos et al., 2014 235 patients with colon cancer, 64% at an advanced stage, with a mean age of 72 years. Patients underwent surgery related to their cancer; 91% reported comorbidities. Medical records 30 days 8% Liang et al., 2013 395 with endometrial cancer, with a mean age of 61 years. Patients underwent surgery related to their cancer; 62% re- ported comorbidities. Medical records 90 days < 3% Offodile et al., 2015 249 patients with head and neck cancer, 46% at an advanced stage, with a mean age of 59 years. Patients underwent surgery related to their cancer; 74% reported comorbidities. Medical records 30 days 15% Continued on the next page 180 VOL. 44, NO. 2, MARCH 2017 • ONCOLOGY NURSING FORUM TABLE 1. Studies of Readmissions Among Patients With Cancer (N = 56) (Continued) Readmission Study Samplea Data Source Definition Rateb Single Institution (n = 24) (continued) Spring et al., 2015 1,141 patients with hematologic cancer, with a median age of 45 years. Patients underwent a medical procedure related to their cancer. Medical records 30, 100+ days 21% Stimson et al., 2010c 753 patients with bladder cancer, 54% at an advanced stage, with a median age of 69 years. Patients underwent surgery related to their cancer; 95% reported comorbidities. Medical records, research database 90 days 9% Tamandl et al., 2015 746 patients with colorectal cancer, with a median age of 58 years. Patients underwent surgery related to their cancer; 46% reported comorbidities. Hospital database, medical records 30 days 13% Tevis et al., 2013 355 patients with rectal cancer, 45% at an advanced stage, with a median age of 60 years. Patients underwent surgery related to their cancer. Hospital database 30 days 9% Walters et al., 2013 384 patients with ovarian cancer, 85% at an advanced stage. Patients underwent surgery related to their cancer. Medical records 30 days 15% Weber et al., 2010 2,618 patients with head and neck cancer underwent surgery related to their cancer; 52% reported comorbidities. CRPR, ICD, medical records, research database 30 days 6%–14% White et al., 2015 263 patients with colorectal cancer, 42% at an advanced stage, with a median age of 67 years. Patients underwent surgery related to their cancer. ICD, medical records 30 days 13% Worley et al., 2013 165 patients with ovarian cancer, 100% at an advanced stage, with a mean age of 75 years. Patients underwent surgery and a medical procedure related to their cancer. Medical records 30 days 13% Multiple Institutions (n = 32) Ahmad et al., 2012c 1,302 patients with pancreatic cancer, with a mean age of 64 years. Patients underwent surgery related to their cancer; about 34% reported comorbidities. Hospital database, medical records, research database 90 days 6% Brown et al., 2014 2,517,886 patients with all types of cancer underwent surgery related to their cancer. ICD, University Health System Con- sortium 7, 14, 30 days 6% Duska et al., 2015 1,873 patients with ovarian cancer, 100% at an advanced stage, with a mean age of 61 years. Patients underwent sur- gery and a medical procedure related to their cancer; about 39% reported comorbidities. Medical records, research database 30 days 11% Farjah et al., 2009 21,067 patients with lung cancer underwent surgery related to their cancer. SEER-Medicare, CRPR, ICD 30 days 15% Fox et al., 2014 14,790 patients with colon cancer, none at an advanced stage, with a median age of 72 years. Patients underwent surgery related to their cancer. HCUP, ICD 30 days 12% Friedman et al., 2008 46,392 patients with all types of cancer, with a mean age of 59–64 years. Patients underwent surgery and a medical pro- cedure related to their cancer; 23% reported comorbidities. HCUP, ICD 30 days 16% Gaitonde et al., 2015 6,737 patients with esophageal cancer underwent surgery related to their cancer. ICD, research data- base 30 days 18% Continued on the next page ONCOLOGY NURSING FORUM • VOL. 44, NO. 2, MARCH 2017 181 TABLE 1. Studies of Readmissions Among Patients With Cancer (N = 56) (Continued) Readmission Study Samplea Data Source Definition Rateb Multiple Institutions (n = 32) (continued) Goffredo et al., 2015 103 patients with adrenal cancer, 26% at an advanced stage, with a mean age of 53 years. Patients underwent surgery related to their cancer; 27% reported comorbidities. ICD, National Cancer Database 30 days 4% Greenblatt et al., 2010 42,348 patients with colon cancer, 32% at an advanced stage, with a mean age of 78 years. Patients underwent surgery and a medical procedure related to their cancer. SEER-Medicare, CRPR, ICD 30 days 11% Hansen et al., 2013 6,760 patients with colon cancer underwent surgery related to their cancer; 84% reported comorbidities. HCUP, ICD 30 days 12% Hechen- bleikner et al., 2013 735 patients with colorectal cancer, with a mean age of 56 years. Patients underwent surgery related to their cancer. ICD, National Surgery Quality Improvement Plan, University Health- System Consortium 30 days 18% Hendren et al., 2011 477,461 patients with colon cancer, with a mean age of 77 years. Patients underwent surgery related to their cancer. CRPR, ICD, Medicare Provider Analysis and Review files 30 days 14%–17% Hu, Jacobs, et al., 2014 1,782 patients with bladder cancer, with a mean age of older than 65 years. Patients underwent surgery related to their cancer; 49% reported comorbidities. SEER-Medicare, CRPR, ICD 30 days 26% Hu, Mc- Murry, et al., 2014 11,432 patients with lung cancer, 18% at an advanced stage, with a median age of 75 years. Patients underwent surgery related to their cancer; about 62% reported comorbidities. SEER-Medicare, CRPR, ICD 30 days 13% Huang et al., 2014 7,534 patients with prostate cancer, 2% at an advanced stage. Patients underwent surgery related to their cancer; 22% reported comorbidities. SEER-Medicare, CRPR, ICD 90 days 3% Hyder et al., 2013 1,488 patients with pancreatic cancer, 4% at an advanced stage, with a median age of 74 years. Patients underwent surgery related to their cancer; 97% reported comorbidities. SEER-Medicare, CRPR, ICD 30 days 21% Kunitake et al., 2010 26,108 patients with colorectal cancer, 15% at an ad- vanced stage, with a mean age of 68–72 years. Patients underwent surgery related to their cancer; 44% reported comorbidities. CRPR, CCR-OSHPD, ICD 30 days 10%–13% Langan et al., 2015 2,797 patients with lung or colon cancer, with a mean age of older than 65 years. Patients underwent surgery related to their cancer; 82% reported comorbidities. Hospital database, ICD, medical records 30, 90 days 16% Lucas et al., 2014 44,822 patients with colorectal cancer, with a median age of 78 years. Patients underwent surgery related to their cancer; about 15% reported comorbidities. SEER-Medicare, CRPR, ICD 30 days 12% Moghavem et al., 2015 19,178 patients with brain cancer, with a median age of younger than 65 years. Patients underwent surgery related to their cancer. HCUP, ICD 30 days 17% Puri et al., 2015 129,893 patients with lung cancer, 15% at an advanced stage, with a mean age of 67 years. Patients underwent surgery related to their cancer; 47% reported comorbidities. ICD, National Cancer Database 30 days 4% Reddy et al., 2009 1,730 patients with pancreatic cancer, 14% at an advanced stage, with a median age of 73 years. Patients underwent surgery related to their cancer; 36% reported comorbidities. SEER-Medicare, CRPR, ICD 30 days 16% Continued on the next page 182 VOL. 44, NO. 2, MARCH 2017 • ONCOLOGY NURSING FORUM data linked to insurance claims served as the under- lying data source for most of the multiple-institution studies. Thirty-one studies had sample sizes greater than 1,000, with smaller samples in the single- versus multiple-institution studies. Nineteen multiple-institution studies focused on older adults, given their use of Medicare claims linked to SEER data, whereas seven of the single-institution studies focused on this population. Forty-eight stud- ies focused on one primary cancer type, and only TABLE 1. Studies of Readmissions Among Patients With Cancer (N = 56) (Continued) Readmission Study Samplea Data Source Definition Rateb Multiple Institutions (n = 32) (continued) Schneider et al., 2013 120,832 patients with colorectal cancer, 15% at an advanced stage, with a mean age of 76 years. Patients