Discussion question 1
Failure can be used as a strategy for success. As a leader how have you or how could you, use failure to move forward for evidence-based innovation/change in your practice setting? Please use the concepts from the book chapter and assigned article to highlight your discussion.
My anticipated system problem or challenge within the Mercris Home Health: Why Direct Hire NPs by the Faculty can cut Cost because now we have to go through a doctor, and they have to pay a doctor more.
This anticipated system problem or challenge within the Mercris Home Health was created by AI. Please help modify appropriately it as it did not sound well.
Discussion question 2 –
Respond to questions for the case study on page 203 of your textbook entitled “Staffing Adequacy” Please use the textbook and other outside resources to support your answers. (300 WORDS)
TEXTBOOK: Leadership for Evidence-Based Innovation in Nursing and Health Professions (Daniel Weberg; Sandra Davidson)
The textbook is available at www.vitalsource.com PLEASE SEE LOGIN INFO IN THE ATTACHMENT.
Please provide data and examples to support
PLEASE SEE DETAILED AND MORE QUESTIONS ATTACHED.
2
www.vitalsource.com
USERNAME: [email protected]
PASSWORD: Temitayo28@
This is an oral presentation that has two parts. Part one includes the research of the topic of your oral presentation and part two is the oral presentation event.
Description of the Assignment:
You will pitch or summarize a system problem or challenge occurring in your organization to your faculty during a one-on-one Zoom session. This will be similar to an “elevator speech”. Think of the faculty as a leader in the C- Suite of your organization.
An elevator speech is a term that means to pitch your idea and solution in a logical sequence in about the time it takes one to ride on an elevator with a person ( leader) who you are hoping to sell your idea. In other words, it is a succinct, orderly, presentation of an idea or problem with a solution. Leaders in the “C- Suite” of healthcare organizations are short on time so it is important for you to have your facts and thoughts together, succinctly, when meeting with the C- Suite leader to present a problem, idea, and or solution. The “C” in C- suite refers to ” Chief” as in chief executive officer, chief nursing officer, or chief operating officer, in other words – the leadership.
Resources for examples of elevator Speech
Business News Daily. (2023). Entrepreneurs: What is an elevator pitch? https://www.businessnewsdaily.com/3937-elevator-pitch.htmlLinks to an external site.
The Assignment: What you will do.
You will identify a system problem in your practice organization and then provide strategies for solutions to the problem from a leadership perspective. Please focus on a leadership issue that is different from your scholarly project. This perspective should be from a leadership position higher than your current position (if you are in a leadership position) and include evidence of research of concepts and references about the leadership issue from your text, the literature, an artificial intelligence (AI) tool called Elicit, class discussion, and/or the book presentations.
PART 1
As part of your Leadership Challenge, you are tasked with providing recommendations to manage a current or anticipated system problem or challenge within the organization of your choosing. These recommendations are to be supported by concepts from current research or references.
Research methods are constantly changing, with new tools and systems being developed regularly. It’s rare to check out a physical academic journal article nowadays! Recent developments in artificial intelligence (AI) have brought about even more new and powerful ways to research a topic. While fast and thorough, these new tools have drawbacks and flaws that may not be readily apparent. Part of this assignment will be to investigate useful new AI tools can be, and how they compare to more traditional search and research methods and references you may be used to,
What tool to use
The AI tool you’ll be using for this course is Elicit.org.Links to an external site. Give yourself time to familiarize yourself with this tool! Play around with it.
On the main page of Elicit, you can ask it a research question or topic question in natural language. Elicit will then attempt to understand the meaning of your question and find articles related to it.
How do I use Elicit?
After asking it a question, you can review the papers/articles it provides and the abstract summary. Try asking direct and simple questions first, then move to more complex questions and determine the difference in Elicit’s quality of articles.
You can filter Elicit’s results by when it was published, keywords, whether it has a readable PDF (important if you do not have access to the article otherwise!), and even by study type!
