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Health services research
Research
Statistical tools used for analyses of frequent users of emergency department: a scoping review
  1. Yohann Chiu1,
  2. François Racine-Hemmings1,
  3. Isabelle Dufour1,
  4. Alain Vanasse1,
  5. Maud-Christine Chouinard2,
  6. Mathieu Bisson1,
  7. Catherine Hudon1
  1. 1 Department of Family Medicine and Emergency Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
  2. 2 Department of Health Sciences, Université du Québec à Chicoutimi, Chicoutimi, Quebec, Canada
  1. Correspondence to Yohann Chiu; yohann.chiu{at}usherbrooke.ca

Abstract

Objective Frequent users represent a small proportion of emergency department users, but they account for a disproportionately large number of visits. Their use of emergency departments is often considered suboptimal. It would be more efficient to identify and treat those patients earlier in their health problem trajectory. It is therefore essential to describe their characteristics and to predict their emergency department use. In order to do so, adequate statistical tools are needed. The objective of this study was to determine the statistical tools used in identifying variables associated with frequent use or predicting the risk of becoming a frequent user.

Methods We performed a scoping review following an established 5-stage methodological framework. We searched PubMed, Scopus and CINAHL databases in February 2019 using search strategies defined with the help of an information specialist. Out of 4534 potential abstracts, we selected 114 articles based on defined criteria and presented in a content analysis.

Results We identified four classes of statistical tools. Regression models were found to be the most common practice, followed by hypothesis testing. The logistic regression was found to be the most used statistical tool, followed by χ2 test and t-test of associations between variables. Other tools were marginally used.

Conclusions This scoping review lists common statistical tools used for analysing frequent users in emergency departments. It highlights the fact that some are well established while others are much less so. More research is needed to apply appropriate techniques to health data or to diversify statistical point of views.

  • Frequent users
  • Statistical methods

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2018-027750

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    Strengths and limitations of this study

    • First overview of statistical tools used in frequent users analysis.

    • Follows a well-defined methodological framework in an extensive body of literature.

    • Quality assessment is not performed in a scoping review.

    • Studies in other languages than English or French might have been missed.

    Background

    Emergency department (ED) ‘frequent users’ are a sub-group of ED users that make repeated, multiple visits during a given amount of time. Though there is no consensus about definition for frequent users, thresholds in the literature range from two to more than 10 ED visits per year,1 2 while the most common one is more than four ED visits per year.1 2 Frequent users represent a small proportion of ED users but account for a large number of visits.3–5 They often display complex characteristics such as low socioeconomic status combined with physical and mental health issues.6 As such, their ED use is considered suboptimal,7 as the best strategy would be to identify those patients at an earlier stage in their health problem trajectory, in order to treat them more efficiently.8 Furthermore, frequent users’ visits may lead to overcrowding in EDs and decreased quality of care.2 Identifying factors that best describe those users and predict their ED use is therefore an essential task to improve ED care as well as frequent users’ health problems. Adequate statistical tools are needed to that end. Although they are numerous, no literature review has been published yet about statistical tools used for analysing ED frequent users. Therefore, the aim of our study was to draw up a list of statistical tools used in identifying variables associated with frequent use or predicting the risk of becoming a frequent user.

    Methods

    In order to list the statistical tools used in describing variables associated with and prediction of frequent ED use, we conducted a scoping review. We followed the 5-stage methodology of Arksey and O’Malley9 adapted by Levac et al.10 The methodological framework of a scoping review allows ‘mapping rapidly the key concepts underpinning a research area and the main sources and types of evidence available’,11 thus allowing us to identify gaps in the literature and future research opportunities.

    Stage 1: Identifying the research question

    We defined our research question as follows: What statistical tools are used in the identification of variables associated with frequent ED users and in their prediction?

    Stage 2: Identifying relevant studies

    We searched PubMed, CINAHL and Scopus databases in February 2019, using search strategies developed with the help of an information specialist (see the online supplementary appendix for the complete search strategy). Keywords included variants of ‘frequent users’, ‘emergency departments’ and ‘statistical tools’.

