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This article was written on 03 Nov 2012, and is filled under Volume 16 Number 3.

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Danish national framework for collecting information about patients’ nutritional status. Nursing Minimum Dataset (N-MDS)

by Sasja Jul Håkonsen, RN, MScN, Inge Madsen, RN, MI,

Merete Bjerrum, MA, PhD, and Preben Ulrich Pedersen, RN, PhD

CITATION

Håkonsen, S., Madsen, I., Bjerrum, M. and Pedersen, U. (October 2012). Danish national framework for collecting information about patients’ nutritional status. Nursing Minimum Dataset (N-MDS). Online Journal of Nursing Informatics (OJNI), 16 (3), Available at  http://ojni.org/issues/?p=2044

 

Abstract

In Denmark the national guidelines for nursing documentation outlines twelve areas in which nurses have to systematically document in daily care. Nutrition is one of these areas. However, the guidelines are frameworks that do not specify exactly what data nurses have to collect and which areas nurses need to document about nutrition in order to make a nursing specific documentation.

This present study set out to identify a nursing minimum data set for nutrition in a clinical setting. Data used was validated and available tools to screen or assess patients’ nutritional risk.

A systematic literature search was undertaken identifying 42 eligible instruments. An inductive content analysis identified eighteen subcategories that were divided into five main categories:

1 Anthropometry such as weight, height, biochemistry, muscle mass and fat etc., 2 Ability to eat, 3 Intake, 4 Factors which indirectly affect intake and needs and 5 Stress factors.

The five main categories are intended to help clinical staff make a complete nursing assessment of patients’ nutritional status in order to guide nurses to make a relevant and complete nursing documentation.

 Key Words

Nutrition, documentation, Nursing Minimum-Dataset (N-MDS), nursing, informatics, content analysis

Introduction

The rate of malnutrition in Danish hospitals is estimated to be approximately 40%, with the highest prevalence, at 57% in departments of gastroenterology surgery (Rasmussen, Kondrup, Staun, Ladefoged, Kristensen, & Wengler, 2004).

There is increasing evidence that the use of nutrition support in hospitals reduces mortality, decreases the rate of complications and shortens the hospital stay (Stratton, Green, & Elia, 2003).

A cross-sectional study in Danish hospitals showed that a minority of the patients (7.6%) had some form of nutritional evaluation done on them, and that weight loss and food intake were seldom recorded and documented (4% and 20% respectively). A nutrition plan and monitoring were made and carried out for 14.2% of the patients (Rasmussen et al. 2004).

In addition, it was found that among the obvious triggers for a nutrition plan (BMI, recent weight loss, recent dietary intake and severity of disease), only severity of disease was clearly associated with the presence of a nutritional plan while recent weight loss was broadly associated with a nutritional plan. This indicated that it is the clinical condition rather than nutritional observations that trigger a nutritional plan. This is somewhat surprising since recording of recent weight loss, dietary intake and nutritional evaluation each occurred more often in patients at nutritional risk.

This study therefore concluded that a nutritional risk condition in a patient does increase the attention given to nutritional problems, but this attention is not linked to well-defined actions or documentation of patients’ nutritional status (Rasmussen et al. 2004).

In 1992 Norma Lang, a professor of healthcare quality and informatics, stated that “If you can’t name it, you can’t control it, finance it, research it, teach it or put into public policy” (MacNeela, Scott, Treacy & Hyde 2006, p. 44).

If nurses do not know which terminology to use about nutritional care, it is obviously difficult to identify areas that are  both of importance to the patient and need to be documented.

The nursing minimum dataset has been proposed as a method of routinely collecting information on core aspects of the nursing contribution to care, organized primarily in terms of phenomena, interventions and outcomes (MacNeela et al. 2006).

