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Patients’ Care Needs: Documentation Analysis in General Hospitals Wolter Paans, PhD, RN, and Maria Müller-Staub, PhD, EdN, RN, FEANS Wolter Paans, PhD, RN, is an Associate Professor, Research and Innovation Group in Nursing Diagnostics, Hanze University of Applied Sciences, Groningen, the Netherlands, and Maria Müller-Staub, PhD, EdN, RN, FEANS, is the Director and Founder, Pflege PBS, Bronschhofen, Switzerland.

Search terms: NANDA-I classification, nursing diagnosis, nursing documentation, record review Author contact: [email protected], with a copy to the Editor: [email protected] Conflicts of interest: No conflict of interest is declared by the authors. Ethical approval: The authors declare that ethical approval by the ethical committee was not required for this study. Contributions: Study design: WP; study design consultation: MM-S; data collection and analysis: WP; manuscript preparation: WP, MM-S.

PURPOSE: The purpose of the study is (a) to describe care needs derived from records of patients in Dutch hospitals, and (b) to evaluate whether nurses employed the NANDA-I classification to formulate patients’ care needs. METHODS: A stratified cross-sectional random-sampling nursing documentation audit was conducted employing the D-Catch instrument in 10 hospitals comprising 37 wards. FINDINGS: The most prevalent nursing diagnoses were acute pain, nausea, fatigue, and risk for impaired skin integrity. CONCLUSIONS: Most care needs were determined in physiological health patterns and few in psychosocial patterns. IMPLICATIONS FOR NURSING PRACTICE: To perform effective interventions leading to high-quality nursing-sensitive outcomes, nurses should also diagnose patients’ care needs in the health management, value-belief, and coping stress patterns. DOEL: Het doel van de studie is: (a) te beschrijven wat de gezondheidsproblemen / verpleegkundige diagnosen zijn die beschreven staan in patiëntendossiers in ziekenhuizen in Nederland en (b) te evalueren of verpleegkundigen de NANDA-I classificatie gebruiken om gezondheidsproblemen te formuleren. METHODE: Uit alle ziekenhuizen in Nederland werd een willekeurige steekproef getrokken om aan de hand van het D-Catch instrument een review naar verpleegkundige documentatie uit te voeren in 10 ziekenhuizen, op 37 verschillende verpleegafdelingen. RESULTATEN: De meest prevalente verpleegkundige diagnosen waren: Acute pijn, Misselijkheid, Vermoeidheid, Risico op huidbeschadiging i.c. decubitus. CONCLUSIES: De meeste gezondheidsproblemen werden gevonden in het fysiologische domein. Er werden aanmerkelijk minder psychologische gezondheidsproblemen gevonden. De NANDA-I classificatie was herkenbaar in de documentatie. Meer eenheid in het gebruik van diagnostische begrippen is gewenst. IMPLICATIE VOOR DE PRAKTIJK: Om effectieve interventies uit te kunnen voeren is het van belang specifiek en nauwkeurig te documenteren wat gezondheidsproblemen van patiënten zijn. Meer aandacht voor diagnostische domeinen die gerelateerd zijn aan gezondheidsbevordering, rollen en relaties, coping en stresstolerantie en welbevinden lijkt gewenst.

Assessing and documenting a patient’s care needs are essential to provide effective interventions that lead to high-quality nursing-sensitive outcomes. As nursing diagnoses represent patients’ care needs and are the beginning point for planning nursing-sensitive patient outcomes and effective nursing interventions, the application of nursing diagnoses in clinical practice is important in order to foster evidence-based nursing care (Lunney, 2008; 178

Müller-Staub, 2009; Müller-Staub, Needham, Odenbreit, Lavin, & van Achterberg, 2008). Accurately assessing patients’ care needs and precise formulation of nursing diagnoses are vital because nursing diagnoses guide interventions (Gordon, 2008; Lunney, 2008, 2009). As indicated by the World Alliance for Patient Safety (2008), the lack of accurate diagnoses and documentation may lead to adverse events and, therefore, inaccurate documentation © 2014 NANDA International, Inc. International Journal of Nursing Knowledge Volume 26, No. 4, October 2015

