Developing and validating a prediction model for frequent attenders at a Swedish emergency department using an electronic medical record system, a retrospective observational study

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The aim of this research was to identify and describe FA using readily available data provided by electronic medical records and create a prediction model to identify future FA Method: Adults ≥18 years that visited the ED during 2015 were included. Patients with ≥4 visits were defined as FA, and patients with ≤3 visits were placed in the control group. Numerous variables were analyzed and differences between the groups compared. Logistic regression analysis was used to determine the predictor variables and the model validated using Receiver Operating Characteristic (ROC) on an independent sample. Results: 6635 patients were included in developing the model: 15.3 (n=1012) were classified as FA and 15.4 (n=1011) as the control group. Variables associated with at risk of becoming a FA were the following: age above 60 years OR 1.52 [CI 1.27 – 1.82], ED arrival by ambulance or helicopter OR 1.31 [CI 1.08 – 1.58], sheltered living OR 3.82 [CI 2.37 – 6.17], previous contact with psychiatric department OR 1.52 [CI 1.23 – 1.89], 10 outpatient care visits or more OR 4.81 [CI 3.81 – 6.08] and 10 outpatient care physician visits or more OR 3.94 [CI 3.25 – 4.78]. The ROC in the validation set had an area under the curve of 0.85 [CI 0.84 – 0.86]. Conclusion: Data from electronic medical record software can be used to create and validate the risk of becoming a FA in the ED. We found that age over 60 years, ED arrival by ambulance or helicopter, sheltered living, previous contact with psychiatric departments, and frequent visits at outpatient care together predict the risk of becoming a FA." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/10-909/v1", "name": "Developing and validating a prediction model for frequent attenders..." } } ] } Home Browse Developing and validating a prediction model for frequent attenders... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Abazi L, Lindqvist E, Edman G et al. Developing and validating a prediction model for frequent attenders at a Swedish emergency department using an electronic medical record system, a retrospective observational study [version 1; peer review: 1 not approved] . F1000Research 2021, 10 :909 ( https://doi.org/10.12688/f1000research.53193.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Developing and validating a prediction model for frequent attenders at a Swedish emergency department using an electronic medical record system, a retrospective observational study [version 1; peer review: 1 not approved] Lis Abazi https://orcid.org/0000-0003-0531-815X 1,2 , Elin Lindqvist 1,2 , Gunnar Edman 1,3 , [...] Magnus Norberg 4 , Jan Bergman 1 , Ingmar Zachrisson 1 , Sune Forsberg 1,2 Lis Abazi https://orcid.org/0000-0003-0531-815X 1,2 , Elin Lindqvist 1,2 , [...] Gunnar Edman 1,3 , Magnus Norberg 4 , Jan Bergman 1 , Ingmar Zachrisson 1 , Sune Forsberg 1,2 PUBLISHED 10 Sep 2021 Author details Author details 1 Norrtälje hospital, Tiohundra AB, Norrtälje, Sweden 2 Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden 3 3Department of Clinical Sciences, Karolinska Institutet, Danderyd Hospital, Danderyd, Sweden 4 IT Department, Tiohundra AB, Norrtälje, Sweden Lis Abazi Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Elin Lindqvist Roles: Conceptualization, Investigation, Methodology, Resources, Writing – Original Draft Preparation Gunnar Edman Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Magnus Norberg Roles: Data Curation, Investigation, Resources, Software Jan Bergman Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Ingmar Zachrisson Roles: Conceptualization, Investigation, Methodology, Writing – Review & Editing Sune Forsberg Roles: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background: Frequent attenders (FA) account for a significant number of emergency department (ED) visits but to date there is no prediction model to identify patients at risk of becoming a FA. The aim of this research was to identify and describe FA using readily available data provided by electronic medical records and create a prediction model to identify future FA Method: Adults ≥18 years that visited the ED during 2015 were included. Patients with ≥4 visits were defined as FA, and patients with ≤3 visits were placed in the control group. Numerous variables were analyzed and differences between the groups compared. Logistic regression analysis was used to determine the predictor variables and the model validated using Receiver Operating Characteristic (ROC) on an independent sample. Results: 6635 patients were included in developing the model: 15.3 (n=1012) were classified as FA and 15.4 (n=1011) as the control group. Variables associated with at risk of becoming a FA were the following: age above 60 years OR 1.52 [CI 1.27 – 1.82], ED arrival by ambulance or helicopter OR 1.31 [CI 1.08 – 1.58], sheltered living OR 3.82 [CI 2.37 – 6.17], previous contact with psychiatric department OR 1.52 [CI 1.23 – 1.89], 10 outpatient care visits or more OR 4.81 [CI 3.81 – 6.08] and 10 outpatient care physician visits or more OR 3.94 [CI 3.25 – 4.78]. The ROC in the validation set had an area under the curve of 0.85 [CI 0.84 – 0.86]. Conclusion: Data from electronic medical record software can be used to create and validate the risk of becoming a FA in the ED. We found that age over 60 years, ED arrival by ambulance or helicopter, sheltered living, previous contact with psychiatric departments, and frequent visits at outpatient care together predict the risk of becoming a FA. READ ALL READ LESS Keywords Emergency department, Frequent attenders Corresponding Author(s) Lis Abazi ( [email protected] ) Close Corresponding author: Lis Abazi Competing interests: No competing interests were disclosed. Grant information: This study was funded by Research and Development Norrtälje, grant number 116067, assigned to the corresponding author Lis Abazi. The funder had no role in the study design, data collection, analysis, or writing of the article. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2021 Abazi L et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Abazi L, Lindqvist E, Edman G et al. Developing and validating a prediction model for frequent attenders at a Swedish emergency department using an electronic medical record system, a retrospective observational study [version 1; peer review: 1 not approved] . F1000Research 2021, 10 :909 ( https://doi.org/10.12688/f1000research.53193.1 ) First published: 10 Sep 2021, 10 :909 ( https://doi.org/10.12688/f1000research.53193.1 ) Latest published: 10 Sep 2021, 10 :909 ( https://doi.org/10.12688/f1000research.53193.1 ) Introduction Several reports and studies report a steadily increasing demand for services at emergency departments (ED). 1 – 4 A small percentage of patients (1-8%) account for a large percentage of ED visits per year (8.5-28%). 1 , 4 - 6 These patients, who are commonly referred to as “frequent attenders” (FA) are defined by the number of their visits and definitions range from 3-12 ED visits per year. 1 , 4 Although this patient group was identified and described decades ago, 7 to date there are no effective interventions that reduce the frequency of their ED visits. 6 FA consist of a heterogenic group of patients. Their demographics, their diseases, and their social conditions vary significantly. Thus, the group is not easily described. 8 While it is claimed that some FA visit the ED for non-emergency care, several studies reveal FA are a vulnerable group with a verifiably increased risk of mortality. 9 Previous studies have described FA as somewhat older and more likely to seek ED care in non-daytime hours than non-FA. 5 , 10 , 11 Poverty, homelessness, chronic disease, mental disorders, and drug and alcohol misuse are characteristics more often associated with FA than with non-FA. 8 , 10 , 12 , 13 A general assumption is that health care services do not sufficiently meet the medical and social needs of FA. 1 , 4 , 6 Given this situation, plus the increasing pressure on limited ED resources from frequent attendance, a solution is needed that can address the needs of this group and the demands on the health care system. 1 , 4 , 6 , 10 , 11 , 13 , 14 Numerous attempts have been made to solve this problem. These attempts include interventions such as case management, patient education, management care plans, social interventions, and health care centres. However, these attempts have not had a major effect in reducing the number of ED visits by FA. 4 , 6 , 12 One explanation for this failure may be that these interventions are only initiated after ED patients are identified as FA. Possibly the interventions might be more successful if patients were identified as likely FA earlier in the identification phase. Furthermore, very little scientific data exist on FA in Swedish emergency departments and the few publication that exist on frequent attendance focus on primary health care. 15 The Swedish health care and social system differs significantly from many of those countries studying FA (e.g. USA, United Kingdom etc.) and therefore it is of interest to investigate if the experiences of FA are similar in a Swedish emergency department compared to that reported internationally. The aim of this study was to identify and characterise FA and to create and validate a prediction model to find FA by using electronic medical record software in an emergency department in Sweden. Methods Ethical approval The Stockholm Regional Ethics Review Board approved this research (D. nr 2017/1695-31/2). There was no requirement to obtain patient consent set by the ethical committee. Study population The patients in the study were adults (≥18 years) who had visited the ED at Norrtälje Hospital, Sweden, during 2015. The patients’ initial visit during 2015 was used for inclusion and therefore labelled the “inclusion visit” [ Figure 1 ]. This visit, which was used for patient selection only, was not included in the analysis. A study period of 12 months was chosen for each included patient. This individual 12-month observational period was defined by tracking a 12-month period before and after the inclusion visit in such a way that a maximum number of visits were included during a 12-month period [ Figure 1 ]. Patients with four or more ED visits during this 12-month period were identified as FA and patients with three or fewer ED visits were identified as controls. The first visit (at the beginning of the 12-month observational period) was labelled the “first visit”, the second visit was labelled the “second visit”, etc. The purpose of this inclusion process was to observe and compare each individual patient in the same way instead of basing the observational period on calendar time only. The cut of value of four visits or more was chosen prospectively based on previous studies. Figure 1. Example of inclusion process for one included individual. Design and data collection This is a retrospective observational register study. The medical record system – Take Care (TC) at Norrtälje Hospital (Sweden) – was used to identify patients. We identified patients by their personal identity numbers (encrypted before the analysis). The data variables from the first visit were age, gender, number of ED visits in the 12-month observational period, number of outpatient care visits, sheltered living (care facility or not), and enrolment at a health care centre. The