Prevalence and documentation of malnutrition in hospitals: a case study in a large private hospital setting.
Objectives: To determine the prevalence of malnutrition and whether the malnourished participants were being identified and documented as malnourished. To evaluate the impact of poor documentation on financial reimbursement to the hospital.
Subjects: Three hundred and twenty-four inpatients from a total of 690 randomly selected patients consented to participate in the study.
Design and setting: Subjective Global Assessment (SGA) was used to assess the nutritional status of inpatients. There were 1906 patients were admitted over a three-month period. Of these, 1860 were eligible and 690 were randomly selected from computer generated ward lists. The referral rate for nutrition intervention of malnourished participants was determined by viewing the patient medical records retrospectively. The Australian National Diagnostic Related Group (AN-DRG) of the malnourished subjects, not documented in the medical record as malnourished, were redetermined with the addition of the malnutrition code. The potential shortfall in financial reimbursement to the hospital was calculated by subtracting the average costing based on original AN-DRGs from the average costing based on the revised AN-DRGs.
Main outcome measures: Prevalence of malnutrition, levels of malnourished patients identified and documented, revenue losses under case payment system.
Statistical analyses: Logistic regression analyses were used to evaluate group differences in sex across SGA categories and to investigate predictors of referral versus non referral. Analysis of variance was used to evaluate group differences in age across SGA categories.
Results: One hundred and twenty-seven (42.3%) of the 324 subjects were malnourished. Only one of 137 malnourished patients was documented as malnourished in the medical records and only 21 (15.3%) were referred for nutrition intervention. The inclusion of the malnutrition code to the AN-DRG of the identified malnourished patients highlighted a shortfall of $125 311 in reimbursements to the hospital.
Conclusions: The degree of malnutrition in this hospital is similar to that found internationally. Malnourished patients are not being identified using the current referral method. Failure to flag malnourished patients requiring nutrition intervention potentially impacts on length of stay, hospital costs and patient outcomes and ultimately results in a shortfall for case payment funded institutions.
Key words: diagnostic related groups, subjective global assessment, reimbursement, malnutrition, prevalence, hospitalisation, casemix, nutritional status
(Nutr Diet 2005;62:41-47)
There has been little recent change to prevalence rates of malnutrition in hospitals (1-3) yet it often remains unrecognised and uncorrected (1,4-8) despite first being acknowledged over 35 years ago (1,9-11). It is well established that malnutrition results in an increased risk of morbidity and mortality (12-16) with subsequent increases in hospitalisation costs and length of stay (17-19). Despite the evidence, effective outcomes are often complicated and difficult to achieve. Malnutrition is infrequently diagnosed (1,6,8,11,17,20-23) or documented in medical notes (1,17,20,21,24) and administrative changes can compound the problem. Staff cuts and budget constraints can impinge upon the ability to provide a flexible, appropriate and timely meal service. This is a powerful combination for patients fasting for tests, or who have nausea, depression or feeding difficulties (1,4,19).
Implementation of a screening tool provides the means to identify malnutrition. Failure or lack of documentation can result in inappropriate acuity downgrades and reduced payments (25). Hospitals are critically dependent on the malnutrition diagnosis and documentation by medical practitioners under a case payment environment (23,26). The shortfall resulting from the omission of malnutrition documentation can amount to millions of dollars as recent studies have suggested (2,20,23,24). Addition of a malnutrition code has the potential to change the patient's Australian National DRG (AN-DRG), allocating a greater average cost to the hospital for the patient's episode of care. Ferguson et al. (20) projects financial reimbursement of AUD $1.67 million when malnutrition is coded as the sole comorbidity.
Overseas and Australian studies indicate malnutrition prevalence ranges from 4% (20) to 68% (27). The wide variation depends partly on the different criteria used to diagnose malnutrition and the methods of assessment such as biochemical and anthropometric data (1,28) or SGA (12). Difficulties comparing malnutrition prevalence rates are aptly acknowledged by Kelly et al. (1). Consequently, malnutrition prevalence rates from the literature cannot be applied to predicting malnutrition in the hospital.