underwent sur- gery related to their cancer. SEER-Medicare, CRPR, ICD 30 days 11% Schneider, Hy- der, Brooke, et al., 2012 149,622 patients with colon cancer, 63% at an advanced stage, with a mean age of 77 years. Patients underwent surgery re- lated to their cancer; about 52% reported comorbidities. SEER-Medicare, CRPR, ICD 30 days 11% Schneider, Hyder, Wolf- gang, et al., 2012 9,957 patients with hepatic or pancreatic cancer, about 30% at an advanced stage, with a mean age of 73 years. Patients underwent surgery related to their cancer; about 47% reported comorbidities. SEER-Medicare, CRPR, ICD 30 days 16% Skolarus et al., 2015 1,782 patients with bladder cancer, with a mean age of older than 65 years. Patients underwent surgery related to their cancer. SEER-Medicare, CRPR, ICD 30 days 26% Speicher et al., 2015 16,275 patients with rectal cancer, 66% at an advanced stage, with a mean age of older than 60 years. Patients underwent surgery related to their cancer; 21% reported comorbidities. ICD, National Can- cer Database 30 days 6% Stitzenberg et al., 2015d 29,719 patients with bladder, lung, pancreatic, or esophageal cancer, 31% at an advanced stage, with a mean age of 74 years. Patients underwent surgery related to their cancer; 54% reported comorbidities. SEER-Medicare, CRPR, ICD 30 days Tan et al., 2011 8,003 patients with kidney cancer, 26% at an advanced stage. Patients underwent surgery related to their cancer; 42% re- ported comorbidities. SEER-Medicare, CRPR, ICD 30 days 10%–12% Tuggle et al., 2010 2,127 patients with thyroid cancer, 48% at an advanced stage, with a mean age of 74 years. Patients underwent surgery related to their cancer; 43% reported comorbidities. SEER-Medicare, CRPR, ICD 30 days 8% Yermilov et al., 2009 2,185 patients with pancreatic cancer, 71% at an advanced stage, with a mean age of 66 years. Patients underwent surgery related to their cancer; 43% reported comorbidities. CRPR, CCR- OSHPD, ICD 30 days 19% Zheng et al., 2015 45,876 patients with colon cancer, 37% at an advanced stage, with a median age of older than 65 years. Patients underwent surgery related to their cancer; 33% reported comorbidities. ICD, National Can- cer Database 30 days 5% a Advanced stage defined as overall stage III or IV; tumor, node, metastasis (TNM) stage III; or with variables indicating distant, advanced, or metastatic disease. Some studies included these variables but did not specify the sample proportions. b Percentage readmitted within 30 days was calculated for studies with longer time frames, assuming a constant readmission rate over time. c Prospective consecutive cohort design d Rate of readmission was 30% for patients with bladder, 13% for lung, 22% for pancreatic, and 22% for esophageal cancer. CCR-OSHPD—California Cancer Registry linked to the Office of Statewide Health Planning and Development files; CRPR—cancer registry or pathology report; HCUP—Healthcare Cost and Utilization Project; ICD—International Classification of Diseases diagnosis or procedure codes; SEER—Surveillance, Epidemiology, and End Results Program Note. All studies were retrospective cohort unless otherwise indicated. Note. Studies of SEER-Medicare data use ICD codes based on histology at the time of diagnosis to define cancer cases. ONCOLOGY NURSING FORUM • VOL. 44, NO. 2, MARCH 2017 183 two studies considered all cancer types. Thirty-three studies accounted for cancer stage or comorbidities, albeit with heterogeneous measures across the studies. Hospitalization within 30 days of discharge from an index admission was the most commonly used readmission definition, appearing in 50 studies. Of the alternative definitions, most considered readmission within 90 days, with the remainder using time periods of as much as a year. Rates of Readmission The percentage of patients experiencing readmis- sion within 30 days ranged from less than 3%–34% across the reviewed studies. Thirty-five studies reported readmission rates from 10%–19%, and the highest rates were reported in studies of patients with bladder, pancreatic, hematologic, and ovarian can- cers. The lowest 30-day readmission rates were the author-calculated rates, which had been presented within longer time frames in the original studies. Significant Predictors of Readmission Across the studies with multivariable models (n = 30) examining predictors of readmission (see Table 2), comorbidities were consistently associated with higher rates of readmission. Most studies controlled for gender, with men having higher readmission rates than women. Other patient factors associated with significantly higher rates of readmission included older age; more advanced disease as measured by cancer stage, tumor size, or lymph node involvement; low socioeconomic status; unmarried status; African American (compared to Caucasian) and non-Hispanic race/ethnicity; and dual eligible insurance status. Residence in low population areas, rural areas, or the Midwest or South was also associated with higher readmission rates. Surgical factors, such as postoperative complica- tions and operative methods, were associated with higher readmission rates, as were longer and shorter index hospital stays and high and low hospital volume. Other characteristics of the index hospitalization as- sociated with higher rates included having a medical (versus surgical) discharging physician, greater travel distance, discharge to a place other than home, and emergent admission. Top Reported Reasons for Readmission Of the studies reviewed, 31 reported reasons for readmission, based primarily on ICD-9 CM codes for the principal diagnosis. A tally of the top five reported reasons for readmission (see Table 3) included gastro- intestinal complications (e.g., nausea, vomiting, diar- rhea, ileus), infection, nutritional complications (e.g., malnutrition, dehydration, failure to thrive), surgical complications, and cardiopulmonary complications. Other reasons included genitourinary complications, disease progression or recurrence, coagulation disor- ders, and pain. Definitions of Preventability Eleven studies considered whether readmissions were potentially preventable (AlHilli et al., 2015; Brown, Burgess, Li, Canter, & Bold, 2014; Fox, Tyler, Vashi, Hsia, & Saxe, 2014; Glasgow, Shields, Vogel, Teoh, & Argenta, 2014; Grant, Cooke, Bhatia, & For- man, 2005; Hansen, Fox, Gross, & Bruun, 2013; Hech- enbleikner et al., 2013; Hynes et al., 2004; Moghavem, Morrison, Ratliff, & Hernandez-Boussard, 2015; Puri et al., 2015; Tuggle, Park, Roman, Udelsman, & Sosa, 2010); only one study (Brown et al., 2014) evaluated individual cases to assess their preventability. Brown et al. (2014) concluded that 33% of readmissions within seven days of the index hospitalization were for issues deemed potentially preventable by the authors, including nausea, vomiting, dehydration, and postoperative pain, with improved discharge follow-up, care coordination, and palliative care. Most studies conceptualized readmissions as planned ver- sus unplanned, using this dichotomy to identify and exclude planned readmissions for chemotherapy, radiotherapy, or rehabilitation (AlHilli et al., 2015; Brown et al., 2014; Fox et al., 2014; Glasgow et al., 2014; Hansen et al., 2013; Hechenbleikner et al., 2013; Puri et al., 2015; Tuggle et al., 2010). Extensions to this conceptualization included focusing on readmission diagnosis related to initial admission (Brown et al., 2014) and including only readmissions originating in the emergency department (Fox et al., 2014). In one study (Hynes et al., 2004), an expert panel used an iterative consensus process to identify diag- nosis codes for surgical complications (within 30–365 days of surgery) that could result in readmission; however, the article did not specify whether these complications were deemed potentially preventable. In another study (Grant et al., 2005), readmissions were conceptualized as unscheduled versus sched- uled, again without an explicit definition, although this categorization could have been determined by the researchers through medical chart review. Moghavem et al. (2015) examined “unplanned read- missions” but did not provide a definition or other- wise explain how these readmissions were identified. Discussion This systematic