key: cord-0862240-96zqo6tx authors: Giunta, Diego Hernán; Marquez Fosser, Santiago; Boietti, Bruno Rafael; Ación, Laura; Pollan, Javier Alberto; Martínez, Bernardo; Luna, Daniel; Bonella, Maria Belen; Grande Ratti, María Florencia title: Emergency department visits and hospital readmissions in an Argentine health system date: 2020-07-22 journal: Int J Med Inform DOI: 10.1016/j.ijmedinf.2020.104236 sha: 16d0173d9c8489f87b75b203799b9929a2903547 doc_id: 862240 cord_uid: 96zqo6tx BACKGROUND AND GOAL OF STUDY: The scope of health in the Sustainable Development Goals is much broader than the Millennium Development Goals, spanning functions such as health-system access and quality of care. Hospital readmission rate and ED-visits within 30 days from discharge are considered low-cost quality indicators. This work assesses an indicator of quality of care in a tertiary referral hospital in Argentina, using data available from clinical records. PURPOSE: To estimate the rate of ED-visits and the hospital readmission rate (HRR) after a first hospitalization (First-H), and to identify associated factors. METHODS: This retrospective cohort included patients who had a First-H in Hospital Italiano de Buenos Aires between 2014-2015. Follow-up occurred from discharge until ED-visit, readmission, death, disaffiliation from health insurance, or 13 months. We present HRR at 30 days and ED-visits rate at 72 hours, using the Cox proportional-hazards regression model to explore associated factors, and reporting adjusted hazard ratios (HR) with their respective 95%CI. RESULTS: The study comprised 10,598 hospitalizations (median age was 68 years). Of these, 5,966 had at least one consultation to the ED during follow up, resulting in a 24 hour rate of consultations to ED of 1.51% (95%CI 1.29-1.72); at 48 hours 3.18% (95%CI 2.86-3.54); at 72 hours 4.71% (95%CI 4.32-5.13). In multivariable models, factors associated for 72 hours ED-visits were: age (aHR 1.06), male (aHR 1.14), Charlson Comorbidity Index (aHR 1.16), unscheduled hospitalization (aHR 1.39), prior consultation with the ED (aHR 1.08) and long hospital stay (aHR 1.39). Meanwhile, 2,345 patients had at least one hospital readmission (98% unscheduled), resulting a 24 hour rate of 0.5% (95%CI 0.42-0.71), at 48 hours 0.98% (95%CI 0.80-1.18), at 72 hours 1.4% (95%CI 1.2-1.6); at 30 days 7.7% (95%CI 7.2-8.2); at 90 days 13% (95%CI 12.4-13.8); and one-year 22.5% (95%CI 21.7-23.4). Associated factors for HRR at 30 days were: age (HR 1.16), male (HR 1.09), Charlson comorbidities score (HR 1.27), social service requirement during First-H (HR 1.37), unscheduled First-H (HR 1.16), previous ED-visits (HR 1.03) and length of stay (HR 1.08). CONCLUSION: Priorities efforts to improve must include greater attention to patients' readiness prior discharge, to explore causes of preventable readmissions, and better support for patient self-management. The 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, provides a shared blueprint for peace and prosperity for the planet, now and into the future [1] . At its heart are the 17 Sustainable Development Goals (SDGs), which are an urgent call for action by all countries as a global partnership [2] . The SDGs are a set of global goals for fair and sustainable health at every level: from the planetary biosphere to the local community [3] . While only SDG 3 focuses on human health, a goal that aims "to ensure healthy lives and promote well-being for all at all ages", all goals are interrelated [4] . In fact, it's recognized that ending poverty and other deprivations must go hand-in-hand with strategies that improve health, education, reduction of inequality, and spur economic growth [5] . The scope of healthcare in the SDGs is much broader than the Millennium Development Goals, spanning functions such as health-system access and quality care. It especially has two monitoring indicators: 1) coverage of essential health services, and 2) catastrophic expenditure on health [6] . Changing health needs, growing public expectations, and ambitious new health goals are raising the bar for health systems to produce better health outcomes and greater social value. Monitoring indicators plays a crucial role in health care. Hospital readmissions and Emergency Department (ED) visits within 30 days from hospital discharge are considered low-cost quality indicators once access to data through electronic health records are available [7] [8] [9] . This indicator has an estimated prevalence of 27%, representing a frequent problem in all health care systems [10] . It is known that some readmissions can be avoided by improving the hospital discharge process [11] . Patients and their families are the main ones who suffer from this disruption in the health care process, as well as significant economic expenses [12] . Risk factors related to increased rates of readmissions have been already studied, Wang et al. reported an association between the Charlson Comorbidity Index (CCI) and the use of a nasogastric tube, showing an increased risk [13] . Another study by Auerbach et al reported as risk factors for readmissions, a premature discharge, the lack of medical follow-up and the absence of hospital visits after discharge [14] . Over the years, predictive models have been generated to J o u r n a l P r e -p r o o f assess patients with higher probability of being readmitted, detecting vulnerable