Despite the large number of studies indicating the prevalence of malnutrition in hospitalised patients, few studies until very recently have been done in Australia, and even fewer in the private hospital setting. This provided the impetus to conduct a study to investigate what is happening in an inpatient population. The validated SGA tool was selected for this study as the most appropriate tool based on the evidence of reliability and reproducibility (12,13,29,30).
The purpose of this study was to determine the prevalence of malnutrition at a large private hospital and to determine whether malnourished patients were being identified and documented as malnourished. This data was then used to determine the potential financial shortfall to the hospital under a case payment system should the at risk subjects fail to have been identified and documented as malnourished.
Methods and subjects
The case study was conducted on a not-for-profit private hospital with 228 acute care beds. The main specialities are general surgery, cardiology, neurosurgery, orthopaedics and urology. Patients excluded from the study were intensive care and day surgery patients, those too ill to participate, patients under 18 years of age and those who could not speak English where an interpreter could not be accessed. Patients eligible for the study were randomly selected from computer generated ward lists using Cbord software (Cbord Group Inc, Sydney, Cbord Diet Office System version 5.0.04).
During the period of the study (August-October 1999) 1906 patients were admitted to the hospital. There were 1868 patients who were eligible to be included at the commencement of the study. Of these, 690 patients were randomly selected for inclusion in the study. Data were collected on 324 consenting patients, representing 17.3% of the eligible hospital population during the study period. There were 297 subjects who declined to participate or were not available at the time of data collection, 48 subjects had been discharged before data was collected, seven were too sick to participate and 14 subjects were identified at the bedside as being of non-English speaking background and met the exclusion criteria so were removed from the study.
Two clinical dietitians, proficient in the use of the Subjective Global Assessment (SGA) tool collected data from the 324 consenting participants. In 100% of cases there was agreement that subjects were well nourished and severely malnourished, and for the moderately malnourished subjects, there was consensus in 90% of the cases. The SGA data collected was recorded on a standardised SGA questionnaire. SGA is a valid and reliable assessment tool (12,13,15,20,29) and is based on the patient's medical history (weight change, dietary intake change, gastrointestinal symptoms, functional capacity) and physical examination (muscle mass, subcutaneous fat, presence of ascites and oedema) (30). The application of this validated prognostic tool divides patients into three groups:
SGA-A: Patient is well nourished
SGA-B: Patient is moderately/mildly malnourished
SGA-C: Patient is severely malnourished
Using the SGA tool, malnutrition occurs when there is a greater than 10% continuing weight loss, continuing loss of subcutaneous fat and muscle, poor appetite, suboptimal intake, excess nutrient losses and loss of functional ability. The prevalence of malnutrition was determined by calculating the frequency of categories SGA-B and SGA-C. The rate of referral to Dietetic Services for nutrition intervention for malnourished patients (SGA-B and SGA-C) was determined retrospectively by viewing the patient order screen on the computerised medical records system (deLacy Patient Information System, 1993) to see if a referral had been generated.
Statistical analysis was performed with SPSS version 6.1.3 (SPSS Inc, Chicago, version SPSS 6.1.3 1995) statistics package. Analysis of variance was used to evaluate group differences in age across SGA categories. Logistic regression was used to evaluate group differences in gender across SGA categories. Logistic regression was also used to investigate the predictors of referral (versus non-referral): two comparisons were constructed, contrasting mild/moderately malnourished subjects (SGA-B) with well nourished subjects (SGA-A), and contrasting severely malnourished subjects (SGA-C) with well nourished subjects (SGA-A). Comparison of means of the study sample against the eligible patient sample was achieved by use of unpaired t tests.
The computerised and paper medical records were reviewed retrospectively to determine whether malnutrition had been documented. The data was then used to determine the percentage of malnourished patients identified in the study but not documented as malnourished in the medical records. The AN-DRGs for the malnourished subjects (SGA-B and SGA-C) identified were obtained from the medical record department. Patients' AN-DRGs were assigned by the medical record department according to patients' diagnoses and procedures using a grouping software package, 3M Health Information Systems Australian National Diagnosis Related Groups Grouper Version 3.1, (3M Australia Pty Ltd and Commonwealth of Australia, 1996) to determine their Australian National Diagnostic Related Group (AN-DRG).
