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Data Utilization for the Palliative Advanced Practice Registered Nurse 

Data Utilization for the Palliative Advanced Practice Registered Nurse
Chapter:
Data Utilization for the Palliative Advanced Practice Registered Nurse
Author(s):

Marilyn Bookbinder

and Debbie Rochester-Gibbons

DOI:
10.1093/med/9780190204747.003.0005
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Key Points

  • Various types of data and related terms are used frequently in palliative care. Advanced practice registered nurses (APRNs) should be familiar with the opportunities for, and barriers to, their use.

  • APRNs must develop competencies in data to use in U.S. healthcare reform.

  • There are many examples of APRNs using data to add more value to their patient care and practices.

Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.

—Atul Butte, Stanford

Defining Data

Data are plain facts. When data are processed, organized, structured, or presented in a given context to be useful, they are called information. Pieces of information can be used in an analysis of a problem, such as the diagnosis of a health problem. Data are also facts stored and processed by a computer.1 Data can also be described by type:

  • Quantitative data are measurable and can be expressed in statistical form with numbers.

  • Qualitative data are narrative or subjective and often describe attitudes, beliefs, and feelings. They are not arrived at by statistical or other quantitative techniques. Rather, the goal is to understand behavior in a natural setting and the perspective of research participants within the context of their everyday life. Methods used for conducting inquiry include interviewing, observation, ethnography, participant observation, and focus groups.

  • Research data can be quantitative or qualitative information that is collected, observed, or created for purposes of analysis to produce original research results. Research data are generated for different purposes and through different processes and may be grouped into different categories. Each category of research may require a different type of data management plan.

Defining the types of data and their uses is a huge task. To organize the concepts, we borrowed from others in the field of implementation science, informatics, and biomedical engineering and organized information as “small” and “big” data.2

Small data are individual-level pieces of information specific to an individual or circumstance. They are specific and take a microscopic view of an individual or event. One familiar example of small data is the information entered into the electronic health record (EHR) for a new patient admission. This can include the individual’s demographics, medical, social, and psychosocial history, findings from physical exams, allergies, and medications.

Big data are patterns of data and information at the population level. The goal of big data is to obtain a macroscopic view of health. Large datasets can help us recognize patterns that are not readily apparent and allow us to go from pieces of data to collective wisdom and knowledge shared by individuals and groups. One example of big data that is high on the radar screen of most APRNs today is the Centers for Medicare and Medicaid Services (CMS)-driven Meaningful Use initiative, described more in the next section. Table 5.1 lists some frequently used terms and phrases associated with data and their definitions.

Table 5.1 Data Definitions and Related Phrases

Categorical data

Categorical data are qualitative and suited to classification into categories. Further divisible into nominal (names), ordinal (levels of quality, development), and dichotomized (mutually exclusive).

Continuous data

Data that have an infinite number of possible values

Data

Plural of datum. A collection of information or facts processed and interpreted to yield information.

Database

An organized collection of data. A medical database is all the information that exists in the practice at any time.

Data adjustment

For useful results, data often need to be modified before analysis—for example, for age, for sex, or for difficulty or for number of attempts.

Data aggregation

A collection of protected health information used to conduct data analysis relating to the healthcare operations of the entity

Data analysis

Submission of data to statistical analysis; includes sorting into categories and determining relationships between variables

Data capture

A mechanism for collecting specified segments or categories of data from a stream of automatically recorded data, some of which may be irrelevant for the specific purpose

Data processing

The collection of data, processing of the data to obtain usable information, and communication of this usable information

Derived or compiled data

Examples include text and data mining and 3D models.

Diagnostic data

Lists of diagnoses and data of clinical signs, clinical pathology results, and pathology lesions used in making diagnoses

Dimensional data

Numerical or quantitative data. May be explicit and therefore continuous, or grouped into approximate groups (e.g., nearest whole number)—that is, discrete data.

Discrete data

Data that have finite (usually whole integer) values and therefore fall naturally into groups of similar values; opposite of continuous data

e-measurement

The secondary use of electronic data to populate standardized performance measures. The prerequisites include standardized performance measures in an electronic format; clinical information systems that capture structured, coded data; and administrative and clinical workflows that facilitate consistent documentation or capture of the data needed to populate the electronic measures.