You can also add more information than just the summary. On the left side, you will see “Add information about all papers” and can add in other information like a more detailed abstract summary, limitations, outcomes measured, number of participants, methodology, and more. Just click in the “Search for paper information” search box to find more options.
What do I submit?
Submit a one-page summary of the experience using Elicit to find your topic for your leadership challenge. Answer the following questions in your summary:
· What questions did you ask Elicit in order to find articles on your topic? Include both the simple and complex questions that you used to obtain your results. The results are the articles on your topic.
· What advantages did you find using Elicit for researching your topic? What disadvantages?
· What errors did you find in Elicit’s findings, such as incorrect or omitted authors, incorrect or incomplete abstract summaries, etc?
o How did you locate these errors? What was your process for determining if the articles were strong evidence to support your oral presentation?
· What did you look for in Elicit’s results to determine if a paper was useful enough to research further? (Abstract, paper title, participant number, etc.)
· Will you use the information in your oral presentation why or why not?
· Submit this summary along with the list of references that you will use in your oral presentation one day prior to your scheduled presentation.
PART 2
Your course faculty will schedule a 30-minute block of time with you using Zoom.
All oral presentations should be scheduled and completed by the end of the sixth week of the course.
Your oral presentation time should be between 5-10 minutes. The remaining time will be for debriefing and discussion.
The pitch/oral presentation should include 1) a brief description of your organization’s structure and culture, 2) the organization’s mission and vision, 3) the use of evidence/references from the sources listed in part one for recommendations to support change, 4) reaction and adaptation to change/challenges of the organization, 5) communication patterns of the organization and 6) innovations needed to overcome barriers to solve the problem. In other words, after presenting the problem, state how you as a leader would manage the challenge to achieve success within the organization.
Refer to the rubric for a complete list of items that you will address during your oral presentation.
Tips for presentation success:
Introduce yourself
Follow the rubric
Speak slowly – be succinct
Include the referenced information to support your points
Include any AI information if appropriate
Be persuasive and positive
Practice! Practice! Practice!
My anticipated system problem or challenge within the Mercris Home Health: Why Direct Hire NPs by the Faculty can cut Cost because now we have to go through a doctor, and they have to pay a doctor more.
This anticipated system problem or challenge within the Mercris Home Health was created by AI. Please help modify appropriately it as it did not sound well.
Please provide data and examples to support
RESOURCES
2. Read the following articles:
Nursing Value of Work Use of Big Data.pdf ATTACHED.
Jenkins, A., Garcia, A., Farm-Fran, D., Choromanski,L.,& Welton, W. (2018).Academic/practice/industry collaboration to develop nursing value research data warehouse governance, Nursing Economics$, 36(5), p. 207-212,251. (MO) 1,2)
Frontline innovations in health care – failure, microclimates and leadership.pdf ATTACHED
Actions
Zuber, C. & Weberg, D. (2020). Frameworks for leading frontline innovation in health care: Failure, microclimates, and leadership. Nurse Leader, 18(3), p. 290-295. https://doi.org/10.1016/j.mnl.2020.03.005 Links to an external site. (MO 3,4)
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Weekly Discussion Board Grading Criteria
DISCUSSION BOARD RUBRIC
Student Points
QUALITY
POINT RANGE
Demonstrates thorough preparation, synthesis and informed opinion; addresses topic thoroughly; comments/ asks questions that contribute to group learning. Brings appropriate outside materials into the discussion; always listens to and supports the work of others. Meets the deadlines for posting responses as outlined by course requirements. Includes citations in the body of the post as well as references at the end of the post using APA format.
5- 4.5
Demonstrates some preparation and generally addresses topic; comments/asks questions; brings outside materials to class but may lack appropriateness; usually listens to and supports the work of others. May be inconsistent in responses or delayed in response to others. APA formatting errors in citations and references.
