Despite its negative impact on patients and health care expenditures, malnutrition remains an under-recognized problem in hospitals. The objectives were thus: 1) to study the prevalence of malnutrition risk, protein-calorie malnutrition and cachexia in a Belgian tertiary care hospital, 2) to evaluate the impact thereof on patient outcomes, and 3) to evaluate the impact of optimizing malnutrition screening, diagnosing, registration and coding on hospital reimbursement.Data was included from all multi-day admissions between January 1, 2017 and December 31, 2019. The NRS2002 was used as screening tool. Patient outcomes were modeled using (generalized) linear mixed models, with pathology and severity of illness as random effects. The financial impact of the screening, diagnosing and registration process was evaluated comparing net revenues related to a malnutrition diagnosis in the year before (2017) and the year after (2019) the optimization process.55,345 patients were evaluated for malnutrition risk at admission of whom 23.6% are considered malnourished or at risk for malnutrition, 0.6% have cachexia and 4.6% protein-calorie malnutrition. Overall length of stay is 2.2 days (p < 0.001) longer in the at-risk population, and 6.2 and 5.0 days longer in patients with cachexia and protein-calorie malnutrition as compared to patients not at risk. Odds ratio for in-hospital mortality is 2.9 (p < 0.001) for the at-risk patients and 3.0 (p < 0.001) for patients with cachexia. Optimization of dietetic workflow and registration, specifying malnutrition severity and facilitating malnutrition coding can increase hospital reimbursement, with approximately 0.4% of all justified beds.Malnutrition still affects both patients and health care finances. Patients at risk for, or having malnutrition at admission have worse outcomes than those without. Importantly, hospital reimbursement for these patients can effectively be increased by implementing an automated nutritional screening and diagnosing protocol with optimized dietetic registration and enhanced nutritional coding. 

Malnutrition risk and severity: Impact on patient outcomes and financial hospital reimbursement in a tertiary teaching hospital.

AIM
Routine malnutrition risk screening of patients is critical for optimal care and part of National Australian Hospital Standards. Identification of malnutrition also ensures reimbursement for hospitals to adequately treat these high-risk patients. However timely, accurate screening, assessment and coding of malnutrition remains suboptimal. This study aimed to investigate manual and digital interventions to overcome barriers to malnutrition identification for improvements in the hospital setting.


METHODS
Retrospective reporting on malnutrition identification processes was done through two stages: 1. manual auditing intervention, and 2. development of a digital solution - the electronic Malnutrition Solution (eMS). Repeated process audits were completed at approximately 6 monthly intervals through both stages between 2016 and 2019 and results analysed. In stage 2, time investment and staff adoption of the digital solution were measured.


RESULTS
Overall, the combined effect of both regular auditing and use of the eMS resulted in statistically significant improvements across all six key measures: patients identified (97% to 100%; p<0.001), screened (68% to 95%; p<0.001), screened within 24 hours (51% to 89%; p<0.001), assessed (72% to 95%; p<0.001), assessed within 24 hours (66% to 93%; p<0.001) and coded (81% to 100%; p=0.017). The eMS demonstrated reduction in screening time by over 60% with user adoption 100%. Data analytics enabled automated, real-time auditing with a 95% reduction in time taken to audit.


CONCLUSIONS
A single digital solution for management of malnutrition and automation of auditing demonstrated significant improvements where manual or combinations of manual and electronic systems continue to fall short. This article is protected by copyright. All rights reserved.

Implementation of an electronic solution to improve malnutrition identification and support clinical best practice.

This research aimed to assess the rate and reliability of routine nurse-completed malnutrition screening and report the prevalence of malnutrition risk on admission to Christchurch Hospital.Student dietitians administered the Malnutrition Screening Tool to patients in three speciality wards within 48 h of admission. Student dietitians' Malnutrition Screening Tool scores were compared against documented nurse-completed Malnutrition Screening Tool scores. Data were analysed using descriptive statistics, Fisher's exact test, and Cohen's kappa tests (interrater reliability ⱪ). A p-value <0.05 was considered statistically significant.Student dietitians, using the Malnutrition Screening Tool, screened 360 (96%) of 377 eligible patients while nurses screened 332 (88%) patients. Student dietitians and nurses screened 119 patients (33%) and 63 patients (18%) respectively at risk of malnutrition. There was fair agreement (ⱪ = 0.311) in Malnutrition Screening Tool total scores between nurses and student dietitians. There was a significant difference in the proportion of patients screened at risk of malnutrition between nurses and student dietitians (p < 0.0001).Malnutrition risk remains high in acute care settings. Nurses were less likely to screen patients as at risk of malnutrition than student dietitians. Regular support and training in using Malnutrition Screening Tool may help improve the rate and reliability of routine malnutrition screening. 