The AN-DRGs of the malnourished patients (SGA-B and SGA-C) identified in the study, but not in the medical records, were redetermined with the addition of the appropriate malnutrition codes by the medical record department using 3M Health Information Systems Australian National Diagnosis Related Groups Grouper Version 3.1 software. Unspecified severe protein-energy malnutrition was assigned for SGA-C and (E43) moderate protein-energy malnutrition (E44.0) was assigned for SGA-B. The NSW Health Acute Care Costs (NSW Health, Sydney, Acute Care Performance Indicators by Hospital Type and AN-DRG version 3.1, 1996/97) were used to determine the average cost per episode of care for the original AN-DRGs and the revised AN-DRGs. The potential shortfall in financial reimbursement to the hospital was determined by subtracting the average costs based on the original AN-DRG from the average costing based on the revised AN-DRG.
The number of patients whose AN-DRG changed with the addition of the malnutrition code were recorded using the malnourished patients identified in the study. The percentage of malnourished patients identified in the study was extrapolated to the entire patient population, for a three-month period and annually. These figures were used to predict the number of malnourished patients whose AN-DRGs would be altered and a projection was made using the NSW Health Acute Care Costs to obtain the annual potential financial shortfall to the hospital.
The hospital in 1999 received DRG-based case payments from one major health fund for patients whose AN-DRG fell into the joint replacement AN-DRGs 404-407. Calculations previously described above were made for malnourished patients identified in the study who fall into the joint replacement AN-DRGs. Three-month and annual projections were calculated to determine the financial shortfall to the hospital under this case payment system.
Approval for the study was obtained from the hospital's ethics committee and informed consent was obtained from patients.
The mean age of the study sample was 66.8 [+ or -] 14.8 years compared with 65.1 [+ or -] 25.4 for the eligible patient population. There was no significant difference between the age of the subjects compared with the eligible patient population (P = 0.2). Forty-nine percent of the subjects were male and 51% female compared with 52% male and 48% female in the eligible patient population. There were no significant differences in other measured variables between the study sample and the eligible patient population.
One hundred and thirty-seven of the 324 subjects (42.3%) were malnourished. Of these, 118 (36.4%) subjects were mild to moderately malnourished (SGA-B) and 19 (5.9%) subjects were severely malnourished (SGA-C). There was a significant difference in age between the SGA groups Logistic regression indicated that the proportion of male subjects did not vary significantly across the three groups ([X.sub.(2).sup.2] = 3.54; P = 0.1703) as seen in Table 1. Thus age was a potential confounder, while sex might still confound the results.
Of the 137 malnourished patients, only 21 (15.3%) patients had been referred for nutrition intervention. Thirteen of the 118 (11%) mild to moderately malnourished patients (SGA-B) had been referred for nutrition intervention. Eight of the 19 (42%) severely malnourished patients (SGA-C) were referred for nutrition intervention.
We firstly predicted referral odds with a logistic regression model having four predictors: two comparisons--for mild/moderately malnourished (SGA-B) and for severely malnourished (SGA-C), with well nourished subjects (SGA-A) as the 'control' group, and with age and sex as covariates. We found the latter two had nonsignificant partial betas (age, P = 0.9478, sex P = 0.1233), so we fit the model predicting referral from the two comparisons.
The odds of referral among the three groups were: well nourished (SGA-A)--11/176=6%; mild/moderate (SGA-B)--13/10=12%; severe (SGA-C)--8/11=73%. The estimated odds ratio (and 95% confidence interval; CI) for the first comparison was 1.98 (0.86-4.58, P = 0.110). That is, the odds of referral for subjects in the mild/moderately malnourished group was about twice the odds of referral for the well nourished group. The CI, however, included unity, indicating that this estimate could be the result of a purely chance fluctuation. The estimated odds ratio for the comparison of severe and well groups was 11.64 (3.89, 34.78), indicating that referral among the severely malnourished is far more likely than among well subjects. Only one out of 137 malnourished patients were documented as malnourished in the paper or computerised medical records. This patient was classified as SGA-C, severely malnourished.