review of 56 studies indicated that 30-day hospital readmission rates among patients with cancer were comparable to and sometimes exceeded 184 VOL. 44, NO. 2, MARCH 2017 • ONCOLOGY NURSING FORUM those of patients with cardiovascular (15%), cardiore- spiratory (21%), and general medical (18%) conditions (Horwitz et al., 2012; Macartney, Stacey, Carley, & Har- rison, 2012; Van Walraven, Bennett, Jennings, Austin, & Forster, 2011). The wide range of readmission rates in this population is likely attributable to the heteroge- neity of cancer case definitions, settings, and popula- tions across the available literature—factors that also complicate the comparison of rates across studies. Collectively, the reviewed articles do not include cancer-specific rates for all cancer types, pointing to the need for future population-based research to more fully enumerate cancer readmission rates. The reported rates, particularly from single-institution studies, may underestimate the true burden of read- missions among patients with cancer because not all studies in this review accounted for readmission to different facilities. Readmissions do not always occur at the index admitting facility; for instance, in a study of patients discharged after pancreaticoduodenec- tomy (Yermilov et al., 2009), 47% were readmitted to different hospitals. This issue may be particularly sa- lient if patients receiving ongoing care from relatively distant regional cancer facilities seek local readmis- sion for symptoms, such as pain or dehydration, which may be effectively treated in ambulatory care settings or with care management interventions. In addition, some individuals may elect to seek care at alternative hospitals because of perceived or actual deficiencies in care during the index admission (RWJF, 2013), resulting in underestimates of readmissions from poor quality care. The studies focused almost exclusively on readmis- sions following surgical procedures; few examined readmissions following index admissions for nonsur- gical indications, although one study (Brown et al., 2014) reported that discharge by a physician with a medical versus surgical specialty was a significant predictor of readmission. The authors of the current study would have preferred to present results sepa- rately for readmissions following index medical ver- sus surgical admissions; however, few studies focused on readmissions following medical index admissions. Studies or readmission after index hospitalizations for medical indications are required to understand differences in the reasons and risk factors for re- admissions following index medical versus surgical admissions. Such a focus is of particular importance, as the results of the current review suggest that the rates of readmission may be higher following an index medical admission (Brown, Bornstein, & Wilcox, 2012; Schneider et al., 2013). The exemption of cancer specialty hospitals from CMS readmission penalties and the exclusion of medical oncology admissions from the hospitalwide, all-cause unplanned readmission rate (Horwitz et al., 2012) create the impression that the reasons and risk factors for readmissions among patients with cancer may differ from those of other inpatient groups. How- ever, the authors found that many sociodemographic predictors of readmission among patients with cancer are consistent with those reported in other work (Kansagara et al., 2011). Cancer-specific variables (e.g., disease stage, treatment, operative method) also had significant independent effects. The reasons for readmissions across the studies were broadly categorized, with the most reported complications (e.g., gastrointestinal, infection, nutritional, surgical) arguably preventable. Future research is needed to better understand potentially preventable healthcare use among patients with cancer, and to more fully examine readmissions after medical procedures and their underlying reasons. As others have noted (Van Walraven et al., 2011), the value of hospital readmissions as quality indica- tors depends on the ability to identify the propor- tion of avoidable readmissions. In one large study of Medicare beneficiaries with the highest costs (Carroll & Frakt, 2013), only 10% of spending was attributed to preventable hospital (re)admissions or emergency care, suggesting that a focus on readmission may not yield the savings some have anticipated. The extent to which this finding applies to readmission among patients with cancer is unknown. Most of the studies that considered the issue of preventability in this review did so only indirectly. In fact, none of the studies presented rates for the presumed preventable readmissions as a proportion of all oncology readmis- sions. Instead, they presented summary readmission rates for only those hospitalizations meeting their definition of potentially preventable. Although Brown et al. (2014) concluded that 33% of readmissions within seven days were because of issues deemed potentially preventable, the rate was presented for a subset already restricted to readmissions meeting the University Health Consortium definition of related readmissions, all of which are considered potentially preventable (Hechenbleikner et al., 2013). Accord- ingly, the rates presented in these studies cannot be interpreted as the proportion of preventable readmis- sions for patients with cancer. The infrequent consideration of the preventability of readmissions among patients with cancer may re- flect the lack of consensus in the literature, generally, about how to identify preventable healthcare use. A review by Van Walraven et al. (2011) suggested that 5%–79% of readmissions for all conditions, including cancer, may be preventable, with the wide-ranging estimates resulting from the use of subjective criteria to determine preventability. None of the reviewed ONCOLOGY NURSING FORUM • VOL. 44, NO. 2, MARCH 2017 185 TABLE 2. Predictors of Higher Rates of Readmission Among Patients With Cancer Studies (N) Significant Not Significant Predictor n Studies n Studies Patient Characteristic Comorbidities (great- er number or specific condition) 25 22 Ahmad et al., 2014; AlHilli et al., 2015; Farjah et al., 2009; Fauci et al., 2011; Hendren et al., 2011; Hu, Jacobs, et al., 2014; Hu, McMurry, et al., 2014; Hyder et al., 2013; Kimbrough et al., 2014; Kunitake et al., 2010; Langan et al., 2015; Lucas et al., 2014; Moghavem et al., 2015; Puri et al., 2015; Schneider et al., 2013; Schneider, Hyder, Brooke, et al., 2012; Schneider, Hyder, Wolfgang, et al., 2012; Spring et al., 2015; Stitzenberg et al., 2015; Tuggle et al., 2010; Yermilov et al., 2009; Zheng et al., 2015 3 Reddy et al., 2009; Stimson et al., 2010; Tan et al., 2011 Male (versus female) 21 12 Farjah et al., 2009; Greenblatt et al., 2010; Hendren et al., 2011; Hu, McMurry, et al., 2014; Kunitake et al., 2010; Lucas et al., 2014; Moghavem et al., 2015; Schneider et al., 2013; Schneider, Hyder, Brooke, et al., 2012; Schneider, Hyder, Wolfgang, et al., 2012; Stimson et al., 2010; Stitzenberg et al., 2015 9 Ahmad et al., 2014; Hyder et al., 2013; Langan et al., 2015; Puri et al., 2015; Reddy et al., 2009; Spring et al., 2015; Tan et al., 2011; Yermilov et al., 2009; Zheng et al., 2015 Older age 21 9 Farjah et al., 2009; Hendren et al., 2011; Hu, McMurry, et al., 2014; Kunitake et al., 2010; Lucas et al., 2014; Puri et al., 2015; Schneider, Hyder, Brooke, et al., 2012; Stitzenberg et al., 2015; Yermilov et al., 2009 12 Clark et al., 2013; Fauci et al., 2011; Gaitonde et al., 2015; Greenblatt et al., 2010; Hyder et al., 2013; Langan et al., 2015; Moghavem et al., 2015; Reddy et al., 2009; Spring et al., 2015; Stimson et al., 2010; Tan et al., 2011; Zheng et al., 2015 Advanced disease stage (stage III or IV, large tumor size, lymph node involve- ment) 19 13 Farjah et al., 2009; Gaitonde et al., 2015; Greenblatt et al., 2010; Kunitake et al., 2010; Moghavem et al., 2015; Offodile et al., 2015; Puri et al., 2015; Schneider et al., 2013; Spring et al., 2015; Stitzenberg et al., 2015; Tuggle et al., 2010; Yermilov et al., 2009; Zheng et al., 2015 6 Hendren et al., 2011; Hyder et al., 2013; Puri et al., 2015; Reddy et al., 2009; Stimson et al., 2010; Tan et al., 2011 Other factors (low so- cioeconomic