Experimental data

Data obtained from lab equipment; often reproducible, but can be expensive (e.g., gene sequences, chromatograms, magnetic field data)

Incidence data

Data related to the occurrence of specific disease incidents

Non-normal data

Data whose frequency distribution is markedly different from that of normal data

Normal data

Data that manifest graphically as a bell-shaped curve distributed symmetrically about the peak value

Observational data

Data captured in real time, usually irreplaceable (e.g., sensor data, survey data, sample data, neurological images)

Ordinal data

A type of data containing limited categories with a ranking from the lowest to the highest (e.g., none, mild, moderate, severe). Subjects placed in order from high to low. For instance, an employer might rank applicants for a job on their professional experience, giving a rank of 1 to the subject who has the least experience, 2 to the next highest, and so on. This rank does not tell us by how much subjects differ.

Paired data

Values that fall normally into pairs and can therefore be expected to vary more between pairs than within pairs

Preexisting data

Data that were in existence before the commencement of a study. Of limited value unless they are exactly the data required, they have been collected adequately, and a group of pre-existing controls with their corresponding data can be identified.

Prevalence data

Disease occurrences are recorded against the size of the population at risk at the time.

Ratio-level data

A higher level of data than the interval level because the ratio has an absolute zero point that we know how to measure. Thus, weight is an example of the ratio scale because it has an absolute zero that we can measure.

Raw data

Data as they are collected, before any calculation, ordering, etc., has been done

Adapted from The Free Dictionary website (http://medical-dictionary.thefreedictionary.com/data)

APRNs Are Poised to Lead Healthcare Reform

Since 2010, two landmark federal initiatives, the Affordable Care Act (ACA) and the CMS-driven Meaningful Use initiative, as well as professional nursing organization imperatives, have poised APRNs in the next decade to maximize their education, scope of practice, and influence to use information wisely for their patients, their profession, and the healthcare industry.

The ACA

The signing of the ACA created enormous pressures on federal, professional, and healthcare organizations to reform and give Americans more access to affordable, quality health insurance and to reduce the growth in healthcare spending in the United States.3

The Institute of Medicine’s landmark Future of Nursing report (2010) suggests that the ACA puts consumers back in charge of their healthcare, giving Americans a new “Patient’s Bill of Rights” and the stability and flexibility they need to make informed choices about their health and insurance plans. The report describes the positive outcomes from studies of APRNs in delivering safe, high-quality primary care and why healthcare organizations have increased their roles and responsibilities in patient care.4

Meaningful Use Guidelines

Another powerful driver for APRNs in the use of data was the American Recovery and Reinvestment Act of 2009 (Recovery Act), including the Health Information Technology for Economic and Clinical Health Act (HITECH Act), which established programs under Medicare and Medicaid to provide incentive payments to eligible professionals, eligible hospitals, and critical access hospitals for the adoption and meaningful use of Certified Electronic Health Record Technology. As of November 2013, more than 93% of all eligible hospitals had registered to participate in the incentive programs. APRNs are at the heart of this initiative, along with physicians and other providers. Meaningful use guidelines are meant to support those eligible by using the EHR information in a meaningful way to help improve the quality and safety of the nation’s healthcare system and its consumers. It can be viewed as a national effort to collect big data to control the cost of healthcare while delivering quality care. Implementation is planned over 5 years. Stage 1 of the EHR incentive program began in 2011, with Stages 2 and 3 to be established by future CMS rules. For more information about meaningful stages and expectations, go to http://www.hrsa.gov/healthit/meaningfuluse/MU%20Stage1%20CQM/mu.html.

After 2015, Medicare will require that all eligible professionals and hospitals meet meaningful use or they may be subject to a financial penalty. HITECH’s incentives and assistance programs seek to improve the health of Americans and the performance of their healthcare system with a focus on five goals:

  • Improve the quality, safety, and efficiency of care while reducing disparities

  • Engage patients and families in their care

  • Promote public and population health

  • Improve care coordination

  • Promote the privacy and security of patient information

The new meaningful use standards are changing the way professionals document medical information. To meet the detailed criteria developed by CMS, thorough charting and documentation are essential for everyone involved in patient care. Meaningful use will also affect how providers engage patients and families in care. To do this, APRNs and others will need to approach and treat patients with certain goals in mind, including the following:

  • Providers will have a broader view of patient data. The EHR’s interface can offer providers better information about tests, screening tools, or treatments that may have been missed. EHRs can reveal lapses in testing or preventive screenings and alert the provider so that necessary treatments are not overlooked.