    Supplemental material

    There were no restriction regarding the population age or sex, health conditions, study period or country.

    Stage 3: Study selection

    Articles written in French or in English were included using the following criteria:

    • The study must focus on frequent users of EDs (studies focusing on re-visits or on frequent visits other than in EDs were excluded).

    • The study must have an explicit definition of frequent users, such as four visits in 1 year (reviews were excluded).

    • The study must use at least one statistical tool that is classified as inferential (not descriptive, as defined by The Cambridge Dictionary of Statistics12), such as hypothesis tests, regression models, decision trees or others.

    • The study’s objectives must include identifying variables associated with frequent use or predicting the risk of becoming a frequent user.

    We collected 4534 potential abstracts (figure 1). Of those, 32 were duplicates and 4344 were excluded by an investigator (YC) after reading the title and the abstract. At this stage, studies were discarded if it was explicit from the title and the abstract that they were unfit for the scoping review (for instance studies about frequent use of inpatient services, systematic reviews, etc). In case of uncertainty, studies were kept for complete reading. Then, YC and FRH or ID independently evaluated the remaining 158 full text articles, of which 109 matched the above criteria. A third evaluator was consulted in case of discrepancy. Reasons for exclusion were: not in French or English (1), duplicate (3), systematic review (4), no inferential statistics (5), no explicit definition of frequent users (5), focus not on ED (14), no description or prediction of frequent users (17). A reference search among the references of the 109 included articles yielded five relevant articles. Thus, 114 articles were included in this study, of which YC, ID and MB examined the full texts.

    Figure 1
    Figure 1

    Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram. ED, emergency department.

    Stage 4: Charting the data

    YC, MB and ID independently extracted the corresponding data. Reported characteristics were the first (two) author(s), the publication year, the study location, the population, the frequent users’ definition, the objectives, the sample size and the statistical tools used concerning the research question.

    Stage 5: Collating, summarising and reporting the results

    The results are reported via a content analysis.13

    Patient and public involvement

    Patients or public were not involved in this study.

    Results

    The studies’ main characteristics are presented in table 1. Out of 114 studies, 65 were conducted in the USA, 17 in Canada and 8 in Australia (figure 2). The various statistical tools were classified into four main categories: regression, hypothesis testing, machine learning and other tools.

    View this table:
    Table 1

    Main characteristics of the 86 included studies

    Figure 2
    Figure 2

    Number of studies by country.

    Regression

    Regression tools consist of a set of processes aimed at quantifying the relationships between a dependent variable and other explanatory variables.14 They are useful for description and prediction. Some regression models may be regularised, which in this case means avoiding overfitting with too many explanatory variables, or zero-truncated, which means that the model is not allowed to take null values.

    Out of the four categories (regression, hypothesis testing, machine learning and other tools), the most reported tool was the logistic regression (90 studies,3–5 15–101 two of which are regularised by LASSO or elastic net techniques), followed by the binomial regression (13 studies,18 46 55 73 76 77 82 89 102–106 2 of which are zero-truncated). To a lesser extent, the Poisson regression (seven studies,77 107–112 one of which is zero-truncated), the linear regression (six studies74 76 102 113–115), the analysis of variance (six studies44 59 73 96 103 104), the Cox regression (four studies87 93 105 116) and hierarchical models (one study90) were also used. In those studies, the results are often associated with ORs. The mixed-effects models were mentioned three times.39 91 117 Regression parameters were estimated by generalised estimating equations in four studies18 103 106 118 while parameter confidence intervals were estimated by the bootstrap procedure (two studies25 67) and the Clopper-Pearson method (one study25). The receiver operating characteristic curve, or equivalently the sensitivity, specificity or area under the curve (‘c-statistic’), was computed in 10 studies.4 36 48 64 75 83 88 107 117 119 Finally, two studies performed imputation to account for missing data (Markov chain Monte Carlo and multiple imputations78 90).