status, unmarried, African American, dual eli- gible) 16 7 Gaitonde et al . , 2015 ; Hendren et al., 2011; Hu, McMurry, et al., 2014; Moghavem et al., 2015; Puri et al., 2015; Stitzenberg et al., 2015; Zheng et al., 2015 9 Dickinson et al., 2015; Farjah et al., 2009; Hendren et al., 2011; Hyder et al., 2013; Kunitake et al., 2010; Langan et al., 2015; Reddy et al., 2009; Spring et al., 2015; Tan et al., 2011 Treatment Characteristic Residence (low popu- lation density, rural, Midwest, South) 8 5 Farjah et al., 2009; Greenblatt et al., 2010; Hu, McMurry, et al., 2014; Moghavem et al., 2015; Stitzenberg et al., 2015 3 Hyder et al., 2013; Kunitake et al., 2010; Puri et al., 2015 Prior chemoradiation 3 2 Hu, McMurry, et al., 2014; Puri et al., 2015 1 Dickinson et al., 2015 Continued on the next page 186 VOL. 44, NO. 2, MARCH 2017 • ONCOLOGY NURSING FORUM studies used existing methods to classify potentially preventable admissions, such as the AHRQ’s (2001) definitions of ambulatory care sensitive conditions; however, such approaches may be insufficient in this population, as they do not account for cancer-specific conditions. Future studies are warranted to better understand which conditions lead to preventable readmissions and TABLE 2. Predictors of Higher Rates of Readmission Among Patients With Cancer (Continued) Studies (N) Significant Not Significant Predictor n Studies n Studies Treatment Characteristic (continued) Surgical complications (infection, blood loss, postoperative compli- cations) 21 15 Ahmad et al., 2014; AlHilli et al., 2015; Clark et al., 2013; Fauci et al., 2011; Greenblatt et al., 2010; Hendren et al., 2011; Hu, Jacobs, et al., 2014; Hu, Mc- Murry, et al., 2014; Kastenberg et al., 2013; Kimbrough et al., 2014; Langan et al., 2015; Schneider, Hyder, Brooke, et al., 2012; Spring et al., 2015; Stim- son et al., 2010; Tuggle et al., 2010 6 Hyder et al., 2013; Kunitake et al., 2010; Offodile et al., 2015; Reddy et al., 2009; Schneider, Hyder, Wolfgang, et al., 2012; Stitzenberg et al., 2015 Operative method 18 10 Ahmad et al., 2014; Farjah et al., 2009; Hu, McMurry, et al., 2014; Langan et al., 2015; Lucas et al., 2014; Puri et al., 2015; Reddy et al., 2009; Schneider, Hyder, Brooke, et al., 2012; Stitzenberg et al., 2015; Zheng et al., 2015 8 Clark et al., 2013; Fauci et al., 2011; Gaitonde et al., 2015; Greenblatt et al., 2010; Kunitake et al., 2010; Offodile et al., 2015; Schneider, Hyder, Wolfgang, et al., 2012; Stimson et al., 2010 Index Hospitalization Characteristic Length of stay (LOS) 14 9 Longer LOS: Greenblatt et al., 2010; Hendren et al., 2011; Puri et al., 2015; Reddy et al., 2009; Schneider et al., 2013; Schneider, Hyder, Brooke, et al., 2012; Schneider, Hyder, Wolfgang, et al., 2012; Stitzenberg et al., 2015; Tuggle et al., 2010 Shorter LOS: Tuggle et al., 2010 5 Ahmad et al., 2014; Dickinson et al., 2015; Fauci et al., 2011; Hyder et al., 2013; Stimson et al., 2010 Other (intensive care unit stay, medical ver- sus surgical discharg- ing physician, greater travel distance) 11 8 Greenblatt et al., 2010; Hu, Jacobs, et al., 2014; Hu, McMurry, et al., 2014; Kastenberg et al., 2013; Langan et al., 2015; Stitzenberg et al., 2015; Tuggle et al., 2010; Zheng et al., 2015 3 Kunitake et al., 2010; Puri et al., 2015; Spring et al., 2015 Discharge to a place other than home 3 3 Dickinson et al., 2015; Greenblatt et al., 2010; Stitzenberg et al., 2015 – – Emergent admission/ urgent surgery 3 3 Greenblatt et al., 2010; Hendren et al., 2011; Moghavem et al., 2015 – – Hospital Characteristic Patient volume/ hospital size 9 8 Higher volume: Gaitonde et al., 2015; Hyder et al., 2013; Stitzenberg et al., 2015; Zheng et al., 2015 Lower volume: Greenblatt et al., 2010; Kunitake et al., 2010; Tan et al., 2011; Tuggle et al., 2010; Zheng et al., 2015 1 Moghavem et al., 2015 Note. All listed predictors derived from studies, including multivariable regression models, and reported as statistically signifi- cant (p < 0.05) ONCOLOGY NURSING FORUM • VOL. 44, NO. 2, MARCH 2017 187 whether discharge follow-up, care coordination, and palliative care interventions can reduce readmission rates among patients with cancer. Such efforts are consistent with national recommendations that hospi- tal staff interview patients and caregivers to elicit the “story behind the story” to better understand their ex- periences of communication, coordination, or logistical barriers leading to readmission (AHRQ, 2014). The authors opted not to grade the quality of the evidence in this review for several reasons. First, they reported unadjusted readmission rates rather the measured effects of any exposure or intervention. Second, none of the studies could be rated as produc- ing the highest quality evidence because random- ized, controlled trials were inapplicable. Third, they separated single- versus multiple-institution studies, which could be viewed as lower versus higher quality evidence, respectively. Limitations Given the reliance on secondary analysis of extant administrative or clinical data, most of the reviewed studies included risk of bias. Administrative data may underreport untreated comorbid conditions or those reimbursed as part of the hospital stay (e.g., substance abuse, mental health conditions) and, subsequently, underestimate the effects of these conditions on readmissions. In addition, most of the studies lacked variables to adequately measure so- cioeconomic status, social support, self-care ability, transportation, health literacy, receipt of timely or ongoing follow-up care, or the quality of discharge instructions. Accordingly, the effect of these variables on readmission is unknown, although they may be just as important as those reported, or perhaps even more salient. Also, most of the studies either excluded or did not describe the proportion of individuals who died within 30 days of hospital discharge, therefore in- troducing bias from semicompeting risk (i.e., reduced readmission rates attributable to death), which may be particularly applicable to patients with advanced cancer. Interpretation of the findings from this review is subject to additional limitations. The abstraction and classification are subject to interpretation, although this subjectivity was mitigated through a dual review and consensus process. The authors may have inad- vertently missed relevant publications that included readmission rates among patients with cancer in their review; however, additional studies changing TABLE 3. Leading Reported Reasons for Patient Readmission Variable Studies (N) Studies Reporting Finding Gastrointestinal com- plications (ileus, colitis, nausea, vomiting, and diarrhea) 24 Ahmad et al., 2014; AlHilli et al., 2015; Brown et al., 2014; Clark et al., 2013; Fauci et al., 2011; Glasgow et al., 2014; Grant et al., 2005; Greenblatt et al., 2010; Gustafson et al., 2012; Hansen et al., 2013; Hari & Rosenzweig, 2012; Hu, Jacobs, et al., 2014; Hu, McMurry, et al., 2014; Hyder et al., 2013; Kimbrough et al., 2014; Langan et al., 2015; Liang et al., 2013; Offodile et al., 2015; Schneider et al., 2013; Stimson et al., 2010; Tamandl et al., 2015; White et al., 2015; Worley et al., 2013; Yermilov et al., 2009 Infection (fever, cellulitis, septicemia) 21 AlHilli et al., 2015; Brown et al., 2014; Dickinson et al., 2015; Grant et al., 2005; Green- blatt et al., 2010; Hansen et al., 2013; Hari & Rosenzweig, 2012; Hu, Jacobs, et al., 2014; Hu, McMurry, et al., 2014; Kastenberg et al., 2013; Kimbrough et al., 2014; Kunitake et al., 2010; Liang et al., 2013; Moghavem et al., 2015; Offodile et al., 2015; Schneider et al., 2013; Schneider, Hyder, Brooke, et al., 2012; Tamandl et al., 2015; White et al., 2015; Worley et al., 2013; Yermilov et al., 2009 Nutritional complications (dehydration, malnutri- tion, failure to thrive) 17 Ahmad et al., 2014; AlHilli et al., 2015; Brown et al., 2014; Glasgow et al., 2014; Grant et al., 2005; Hansen et al., 2013; Hari & Rosenzweig, 2012; Hu, Jacobs et al., 2014; Hu, McMurry et al., 2014; Hyder et al., 2013; Kimbrough et al., 2014; Schneider et al., 2013; Schneider, Hyder, Brooke, et al., 2012; Stimson et al., 2010, White et al., 2015; Worley et al, 2013; Yermilov et al., 2009 Surgical complications (blood loss, postopera- tive