NRSE 4580 OU Elements of an Organizational Model of Health Care Paper

NRSE 4580 OU Elements of an Organizational Model of Health Care Paper NRSE 4580 OU Elements of an Organizational Model of Health Care Paper ELEMENTS OF AN ORGANIZATIONAL MODEL OF HEALTH CARE PERFORMANCE, QUALITY ASSESSMENT AND MANAGEMENT This assessment requires you to use information from your assigned readings, the literature and leaders in your organization to answer the following questions. If you are not currently employed by an organization, gather information from a nurse leader or quality management colleague. Organization name: Ohio Heath What are the organization’s quality program goals and objectives? What is the organization’s quality management structure? If there is not a formal structure, who is responsible for quality management in the organization? How are quality improvement projects selected, managed and monitored? Does nursing staff have any input? State if quality improvement inservice programs are available for staff in your facility and describe a brief overview of the content. What quality methodology and quality tools/techniques are utilized? Are they effective? Why or why not? Provide rationale. How are QI activities and processes communicated to staff? Is the communication effective? How could it be improved? How does the organization evaluate QI activities for effectiveness? What is the process when the QI activity is not effective? Provide 2 examples of a QI initiative that has been effective in your organization. Describe the QI process that occurred. What was the impact on patient outcomes? Did it result in a change in practice? Objectives Correlate a model of healthcare performance and quality to your organization. Identify the nurse’s role in measuring, monitoring and improving health care quality and safety. Discuss terms and concepts related to health care quality and safety. References Minimum of four (4) total references: two (2) references from required course materials and two (2) peer-reviewed references. All references must be no older than five years (unless making a specific point using a seminal piece of information) Peer-reviewed references include references from professional data bases such as PubMed or CINHAL applicable to population and practice area, along with evidence based clinical practice guidelines. Examples of unacceptable references are Wikipedia, UpToDate, Epocrates, Medscape, WebMD, hospital organizations, insurance recommendations, & secondary clinical databases. Style Unless otherwise specified, all the written assignment must follow APA 6th edition formatting, citations and references Number of Pages/Words Unless otherwise specified all papers should have a minimum of 600 words (approximately 2.5 pages) excluding the title and reference pages. carol_2019_synthesis_of_nursing_knowledge.pdf reif_et_al_2016_using_health_information_technology.pdf p3.docx ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS NURSING INFORMATICS The synthesis of nursing knowledge and predictive analytics By Whende M. Carroll, MSN, RN-BC A s healthcare organizations enter the maintenance and optimization phases of electronic health record (EHR) implementation, the time has come for us to leverage the vast amounts of data generated by the EHR and associated technology to improve information sharing and deliver excellent clinical care and patient experience. The evolution from simple data collection to aggregating, tracking, trending, and analyzing big data to enhance care is in flight. Now, the ability to use even more advanced data manipulation techniques for care planning and delivery is, in many cases, required to meet the needs of modern nursing practice.1 Through the application of emerging technologies, such as predictive analytics and machine learning, nurses can add tremendous value to the future of care delivery and operations. Nurses as knowledge workers Nurses are knowledge workers, performing highly variable, focused work that involves a significant amount of information.2 In our daily work, we use our specialized nursing skills to compile, sift through, and find actionable solutions using disparate data sources and large datasets. With explicit knowledge of clinical science and by applying the nursing www.nursingmanagement.com process and critical thinking, nurses instinctively take discrete data elements and organize them into information to use in every patient experience. The application of our nursing knowledge and experience, married with successful data handling, allows us to make critical decisions at the point of