Research in Gerontological Nursing

Original Research: Clinical Documentation and Meaningful Use 

Nursing Diagnoses, Interventions, and Patient Outcomes for Hospitalized Older Adults with Pneumonia

Barbara J. Head, PhD, RN; Cindy A. Scherb, PhD, RN; David Reed, PhD; Deborah Marks Conley, MSN, APRN-CNS-BC, FNGNA; Barbara Weinberg, BS, RN, CCRN; Marie Kozel, RN, BSN, MBA; Susan Gillette, RN; Mary Clarke, PhD, RN, BC; Sue Moorhead, PhD, RN

Abstract

A study was conducted by academic and community hospital partners with clinical information systems that included the standardized nursing language classifications of the North American Nursing Diagnosis Association International (NANDA-I), Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC). The aim of the study was to determine the frequency of NANDA-I, NIC, and NOC (NNN) terms documented for older adults with pneumonia who were discharged from three hospitals during a 1-year period. NNN terms were ranked according to frequency for each hospital, and then the rankings were compared with previous studies. Similarity was greater across hospitals in rankings of NANDA-I and NOC terms than in rankings of NIC terms. NANDA-I and NIC terms are influenced by reimbursement and regulatory factors as well as patient condition. The 10 most frequent NNN terms for each hospital accounted for only a small to moderate percentage of the terms selected.

Abstract

A study was conducted by academic and community hospital partners with clinical information systems that included the standardized nursing language classifications of the North American Nursing Diagnosis Association International (NANDA-I), Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC). The aim of the study was to determine the frequency of NANDA-I, NIC, and NOC (NNN) terms documented for older adults with pneumonia who were discharged from three hospitals during a 1-year period. NNN terms were ranked according to frequency for each hospital, and then the rankings were compared with previous studies. Similarity was greater across hospitals in rankings of NANDA-I and NOC terms than in rankings of NIC terms. NANDA-I and NIC terms are influenced by reimbursement and regulatory factors as well as patient condition. The 10 most frequent NNN terms for each hospital accounted for only a small to moderate percentage of the terms selected.

Although patient care provided by nurses is a pivotal influence in quality of health care (Institute of Medicine, 2004), it is mostly invisible due to limited and inaccessible records of nursing clinical practices. The documentation of nursing in electronic clinical information systems (CISs) using nursing language classifications makes nursing visible and is potentially useful in evaluating and improving the quality of patient care (Jennings & McClure, 2004; Simpson, 1991) and in improving perceptions about the profession of nursing. It is important, however, that data entered into a CIS can be processed into information, that knowledge is gained by identifying relationships among patient and nursing phenomena, and that new knowledge is acquired to improve nursing practice (Graves & Corcoran, 1989).

The use of standardized nursing languages is necessary for nurses to describe their contributions to patient care (Lundberg et al., 2008) and to create an evidence base that can be used for decision making and improved accountability for nursing practice (Jennings & McClure, 2004). Standardized nursing languages are also important for clear communication with patients and families and are essential for efficient and effective communication and collaboration among nurses, between nurses and members of other disciplines, and between nurses and policy makers. Finally, standardized nursing languages in electronic CISs are needed for the development of interoperable clinical nursing datasets that can be analyzed for the effectiveness of nursing and compared among health care settings (Moorhead, Johnson, Maas, & Swanson, 2008).

Retrieval and storage of nursing data elements such as nursing diagnoses of patients, nursing interventions, and patient outcomes using standardized terminologies are critical to the ability of nurses to conduct nursing outcomes effectiveness research. Outcomes effectiveness research using large datasets containing nursing data elements and other individual-level patient and unit/organizational-level data, when analyzed together, will enable nurse researchers to determine what, if any, effect nursing interventions have on patient outcomes (Moorhead et al., 2008).

Clinicians are also interested in nursing data for their usefulness in guiding clinical practice. Having knowledge about and being able to describe the types of problems nurses treat, as well as the variations in treatment for specific nursing problems and for the same nursing problems across settings, has implications for nursing administration in planning for staffing and staff development programs. Beyond that, information about nursing practice allows nurses and managers to express, claim, and better explain their unique methods of intervening with patients’ specific problems (Dochterman et al., 2005; Shever, Titler, Dochterman, Fei, & Picone, 2007). Nurse clinicians and nurse administrators need to know what specific outcomes and changes in these outcomes are associated with specific nursing interventions. It is also important to know what is possible during the current health care system’s short hospital stays of patients and what nursing interventions need to be communicated and continued for optimal outcome achievement when patients are discharged to other settings.

Although a growing number of health care organizations are using standardized nursing classifications (Titler, Dochterman, & Reed, 2004), many report difficulty using the data to evaluate and improve patient care quality. Reasons for this may be a focus on developing the documentation systems versus data retrieval capability, disagreement over variable definitions, inadequate resources, and lack of expertise in data warehouse development (Titler et al., 2004).