data variables from the first visit and (if applicable) from the second visit were ED visit date and time, ED transport, triage priority, main complaint, ED care unit (medicine/surgery/orthopaedic), medication number/type, and ICD-10 diagnosis at discharge (if hospitalised). Physical findings recorded for the patients were blood pressure (mmHg), heart rate (beats per minute/bpm), temperature (°C), peripheral oxygen saturation (%), height (cm), weight (kg). Laboratory statistics recorded for the patients were haemoglobin g/L (Hb), thrombocyte particle concentration ×10 9 /L (TPK), leukocyte particle concentration ×10 12 /L (LPK), creatinine (μmol/L), glucose (mmol/L), C-reactive protein (mg/L) (CRP), sodium (mmol/L), potassium (mmol/L), bilirubin (μmole/L), aspartate aminotransferase (μkat/L) (ASAT), alanine aminotransferase (μkat/L) (ALAT), alkaline phosphatase (μkat/L) (ALP), lactate dehydrogenase (μkat/L) (LD), albumin (g/L). Information on previous contact with psychiatric departments and death within one year following the first visit was also collected. These data were chosen because they were easily available to extract from the electronic medical record software and were thought to be clinically relevant in the light of previous studies. Norrtälje Municipality and Norrtälje Hospital The Norrtälje Municipality is approximately seventy kilometres from Stockholm, Sweden. The Municipality has a total area of 6030.42 km 2 with a population of 61 864 inhabitants (2019). One-third of the Municipality's inhabitants (20 721 inhabitants, 2018) live in the central town. The surrounding rural area is sparsely inhabited. The Municipality is a somewhat deprived socioeconomic area (compared to many other areas in Sweden). The average yearly income and the average education level are below national averages. 16 , 17 Norrtälje Hospital is a small hospital that serves the Municipality and its environs. The Hospital, which has specialist clinics and four wards with 96 beds, is integrated with local primary health care in a way that the both the hospital and the primary health care are within the same organisation with the same management and board. The health care is publicly funded, requires no health insurance and the nearest next emergency department which also is of a higher level, is 60 km away. Attendance to the emergency department does not require referral and can be done at the initiative of the patient. The emergency department treats approximately 20 000 patients yearly. Statistical methods The Statistical Package for Social Sciences , SPSS Version 25.0 (IBM SPSS Statistics, RRID:SCR_019096) was used for the statistical analysis. The data was randomly split into two equally large samples where the first was used for analysis (training set) and the second for validation (validation set). All variables were summarized using standard descriptive statistics such as frequency, mean and standard deviation. Group differences between the categorical variables (e.g., gender, weekday of the first visit, death within one year after the first visit) were analysed using Pearson's chi-square method. Group differences between the continuous variables (e.g., age) were analysed using Student's t-test for independent groups. The significance level in all analyses was 5 % (two-tailed). If a variable was severely skewed (skewness above 1.5 as for length of hospital stay and for the number of pharmaceutical drugs), a non-parametric Mann-Whitney U test was conducted. The model was created using logistic regression analysis on complete cases on the training set on the relationships between group (FA – “Yes”/”No”) and the identified risk variables. After the model was defined, a predicted risk score for each patient was recorded in both the training set and the validation set. The risk score of the training set and validation set was then used in a Receiver Operating Characteristic (ROC) analysis comparing these two and the sensitivity and specificity determined. Results In total 13,193 patients were included in the study and randomly split into two groups of 6635 patients (training set) and 6558 patients (validation set). In the training set 15.3% (n = 1012) were classified as FA and 15.4% (n = 1011) were placed in the control group as non-FA. Significant differences between the two groups were found in the following variables: age, sheltered living, number of outpatient care visits, number of outpatient care physician visits, ED arrival by ambulance or helicopter, triage priority, weekday of the first visit, length of hospital stay, previous contact with a psychiatric department, and one-year survival after the first visit [ Table 1 ]. No significant differences were found between gender and time of visit [ Table 1 ]. Table 1. Baseline data. Variable Control Frequent attender p Age M/Md (SD) 54.5/56 (20.25) 65.3/70 (20.06) <0.001 Women % (n) 49.9 (2,806) 47.0 (476) 0.093 Survival, 12 mo's % (n) 92.2 (5,187) 75.5 (764) <0.001 Sheltered living % (n) 1.5 (85) 3.6 (36) <0.001 Number of outpatient care visits M/Md (SD) 9.0/5 (12.20) 27.2/21 (23.54) <0.001 Number of emergency visits M/Md (SD) 1.5/1 (0.65) 6.1/5 (3.44) <0.001 Outpatient care at physician M/Md (SD) 4.9/3 (5.37) 15.5/13 (11.04) <0.001 <10 visits % (n) 85.8 (4,825) 33.4 (338) <0.001 10- visits % (n) 14.2 (798) 66.6 (674) Arrived by ambulance or helicopter % (n) 17.5 (982) 28.3 (286) <0.001 Triage priority 1 (immediately) % (n) 1.3 (71) 1.3 (13) <0.001 2 (≤15 min's) % (n) 6.8 (381) 8.6 (87) 3 (≤60 min's) % (n) 50.7 (2,836) 57.0 (575) 4 (≤120 min's) % (n) 36.7 (2,054) 30.4 (307) 5 (≤240 min's) % (n) 4.5 (254) 2.7 (27) Time at visit M/Md (SD) 13:33/13:28 (5:13) 13:25/13:21 (5:14) 0.473 Weekday % (n) 71.1 (3,997) 74.8 (757) 0.016 Length of hospital stay M/Md (SD) 1.1/0 (3.53) 7.9/3 (12.23) <0.001 