Malnutrition screening tool use in a New Zealand hospital: Reliability and rates of malnutrition screening on admission.

This study aimed to explore the diagnostic accuracy of the Patient-Generated Subjective Global Assessment (PG-SGA) malnutrition risk screening tool when used to score patients based on their electronic medical records (EMR), compared to bedside screening interviews. In-patients at a rural health service were screened at the bedside (n = 50) using the PG-SGA, generating a bedside score. Clinical notes within EMRs were then independently screened by blinded researchers. The accuracy of the EMR score was assessed against the bedside score using area under the receiver operating curve (AUC), sensitivity, and specificity. Participants were 62% female and 32% had conditions associated with malnutrition, with a mean age of 70.6 years (SD 14.9). The EMR score had moderate diagnostic accuracy relative to PG-SGA bedside screen, AUC 0.74 (95% CI: 0.59–0.89). The accuracy, specificity and sensitivity of the EMR score was highest for patients with a score of 7, indicating EMR screen is more likely to detect patients at risk of malnutrition. This exploratory study showed that applying the PG-SGA screening tool to EMRs had enough sensitivity and specificity for identifying patients at risk of malnutrition to warrant further exploration in low-resource settings.

/pdf/testing-the-accuracy-of-a-bedside-screening-tool-framework-2skw5c6n.pdf

Testing the Accuracy of a Bedside Screening Tool Framework to Clinical Records for Identification of Patients at Risk of Malnutrition in a Rural Setting: An Exploratory Study

Malnutrition is a serious health risk facing older people living in residential aged care facilities. Aged care staff record observations and concerns about older people in electronic health records (EHR), including free-text progress notes. These insights are yet to be unleashed.This study explored the risk factors for malnutrition in structured and unstructured electronic health data.Data of weight loss and malnutrition were extracted from the de-identified EHR records of a large aged care organization in Australia. A literature review was conducted to identify causative factors for malnutrition. Natural language processing (NLP) techniques were applied to progress notes to extract these causative factors. The NLP performance was evaluated by the parameters of sensitivity, specificity and F1-Score.The NLP methods were highly accurate in extracting the key data, values for 46 causative variables, from the free-text client progress notes. Thirty three percent (1,469 out of 4,405) of the clients were malnourished. The structured, tabulated data only recorded 48% of these malnourished clients, far less than that (82%) identified from the progress notes, suggesting the importance of using NLP technology to uncover the information from nursing notes to fully understand the health status of the vulnerable older people in residential aged care.This study identified 33% of older people suffered from malnutrition, lower than those reported in the similar setting in previous studies. Our study demonstrates that NLP technology is important for uncovering the key information about health risks for older people in residential aged care. Future research can apply NLP to predict other health risks for older people in this setting. 

Malnutrition and its contributing factors for older people living in residential aged care facilities: Insights from natural language processing of aged care records

Summary Background and aims During conversion from paper to electronic documentation at a tertiary hospital, the Malnutrition Screening Tool was embedded into the electronic health record (e-HR) with nursing staff's completion continued as part of admission procedures with dietetic referrals automated. Currently, the impact of e-HR implementation on malnutrition identification is unknown. Consequently, this retrospective pre-test post-test study compared one year of malnutrition coding in a tertiary teaching hospital two years before and after e-HR implementation automating malnutrition screening referrals to dietitians with subsequent malnutrition assessment completion. Methods Eligibility included adults (≥18yrs) admitted overnight or longer during the 2013/2014 and 2017/2018 financial years. Requested hospital data included demographics, admission data and coding for malnutrition and dietitian intervention. Eligible admissions prior to e-HR implementation were classified as pre-e-HR group, with admissions after classified as post-e-HR. Descriptive, Fisher's exact, Mann–Whitney U and independent samples t-tests were used to compare groups. Results Patient admissions pre-e-HR (n = 37,143) and post-e-HR (n = 36,625) implementation were clinically similar in age, gender and length of stay (57 ± 19 years, 60% male, 3 (1–918) days). However, the numbers of admissions coded annually with malnutrition increased by 47% from 1436 to 2116 following e-HR implementation (p  Conclusions Malnutrition coding increased by 47% after an e-HR implementation with an embedded malnutrition screening tool that automated referrals to dietitians impacting the identification of care to optimize nutritional status.

Capitalising on opportunities: Malnutrition coding in hospital before and after the introduction of electronic health records with an embedded malnutrition screening tool.

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Capitalising on opportunities: Malnutrition coding in hospital before and after the introduction of electronic health records with an embedded malnutrition screening tool.