Study Aims

To date, nursing outcomes and effectiveness research has consisted of primarily single-site studies, few of which have been conducted with clinical nursing data documented using standardized nomenclature. As more hospitals use standardized nursing nomenclature in their electronic documentation systems, there is a need to describe and compare actual clinical data from across multiple sites and evaluate the usefulness of the data for subsequent effectiveness studies. With the long-term goal of conducting multivariate outcomes effectiveness studies, the investigators conducted a study in partnership with three community hospitals that had implemented CISs using the North American Nursing Diagnosis Association International (NANDA-I) (2008) classification, the Nursing Interventions Classification (NIC) (Bulechek, Butcher, & Dochterman, 2008), and the Nursing Outcomes Classification (NOC) (Moorhead et al., 2008). The primary aim of the study was to describe and compare the 10 most frequent NANDA-I nursing diagnoses, NIC interventions, and NOC outcomes documented by nurses for hospitalized patients ages 60 to 89 with a primary discharge diagnosis-related group (DRG) of pneumonia (DRG 89 or 90). A secondary aim was to evaluate the quality and limitations of the data for further effectiveness research.

Literature Review

Pneumonia is a serious problem for older adults and often results in costly health care, including repeated hospitalization. In addition to problems related to the medical diagnoses, older adults commonly experience problems with hospitalization such as functional decline, incontinence, decreased appetite, and altered sleep/wake patterns (Creditor, 1993).

Direct health care costs for individuals with pneumonia in the United States, including 1.2 million annual hospitalizations, are estimated to be $20 billion annually (Lindenauer et al., 2006). The rationale for selecting this population to study was hospital partner concern about the quality and costs of caring for older adults with pneumonia. Hospital experience suggested that nursing interventions with this population influenced the achievement of positive patient outcomes, although most of the interventions had not been well evaluated. Positive patient-level outcomes would be expected to improve system-level quality and cost indicators.

In 2006, 17% of all Americans were 60 and older (U.S. Census Bureau, 2006), and this population is rapidly increasing (Sutherland, 2005). Kaplan et al. (2002) estimated that the number of pneumonia cases will rise to 750,000 in 2010 and to 1 million in 2020 due to the growth of the aging population. Pneumonia is the sixth most common cause of death in the United States, the leading infectious cause of death in older adults (Chan & Fernandez, 2001), and is highly costly in health care resources such as intensive care and life support measures due to frequent comorbidities (Kaplan et al., 2002).

Older adults who require hospitalization often have a complex array of responses to pneumonia that require nursing surveillance and other interventions. Patients are frequently acutely confused and agitated, resulting in safety and communication concerns (Coleman, 2004). Nursing interventions recommended in the literature include surveillance, respiratory monitoring, medication administration (both intravenous and oral), individual teaching, exercise therapy (ambulation), pain management, immunization management, smoking/tobacco cessation assistance (Mandell et al., 2007), oral health promotion (Coleman, 2004; O’Connor, 2008), oxygen therapy, positioning, nutritional counseling, fluid/electrolyte management, spiritual support, dying care, self-care assistance, self-care assistance with feeding (Coleman, 2004), and airway management (Hecht, Siple, Deitz, & Williams, 1995). These problems and interventions suggest the need to measure outcomes such as immune status, respiratory status (airway patency, gas exchange, and ventilation), ambulation, nutritional status, smoking/tobacco cessation behavior, pain level, hydration, infection severity, and self-care (activities of daily living).

Nursing diagnoses, interventions, and patient outcomes for the pneumonia population are described in the literature, but not with large database research reporting their use by nurse clinicians or the effectiveness of nursing interventions. This makes it highly important to describe the nursing intervention and outcomes documented in clinical data systems and to explore the capacity of electronic documentation systems that use standardized nursing nomenclatures to form data warehouses that can be analyzed for effectiveness research.

Studies Involving Actual Clinical Data

Only a few studies have described nursing phenomena using NANDA-I (2008), NIC (Bulechek et al., 2008), and NOC (Moorhead et al., 2008) with clinical nursing data for hospitalized older adults. Scherb (2002a) conducted an exploratory, descriptive study using a large dataset of patients with pneumonia hospitalized in one Midwestern community hospital to describe the most frequent nursing diagnoses, interventions, and patient outcomes (Table 1). The effect of nursing interventions on the five most prevalent outcomes for the pneumonia population was analyzed. Statistically significant (p < 0.05) changes in patient outcomes for the pneumonia population were found with Oxygen Therapy and Family Involvement nursing interventions. In another study, Scherb (2002b) worked with two Midwestern community hospitals to determine the difference in outcome admission and discharge ratings of primarily older adults with pneumonia. The three most frequent outcomes—Knowledge: Illness Care, Respiratory Status: Gas Exchange, and Respiratory Status: Ventilation—are shown in Table 1. All of the outcome ratings for this population were significantly and positively different (p < 0.05) from admission to discharge (Scherb, 2002b).