W. Paans and M. Müller-Staub Patients’ Care Needs: Documentation Analysis in General Hospitals is considered a hindrance to the quality of care (Wang, Hailey, & Yu, 2011; World Alliance for Patient Safety, 2008). Based on study results, knowledge development concerning the use of standardized nursing diagnoses is strongly recommended (Odenbreit, Müller-Staub, Brokel, Avant, & Keenan, 2012; Paans, Muller-Staub, & Nieweg, 2013). When employing knowledge-based standardized nursing language (SNL), the processes and the value of nursing diagnoses documentation are made easily assessable for scientific evaluations (Odenbreit, 2008, 2010). On the contrary, utilizing nonprofessional, nonstandardized, or incomplete documentation in nursing portrays a documentation result that misinforms hospital administration (Paans, Sermeus, Nieweg, & van der Schans, 2010b; Zegers et al., 2011). To obtain comparable and reliable nursing datasets for quality evaluations and to explain expenditures, the implementation of SNL into health records was highly recommended (Institute of Medicine, 2004; Keenan, Yakel, Dunn Lopez, Tschannen, & Ford, 2013; Saranto & Kinnunen, 2009) as healthcare expenditures vary significantly within the various healthcare settings, populations, diseases, and conditions, whereas political perspectives focus on cost controlling. Prevalence and accuracy studies are imperative for launching meaningful nation- or state-wide nursing datasets to evaluate nursing diagnoses, interventions, and outcomes (Lunney & Müller-Staub, 2012; Odenbreit et al., 2012).

Background Nursing diagnoses are defined as “a clinical judgment about individual, family, or community experiences/ responses to actual or potential health problems/life processes” (Herdman, 2012, p. 515). An accurate diagnosis describes a patient’s problem (label and definition), related factors (etiologies), and defining characteristics (signs and symptoms) in an unequivocal, unambiguous language (Gordon, 2008; Lunney, 2008). Describing a problem solely in terms of its nursing diagnostic label in the absence of related factors and defining characteristics can lead to misinterpretation (Lunney, 2003, 2009; Müller-Staub, Lavin, Needham, & van Achterberg, 2006; Müller-Staub, Needham, et al., 2008). In Dutch hospitals, nurses often exploit literature, for example, prestructured admission forms based on Gordon’s functional health patterns (FHPs), to support the formulation of nursing diagnoses (Gordon, 2005, 2008). Even though the NANDA-I taxonomy and the FHP are taught in basic nursing education in the Netherlands, several authors reported that patient records contained only minimal precisely formulated diagnoses, pertinent signs and symptoms, and related factors, as well as poorly documented nursing interventions and outcomes (Florin, Ehrenberg, & Ehnfors, 2005; Müller-Staub et al., 2006; Paans et al., 2010b).

Studies addressing the combination of prevalence and accuracy in nursing diagnoses began with instrument development in 1984 with the Ziegler Criteria for Evaluating the Quality of the Nursing Process (ZCEQNP) instrument of Ziegler (Dobrzyn, 1995). This instrument was developed to assess nursing diagnoses derived by students. To study the effects of education in nursing diagnoses, interventions, and outcomes, Nordström and Gardulf (1996) developed the NoGa instrument, whereby a scale measures the degrees of accuracy in nursing diagnoses (Nordström & Gardulf, 1996). Lunney developed an instrument to evaluate the influence of educational interventions on nursing students (Collins, 2013; Lunney, 2001). Several audit instruments, such as the catching instrument (Björvell, Wredling, & Thorell-Ekstrand, 2002), the Quality of Nursing Diagnosis (QOD; Florin et al., 2005), the Quality of Diagnosis, Interventions and Outcomes (Q-DIO; Müller-Staub et al., 2009; Müller-Staub, Lunney, et al., 2008), and the D-Catch (Paans, Sermeus, Nieweg, & van der Schans, 2010a), have also been developed to review and measure the quality of nursing diagnoses for educational purposes and in clinical studies. Studies focusing on the prevalence of nursing diagnoses in specific healthcare settings and/or in specific patient populations are increasing but continue to be insufficient. For example, in a geriatric rehabilitation setting, the greatest prevalence of diagnoses was chronic confusion and imbalanced nutrition (Heering, 2010). In a study based on a record review in a hospital in Iceland, altered comfort, selfcare deficit, impaired physical mobility, and emotional discomfort were the most prevalent nursing diagnoses (Thoroddsen & Thorsteinsson, 2002). Examples of studies addressing the prevalence of nursing diagnoses in specific populations are critical care patients (Gordon & Hiltunen, 1995), perioperative care patients (Killen et al., 1997), oncology care patients (Cáceres Manrique & Puerto Pedraza, 2008; Courtens & Huijer Abu-Saad, 1998; Speksnijder, Mank, & van Achterberg, 2011), patients with cardiac insufficiency (Matos, Guimaraes, Brandao, & Santoro, 2012), oncology patients in the dying phase (van der Werf, Paans, & Nieweg, 2012), and hospitalized elderly (Almeida et al., 2008). Most of these studies were conducted in one or two institutions as multicenter overviews regarding the prevalence of NANDA-I nursing diagnoses in general hospitals are presumably nonexistent. According to our knowledge, no study has addressed the prevalence and accuracy of nursing diagnoses in relationship to the NANDA-I theoretical framework in general Dutch hospitals.