complications) 13 Ahmad et al., 2014; Clark et al., 2013; Fauci et al., 2011; Greenblatt et al., 2010; Hansen et al., 2013; Langan et al., 2015; Moghavem et al., 2015; Offodile et al., 2015; Reddy et al., 2009; Schneider et al., 2012; Tuggle et al., 2010; White et al., 2015; Yermilov et al., 2009 Cardiopulmonary com- plications (respiratory complaints, pneumonia) 11 Ahmad et al., 2014; Fauci et al., 2011; Greenblatt et al., 2010; Hu, McMurry, et al., 2014; Hyder et al., 2013; Kimbrough et al., 2014; Moghavem et al., 2015; Langan et al., 2015; Tamandl et al., 2015; Tuggle et al., 2010; White et al., 2015 188 VOL. 44, NO. 2, MARCH 2017 • ONCOLOGY NURSING FORUM the conclusions of the review is unlikely given the wide range of rates found in this literature. As noted, direct comparison of readmission rates by cancer type across studies was not undertaken because of the heterogeneity of study populations and measures that would confound such comparisons. In addi- tion, the lack of a standard definition of readmission across the studies in the review may complicate comparison of the reported results. The authors’ standardized 30-day readmission rate assumed a constant rate of readmissions over time, which may introduce bias in the rates calculated for studies us- ing alternative time frames, particularly if systematic differences exist in the the timing of readmission rates overall or by cancer type. Although most stud- ies reported 30-day rates, those with longer time frames for which the authors calculated 30-day rates had the lowest rates of readmission. Consistent with work illustrating rapid early accrual of readmissions using time-to-event curves (Horwitz et al., 2012), this finding suggested higher readmission rates closer to index admission discharge, with the calculated rates likely to be underestimates. Implications for Nursing People diagnosed with cancer—particularly blad- der, pancreatic, ovarian, and liver cancer—experience high rates of readmission; however, little evidence indicates the degree to which these readmissions may be preventable. At the same time, all commonly reported reasons for readmission among patients with cancer include at least some modifiable facets within the scope of nursing practice. This assertion is consistent with findings from a retrospective chart re- view (Weaver et al., 2006) reporting that readmission risk may be driven by complex medical care needs as well as psychosocial issues (e.g., living alone, care- giver difficulties, financial and insurance concerns). In fact, many reasons for readmission reported in the authors’ review, such as infection, nausea and vomit- ing, and nutritional difficulties, have been identified by the Oncology Nursing Society as nursing-sensitive patient outcomes (NSPOs), which are amenable to nursing interventions in the oncology setting (Given & Sherwood, 2005). Therefore, efforts among oncol- ogy nurses to reduce readmissions might focus on implementing evidence-based interventions to ad- dress these NSPOs through symptom management and infection prevention, particularly among high-risk patients with advanced stage cancers, older age, and multiple chronic conditions. Oncology nurses are in a unique position to contrib- ute to future research on the impact of specific and multicomponent nursing interventions on readmis- sion rates. Based on work involving other chronic conditions (Coleman et al., 2006; Epstein et al., 2011; Feigenbaum et al., 2012; Hansen et al., 2011; Hari & Rosenzweig, 2012; Jack et al., 2009; Naylor et al., 2011; Peikes et al., 2009; VanSuch et al., 2006), such interventions might incorporate comprehensive dis- charge planning and instructions with follow-up, home visits, individualized care planning, clinical manage- ment, education, and behavioral support. Nursing perspectives that account for medical and psychoso- cial needs are also needed in future population-based research to more fully enumerate cancer readmission rates, to better understand preventable healthcare use among patients with cancer, and to study readmis- sions and their underlying reasons. Conclusion Readmission rates among patients with cancer are substantial and comparable to those among patients with other chronic conditions. At the same time, the extent to which cancer-related readmissions and as- sociated spending may be avoidable is unclear. The lack of consensus on the definition of preventable readmission, either general or specific to cancer, limits the authors’ ability to identify specific condi- tions that could be influenced by care coordination or discharge planning interventions. Future research is needed to describe readmission rates by cancer type using comparable methods, to examine readmissions following medical index admissions, and to develop and assess the effectiveness of readmission reduction interventions among patients with cancer. Clinical efforts to reduce readmission among patients with cancer may target conditions with the highest rates of readmissions, and include interventions such as the prevention of infection, the proactive manage- ment of nausea and vomiting, and care coordination interventions to address patient-level risk factors, including older age, multiple comorbid conditions, and advanced cancer. Knowledge Translation • Readmission rates among patients with cancer are compa- rable to and sometimes exceed the rates of patients with other chronic conditions. • Commonly reported reasons for readmission may be amena- ble to nursing intervention, as they are are nursing-sensitive patient outcomes. • Clinical efforts to reduce readmissions among patients with cancer might target conditions with the highest rates of readmission. ONCOLOGY NURSING FORUM • VOL. 44, NO. 2, MARCH 2017 189 References Agency for Healthcare Research and Quality. (2001). Guide to preven- tion quality indicators. 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Com- parative effectiveness of laparoscopy vs open colectomy among nonmetastatic colon cancer patients: An analysis using the Na- tional Cancer Data Base. Journal of the National Cancer Institute, 107(3), dju491. doi:10.1093/jnci/dju491 Copyright of Oncology Nursing Forum is the property of Oncology Nursing Society 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. Paes GO, Mesquita MGR, Moreira MB. Best practices applied to patient safety… English/Portuguese J Nurs UFPE on line., Recife, 10(Suppl. 6):4969-73, Dec., 2016 4969 ISSN: 1981-8963 ISSN: 1981-8963 DOI: 10.5205/reuol.8200-71830-3-SM.1006sup201633 BEST PRACTICES APPLIED TO PATIENT SAFETY IN THE ADMINISTRATION OF MEDICINES MELHORES PRÁTICAS APLICADAS À SEGURANÇA DO PACIENTE NA ADMINISTRAÇÃO DE MEDICAMENTOS MEJORES PRÁCTICAS APLICADAS A LA SEGURIDAD DEL PACIENTE EN LA ADMINISTRACIÓN DE MEDICAMENTOS Graciele Oroski Paes. PhD Professor, Department of Fundamental Nursing, Anna Nery School of Nursing, Federal University of Rio de Janeiro/UFRJ. Rio de Janeiro (Rio de Janeiro), Brazil. Email: gracieleoroski@gmail.com Maria Gefé da Rosa Mesquita. PhD Professor, Department of Methodology in Nursing, Anna Nery School of Nursing, Federal University of Rio de Janeiro/UFRJ. Rio de Janeiro (Rio de Janeiro), Brazil. Email: mariagefe@gmail.com Maiara Benevides Moreira. Nurse, Master’s Student of Nursing, Anna Nery School of Nursing, Federal University of Rio de Janeiro/UFRJ. Rio de Janeiro (Rio de Janeiro), Brazil. Email: maiarabenevides@hotmail.com ABSTRACT Objectives: to identify how the nursing team handles medication administration in low and medium complexity hospital admission units, to analyze the practice of medication administration by the nursing team in light of the best practices focused on patient’s safety and to develop protocols directed to practice of medication administration as a subsidy to nursing teams. Method: translational research, quantitative approach, descriptive and exploratory typology subsidized by practice based on evidence, based on a University Hospital of Rio de Janeiro/RJ, Brazil and as participants the nursing professionals. The data will be produced through non-participant observation along with semi-structured interview. Expected results: the optimization of the work process in drug administration mediated by guiding and updated