Most Frequent Nursing Diagnoses, Interventions, and Patient Outcomes for the Pneumonia Population: Comparison Across Multiple Studies

Table 1: Most Frequent Nursing Diagnoses, Interventions, and Patient Outcomes for the Pneumonia Population: Comparison Across Multiple Studies

NANDA-I, NIC, and NOC

Nursing diagnoses were first developed in 1973 by NANDA-I (2008) and were the first of the standardized nursing languages. A nursing diagnosis is “a clinical judgment about individual, family, or community responses to actual or potential health problems/life processes” (NANDA-I, 2008, p. 419). NANDA-I standardized nursing diagnoses are terms to communicate nurses’ professional judgments to patients, other nurses, and the interdisciplinary team (NANDA-I, n.d.).

NIC is a classification of nursing interventions. Development began in 1987, and the current edition contains 542 interventions (Bulechek et al., 2008). A nursing intervention is “any treatment, based upon clinical judgment and knowledge, that a nurse performs to enhance patient/client outcomes” (Bulechek et al., 2008, p. xxi). These interventions may reflect direct or indirect care and may be nurse-, physician-, or other provider-initiated treatments administered by nurses. The interventions are aimed at assisting the individual, family, or community to reach desired outcomes (Bulechek et al., 2008).

NOC is a classification of nursing-sensitive patient outcomes. The development of NOC began in 1991, and the current classification contains 385 standardized outcomes (Moorhead et al., 2008). A nursing-sensitive patient outcome is “an individual, family, or community state, behavior, or perception that is measured along a continuum in response to nursing intervention(s)” (Moorhead et al., 2008, p. 30). NOC outcomes are measured on a 5-point Likert scale, with 5 being the most desired state and 1 being the least desired state. It is recommended that the outcomes be measured at least on admission and discharge, with any significant change in status, or at intervals judged by nurses as appropriate for evaluating interventions. Measuring the outcome over time allows nurses to evaluate the effectiveness of their interventions (Moorhead et al., 2008).

Method

Data were collected from three community hospitals for a larger study to determine hospital partner documentation and feasibility of retrieval of data for nursing outcomes effectiveness research (Head, 2005; Scherb, 2004). Hospital A was a 136-bed community hospital located in the eastern United States. Hospitals B and C were Midwestern community hospitals with 255 and 430 licensed beds, respectively. Research proposals and budgets were discussed with Vice Presidents (VPs) of Patient Care at each hospital. The VPs expressed strong support for the project as having benefit for meeting the hospitals’ research aims and interest in retrieving clinical data for analysis and use in quality improvement.

All data received from the hospitals met the Safe Harbor requirements for de-identification of personal health information (National Institutes of Health, 2004). Following approval of the studies by the two principal investigators’ universities and each of the hospitals’ Institutional Review Board procedures, principal investigators organized a meeting of the interdisciplinary research team of nurse investigators, a statistician, and data retrieval specialists at each hospital to discuss proposed data elements. Following agreement on the data to be collected, telephone conferences were held every 1 to 2 months to discuss data retrieval progress and provide further clarification. With the assistance of data retrieval specialists at each hospital consulting with the statistician about format, data were assembled for each variable for a 1-year period (February 1, 2005 through February 28, 2006) for Hospitals A and C. Only 4 months (November 1, 2005 through February 28, 2006) of data were available for Hospital B because the CIS was implemented during the study period. Most data were available in electronic information systems, although a few items required retrieval from paper systems at one hospital. Data from each hospital were sent in electronic form with a data dictionary to a central data storage location (warehouse) for analysis by the project statistician. Data were then imported into an SPSS version 15 database.

Clinical Documentation Systems

All three community hospitals included in this study had electronic CISs using the standardized nursing languages of NANDA-I, NIC, and NOC (NNN). The Midwestern hospital sites (Hospitals B and C) were part of the same health system and shared the same CIS and support. The Eastern hospital (Hospital A) had a separate system with a different software vendor.

The hospitals had been using electronic documentation for varying lengths of time prior to data collection. Hospital A had been documenting via its CIS for 2 years, Hospital C for 2 months prior to data collection, and Hospital B implemented the CIS during the study period, resulting in only 4 months of data collection. All three hospitals used templates or standard care plans to provide decision support for nurses in developing plans of care. The templates were developed by expert nurses in the organizations and were specific to that organization. The template developers were guided and influenced by the NNN linkages (Johnson, Bulechek, Dochterman, Maas, & Moorhead, 2001) published by the NIC and NOC research teams. The published linkages were deemed appropriate by expert clinical staff. Hospital B’s and C’s templates were similar because they shared the same CIS and support staff. Nurses were able to individualize the templates to reflect the needs of the patient. Each of the hospitals provided staff education on use of the electronic documentation system and NANDA-I, NIC, and NOC.