  • More hands-on patients. Through patient portals and other online tools, patients will have digital access to their own records, lab results, and medication lists. This means patients will take a more informed and leading role in their own plan of care.

  • More collaboration and coordination between providers. The ultimate goal of EHR use is to provide one, real-time, current picture of a patient’s health that can be viewed by providers, hospitals, and clinics across the United States. Having this current information on hand makes it easier for providers to compare treatments and decide what will work best for a particular patient.5

Professional Nursing Imperatives Improving Quality

The 2011 report by the American Nurses Association, Advanced Practice Nursing: The New Age in Health Care, reminds us of the powerful position of advanced practice nurses to produce revenue and have influence over patient outcomes. Nurse practitioners (NPs) and clinical nurse specialists (CNSs) are being reimbursed by Medicare, Medicaid, and private insurers and have prescriptive authority in all 50 states. In 22 states and the District of Columbia, NPs can practice independently without physician involvement, and 39% of NPs nationwide have hospital privileges. In 19 recent studies, results confirmed that NPs delivered care equivalent to physician-provided care “and, in some studies, more effective care among selected measures than that provided by physicians.” NPs also consistently demonstrated better results for patient follow-up, satisfaction, consultation time, and providing screening, assessment, and counseling.6,7,8

In June 2014, Press Ganey, a recognized leader in performance improvement for nearly 30 years, announced the acquisition of the American Nurses Association’s National Database of Nursing Quality Indicators (NDNQI), the leading quality improvement and nurse engagement tool in the United States, managed by the University of Kansas School of Nursing since 2001. Press Ganey, also a vendor for patient satisfaction reporting of the patient experience, is partnering with more than 11,000 healthcare organizations worldwide and 98% of U.S. Magnet-designated hospitals to ultimately improve the overall healthcare experience. Use of NDNQI data strengthens the ability of nurses and leaders in their mission to reduce patient suffering, improve the patient experience, and make improvements on 18 nursing-sensitive measures, including hospital-acquired conditions and adverse events subject to the CMS non-payment rule—such as pressure ulcers, falls, and bloodstream infections. With a robust comparative database, organizations can compare themselves to peer institutions, both nationally and regionally, in key quality areas. NDNQI also measures characteristics of the nursing workforce that have been related to the quality of patient care, such as staffing levels, turnover, and registered nurse education and certification. Organizations contributing to NDNQI have demonstrated improved nursing quality: infection rates decreased by 87% in 2 years; injury fall rates decreased by 17% in 4 years; and hospital-acquired pressure ulcer rates decreased by 24% to 59% in 2 years.9 Coupled with Press Ganey’s broad benchmarking data, and advanced analytics, “the addition of NDNQI in approximately 2,000 hospitals nationwide is the largest provider of unit-level performance data to hospitals, offering those accountable for making changes in structure and process more targeted insights into nursing performance to improve the overall patient experience and outcomes.” Nurse leaders claim that this strategic alignment will enhance the power of nursing data, generate even better normative comparisons, and allow for expanded linkages to outcomes.10

APRNs in every role, including management, administration, education, informatics, and direct patient care, will move their thinking from volume-based care to value-based care. They will need the ability to understand results from nursing quality indicators and “retain nursing staff to maintain their vital role in new coordinated models of care.” For more information, visit www.ndnqi.org.

APRNs are also leading healthcare reform by assuming new executive roles, such as deputy chief information officers, senior nurse informaticists, and nursing informatics executives. These nurse leaders are joining forces to educate, train, coach, and mentor APRNs on how to integrate technologies to manage information and improve healthcare. APRNs can assess their levels of competency in data management and translation to lead and support new models for delivering patient care in all settings. Specific competencies in informatics address the following:

  • Education/coaching: Can APRNs translate technical and scientific health information to meet patients’ information and learning needs? Can they assess patient and caregiver educational needs? Can they coach patients and caregivers in positive behavioral change?