instruments incorporates qualified recommendations appropriate to the reality investigated, and essentially guarantees that the precepts aimed at patient safety are implemented and validated. Descriptors: Patient’s Safety; Nursing; Administration of Medicinal Therapy. RESUMO Objetivos: identificar como processa a administração de medicamentos pela equipe de enfermagem nas unidades de internação hospitalar de baixa e média complexidade, analisar a prática de administração de medicamentos pela equipe de enfermagem a luz das melhores práticas voltadas para segurança do paciente e elaborar protocolos direcionados a prática de administração de medicamentos como subsídio as equipes de enfermagem. Método: pesquisa translacional, de abordagem quantitativa, tipologia descritiva e exploratória subsidiada pela prática baseada em evidência, tendo como cenário um Hospital Universitário do Rio de Janeiro/RJ, Brasil e como participantes os profissionais de enfermagem. Os dados serão produzidos por meio de observação não participante juntamente com entrevista semiestruturada. Resultados esperados: que a otimização do processo de trabalho na administração medicamentosa mediada por instrumentos norteadores e atualizados incorpore recomendações qualificadas e apropriadas à realidade investigada, e garanta essencialmente que os preceitos voltados para segurança do paciente sejam implementados e validados. Descritores: Segurança do Paciente; Enfermagem; Administração de Terapia Medicamentosa. RESUMEN Objetivos: identificar como procesa la administración de medicamentos por parte del personal de enfermería en unidades de hospitalización de complejidad baja y mediana, analizar la práctica de la administración de medicamentos por parte del personal de enfermería a la luz de las mejores prácticas relativas a la seguridad del paciente y desarrollar protocolos específicos a la práctica de la administración de medicamentos como apoyo para el personal de enfermería. Método: una investigación traslacional, de enfoque cuantitativo, de tipología descriptiva y exploratoria subvencionada por la práctica basada en la evidencia, en el contexto de un hospital universitario de Río de Janeiro/RJ, Brasil, y como participantes los profesionales de enfermería. Los datos se produjeron a través de la observación no participante con la entrevista semi-estructurada. Resultados esperados: que la optimización del proceso de trabajo en la administración del fármaco mediada guía e instrumentos actualizados incorpore las recomendaciones cualificadas y adecuadas a la realidad investigada y garantiza esencialmente que los preceptos centrados en la seguridad del paciente sean implementados y validados. Descriptores: Seguridad del Paciente; Enfermería; Gestión de la Terapia con Medicamentos. NOTE PREVIEW ARTICLE mailto:gracieleoroski@gmail.com mailto:mariagefe@gmail.com mailto:maiarabenevides@hotmail.com Paes GO, Mesquita MGR, Moreira MB. Best practices applied to patient safety… English/Portuguese J Nurs UFPE on line., Recife, 10(Suppl. 6):4969-73, Dec., 2016 4970 ISSN: 1981-8963 ISSN: 1981-8963 DOI: 10.5205/reuol.8200-71830-3-SM.1006sup201633 In hospital environment patient’s safety has generated worldwide debate and has received a number of interpretations, for example, that safety consists in reducing risk and unnecessary harm to the patient associated with health care to the least acceptable level. This refers to what is viable in the face of current knowledge, available resources and the context in which assistance was provided. Among available resources, drug use is one of the most commonly used; however, adverse events and drug-related errors are frequent in the hospital setting.1 Among the various stages foreseen in the process involving the manipulation of medications, the nursing team is generally responsible for scheduling, preparing and administering medications. Drug administration is the final phase of the medication system and offers the last opportunity to avoid the error in the process. Studies have shown that 38% of errors occur during drug administration and only 2% of errors are intercepted, which calls us attention to the vulnerability of this phase of the process.2-3 In order for this phase of the drug process to be not so defenseless and for the professional to provide quality care, scientific knowledge is of vital importance. The multidisciplinary team, especially the nursing team, needs to know the process as a whole and how fragile it can be, so that they identify potential failures and prevent them from occurring. Thus, knowledge will be a strong ally of safe and quality care.4 Pharmacology is a very important field for the training of nurses and sometimes this knowledge is insufficient to subsidize professional practice. Since the nursing team is largely responsible for the follow-up of the patient’s therapy, these professionals need specific academic training in the field of pharmacology.5 Another way to make care safer is to adopt professional practices based on protocols and clinical evidences. The protocolized practice follows a line established and standardized by protocols established in health institutions for the performance of procedures, which contributes to the organization of the work process and are strong allies in the decision- making process. However, protocols should only guide practice, since each case requires a different solution, and thus, the use of protocols cannot mechanize the work process, but somewhat assists it.6 In order for safety practices to be discussed and implemented, it is necessary for the leaders of the institutions to create a patient-oriented safety culture and to organize a multidisciplinary team to lead these discussions, seeking to analyze and evaluate each existing process, seeking improvements and incorporation of new technologies and evidence.1 The use of techniques and technologies applied to health care can have a positive impact on the quality of care provided. It is well known that in health care it is more useful to define technology as tools in a general sense, applications of different knowledge, practices and strategies of construction or deconstruction of knowledge, care in its entire dimension. Considering that technology is not merely applied science, nursing praxis is also technology.7 The protocols represent, from this perspective, the application of a type of technology directed directly to the care. They should therefore be developed in a systematic way to assist professionals and clients in deciding on appropriate care in meeting specific health conditions.8 The establishment of care protocols capable of early risk screening and the application of timely interventions may represent a gain in the quality of nursing care, especially in situations where decisions should be made on time, both in relation to the diagnosis and possible health damages of the customer. This study expands the knowledge about the drug preparation and administration process and offers elements to contribute to the care process, aiming at improving the quality of care provided to the patient, promoting the safe and rational use of medications and subsidizing safe practices. Facing the above, the object of the project in question deals with the practice of medication administration by the nursing team in the hospital setting. ● To identify how the administration of drugs by the nursing team is processed in the hospital units of low and medium complexity. ● To analyze the practice of medication administration by the nursing team in light of the best practices focused on patient safety. ● To elaborate protocols directed to the practice of administration of medicines as subsidy to the nursing teams. OBJECTIVES INTRODUÇÃO Paes GO, Mesquita MGR, Moreira MB. Best practices applied to patient safety… English/Portuguese J Nurs UFPE on line., Recife, 10(Suppl. 6):4969-73, Dec., 2016 4971 ISSN: 1981-8963 ISSN: 1981-8963 DOI: 10.5205/reuol.8200-71830-3-SM.1006sup201633  Type of study It is a translational, descriptive and exploratory research having as methodological reference the practice based on evidence, with a quantitative approach. The general purpose will be the collection of detailed information about the variable process of drug preparation and administration in hospital admission scenarios.  