    Hypothesis testing

    Statistical tests aim at testing a specific hypothesis about data and rely on probability distributions.120 In the selected studies, the tests aimed mainly at comparing two samples (frequent users and non-frequent users).

    The most common statistical tests were the χ2 test (53 studies17 28 31 34 36–38 40–42 47 49 52 54 56 58 60 62–69 72–74 76 77 79–82 85 88 89 94 96 97 101–104 109 110 112 115 119 121–124) and the t-test (24 studies40 45 47 49 62 63 65 66 74 77 79 81 85 89 94 95 97 98 109 114 115 122 124 125) which measured association between variables or goodness-of-fit. As an alternative to the χ2 test for association, five studies used the Fisher exact test.63 72 94 98 119 Sample mean differences were assessed by 23 studies with the Mann-Whitney U test (also called the Wilcoxon rank-sum test20 23 31 47 58 66 77 98 110 119 121 123–125), its variant for dependent samples the Wilcoxon signed rank test,40 101 or the Kruskal-Wallis test.23 37 42 68 73 76 102 The difference in proportions test,126 Mantel-Haenszel test (test for differences in contingency tables, two studies44 117), the likelihood ratio test (significance test for nested models, two studies64 67), the Hosmer-Lemeshow test (goodness-of-fit for logistic regression, two studies67 70), the Wald test (significance test for regression coefficients, two studies30 96) and the Breslow-Day test (test for homogeneity in contingency tables OR53) were also used to a lesser degree. Finally, one study checked the assumption of normality with the Kolmogorov-Smirnov test.66

    Machine learning

    Machine learning tools are a set of algorithms that can learn and adapt to data in order to classify or predict, for instance.127 In the selected studies, the machine learning tools aimed mainly at classifying users (frequent vs non-frequent).

    Two studies used random forests31 36 along with support vector machines. Decision trees, which include classification and regression trees, were implemented by five studies.5 31 36 61 113 Adaptive boosting, or AdaBoost, is a meta-algorithm that combines with other algorithms and helps for better performances. It was computed in two studies.36 61

    Other tools

    Two studies used survival analysis,50 116 while another one fitted a non-parametric distribution to their data.25 Finally, maximum likelihood monotone coarse classifier algorithm was used as a binning method91 and non-negative matrix factorisation as a clustering technique.115

    Discussion

    The most exploited statistical tools arguably came from regression analysis. This may be because regression is well established in medical statistics or also because it is the most natural tool when trying to find significant variables to explain a dependent variable (in this case, to be a frequent user). Moreover, it allows predicting easily the risk of a new user becoming a frequent user, depending on its covariates. Other tools from hypothesis testing or machine learning also proved to be popular, although to a much lesser extent. Combining these statistical techniques may help in discovering significant and complementary patterns, compared with using tools from one class only. In our scoping review, two studies mixed statistical tools from regression, hypothesis testing and machine learning.31 36 In those studies, the author evaluated various performance criteria. While logistic regression performed well, other techniques such as random forests or LASSO regression were also competitive. Besides the fact that logistic regression can display modest performances,128 random forests and LASSO regression can complete logistic regression. The first technique can be used to assess the importance of each independent variable in the model, while the second technique can be useful for automatic selection of features. Likewise, using a variety of statistical tools can help complete or confirm results obtained with established methodologies. Different tools from one class can also be mixed in order to achieve different stages of the analysis (for instance, different types of regression82).