The frequencies for the nursing diagnoses, nursing interventions, and nursing-sensitive outcomes for pneumonia patients at each hospital were determined and rank ordered for each hospital. Diagnosis, intervention, and outcome rankings were compared across study hospitals. Data analysis and interpretation were discussed among the research investigators and team members representing each hospital to identify clinical implications as well as advantages and limitations for subsequent analyses of intervention effectiveness.

Results

Four hundred fifty-one patients with pneumonia were discharged (A = 65, B = 67, C = 319) during the study period. Two hundred thirty-eight (53%) of the patients were men (A = 30, B = 36, C = 172). The age of the participants ranged from 60 to 89 (mean age: A = 76.2 [SD = 8.2 years], B = 75.8 [SD = 8.1 years ], C = 76.7 [SD = 7.9 years]). At all three hospitals, the majority of the participants were married. Lengths of stay (LOS) ranged from 1 to 41 days (mean LOS: A = 4.3 [SD = 2.2], B = 6.3 [SD = 3.8], C = 5.5 [SD = 3.8]). Eight patients died before discharge.

The 10 most frequent NANDA-I diagnoses documented at each hospital are displayed in Table 2. Five diagnoses in the top 10 were used by all three hospitals: Acute Pain, Deficient Knowledge, Imbalanced Nutrition: Less than Body Requirements, Ineffective Breathing Pattern, and Risk for Falls. The 10 most frequent diagnoses were identical for Hospitals B and C, while five diagnoses used in Hospital A were unique (Impaired Gas Exchange, Altered Body Temperature, Effective Therapeutic Regimen Management, Ineffective Health Maintenance [Tobacco Cessation], and Potential for Altered Tissue Perfusion).

Ten Most Frequent NANDA-I, NIC, and NOC Terms for the Pneumonia Population

Table 2: Ten Most Frequent NANDA-I, NIC, and NOC Terms for the Pneumonia Population

The most frequent NIC interventions for this older adult pneumonia population were also dissimilar between Hospital A and Hospitals B and C. Pain Management was the only NIC intervention that the hospitals had in common. Hospitals B and C were similar except that Hospital B nurses frequently documented use of Skin Surveillance, and Hospital C frequently documented use of Dysrhythmia Management (Table 2). At all of the hospitals, the 10 most frequent interventions represented only a small to moderate percentage of the total number of interventions documented (A = 23%, B = 32%, C = 24%).

Only four outcomes occurred frequently for all three sites for the pneumonia population: Fall Prevention Behavior, Knowledge: Illness Care, Respiratory Status: Gas Exchange, and Respiratory Status: Ventilation. The difference between Hospitals B and C were minimal. Hospital B had Nutritional Status and Hospital C had Pain Level. Several outcomes were again unique to Hospital A, including Vital Signs Status, Tissue Perfusion: Peripheral, and Participation in Health Care Decisions (Table 2).

Discussion

Comparison Across Study Hospitals

In comparing the nursing diagnoses, interventions, and patient outcomes (NNN) across the three study hospitals for patients discharged with the pneumonia DRGs, a greater variation was found in the 10 most frequent NNN terms than expected. Possible reasons for this finding may be the differences in prevalence of comorbid conditions and demographic characteristics of patients hospitalized in the three sites. Hospitals B and C were part of the same health system and shared the same CIS, developed and supported by local experts; therefore, the finding that the most frequently documented NNN terms were comparable was anticipated. One probable reason for the differences between Hospitals B and C and Hospital A was the use of locally developed, hospital-specific care-planning templates. Another was the length of time each hospital had used the CIS before the study period.

The NNN terms most frequently selected by staff were those included in the templates, although many other NNN terms were documented for the population. The most frequently documented NNN terms that were also included on the care-planning templates are marked in bold in Table 2. Care-planning templates are useful for guiding nursing practice, but use of other decision support strategies may allow nurses to develop more robust care plans to reflect patient problem-specific care.

Interpretation of the NNN frequency data by investigators and clinical partners led to recognition that the most frequent NNN terms reported included not only concepts relevant to the specific patient condition (i.e., pneumonia), but those that represented national quality indicators, such as the American Nurses Association’s (n.d.) nursing-sensitive quality indicators for submission to the National Database of Nursing Quality Indicators. Pneumonia-appropriate NNN terms were documented by the hospitals; however, sometimes they were not among the 10 most frequent. This recognition resulted in Hospitals B and C reassessing their care plan templates to identify the NNN terms that were related to general patient surveillance and to refocus the templates on condition-specific NNN terms. Scherb (2002a) described the use of a “general patient care” area on care plan templates that captured the interventions completed for most patients regardless of health/medical problem. Scherb’s data were analyzed excluding “general patient care,” and thus the most frequent pneumonia-specific NNN terms were identified.