4.4 -2.5
Demonstrates inadequate preparation, marginal or off-topic posts, lacks synthesis, and reaffirms existing information, minimal / no contribution to group learning. Posts only within the last 24 hours of the week; rarely supports the efforts, ideas, and work of others. Late or no response to others. Posting lacks citations and references.
2.4- 0
ASSESSMENT, REASONING, AND SYNTHESIS
POINT RANGE
Sound synthesis of material and data analysis from assigned readings and activities; Draws appropriate conclusions; facilitates/stimulates interaction and discussion with peers and instructor. Presents new ideas to the group.
5- 4.5
Faulty or incomplete synthesis and conclusions of data analysis, offers superficial opinion; Responses are limited and may not reflect assigned weekly reading or activities. Interactions and ideas are limited.
4.4 -2.5
Unsatisfactory synthesis of data analysis and conclusions; content not relevant; absence of informed opinion; absence of new ideas; responses are weak and disjointed.
2.4- 0
POINT TOTAL
/10
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Peggy Jenkins Amy Garcia Lynn Choromanski
Diana Farm-Franks John M. Welton
Academic/Practice/Industry Collaboration to Develop Nursing Value Research Data Warehouse
Governance EXECUTIVE SUMMARY
Big data for nursing research is complex and requires collabora- tion from multiple organizations. The Nursing Value Workgroup (NVW), formed from the Big Data Science initiative in 2014, plans to measure nursing value and develop new real-time met- rics to monitor costs, quality, and effectiveness of nursing care through understanding the indi- vidual contribution of a nurse to a patient. The vision of the NVW is to cre- ate a warehouse that hosts data from multiple disparate data sources within multiple organiza- tions for use in research to dis- cover, explore, and test ques- tions related to nursing value. Business and managerial acu- men is necessary to take on governance and board develop- ment work. The collective open minds of several diverse partners includ- ing academia, practice, and industry allowed for robust dis- cussion, freethinking, and led to development of an innovative governance structure for a nurs- ing value research data ware- house.
B IG DATA USED FOR NURSING research can provide greater precision and granularity for measuring care quality,
patient outcomes, and cost of nurs- ing care. However, big data for nurs- ing research is complex and requires collaboration from multiple organi- zations. A national action plan to make nursing data sharable, compa- rable, timely, and more relevant to improving health (Delaney, Pruinelli, Alexander, & Westra, 2016) was developed at the 2013 Nursing Knowledge: Big Data Science Initiative hosted by the University of Minnesota. The Nursing Value Workgroup (NVW)
formed from the Big Data Science initiative in 2014 plans to measure nursing value and develop new real- time metrics to monitor costs, quali- ty, and effectiveness of nursing care through understanding the individ- ual contribution of a nurse to a patient (Welton & Harper, 2015). The vision of the NVW is to create a warehouse that hosts data from mul- tiple disparate data sources within multiple organizations for use in research to discover, explore, and test questions related to nursing value.
The NVW defines value of nursing as quality patient out- comes divided by cost of nursing
PEGGY JENKINS, PhD, RN, is Assistant Professor/Specialty Director iLEAD, University of Colorado College of Nursing, Aurora, CO.
AMY GARCIA, DNP, MSN, RN, CENP, is Healthcare Executive, Cerner Corporation, Kansas City, MO.
DIANA FARM-FRANKS, DNP, RN-BC, L-HTT-HP, CPHIMS, CPPS, CCRN-K, CPHQ, is Senior Clinical Analyst, Fairview Health Services, Minneapolis, MN.
LYNN CHOROMANSKI, PhD, RN-BC, is Informatics Specialist, HCMC, Minneapolis, MN.
JOHN M. WELTON, PhD, RN, FAAN, is Professor and Senior Scientist, Health Systems Research, University of Colorado College of Nursing, Anschutz Health Sciences Campus, Aurora, CO; and a Nursing Economic$ Editorial Board Member.