care. The result is nurses disseminating wisdom and the improved application of evidencebased practice, adding immeasurable value to the clinical setting and moving toward improving the health of populations and communities. Through advanced data analytics, we can use this information to our advantage and distribute the subsequent wisdom with greater impact. Studies have shown that nurses spend upwards of 50% of their time recording and managing this assimilated information.3 By using acquired patient data, nurses gain information and apply knowledge to guide practice.4 Nursing knowledge identifies information and creates relationships so it can be synthesized and formalized.2 These relationships leverage the nurse’s ability to apply inferences to information and make a judgment to determine patient progress toward expected outcomes or identify nursing problems and interventions appropriate for the challenge. A set of vital signs is information; however, the interpretation of that information as abnormal indicates knowledge.5 Increasingly, new ways of using data enhance the clinical experience by allowing nurses to make informed, data-driven decisions. Today’s nurses need constant involvement in technical innovation to stay current and forwardthinking in care delivery.6 To that end, technologic advances enabled through the EHR, medical claims, patient prescription history, and digital sensor data now allow nurses to provide more precise, higher quality, and safer care. The application of emerging technologies enables nurses to reap the benefits of data manipulated though nonhuman processing, accelerating and expanding nursing Nursing Management • March 2019 15 Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved. NURSING INFORMATICS knowledge generation and prioritizing care based on patient needs. Applied predictive analytics Advanced computational analysis of healthcare data, particularly predictive analytics, can help nurses unearth unidentified trends within multiple sources of data. Predictive analytics is the statistical science of data analysis that discovers various patterns.7 By applying computational models and analysis, nurses can draw on historical, Machine learning methods take historical data and compare them with current data to predict what will happen in the future. With every refresh of new data from designated sources, the machine learns how to be more precise in predicting.10 Predictive analytics and machine learning in clinical care function as “assistive intelligence.”12 Nurses’ critical thinking is still needed to assess the clinical situation, synthesize the derived information to gence of predictive analytics and machine learning along with nursing knowledge can keep patients from: • rapid deterioration. NRSE 4580 OU Elements of an Organizational Model of Health Care Paper Predictive analytics can help nurses identify when a patient is declining by sending a warning or risk score based on patient-specific data, such as vital signs and lab or radiology results, along with external data sources from sensors and remote devices.14 A machineassimilated risk score, in addition By applying computational models and analysis, nurses can draw on historical, present, and simulated future data to provide actionable insights into real-world clinical and operational problems. present, and simulated future data to provide actionable insights into real-world clinical and operational problems.8 Predictive analytics allows a machine approach to refine these data and extract hidden value from the newly discovered patterns to dynamically inform data-driven decision-making so we can know what will happen in healthcare settings, when, and what to do about it.9 Further robust exploration of data is needed to harness the power of prediction in clinical care. The addition of advanced algorithms through machine learning is a way to guide and standardize best practices and expedite treatment. Machine learning is the study of computer algorithms that improve automatically through experience.10 It’s a form of artificial intelligence that enables software applications to become more accurate in predicting outcomes without being explicitly programmed.11 make the best decision, and put the decision into action. Although human judgment is paramount to the success of predicting trends and identifying variation, the use of algorithms is promising in attaining the best outcomes, expounding on existing clinical decision support systems, and adding a helpful layer of precision. Looking toward the future, nurses can count on advanced technologies to