Critique theCarpenter, Short, Willians, Yandell, and Bowers (PDF)(Links to an external site.)article (attached). Using this discussion's quantitative critique rubric (attached, rubric must be closely...

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  • Critique theCarpenter, Short, Willians, Yandell, and Bowers (PDF)(Links to an external site.)article (attached).

  • Using this discussion's quantitative critique rubric (attached, rubric must be closely followed) This is part of a larger paper, this assignment is to critically review and breakdown three sections. The sections needed reviewed are 1. Design 2. Sample and 3. measurement.

  • The goal here is beyond identification and description; a critical analysis of this quantitative method is expected. APA format, utilize the article with apa citations.

  • 1. Design-what design was used in the article, describe the design and how it was utilized in this article.

  • 2. Sample-what was the sample of this article, how was it collected, who was involved and how were they selected.

  • 3. Measurement- how was the measurements and results determined (not what are the results but how was it collected and measured). was certain tools, or programs utilized?

  • a couple paragraphs for each sections at a masters level article review for the sections above. No title page needed, just references and works cited. utilized.

  • Thank you so much for you help, so far the assignments from here have been top notch and at a masters level. looking forward to reading this as well.




255NURSING ECONOMIC$/September-October 2015/Vol. 33/No. 5 A CROSS THE NATION, hospi-tals are realizing the fi -nancial impact of alteredMedicare reimbursement processes based on pay-for-perfor- mance programs called Value-Bas - ed Purchasing (VBP) and the Re - admissions Reduction Program (RRP). These evolving Centers for Medicare & Medicaid Services (CMS) initiatives incorporate meth - ods to realign hospital reimburse- ment rates based on algorithms that utilize scores for quality, patient satisfaction measures, mortality, and readmission rates. VBP payment reductions of up to 1% of eligible Medicare reim- bursement began October 1, 2012, with planned 0.5% annual in - creases up to a level of 3% by ap - proximately 2017. With VBP, org - anizations can receive improved reimbursement for performance better than national rates at levels similar to penalties (Department of Health and Human Services, 2011). The RRP, rates of patients re - admitted within 30 days of hospi- tal discharge, began affecting hos- pitals in October 2012 (the be - ginning of CMS fiscal year 2013) with a potential rate reduction on all Medicare admissions of up to 1% (CMS, 2013). The RRP is CMS’s most significant program, with up to a 3% reduction in future reim- bursement beginning October 2014. Unlike VBP, the RRP offers no o - pportunity to ex ceed performance expectations and increase reim- bursement. There is an inverse relationship between readmission rates and payments: performance at or above national levels allows a hospital to retain the at-risk amount, while below standard rates decrease reimbursement. The combined impact of VBP and EXECUTIVE SUMMARY Evidence supporting the devel- opment of Clinical Decision Units (CDUs) to impact conges- tive heart failure readmission rates comes from several cate- gories of the literature. In this study, a pre-post design with comparison group was used to evaluate the impact of the CDU. Early changes in clinical and financial outcome indicators are encouraging. Nurse leaders seek ways to improve clinical outcomes while managing the current financially challenging environment. Implementation of a CDU pro- vides many opportunities for nurse leaders to positively impact clinical care and finan- cial performance within their institutions. Jo Ellen Carpenter Nancy Short Tracy E. Williams Ben Yandell Margaret T. Bowers Improving Congestive Heart Failure Care with a Clinical Decision Unit JO ELLEN CARPENTER, DNP, MBA, RN, NEA-BC, CENP, is Assistant Vice President, Nursing Operations, MedStar Georgetown University Hospital, Washington, DC. NANCY SHORT, DrPH, MBA, RN, is Associate Professor, Duke University School of Nursing, Durham, NC. TRACY E. WILLIAMS, DNP, RN, is Senior Vice President and System Chief Nursing Officer, Norton Healthcare, Louisville, KY. BEN YANDELL, PhD, is System Associate Vice President of Clinical Information Analysis, Norton Healthcare, Louisville, KY. MARGARET T. BOWERS, DNP, RN, FNP-BC, AACC, is Assistant Professor Coordinator, Adult/Gerontology Nurse Practitioner Program, and Lead Faculty, Cardiovascular Concentration, Duke University School of Nursing, Durham, NC. NURSING ECONOMIC$/September-October 2015/Vol. 33/No. 5256 RRP will be up to a 6% reduction in all hospital Medicare reim- bursement when the programs fully deploy in 2017. This is an important point for hospitals to consider, as all future CMS reim- bursements are at risk based on penalties earned, not just the diag- nostic categories performing be - low target. Jencks, Williams, and Coleman (2009) provide context for the CMS focus on readmis- sions. These authors identified 19.6% of Medicare patients dis- charged from hospitals were read- mitted within 30 days. The au - thors found that patients with a diagnosis of congestive heart fail- ure (CHF) had a 30-day readmis- sion rate of 26.9%, the highest of all diagnostic categories reported. The estimated annual cost to Medicare of unplanned readmis- sions was $17.4 billion in 2004. Norton Audubon Hospital (NAH), Louisville, KY, is a 275- bed community hospital that is part of Norton Healthcare, the largest not-for-profit integrated health care system in the state. Cardiology is the major service line for NAH and it provides terti- ary-level care for patients from local and extended service areas. In addition, the local community has a large base of patients with Medicare as their primary payer. Based on its service line focus and community demographics, NAH cares for more than 50% (>7,000 annual admissions) of all Medi - care patients for the entire Norton system. Stellar performance in all categories of VBP and RRP is of paramount importance for NAH and the health system in terms of optimizing CMS reimbursement and quality care for patients. The NAH readmission rate for CHF is 25.35% for January and February 2013, representing 18 re - admissions out of 71 patient index admissions. To retain full reim- bursement, a hospital must not ex - perience excess readmissions. An excess readmission is defined as “a hospital’s readmission perform- ance compared to the national average for the