  • Decision making: Can APRNs demonstrate information literacy and analytic skills in situations that require complex decision making?

  • System design: To what extent can APRNs contribute to the design of clinical information systems?

  • Evaluation: Can APRNs use technology to evaluate the quality, safety, and efficiency of nursing care?

APRNs who demonstrate the ability to generate revenue and who have the knowledge and skills to use data in leadership, care delivery, informatics, finance, and education will soar in the next decade and lead others to create better (faster and less expensive) workflows and improved patient outcomes. For more about the decades of contributions by advanced practice nurses to informatics, analytics, and the role of translating big data into knowledge and patient outcomes, we refer readers to the Healthcare Research Information website at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3717442/.

Barriers to Using Data

Several studies have highlighted the barriers facing APRNs when trying to use data as care providers in their daily practice. We will discuss the obstacles they face using research results, information on the Internet, and data needed for decision making in daily clinical practice.

In a systematic review of 63 studies spanning nearly two decades (1991–2009) that was reported in Implementation Science, nurse researchers examined the state of knowledge of the validated BARRIERS scale and nurses’ use of research and its usefulness to improve practice. They found that barriers were consistent over time and across geographic locations despite varying sample size, response rate, study setting, and the assessment of quality research.

Approximately one-third of the sample included APRN-level positions. The most frequently reported barriers to using research were related to knowledge and skills in research, their setting, and the presentation of the research. Further, nurses reported not having the skills to read research, particularly evaluating the merit and statistical sections, not having the protected time needed at work to read research, and not having the authority to use the findings or support from other colleagues, including physicians, to implement a research-based change.11

The researchers concluded that the BARRIERS scale, although reliable as reflected in assessments of internal consistency and identifying general barriers to research utilization, was less useful for planning implementation interventions. They recommend that barriers be identified in real time and be specific to the particular context of implementation and the intended evidence to be translated. Similar results were found in a Magnet-designated community hospital study and its 1,100 nurses. Barriers to using research data included the characteristics of the organization, including lack of protected time and practice authority as the greatest barriers.12

In another study, researchers explored the barriers experienced by NPs and other nurses when trying to use the Internet to find best practices. In this smaller purposive sample of 29 practicing nurses and 4 APRNs from general practices in northern England, investigators examined which information sources the nurses used in their daily clinical decision making. Decisions were categorized into seven clinical areas: assessment, diagnosis, intervention, referral, communication, service delivery and organization, and information seeking. The majority of the nurses reported their uncertainty in making decisions about what they read and instead relied on personal experience or simply obtained advice and information from physicians or other colleagues nearby.13

Human sources of information were overwhelmingly preferred to text or online resources. Despite encounters with evidence-based resources through continuing professional development, the nurses rarely used them to seek answers to routine clinical questions. In most decisions, nurses were trying to get answers about how to best communicate to patients the risks and benefits associated with various interventions. This study supports the need for expert clinical resources, such as CNSs or specialty-level NPs who can provide information, in “real time” in clinical areas for adult learners.

Reasons cited for not using the Internet included lack of knowledge and skills to use the Internet and computer, lack of time to conduct searches and download the key information from an overwhelming amount of data, and uncertainty about the quality of the information. Findings from these studies support previous nursing research on barriers to using data over the past three decades and concluded that most nurses still lack the knowledge, skill, and time to use evidence in daily practice.

Roles of APRNs and Data Utilization

The Department of Veterans Affairs,14 the Geisinger Health System,15 and Kaiser Permanente16 are recognized healthcare delivery organizations that maximize advanced practice nurses’ scope of practice. APRNs in these organizations work in a range of practice settings with exposure to small individual data, specialty databases, and big datasets from national samples. Some exemplary roles are described below.

NPs can provide primary and subspecialty-level care to patients. They might practice independently and collaborate closely with doctors and other healthcare professionals. Together or alone, they are responsible for accurate documentation to ensure patient safety, improved goals, and reimbursement. They perform comprehensive physical examinations, diagnose and manage common health conditions, order diagnostic tests, and prescribe therapeutic medications and treatments. NPs may provide family healthcare as well, or they may limit their practice to a specific population, such as adults, children, or the elderly. NPs, depending on their role, technology environment, and resources, may have access to data at the individual and population levels.