drive cutting-edge, enhanced practices and research-based evidence to the point of care to help make the most complex clinical decisions with a higher degree of confidence.13 Using data for prediction Nurses have the influence to proactively adopt and expertly apply emerging technologies, adding value to care delivery by making the best data-driven decisions to improve outcomes and patient experience. Using the assistive intelli- 16 March 2019 • Nursing Management to patient assessment and presentation, quickly enables nurses to determine if the patient’s status is indeed declining, which allows us to begin immediate care, prevent further deterioration, and move the patient to a higher level of care if needed. • staying in the hospital for too long or not long enough. An aggregate of the patient’s demographics, comorbidities, number of medications, and lab and vital signs values derived from the EHR can determine the risk of readmission. Understanding a patient’s risk of rehospitalization powered by advanced analytics such as machine learning will better enable nurses to personalize care, discharge planning, and outpatient care needs earlier—all factors that can prevent rehospitalization.15 Conversely, with predictive analytics, nurses can recognize what may inappropriately lengthen a patient’s stay, such as ineffective medication www.nursingmanagement.com Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved. NURSING INFORMATICS management, missed treatments and procedures, and not meeting discharge criteria. • failing to receive the best options at end of life. Predicting mortality using machine learning is also on the horizon. Machine learning algorithms interpret multiple data sources, including the EHR, medical claims, and geographic data, to discover patterns indicating imminent mortality in patients.16 Predictive analytics can help nurses lead data-driven critical conversations to ensure that patients receive appropriate care. These knowledge-derived discussions help patients and family members consider the best care options approaching death, including palliative and hospice care. Using analytics can aid nurses to engage patients and families with end-of-life choices to improve quality of life.17 Into the future The value of nursing knowledge synthesized with predictive analytics enables the provision of evidencebased care and the promotion of safety, quality, and appropriate patient outcomes—the end goal of using all health information technology. NRSE 4580 OU Elements of an Organizational Model of Health Care Paper technologies, such as predictive analytics and machine learning, will strengthen our ability to collect data, assimilate these data into information, apply newly discovered knowledge, and gain wisdom to improve care delivery. Moving forward, we’ll use these technologies to enhance EHR clinical decision support tools and help optimize operational workforce issues such as inadequate staffing through more precise scheduling. We’ll also decrease inefficiencies that hinder caregiver satisfaction, such as breakdowns in multidepartmental processes and patient throughput, www.nursingmanagement.com and become key players in solving the challenges of transitional care. Harnessing the power of using data to extract valuable patterns to inform better decision-making gives nurses an edge in healthcare. We’ll collectively add influence as we provide appropriate, evidencebased care and advance the nursing profession. NM REFERENCES 1. Lutfiyya MN, Schicker T, Jarabek A, Pechacek J, Brandt B, Cerra F. Generating the data for analyzing the effects of interprofessional teams for improving triple aim outcomes. In: Delaney C, Weaver C, Warren J, Clancy T, Simpson R, eds. Big Data-Enabled Nursing: Education, Research and Practice (Health Informatics). New York, NY: Springer; 2017:136. 2. McGonigle D, Mastrian K. Nursing Informatics and the Foundation of Knowledge. 4th ed. Burlington, MA: Jones & Bartlett Publishers; 2017. 3. Ommaya AK, Cipriano PF, Hoyt DB, et al. Care-centered clinical documentation in the digital environment: solutions to alleviate burnout. NAM Perspectives. 2018. https://nam.edu/ wp-content/uploads/2018/01/CareCentered-Clinical-Documentation. pdf. 4. McGonigle D, Mastrian K. Nursing Informatics and the Foundation of Knowledge. 2nd ed. Burlington, MA: Jones & Bartlett Publishers; 2012. 5. Matney SA, Maddox LJ, Staggers N. Nurses as knowledge workers: is there evidence of knowledge in patient handoffs? West J Nurs Res. 2014;36(2):171-190. 6. Herron J, Sampson C. The knowledge worker in healthcare. Royer Maddox Herron Advisors. 