subgroups of patients to implement accurate strategies to target them [15] . Despite continuous and robust efforts, few data exist to define the frequency of preventable readmissions [16, 17] ; but there is no doubt that hospital readmissions are expensive and may reflect poor quality of care [18] . In Argentina, risk factors associated have not been deeply studied. The aim of this study was to estimate the consulting rate to ED and the hospital readmission rate (HRR) after a first hospitalization (First-H), and to identify associated factors. Our study took place in the Hospital Italiano de Buenos Aires (HIBA), which includes two highly complex hospitals with a total bed capacity of 785. Additionally, we also have our own private insurance plan (PS-HIBA) with approximately 160.000 affiliated patients. Eligible patients were 18 years or older, affiliated to PS-HIBA, who had a first episode of hospitalization (first-H) between January 2014 and December 2015. Patients who died during this first-H were excluded from analyses. Regarding sample size, we assume a readmission rate 30 days after hospital discharge of 11%, according from bibliography [19] , using a precision estimation of 5,000 hospitalizations with a hemi amplitude of +/-1% and 99%CI, we decided to include all hospitalizations during the study period. We conducted a retrospective cohort using secondary databases. Patients were followed from discharge until consulting to ED, readmission, death, disaffiliation from health insurance or 13 months. The main outcomes were: the first readmission and/or the first ED-visit during the follow-up period. The following variables were considered as possible risk factors associated with readmissions or ED-visit: patient's characteristics such as gender, age, marital J o u r n a l P r e -p r o o f status, nationality, comorbidities (CCI, COPD). Also, we used administrative information such as first-H type (emergency or scheduled), length of stay, social service or critical care requirements, time using PS-HIBA services, amount of hospitalizations or ED-visits in previous years. All patient health information is stored in a single Clinical Data Repository (CDR) fed by the hospital electronic health record (EHR). The hospital health system has been Ethics approval was obtained from IRB and the project has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. All data were treated with confidentiality and restricted access was given only to researchers involved. Regarding time to the event for readmissions and ED-visits, it was calculated as a starting point the discharge date of the first-H. The readmission rate and ED-visit rate are presented at 24 hours, 48 hours, 3 days, 30 days, 90 days, as well as one-year after hospital discharge. To explore associated factors, we first characterized the study patients using univariable methods. Then, we selected potential contributing factors using multivariable models, using the cox proportional-hazards regression model, due this model is a powerful and popular method to analyze survival data (which allows us to formulate the results, when the factor is present, according to the hospitalizations for the analyses (Figure 1) . The median age of this cohort was 68 years (IQR 45-81), 35.1% (3, 717) were male, with a median hospital stay of 3 days (IQR 1-5). Other characteristics of the cohort are listed in Table 1 . Table 2 summarizes the cumulative incidences of readmissions for each period. Figure 3 shows the cumulative readmission incidence after the First-H. Regarding associated factors after adjusting for co-variables with 30-day readmissions, the following were significant: age (HR 1. 16 Table 4 shows other potential underlying factors that are not significantly associated with 30-day readmissions. In our hospital, 18% of patients made a new consultation at the ED and 8% of patients were readmitted within 30 days after discharge. These rates represent two manifestations of the same phenomenon underlying the need for medical care, both are easy to measure and the data necessary for their calculation were highly available. Our findings were similar to other contexts, Wang et al. estimated these rates at a tertiary hospital in Taiwan yielding 12% and 14% respectively [20] . Although they share characteristics of the population in terms of age, sex, and comorbidities, they differ in the type of cohort: our study was based on a closed cohort of patients with exclusive care within the same system, while the other used an open system with patients referred or attended in other centers from which the follow-up information is lost. However, it was lower compared to that reported by Padilla et al. (11.16%, 95%CI 10.99-11.33) at the Hospital Alemán [21] . Although both studies were carried out in private hospitals with similar characteristics in the same city, they were not comparable as they included 12% of first index hospitalizations corresponding to pediatric patients. It is of great clinical relevance to defining the readmission time window since very short time windows could lose readmissions while very long time windows increase the probability that readmissions are not related to index hospitalization [22] . That is J o u r n a l P r e -p r o o f why the use of these rates as an indicator of quality of care is useful as a selfcomparison measure over time with the aim of detecting the associated factors