Study Aims The aim of the study was twofold: (a) to describe care needs derived from records of medical/surgical patients in general Dutch hospitals, and (b) to evaluate whether nurses employed the NANDA-I classification to formulate patients’ care needs. 179

Patients’ Care Needs: Documentation Analysis in General Hospitals W. Paans and M. Müller-Staub Methods A retrospective nursing documentation audit was performed by conducting a data analysis of nursing records and by utilizing the D-Catch instrument. Population and Sample Stratified cross-sectional sampling was applied to represent nursing records of all medical centers of each province in the Netherlands. From a total of 94 medical centers (86 general hospitals and 8 university hospitals), 10 hospitals were randomly selected for study participation. After four hospital directors declined participation, four other hospitals of the same province were approached (response rate: four out of six hospitals). The sample size of nursing records was 369 and represents 37 wards. The director of the hospitals selected the wards for participation as it appeared unfeasible to take a random sample of wards per hospital. On the day planned for measurement, the managers of the participating wards volunteered to extract a random sample of 10 nursing records (out of all available records). The inclusion of nursing records was based on two criteria: (a) the patient’s length of stay was at least 3 days and (b) the patient’s written informed consent to study their nursing documentation. Measurement Instrument The D-Catch instrument was employed to measure the prevalence of the terms used for nursing diagnoses (labels), and whether the diagnostic labels that were used in nursing records demonstrate the application of the NANDA-I classification. A diagnostic label includes—at a minimum—the diagnostic focus and the nursing judgment (e.g., urinary elimination, impaired). According to NANDA-I, a diagnostic label is a concise term or phrase that represents a pattern of related cues (Herdman, 2012). The D-Catch was developed to measure the accuracy of the following: (a) nursing record structure (according to the phases of the nursing process); (b) admission data (information from the admission interview); (c) nursing diagnoses (problem label, etiologies or related factors, and signs and symptoms, which is also known as the PES structure of nursing diagnoses, and if the diagnosis establishes the possibility of a nursing intervention); (d) nursing interventions (and their relationships in terms of effectiveness on the documented nursing diagnoses); (e) progress notes and outcome evaluations (related to the documented nursing diagnoses); and (f) legibility (readability of handwritten or typed records) (Paans et al., 2010a, 2010b). Internal consistency of the D-Catch (Cronbach’s alpha) was 0.722, and the inter-rater reliability (Kw) varied between 0.742 and 0.896 (Paans et al., 2010a). This study focused on the prevalence of nursing diagnoses; therefore, only the diagnosis domain (3) of the D-Catch was used. This D-Catch domain contains the section “Write the 180

diagnostic findings from the record here.” The findings of this section were subsequently employed for further analyses. Ethical Considerations In the Netherlands, retrospective record research is not subject to approval by a research ethics board (Vereniging van Samenwerkende Nederlandse Universiteiten [VSNU], 2004, p. 15). Patients were informed of the details of the original study, and that information obtained from their records would be treated anonymously and would not be identified in reports. The records were incorporated into the study after patients signed a consent form allowing aggregation of the data from their records. Data Analysis To achieve expert consensus, two groups of reviewers performed data analyses. First, the principal investigator and a second researcher independently assessed each patient record by applying the D-Catch section 3 in order to evaluate nursing diagnostic descriptions. Second, registered nurses (n = 4) and fourth-year bachelor’s degree nursing students (n = 8) served as external reviewers. To qualify as an external reviewer, completing 20 hr of training that addressed the theoretical background of nursing diagnosis documentation and measurement was required. The paired external reviewers analyzed the prevalence of nursing diagnoses and rated if the diagnostic description was stated as either (a) diagnostic label according to the NANDA-I classification or (b) diagnostic description not related to NANDA-I. The external reviewers applied the 4-point quality and quantity scales of the D-Catch as a reference guide (Paans et al., 2010a), and paired discussions of findings continued until consensus was reached regarding the following three categories: (a) diagnostic issues not related to NANDA-I classification, (b) diagnostic issues written ambiguous, and (c) nursing diagnosis according to NANDA-I classification. A diagnosis label was considered to be a NANDA-I nursing diagnosis label when the reviewers had reached consensus on the comparability in nurses’ descriptions in the records that evidenced the NANDA-I diagnosis definition. This mapping procedure between what was discovered in the records and in the NANDA-I classification was conjointly discussed as there were instances where no sufficient match occurred due to a slightly different ordering of words in the records compared with the NANDA-I diagnoses (see example in Table 4). To be evaluated as a sufficient match, a diagnosis was required to be distinctly formulated as published in the newest Dutch version of the NANDA-I handbook for practical use in education and clinical practice (Herdman, 2012). Third, to specify and categorize the prevalence of nursing diagnoses, Gordon’s 11 FHPs were applied. For this analysis step, the FHP including the definition of each