n of pharmaceutical drugs M/Md (SD) 4.9/3 (5.49) 6.1/5.0 (4.52) <0.001 Psychiatric contact % (n) 10.7 (604) 21.8 (221) <0.001 On average, FA compared to non-FA were older (65.3 years vs 54.5 years), made more outpatient care visits (27.2 visits vs 9.0 visits) and more physician visits (15.5 visits vs 4.9 visits), and had a lower survival rate within one year after the first visit (75.5% survival rate vs 92.2% survival rate). The ED arrival rate by ambulance or helicopter was higher for the FA than for the non-FA (28.3% of arrivals vs 17.5% of arrivals). The FA also had a higher triage priority and made more visits on a weekday (74.8% of visits vs 71.1% of visits). FA also had longer lengths of hospital stays (7.9 days vs 1.1 days) and had a higher rate of previous contact with psychiatric departments (21.8% of contacts vs 10.7% of contacts). Group comparisons of laboratory and physical findings revealed significant patient group differences in creatinine, potassium, haemoglobin, and glucose [Extended data – Table 1 ]. Significant differences were found between the groups for systolic blood pressure, saturation and thrombocytes, but these differences, although significant, applied to only a few patients. Approximately 50 % of the laboratory findings and approximately 20 % of the physical findings were missing in the entire study population. We were therefore unable to use these variables in the analysis. Therefore, they are only briefly presented in the supplementary material [Extended data – Table 1 ]. Multivariable logistic regression found the following variables posed a risk to patients of becoming a FA [ Figure 2 ]; age above 60 years OR 1.52 [CI 1.27 – 1.82], ED arrival by ambulance or helicopter on the first visit OR 1.31 [CI 1.08 – 1.58], sheltered living OR 3.82 [CI 2.37 – 6.17], previous contact with psychiatric department OR 1.52 [CI 1.23 – 1.89], 10 outpatient care visits or more in the observational period OR 4.81 [CI 3.81 – 6.08], 10 outpatient care physician visits or more in the observational period OR 3.94 [CI 3.25 – 4.78]. Figure 2. Forest plot with odds ratio of variable predicting frequent attenders. Data from the multivariable logistic regression was used to perform a ROC curve analysis which resulted in an AUC of 0.841 [CI 0.828-0.854] for the training set and an AUC of 0.849 [CI 0.836-0.861] for the validation set [ Figure 3 ] indicating a good model. The ROC curve was then used to find the optimal cut of value and resulted in a sensitivity of 44.5% and a specificity of 91.9% with a positive predictive value of 44.5% and negative predictive value of 91.9%. Figure 3. ROC-curve for training set and validation set. Discussion To our knowledge this is the first time readily available electronic medical record data has been used to identify and characterize FA and to create a FA prediction model. Our study shows that these data, which can be obtained technologically using electronic medical record software, can be used to predict ED patients’ risk of becoming FA. We found that the variables that most closely correlated with FA status were the following: age above 60 years, ED arrival by ambulance or helicopter, sheltered living, previous contact with psychiatric department, 10 or more outpatient care visits and 10 or more outpatient care visits at physician. This model resulted in an AUC of 0.849 [CI 0.836-0.861] when validated in an independent sample. The sensitivity and specificity of the model can be adjusted according to the needs the purpose of the model. In our case we aim to use the model for early identification of potential FA and target them with an intervention with low risk of harm. In such case false negative cases are of less importance. However, too many false cases will result in extra work and make the intervention more difficult to accomplish. Furthermore, it is of importance that we correctly identify enough future FA, so an intervention makes a clinical difference. Therefore, we finally settled with a sensitivity of 44.5% and a specificity of 91.9%. Although the sensitivity is low and many FA will be missed, there will be enough correctly identified for an intervention without including too many false positive. Many studies have described ED FA as a heterogenous group. 9 Studies also associate older age, poorer physical and mental health, and greater social vulnerability with these patients. 8 – 13 These studies are performed in health care and social systems that differ from the Swedish. Still, our study confirms these findings. We found that the FA (compared to the control group) were older, were in a worse physical health state, had a higher mortality rate, arrived at the ED more often by ambulance or helicopter, had more deranged blood sample values and physical parameters, had been in contact with the psychiatric department more often, and had been hospitalized for more days. In confirmation of other research, we found that the FA are recurrent visitors at most health care system levels. 18 We found very high usage of outpatient care among the FA – 27 vs 9 outpatient care visits/year – and the highest OR for frequent attendance. With this descriptive model of FA, we can better identify them at early ED visits, provide better care for them by combining hospital and outpatient care resources, and perhaps reduce the number of their ED visits. Several interventions have been tested that are designed to address the needs of FA. These interventions have had little success. 