Understanding Emergency Department 72-Hour Revisits Among Medicaid Patients Using Electronic Healthcare Records

Risk Factors for Early Return Visits to the Emergency Department in Patients Presenting with Nonspecific Abdominal Pain and the Use of Computed Tomography Scan

Over a quarter of patients presenting with abdominal pain at emergency departments (EDs) are diagnosed with nonspecific abdominal pain (NSAP) at discharge. This study investigated the risk factors associated with return ED visits in Taiwanese patients with NSAP after discharge. We divided patients into two groups: the study group comprising patients with ED revisits after the index ED visit, and the control group comprising patients without revisits. During the study period, 10,341 patients discharged with the impression of NSAP after ED management. A regression analysis found that older age (OR [95%CI]: 1.007 [1.003–1.011], p = 0.004), male sex (OR [95%CI]: 1.307 [1.036–1.650], p = 0.024), and use of NSAIDs (OR [95%CI]: 1.563 [1.219–2.003], p < 0.001) and opioids (OR [95%CI]: 2.213 [1.643–2.930], p < 0.001) during the index visit were associated with increased return ED visits. Computed tomography (CT) scans (OR [95%CI]: 0.605 [0.390–0.937], p = 0.021) were associated with decreased ED returns, especially for those who were older than 60, who had an underlying disease, or who required pain control during the index ED visit.

Improving Prediction of High-Cost Health Care Users with Medical Check-Up Data

Studies found that a small portion of the population spent the majority of health care resources, and they highlighted the importance of predicting high-cost users in the health care management and policy. Most prior research on high-cost user prediction models are based on diagnosis data with additional cost and health care utilization data to improve prediction accuracy. To further improve the prediction of high-cost users, researchers have been testing various new data sources such as self-reported health status data. In this study, we use three categories of medical check-up data, laboratory tests, self-reported medical history, and self-reported health behavior data to build high-cost user prediction models, and to assess the medical check-up features as predictors of high-cost users. Using three data-mining models, logistic regression, random forest, and neural network models, we show that under the diagnosis-based approach, medical check-up data marginally improve diagnosis-based prediction models. Under the cost-based approach, we find that medical check-up data improve cost-based prediction models marginally and medical check-up data can be a viable alternate data source to diagnosis data in predicting high-cost users.

Large observational study on risks predicting emergency department return visits and associated disposition deviations

Objective

A common emergency department (ED) patient care outcome metric is 72-hour ED return visits (EDRVs). Risks predictive of EDRV vary in different studies. However, risk differences associated with related versus unrelated EDRV and subsequent EDRV disposition deviations (EDRVDD) are rarely addressed. We aim to compare the potential risk patterns predictive of related and unrelated EDRV and further determine those potential risks predictive of EDRVDD.

Methods

We conducted a large retrospective observational study from September 1, 2015 through June 30, 2016. ED Patient demographic characteristics and clinical metrics were compared among patients of 1) related; 2) unrelated; and 3) no EDRVs. EDRVDD was defined as obvious disposition differences between initial ED visit and return visits. A multivariate multinomial logistic regression was performed to determine the independent risks predictive of EDRV and EDRVDD after adjusting for all confounders.

Results

A total of 63,990 patients were enrolled; 4.65% were considered related EDRV, and 1.80% were unrelated. The top risks predictive of EDRV were homeless, patient left without being seen, eloped, or left against medical advice. The top risks predictive of EDRVDD were geriatric and whether patients had primary care physicians regardless as to whether patient returns were related or unrelated to their initial ED visits.

Conclusion

Over 6% of patients experienced ED return visits within 72 hours. Though risks predicting such revisits were multifactorial, similar risks were identified not only for ED return visits, but also for return ED visit disposition deviations.

Predictors of Admission in Adult Unscheduled Return Visits to the Emergency Department

Introduction

The 72-hour unscheduled return visit (URV) of an emergency department (ED) patient is often used as a key performance indicator in emergency medicine. We sought to determine if URVs with admission to hospital (URVA) represent a distinct subgroup compared to unscheduled return visits with no admission (URVNA).