FUNDING: This article was supported by grants from the Agency for Healthcare Research and Quality (AHRQ) 1R03 HS025495-01 and the University of Colorado School of Medicine Data to Value (D2V) project.
NURSING ECONOMIC$/September-October 2018/Vol. 36/No. 5 207
Table 1. Definitions for Data Governance
Term Definition Data Collation Assembling data obtained from disparate data sources such as electronic medical records,
administrative, billing, plan enrollment, pharmacy/laboratory, patient behaviors/preferences, human resources, scheduling/payroll systems, education platforms, competency records
Data Dictionary A database about the database defining structure/individual data points. Mandatory for the control/maintenance of large databases.
Data Stewardship Focuses on providing the appropriate access to users, helping users understand the data and taking ownership of data quality (Saba & McCormick, 2015). Includes negotiating data use agreements, determining institutional review boards, coordinating executive leadership oversight, auditing, compliance, legal departments.
Data Privacy Keeping confidential patient and organizational data. Includes reviewing/approving policies/standards that reflect ethical, regulatory, and statutory compliance with data privacy requirements.
Data Security Protecting data from unauthorized access and data corruption. Includes overseeing establishment of policies/procedures for secure transmission, storage of data via networks, appropriate data encryption, firewalls, password protection (Ballard et al., 2014).
Data Use Purposes for which data are requested, accessed, and analyzed Data Queries Process and tools used to pull requested data from a large database Data Retention Period of time data are maintained; Dictated by legal, organizational continuity, research
standards Data Audits Evaluate information system/date integrity, identify unauthorized system access, ensure
appropriate collection/representation of data. User Training Safeguard quality researchers will obtain training on data warehouse.
care (Pappas, 2013). Nurse staffing software available today provides data that can potentially measure nursing cost more precisely by recording variables such as indi- vidual nurse work time, salary, benefits, and patient acuity. Electronic health record (EHR) data captures the interaction between nurse and patient, which can be translated into time spent per patient. These data can be linked directly to each patient through the nurse-patient assign- ment. The linkage of nurses and patients is accomplished through unique identifiers; therefore, other data can also be linked such as patient demographics, diagnosis data (e.g., diagnosis-related group), and other hospital-level or clini- cally relevant information. In addi- tion, data unique to each nurse can be collected such as nurse demo- graphics and experience level. The
combination of unique patient and nurse data allows analysis that until now has been nearly impos- sible (Welton, 2016). One author completed a pilot study using data to explore patient and nurse char- acteristics contributing to nursing cost per patient using unique patient and nurse data (Jenkins & Welton, 2014). Extracting, organiz- ing, warehousing, and analyzing such data can be a complex under- taking involving many different players and organizations. To ensure validity, reliability, and standardization of these data for research, a data warehouse gover- nance structure is imperative.
Methods A collaborative inter-organiza-
tional data warehouse requires explicit governance so all parties understand and agree to roles, requirements, processes, and legal
implications for use of data for research. A task force of the NVW focused on creating a governance structure for the data warehouse. The consortium model, governing operational board structure, gover- nance charter, and lessons learned of a nursing value research data warehouse (NVRDW) are present- ed.
Task Force Members The original NVRDW gover-
nance task force was a subcommit- tee of the NVW comprised of nine members including representatives from hospitals, academia, and industry. Additional members from primary care and industry lent expertise for thorough planning. Representatives were from diverse settings, both national and interna- tional. The task force included informatics experts such as clinical analyst, chief nurse executive strate-
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Figure 1. Nursing Value Research Data Warehouse Governance Structure
Consortium Executive Board
(MOU) Strategic oversight academic
Board of Directors (Bylaws)
Volunteers to develop strategy for development, maintenance,
use of data
Operational Committee
Volunteers to solve specific issues
Operational Committee
Volunteers to solve specific issues
Host Organization Physical location of warehouse,
responsible for data maintenance, security and privacy; funded by
grants and data use fees
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