6 The most promising intervention is perhaps “case management,” which has sometimes reduced the number of ED visits. We think the results from our study may be used to identify FA at early care stages and easier randomize FA to comparable interventions. We acknowledge there is a difficulty in generalizing our findings (certainly beyond Sweden's national boundaries) because we use data only from one small Swedish hospital. In any case, it is challenging to draw generalizations in an international dimension regarding FA and to make comparisons among FA studies. Health care/social systems, emergency care, and populations vary greatly – country to country. 19 – 21 The burden of disease (both physical and mental) in the ED also differ among countries. 22 Nonetheless we believe that our method and findings can be of interest to the scientific community and add to the general knowledge. Limitations Our study sample was drawn from ED patients only at Norrtälje Hospital during a relatively brief time period (one year) which makes the results difficult to generalize. Nonetheless we believe that our method and findings can be of interest to the scientific community and add to the general knowledge. We may also have underestimated the number of FA as patients in the control group may have sought emergency health care at another caregiver which we would have missed. The nearest emergency department is 60 km away and requires therefore a certain effort from the patient to visit. We therefore estimate this to be a smaller group and not conflict significantly with the results. Furthermore, the definition of when a patient does his or her first visit at the ED is also very difficult to define. Our analysis was based on this first visit with the ambition of finding FA as early as possible. Even though we cannot say that we have correctly identified the first ED visit for the patients, we still believe our model of defining the first visit is a better way of finding FA at an earlier stage compared to only looking at a calendar year. Conclusion Our research finds that frequent attendance at the ED may be predicted by the following variables: age above 60 years, ED arrival by ambulance or helicopter, sheltered living, previous contact with psychiatric department, 10 or more outpatient care visits and 10 or more outpatient care visits at physician. These patient variables collectively observed in the first ED visit resulted in a good model with an AUC of 0.849 [CI 0,836-0,861] in the validation sample. Data availability statement Underlying data We want to do our utmost to contribute to the scientific community by sharing data and being transparent. We are however unable to give our raw underlying data fully and unconditionally to anyone as we have asked and received permission to this data with condition of it being used for our publication purpose only and presented in an aggregated form. Moreover, our raw data contains several detailed variables and although the data is pseudonymized, if combined with other databases we see a risk of individuals being identified. We therefore need to restrict our data sharing. However, if request is made for our data for scientific purpose in line with our research question, we commit to providing our raw data. This is done by a contacting the corresponding author at [email protected] and a reply will be given at the latest within a month. Extended data Open Science Framework: Developing and validating a prediction model for frequent attenders at a Swedish emergency department using an electronic medical record system, a retrospective observational study. https://doi.org/10.17605/OSF.IO/BK6S4 . 23 This project contains the following underlying data: • Extended data table 1.docx (supplemental table with laboratory results) Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). References 1. LaCalle E, Rabin E: Frequent users of emergency departments: the myths, the data, and the policy implications. Ann Emerg Med. 2010; 56 (1): 42–8. [published Online First: 2010/03/30]. PubMed Abstract | Publisher Full Text 2. Pines JM, Hilton JA, Weber EJ, et al. : International perspectives on emergency department crowding. Acad Emerg Med. 2011; 18 (12): 1358–70. [published Online First: 2011/12/16]. PubMed Abstract | Publisher Full Text 3. Williams ER, Guthrie E, Mackway-Jones K, et al. : Psychiatric status, somatisation, and health care utilization of frequent attenders at the emergency department: a comparison with routine attenders. J Psychosom Res. 2001; 50 (3): 161–7. [published Online First: 2001/04/24]. PubMed Abstract | Publisher Full Text 4. Olsson M: Akutmottagningens mångbesökare: hur kan vården förbättras?. Stockholm: Forum för kunskap och gemensam utveckling, Stockholms läns landsting; 2006. 5. Moore L, Deehan A, Seed P, et al. : Characteristics of frequent attenders in an emergency department: analysis of 1-year attendance data. Emerg Med J. 2009; 26 (4): 263–7. [published Online First: 2009/03/25]. PubMed Abstract | Publisher Full Text 6. Soril LJ, Leggett LE, Lorenzetti DL, et al. : Reducing frequent visits to the emergency department: a systematic review of interventions. PLoS One. 2015; 10 (4): e0123660. [published Online First: 2015/04/16]. PubMed Abstract | Publisher Full Text | Free Full Text 7. Andren KG, Rosenqvist U: Heavy users of an emergency department: psycho-social and medical characteristics, other health care contacts and the effect of a hospital social worker intervention. Soc Sci Med. 1985; 21 (7): 761–70. [published Online First: 1985/01/01]. PubMed Abstract | Publisher Full Text 8. Jacob R, Wong ML, Hayhurst C, et al. : Designing services for frequent attenders to the emergency department: a characterisation of this population to inform service design. Clin Med (Lond). 