Another consideration in evaluating the frequency data was the length of time the organizations had been using NNN terminology in their CIS. Hospital A’s CIS had been operational for 2 years prior to data collection. Hospital C had been operational for 2 months prior to data collection, and Hospital B implemented the CIS during the study period. Similar to Scherb’s (2002a) study, the nursing staff in Hospitals B and C did not have much time to become familiar with the documentation system before data collection began. There was a learning curve associated with understanding how to accurately document using both NNN terminology and the new CIS.

Other reasons for the discrepancies found among study hospitals’ documentation of NNN terms for the pneumonia population might include geographical variations in practice and lack of research evidence related to nursing diagnoses, interventions, and outcomes. Nurses are often guided by strong local practice traditions passed along from one professional cohort to the next, and traditions may vary widely from one location to another, for example from the midwestern to the eastern United States locations in this study. Without research evidence about nursing problems, care, and results with pneumonia patients, nurses in the study hospitals using CISs guided by templates based on expert opinion may have found the need for a variety of additional diagnoses, interventions, and patient outcomes to accurately document the patient care provided and respond to health system-specific expectations.

To summarize, a comparison of the frequency of nursing diagnoses and outcomes indicates a better conceptual match than is observed between interventions and diagnoses and outcomes. There is often a one-to-one or one-to-two ratio between nursing diagnoses and nursing-sensitive patient outcomes. In contrast, a larger number of interventions than outcomes are usually documented per nursing diagnosis, resulting in a one-to-many relationship between nursing diagnoses and nursing interventions. Thus, nursing interventions that appear in the 10 most frequent category often do not correspond with the nursing diagnoses and outcomes used most frequently.

Comparison of Rankings with Results of Studies in Similar Settings

Comparing the nursing diagnoses results of this study to Scherb’s (2002a) findings for the pneumonia population, three nursing diagnoses were among the 10 most frequent in all four hospitals: Acute Pain, Deficient Knowledge, and Imbalanced Nutrition: Less than Body Requirements. Hospitals B, C, and Scherb’s (2002a) study shared high rankings for risk for Impaired Skin Integrity, and Hospital A and Scherb’s site had Impaired Gas Exchange as most frequent. Only the Scherb study found the following diagnoses as most frequent: Activity Intolerance, Ineffective Airway Clearance, Decreased Cardiac Output, Impaired Physical Mobility, and Altered Urinary Elimination (Table 1). Three different nursing diagnoses (Impaired Gas Exchange, Ineffective Airway Clearance, and Ineffective Breathing Pattern) were used to document patient respiratory difficulties.

In comparing NIC intervention frequency for the pneumonia population between this study and Scherb’s (2002a) study, no intervention was on the 10 most frequent lists for all four hospitals in the two studies. However, several interventions appeared on the most frequent lists across two or three hospitals. Hospitals B, C, and Scherb’s (2002a) site had Airway Management, Discharge Planning, Skin Surveillance, and Teaching: Individual as most frequent interventions. Hospital A and Scherb both listed Respiratory Monitoring in the top 10. Most frequent interventions reported exclusively by Scherb (2002a) were Acid-Base Management, Cardiac Care, Energy Management, Family Involvement (now discontinued by NIC), and Oxygen Therapy, indicating a considerable difference between Scherb’s findings and those of this study (Table 1). This variation in the most frequently documented NIC interventions does not appear to be related to different nursing diagnoses among the three study hospitals. The variation is more likely due to the different standard templates or plans of care used by each hospital.

The NOC frequency for the pneumonia population in this study compared with that of Scherb’s (2002a) indicated that three outcomes were ranked as most frequent across the four hospitals in the two studies: Knowledge: Illness Care, Respiratory Status: Gas Exchange, and Respiratory Status: Ventilation. Hospital A, B, and Scherb (2002a) reported the outcome Nutritional Status among the most frequent. Hospitals B, C, and Scherb (2002a) all reported Tissue Integrity: Skin and Mucous Membranes among the 10 most frequent patient outcomes. Hospital A and Scherb (2002a) both documented Pain Control. In another study of this population, Scherb (2002b) found the four most frequent NOC outcomes were Knowledge: Illness Care, Respiratory Status: Gas Exchange, Respiratory Status: Ventilation, and Energy Conservation (Table 1). Other outcomes found only in the Scherb (2002a) study were: Cardiac Pump Effectiveness, Mobility Level, and Urinary Elimination. It was noted that Hospital C used two outcomes related to pain: Pain Level and Comfort/Pain Level. Pain Level was used to document the actual numerical pain level score, and the Comfort/Pain Level outcome was measured using the Faces Pain Scale (Herr, Mobily, Kohout, & Wagenaar, 1998) or nonverbal behaviors when it was not possible to assign a numerical pain score.

