Factors associated with early 14-day unplanned hospital readmission: a matched case-control study

Outcomes of ED chest pain visits: the prognostic value of negative but measurable high-sensitivity cardiac troponin (hs-cTn) levels

BACKGROUND

Chest pain is a common condition in the emergency department (ED). High-sensitivity cardiac troponin (hs-cTn) assays are crucial for diagnosing acute coronary syndrome, but the implications of "negative but measurable" hs-cTn levels are not well understood. This study assesses the outcomes of patients with acute chest pain discharged from the ED based on their hs-cTn levels.

METHODS

This retrospective cohort study analyzed medical records of patients aged 18 and older presenting with chest pain to the Tel Aviv Sourasky Medical Center ED from 2017 to 2022. We compared patients with negative but measurable hs-cTn levels (3-50 ng/L) to those with very low hs-cTn levels (< 3 ng/L). Primary outcomes included 90- days coronary angiogram (CAG), and secondary outcomes were 7- days ED revisits, 14-days hospital admissions, and 30- days mortality.

RESULTS

Of 32,162 eligible patients, 23,297 had hs-cTn levels ≤ 50 ng/L. Patients with negative but measurable hs-cTn levels had higher rates of 90-days CAG (1.8% vs. 0.5%, p < 0.001), 7-day ED revisits (5.2% vs. 3.3%, p < 0.001), 14-day hospital admissions (3.1% vs. 0.9%, p < 0.001), and 30-day mortality (0.3% vs. 0.01%, p < 0.001) compared to those with very low hs-cTn levels. Independent predictors for 90 days CAG included age ≥ 57 years, male sex, and hs-cTn ≥ 3.5 ng/L.

CONCLUSIONS

Negative but measurable hs-cTn levels are linked to worse outcomes than very low hs-cTn levels in discharged ED patients. Closer follow-up and further cardiac evaluation may be warranted for these patients.

Enhancing readmission prediction models by integrating insights from home healthcare notes: Retrospective cohort study

BACKGROUND

Hospital readmission is an important indicator of inpatient care quality and a significant driver of increasing medical costs. Therefore, it is important to explore the effects of postdischarge information, particularly from home healthcare notes, on enhancing readmission prediction models. Despite the use of Natural Language Processing (NLP) and machine learning in prediction model development, current studies often overlook insights from home healthcare notes.

OBJECTIVE

This study aimed to develop prediction models for 30-day readmissions using home healthcare notes and structured data. In addition, it explored the development of 14- and 180-day prediction models using variables in the 30-day model.

DESIGN

A retrospective observational cohort study.

SETTING(S)

This study was conducted at Ajou University School of Medicine in South Korea.

PARTICIPANTS

Data from electronic health records, encompassing demographic characteristics of 1819 participants, along with information on conditions, drug, and home healthcare, were utilized.

METHODS

Two distinct models were developed for each prediction window (30-, 14-, 180-day): the traditional model, which utilized structured variables alone, and the common data model (CDM)-NLP model, which incorporated structured and topic variables extracted from home healthcare notes. BERTopic facilitated topic generation and risk probability, representing the likelihood of documents being assigned to specific topics. Feature selection involved experimenting with various algorithms. The best-performing algorithm, determined using the area under the receiver operating characteristic curve (AUROC), was used for model development. Model performance was assessed using various learning metrics including AUROC.

RESULTS

Among 1819 patients, 251 (13.80 %) experienced 30-day readmission. The least absolute shrinkage and selection operator was used for feature extraction and model development. The 15 structured features were used in the traditional model. Moreover, five additional topic variables from the home healthcare notes were applied in the CDM-NLP model. The AUROC of the traditional model was 0.739 (95 % CI: 0.672-0.807). The AUROC of the CDM-NLP model was high at 0.824 (95 % CI: 0.768-0.880), which indicated an outstanding performance. The topics in the CDM-NLP model included emotional distress, daily living functions, nutrition, postoperative status, and cardiorespiratory issues. In extended prediction model development for 14- and 180-day readmissions, the CDM-NLP consistently outperformed the traditional model.

CONCLUSIONS

This study developed effective prediction models using both structured and unstructured data, thereby emphasizing the significance of postdischarge information from home healthcare notes in readmission prediction.

[Discharge management strategies in German general hospitals : A nationwide survey of professionals responsible for clinical risk management]

BACKGROUND

Transitions from inpatient care are associated with risks for the safety of patients. In 2017, the framework agreement on discharge management was legally defined. There is currently a lack of empirical data in Germany on the implementation of measures to ensure safe transitions of patients after inpatient care. The aim of this study is to provide an overview of the discharge management strategies implemented by German general hospitals.