hospital’s set of patients with that applicable con- dition” (CMS, 2013, para. 5). The addition of strategies to address CHF readmissions is now critical due to the future impact on Medi - care reimbursement. Literature Review and Synthesis Evidence supporting the de - velopment of Clinical Decision Units (CDUs) to impact CHF read- mission rates comes from several categories of literature, including disease management and patient education programs, CDU/Ob - servation Unit efficacy studies, and reports on the use of predic- tive index tools. A summary of pertinent literature supporting the development of a CDU specifical- ly for patients with CHF is provid- ed in Table 1. CDU Development and Design Factors Norton Healthcare has been exploring new systems in chronic disease management, including the development of an outpatient, nurse practitioner-run CHF clinic on the NAH campus. The NAH leadership and case management teams visited a successful CHF clinic at Piedmont Hospital, Atlanta, GA, and learned they were in the process of strengthen- ing their program through the addition of a CDU. Further, key members of a leading cardiology practice voiced an interest in man- aging patients with CHF in a CDU based on prior experiences and knowledge of care trends in their specialty. Deployment of an out- patient CHF clinic and a CDU as a bundled approach to CHF man- agement emerged as a viable plan. This project is designed to bring about significant change in care delivery for patients with CHF and provides a foundation through which leadership seeks to create a vision and program for patient care that serves as a basis for future work around chronic dis- ease management. The primary aim of the CDU development proj- ect is to improve care and reduce NAH’s overall 30-day readmission rate for patients with CHF. Table 2 provides a summary of the compo- nents of this project identified as contributory to its success. At the time of this project, Norton Healthcare used Microsoft Amalga™ Readmissions Manager (Redmond, WA) as a predictive index tool to aggregate data and provide analysis via a set of mark- ers designed to predict the proba- bility of an individual pa tient’s risk for readmission. Examples of mark- ers include patient age, history of prior admissions, number of emer- gency department (ED) visits in the prior year, presence of certain chronic diseases, and marital sta- tus. Based upon analysis of histor- ical data, Norton Healthcare has established any predictive index score above 26 as higher risk for readmission (scores can range from below zero to greater than 100). The predictive index deter- mines individual patient risk for readmission and triggers aggres- sive discharge planning, including a followup visit with a primary care or cardiology provider within 24-48 hours post-discharge. An at-risk patient receives education based on RN assess- ment of risk factors from the pre- dictive index elements and/or fail- ures of the prior discharge plan, as well as planned transitions to home care or other post-discharge care sources. If a CDU patient is unable to discharge home safely, nursing and case management make alternate arrangements such as transfer to a rehabilitation or skilled nursing facility. This sce- nario typically prompts conver- sion to an inpatient admission and transfer to an inpatient unit so that adequate time can be devoted to proper discharge planning. Project Resources A summary of the project implementation costs is shown in Table 3. The major capital expens- es were new cardiac monitoring and nurse call systems, as well as 257NURSING ECONOMIC$/September-October 2015/Vol. 33/No. 5 Table 1. Relevant Literature and Clinical Decision Unit (CDU) Application Study/Citation Main Conclusions/Relevance CDU Design Elements Short-stay units and observation medi- cine: A systematic review (Daly, Campbell, & Cameron, 2003) Found positive impacts on avoiding hospital admissions, cost effectiveness, and patient satisfaction. Support for development of unit. A systematic meta-analysis of the effi- cacy and heterogeneity of disease man- agement programs in congestive heart failure (Gohler et al., 2006) Identified program components most effective in decreasing mortality and readmissions: Personal contact with the patient (home or phone visits) post dis- charge, management of transitions to the next level of care, use of a multidis- ciplinary team to manage hospital care and discharge plans. Transition management and use of mul- tidisciplinary team for inpatient care, discharge planning, and post-discharge phone calls. Effectiveness of a multipurpose obser- vation unit: Before and after study (Iannone & Lenzi, 2009). Found a statistically significant decrease in observational unit length of stay after implementation of prescribed unit admission/exclusion criteria and clinical protocols. Development of admission and dis- charge criteria and clinical protocols. 2009 focused update: ACCF/AHA guidelines for the diagnosis and man- agement of heart failure in adults (Jessup et al., 2009). Update of national guidelines, incorpo- rating new evidence. Level I recommendations for compre- hensive discharge instructions, care transitions, medication reconciliation, and primary care provider office visits. Effects of self-management intervention on health outcomes of patients with heart failure: A systematic review of ran- domized controlled trails (Jovicic, Holroyd-Leduc, & Straus, 2006) Identified decreased readmission risk with intensive disease/drug education, self-management of medicines/symp- toms and home visits/calls. Patient education and post-discharge phone calls. Risk prediction models for hospital readmission: A systematic review (Kansagara et al., 2011) Found predictive index tools can be useful in directing care transitions by allowing resources to be applied toward patients with higher risk. Use of predictive index score to direct focused attention on discharge plans. Can an emergency department-based clinical decision unit successfully utilize alternatives to emergency hospitaliza- tion? (Roberts, Baird, Kerr, & O’Reilly, 2010) Found the use of a CDU significantly decreased hospital admissions and improved discharge to post-hospital services. Support for development of the unit and discharge plans to include referrals to primary care providers. Statistical models and patient predictors of readmission for heart failure (Ross et al., 2008) Support the use of predictive index tools to guide care transitions. Use of predictive index tool. Improvement guide: Implementing a clinical decision unit to reduce avoid- able readmissions
Answered Same DayApr 06, 2021