2016; 16 (4): 325–9. [published Online First: 2016/08/03]. PubMed Abstract | Publisher Full Text | Free Full Text 9. Moe J, Kirkland S, Ospina MB, et al. : Mortality, admission rates and outpatient use among frequent users of emergency departments: a systematic review. Emerg Med J. 2016; 33 (3): 230–6. [published Online First: 2015/05/09]. PubMed Abstract | Publisher Full Text 10. Boh C, Li H, Finkelstein E, et al. : Factors Contributing to Inappropriate Visits of Frequent Attenders and Their Economic Effects at an Emergency Department in Singapore. Acad Emerg Med. 2015; 22 (9): 1025–33. [published Online First: 2015/08/19]. PubMed Abstract | Publisher Full Text 11. Locker TE, Baston S, Mason SM, et al. : Defining frequent use of an urban emergency department. Emerg Med J. 2007; 24 (6): 398–401. [published Online First: 2007/05/22]. PubMed Abstract | Publisher Full Text | Free Full Text 12. Phillips GA, Brophy DS, Weiland TJ, et al. : The effect of multidisciplinary case management on selected outcomes for frequent attenders at an emergency department. Med J Aust. 2006; 184 (12): 602–6. [published Online First: 2006/06/29]. PubMed Abstract | Publisher Full Text 13. Wooden MD, Air TM, Schrader GD, et al. : Frequent attenders with mental disorders at a general hospital emergency department. Emerg Med Australas. 2009; 21 (3): 191–5. [published Online First: 2009/06/17]. PubMed Abstract | Publisher Full Text 14. Skinner J, Carter L, Haxton C: Case management of patients who frequently present to a Scottish emergency department. Emerg Med J. 2009; 26 (2): 103–5. [published Online First: 2009/01/24]. PubMed Abstract | Publisher Full Text 15. Strombom Y, Magnusson P, Karlsson J, et al. : Health-related quality of life among frequent attenders in Swedish primary care: a cross-sectional observational study. BMJ open. 2019; 9 (7): e026855. [published Online First: 2019/08/02]. PubMed Abstract | Publisher Full Text | Free Full Text 16. Income and tax statistics: Statistics Sweden: [cited 2019 2019-06-19]; accessed 2019-06-19 2019. Reference Source 17. Educational attainment of the population: Statistics Sweden: [cited 2019 2019-06-19]; accessed 2019-06-19 2019. Reference Source 18. Hansagi H, Olsson M, Sjoberg S, et al. : Frequent use of the hospital emergency department is indicative of high use of other health care services. Ann Emerg Med. 2001; 37 (6): 561–7. [published Online First: 2001/06/01]. PubMed Abstract | Publisher Full Text 19. Schutte S, Acevedo PNM, Flahault A: Health systems around the world - a comparison of existing health system rankings. J Glob Health. 2018; 8 (1): 010407. [published Online First: 2018/03/23]. PubMed Abstract | Publisher Full Text | Free Full Text 20. Ridic G, Gleason S, Ridic O: Comparisons of health care systems in the United States, Germany and Canada. Materia socio-medica. 2012; 24 (2): 112–20. [published Online First: 2012/01/01]. PubMed Abstract | Publisher Full Text | Free Full Text 21. Anell A, Willis M: International comparison of health care systems using resource profiles. Bull World Health Organ. 2000; 78 (6): 770–8. [published Online First: 2000/08/05]. PubMed Abstract | Free Full Text 22. 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 10 Sep 2021 ADD YOUR COMMENT Comment Author details Author details 1 Norrtälje hospital, Tiohundra AB, Norrtälje, Sweden 2 Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden 3 3Department of Clinical Sciences, Karolinska Institutet, Danderyd Hospital, Danderyd, Sweden 4 IT Department, Tiohundra AB, Norrtälje, Sweden Lis Abazi Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Elin Lindqvist Roles: Conceptualization, Investigation, Methodology, Resources, Writing – Original Draft Preparation Gunnar Edman Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Magnus Norberg Roles: Data Curation, Investigation, Resources, Software Jan Bergman Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Ingmar Zachrisson Roles: Conceptualization, Investigation, Methodology, Writing – Review & Editing Sune Forsberg Roles: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This study was funded by Research and Development Norrtälje, grant number 116067, assigned to the corresponding author Lis Abazi. The funder had no role in the study design, data collection, analysis, or writing of the article. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 10 Sep 2021, 10:909 https://doi.org/10.12688/f1000research.53193.1 Copyright © 2021 Abazi L et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Abazi L, Lindqvist E, Edman G et al. Developing and validating a prediction model for frequent attenders at a Swedish emergency department using an electronic medical record system, a retrospective observational study [version 1; peer review: 1 not approved] . F1000Research 2021, 10 :909 ( https://doi.org/10.12688/f1000research.53193.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 10 Sep 2021 Views 0 Cite How to cite this report: Scott J. Reviewer Report For: Developing and validating a prediction model for frequent attenders at a Swedish emergency department using an electronic medical record system, a retrospective observational study [version 1; peer review: 1 not approved] . F1000Research 2021, 10 :909 ( https://doi.org/10.5256/f1000research.56552.r270992 ) The direct URL for this report is: https://f1000research.com/articles/10-909/v1#referee-response-270992 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 21 May 2024 Jason Scott , Northumbria University, Newcastle upon Tyne, England, UK Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.56552.r270992 Thank you for the invitation to review this study. In its current form I cannot recommend indexing, specifically that it appears to be nearly a decade out of date in both the data used and the use of literature. More ... Continue reading READ ALL Thank you for the invitation to review this study. In its current form I cannot recommend indexing, specifically that it appears to be nearly a decade out of date in both the data used and the use of literature. More detailed comments are below: 1. Literature used is outdated and not comprehensive. One example is the first sentence of the introduction, which refers to a (current) increasing demand for ED, yet cites studies published in 2001, 2006, 2010 and 2011. 