    The analysis of frequent ED users could benefit from using more machine learning techniques. Those were found to be not as common as regression or hypothesis testing, although they are especially appropriate when dealing with classification, prediction or big data. Tools such as support vector machines (which were used by two studies in this scoping review31 36), artificial neural networks or Bayesian networks are common classifiers and predictors in the artificial intelligence community.129 They are popular for instance in cancer diagnostic and prognosis, which strongly rely on classification and prediction.130–132 In particular, support vector machines, decision trees or self-organising maps can deal with binary outcomes, which is usually the case for frequent use outcomes. They usually require large datasets in order to overcome overfitting, but this is becoming less and less of an issue in health sciences.133 Nevertheless, machine learning tools often use a black box approach as there are many intermediary steps leading to the final solution. While each step usually consists of simple arithmetic operations, their multiple interactions can be more difficult to interpret. In spite of this opacity, they still display good performances in classifying and predicting. In some cases, they may be more accurate than the widely used logistic regression.134 Those methods would thus turn out to be less useful in data exploration.135 Machine learning tools are getting popular in other fields in health sciences, such as critical care,136 cardiology137 or emergency medicine.138 The authors state that their fields would benefit from this growing popularity, though results need to be analysed and interpreted in collaboration with clinicians.

    Other tools exist that may also be suitable for describing the associated variables or the prediction of frequent ED users but were not reported in the literature. Among those, principal component analysis (PCA) is a dimensional reduction and visualisation technique, sometimes used with cluster or discriminant analysis.139 Based on all the original explanatory variables, PCA constructs new ones by summing and weighing them differently. More weight is given to relevant variables so that those latter become dominant in the new constructions while still including all variables. For instance, Burgel et al 140 built chronic obstructive pulmonary disease clinical phenotypes by constructing new relevant variables with PCA and by grouping similar subjects in this new space with cluster analysis.140 Moreover, PCA has already been used for the construction of questionnaires and diagnosis tools in a medical context,141 142 both of which can prove useful in the identification of frequent users.

    As mentioned, regression techniques were common in the selected studies. Yet, quantile regression (QR)143 was not mentioned. QR is a generalisation of mean regression in the sense that its focus is not only the mean of the dependent variable distribution (such as in classical linear regression) but any quantile of it. QR thus represents an alternative to define frequent users by the high quantiles of ED visit distribution (eg, the 90th quantile). Eight studies25 27 46 48 51 54 62 121 defined frequent users with quantiles, but they did not use QR. QR would allow for finer investigations in the different quantiles of ED users in relationship to the explanatory variables. For instance, the association between age and the number of ED visits may be significantly different across the 10th (low users) and 90th (frequent users) quantiles. Such a heterogeneous association would be uncovered by QR, while usually unseen with a classical mean regression. Ding et al 144 used QR to characterise waiting room and treatment times in EDs.144 They explored the lowest, median and highest of those times and highlighted predictors that were significant only in particular quantiles. Usually, QR requires a continuous dependent variable as opposed to a logistic regression, though it is possible to combine these two regressions.145 Furthermore, defining frequent users by quantiles would allow for better comparison between studies as there is no common definition for frequent users.

    Strengths and limitations

    To the best of our knowledge, this scoping review is the first to list statistical tools that are used in the identification of variables associated with frequent ED use and the prediction of frequent users. Besides, it was conducted following a well-defined methodological framework. The search strategies were designed with an information specialist in three different databases. Two independent evaluators selected the articles and extracted the data while a third independent evaluator settled disagreements, ensuring that all included studies were relevant. One limitation of our study is that quality assessment is not performed in a scoping review. However, this should not alter the results, since the aim was to list which statistical tools have been applied in the literature. Moreover, the majority of articles were in English which may introduce a selection bias (for instance, one excluded article was in Spanish). More than half of the reviewed studies were indeed conducted in the USA, making the results difficult to compare to other countries.

    Conclusions

    Frequent ED users represent a complex issue, and their analysis require adequate statistical tools. In this context, this scoping review shows that some tools are well established, such as logistic regression and χ2 test, while others such as support vector machines are less so, though they would deserve to get more attention. It also outlines some research opportunities with other tools not yet explored.

    Acknowledgments

    We would like to thank information specialist Josée Toulouse for her help in defining the search strategies and Tina Wey (PhD) for revising the text.