W. Paans and M. Müller-Staub Patients’ Care Needs: Documentation Analysis in General Hospitals health pattern, as provided by Wilkinson in a Dutch edition (Wilkinson, 2007, pp. 107–109), was employed. For statistical analyses, the SPSS Benelux software package version 17 (SPSS Statistics for Windows, Version 17.0, SPSS Inc., Chicago, IL, USA) was applied. Frequencies and percentages scores of diagnostic findings were calculated, as well as means and standard deviations.

The cognitive-perceptual pattern (n = 420 diagnoses) and the nutritional-metabolic pattern (n = 378 diagnoses) comprised the majority of diagnoses. The sexualityreproductive pattern and the value-belief pattern appeared to be nonexistent as no diagnoses were detected in the nursing documentation regarding these FHPs (Table 6).

Results

Patients’ care needs were formulated according to NANDA-I labels and were ascertained to be prevalent in all

The results demonstrate the prevalence of nursing diagnoses in 8 different specialties and 37 wards in 10 hospitals (Table 1). Based on the review of 369 records, 1,635 diagnostic labels were ascertained. The mean number (SD) of diagnoses per record was 4.4 (3.1) diagnoses. Five records contained no diagnosis, and eight records contained 20 diagnoses, which was the maximum number of determined diagnoses. The top five prevalent nursing diagnoses in Dutch hospitals were acute pain (n = 212), nausea (n = 125), fatigue (n = 117), risk for impaired skin integrity (n = 79), and nutrition: less than body requirements (n = 69) (Table 2). Except for the diagnosis nutrition: less than body requirements, the other four aforementioned diagnoses were ascertained to be prevalent in almost all of the wards included in the sample. From all of the detected diagnostic labels, 94.1% were determinedly related to one of 47 NANDA-I problem labels (Table 3). There were minor variances in descriptions of diagnostic labels. For instance, in the most prevalent diagnosis “acute pain,” nurses’ descriptions were denoted in several related manners or as contextual clarifications (Table 4). Diversity was evident in the prevalence of nursing diagnoses per specialty. The greatest mean number of nursing diagnoses per documentation was discovered in a geriatric ward, n = 8.5, and the lowest in the rheumatology ward, n = 2.3 (Table 5).

Table 1. Nursing Wards in Sample Wards

Hospitals (n)

Wards (n)

Internal medicine warda Intensive care Neurological ward Pediatric wardb Orthopedic ward Rheumatology ward Surgery wardc Geriatric ward Total

9/10 2/10 6/10 1/10 4/10 1/10 10/10 2/10 10/10

11 2 6 1 4 1 10 2 37

a

Internal medicine wards, including specific care units with nonsurgical oncology, hematology, cardiology, and pulmonology. b Pediatric ward with units for specialized care for children under 16 years of age. c Surgery wards, including specific care units with surgical oncology, urology, and plastic surgery.

Discussion

Table 2. Diagnostic Labels According to NANDA-I Classification Ordering 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Diagnoses

n

%

Acute pain Nausea Fatigue Risk for impaired skin integrity Imbalanced nutrition: less than body requirements Impaired skin integrity Insomnia Impaired urinal elimination Peripheral neurovascular dysfunction Impaired gas exchange Ineffective breathing patterns Diarrhea Hyperthermia Constipation Anxiety Hopelessness Impaired physical mobility Sensory perception alterations Self-care deficit syndrome Impaired swallowing Functional incontinence Acute confusion Deficient fluid volume Risk for falls Hypothermia Decreased cardiac output Bowel incontinence Risk for infection Readiness for enhanced knowledge Risk for spiritual distress Dressing self-care deficit Toileting self-care deficit Bathing self-care deficit Feeding self-care deficit Fear Risk for acute confusion Impaired verbal communication Delayed surgical recovery Risk for spiritual distress Risk for other-directed violence Altered thought processes Impaired memory Decisional conflict Defensive coping Risk for electrolyte imbalances Noncompliance Impaired bed mobility Total