These data suggest that study hospitals often use nursing diagnoses, interventions, and outcomes found in the literature for the pneumonia population. Nursing problems stated in the literature and reported by study hospitals indicate that nursing is concerned with ineffective breathing pattern, pain, safety, and nutrition. Interventions selected most frequently by the study hospitals and supported in the literature are Respiratory Monitoring, Pain Management, Airway Management, and Teaching: Individual. Outcomes frequently selected by the study hospitals (i.e., Respiratory Status, Safety, Nutrition, Pain Control) were supported by the literature. Although many nursing diagnoses, interventions, and outcomes for pneumonia patients suggested by the literature do not appear among the 10 most frequent, some may have been documented by the study hospitals with rankings lower than tenth. As noted previously, only a small to moderate percentage of the total number of NNN terms documented are represented by these results. This study focused on only the 10 most frequently used NNN terms, so a complete description of NNN terms selected is beyond the scope of this study.

Scherb’s (2002a) findings were unique in their high rankings of psychosocial phenomena, as there was a notable absence of psychosocial phenomena within the most frequently documented NNN terms at this study’s sites. This finding may inform future developers of CISs of the need to consciously include psychosocial content within their nursing care plan templates. In addition, NNN terms related to self-care, risk management, patient discharge readiness, and caregiver health and knowledge were infrequently found in the NNN documentation of nursing care of older adults in this study. It is likely that nurses in today’s acute health care system with its shorter stays and more acutely ill patients are less focused on these areas than might be possible with longer stays. It may not be feasible or realistic for acute care nursing staff to document in areas such as these and still record data that are focused on the standard of care essential for a particular population or needed for regulatory compliance. We encourage discussion of these issues among nursing professionals and information system developers in all clinical settings.

Limitations

The major limitation of the study is the inability of the hospital CISs to provide reports that elucidate the linkages between nursing diagnosis, nursing intervention, and patient outcome data. This limitation affects the usefulness of the CIS data for quality improvement within organizations, as well as the study of nursing effectiveness in research studies. While the aim of description of the 10 most frequent diagnoses, interventions, and outcomes could be achieved, the lack of these linkages poses potentially serious problems for future nursing research. The linkages must be analyzed along with a considerable number of patient-, unit-, and organizational-level variables that affect patient outcomes to conduct nursing outcomes effectiveness research. Nurse administrators and researchers must begin to demand that information system developers and vendors of systems allow for the capture of NNN linkage data and build in the ability to analyze NNN linkages that will allow institutions, payers, client health care advocates, and other leaders to estimate the quality and effectiveness of nursing interventions in resolving patient problems and achieving patient outcomes. Until such linkage reports are available in the CIS, data mining techniques may be useful for analysis of data from information systems such as those in this study. We are currently exploring the value of data mining for matching problems, interventions, and outcomes (Lu, 2009) in the study dataset.

Despite the absence of NNN linkages in the study data, results are useful to the hospitals for purposes of quality improvement and CIS development. Identification and ranking of nursing diagnoses, interventions, and patient outcomes suggest which NNN terms should be considered for inclusion in systems serving the population and point out areas for staff development focused on the most frequently documented nursing diagnoses, interventions, and patient outcomes. Nursing diagnoses, interventions, and patient outcomes that are documented by nursing staff can be compared with the nursing literature and care plan templates evaluated for the basis of their interventions in various levels of evidence and the consistency of staff selection for documentation. Review of outcomes data, including change scores across time, is essential to organizations and staff in the monitoring of the results of hospital care.

Additional study limitations were the quality and accuracy of documentation. Reliability and validity of secondary data are always suspect. Nurses’ knowledge of NNN terms, the time spent documenting, and the accuracy of their selection of NNN terms affect the quality of the data retrieved. These limitations may be diminished by providing adequate training, continuing education, and staff supervision until they become expert at documentation. Studying only the 10 most frequent NNN terms does not give the total picture of the care provided for these older adults. These patients have complex problems and require extensive nursing care. The most frequent 10 nursing interventions represent only 16% to 53% of the total interventions documented in each site.

Data were drawn from three hospitals and the pneumonia population in two U.S. regions. Generalization of findings to other settings and regions must be made cautiously. Data were collected to represent nursing of older adults but were restricted by Health Insurance Portability and Accountability Act regulations of de-identified data, thus no patients older than 89 were included in the study. Considering the growing number of the oldest-old population, future research should explore ways to address this limitation.