Inclusion of the malnutrition codes (E43 and E44.0) to the original medical record coding and regrouping of the record changed the AN-DRG in 30 out of 137 (21.9%) of the malnourished subjects to an AN-DRG with complication or comorbidity. This is demonstrated in Table 2. The remaining 107 out of 137 (78.1%) malnourished subjects were already in an AN-DRG with a complication or comorbidity code or the AN-DRG did not have a complication or comorbidity split, so the AN-DRG did not change. Using the NSW Health Acute Care Costs 1996/97, the malnutrition code added to the coding of the malnourished patients, changed the AN-DRG and resulted potentially in $AUD125 311 additional reimbursement for these 137 patients.
A total of 137 of the 324 subjects (42.3%) were malnourished. With the patient population of 1906 over the study period, it is predicted that 806 patients may be malnourished for any three-month period and 3224 patients malnourished over the year. With 21.9% (30 out of 137) patients having malnutrition as the complication or sole comorbidity, we predict each of these patients potentially attracts an additional AUD$4177 in reimbursement. For the year, this extrapolates to an annual potential increase in reimbursement of AUD$2 948 962.
Of the patients with an AN-DRG in the range 404-407 which currently qualify for case payments with one of the major health funds, nine
of the 14 patients (64.3%) had changes to the AN-DRG with the addition of the malnutrition code. This made a difference in AUD of $26 938 in reimbursement to the hospital under a case payment arrangement as seen in Table 3. The remaining five malnourished patients were already in an AN-DRG with a complication or comorbidity code or the AN-DRG did not have a complication or comorbidity split, so the AN-DRG did not change.
With the patient population of 1906 over the study period, 82 patients with an AN-DRG in the range 404-407 would be malnourished for any three-month period and 328 over the year. With 64.3% (nine out of 14) patients having malnutrition as the sole comorbidity or complication, we predicted each of these patients would potentially attract AUD$2993 in reimbursement. This extrapolates to AUD$158 629 for any three-month period and AUD$634 516 for the year under a case payment system.
The high prevalence of malnutrition in hospitals highlighted in recently published papers (20,31) suggests that malnutrition occurs in Australia to an extent comparable with overseas studies (6,32,33). Our finding of malnutrition in 42.3% of the study sample is similar to that found in some major teaching hospitals (31,34) and most likely reflects the high acuity of patients in the case study hospital. In 1999, the study hospital had an average acute casemix weight of 1.61, comparable with a tertiary referral hospital. This is the NSW Health Department calculated average acute casemix weight.
The prevalence of malnutrition found in the hospital under study is likely to be an underestimation as some patients at high risk of malnutrition were excluded from the study as they were too ill to participate or were in intensive care. It is emphasised that the financial reimbursements to this hospital were estimated values, based on the current malnutrition codes and tools in use. There are limitations pertaining to lack of definitions of under-nutrition and absence of cut-off values for variables used to measure nutritional status (39). Swails et al. (2) referred to the shortcomings of the ICD-9-CM malnutrition diagnosis codes and the same could be said about the ICD-10-AM codes, which are based on malnutrition in developing countries. These are the only malnutrition codes available and are currently in use nationally and internationally for classifying adult malnutrition in hospitalised patients, and in determining financial reimbursements (20,24) despite their relevance being questionable.
The prevalence of severe malnutrition in the hospital (6%) fell within the range indicated in previous studies overseas and in Australia (1,3,20,35). Age differed significantly (while sex did not) across the three malnutrition groups, indicating that age was associated with nutritional status in the study sample. However when both age and sex were fitted as predictors of referral together with the malnutrition groups, there was no association.
Referral rates for malnourished patients were poor overall. While the severely malnourished patients were more likely to be referred, the rates remained at less than half. This result is not surprising, as severely malnourished patients are easier to identify. The cost weighting and financial reimbursement estimates based on the NSW Health Acute Care Costs remained unchanged when different malnutrition codes were assigned because both codes caused the patients to group to the applicable 'with CC' DRG. The addition of any malnutrition code was sufficient to cause the patient to group to a 'with complication/comorbidity (CC)' DRG, except where the patient was already in a 'with CC' DRG or where there was no CC split for the DRG. SGA-B patients were assigned the malnutrition code E44.0 and SGA-C were assigned the malnutrition code E43. In each case where the patient's DRG changed as a result of the addition of a malnutrition code, this occurred regardless of whether code E44.0 or code E43 was assigned. Both malnutrition codes caused the patient to group to the applicable 'with CC' DRG.
The documentation of malnutrition was minimal. Only one in 137 malnourished patients was documented as malnourished in the medical record by a medical practitioner. Similar results are demonstrated in overseas and Australian studies (17,20,21). We suggest doctors may fail to document malnutrition in the medical notes due to difficulties encountered when interpreting the existing malnutrition definitions specified in the ICD-9-CM (36) and ICD-10-AM tabular list of diseases (37). These definitions are designed principally in relation to clinical syndromes of primary protein-energy malnutrition seen in paediatric populations in developing countries and are not easily applied to the hospitalised adult population in industrialised countries (2). It is possible doctors interpret malnutrition to be a pre-existing comorbidity or an expected comorbidity resulting from the patient's hospital admission.
Under the hospital's case payment arrangement, full reimbursement was not realised for patients with an AN-DRG in the range 404-407, based on the NSW Health Acute Care Costs. The potential shortfall to the hospital of AUD$26 938 was not recognised because the 14 malnourished subjects with AN-DRGs 404-407 were not identified or documented as malnourished, so could not be assigned a malnutrition code. Annually, the potential revenue loss of AUD$634 516 cannot be ignored. Identifying and managing malnutrition is probably the best attainable practice. A recent study has shown that early and intensive nutrition intervention appears to be beneficial in minimising weight loss and deterioration of nutritional status in ambulatory oncology patients (38). Failure to identify and document malnourished patients is an important issue, not only because it compromises best practice of care, but because it has the potential to incur a significant revenue deficit to Australian hospitals under a case payment system. No practitioner would debate that patient care is the most important issue, but it cannot be ignored that a significant dollar shortfall may also occur through lack of identification, documentation and therefore coding of malnutrition in the medical record.
No single assessment tool or measure has been accepted as a gold standard. We selected SGA as the most appropriate valid assessment tool because it incorporated a range of clinical criteria for assessing nutritional status (30), and did not emphasise one measure compared to a reference population which is no longer relevant (1,28).
Dietitians at some Australian hospitals already have malnutrition screening programs in place and use SGA to identify malnutrition. The National Centre for Classification in Health recognises malnutrition diagnosis for coding purposes by a dietitian if verified by the primary treating clinician. Dietitians can support hospitals in maximising reimbursement under a case payment system by documenting malnutrition.
The degree of malnutrition identified in the case study hospital was similar to that seen internationally and in local major teaching hospitals. Failure to adequately recognise and document its existence would have a financial impact on the hospital. We consider there is an underlying global complacency towards malnutrition within hospital culture because it is regarded as an expected occurrence among hospitalised patients and it is difficult to diagnose routinely. A set of criteria approved by each hospital for diagnosing malnutrition and a preadmission screening program would facilitate recognition, diagnosis, documentation and treatment. A multidisciplinary team approach that includes hospital administrators, medical practitioners and clinical dietitians is needed to assess reimbursement opportunities to maximise revenue. Until this occurs, it is unlikely that the levels of documentation will reflect the true prevalence of malnutrition in hospitals and allow these facilities to fully realise their revenue.
