Evaluation of an electronic health record structured discharge summary to provide real time adverse event reporting in thoracic surgery

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.

A Multiple Baseline Trial of an Electronic ICU Discharge Summary Tool for Improving Quality of Care

OBJECTIVE

Effective communication between clinicians is essential for seamless discharge of patients between care settings. Yet, discharge summaries are commonly not available and incomplete. We implemented and evaluated a structured electronic health record-embedded electronic discharge (eDischarge) summary tool for patients discharged from the ICU to a hospital ward.

DESIGN

Multiple baseline trial with randomized and staggered implementation.

SETTING

Adult medical-surgical ICUs at four acute care hospitals serving a single Canadian city.

PATIENTS

Health records of patients 18 years old or older, in the ICU 24 hours or longer, and discharged from the ICU to an in-hospital patient ward between February 12, 2018, and June 30, 2019.

INTERVENTION

A structured electronic note (ICU eDischarge tool) with predefined fields (e.g., diagnosis) embedded in the hospital-wide electronic health information system.

MEASUREMENTS AND MAIN RESULTS

We compared the percent of timely (available at discharge) and complete (included goals of care designation, diagnosis, list of active issues, active medications) discharge summaries pre and post implementation using mixed effects logistic regression models. After implementing the ICU eDischarge tool, there was an immediate and sustained increase in the proportion of patients discharged from ICU with timely and complete discharge summaries from 10.8% (preimplementation period) to 71.1% (postimplementation period) (adjusted odds ratio, 32.43; 95% CI, 18.22-57.73). No significant changes were observed in rapid response activation, cardiopulmonary arrest, death in hospital, ICU readmission, and hospital length of stay following ICU discharge. Preventable (60.1 vs 5.7 per 1,000 d; p = 0.023), but not nonpreventable (27.3 vs 40.2 per 1,000d; p = 0.54), adverse events decreased post implementation. Clinicians perceived the eDischarge tool to produce a higher quality discharge process.

CONCLUSIONS

Implementation of an electronic tool was associated with more timely and complete discharge summaries for patients discharged from the ICU to a hospital ward.

Effectiveness of an Electronic Communication Tool on Transitions in Care From the Intensive Care Unit: Protocol for a Cluster-Specific Pre-Post Trial

BACKGROUND

Transitions in care are vulnerable periods in health care that can expose patients to preventable errors due to incomplete or delayed communication between health care providers. Transitioning critically ill patients from intensive care units (ICUs) to other patient care units (PCUs) is particularly risky, due to the high acuity of the patients and the diversity of health care providers involved in their care. Instituting structured documentation to standardize written communication between health care providers during transitions has been identified as a promising means to reduce communication breakdowns. We developed an evidence-informed, computer-enabled, ICU-specific structured tool-an electronic transfer (e-transfer) tool-to facilitate and standardize the composition of written transfer summaries in the ICUs of one Canadian city. The tool consisted of 10 primary sections with a user interface combination of structured, automated, and free-text fields.

OBJECTIVE

Our overarching goal is to evaluate whether implementation of our e-transfer tool will improve the completeness and timeliness of transfer summaries and streamline communications between health care providers during high-risk transitions.

METHODS

This study is a cluster-specific pre-post trial, with randomized and staggered implementation of the e-transfer tool in four hospitals in Calgary, Alberta. Hospitals (ie, clusters) were allocated randomly to cross over every 2 months from control (ie, dictation only) to intervention (ie, e-transfer tool). Implementation at each site was facilitated with user education, point-of-care support, and audit and feedback. We will compare transfer summaries randomly sampled over 6 months postimplementation to summaries randomly sampled over 6 months preimplementation. The primary outcome will be a binary composite measure of the timeliness and completeness of transfer summaries. Secondary measures will include overall completeness, timeliness, and provider ratings of transfer summaries; hospital and ICU lengths of stay; and post-ICU patient outcomes, including ICU readmission, adverse events, cardiac arrest, rapid response team activation, and mortality. We will use descriptive statistics (ie, medians and means) to describe demographic characteristics. The primary outcome will be compared within each hospital pre- and postimplementation using separate logistic regression models for each hospital, with adjustment for patient characteristics.

RESULTS

Participating hospitals were cluster randomized to the intervention between July 2018 and January 2019. Preliminary extraction of ICU patient admission lists was completed in September 2019. We anticipate that evaluation data collection will be completed by early 2021, with first results ready for publication in spring or summer 2021.

CONCLUSIONS

This study will report the impact of implementing an evidence-informed, computer-enabled, ICU-specific structured transfer tool on communication and preventable medical errors among patients transferred from the ICU to other hospital care units.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03590002; https://www.clinicaltrials.gov/ct2/show/NCT03590002.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/18675.

Machine Learning Based Sentiment Text Classification for Evaluating Treatment Quality of Discharge Summary

Patients’ discharge summaries (documents) are health sensors that are used for measuring the quality of treatment in medical centers. However, extracting information automatically from discharge summaries with unstructured natural language is considered challenging. These kinds of documents include various aspects of patient information that could be used to test the treatment quality for improving medical-related decisions. One of the significant techniques in literature for discharge summaries classification is feature extraction techniques from the domain of natural language processing on text data. We propose a novel sentiment analysis method for discharge summaries classification that relies on vector space models, statistical methods, association rule, and extreme learning machine autoencoder (ELM-AE). Our novel hybrid model is based on statistical methods that build the lexicon in a domain related to health and medical records. Meanwhile, our method examines treatment quality based on an idea inspired by sentiment analysis. Experiments prove that our proposed method obtains a higher F1 value of 0.89 with good TPR (True Positive Rate) and FPR (False Positive Rate) values compared with various well-known state-of-the-art methods with different size of training and testing datasets. The results also prove that our method provides a flexible and effective technique to examine treatment quality based on positive, negative, and neutral terms for sentence-level in each discharge summary.

Unlocking the Potential of Electronic Health Records for Health Research

Electronic health records (EHRs), originally designed to facilitate health care delivery, are becoming a valuable data source for health research. EHR systems have two components, both of which have various components, and points of data entry, management, and analysis. The “front end” refers to where the data are entered, primarily by healthcare workers (e.g. physicians and nurses). The second component of EHR systems is the electronic data warehouse, or “back-end,” where the data are stored in a relational database. EHR data elements can be of many types, which can be categorized as structured, unstructured free-text, and imaging data. The Sunrise Clinical Manager (SCM) EHR is one example of an inpatient EHR system, which covers the city of Calgary (Alberta, Canada). This system, under the management of Alberta Health Services, is now being explored for research use. The purpose of the present paper is to describe the SCM EHR for research purposes, showing how this generalizes to EHRs in general. We further discuss advantages, challenges (e.g. potential bias and data quality issues), analytical capacities, and requirements associated with using EHRs in a health research context.