2016. www.royer maddoxherronadvisors.com/Blog/2016/ April/The-Knowledge-Worker-inHealthcare.aspx. 7. Bari A, Chaouchi M, Jung T. Predictive Analytics for Dummies. 2nd ed. Hoboken, NJ: John Wiley & Sons, Inc.; 2017. 8. Thornton C, O’Flaherty B. Improving customer centric design for self-service predictive analytics. In: Donnellan B, Helfert M, Kenneally J, VanderMeer D, Rothenberger M, Winter R, eds. New Horizons in Design Science: Broadening the Research Agenda. New York, NY: Springer; 2015:230-245. 9. Carroll WM, Hofmeister N. Predictive analytics and the impact on nursing care delivery. HIMSS18 Conference & Exhibition. 2018. http://365.himss.org/ sites/himss365/files/365/handouts/550229529/handout-NI2.pdf. 10. Mitchell TM. Machine Learning. New York, NY: McGraw Hill; 1997. 11. Posadas S, Kunkel K. Machine learning: a brave new world. Valve Magazine. 2017. www.valvemagazine.com/ web-only/categories/technicaltopics/8687-machine-learning-a-bravenew-world.html. 12. McGrane C. Health tech podcast: how AI is making humans the “fundamental thing in the internet of things.” GeekWire. 2018. www.geekwire.com/2018/ health-tech-podcast-ai-making-hu mans-fundamental-thing-internetthings. 13. Simpson RL. Technology enables value-based nursing care. Nurs Adm Q. 2012;36(1):85-87. 14. O’Dowd E. IoT devices significantly lower nurse response times. HITInfrastructure. 2017. https://hitinfra structure.com/news/iot-devicessignificantly-lower-nurse-responsetimes. 15. Nelson JM, Rosenthal L. How nurses can help reduce hospital readmissions. Am Nurse Today. 2015;10(5) (suppl):18-20. 16. Ahmad MA, Eckert C, McKelvey G, Zolfaghar K, Zahid A, Teredesai A. Death versus data science: predicting end of life. The Thirtieth AAAI Conference on Innovative Applications of Artificial Intelligence. New Orleans, LA. February 4-6, 2018. 17. Ferguson R. Care coordination at end of life: the nurse’s role. Nursing. 2018;48(2):11-13. Whende M. Carroll is the founder of Nurse Evolution and the senior editor of the Online Journal of Nursing Informatics. The author has disclosed no financial relationships related to this article. DOI-10.1097/01.NUMA.0000553503.78274.f7 Nursing Management • March 2019 17 Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved. Health Communication ISSN: 1041-0236 (Print) 1532-7027 (Online) Journal homepage: https://www.tandfonline.com/loi/hhth20 Using Health Information Technology to Foster Engagement: Patients’ Experiences with an Active Patient Health Record John J. Rief, Megan E. Hamm, Susan L. Zickmund, Cara Nikolajski, Dan Lesky, Rachel Hess, Gary S. Fischer, Melissa Weimer, Sunday Clark, Caroline Zieth & Mark S. Roberts To cite this article: John J. Rief, Megan E. Hamm, Susan L. Zickmund, Cara Nikolajski, Dan Lesky, Rachel Hess, Gary S. Fischer, Melissa Weimer, Sunday Clark, Caroline Zieth & Mark S. Roberts (2017) Using Health Information Technology to Foster Engagement: Patients’ Experiences with an Active Patient Health Record, Health Communication, 32:3, 310-319, DOI: 10.1080/10410236.2016.1138378 To link to this article: https://doi.org/10.1080/10410236.2016.1138378 Published online: 25 May 2016. Submit your article to this journal Article views: 1048 View related articles View Crossmark data Citing articles: 6 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=hhth20. NRSE 4580 OU Elements of an Organizational Model of Health Care Paper HEALTH COMMUNICATION 2017, VOL. 32, NO. 3, 310–319 http://dx.doi.org/10.1080/10410236.2016.1138378 Using Health Information Technology to Foster Engagement: Patients’ Experiences with an Active Patient Health Record John J. Riefa, Megan E. Hammb, Susan L. Zickmundc, Cara Nikolajskid, Dan Leskye, Rachel Hessf, Gary S. Fischerg, Melissa Weimerd, Sunday Clarkh, Caroline Ziethd, and Mark S. Robertsg,i a Department of Communication and Rhetorical Studies, Duquesne University; bQualitative, Evaluation and Stakeholder Engagement Services, Center for Research on Health Care, University of Pittsburgh; cCenter for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, University of Pittsburgh; d Center for Research on Health Care, University of Pittsburgh; eUniversity of Pittsburgh School of Medicine, University of Pittsburgh; fDepartments of Population Health Sciences and Internal Medicine, University of Utah; gDivision of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, University of Pittsburgh; hDepartment of Emergency Medicine, Weill Cornell Medical College; iDepartment of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, University of Pittsburgh ABSTRACT Personal health records (PHRs) typically employ “passive” communication strategies, such as nonpersonalized medical text, rather than direct patient engagement in care. Currently there is a call for more active PHRs that directly engage patients in an effort to improve their health by offering elements such as personalized medical information, health coaches, and secure messaging with primary care providers. As part of a randomized clinical trial comparing “passive” with “active” PHRs, we explore patients’ experiences with using an “active” PHR known as HealthTrak. The “passive” elements of this PHR included problem lists, medication lists, information about patient allergies and immunizations, medical and surgical histories, lab test results, health reminders, and secure messaging. The active arm included all of these elements and added personalized alerts delivered through the secure messaging platform to patients for services coming due based on various demographic features (including age and sex) and chronic medical conditions. Our participants were part of the larger clinical trial and were eligible if they had been randomized to the active PHR arm, one that included regular personalized alerts. We conducted focus group discussions on the benefits of this active PHR for patients who are at risk for cardiovascular disease. Forty-one patients agreed to participate and were organized into five separate focus group sessions. Three main themes emerged from the qualitatively analyzed focus groups: participants reported that the active PHR promoted better communication with providers; enabled them to more effectively partner with their providers; and helped them become more proactive about tracking their health information. In conclusion, patients reported improved communication, partnership with their providers, and a sense of self-management, thus adding insights for PHR designers hoping to address low adoption rates and other patient barriers to the development and use of the technology. Introduction This study investigates participant experiences of and satisfaction with an active Personal Health Record (PHR). In our parent study design (a randomized controlled trial), we differentiated between “active” and “passive” PHRs, in line with previous research in this area (Fischer et al., 2013; Hess et al., 2014). The “passive” PHR included problem lists, medication lists, information about patient allergies and immunizations, medical and surgical histories, lab test results, health reminders, and secure messaging. Such “passive” PHR designs typically provide both information and the capacity to make contact with providers, but only to patients who actively pursue these elements (Pagliari, Detmer, & Singleton, 2007, pp. 330–332; Tang, Ash, Bates, CONTACT John J. Rief USA. Tele: 412-396-2639. © 2016 Taylor & Francis [email protected] Overhage, & Sands, 2006). The active arm included all of these elements but added personalized alerts to patients for services coming due based on various demographic features (including age and sex) and chronic medical conditions (Fischer et al., 2013; Hess et al., 2014). Our hypothesis in this paper is that “active” PHR designs may have the capacity to address the gap that exists between “information-only” (Bodenheimer, Lorig, Holman, & Grumbach, 2002, pp. 2473–2474) approaches that treat patients as “passive” consumers of information (described by Bodenheimer, 2005, p. 319; Bodenheimer et al., 2002, p. 2470; Charles, Gafni, & Whelan, 1997, pp. 682–683; Roter, 1977; Wagner et al., 2005) and those that inspire patient agency, action, and self-management (Roter, Stashefsky-Margalit, & Rudd, 2001; Rubinelli, Schulz, & Nakamoto, 2009; Wagner Department of Communication and Rhetorical Studies, Duquesne University, 600 Forbes Avenue, Pittsburgh, PA 15282, HEALTH COMMUNICATION et al., 2001, 2005). They may also provide an impetus for “patients [to] become active participants in their own health care” (Tang & Lansky, 2005, p. 1291; see also; Tang et al., 2006), thus transcending the more common and often less efficacious “passive role” that fails to inspire patient autonomy, engagement, activation, and shared decision-making (Alexa …NRSE 4580 OU Elements of an Organizational Model of Health Care Paper Purchase answer to see full attachment Student has agreed that all tutoring, explanations, and answers provided by the tutor will be used to help in the learning process and in accordance with Studypool’s honor code & terms of service . Get a 10 % discount on an order above $ 100 Use the following coupon code : NURSING10

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