Implications

System developers are most concerned about developing a system for inputting data, devoting less thought and time to ensuring data structures will enable easy and useful retrieval of data. An example is that many nursing CISs do not provide reports that link NNN terms. This is a serious constraint on the ability to evaluate nursing practices and the effectiveness of nursing interventions on patient outcomes, for nursing science, and for organizational cost and quality studies. As noted above, data mining techniques are being explored for their value in linking NNN data in this study. The relationships among NNN terms need to be studied in light of other patient characteristics (e.g., age, gender, comorbid conditions).

Evidence-based guidelines for treatment of the pneumonia population were found in the literature, all primarily driven by the medical model. Effectiveness research using nursing clinical data would assist in developing evidence-based guidelines that reflect nursing care that is necessary for patients to achieve their desired outcomes. Such guidelines would provide the basis for the provision of quality, safe, and cost-effective nursing care.

Analysis of NNN frequencies provides the opportunity for nurse administrators and clinicians to assess nursing practice, the resources needed to support practice, documentation procedures, and potential refinements of CISs. Such analyses also allow comparison of practice patterns across settings in various regions of the country. In addition, these findings may raise research questions that need answers to provide stronger evidence for nursing practice. For example, knowledge of practice patterns provides opportunities for health systems and researchers to study the relationship of different practice patterns to national quality indicators.

Conclusions

This study demonstrates that nursing can describe its contribution to patient care for a specific population, nursing data can be retrieved if systems are appropriately designed, and that there is potential for using these data to improve quality of patient care, build ongoing programs of nursing effectiveness research, and define guidelines for best practices. Community hospitals can play an important role in nursing research and contribute to the development of knowledge about effective nursing practice. These study results could be used by CIS developers in discussions of appropriate NNN terms in care plan templates and clinical care pathways, with consideration of specific patient conditions, regulatory standards, and potential research questions. Acute care nurses are excellent at documenting the physiological condition of patients but may be less inclined to document psychosocial aspects of care, nursing interventions toward successful discharge planning, and preventive care; thus, these phenomena should be included in care plan templates to encourage their documentation. For effectiveness research to move forward, it will be necessary to link NNN terms in CISs. These linkages will provide the necessary bridge for transition from paper documentation to the CIS with decision support and will enhance the visibility and effectiveness of nursing practice.

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Most Frequent Nursing Diagnoses, Interventions, and Patient Outcomes for the Pneumonia Population: Comparison Across Multiple Studies

Literature Current Study
Term Scherb (2002a) Scherb (2002b) Hospital A Hospital B Hospital C
NANDA-I diagnoses
Acute Pain X X X X
Deficient Knowledge X X X X
Imbalanced Nutrition: Less than Body Requirements X X X X
Risk for Falls X X X
Ineffective Breathing Pattern X X X
Risk for Impaired Skin Integrity X X X
Impaired Gas Exchange X X
Activity Intolerance X
Ineffective Airway Clearance X
Decreased Cardiac Output X
Impaired Physical Mobility X
Altered Urinary Elimination X
NIC interventions
Pain Management X X X
Airway Management X X X
Discharge Planning X X X
Skin Surveillance X X X
Teaching: Individual X X X
Respiratory Monitoring X X
Acid-Base Management X
Cardiac Care X
Energy Management X
Family Involvement X
Oxygen Therapy X
NOC outcomes
Knowledge: Illness Care X X X X X
Respiratory Status: Gas Exchange X X X X X
Respiratory Status: Ventilation X X X X X
Fall Prevention Behaviora X X X
Nutritional Status X X X
Tissue Integrity: Skin and Mucous Membranes X X X
Pain Control Behavior X X
Energy Conservation X X
Cardiac Pump Effectiveness X
Mobility Level X
Urinary Elimination X