 

Background

Nursing Minimum Data sets (NMDSs) have been developed in several countries around the world (Goossen, Epping, Feuth, Dassen, Hasman, van der Heuvel,1998; Butter, 2006; Griens, Goossen, Van der Kloot, 2001). A NMDS is comprised of “essential nursing data” which are defined as “those specific items of information that are used on a regular basis by the majority of nurses across all settings in the delivery of care” (Devine & Werley, 1988). Uniform definitions and categories are used to describe these items of information, and the aim is to meet the information needs of various data users in healthcare systems (Goossen et al. 1998).

To perform high quality nursing, nurses firstly need a structured and standardized clinical language for nursing practice and nursing science, secondly, they need their patients and their families to be able to understand what it is that nurses do in response to certain human needs and in producing certain outcomes, and thirdly, they need for managers to be able to access standardized data on nursing for resource management and the evaluation of outcomes (Goossen et al. 1998; Rutherford, 2008). Nursing documentation is therefore a prerequisite for high quality patient care.

An accurate, complete and process oriented record has been described as the central basis for patient care, and it is believed to be essential for safe and effective practice and for maximizing positive patient outcomes (Marinis, Piredda, Pascarella, Vincenzi, Spiga, Tartaglini, Alvaro & Matarese 2010).

Documentation by nurses has however received some criticism in recent years. The critique comes from nurses, doctors and researchers who point out that documentation is time consuming, lacks useful information, produces too much text and suffers from a lack of structure (Andersen & Lindgaard 2009).

A Cochrane review has stated that global nursing record systems that are unstructured and undocumented, in general lead to a lower level of accuracy and consistency of patient described data in the nursing records (Urquhart, Curell, Grant & Hardiker, 2009). However,  nursing record systems aiming at a specific problem, guiding nurses in a structured way, to report patient data, such as patients’ nutritional care data, may be successful and lead to improvement in quality of care. (Urquhart et al. 2009).

In Denmark the national guidelines for nursing documentation outline 12 areas in which nurses have to systematically document daily care. Nutrition is one of these areas. However, the guidelines are a framework that does not specify exactly what data nurses have to collect and what nurses need to document about nutrition in order to identify areas that is relevant and counts as specific nursing documentation. It is an empty framework where nurses themselves must assess what is relevant to document about nutrition in the specific patient situations.

Several studies describe the lack of nutrition-related information in the patients’ nursing records (Brown et al. 2006; Hosseini, Amirkalali, Nayebi, Heshmat & Larijani, 2006). Nurses have indicated that they do know that nutrition is important (Rasmussen et al. 2004; Bjerrum, Tewes & Pedersen, 2012), but they have difficulties identifying what needs to be documented about patients’ nutritional care, what is relevant and what is important (Rasmussen et al. 2004; Persenius, Hall-Lord, Bååth & Larsson, 2008).

Using general terms like nutrition does not guide nurses to make adequate and relevant observations and systematically document  the patient’s nutritional care. Nurses need more specific guidance in order to collect information, to assess the patients’ needs, prepare a plan for  nursing care, carry out nursing interventions and evaluate the outcome of the interventions.

Nutritional screening tools designed for nurses, encompasses the areas that nurses need to know about before they can make a care plan for the individual patient. Therefore in this study we set out to derive specific minimum data within the area nutrition, by using validated instruments for identification of nutritional risk, as screening instruments ideally contain the minimal number of items for necessary nutritional problem identification.

 

Aim, purpose and assumptions

Aim:

The study’s aim is to identify a nursing minimum data set within nutrition in a clinical setting by comparing existing and available screening and assessment tools to screen or assess the nutritional risk of patients.

Purpose:

The purpose of producing a NMDS specifically for nutritional care is to promote the comparability of nursing data, describe the nursing care of patients, clients or families in a variety of settings, stimulate nursing research, provide data about nursing care to facilitate and influence clinical, administrative and health policy decision making. A NMDS contains core data elements that need to be collected from all patients receiving nursing nutritional care, as we expect that these data will help nurses make relevant decisions leading to improved patient outcomes (Ryan & Delaney, 1995).