2. The problem of outdated literature continues with the statement that there are no effective interventions. This is highly contested; causes of frequent use are highly complex and people who frequently use services are heterogeneous, which likely contributes to mixed evidence around effectiveness. Take for instance people who frequently use ED due to poor mental health, a recent review by Gabet et al (2023) identified several interventions with promising evidence around effectiveness. Many other reviews since 2015 have similar results, though some highly anticipated interventions (Eg. hotspotting) have then shown to be ineffective (Finkelstein et al., 2020). The authors need to update the entire introduction and discussion using contemporary literature and with greater critical engagement of the wider evidence base, including the increasing qualitative evidence base around frequent use of other services that the authors themselves found contribute to ED use (Eg. ambulance services; Evans et al., 2024 – I disclose being a co-author so feel welcome to identify other relevant literature). 3. Regardless of the issues around the literature, the premise that earlier identification of people at risk of becoming a frequent attender is valid and does not need changing. 4. The data used in the study are from 2015, which is nearly a decade old. A lot has happened since then that has changed health systems throughout the world. Could the authors please comment on whether the data (and thus the study) is still relevant. 5. The index visit was categorised as the first visit during the study period. Was any consideration given as to whether some of the patients may have been either long-standing or relatively new frequent attenders prior to the study period and how this was factored into the analysis. For instance, it may be that most people recruited into the study in month 1 were longer-standing frequent attenders, and subsequent months may have been newer frequent attenders, thus examining two different groups of people (we know there are almost certainly differences in characteristics between the groups). 6. Frequent attenders were split into a training set and validation set. Were any analyses conducted to confirm that the two groups were similar, or was there a reliance on randomisation? It would also be helpful to describe, in the methods, how they were randomised. 7. The authors state this is the first time routine data have been used to create a prediction model; this is absolutely not the case. Authors need to examine more recent literature on the topic. Notably, given my previous comment (5) around index visits, Chiu et al identified that the greatest predictor of frequent use was being a past frequent user. 8. A comment in the discussion says that existing studies were conducted in health systems not comparable to the Swedish health system. It would be helpful if the authors could explain and unpick the differences. References Chiu, Y. M. et al (2023 [ref - 1]). Machine learning to improve frequent emergency department use prediction: a retrospective cohort study. Scientific Reports, 13(1), 1981. Evans, B. A. et al (2024 [ref - 2]). Experiences and views of people who frequently call emergency ambulance services: A qualitative study of UK service users. Health Expectations, 27(1), e13856. Finkelstein, A. et al (2020 [ref - 3]). Health care hotspotting—a randomized, controlled trial. New England Journal of Medicine, 382(2), 152-162. Gabet, M. et al (2023 [ref - 4]). Effectiveness of emergency department based interventions for frequent users with mental health issues: A systematic review. The American Journal of Emergency Medicine, 74, 1-8. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? No References 1. Chiu YM, Courteau J, Dufour I, Vanasse A, et al.: Machine learning to improve frequent emergency department use prediction: a retrospective cohort study. Sci Rep . 2023; 13 (1): 1981 PubMed Abstract | Publisher Full Text 2. Evans BA, Khanom A, Edwards A, Edwards B, et al.: Experiences and views of people who frequently call emergency ambulance services: A qualitative study of UK service users. Health Expect . 2023; 27 (1). PubMed Abstract | Publisher Full Text 3. Finkelstein A, Zhou A, Taubman S, Doyle J: Health Care Hotspotting - A Randomized, Controlled Trial. N Engl J Med . 2020; 382 (2): 152-162 PubMed Abstract | Publisher Full Text 4. Gabet M, Armoon B, Meng X, Fleury MJ: Effectiveness of emergency department based interventions for frequent users with mental health issues: A systematic review. Am J Emerg Med . 2023; 74 : 1-8 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Health and social care quality including specifically frequent use of emergency care services. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Scott J. Reviewer Report For: Developing and validating a prediction model for frequent attenders at a Swedish emergency department using an electronic medical record system, a retrospective observational study [version 1; peer review: 1 not approved] . F1000Research 2021, 10 :909 ( https://doi.org/10.5256/f1000research.56552.r270992 ) The direct URL for this report is: https://f1000research.com/articles/10-909/v1#referee-response-270992 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 10 Sep 2021 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 1 10 Sep 21 read Jason Scott , Northumbria University, Newcastle upon Tyne, UK Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Scott J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 21 May 2024 | for Version 1 Jason Scott , Northumbria University, Newcastle upon Tyne, England, UK 0 Views copyright © 2024 Scott J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Thank you for the invitation to review this study. In its current form I cannot recommend indexing, specifically that it appears to be nearly a decade out of date in both the data used and the use of literature. More detailed comments are below: 1. Literature used is outdated and not comprehensive. One example is the first sentence of the introduction, which refers to a (current) increasing demand for ED, yet cites studies published in 2001, 2006, 2010 and 2011. 