Validation of two case definitions to identify pressure ulcers using hospital administrative data

Ho, Chester; Jiang, Jason; Eastwood, Catherine; Wong, Holly; Weaver, Brittany; Quan, Hude

doi:10.1136/bmjopen-2017-016438

Cited by 9 publications

(12 citation statements)

References 40 publications

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“…We used the structured fields from the nursing documentation section of the EHR to record HAPrIs. We chose to use the structured fields from nursing documentation because a prior study showed that International Classification of Diseases codes underreport HAPrIs [ 21 ], likely because HAPrIs are noticed by nurses during head-to-toe skin assessments but not always flagged for inclusion in physician notes or billing codes. Furthermore, we relied on nursing documentation because it is the only accurate source of data for the date and time the HAPrI developed.…”

Section: Methodsmentioning

confidence: 99%

Hospital acquired pressure injury prediction in surgical critical care patients

Alderden

Drake

Wilson

et al. 2021

BMC Med Inform Decis Mak

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Background Hospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5–10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data. Methods In this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F1 score. Results Among 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F1 scores of the five classification algorithms proved to be a valuable evaluation metric for model performance considering the class imbalance. Models developed with the parsimonious data set had comparable F1 scores to those developed with the larger set of predictor variables. Conclusions Results from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.

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Section: Methodsmentioning

confidence: 99%

Hospital acquired pressure injury prediction in surgical critical care patients

Alderden

Drake

Wilson

et al. 2021

BMC Med Inform Decis Mak

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“…Although, cross-sectional surveys by the ANQ showed similar but even lower prevalence rates (1.8%) compared to our study data (1.92%). It has been reported that administrative data do not provide valid data for this NSO due to several reasons [48][49][50]. This fact seems to…”

Section: Discussionmentioning

confidence: 96%

Derivation and validation of a prediction model to establish nursing-sensitive quality benchmarks in medical inpatients: a secondary data analysis of a prospective cohort study

Kutz¹,

Volken²,

Gregoriano³

et al. 2022

Swiss Med Wkly

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BACKGROUND: Hospitals are using nursing-sensitive outcomes (NSOs) based on administrative data to measure and benchmark quality of nursing care in acute care wards. In order to facilitate comparisons between different hospitals and wards with heterogeneous patient populations, proper adjustment procedures are required. In this article, we first identify predictors for common NSOs in acute medical care of adult patients based on administrative data. We then develop and cross-validate an NSO-oriented prediction model. METHODS: We used administrative data from seven hospitals in Switzerland to derive prediction models for each of the following NSO: hospital-acquired pressure ulcer (≥ stage II), hospital-acquired urinary tract infection, non-ventilator hospital-acquired pneumonia and in-hospital mortality. We used a split dataset approach by performing a random 80:20 split of the data into a training set and a test set. We assessed discrimination of the models by area under the receiver operating characteristic curves. Finally, we used the validated models to establish a benchmark between the participating hospitals. RESULTS: We considered 36,149 hospitalisations, of which 51.9% were male patients with a median age of 73 years (with an interquartile range of 59–82). Age and length of hospital stay were independently associated with all four NSOs. The derivation and validation models showed a good discrimination in the training (AUC range: 0.75–0.84) and in the test dataset (AUC range: 0.77–0.81), respectively. Variation among different hospitals was relevant considering the risk for hospital-acquired pressure ulcer (≥ stage II) (adjusted Odds ratio [aOR] range: 0.51 [95% CI: 0.38–0.69] – 1.65 [95% CI: 1.33–2.04]), the risk for hospital-acquired urinary tract infection (aOR range: 0.46 [95% CI: 0.36–0.58] – 1.45 [95% CI: 1.31–1.62]), the risk for non-ventilator hospital-acquired pneumonia (aOR range: 0.28 [95% CI: 0.09–0.89] – 2.87 [95% CI: 2.27–3.64]), and the risk for in-hospital mortality (aOR range: 0.45 [95% CI: 0.36–0.56] – 1.39 [95% CI: 1.23–1.60]). CONCLUSION: The application of risk adjustment when comparing nursing care quality is crucial and enables a more objective assessment across hospitals or wards with heterogeneous patient populations. This approach has potential to establish a set of benchmarks that could allow comparison of outcomes and quality of nursing care between different hospitals and wards.

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“…The described efforts to “improve the accuracy of clinical documentation” are consistent with other studies conducted in Australia ( 25 ) and specifically on PI ( 1 , 2 ). Studies conducted in Canada ( 29 , 30 , 36 ), Portugal ( 37 ), UK ( 38 ), and USA ( 39 ) also highlighted the need for quality improvement processes for PI clinical documentation. Nursing documentation improvement is a vital component of the complex capacity building programs on PI prevention in acute care services and is relied on by coders ( 40 , 41 ).…”

Section: Discussionmentioning

confidence: 99%

Clinical Coders' Perspectives on Pressure Injury Coding in Acute Care Services in Victoria, Australia

Weller

Turnour

Connelly

et al. 2022

Front. Public Health

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Pressure injuries (PIs) substantively impact quality of care during hospital stays, although only when they are severe or acquired as a result of the hospital stay are they reported as quality indicators. Globally, researchers have repeatedly highlighted the need to invest more in quality improvement, risk assessment, prevention, early detection, and care for PI to avoid the higher costs associated with treatment of PI. Coders' perspectives on quality assurance of the clinical coded PI data have never been investigated. This study aimed to explore challenges that hospital coders face in accurately coding and reporting PI data and subsequently, explore reasons why data sources may vary in their reporting of PI data. This article is based upon data collected as part of a multi-phase collaborative project to build capacity for optimizing PI prevention across Monash Partners health services. We have conducted 16 semi-structured phone interviews with clinical coders recruited from four participating health services located in Melbourne, Australia. One of the main findings was that hospital coders often lacked vital information in clinicians' records needed to code PI and report quality indicators accurately and highlighted the need for quality improvement processes for PI clinical documentation. Nursing documentation improvement is a vital component of the complex capacity building programs on PI prevention in acute care services and is relied on by coders. Coders reported the benefit of inter-professional collaborative workshops, where nurses and coders shared their perspectives. Collaborative workshops had the potential to improve coders' knowledge of PI classification and clinicians' understanding of what information should be included when documenting PI in the medical notes. Our findings identified three methods of quality assurance were important to coders to ensure accuracy of PI reporting: (1) training prior to initiation of coding activity and (2) continued education, and (3) audit and feedback communication about how to handle specific complex cases and complex documentation. From a behavioral perspective, most of the coders reported confidence in their own abilities and were open to changes in coding standards. Transitioning from paper-based to electronic records highlighted the need to improve training of both clinicians and coders.

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Validation of two case definitions to identify pressure ulcers using hospital administrative data

Cited by 9 publications

References 40 publications

Hospital acquired pressure injury prediction in surgical critical care patients

Hospital acquired pressure injury prediction in surgical critical care patients

Derivation and validation of a prediction model to establish nursing-sensitive quality benchmarks in medical inpatients: a secondary data analysis of a prospective cohort study

Clinical Coders' Perspectives on Pressure Injury Coding in Acute Care Services in Victoria, Australia

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