The NMDS should help nursing staff to identify problem areas and amplify clinical assessments and potentially lead to improved documentation and high quality patient care (Goossen et al. 1998). Furthermore, a NMDS should serve as a clinical “red flag” to initiate proactive interventions and thereby identify people who are at nutritional risk and in need for targeted nursing interventions (Corbett, Crogan & Short 2002).

 

Assumptions:

  • Specific nursing minimum data within nutrition will increase nurses’ knowledge about assessing patients at potential nutritional risk.
  • Specific nursing minimum data within nutrition will guide nurses in a structured way to report patient data and documentation.
  • Specific nursing minimum data within nutrition will improve nurses’ documentation regarding nutrition, leading to improved patient quality care.
  • Specific nursing minimum data within nutrition will improve nurses’ clinical language for practice and result in a common terminology within nutrition.

 

Method

Data collection:

We conducted a systematic thesaurus based search in PubMed (Medline), Embase, CINAHL, and the Cochrane Library to locate studies which responded to the present study’s criteria for inclusion. Search terms included “validity”, “reliability”, “sensitivity”, “specificity”, “valid”, “reliable”, “instrument”, “nutritional screening”, “nutritional assessment”, “malnutrition screening” and “nutrition”.

Furthermore a broad based search was conducted and relevant studies were retrieved from those listed in key articles.

Titles and abstracts were examined and when abstracts met the inclusion and exclusion criteria the full text of the article was obtained.

 Inclusion criteria:

The criteria for selecting articles were:

  • Articles in which the screening or assessment of nutritional status by nurses using a tool were described.
  • Articles where the patient population is 18+ years old.
  • Articles in which screenings or assessment tools of patients’ nutritional status are used/described within a clinical setting.

 Exclusion criteria:

  • Articles which were not available in English, Swedish, Norwegian or Danish.
  • Articles that did not at a minimum assess the validity and reliability of the screening instruments.
  • Articles which described screening or assessment methods which are not relevant for nurses.
  • Articles where screening or assessment are reserved for a specific patient population (patients with HIV, dementia etc.)

 Materials:

In a preliminary search a literature review published in 2002 was identified (Green & Watson, 2005). This literature review describes the range of published tools available for nurses to screen or assess the nutritional status of patients. The literature review identified searched literature from 1982-2002, therefore the present study searched for relevant articles in the period 2002-February 2011.

The search from 2002-2011 of all electronic databases yielded 37 articles in total. After reading abstracts 16 studies were excluded. 21 articles were obtained in full text. 16 articles were then excluded and five articles were used in the final analysis.

The five included articles consist of one literature review (Green & Watson, 2005), one prospective study (Nursal et al. 2005), one retrospective study (Brugler, Stankovis, Schlefer & Bernstein 2005), one comparison study (Brown, Heeg, Turek & O´Sullivan Maillet 2006) and a quasi-experimental study (Jordan, Snow, Hayes, & Williams, 2003).

The literature review (Green & Watson, 2005) included 35 studies, using a search period of 1982-2002, of which 28 of these met the inclusions criteria in the present study. The other seven studies identified specific instruments and assessment tools and were therefore excluded.

A total number of 42 screening instruments/tools were assessed and evaluated.

 

Table 1: Overview of included studies.