Research scenario The research will be conducted in hospital units of low and medium complexity of a University Hospital located in the city of Rio de Janeiro, where the drug therapy is composed of a great variety of medications and most of the time it takes a long period. The Hospital to be investigated is a center of excellence in research and teaching, which brings together health academics, including nursing undergraduates, who act directly in the care and research within the institution, corroborating for intellectual production and for the practice of health care. In addition, this institution is part of the Network of Sentinel Hospitals of ANVISA, which is of paramount importance for Patient Safety.  Participants of the research The population will be composed of professionals of the nursing team who participate in the process of preparation and administration of drugs in the units investigated. The sample will be made up of all the nursing professionals who acted in the process of preparation and administration of medicines in the clinics under study during the period of the investigation and who accept, in writing, to be observed and interviewed.  Data production  First stage It will review the best evidence for the practice of drug administration. For this purpose we will use the databases and virtual libraries: Portal Evidence – VHL, Cochrane – VHL, BDEnf – VHL, Medline, PubMed, Scielo and ISI Web of Science, Embase, SciVerse Scopus, Cinahl. In addition, a dense bibliographic review of the pathophysiology, diagnostic and therapeutic procedures recommended for drug administration cases will be conducted, including a review of the national and international consolidated literatures in the area of knowledge. Next, we will synthesize the knowledge applied specifically to nursing care and the practice of medication administration.  Second stage We will give the field research, where we choose two inter-involved and associated techniques: the technique of non-participant observation along with the application of a semi-structured questionnaire. In order to collect the data, non- participant and direct observations will be made following an observation script. To this end, four research assistants, after receiving 16-hour training, will observe the activities of the nursing professionals responsible for receiving medication from the pharmacy, packaging, conference, preparation, administration, checking and registration of medications in prescriptions and monitoring of patients for adverse drug effects.In this stage, the compilation of the data emanated from the interviews together with the synthesis of the best evidence extracted in the first stage will allow the preparation of a previous flowchart of the protocol to be applied in the third stage.  Third stage It will involve the application of the pilot flowchart, through simulated situations that allow the analysis and interpretation of conditions involving patients who need the practice of medication administration. The members of the nursing team that answered the questionnaire should participate in this stage. Data collection will be carried out exclusively by the researcher, requested scholars and research assistants responsible for the project, in order to avoid bias arising from the application process of the questionnaires. The research team will request the signature in the consent term for participation in research, and will have the function of explaining and clarifying doubts about completing the questionnaire. Participants will be guaranteed voluntary participation and anonymity at all stages of the research process (pre-test and data collection proper).  Data analysis The consistency of the information contained in the second and third stages will be evaluated; through the presented answers, we will make a comparative test of validity of measures, to identify the effectiveness / efficiency of the use of the protocol of interpretation of blood gases in the clinical practice of the nursing team. The comparison of the answers given in the test (first application of the questionnaire) and in the METHOD Paes GO, Mesquita MGR, Moreira MB. Best practices applied to patient safety… English/Portuguese J Nurs UFPE on line., Recife, 10(Suppl. 6):4969-73, Dec., 2016 4972 ISSN: 1981-8963 ISSN: 1981-8963 DOI: 10.5205/reuol.8200-71830-3-SM.1006sup201633 retest (second application of the questionnaire) will be done through the Kappa Index. After testing the flowchart, we will submit it to an initial validation process, aiming at the evaluation by a group of specialists. Therefore, an opinion about the product will be requested from ad hoc evaluators. With vast experience and unquestionable reputation in the area of performance related to the dimension evaluated. Three criteria will be adopted for evaluation: relevance, probability and validity of the instrument. Relevance takes into account the applicability and relevance of the protocol; Feasibility involves the operational ease, costs, required data and other barriers associated with the application of the protocol; The validity verifies the degree to which the protocol reaches its objectives, that is, it reflects the event or aspect that it proposes to measure. Three aspects of validity can be evaluated operationally: validity can be content (legitimacy of measurement), construction validity (degree of correlation with other measures of the same event), and criterion validity (logical sense for specialists).9 After completing these steps, the flowchart has undergone adjustments that should consider institutional, administrative, and scientific aspects, only then to take the methodological design and all scope that requires an assistance protocol. We used as inclusion criteria: to have an employment relationship with the participating institution and to have experience of at least one year in the participating hospital and as criteria for exclusion: you will be prevented from exercising the profession due to official licenses or extra offices.  Ethical aspects In view of the ethical-legal issues advocated by the National Health Council, this research was approved by the CAAE 17589513.0.0000.5238 from the Research Ethics Committee (CEP) of the University Hospital Clementino Fraga Filho HUCFF/UFRJ and the CEP of the School of Nursing Anna Nery (Opinion N 336,436), pursuant to the guidelines of Resolution 466/12, which seeks to ensure the rights and duties of the scientific community of research subjects and the state, based on the four basic bioethics references, not maleficence, beneficence, and justice and equity. It is expected, with the results of this study, the optimization of the work process in drug administration mediated by guiding and updated instruments, to incorporate qualified and appropriate recommendations to the reality investigated, and essentially guarantee that the precepts aimed at patient’s safety be implemented and validated. Researches of this feature generate a potential impact on the quality of direct and indirect nursing care and of all health care body, confirming the scientific, ethical and legal commitment, with the guarantee of the promotion of a greater good for the patient. 1. Camerini FG, silva LD. Segurança do paciente: análise do preparo de medicação intravenosa em hospital da rede sentinela. Texto Contexto Enferm [Internet] 2011 Jan- Mar [cited 2014 Dec 15] 20(1):41-9. Available from: http://www.scielo.br/scielo.php?script=sci_a rttext&pid=S0104-07072011000100005 2. Wachter RM. Compreendendo a segurança do paciente. [Tradução: Buss C, Schrotberger CPL, Silva AA; revisão técnica: Barcellos GB]. 2th ed. Porto Alegre: AMGH Editora Ltda; 2013. 3. Silva LD, camerini FG. Analisys of intravenous medication administration in sentinel network hospital. Text Context Nursing [Internet] 2012 Jul-Sep [cited 2014 Dec 15] 21(3): 633-41. Available from: http://www.scielo.br/pdf/tce/v21n3/en_v21 n3a19.pdf 4. Galiza DDF de, Moura OS de, Barros VL de, Luz GOA. Preparo e administração de medicamentos: erros cometidos pela equipe de enfermagem. Rev. Bras. Farm. Hosp. Serv. Saúde São Paulo [Internet] 2014 Apr-Jun [cited 2015 Feb 03] 5(2):45-50. Available from: http://enfermeirosdeplantao.com.br/artigos/ PREPARO%20E%20ADMINISTRA%C3%87%C3%83O %20DE%20MEDICAMENTOS%20ERROS%20COMET IDOS%20PELA%20EQUIPE%20DE%20ENFERMAGE M.pdf 5. Gimenes FRE, Mota MLS, Teixeira TCA, Silva AEBC, Opitz SP, Cassiani SH de B. Patient Safety in Drug Therapy and the Influence of the Prescription in Dose Errors. Rev Latino-Am Enfermagem [Internet]. 