2. The problem of outdated literature continues with the statement that there are no effective interventions. This is highly contested; causes of frequent use are highly complex and people who frequently use services are heterogeneous, which likely contributes to mixed evidence around effectiveness. Take for instance people who frequently use ED due to poor mental health, a recent review by Gabet et al (2023) identified several interventions with promising evidence around effectiveness. Many other reviews since 2015 have similar results, though some highly anticipated interventions (Eg. hotspotting) have then shown to be ineffective (Finkelstein et al., 2020). The authors need to update the entire introduction and discussion using contemporary literature and with greater critical engagement of the wider evidence base, including the increasing qualitative evidence base around frequent use of other services that the authors themselves found contribute to ED use (Eg. ambulance services; Evans et al., 2024 – I disclose being a co-author so feel welcome to identify other relevant literature). 3. Regardless of the issues around the literature, the premise that earlier identification of people at risk of becoming a frequent attender is valid and does not need changing. 4. The data used in the study are from 2015, which is nearly a decade old. A lot has happened since then that has changed health systems throughout the world. Could the authors please comment on whether the data (and thus the study) is still relevant. 5. The index visit was categorised as the first visit during the study period. Was any consideration given as to whether some of the patients may have been either long-standing or relatively new frequent attenders prior to the study period and how this was factored into the analysis. For instance, it may be that most people recruited into the study in month 1 were longer-standing frequent attenders, and subsequent months may have been newer frequent attenders, thus examining two different groups of people (we know there are almost certainly differences in characteristics between the groups). 6. Frequent attenders were split into a training set and validation set. Were any analyses conducted to confirm that the two groups were similar, or was there a reliance on randomisation? It would also be helpful to describe, in the methods, how they were randomised. 7. The authors state this is the first time routine data have been used to create a prediction model; this is absolutely not the case. Authors need to examine more recent literature on the topic. Notably, given my previous comment (5) around index visits, Chiu et al identified that the greatest predictor of frequent use was being a past frequent user. 8. A comment in the discussion says that existing studies were conducted in health systems not comparable to the Swedish health system. It would be helpful if the authors could explain and unpick the differences. References Chiu, Y. M. et al (2023 [ref - 1]). Machine learning to improve frequent emergency department use prediction: a retrospective cohort study. Scientific Reports, 13(1), 1981. Evans, B. A. et al (2024 [ref - 2]). Experiences and views of people who frequently call emergency ambulance services: A qualitative study of UK service users. Health Expectations, 27(1), e13856. Finkelstein, A. et al (2020 [ref - 3]). Health care hotspotting—a randomized, controlled trial. New England Journal of Medicine, 382(2), 152-162. Gabet, M. et al (2023 [ref - 4]). Effectiveness of emergency department based interventions for frequent users with mental health issues: A systematic review. The American Journal of Emergency Medicine, 74, 1-8. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? No References 1. Chiu YM, Courteau J, Dufour I, Vanasse A, et al.: Machine learning to improve frequent emergency department use prediction: a retrospective cohort study. Sci Rep . 2023; 13 (1): 1981 PubMed Abstract | Publisher Full Text 2. Evans BA, Khanom A, Edwards A, Edwards B, et al.: Experiences and views of people who frequently call emergency ambulance services: A qualitative study of UK service users. Health Expect . 2023; 27 (1). PubMed Abstract | Publisher Full Text 3. Finkelstein A, Zhou A, Taubman S, Doyle J: Health Care Hotspotting - A Randomized, Controlled Trial. N Engl J Med . 2020; 382 (2): 152-162 PubMed Abstract | Publisher Full Text 4. Gabet M, Armoon B, Meng X, Fleury MJ: Effectiveness of emergency department based interventions for frequent users with mental health issues: A systematic review. Am J Emerg Med . 2023; 74 : 1-8 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Health and social care quality including specifically frequent use of emergency care services. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Scott J. Peer Review Report For: Developing and validating a prediction model for frequent attenders at a Swedish emergency department using an electronic medical record system, a retrospective observational study [version 1; peer review: 1 not approved] . F1000Research 2021, 10 :909 ( https://doi.org/10.5256/f1000research.56552.r270992) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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