AUTHORS TYPE SAMPLE TOOLS REVIEWED
 Green, Sue M & Watson, R. UK. 2005 Literature review All hospital and primary care patients. They included 35 articles assessing 35 screening instruments. 7 assessed tools within a specific patient population such as HIV infections, children with special health needs etc. Included 28 studies of which following risk categories were identified within a general hospital and primary care setting: BMI (weight/height), Weight gain / weight loss, Clinical assessment, Biochemistry, muscle and fat mass, Need for assistance, Oral cavity state, Chewing and swallowing problems, Types of diet, Changes in food intake, Age, Taste, Appetite, Eating habits, Gastrointestinal symptoms, Gastrointestinal functions, Mental, condition / physiological state, Medical condition / disease presence / Co-morbidity / stress metabolism. 24 articles included in the literature review assessed for reliability, validity, sensitivity and specificity. 11 articles examined for reliability, validity, acceptability, sensitivity, specificity, positive predictive value and negative predictive value.
 Brown, B; Heeg, A; Turek, J; Maillet, J. USA. 2006 Comparison study Hospitalized patients. BMI (weight/height), Weight gain / weight loss, Biochemistry , Medical condition / disease presence The study assessed for reliability, validity, sensitivity and specificity
 Nursal, TZ; Noyan, N; Atalay, BG; Köz, N; Karakayali, H. Turkey. 2004. Prospective study Hospitalized patients BMI (weight/height), Weight gain / weight loss, Clinical assessment, Biochemistry, muscle and fat mass The study assessed for reliability, validity, acceptability, sensitivity, specificity, positive predictive value and negative predictive value.
 Brugler, L; Stankovic, AK; Schlefer, M; Bernstein, L. USA: 2004. Retrospective study Hospitalized patients. Biochemistry, BMI (weight/height), Types of diet, Changes in food intake The study assessed for sensitivity and specificity
 Jordan, S; Snow, D; Hayes, C; Williams, A. UK. 2003. Quasi-experimental study Hospitalized patients Appetite, ability to eat, mental condition, weight loss, weight, gastrointestinal function The study assessed for sensitivity and specificity

 

Data analysis:

The included studies were analyzed using inductive content analysis in order to ensure validity (Bryman, 2008). By using an inductive content analysis process, the aim was to build a model or a thematic form to describe a certain phenomenon in a conceptual way, in relation to a topic that has not been previously described. Content analysis is a technique used in health sciences to analyze verbal and written material. The key feature is to classify text into smaller content categories using a set of codes to reduce volumes of verbal or print material into more manageable data from which researchers identify patterns and gain insight (Krippendorf, 2004; Elo & Kyngäs, 2007).

The key feature of all content analysis is that the many words of the text in the articles are classified into much smaller content categories (Krippendorf, 2004; Elo & Kyngäs, 2007). Content analysis is a research technique used in health science to analyze verbal and written material. The technique uses a set of codes to reduce volumes of verbal or print material. The technique uses a set of codes to reduce volumes of verbal or print material into more manageable data from which researchers identify patterns and gain insight (Elo & Kyngäs, 2007).

In the present study the process of analyzing the data (the screening instruments) within the articles consisted of three main phases; preparation, organizing and reporting. In the preparation phase the aim is to become immersed  in the data, which is why the written material, in this study articles and screening instruments, are read through several times. No insights or theories can spring forth from the data without the researcher becoming completely familiar with them. After making sense of the data, analysis was conducted using an inductive approach (Krippendorf, 2004; Elo & Kyngäs, 2007). This process included open coding, creating categories and abstraction.

 

Results

The open coding in the inductive content analysis process means that notes and headings are written in the text while reading it. The written material was read through again and as many headings as necessary was written down in a schedule to describe all aspects of the content.

18 headings – or subcategories – were written down during the reading process:

-       BMI (weight/height)

-       Weight gain/weight loss

-       Clinical assessment

-       Biochemistry

-       Muscle and fat mass

-       Need for assistance

-       Oral cavity state

-       Chewing and swallowing problems

-       Types of diet

-       Changes in food intake

-       Mental condition/physiological state

-       Medical condition/disease presence /co-morbidity/metabolic stress

-       Age

-       Taste

-       Appetite

-       Eating habits

-       Gastrointestinal symptoms

-       Gastrointestinal functions

 

At the final stage of the inductive content analysis work process categories are then freely generated. Subcategories with similar events and incidents are grouped together as categories and categories are grouped as main categories (Elo & Kyngäs, 2007).

The 18 subcategories were divided into five main categories after discussion among the research group of the article: 1), anthropometry such as weight, height, biochemistry, muscle mass and fat etc. 2), ability to eat, 3), intake 4), factors which indirectly affect intake and needs and 5) stress factors.