    References

    1. 1.↵
      2. Kumar GS ,
      3. Klein R
      . Effectiveness of case management strategies in reducing emergency department visits in frequent user patient populations: a systematic review. J Emerg Med 2013;44:717–29.doi:10.1016/j.jemermed.2012.08.035
      OpenUrlCrossRefPubMed
    2. 2.↵
      2. LaCalle E ,
      3. Rabin E
      . Frequent users of emergency departments: the myths, the data, and the policy implications. Ann Emerg Med 2010;56:42–8.doi:10.1016/j.annemergmed.2010.01.032
      OpenUrlCrossRefPubMedWeb of Science
    3. 3.↵
      2. Hunt KA ,
      3. Weber EJ ,
      4. Showstack JA , et al
      . Characteristics of frequent users of emergency departments. Ann Emerg Med 2006;48:1–8.doi:10.1016/j.annemergmed.2005.12.030
      OpenUrlPubMedWeb of Science
    4. 4.↵
      2. Doupe MB ,
      3. Palatnick W ,
      4. Day S , et al
      . Frequent users of emergency departments: developing standard definitions and defining prominent risk factors. Ann Emerg Med 2012;60:24–32.doi:10.1016/j.annemergmed.2011.11.036
      OpenUrlCrossRefPubMed
    5. 5.↵
      2. Hudon C ,
      3. Courteau J ,
      4. Krieg C , et al
      . Factors associated with chronic frequent emergency department utilization in a population with diabetes living in metropolitan areas: a population-based retrospective cohort study. BMC Health Serv Res 2017;17:525.doi:10.1186/s12913-017-2453-3
      OpenUrl
    6. 6.↵
      2. Krieg C ,
      3. Hudon C ,
      4. Chouinard MC , et al
      . Individual predictors of frequent emergency department use: a scoping review. BMC Health Serv Res 2016;16:594.doi:10.1186/s12913-016-1852-1
      OpenUrl
    7. 7.↵
      2. Ruger JP ,
      3. Richter CJ ,
      4. Spitznagel EL , et al
      . Analysis of costs, length of stay, and utilization of emergency department services by frequent users: implications for health policy. Acad Emerg Med 2004;11:1311–7.doi:10.1197/j.aem.2004.07.008
      OpenUrlCrossRefPubMedWeb of Science
    8. 8.↵
      2. Bodenheimer T ,
      3. Berry-Millett R
      . Care management of patients with complex health care needs. Policy 2009;1:6.
      OpenUrl
    9. 9.↵
      2. Arksey H ,
      3. O’Malley L
      . Scoping studies: towards a methodological framework. Int J Soc Res Methodol 2005;8:19–32.doi:10.1080/1364557032000119616
      OpenUrlCrossRef
    10. 10.↵
      2. Levac D ,
      3. Colquhoun H ,
      4. O’Brien KK
      . Scoping studies: advancing the methodology. Implement Sci 2010;5:69.doi:10.1186/1748-5908-5-69
      OpenUrlCrossRefPubMed
    11. 11.↵
      2. Mays N ,
      3. Roberts E ,
      4. Popay J
      . Synthesising research evidence. Studying the organisation and delivery of health services. Research methods 2001:188–220.
    12. 12.↵
      2. Everitt BS ,
      3. Skrondal A
      . The Cambridge Dictionary of Statistics. 4th edn. Cambridge: Cambridge University Press Cambridge, 2010.
    13. 13.↵
      2. Vaismoradi M ,
      3. Turunen H ,
      4. Bondas T
      . Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nurs Health Sci 2013;15:398–405.doi:10.1111/nhs.12048
      OpenUrlCrossRefPubMed
    14. 14.↵
      2. Harrell FE
      . Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. 2 edn. New York: Springer International Publishing, 2015.
    15. 15.↵
      2. Aagaard J ,
      3. Aagaard A ,
      4. Buus N
      . Predictors of frequent visits to a psychiatric emergency room: a large-scale register study combined with a small-scale interview study. Int J Nurs Stud 2014;51:1003–13.doi:10.1016/j.ijnurstu.2013.11.002