Table 1. Average age and degree of malnutrition Mean SGA category (a) n (b) age sd (c) % Male A 187 62.1 14.3 45 B 118 69.4 14.6 56 C 19 68.9 15.6 47 (a) SGA category: A = well nourished; B=mild/moderately malnourished; C=severely malnourished. (b) n = number of subjects. (c) sd = standard deviation. Table 2. Variations in AN-DRG payment with the inclusion of malnutrition codes E43 and E44.0 Original Original Patient SGA category (a) DRG (b) Revised DRG (c) payment (d) 72 B 568 567 6021 19 B 24 23 9865 22 B 24 23 9865 31 B 306 305 8327 125 C 311 310 5679 137 C 161 160 11196 6 B 416 414 5012 13 B 416 414 5012 18 B 416 414 5012 58 B 416 414 5012 87 B 416 414 5012 89 B 416 414 5012 54 B 512 511 3296 83 B 794 793 2661 2 B 405 404 12000 8 B 405 404 12000 33 B 405 404 12000 70 B 405 404 12000 112 B 405 404 12000 114 B 405 404 12000 117 B 405 404 12000 105 B 309 308 8529 104 B 38 37 4478 14 B 407 406 11413 37 B 407 406 11413 32 B 455 454 2368 93 B 455 454 2368 116 B 576 575 3042 35 B 349 348 1405 57 B 349 348 1405 Revised Difference Patient payment (e) payment (f) 72 14359 8338 19 17355 7490 22 17355 7490 31 15359 7032 125 12247 6568 137 17073 5877 6 10716 5704 13 10716 5704 18 10716 5704 58 10716 5704 87 10716 5704 89 10716 5704 54 7922 4626 83 7124 4463 2 15240 3240 8 15240 3240 33 15240 3240 70 15240 3240 112 15240 3240 114 15240 3240 117 15240 3240 105 11470 2941 104 6981 2503 14 13542 2129 37 13542 2129 32 3930 1562 93 3930 1562 116 4415 1373 35 2567 1162 57 2567 1162 Total 125311 (a) SGA category: A = well nourished; B=mild/moderately malnourished; C=severely malnourished. (b) Original DRG: assigned to the patient by the medical records department; DRG=diagnostic related group. (c) Revised DRG results from the inclusion of malnutrition codes with the original DRG of the malnourished patients. The malnutrition codes are E43=unspecified severe protein energy malnutrition, assigned to SGA-C; malnutrition code E44.0=moderate protein energy malnutrition, assigned to SGA-B. (d) Original payment expressed as $AUD; payment for that DRG category using the NSW Health Acute Care Costs 1996/97; AUD=Australian Dollars. (e) Revised payment expressed as $AUD; payment based on regrouping of the DRG with the addition of the malnutrition codes (E43 for SGA-C and E44.0 for SGA-B); AUD=Australian Dollars. (f) Revised payment minus the original payment. Table 3. Financial reimbursement under the current DRG case payment system Original Original Patient SGA category (a) DRG (b) Revised DRG (c) payment (d) 1 B 405 404 12000 2 B 405 404 12000 5 B 405 404 12000 9 B 405 404 12000 11 B 405 404 12000 13 B 405 404 12000 14 B 405 404 12000 3 B 407 406 11413 7 B 407 406 11413 4 B 406 406 13542 6 B 404 404 15240 8 B 406 406 13542 10 B 406 406 13542 12 B 404 404 15240 Revised Patient payment (e) Difference payment (f) 1 15240 3240 2 15240 3240 5 15240 3240 9 15240 3240 11 15240 3240 13 15240 3240 14 15240 3240 3 13542 2129 7 13542 2129 4 13542 0 6 15240 0 8 13542 0 10 13542 0 12 15240 0 Total 26938 (a) SGA category: A=well nourished; B=mild/moderately malnourished; C=severely malnourished. (b) Original DRG: assigned to the patient by the medical records department; DRG=diagnostic related group. (c) Revised DRG results from the inclusion of malnutrition codes with the original DRG of the malnourished patients. The malnutrition codes are E43=unspecified severe protein energy malnutrition, assigned to SGA-C; malnutrition code E44.0=moderate protein energy malnutrition, assigned to SGA-B. (d) Original payment expressed as $AUD; payment for that DRG category using the NSW Health Acute Care Costs 1996/97; AUD=Australian Dollars. (e) Revised payment expressed as $AUD; payment based on regrouping of the DRG with the addition of the malnutrition codes (E43 for SGA-C and E44.0 for SGA-B); AUD=Australian Dollars. (f) Revised payment minus the original payment.
