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
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
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