that allow rethinking and installing improvement cycles [23] . Regarding associated factors, in our study, we report some variables related to indirect indicators of severity of the index hospitalization, such as unscheduled hospitalization in the First-H, transfer to a closed unit, prior ED-visits and long hospital stays. Similarly, Shu et al. reported that an increase in hospital stay in the index hospitalization was associated with an increased risk of unscheduled hospitalizations [24] . On the other hand, and similar to a study by Navathe et al. where they reported an association of social factors such as poor social support in hospitalized patients [25] , we detected that the presence of social service requirements, as an indirect indicator of social risk at discharge, in the index hospitalization was associated with ED-visits and unscheduled readmissions. Another limitation to recognize is the potential information bias in the registry of some of the variables (comorbidities and COPD), also due to the retrospective nature of the study in which secondary bases were used [26] . Although, a strength undoubtedly to be mentioned is the importance of the electronic medical record as a key factor for the feasibility of data collection for our study, as well as for the improvement in its recording. The EHR made it possible to provide local data on the rate of readmissions that have not been studied in-depth in our country. This represents an essential first step to be able to use them as a quality indicator. Additionally, as it was a closed cohort study, it was possible to calculate the rates with better precision, minimizing selection bias. Organizing and a better understanding of hospital monitoring indicators becomes highly important nowadays due to the new COVID-19 threatening the world [27] . In consequence, hospitals and EDs are facing a hard time to adapt. The enormous demand for handling the outbreak challenges the health care personnel, the medical supply, and the health system organization as a whole. The COVID-19 pandemic increases the need to have high-quality monitored health systems able to adapt to each given context, responding to population needs [3] . The hospital, and particularly the ED, undertook the mission of clinical reception, primary diagnosis, and initial treatment for COVID-19 cases [28] . That's why the ED J o u r n a l P r e -p r o o f has become one of the highest priorities. We focused on providing staff and qualified protection equipment, rescheduling non-urgent visits, and in canceling scheduled surgeries to reduce possible hospital overload [28] . Health systems need to develop the capacity to measure, use of data to learn and respond. This article represents an example of the use of digital solutions and data science initiatives to collect, process, analyze, and disseminate data to track progress and informed decision support to achieve the health-related SDGs. Our experience represents an empirical monitoring effort on moving towards more standardized and universal assessments. Continued collaboration is important to bring proxy measures closer to definitions and their metadata. According to a goal that aims to SDG 3 [29] , future data could help to gain insight in order to understand driving mechanisms behind high readmission rates, in order to ensure the quality of care, and that no one is left out due to lack of access to health care. Multicomponent care transition programs are a desired approach to improve patient outcomes in the period after acute care. Our results only suggest a potential approach with interventions that guarantee a continuum of care necessary to support patients and improve outcomes during the period after hospitalization. In this way, efforts must focus to prepare patients more effectively for discharge, and to provide better ability for patients, caregivers, and health care professionals. Further attention to measurement readmissions will be necessary, such as to explore causes, as an opportunity to reduce them. What was already known before the study • The scope in the SDGs spanning functions such as health-system access and quality care; • Low-cost quality indicators represents an initiative to improve health outcomes; What this study has added to the body of knowledge • Describes the development and use of quality indicators: rate of ED-visits and hospital readmission rate (HRR) after hospital discharge; • Our findings suggest a potential approach with the necessary interventions to ensure continuity of care and improve outcomes after hospitalization. • Efforts will need to be improved, as well as strategies to detect causes and avoid potentially preventable factors to reduce these indicators. DHG and JP conceived the original idea. BRB, LA and MFGR wrote the protocol and they made the statistical analyses. SMF, BB and MFGR wrote and translated the manuscript. BM and DL made critical feedback and institutional support. All authors contributed to the final version of the manuscript. Funding: This work has been partly supported by an unrestricted research grant from: "BECA ISALUD de Investigación 2017 Dr. Mario González Astorquiza", by giving the first author 30% of his working time available for research. The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The authors have no conflict of interests to declare. 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