212 125 117 79 69

57.5 33.9 31.7 21.4 18.7

66 56 54 54 50 50 50 49 46 42 33 32 31 30 28 26 21 21 19 18 16 15 14 12 12 11 11 11 10 10 7 5 5 4 4 4 3 3 1 1 1 1 1.539

17.9 15.2 14.6 14.6 13.6 13.6 13.6 13.3 12.5 11.4 8.9 8.7 8.4 8.1 7.6 7.0 5.7 5.7 5.1 4.9 4.3 4.1 3.8 3.3 3.3 3.0 3.0 3.0 2.7 2.7 1.9 1.4 1.4 1.1 1.1 1.1 0.8 0.8 0.3 0.3 0.3 0.3

181

Patients’ Care Needs: Documentation Analysis in General Hospitals W. Paans and M. Müller-Staub Table 3. Number of Diagnoses in 10 Hospitals (37 Wards) n Diagnostic issues not related to NANDA-I classification Diagnostic issues written unclear Nursing diagnosis according to NANDA-I classification Total number of diagnoses Mean (SD) of diagnoses per record

83 13 1.539

% 5.1 0.8 94.1

1.635 4.4 (3.2)

Table 4. Acute Pain No.

Description as in record literally

n

1 2

(Acute) pain (Acute) bellyache, abdominal pain, stomachache Painful, suffering (Acute) headache (Acute) pain in the region of pelvis or hip Other descriptions containing the label “pain”

161 12

3 4 5 6

11 11 7 10

hospitals, wards, and clinical specialties included in this study. The NANDA-I 2009–2011 classification contained 201 validated nursing diagnoses. These diagnoses were published in the updated Dutch version of the NANDA-I handbook for practical use in education and clinical practice (Herdman, 2012). Of these NANDA-I diagnoses, 47 labels were evident in nurses’ patient documentations. It may be concluded that the other 154 nursing diagnoses were not employed in hospital practice in the Netherlands. In comparison with a study regarding the prevalence of nursing diagnoses in Iceland (Thoroddsen & Thorsteinsson, 2002), there are similarities in the compilation of the 20 most prevalent nursing diagnoses discovered in this Dutch study, although the most prevalent Dutch diagnosis, “acute pain,” was not in the top 20 of the Icelandic results. However, the finding of the current Dutch study is supported by a systematic review by Müller-Staub et al. (2006), who demonstrated in several studies, including a review with a total of 4,051 patients from 12 various sites, that pain was the most frequently stated diagnosis. There may be certain cultural differences in assessing and documenting the prevalence of nursing diagnoses (Thoroddsen & Thorsteinsson, 2002). The results of this study demonstrate that, of the total diagnostic labels reviewed, 94.1% of the labels were determinedly related to one of 47 NANDA-I problem labels. In 2001, in the Icelandic study, this was ascertained to be 60%. These variances may be based on cultural diversities on the utilization of SNL, and/or in differences of implementation strategies when employing nursing diagnoses in clinical practice, and/or in differing study methods; for example, the D-Catch was applied only in the Dutch study. 182