CNSs are experts in a specific area of nursing, such as oncology or cardiology. They can focus on specific patient populations or individual patients. CNSs often provide consultation, leadership, education and management skills in healthcare settings, such as emergency room care. They might be experts in a specific type of problem, such as pain or end-of-life care. Whatever the role, the CNS performs basic nursing practices but with a greater depth of knowledge and skill. Inherent in their role is the translation of research for the education and training of other nurses and patient and family populations. They typically have skills in evaluation and using research to improve quality care and develop evidence-based practices that raise the standard of care for organizations and their processes, interventions, policy, protocols, and research.

As direct care providers, APRNs might focus their use of data in a defined scope of practice, such as geriatrics or palliative care. With support from peers, professional resources, a national computerized patient record system, and collaboration within the framework of interdisciplinary care teams, APRNs in this health model, especially those who are ready to generate revenue and be self-supporting, have exciting potential to lead healthcare reform in the decades ahead.

As healthcare researchers, APRNs conduct research that involves nursing issues and outcomes of care, including patient and family satisfaction with care. Master’s prepared nurses have the opportunity to be principal investigators and co-investigators of research projects, as well as translate their findings into practice. As one of the largest research organizations in the United States, the Department of Veterans Affairs, for example, offers funded research opportunities and encourages nurse researchers and clinicians to disseminate their findings in literature and through presentations and publication.

As educators, APRNs have the opportunity to guide the future generation of NPs and CNSs by participating in and leading research through affiliations with nursing schools. APRNs in exemplary organizations can hold university faculty positions and offer leadership as preceptors, and in developing curriculums for observerships, internships, residency and fellowship programs.

As health information technology leaders, APRNs can establish the linkages needed among nurses, informatics, analytics, and patient outcomes. They can improve workflows and help reduce the barriers nurses face in documenting routine care. These linkages are critical to capturing the data needed to populate nursing-sensitive indicators and use health information technology to promote positive outcomes.17

As healthcare leaders, APRNs shape policy, facilitate access to healthcare, and influence resource management. Exemplary organizations encourage their leaders to be involved in professional nursing organizations and to share and benchmark data at conferences and other local, regional, and national committees and taskforces. We end this section by offering readers a self-assessment tool that may help to determine areas of proficiency and deficiency and specific learning needs using data (Table 5.2).

Table 5.2 Self-Assessment of Data Utilization

Data Sources

Assess your level of confidence/competence to:

Administrative and financial data

Lacking

Improving

Competent

Good

Excellent

  1. Review budgets and variance reports

1

2

3

4

5

  2. Translate billing reports

1

2

3

4

5

  3. Develop a justification for additional staff or new program

1

2

3

4

5

  4. Use the computer and software: Excel, Word, PowerPoint, timelines, table of organization

1

2

3

4

5

  5. Participate in an informatics group to redesign your EHR

1

2

3

4

5

  6. Perform a needs assessment (gap analysis)

1

2

3

4

5

  7. Conduct a brainstorming session

1

2

3

4

5

  8. Design a workflow

1

2

3

4

5

  9. Access policies, procedures, and standards of care

1

2

3

4

5

10. Develop a dashboard for your patient caseload of volume statistics and quality indicators

1

2

3

4

5

11. Develop an evidence-based protocol

1

2

3

4

5

12. Understand CPT coding guidelines/rules (how to bill)

1

2

3

4

5

13. Understand profit-and-loss statements; review a business plan

1

2

3

4

5

14. Manipulate data to look at patterns (by using software or spreadsheets)

1

2

3

4

5

15. Redesign staffing model based on needs, resources, and revenue

1

2

3

4

5

16. Develop a database to capture complaints

1

2

3

4

5

Quality data

17. Develop a quality improvement study

1

2

3

4

5

18. Read quality reports and develop action plans

1

2

3

4

5

19. Develop a graph or table with data

1

2

3

4

5

20. Participate in a shared governance or magnet committee

1

2

3

4

5

21. Understand best-practice benchmarks and metrics

1

2

3

4

5

22. Interpret patient satisfaction scores and develop an action plan for improvement

1

2

3

4

5

23. Use Survey Monkey™ to gather information and summarize results

1

2

3

4

5

24. Write up a case study for morbidity/mortality meeting

1

2

3

4

5

25. Develop a competency measuring knowledge and skills

1

2

3

4

5

26. Display data in a control or Pareto chart

1

2

3

4

5

Research data

27. Conduct a literature search

1

2

3

4

5

28. Compile data into graphs or tables

1

2

3

4

5

29. Join a research committee

1

2

3

4

5

30. Participate in a journal club and determine the level of evidence of a research study

1

2

3

4

5

31. Write an abstract on a project with data

1

2

3

4

5

32. Give a presentation displaying data

1

2

3

4

5

33. Evaluate a student’s progress using a standardized tool

1

2

3

4

5

34. Participate in peer review

1

2

3

4

5

35. Co-author a manuscript about clinical practice

1

2

3

4

5

36. Critique a research study for an evidence-based practice project

1

2

3

4

5

Increasing Competencies of APRNs in Data Utilization

The American Association of Colleges of Nursing sets standards for U.S. accredited graduate school curriculums. The association’s 2011 report incorporates data utilization exercises into the quality standards that prepare APRNs in nine knowledge and skill areas.18 Skill areas address how to lead change to improve quality outcomes, advance a culture of excellence through lifelong learning, build and lead collaborative interprofessional care teams, navigate and integrate care services across the healthcare system, design innovative nursing practices, and translate evidence into practice.

APRNs represent less than 8% of our workforce in hospitals and outpatient and community settings. Given that the average age of U.S. practicing nurses is approximately 50 years, we can assume that most may not have had exposure or training in the process of accessing, reading, translating, and using data to create best practices and improve delivery systems.

The Institute of Medicine’s report on the quality areas needed to improve the American healthcare system is over a decade old, yet only minimal improvements in quality and safety have been reported, according to nurse leaders. To boost improvement efforts, the Quality and Safety Education for Nurses initiative was developed to integrate quality and safety competencies into nursing education. Leaders argue that the current challenge is for nurses to move beyond the application of Quality and Safety Education for Nurses competencies to individual patients and families and incorporate systems thinking in quality and safety education and healthcare delivery.19

APRNs with administrative support can use financial and administrative data to justify aspects of their APRN role. Figure 5.1 is the outline of a financial proposal for a new program. In Figure 5.2, a new NP created a dashboard of indicators to monitor volume, quality, and safety of a new service. In Figure 5.3, an NP and nurse manager piloted a fast-track program to reduce walk-ins for a rapidly growing chronic pain service. In Figure 5.4, a CNS demonstrated the positive patient outcomes of her Reiki program. Each of these APRNs partnered with a mentor to develop these projects. Mentors for these APRNs were administrators, directors of nursing, peers, and physician collaborators.

Figure 5.1 Sample program expense sheet

Figure 5.1 Sample program expense sheet

Figure 5.2 New consultation service dashboard

Figure 5.2 New consultation service dashboard

Figure 5.3 Fast-track walk-in documentation form: pain management outpatient clinic

Figure 5.3 Fast-track walk-in documentation form: pain management outpatient clinic


Figure 5.4 Documenting orthopedic patient response to Reiki therapy following surgery

Figure 5.4 Documenting orthopedic patient response to Reiki therapy following surgery

The next examples are related to quality concerns voiced by APRNs in both educator and NP inpatient practitioner roles. In the Case Study and Figure 5.5, APRNs piloted two peer review exercises. The case study of individual patient data was reviewed with peers for the purpose of improving the plan of care. An APRN director recognized the need for better comprehensive documentation by NPs caring for palliative patients in the community. Case reviews were done at interdisciplinary team meetings over a 4-month period. Documentation improved, and fewer cases were rejected for reimbursement because of inadequate documentation.