Assistance provided by Kathryn Tucker, Gina Martin and Archana Gulvady is gratefully acknowledged. We also would like to thank Gabrielle Challis.
1. Kelly IE, Tessier S, Cahill A, Morris SE, Crumley A, McLaughlin D, et al. Still hungry in hospital: identifying malnutrition in acute hospital admissions. QJM 2000;93:93-8.
2. Swails WS, Samour P, Babineau TJ, Bistrian BR. A proposed revision of current ICD-9-CM malnutrition code definitions. J Am Diet Assoc 1996;96:370-3.
3. Royce C, Taylor M. Starvation in hospital. BMJ1994;308:1370.
4. Garrow J. Starvation in hospital. Nutrition is given too little attention by doctors, nurses, and managers. BMJ 1994;308:934.
5. Nightingale JMD, Reeves J. Knowledge about the assessment and management of undernutrition: a pilot questionnaire in a UK teaching hospital. Clin Nutr 1999;18:23-7.
6. Sullivan DH, Sun S, Walls RC. Protein-energy undernutrition among elderly hospitalized patients: a prospective study. JAMA 1999;281:2013-9.
7. Burns JT, Jensen GL. Malnutrition among geriatric patients admitted to medical and surgical services in a tertiary care hospital: frequency, recognition, and associated disposition and reimbursement outcomes. Nutrition 1995;11(2 Suppl):245-9.
8. Gallagher-Aldred CR, Voss AC, Finn SC, McCamish MA. Malnutrition and clinical outcomes: the case for medical nutrition therapy. J Am Diet Assoc 1996;96:361-6.
9. Bistrian BR, Blackburn GL, Hallowell E, Heddle R. Protein status of general surgical patients. JAMA 1974;230:858-60.
10. Butterworth CE, Blackburn GL. The skeleton in the hospital closet. Nutr Today 1975;9:4.
11. Christensen KS, Gstundtner KM. Hospital-wide screening improves basis for nutrition intervention. J Am Diet Assoc 1985;85:704-6.
12. Detsky AS, Baker JP, O'Rourke K, Johnston N, Whitwell J, Mendelson RA, et al. Predicting nutrition-associated complications for patients undergoing gastrointestinal surgery. JPEN 1987;11:440-6.
13. Detsky AS, Smalley PS, Chang J. The rational clinical examination. Is this patient malnourished? JAMA 1994;271:54-8.
14. Marshman R, Fisher MMcD, Coupland GAE. Nutritional status and post operative complications in an Australian hospital. Aust N Z J Surg 1980;50:516-9.
15. Reilly JJ, Hull SF, Albert N, Waller A, Bringardener S. Economic impact of malnutrition: A model system for hospitalized patients. JPEN 1988;12:371-6.
16. Torosian MH. Perioperative nutrition support for patients undergoing gastrointestinal surgery: Critical analysis and recommendations. World J Surg 1999;23:565-9.
17. McWhirter JP, Pennington CR. Incidence and recognition of malnutrition in hospital. BMJ 1994;308:945-8.
18. Robinson G, Goldstein M, Levine GM. Impact of nutritional status on DRG length of stay. JPEN 1987;11:49-51.
19. Holmes S. Nutrition: a necessary adjunct to hospital care? J R Soc Health 1999;119:175-9.
20. Ferguson M, Capra S, Bauer J, Banks M. Coding for malnutrition enhances reimbursement under casemix-based funding. Aust J Nutr Diet 1997;54:102-7.
21. Bruun LI, Bosaeus I, Bergstad K, Nygaard K. Prevalence of malnutrition in surgical patients: evaluation of nutritional support and documentation. Clin Nutr 1999;18:141-7.