Ten Most Frequent NANDA-I, NIC, and NOC Terms for the Pneumonia Population

NANDA-I diagnoses
Hospital A (n = 63) Hospital B (N = 67) Hospital C (N = 319)
Deficient Knowledgea (63) Risk for Fallsa (67) Risk for Fallsa (316)
Impaired Gas Exchangea (48) Risk for Infection (67) Risk for Impaired Skin Integritya (315)
Potential for Paina, b (40) Deficient Knowledge (67) Risk for Infection (314)
Altered Body Temperaturea, b (39) Acute Paina (67) Deficient Knowledge (314)
Risk for Fallsa (32) Self-Care Deficitb (67) Acute Paina (314)
Effective Therapeutic Regimen Managementa (21) Risk for Impaired Skin Integritya (67) Self-Care Deficitb (314)
Ineffective Health Maintenance (Tobacco Cessation) (15) Ineffective Breathing Patterna (58) Risk for Constipation (254)
Ineffective Breathing Pattern (14) Imbalanced Nutrition: Less than Body Requirementsa (54) Ineffective Breathing Patterna (236)
Potential for Altered Tissue Perfusionb (14) Risk for Constipation (29) Imbalanced Nutrition: Less than Body Requirementsa (227)
Imbalanced Nutrition: Less than Body Requirements (12) Nausea (27) Nausea (115)
NIC interventions
Hospital A (n = 63) Hospital B (n = 49) Hospital C (n = 88)
Exercise Therapy: Ambulationa (63) Intravenous (IV) Therapy (47) IV Therapy (85)
Bowel Managementa (63) Teaching: Individual (46) Pain Managementa (84)
Urinary Elimination Managementa (63) Infection Protection (45) Infection Protection (79)
Positioninga (63) Pain Managementa (44) IV Insertion (75)
Pain Managementa (63) IV Insertion (42) Teaching: Individual (74)
Sleep Enhancementa (63) Discharge Planning (32) Fall Preventiona (62)
Fluid/Electrolyte Managementa (63) Airway Managementa (30) Airway Managementa (59)
Neurologic Monitoringa (63) Skin Surveillance (30) Discharge Planning (57)
Respiratory Monitoringa (63) Fall Preventiona (27) Dysrhythmia Management (40)
Skin Surveillancea (63) Pressure Management (26) Pressure Pointb (31)
NOC outcomes
Hospital A (n = 62) Hospital B (N = 67) Hospital C (n = 312)
Vital Signs Statusa (62) Infection Severity (64) Knowledge: Illness Care (282)
Knowledge: Illness Carea (62) Comfort/Pain Levela, b (63) Infection Severity (280)
Pain Control Behaviora, c (40) Knowledge: Illness Care (63) Knowledge: Fall Prevention (220)
Respiratory Status: Gas Exchangea (39) Respiratory Status: Ventilationa (50) Fall Prevention Behaviora, d (212)
Infection Status (39) Fall Prevention Behaviora, d (49) Respiratory Status: Ventilationa (211)
Safety Behavior: Fall Preventiona, c (24) Respiratory Status: Gas Exchangea (49) Pain Level (178)
Respiratory Status: Ventilation (22) Tissue Integrity: Skin and Mucous Membranes (46) Respiratory Status: Gas Exchangea (173)
Tissue Perfusion: Peripheral (13) Knowledge: Fall Prevention (35) Tissue Integrity: Skin and Mucous Membranes (148)
Participation in Health Care Decisionsa (12) Self-Care: Activities of Daily Living (34) Self-Care: Activities of Daily Living (109)
Nutritional Status (11) Nutritional Statusa (23) Comfort/Pain Levela, b (108)
Authors

Dr. Head is Assistant Professor, College of Nursing, University of Nebraska Medical Center, Ms. Conley is Gerontological Clinical Nurse Specialist, Nebraska Methodist Hospital, and Ms. Kozel is Clinical Informatics Lead, Nebraska Methodist Health System, Omaha, Nebraska; Dr. Scherb is Professor, College of Nursing and Health Sciences, Winona State University, Rochester, Minnesota; Dr. Reed is Research Analyst, Cecil G. Sheps Center for Health Services Research, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Ms. Weinberg is Nurse Manager, Intensive Care and Respiratory Therapy, and Ms. Gillette is Clinical Data Analyst, Finger Lakes Health, Geneva, New York; Dr. Clarke is Director of Nursing Practice, Research and Innovation, Genesis Medical Center, Davenport, and Dr. Moorhead is Associate Professor and Director, Center for Nursing Classification and Clinical Effectiveness, College of Nursing, The University of Iowa, Iowa City, Iowa.

Dr. Moorhead discloses that she receives royalties from the sale of books and licenses for electronic use of the Nursing Outcomes Classification. Dr. Head, Dr. Scherb, Dr. Reed, Ms. Conley, Ms. Weinberg, Ms. Kozel, Ms. Gillette, and Dr. Clarke disclose that they have no significant financial interests in any product or class of products discussed directly or indirectly in this activity. Support was obtained from the Gerontological Nursing Interventions Research Center, National Institutes of Health P30 NR03979 (PI: Toni Tripp-Reimer, PhD, RN, FAAN, The University of Iowa College of Nursing) and The John A. Hartford Foundation Hartford Center of Geriatric Nursing Excellence (PI: Kathleen Buckwalter, PhD, RN, FAAN, The University of Iowa College of Nursing). The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the National Institute of Nursing Research or The John A. Hartford Foundation.

Address correspondence to Barbara J. Head, PhD, RN, 8708 Lafayette Avenue, Omaha, NE 68114; e-mail: bhead@unmc.edu.

Received: March 17, 2009
Accepted: March 05, 2010
Posted Online: June 30, 2010

10.3928/19404921-20100601-99

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