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

A scoping review of the methodological approaches used in retrospective chart reviews to validate adverse event rates in administrative data

Patient safety is a key quality issue for health systems. Healthcare acquired adverse events (AEs) compromise safety and quality; therefore, their reporting and monitoring is a patient safety priority. Although administrative datasets are potentially efficient tools for monitoring rates of AEs, concerns remain over the accuracy of their data. Chart review validation studies are required to explore the potential of administrative data to inform research and health policy. This review aims to present an overview of the methodological approaches and strategies used to validate rates of AEs in administrative data through chart review. This review was conducted in line with the Joanna Briggs Institute methodological framework for scoping reviews. Through database searches, 1054 sources were identified, imported into Covidence, and screened against the inclusion criteria. Articles that validated rates of AEs in administrative data through chart review were included. Data were extracted, exported to Microsoft Excel, arranged into a charting table, and presented in a tabular and descriptive format. Fifty-six studies were included. Most sources reported on surgical AEs; however, other medical specialties were also explored. Chart reviews were used in all studies; however, few agreed on terminology for the study design. Various methodological approaches and sampling strategies were used. Some studies used the Global Trigger Tool, a two-stage chart review method, whilst others used alternative single-, two-stage, or unclear approaches. The sources used samples of flagged charts (n = 24), flagged and random charts (n = 11), and random charts (n = 21). Most studies reported poor or moderate accuracy of AE rates. Some studies reported good accuracy of AE recording which highlights the potential of using administrative data for research purposes. This review highlights the potential for administrative data to provide information on AE rates and improve patient safety and healthcare quality. Nonetheless, further work is warranted to ensure that administrative data are accurate. The variation of methodological approaches taken, and sampling techniques used demonstrate a lack of consensus on best practice; therefore, further clarity and consensus are necessary to develop a more systematic approach to chart reviewing.

Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models

BACKGROUND

Surveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs.

OBJECTIVE

This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes.

METHODS

Patients with HAPIs were identified from head-to-toe skin assessments in a local tertiary acute care hospital during a clinical trial that took place from 2015 to 2018 in Calgary, Alberta, Canada. Clinical notes documented during the trial were extracted from the EMR database after the linkage with the discharge abstract database. Different combinations of several types of clinical notes were processed by sequential forward selection during the model development. Text classification algorithms for HAPI detection were developed using random forest (RF), extreme gradient boosting (XGBoost), and deep learning models. The classification threshold was tuned to enable the model to achieve similar specificity to an ICD-based phenotyping study. Each model's performance was assessed, and comparisons were made between the metrics, including sensitivity, positive predictive value, negative predictive value, and F1-score.

RESULTS

Data from 280 eligible patients were used in this study, among whom 97 patients had HAPIs during the trial. RF was the optimal performing model with a sensitivity of 0.464 (95% CI 0.365-0.563), specificity of 0.984 (95% CI 0.965-1.000), and F1-score of 0.612 (95% CI of 0.473-0.751). The machine learning (ML) model reached higher sensitivity without sacrificing much specificity compared to the previously reported performance of ICD-based algorithms.

CONCLUSIONS

The EMR-based NLP phenotyping algorithms demonstrated improved performance in HAPI case detection over ICD-10-CA codes alone. Daily generated nursing notes in EMRs are a valuable data resource for ML models to accurately detect adverse events. The study contributes to enhancing automated health care quality and safety surveillance.

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

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.

Comparative Evaluation of the Effect of Aloe Vera Gel, Olive Oil, and Compound Aloe Vera Gel-Olive Oil on Prevention of Pressure Ulcer: A Randomized Controlled Trial

Background

One of the most common problems in the intensive care units (ICUs) is pressure ulcers (PUs). The present study aimed to evaluate the effectiveness of aloe vera gel, olive oil, and compound aloe vera gel-olive oil in the prevention of PUs.

Materials and Methods

This randomized clinical trial was conducted on 240 patients. They were randomly divided into four groups, aloe vera gel (n = 60), olive oil (n = 60), aloe vera gel-olive oil combination (n = 60), and control (n = 60). Braden scale and National Pressure Ulcer Advisory Panel scale were used to collect data. The intervention was performed for 30 days. In the intervention and control groups, the patient received routine care. In each intervention group, 10-15 ml of olive oil or aloe vera gel or a combination of olive oil and aloe vera was rubbed into body areas under pressure.

Results

There were no PUs detected in all groups before the intervention; after the intervention, 12 patients in the olive group, 20 patients in the aloe vera group, 10 patients in the aloe vera-olive combination group, and 22 patients in the control group developed PUs. The results reported 40% of the patients with stage 1 PU and 10% of them with stage 2.

Conclusion

Due to the effectiveness of olive oil and aloe vera-olive oil combination in preventing PU, it is recommended to use these herbal compounds in preventing PU on ICU patients.

Hospital acquired pressure injury prediction in surgical critical care patients

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.

Effect of Hydrogel Enriched With Alginate, Fatty Acids, and Vitamins A and E on Pressure Injuries: A Case Series

Pressure injuries are a common kind of skin lesion that may be difficult to treat. The objective of this study was to analyze the effect of hydrogel enriched with alginate, fatty acids, and vitamins A and E in the treatment of pressure injuries. This case series with 12-week follow-up included applying daily dressings with hydrogel, maintaining a photographic record, using planimetry to calculate the lesion area, and classifying the healing process using the Pressure Ulcer Scale for Healing (PUSH). In addition, exudate collection from the ulcers was performed in the beginning and after 12 weeks of treatment to determine the dosage of metalloproteinase 9 (MMP9) and tissue inhibitor of metalloproteinase 1 (TIMP1). Of the 13 patients included in the study, 2 died and 11 were monitored for 12 weeks. Only 1 patient showed full wound healing, but all patients showed a significant 12.19% (p = .023) reduction in the lesion area. The PUSH score was also significantly reduced from 15.9 to 10.54 (p = .0052). Relative to the dosage of metalloproteinase and its inhibitor, there was a reduction in the level of MMP9 and there was no change in the level of TIMP1. This study showed that hydrogel enriched with alginate, fatty acids, and vitamins A and E provided promising results for the treatment of pressure injuries by reducing the lesion area, the general PUSH score, and the amount of MMP9 in the wounds' microenvironment.