22. Micklewright A. Nutritional status under submission for dietetic services and screening for malnutrition at admission to hospital. Clin Nutr 1999;18:3-4.
23. Funk KL, Ayton CM. Improving malnutrition documentation enhances reimbursement. J Am Diet Assoc 1995;95:468-75.
24. Cole BJ, Flics S, Levine DB. Optimising hospital reimbursement through physician awareness: a step toward better patient care. Orthopaedics 1998;21:79-83.
25. Leeth L. Are you fiscally fit? Nurs Manage 2004;35:42-7.
26. Raja R, Lim AV, Lim YP, Lim G, Chan SP, Vu CKF. Malnutrition screening in hospitalised patients and its implication on reimbursement. Intern Med J 2004;34:176-81.
27. Gassull MA, Cabre E, Vilar CL, Alastrue A, Montserrat A. Protein-energy malnutrition: an integral approach and a simple new classification. Hum Nutr Clin Nutr 1984;38:419-31.
28. Coats KG, Morgan SL, Bartolucci A, Weinsier R. Hospital-associated malnutrition: a reevaluation 12 years later. J Am Diet Assoc 1993;9:27-33.
29. Baker JP, Detsky AS, Whitwell JA, Langer B, Jeejeebhoy KN. A comparison of the predictive value of nutritional assessment techniques. Hum Nutr 1982;36C:233-41.
30. Detsky AS, McLaughlin GR, Baker JP, Johnston N, Whittaker S, Mendelson RA, et al. What is Subjective Global Assessment of Nutritional Status? JPEN 1987;11:8-13.
31. Middleton MH, Nazarenko G, Nivison-Smith I, Smerdely P. Prevalence of malnutrition and 12-month incidence of mortality in two Sydney teaching hospitals. Intern Med J 2001;31:455-61.
32. Chima CS, Barco K, Dewitt MLA, Maeda M, Teran JC, Mullen KD. Relationship of nutritional status to length of stay, hospital costs, and discharge status of patients hospitalized in the medicine service. J Am Diet Assoc 1997;97:975-8.
33. Azad N, Murphy J, Amos S, Toppan J. Nutrition survey in an elderly population following admission to a tertiary care hospital. CMAJ 1999;161:511-5.
34. Prendergast JS, Whitworth AG, Ryan MS, Nisbet G. Prevalence of Malnutrition amongst North Shore Hospital patients. Proceedings of the AuSPEN 25th Annual Scientific Meeting; October 1999; Australia.
35. Pinchofsky GD, Kaminiski MV. Increasing malnutrition during hospitalisation: Documentation by a nutritional screening program. J Am Coll Nutr 1985;4:471-9.
36. The Australian Version of the International Classification of Diseases, 9th revision, Clinical Modification, 1996, 2nd ed. Sydney: National Coding Centre, University of Sydney; 1996.
37. The Australian Version of the International Classification of Diseases, 10th revision, Australian Modification, 2nd ed. Sydney: National Centre for Classification in Health, University of Sydney; 1996.
38. Isenring EA, Capra S, Bauer J. Nutrition intervention is beneficial in oncology outpatients receiving radiotherapy to the gastrointestinal or head and neck area. Br J Cancer 2004 August 2;91:447-52.
39. Corish CA, Kennedy NP. Protein-energy undernutrition in hospital in-patients. Br J Nutr 2000;83:575-91.
St Vincent's Private Hospital, Sydney
C. Lazarus, BSc, MNutrDiet (Syd), Food Service Nutrition Manager
J. Hamlyn, GradDipNutrDiet (Syd), Clinical Dietitian
Correspondence: J. Hamlyn, St Vincent's Private Hospital, Locked Bag 5, Darlinghurst NSW 2010. Email: firstname.lastname@example.org
Conception, design, acquisition of data, analysis and interpretation of results, manuscript by C. Lazarus and acquisition of data, analysis and interpretation of results, manuscript by J. Hamlyn.
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|Title Annotation:||Original research|
|Publication:||Nutrition & Dietetics: The Journal of the Dietitians Association of Australia|
|Date:||Mar 1, 2005|
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