Figure 5.5 Sample pilot peer review sheet

Figure 5.5 Sample pilot peer review sheet

Case Study

Mr. Li was a 65-year-old Chinese-American man, diagnosed a year earlier with lung cancer. The patient has been told he has “lung disease.” Despite the fact that his disease was clearly advancing, the family insisted that he not be told of his diagnosis or prognosis. Mr. Li lost 20 lbs. in the previous two months and was having difficulty swallowing. He was in pain and expressed shortness of breath. He had a recent long hospital stay resulting in progressive deconditioning, weakness, and functional decline. He lived with his wife in a second-floor apartment. His two sons were both married and lived in the area. He denied any religious affiliation. The home healthcare team was increasingly frustrated with the fact that Mr. Li was not able to fully participate in decisions about his care. As Mr. Li’s disease progressed, he became weaker and unable to move from the bed. When asked how he was feeling, he always whispered “fine” and denied any symptoms. His wife was tearful about her husband’s diminished appetite. She believed he would be cured if only he would “eat” and that he needed to “try harder.” The nurse observed the patient experienced difficulty swallowing and potentially aspirated when given soft food. When (through an interpreter) the nurse attempted to explain the distress and risks involved, Mrs. Li appeared unable to understand.

The team identified three options: they could talk to family hospice services, consider inpatient management, or continue with acute care home care services. During a subsequent home visit by the home care nurse and social worker, the home care team discussed the services that a hospice program could offer the patient and family. The family, patient, and primary medical doctor were agreeable. The home health nurse contacted the nurse at hospice, who was the patient’s primary nurse, and shared the care plan with her. The patient transitioned to a hospice program without difficulty. Over the next week, the team worked with the family regarding symptom management and intake of food and fluids. The goals of care were discussed and the family wished to move forward with a palliative care plan. The patient was successfully managed at home with twice-weekly RN visits and weekly social work visits. The patient experienced more shortness of breath and told his wife he knew he was dying and wished to die outside of the home. The interdisciplinary team worked with the patient and family to arrange for inpatient hospice. The patient was transferred to inpatient hospice, symptoms were managed, and he died peacefully 48 hours after admission, with the family at his bedside.

The second (Fig 5.5) was a peer review tool piloted by NPs in an Advanced Practice Committee following a review of the literature. The two processes were instrumental in raising the knowledge levels of individuals and a group of practitioners in a specific specialty, working toward advanced certification in palliative care.

In the next two examples, inpatient pain management NPs learned the value of using two quality tools to increase efficiency and target improvements in patient satisfaction: the Pareto chart and control chart. Using a Pareto chart (Fig 5.6), an NP identified specific patient diagnoses that were consistently associated with high sustained pain (i.e., 3 consecutive days of moderate to severe pain). Sickle cell patients and chronic pain patients were more frequently admitted through the emergency department and referred to the hospitalist team, who then called a pain consultation. The NP developed a standard protocol for patients with these diagnoses entering the emergency department, reducing delays in pain management and increasing patient reports of comfort. In Figure 5.7, a CNS in pain management used control charts from standardized satisfaction surveys to demonstrate a stable process and how well pain was managed, and helped others see the value of using data to support pain protocols and build staff morale.


Figure 5.7 Control chart showing statistical control and stability of process: Managing pain

Figure 5.7 Control chart showing statistical control and stability of process: Managing pain

One way APRNs can increase their knowledge and skills in the translation of research into evidence-based practice (EBP) is to enroll in a class, such as the Johns Hopkins Nursing EBP Course, a self-learning online course. The course is offered in five modules.20 Module 1 defines what EBP is and why it is important for nurses to learn. Module 2 is the development of questions about a problem in practice and plans for how to get started, how to form your team, and how to get the work done. This is the methods module and helps the learner create the rules and a timeline for each step. Module 3 relates to the evidence. Once the problem question is written, this step helps the learner look for evidence. This module explains what evidence is appropriate and how to find it. In Module 4, the learner summarizes the evidence. Tools are provided to the learner to help evaluate the studies, the strength of the evidence, and what kind of practice change, if any, is appropriate. Module 5, the translation step, is thought to be the hardest part of the EBP process. Learners are guided through how to implement a change in practice and learn the difference between leading change and managing transitions and developing a systematic action planning process.