The majority of diagnoses were observed in physiological health patterns and only a minimal number in the coping stress intolerance and role-relationship patterns, respectively, and no diagnoses in the sexuality-reproductive and in the value-belief pattern. Specification and categorization of prevalent nursing diagnoses into Gordon’s 11 FHPs revealed no diagnoses in two patterns (sexuality-reproductive pattern and value-belief pattern), and the role-relationship pattern revealed only four diagnoses out of 1,539. This signifies that diagnoses such as powerlessness, moral distress, death anxiety, caregiver role strain, ineffective family coping, and grieving were not evident. An experimental study determined that nurses asked no questions related to the sexuality-reproductive pattern in admission interviews and only very infrequently asked questions related to the value-belief pattern (Paans et al., 2013). This assessment bias could possibly be an explanation for the findings of the current study. Deficiencies of documenting psychosocial concerns were also reported elsewhere (Jefferies, Johnson, & Griffiths, 2010). Nurses’ previously obtained case-related diagnostic knowledge, diagnostic experience, critical thinking, dispositions, and reasoning skills, as well as the exploitation of resources in clinical practice, hospital policy, and environmental aspects, have been demonstrated to be influential on the prevalence and accuracy of nursing diagnoses (Bruylands, Paans, Hediger, & Müller-Staub, 2013; Paans et al., 2010b). Future research is required on influencing factors that can serve as a beneficial foundation for future strategies that support nurses and enhance the accuracy and use of nursing diagnoses in practice. This study demonstrated that nurses employed the NANDA-I-related terms to document patients’ care needs as are described in the standardized, theory-based nursing diagnoses represented in the classification. These findings could be interpreted as the NANDA-I classification providing substance to guide practice and are perceived as beneficial when describing patients’ care needs. However, the analyses of this study only addressed the comparing and mapping of nursing diagnoses labels as SNL had not been fully implemented into clinical practice in the studied hospitals. Knowledge regarding a patient’s history and how to interpret relevant patient information is a central factor of deriving accurate nursing diagnoses (Cholowski & Chan, 1992; Hasegawa, Ogasawara, & Katz, 2007; Müller-Staub, 2009; Müller-Staub et al., 2009). In order to make these accurate diagnostic statements, nursing diagnoses that include the defining characteristics and related factors (thusly designated as the PES format) must be documented, as presented by the patients, and stated in standardized, theory-based terms (Lunney, 2008; Lunney & Müller-Staub, 2012; Müller-Staub, 2012; Paans et al., 2013). Studies have indicated that implementing NANDA-I nursing diagnoses into practice by employing the PES format promotes accurately assessing patients’ care needs. Nurses working with the PES format of nursing diagnoses selected more effective nursing interventions that resulted in significantly

1. Acute pain 2. Nausea 3. Fatigue 4. Risk for impaired skin integrity 5. Imbalanced nutrition: less than body requirements 6. Impaired skin integrity 7. Insomnia 8. Impaired urinal elimination 9. Peripheral neurovascular dysfunction 10. Impaired gas exchange 11. Ineffective breathing patterns 12. Diarrhea 13. Hyperthermia 14. Constipation 15. Anxiety 16. Hopelessness 17. Impaired physical mobility 18. Sensory perception alterations 19. Self-care deficit syndrome 20. Impaired swallowing 21. Functional incontinence 22. Acute confusion 23. Deficient fluid volume 24. Risk for falls 25. Hypothermia 26. Decreased cardiac output 27. Bowel incontinence 28. Risk for infection 29. Readiness for enhanced knowledge 30. Risk for spiritual distress 31. Dressing self-care deficit 32. Toileting self-care deficit 33. Bathing self-care deficit 34. Feeding self-care deficit 35. Fear 36. Risk for acute confusion 37. Impaired verbal communication 38. Delayed surgical recovery 39. Risk for spiritual distress 40. Risk for other-directed violence 41. Altered thought processes 42. Impaired memory 43. Decisional conflict 44. Defensive coping 45. Risk for electrolyte imbalances 46. Noncompliance 47. Impaired bed mobility Total number of diagnoses Total number of records Mean number of diagnoses per record

Diagnoses 27 21 11 5 3 4 6 6 3 1 0 3 4 8 6 5 10 4 0 3 4 5 1 0 2 2 1 4 0 2 0 0 0 0 2 0 0 1 0 1 0 0 0 0 0 0 0 155 53 2.9

0 1 1 0 3 2 2 0 0 0 0 0 0 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 30 13 2.3

Orthopedic ward

11 2 3 1 0

Rheumatology ward

Specialism

22 14 9 12 5 9 13 9 8 5 7 3 8 13 6 4 1 1 1 0 2 3 3 2 6 3 3 4 3 2 0 2 1 2 2 1 0 0 0 0 0 0 357 78 4.6

70 39 27 20 13

Surgery ward

Table 5. Number of NANDA-I Diagnoses Related to Hospital Specialism

7 0 6 3 6 2 4 6 5 6 2 1 2 3 2 0 0 3 0 7 3 0 2 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 110 24 4.6

12 9 8 4 5

Pediatric ward

5 3 2 2 3 3 0 0 5 8 6 8 7 5 2 7 6 2 4 0 0 1 0 1 0 7 7 7 7 0 2 0 0 1 1 1 3 0 1 0 0 0 136 16 8.5

5 1 6 0 7

Geriatric ward

4 0 5 5 3 0 2 3 3 2 0 0 1 0 1 1 0 0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 40 14 2.9