2010 Nov-Dec [cited 2015 Feb 05];18(6):1055-61. Available from: http://www.scielo.br/pdf/rlae/v18n6/03.pdf 6. Amorim FDB, Flores PVP, Bosco PS, Menezes AHB, Alóchio KV. O aprazamento de REFERENCES EXPECTED RESULTS http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-07072011000100005 http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-07072011000100005 http://www.scielo.br/pdf/tce/v21n3/en_v21n3a19.pdf http://www.scielo.br/pdf/tce/v21n3/en_v21n3a19.pdf http://enfermeirosdeplantao.com.br/artigos/PREPARO%20E%20ADMINISTRA%C3%87%C3%83O%20DE%20MEDICAMENTOS%20ERROS%20COMETIDOS%20PELA%20EQUIPE%20DE%20ENFERMAGEM.pdf http://enfermeirosdeplantao.com.br/artigos/PREPARO%20E%20ADMINISTRA%C3%87%C3%83O%20DE%20MEDICAMENTOS%20ERROS%20COMETIDOS%20PELA%20EQUIPE%20DE%20ENFERMAGEM.pdf http://enfermeirosdeplantao.com.br/artigos/PREPARO%20E%20ADMINISTRA%C3%87%C3%83O%20DE%20MEDICAMENTOS%20ERROS%20COMETIDOS%20PELA%20EQUIPE%20DE%20ENFERMAGEM.pdf http://enfermeirosdeplantao.com.br/artigos/PREPARO%20E%20ADMINISTRA%C3%87%C3%83O%20DE%20MEDICAMENTOS%20ERROS%20COMETIDOS%20PELA%20EQUIPE%20DE%20ENFERMAGEM.pdf http://enfermeirosdeplantao.com.br/artigos/PREPARO%20E%20ADMINISTRA%C3%87%C3%83O%20DE%20MEDICAMENTOS%20ERROS%20COMETIDOS%20PELA%20EQUIPE%20DE%20ENFERMAGEM.pdf http://www.scielo.br/pdf/rlae/v18n6/03.pdf Paes GO, Mesquita MGR, Moreira MB. Best practices applied to patient safety… English/Portuguese J Nurs UFPE on line., Recife, 10(Suppl. 6):4969-73, Dec., 2016 4973 ISSN: 1981-8963 ISSN: 1981-8963 DOI: 10.5205/reuol.8200-71830-3-SM.1006sup201633 medicamentos pautado na segurança do paciente: um alerta para prática de enfermagem. Rev enferm UFPE on line [Internet] 2014 Jan [cited 2015 Feb 03];8(1):224-8. Available from: http://www.revista.ufpe.br/revistaenfermag em/index.php/revista/article/download/5644 /8408 7. Merhy EE, onocko R. Agir em saúde: um desafio para o público. São Paulo, HUCITEC; 1997. 8. Paes GO, Mello ECP, Leite JL, Mesquira MGR, Oliveira FT, Carvalho SM. Care protocol for clients with respiratory disorder: tool for decision making in nursing. Escola Anna Nery Revista de Enfermagem [Internet] 2014 Apr- June [cited 2015 Feb 09];18(2):303-10. Available from: http://www.scielo.br/pdf/ean/v18n2/en_141 4-8145-ean-18-02-0303.pdf 9. LoBiondo-Wood G, Haber J. Nursing Research: Methods and Critical Appraisal for Evidence-Based Practice (Nursing Research: Methods, Critical Appraisal & Utilization). 8th ed. Rio de Janeiro/RJ. Editora: Elsevier Inc, 2013. Submission: 2016/05/11 Accepted: 2016/11/09 Publishing: 2016/12/15 Corresponding Address Graciele Oroski Paes Universidade Federal do Rio de Janeiro Centro de Ciências da Saúde Avenida Carlos Chagas Filho, 373, Bloco K, 2º andar, sala 38 Cidade Universitária Ilha do Fundão CEP 21941-902 ― Rio de Janeiro (RJ), Brazil http://www.revista.ufpe.br/revistaenfermagem/index.php/revista/article/download/5644/8408 http://www.revista.ufpe.br/revistaenfermagem/index.php/revista/article/download/5644/8408 http://www.revista.ufpe.br/revistaenfermagem/index.php/revista/article/download/5644/8408 http://www.scielo.br/pdf/ean/v18n2/en_1414-8145-ean-18-02-0303.pdf http://www.scielo.br/pdf/ean/v18n2/en_1414-8145-ean-18-02-0303.pdf Copyright of Journal of Nursing UFPE / Revista de Enfermagem UFPE is the property of Revista de Enfermagem UFPE 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. Rubic_Print_Format Course Code Class Code Assignment Title Total Points HLT-362V HLT-362V-OL191 Article Analysis 2 130.0 Criteria Percentage 1: Unsatisfactory (0.00%) 2: Less Than Satisfactory (65.00%) 3: Satisfactory (75.00%) 4: Good (85.00%) 5: Excellent (100.00%) Comments Points Earned Content 100.0% Two Quantitative Articles 10.0% Fewer than two articles are presented. None of the articles presented use quantitative research. N/A Two articles are presented. Of the articles presented, only one articles are based on quantitative research N/A Two articles are presented. Both articles are based on quantitative research. Article Citation and Permalink 10.0% Article citation and permalink are omitted. Article citation and permalink are presented. There are significant errors. Page numbers are not indicated to cite information, or the page numbers are incorrect. Article citation and permalink are presented. Article citation is presented in APA format, but there are errors. Page numbers to cite information are missing, or incorrect, in some areas. Article citation and permalink are presented. Article citation is presented in APA format. Page numbers are used in to cite information. There are minor errors. Article citation and permalink are presented. Article citation is accurately presented in APA format. Page numbers are accurate and used in all areas when citing information. Broad Topic Area/Title 10.0% Broad topic area and title are omitted. Broad topic area and title are referenced but are incomplete. Broad topic area and title are summarized. There are some minor inaccuracies. Broad topic area and title are presented. There are some minor errors, but the content overall is accurate. Broad topic area and title are fully presented and accurate. Hypothesis 10.0% Definition of hypothesis is omitted. The definition of the hypothesis is incorrect. Hypothesis is summarized. There are major inaccuracies or omissions. Hypothesis is generally defined. There are some minor inaccuracies. Hypothesis is defined. Hypothesis is generally defined. There are some minor inaccuracies. Hypothesis is accurate and clearly defined. Independent and Dependent Variable Type and Data for Variable 10.0% Variable types and data for variables are omitted. Variable types and data for variables are presented. There are major inaccuracies or omissions. Variable types and data for variables are presented. There are inaccuracies. Variable types and data for variables are presented. Minor detail is needed for accuracy. Variable types and data for variables are presented and accurate. Population of Interest for the Study 10.0% Population of interest for the study is omitted. Population of interest for the study is presented. There are major inaccuracies or omissions. Population of interest for the study is presented. There are inaccuracies. Population of interest for the study is presented. Minor detail is needed for accuracy. Population of interest for the study is presented and accurate. Sample 10.0% Sample is omitted. Sample is presented. There are major inaccuracies or omissions. Sample is presented. There are inaccuracies. Sample is presented. Minor detail is needed for accuracy. Page citation for sample information is provided. Sample is presented and accurate. Page citation for sample information is provided. Sampling Method 10.0% Sampling method is omitted. Sampling is presented. There are major inaccuracies or omissions. Sampling is presented. There are inaccuracies. Page citation for sample information is omitted. Sampling is presented. Minor detail is needed for accuracy. Sampling method is presented and accurate. How Was Data Collected 10.0% The means of data collection are omitted. The means of data collection are presented. There are major inaccuracies or omissions. The means of data collection are presented. There are inaccuracies. Page citation for sample information is omitted. The means of data collection are presented. Minor detail is needed for accuracy. Page citation for sample information is provided. The means of data collection are presented and accurate. Page citation for sample information is provided. Mechanics of Writing (includes spelling, punctuation, grammar, and language use) 10.0% Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is employed. Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied. Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed. Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech. The writer is clearly in command of standard, written, academic English. Total Weightage 100%