Healthcare leaders in economics predict that 130% more family nurse practitioners will be needed by 2020. Many will enroll in DNP programs to meet professional goals to become independent practitioners. APRNs entering DNP programs will have opportunities to be mentored by faculty in the research and data utilization process as DNP students typically develop EBP projects first hand, using a recognized EBP model. Figures 5.8 and 5.9 illustrate the efforts of two oncology NPs to perform a quality improvement study that would improve the screening, assessment, and treatment of patients with neuropathic pain receiving chemotherapy. The NPs increased their competency in using and conducting research. They developed an algorithm that began with screening by nursing assistants, followed by referral to the NP for a full neuropathic assessment, treatment using evidence-based pharmacology, and follow-up evaluations.


Figure 5.8 Patient satisfaction with treatment for neuropathic pain

Figure 5.8 Patient satisfaction with treatment for neuropathic pain


Figure 5.9 Identifying patients with moderate to severe pain for earlier referral to pain management

Figure 5.9 Identifying patients with moderate to severe pain for earlier referral to pain management

Figure 5.9 describes a nurse manager’s efforts to improve patient satisfaction ratings in pain management on her unit. The Pain Service NP, hospitalists, and nurse manager reviewed all patients and their pain data daily, identifying those with high-sustained pain (i.e., patients with pain levels of 5 or greater). Patients having three consecutive days without a significant decrease were primarily chronic pain patients referred to the pain service. Although decreases occurred, the unit continued to have about one-third of patients with moderate to severe pain. This finding increased awareness of all unit staff about making earlier referrals to the pain service, and other approaches to managing the patient’s pain flares and attention to the patient’s hospital experience.

Future of APRNs and Data Utilization

The Affordable Care Act domino effect is here, with an additional 30 million people with health insurance who will need to access care. The number of primary care providers is inadequate, and APRNs (NPs) will be in demand to help with this problem. Nurse-managed health centers are a well-established community-based model providing primary healthcare services, under the leadership of an advanced practice nurse. They emphasize health education, health promotion, and disease prevention, and their target population is usually the underserved. Unlike “Minute Clinics,” which are also led by NPs, these centers are not-for-profit and usually have sliding scales for payment. There are at least 200 of these nurse-managed health centers currently operating in 37 states, with an estimated 2 million patient encounters per year. APRNs of the future will need to have skills in using administrative, financial, quality, and research data to build viable care centers with best practices and economic models that can be sustained.21

APRNs of the future will be collaborating with researchers using telemedicine to improve a patient’s clinical health status. Telemedicine researchers evaluate information exchanged from one site to another via electronic communications, including applications and services that use two-way video, email, smartphones, wireless tools, and other forms of telecommunications technology. Timothy Landers, RN, CNP, PhD, assistant professor at the Ohio State University and a Robert Wood Johnson Foundation Nurse Faculty Scholar, is a nurse researcher leading the field in testing the Fitbit One and its ability to collect personal health outcomes. This clip-on device is an activity monitor that measures activity level, including steps taken, stairs climbed, distance walked, calories burned, and sleep. The Fitbit smartphone app syncs with a small activity monitor and displays personal activity data. This use of small data will enable patients and clinicians to be more aware of changes in activity and lifestyles.22 APRNs in direct patient care roles are positioned to use individual patient information to make smart clinical assessments and diagnoses and search for best practices for specific diagnoses. Using the same technology, APRNs in management positions can support data collection at the group level, providing the big data that could be useful in evaluating workplace designs in relationship to the activity levels of workers.

Conclusion

Data are all around you and represent a huge task: they can be your enemy or your friend. Make them your friend. Understand what type of data you need to know about. Learn how to read and translate reports and use the data you are responsible for wisely. Ask your supervisor what your key role is in improving outcomes. Gain competency in those areas and keep improving yourself and those around you.

Pressures to be smarter and more efficient with data will increase. Meaningful use is here to offer hospitals incentives to capture data geared toward promoting quality.

APRNs are expected to be knowledgeable about each component at the various stages. Be the one who knows what is ahead, and be prepared to lead others. Report cards measuring patient satisfaction and other indicators of quality are shaping behavior by forcing providers to “look in the mirror.” Drill down the data to the practice level where you can make a difference in scores and create partnerships to move those scores up. Find out what the benchmarks are in your practice area and start a team so that you can compare and improve. The barriers facing APRNs in data utilization have not changed in 20-plus years. APRNs will need new forums for mentorship and education in data utilization in the decades ahead.

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