Table 2: Overview of the process from article to subcategory to main category

Table 2: Overview of the process from article to subcategory to main category Table 2: Overview of the process from article to subcategory to main category.
Row 1 consists of the anthropometry data that the largest number of screening instruments uses to identify patients at nutritional risk. Anthropometry data is the measurement of the size, weight and proportions of the human body, as well as biochemistry data. Weight and height can be used to calculate the Body Mass Index (BMI). The BMI was used in a great number of the screening instruments as a sole predictor of malnutrition.

Row 2 deals with patients’ ability to eat. Does the patient need assistance? What is the state of the oral cavity? Are there any dentures? Are there any blisters? Are there coatings on the tongue etc.? Does the patient have chewing or swallowing problems which result in the patient only being able to take liquid food?

Row 3 refers to the actual intake, the type of diet that the patient consumes, whether it is a diabetic diet etc. Has there been any change in food intake? Has the food intake been reduced or increased?

Row 4 refers to both the physical and psychological condition of the patients. Does the patient have a psychological disorder such as dementia so that he or she is not able to look after their own nutritional needs? What is the patient’s physical condition? Does the patient have cancer, has he/she recently undergone surgery etc? It covers the stress factors that the patient has.

Row 5 relates to the factors which indirectly effects intake and needs such as age, taste and appetite. Eating habits refers to why and how people eat, which foods they eat, and with whom they eat. It deals with both the individual, social, cultural, religious, economic, environmental and political factors. Gastrointestinal symptoms refers to dry mouth, belching, nausea etc. while gastrointestinal functions refers to constipation, diarrhea etc.

 

Discussion

The purpose of this study was to produce a NMDS specifically for nutritional care in order to promote the comparability of nursing data, describe the nursing care of patients, clients or families in a variety of settings, stimulate nursing research and to provide data about nursing care to facilitate and influence clinical, administrative and health policy decisions making.

The five main categories containing the 18 sub categories (NMDS) contain core data elements that need to be considered with all patients receiving nursing nutritional care, as we expect that these data will help us make relevant decisions, leading to improved patient outcomes.

The data should help staff to identify problem areas and amplifies clinical assessments, potentially leading to improved documentation and high quality patient care.

 Strengths and weaknesses of the study

In this study we have attempted to identify and summarize a NMDS by using an inductive content analysis. We used validated screenings and/or assessment tools that screen or assess nutritional risk of patients in order to identify the main areas that are of interest for the planning and documentation of nursing care within nutrition.

The five main categories have yet not been tested in a clinical setting, nor as a pilot study. Even though testing usage of the five main categories (18 subcategories) as a systematic way of documenting patients’ nutritional status in order to identify problem areas and symptoms, initiate interventions and measuring outcomes is of great importance, it is not considered as relevant in this particular study.

The literature included in this study consists of literature published between 1982 and 2011. However,  there may be screening and assessment tools which are in use in clinical practice and have been validated and reliability tested, but have not been published and are therefore not a part of this study. This can be considered as a weakness of the study, as we may not have obtained all relevant screening tools used in nutritional risk assessment and therefore in this study only deal with a selected range of categories of the NMDSs within nutrition.

It must be considered as strength of the study that we have excluded non-validated screening instruments, as numerous nutrition screening tools have never been validated for the care setting, patient population or outcome they strive to identify. Thus, it is unclear if they appropriately identify patients who truly need further nutrition assessment and, potentially, intervention.

The methods we used to identify a NMDS covering nutrition consisted of a literature search and an inductive content analysis. We chose an inductive content analysis, due to the fact that this method is applicable in cases where no studies deal with the phenomenon or in cases where current knowledge is fragmented and there is no cohesion.