4 0 1 2 0

Intensive care

10 15 15 16 14 18 21 21 5 6 4 4 4 4 4 5 4 12 7 3 3 3 5 9 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 0 386 96 4

43 37 40 26 16

Internal medicine ward

14 17 10 13 15 16 5 6 12 9 9 6 5 4 8 4 5 2 7 4 6 7 0 0 3 0 1 0 0 4 4 3 1 1 0 1 0 2 0 0 0 1 325 75 4.3

40 16 21 21 25

Neurological ward

66 56 54 54 50 50 50 49 46 42 33 32 31 30 28 26 21 21 19 18 16 15 14 12 12 11 11 11 10 10 7 5 5 4 4 4 3 3 1 1 1 1 1.539 369 4.2

212 125 117 79 69

Total

W. Paans and M. Müller-Staub Patients’ Care Needs: Documentation Analysis in General Hospitals

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Patients’ Care Needs: Documentation Analysis in General Hospitals W. Paans and M. Müller-Staub Table 6. Diagnoses in Gordon’s Functional Health Patterns Gordon’s functional health patterns 1. Health perception-health management pattern 2. Nutritional-metabolic pattern 3. Elimination pattern 4. Activity-exercise pattern 5. Sleep-rest pattern 6. Cognitive-perceptual pattern 7. Self-perception pattern 8. Role-relationship pattern 9. Sexuality-reproductive pattern 10. Coping stress intolerance pattern 11. Value-belief pattern Total

Diagnoses (n) 16 378 212 246 173 420 79 4 0 11 0 1.539

% 1.0 24.6 13.8 16.0 11.2 27.3 5.1 0.3 0.0 0.7 0.0 100

improved quality patient outcomes compared with not using SNL (Gordon, 1995; Müller-Staub, Needham, Odenbreit, Lavin, & van Achterberg, 2007; Müller-Staub, Needham, et al., 2008). In a cluster randomized trial, the study intervention was to encourage nurses in guided clinical reasoning sessions to utilize standardized, theory-based nursing diagnoses in the PES format. The results demonstrated the documentation of substantially significant and more accurate nursing diagnoses, more effective nursing interventions, and improved patient outcomes (p = .0001) (Müller-Staub, Needham, et al., 2008). Knowledge obtained through sources such as the FHP assessment, which supports nurses in the diagnostic process to derive accurate nursing diagnoses, assists nurses in deducing diagnoses more than assessments without the use of such resources (Spenceley, O’Leary, Chizawsky, Ross, & Estabrooks, 2008). The purpose of exploiting assessment formats based on FHPs (Gordon, 2005, 2008) and standard nursing diagnoses, such as those included in the literature (Ackley & Ladwig, 2011; Doenges & Moorhouse, 2012; Doenges, Moorhouse, & Murr, 2013), is to attain greater accuracy in diagnoses. There is evidence that NANDA-I diagnoses can facilitate the collection and exploitation of data for measuring and monitoring quality of care (Almasalha et al., 2013; Keenan et al. 2013; Keenan, Yakel, Yao, et al., 2012; Müller-Staub, 2007; Müller-Staub, Needham, et al., 2008). Based on studies related to accuracy in nursing documentation (Paans et al., 2010b, 2013), we can conclude that employing SNL is a prerequisite for developing electronic health records (EHRs) that represent state-of-the art nursing (Zegers et al., 2011). EHRs that include SNL provide reliable and valid nursing datasets for research purposes (Keenan et al., 2013; Odenbreit et al., 2012). Additional research on the meaningful and standardized utilization of nursing diagnoses in the nursing record is required in order to provide guidelines for software developers (Anderson, Keenan, & Jones, 2009; Keenan, Tschannen, & Wesley, 2008; Keenan & Yakel, 2005). 184