Answer To: Critique theCarpenter, Short, Willians, Yandell, and Bowers (PDF)(Links to an external site.)article...

Poulami answered on Apr 07 2021
140 Votes
Introduction
The review was based on a strategic design that had several components. The deployment of an outpatient clinic for congestive heart failure (CHF) was the primary step in the designin
g procedure. Along with the CHF clinic, a Clinical Decision Unit (CDU) was formed for assessment of the task. An outpatient setting requires close monitoring for a safe and smooth run.
Design
Along with the CHF clinic, a Clinical Decision Unit (CDU) was formed for assessment of the task. An outpatient setting requires close monitoring for a safe and smooth run. Moreover, the patient population belonged to the group of CHFs, a deadly disease with an approximate life span of 4-5 years after diagnosis. Thus, a bundled approach to the management of CHF emerged as a viable plan for monitoring patients with CHF (Nagy et al., 2017). Norton Healthcare utilized Microsoft Amalga™ Readmissions Manager, as a predictive tool to aggregate the data as well as provide analysis through a set of markers to predict the risk probability of a patient for readmission. The project was conducted by designing a lot of markers that were helpful to predict the re-admission probability of each of the patients. Some examples of the markers included the history of prior admission, patient age, number of emergency department visits in the previous year, the existence of definite chronic diseases, and marital status (Lookman et al., 2019). If a CDU patient was unable to be discharged home safely, the management of nursing care used to make alternate decisions such as transmission to a rehabilitation center or accomplished nursing facility.
Sample
The samples utilized in the project were patients, the family members of patients, the management system, and nursing professionals. The patients were categorized into high-risk, moderate-risk, and lower-risk groups based on their disease condition as well as disease progression. The possibilities of re-admissions were...
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