Methods

We performed a retrospective cohort study of all 72-hour URVs in adults across 10 EDs in the Edmonton Zone (EZ) over a one-year period (January 1, 2015 - December 31, 2015) using ED information-system data. URVA and URVNA populations were compared, and a multivariable analysis identified predictors of URVA.

Results

Analysis of 40,870 total URV records, including 3,363 URVAs, revealed predictors of URVA on the index visit including older age (>65 yrs, odds ratio [OR] 3.6), higher disease acuity (Canadian Emergency Department Triage and Acuity Scale [CTAS] 2, OR 2.6), gastrointestinal presenting complaint (OR 2.2), presenting to a referral hospital (OR 1.4), fewer annual ED visits (<4 visits, OR 2.0), and more hours spent in the ED (>12 hours, OR 2.0). A decrease in CTAS score (increase in disease acuity) upon return visit also increased the risk of admission (-1 CTAS level, OR 2.6). ED crowding at the index visit, as indicated by occupancy level, was not a predictor.

Conclusion

We demonstrate that URVA patients comprise a distinct subgroup of 72-hour URV patients. Risk factors for URVA are present at the index visit suggesting that patients at high risk for URVA may be identifiable prior to admission.

Emergency department 72-hour revisits among children with chronic diseases: a Saudi Arabian study

BACKGROUND

Emergency Department (ED) revisits have often been used as an indicator of medical care quality. This study aimed to quantify the frequency of ED revisits within 72 h of discharge and identify its factors among children with chronic diseases.

METHODS

We designed a retrospective cohort study of children with at least one chronic disease who were also under 18 years of age and had attended and were discharged from the ED at King Abdullah Specialist Children's Hospital (KASCH-RD), Riyadh, Saudi Arabia between April 19, 2015 and July 29, 2017. The outcome measure was the frequency of ED revisits during a period of 72 h after discharge.

RESULTS

The study included 11,057 ED discharges of children with at least one chronic disease. Their revisit rate was 1211 (11%), with 83 (6.9%) having had a second ED revisit within 72 h of ED discharge. According to ICD-10 codes, the most common causes of ED revisits were respiratory, digestive, genitourinary, symptoms, and external causes. Factors of frequent ED revisits within 72 h were young age, institutional health insurance coverage, year of new health information system (2015), external causes, and genitourinary.

CONCLUSION

The rate of 72-h ED revisits after discharge of children with chronic diseases treated at KASCH-RD was relatively high, and was associated with young age, institutional health insurance coverage, year of a new health information system implementation, and external causes of ED visit. These study findings amplify the need for intervention to reduce the rate of early ED revisits among children with chronic diseases.

From Value Assessment to Value Cocreation: Informing Clinical Decision-Making with Medical Claims Data

Big data and advances in analytical processes represent an opportunity for the healthcare industry to make better evidence-based decisions on the value generated by various tests, procedures, and interventions. Value-based reimbursement is the process of identifying and compensating healthcare providers based on whether their services improve quality of care without increasing cost of care or maintain quality of care while decreasing costs. In this article, we motivate and illustrate the potential opportunities for payers and providers to collaborate and evaluate the clinical and economic efficacy of different healthcare services. We conduct a case study of a firm that offers advanced biomarker and disease state management services for cardiovascular and cardiometabolic conditions. A value-based analysis that comprised a retrospective case/control cohort design was conducted, and claims data for over 7000 subjects who received these services were compared to a matched control cohort. Study subjects were commercial and Medicare Advantage enrollees with evidence of CHD, diabetes, or a related condition. Analysis of medical claims data showed a lower proportion of patients who received biomarker testing and disease state management services experienced a MI (p < 0.01) or diabetic complications (p < 0.001). No significant increase in cost of care was found between the two cohorts. Our results illustrate the opportunity healthcare payers such as Medicare and commercial insurance companies have in terms of identifying value-creating healthcare interventions. However, payers and providers also need to pursue system integration efforts to further automate the identification and dissemination of clinically and economically efficacious treatment plans to ensure at-risk patients receive the treatments and interventions that will benefit them the most.