The knowledge base provided by SNL in EHRs provides opportunities to discover the nature of nursing care expenses. Employing SNL can encourage hospital leaders to increase documentation quality by focusing on the procedures and content of documentation (Van Herck et al., 2013). Standardized nursing diagnoses, intervention, and outcome data can be used for further prevalence studies, retrospective quality analyses, safety assurance strategies, and financial controlling (Keenan & Yakel, 2005; Keenan et al., 2008; Keenan, Yakel, Yao, et al., 2012). Limitations of the Study With respect to prevalence patterns of ward specialties, it must be taken into consideration that the sample of the total 37 wards represented one rheumatology and one pediatric ward. However, a random sample was derived from all of the 94 medical centers in the Netherlands. Eight specialties and 37 wards were reviewed that could possibly have differed in conditions of patients, patient-to-staff-ratios, nurses’ educational backgrounds, and years of experience. Nursing records were included by utilizing ward-level sampling, and ward managers volunteered in collecting records which could have created potential selection bias on ward levels. These and other undetected environmental influences on wards and/or of patient conditions may have confounded prevalence outcomes. The influence of diagnostic sources, such as FHPs, was not assessed in the current study. Despite the representative sample of Dutch hospitals that was reviewed, the results of this study, with its sample size, may not be representative of the prevalence of nursing diagnoses in the evaluated eight specialty units. Conclusion Based on the results of this study, all 47 nursing diagnoses—and especially the 10 most prevalent diagnoses that are relevant in many specialties of medical/surgery wards—have priority when utilizing SNL in clinical decision making and for introduction into electronic documentation systems used to develop nursing minimal datasets. There are, most likely, no single determinants that uniquely attribute to the prevalence of nursing diagnoses. Instead, a combination of factors is presumed to affect the nursing diagnosis and must be taken into consideration as a topic for further research. There is evidence that employing NANDA-I diagnoses facilitates the aggregation and use of data to monitor and measure quality of care, and using SNL is a prerequisite when developing EHRs that represent state-of-the art nursing. EHRs that include SNL can provide reliable and valid nursing datasets to be used for research purposes. Standardized nursing diagnoses, intervention, and outcome data can be employed for further prevalence studies, retrospective quality analyses, safety assurance strategies, and financial controlling. Additional research on meaningful

W. Paans and M. Müller-Staub Patients’ Care Needs: Documentation Analysis in General Hospitals and standardized use of nursing diagnoses in the nursing record is required in order to provide guidelines for software developers. Nursing Implications Most nursing diagnoses/care needs were found in the physiological health patterns, only a few diagnoses in psychosocial patterns, and no diagnoses in the sexuality and value-belief patterns. It is suggested that nurses also focus on the psychosocial aspects to assess patients’ overall care needs. To perform effective interventions leading to highquality nursing-sensitive outcomes, nurses should also state nursing diagnoses in the health management, valuebelief, and coping stress patterns. Knowledge regarding a patient’s history and how to interpret relevant patient information is a central factor in deriving accurate nursing diagnoses. Nurses documented patients’ care needs by using NANDA-I labels, but these diagnoses present only labels. To accurately indicate care needs, defining characteristics and related factors—the PES format—should also be performed as these provide the beginning point for meaningful and effective nursing interventions. The results of this study can assist in developing new electronic documentation systems. Regarding the implementation of software for nurses’ use of NANDA-I diagnoses in clinical practice, it is suggested to implement the entire NANDA-I classification into EHRs and not just the diagnostic labels. Related factors and defining characteristics (PES format) should also be implemented into software programs. The entire classification presented in a taxonomic structure, such as the FHP, can support nurses in determining accurate nursing diagnoses. The implementation of SNL, such as NANDA-I, is a prerequisite for electronic nursing documentation systems, and SNL provides the foundation for reliable and valid nursing data. These data can be exploited for nurse staff planning, cost estimations, and efficiency measurements, to develop knowledge by providing education to students and nurses in clinical practice, and for accurate nursing diagnoses documentation, which is an essential factor to ensure patient safety and quality of care. Acknowledgments. The authors gratefully thank Gonneke Jonker, BScN, and Romy Venema, BScN, for their support in preparing data for analysis. References Ackley, B. J., & Ladwig, G. B. (2011). Nursing diagnosis handbook: An evidencebased guide to planning care (9th ed.). St. Louis, MO: Mosby/Elsevier. Almasalha, F., Xu, D., Keenan, G. M., Khokhar, A., Yao, Y., Chen, Y. C., & Wilkie, D. J. (2013). Data mining nursing care plans of end-of-life patients: A study to improve healthcare decision making. International Journal of Nursing Knowledge, 24(1), 15–24. doi:10.1111/j.2047-3095.2012.01217.x Almeida Mde, A., Aliti, G. B., Franzen, E., Thome, E. G., Unicovsky, M. R., Rabelo, E. R., & Moraes, M. A. (2008). Prevalent nursing diagnoses and

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Patients' Care Needs: Documentation Analysis in General Hospitals.

The purpose of the study is (a) to describe care needs derived from records of patients in Dutch hospitals, and (b) to evaluate whether nurses employe...
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