METHODS

Between March and May 2022, specific discharge management strategies as well as structural and organizational characteristics were assessed in a nationwide survey of 401 general hospitals, and descriptive statistics and group comparisons were performed.

RESULTS

Seven of nine strategies surveyed were implemented in > 95% of all hospitals. The evaluation of discharge planning was only implemented in 61% of the hospitals, and systematic documentation, analysis, and evaluation of readmissions in 54%. Hospitals with a higher number of hospital beds reported significantly less often about "early contact with follow-up care providers" and "organization of a seamless transition to follow-up care."

DISCUSSION

A large part of the strategies in discharge management from inpatient treatment is implemented in German general hospitals. However, measures for evaluation and the systematic analysis of discharge processes and readmissions of patients have only been partially implemented. However, these are necessary to systematically evaluate and potentially improve the discharge processes.

Copy-Pasting in Patients' Electronic Medical Records (EMRs): Use Judiciously and With Caution

An electronic medical record (EMR) is an electronic, comprehensive, and up-to-date compilation of a patient's medical history and information stored in a secure digital format. It provides real-time access to patient data, enabling healthcare providers to make informed decisions quickly and accurately. EMR systems streamline a patient's healthcare journey and enable shared care across the medical practice. By providing a comprehensive view of a patient's medical history, EMRs can be invaluable tools for physicians and healthcare providers, allowing them to collaborate more effectively and provide better care. Additionally, EMRs can help reduce paperwork, improve accuracy, and increase efficiency, ultimately leading to improved patient outcomes. The true potential of EMR systems can be realized when they are used in conjunction with evidence-based medicine methodologies, quality improvement initiatives, and team-based care. This combination of technologies and practices can revolutionize healthcare delivery, improving patient outcomes, greater efficiency, and cost savings. "Copy-pasting" is an essential feature of EMR systems, with physicians relying on it for up to 35.7% of their workflow. By leveraging the copy-pasting feature of their EMR system, physicians can ensure that their data capture is accurate and timely, leading to better patient care. Copy-pasting can be a valuable tool for physicians, saving time and allowing them to focus on practical clinical issues. However, it is essential to note that while most clinicians copy-paste, 25% of them believe it can lead to a high frequency of medical errors, with the potential for a significant number of errors being attributed to this practice. Therefore, physicians must exercise caution when copy-pasting and take the necessary steps to ensure accuracy and reduce the risk of errors. Copy-pasting can cause severe adverse patient events by introducing new inaccuracies, rapidly spreading inaccurate or outdated information, leading to discordant notes, and creating long notes that mask essential clinical information. Despite these risks, copy-pasting has become widely used in EMRs. Additionally, copy-pasting can reduce the time spent on documentation, allowing healthcare providers to focus more on patient care. Inappropriate copy-pasting can have serious consequences, such as compromising data integrity, endangering patient safety, increasing costs, and even leading to fraudulent malpractice claims. In conclusion, copy-pasting can be helpful for healthcare professionals, but it must be used cautiously. Proper education and safeguards should be implemented to ensure accuracy and up-to-date patient data. Additionally, healthcare professionals should be aware of the legal implications of copy-pasting, as it may be considered a form of medical malpractice. With the proper precautions, copy-pasting can be a safe and efficient way to save time and reduce errors in patient records.

Early prediction of 30- and 14-day all-cause unplanned readmissions

BACKGROUND

An unplanned readmission is a dual metric for both the cost and quality of medical care.

METHODS

We employed the random forest (RF) method to build a prediction model using a large dataset from patients' electronic health records (EHRs) from a medical center in Taiwan. The discrimination abilities between the RF and regression-based models were compared using the areas under the ROC curves (AUROC).

RESULTS

When compared with standardized risk prediction tools, the RF constructed using data readily available at admission had a marginally yet significantly better ability to identify high-risk readmissions within 30 and 14 days without compromising sensitivity and specificity. The most important predictor for 30-day readmissions was directly related to the representing factors of index hospitalization, whereas for 14-day readmissions the most important predictor was associated with a higher chronic illness burden.

CONCLUSIONS

Identifying dominant risk factors based on index admission and different readmission time intervals is crucial for healthcare planning.

