CLUSTERING AND PRIORITIZING PATIENT SAFETY ISSUES DURING EHR IMPLEMENTATION AND UPGRADES IN HOSPITAL SETTINGS

Association between Electronic Health Record Implementations and Hospital-Acquired Conditions in Pediatric Hospitals

BACKGROUND

Implementing an electronic health record (EHR) is one of the most disruptive operational tasks a health system can undergo. Despite anecdotal reports of adverse events around the time of EHR implementations, there is limited corroborating research, particularly in pediatrics. We utilized data from Solutions for Patient Safety (SPS), a network of 145+ children's hospitals that share data and protocols to reduce harm in pediatric care delivery, to study the impact of EHR implementations on patient safety.

OBJECTIVE

Determine if there is an association between the time immediately surrounding an EHR implementation and hospital-acquired conditions (HACs) rates in pediatrics.

METHODS

A survey of information technology leaders at pediatric institutions identified EHR implementations occurring between 2012 and 2022. This list was cross-referenced with the SPS database to create an anonymized dataset of 27 sites comprising monthly HAC and care bundle compliance rates in the 7 months preceding and succeeding the transition. Six HACs were analyzed: central-line associated bloodstream infection (CLABSI), catheter-associated urinary tract infection (CAUTI), adverse drug events, surgical site infections (SSIs), pressure injuries (PIs), and falls, in addition to four associated care bundle compliance rates: CLABSI and CAUTI maintenance bundles, SSI bundle, and PI bundle. To determine if there was a statistically significant association with EHR implementation, the observation period was divided into three eras: "before" (months -7 to -3), "during" (months -2 to +2), and "after" go-live (months +3 to +7). Average monthly HAC and bundle compliance rates were calculated across eras. Paired t-tests were performed to compare rates between the eras.

RESULTS

No statistically significant increase in HAC rates or decrease in bundle compliance rates was observed across the EHR implementation eras.

CONCLUSION

This multisite study detected no significant increase in HACs and no decrease in preventive care bundle compliance in the months surrounding an EHR implementation.

Developing an Analytical Pipeline to Classify Patient Safety Event Reports Using Optimized Predictive Algorithms

BACKGROUND

 Patient safety event reports provide valuable insight into systemic safety issues but deriving insights from these reports requires computational tools to efficiently parse through large volumes of qualitative data. Natural language processing (NLP) combined with predictive learning provides an automated approach to evaluating these data and supporting the work of patient safety analysts.

OBJECTIVES

 The objective of this study was to use NLP and machine learning techniques to develop a generalizable, scalable, and reliable approach to classifying event reports for the purpose of driving improvements in the safety and quality of patient care.

METHODS

 Datasets for 14 different labels (themes) were vectorized using a bag-of-words, tf-idf, or document embeddings approach and then applied to a series of classification algorithms via a hyperparameter grid search to derive an optimized model. Reports were also analyzed for terms strongly associated with each theme using an adjusted F-score calculation.

RESULTS

 F₁ score for each optimized model ranged from 0.951 ("Fall") to 0.544 ("Environment"). The bag-of-words approach proved optimal for 12 of 14 labels, and the naïve Bayes algorithm performed best for nine labels. Linear support vector machine was demonstrated as optimal for three labels and XGBoost for four of the 14 labels. Labels with more distinctly associated terms performed better than less distinct themes, as shown by a Pearson's correlation coefficient of 0.634.

CONCLUSIONS

 We were able to demonstrate an analytical pipeline that broadly applies NLP and predictive modeling to categorize patient safety reports from multiple facilities. This pipeline allows analysts to more rapidly identify and structure information contained in patient safety data, which can enhance the evaluation and the use of this information over time.

Automatic classification of scanned electronic health record documents

OBJECTIVES

Electronic Health Records (EHRs) contain scanned documents from a variety of sources such as identification cards, radiology reports, clinical correspondence, and many other document types. We describe the distribution of scanned documents at one health institution and describe the design and evaluation of a system to categorize documents into clinically relevant and non-clinically relevant categories as well as further sub-classifications. Our objective is to demonstrate that text classification systems can accurately classify scanned documents.

METHODS

We extracted text using Optical Character Recognition (OCR). We then created and evaluated multiple text classification machine learning models, including both "bag of words" and deep learning approaches. We evaluated the system on three different levels of classification using both the entire document as input, as well as the individual pages of the document. Finally, we compared the effects of different text processing methods.

RESULTS

A deep learning model using ClinicalBERT performed best. This model distinguished between clinically-relevant documents and not clinically-relevant documents with an accuracy of 0.973; between intermediate sub-classifications with an accuracy of 0.949; and between individual classes with an accuracy of 0.913.

DISCUSSION

Within the EHR, some document categories such as "external medical records" may contain hundreds of scanned pages without clear document boundaries. Without further sub-classification, clinicians must view every page or risk missing clinically-relevant information. Machine learning can automatically classify these scanned documents to reduce clinician burden.

CONCLUSION

Using machine learning applied to OCR-extracted text has the potential to accurately identify clinically-relevant scanned content within EHRs.