Furthermore the process is useful when one wants to structure or summarize material – in this case already existing screenings instruments, and thereby create new categories within a nutritional minimum data set. The process also provided us with information on how frequently subcategories, such as “types of diet”, “oral cavity state” etc. each appeared when assessing the instruments,  thereby allowing us to create an overview of the main subcategories used to identify patients at nutritional risk, which, without comparison, was the “body mass index”.

Lastly, the choice of methods must be considered as a strength to the study as these methods are both scientific approaches to explore the focus of the study, thereby basing the new knowledge on already existing categories and items. We could also have chosen another approach such as interviewing nurses, making consensus rounds and then finding validation in the literature. However, we believe that this would have brought forth a higher degree of subjective assessment, potentially leading to a lower degree of validated categories and topics.

 

Implications for practice

As mentioned previously the Danish guidelines for documentation consist of 12 main areas of which nutrition is one of them. It is also an empty framework. In this study we have tried to fill out or supplement the idea of “nutrition” with some information or “headliners” in order to provide for and help clinical practitioners by identifying what they need to consider as relevant when dealing with patients by summing up the content of validated screening instruments.

In Denmark nutrition is, as mentioned before, only one of 12 areas in which nurses have to systematically document daily care, the other 11 areas could be explored in the same way as in this study.

The above mentioned subcategories divided into five main categories may be considered as a minimum dataset with which nurses could cover during, for instance, the admission interview in order to identify patients at nutritional risk during hospitalization.

The areas are not to be considered as a new set of screening instruments. They have, however, proved to be important when summarizing elements of already existing screening instruments.

They can help nursing staff make a complete assessment of the patients’ nutritional status in order to first and foremost recognize that there could be a problem regarding nutrition and secondly, start an appropriate intervention.

This study identifies areas which nurses, as a minimum, should document about patients’ nutritional care, and thereby point out areas which identify the patient at nutritional risk due to either problems related to eating ability, appetite, changes in food intake, weight gain/loss etc.

We are well aware that these areas are all generic although being far more specific than just the word “nutrition”. It is necessary that every institution locally implement the main categories that are relevant for their patient population. Not all of the 18 subcategories divided into five main categories are relevant in every clinical setting facing every single patient.

The results of this study are to be tested in a clinical setting in an intervention study. The aim of the study is to see the effects of implementing the five main categories on both nurses degree of documentation (accuracy, relevance, comprehensiveness, understanding etc.) as well as on patient outcome (identification of a higher number of patients in nutritional risk, fewer complications etc.). The study is intended to be conducted in two Danish Hospitals both within an orthopedic and abdominal surgical setting. Two of the authors of this article will undertake and manage the process as a part of a future PhD study.

As stated in the introduction section, the rate of malnutrition in Danish hospitals is estimated to be approximately 40%, with the highest prevalence at 57% in departments of gastroenterology surgery (Rasmussen et al. 2004). There is evidently a need to intervene against the very high malnutrition rates in Danish Hospitals. Not only has malnutrition proved to decrease the quality of life of patients being malnourished, but also increase mortality, morbidity and prolong hospitalization (Stratton et al. 2003), areas that are resource intensive for the organization, society, health professionals and especially the patients.

Implementing the NMDS identified in this study, is one approach, in which is it expected that a higher number of patients at nutritional risk will be identified, as the NMDS can help make a complete assessment of the patients nutritional status, help recognize problem areas and thereby initiating relevant interventions leading to fewer malnourished patients.

 

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Author Bios:

Sasja Jul Håkonsen, RN, MScN

Centre for Clinical Guidelines – a National Clearinghouse for Nursing. Deparment of Publich Health, Aarhus University, Denmark.

Inge Madsen, MI

Associate Professor, Healthcare Informatics, Aalborg. University & VIA University College, Aarhus, Denmark.

Merete Bjerrum

Associate Professor, MA, PhD, Department of Public Health,

Aarhus University, Denmark

Preben Ulrich Pedersen

Associate Professor, PhD, Centre for Clinical Guidelines – a National Clearinghouse for Nursing. Deparment of Publich Health, Aarhus University, Denmark.

 

 

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