The number of beds occupied is an independent risk factor for discharge of trauma patients

Reducing the burden of limited capacity on medical practitioners and public health systems requires a time-dependent characterization of hospitalization rates, such that inferences can be drawn about the underlying causes for hospitalization and patient discharge. The aim of this study was to analyze non-medical risk factors that lead to the discharge of trauma patients. This retrospective cohort study includes trauma patients who were treated in Switzerland between 2011 and 2018. The national Swiss database for quality assurance in surgery (AQC) was reviewed for trauma diagnoses according to the ICD-10 code. Non-medical risk factors include seasonal changes, daily changes, holidays, and number of beds occupied by trauma patients across Switzerland. Individual patient information was aggregated into counts per day of total patients, as well as counts per day of levels of each categorical variable of interest. The ARIMA-modeling was utilized to model the number of discharges per day as a function of auto aggressive function of all previously mentioned risk factors. This study includes 226,708 patients, 118,059 male (age 48.18, standard deviation (SD) 22.34 years) and 108,649 female (age 62.57, SD 22.89 years) trauma patients. The mean length of stay was 7.16 (SD 14.84) days and most patients were discharged home (n = 168,582, 74.8%). A weekly and yearly seasonality trend can be observed in admission trends. The mean number of occupied trauma beds ranges from 3700 to 4000 per day. The number of occupied beds increases on weekdays and decreases on holidays. The number of occupied beds is a positive, independent risk factor for discharge in trauma patients; as the number of occupied beds increases at any given time, so does the risk for discharge. The number of beds occupied represents an independent non-medical risk factor for discharge. Capacity determines triage of hospitalized patients and therefore might increase the risk of premature discharge.

The Impacts of COVID-19 on Healthcare Quality in Tertiary Medical Centers-A Retrospective Study on Data from Taiwan Clinical Performance Indicators System

To date, COVID-19 is by far the most impactful contagious disease of the 21st century and it has had a devastating effect on public health in countries around the globe. Elective medical services have declined markedly since the outbreak of the COVID-19 pandemic. Few studies have compared changes in healthcare quality before and during the outbreak of COVID-19 in Eastern Asian countries. We aimed to explore the impacts of COVID-19 on healthcare quality among medical centers in Taiwan. This was a retrospective study that collected anonymized data from the Taiwan Clinical Performance Indicator system, which was founded by the Joint Commission of Taiwan, an organization to promote, execute, and certify the nation’s healthcare quality policies. We explored quality indicators reported by more than three-quarters of medical centers in Taiwan from January 2019 to December 2020. The year 2019 was defined as the baseline period and 2020 was defined as the period after the start of the outbreak of COVID-19. Quality indicators from different regions were analyzed. Unscheduled returns of emergency patients within 72 h of their discharge, unscheduled returns of hospitalized patients within 14 days of their discharge, and unscheduled returns of surgical patients to the operating room during hospitalization all declined during the COVID-19 outbreak. Interestingly, the proportion of acute ischemic stroke patients receiving intravenous tissue-type plasminogen activator (IV-tPA) increased during outbreak of COVID-19. There were significant regional variations in healthcare quality indicators among medical centers in northern and middle/southern Taiwan. The outbreak of COVID-19 changed different patterns of healthcare systems. Although healthcare quality seemed to improve, further investigation is warranted to better understand whether those who were in need of returning to the emergency room or hospital were reluctant or were prevented from travel by the shelter-in-place policy.

Restricted use of copy and paste in electronic health records potentially improves healthcare quality

ABSTRACT

The copy-and-paste feature is commonly used for clinical documentation, and a policy is needed to reduce overdocumentation. We aimed to determine if the restricted use of copy and paste by doctors could improve inpatient healthcare quality.Clinical documentation in an inpatient dataset compiled from 2016 to 2018 was used. Copied-and-pasted text was detected in word templates using natural language programming with a threshold of 70%. The prevalence of copying and pasting after the policy introduction was accessed by segmented regression for trend analysis. The rate of readmission for the same disease within 14 days was assessed to evaluate inpatient healthcare quality, and the completion of discharge summary notes within 3 days was assessed to determine the timeliness of note completion. The relationships between these factors were used cross-correlation to detect lag effect. Poisson regression was performed to identify the relative effect of the copy and paste restriction policy on the 14-day readmission rate or the discharge note completion rate within 3 days.The prevalence of copying and pasting initially decreased, then increased, and then flatly decreased. The cross-correlation results showed a significant correlation between the prevalence of copied-and-pasted text and the 14-day readmission rate (P < .001) and a relative risk of 1.105 (P < .005), with a one-month lag. The discharge note completion rate initially decreased and not affected long term after restriction policy.Appropriate policies to restrict the use of copying and pasting can lead to improvements in inpatient healthcare quality. Prospective research with cost analysis is needed.