The document discusses an organizational model of health care performance, quality assessment, and management for Ohio Health. It addresses the organization's quality program goals and structure, how quality improvement projects are selected and monitored, quality improvement training for staff, quality tools and techniques used, how quality information is communicated, and how the effectiveness of quality initiatives is evaluated. Two examples of effective quality improvement initiatives are also provided that improved patient outcomes and changed practices.

1. NRSE 4580 OU Elements of an Organizational Model of Health Care Paper
NRSE 4580 OU Elements of an Organizational Model of Health Care Paper ON NRSE 4580
OU Elements of an Organizational Model of Health Care PaperELEMENTS OF AN
ORGANIZATIONAL MODEL OF HEALTH CARE PERFORMANCE, QUALITY ASSESSMENT AND
MANAGEMENTThis 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 HeathWhat 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?ObjectivesCorrelate 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.ReferencesMinimum 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.StyleUnless otherwise
specified, all the written assignment must follow APA 6th edition formatting, citations and
referencesNumber of Pages/WordsUnless otherwise specified all papers should have a
minimum of 600 words (approximately 2.5 pages) excluding the title and reference
pages.NRSE 4580 OU Elements of an Organizational Model of Health Care

2. Paperattachment_1attachment_2attachment_3Unformatted Attachment PreviewNURSING
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 NRSE 4580 OU Elements of an
Organizational Model of Health Care PaperNursing 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

3. 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. 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

4. 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 NRSE
4580 OU Elements of an Organizational Model of Health Care PaperPredictive 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. Emerging 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:

5. 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. NRSE 4580 OU Elements of an Organizational Model of
Health Care PaperAhmad 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 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

6. 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. NRSE 4580 OU Elements of an Organizational Model of Health
Care PaperOur 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

7. (Pagliari, Detmer, & Singleton, 2007, pp. 330–332; Tang, Ash, Bates, CONTACT John J. Rief
USA. Tele: 412-396-2639. © 2016 Taylor & Francis riefj@duq.edu 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