      OpenUrlCrossRefPubMed
    16. 16.↵
      2. Adams RJ ,
      3. Smith BJ ,
      4. Ruffin RE
      . Factors associated with hospital admissions and repeat emergency department visits for adults with asthma. Thorax 2000;55:566–73.doi:10.1136/thorax.55.7.566
      OpenUrlAbstract/FREE Full Text
    17. 17.↵
      2. Alghanim SA ,
      3. Alomar BA
      . Frequent use of emergency departments in Saudi public hospitals: Implications for primary health care services. Asia-Pac J Public Health 2015;27:NP2521–NP30.
      OpenUrl
    18. 18.↵
      2. Alpern ER ,
      3. Clark AE ,
      4. Alessandrini EA , et al
      . Pediatric Emergency Care Applied Research Network (PECARN). Recurrent and high-frequency use of the emergency department by pediatric patients. Acad Emerg Med 2014;21:365–73.doi:10.1111/acem.12347
      OpenUrlCrossRefPubMedWeb of Science
    19. 19.↵
      2. Arfken CL ,
      3. Zeman LL ,
      4. Yeager L , et al
      . Case-control study of frequent visitors to an urban psychiatric emergency service. Psychiatr Serv 2004;55:295–301.doi:10.1176/appi.ps.55.3.295
      OpenUrlCrossRefPubMedWeb of Science
    20. 20.↵
      2. Bieler G ,
      3. Paroz S ,
      4. Faouzi M , et al
      . Social and medical vulnerability factors of emergency department frequent users in a universal health insurance system. Acad Emerg Med 2012;19:63–8.doi:10.1111/j.1553-2712.2011.01246.x
      OpenUrlCrossRefPubMedWeb of Science
    21. 21.↵
      2. Billings J ,
      3. Raven MC
      . Dispelling an urban legend: frequent emergency department users have substantial burden of disease. Health Aff 2013;32:2099–108.doi:10.1377/hlthaff.2012.1276
      OpenUrlAbstract/FREE Full Text
    22. 22.↵
      2. Boyer L ,
      3. Dassa D ,
      4. Belzeaux R , et al
      . Frequent visits to a French psychiatric emergency service: diagnostic variability in psychotic disorders. Psychiatr Serv 2011;62:966–70.doi:10.1176/ps.62.8.pss6208_0966
      OpenUrlCrossRefPubMedWeb of Science
    23. 23.↵
      2. Brennan JJ ,
      3. Chan TC ,
      4. Hsia RY , et al
      . Emergency department utilization among frequent users with psychiatric visits. Acad Emerg Med 2014;21:1015–22.doi:10.1111/acem.12453
      OpenUrl
    24. 24.↵
      2. Buhumaid R ,
      3. Riley J ,
      4. Sattarian M , et al
      . Characteristics of frequent users of the emergency department with psychiatric conditions. J Health Care Poor Underserved 2015;26:941–50.doi:10.1353/hpu.2015.0079
      OpenUrl
    25. 25.↵
      2. Cabey WV ,
      3. MacNeill E ,
      4. White LN , et al
      . Frequent pediatric emergency department use in infancy and early childhood. Pediatr Emerg Care 2014;30:710–7.doi:10.1097/PEC.0000000000000233
      OpenUrlCrossRefPubMed
    26. 26.↵
      2. Castner J ,
      3. Wu YW ,
      4. Mehrok N , et al
      . Frequent emergency department utilization and behavioral health diagnoses. Nurs Res 2015;64:3–12.doi:10.1097/NNR.0000000000000065
      OpenUrlCrossRefPubMed
    27. 27.↵
      2. Chambers C ,
      3. Chiu S ,
      4. Katic M , et al
      . High utilizers of emergency health services in a population-based cohort of homeless adults. Am J Public Health 2013;103(S2):S302–10.doi:10.2105/AJPH.2013.301397
      OpenUrlCrossRefPubMed
    28. 28.↵
      2. Chang G ,
      3. Weiss AP ,
      4. Orav EJ , et al
      . Predictors of frequent emergency department use among patients with psychiatric illness. Gen Hosp Psychiatry 2014;36:716–20.doi:10.1016/j.genhosppsych.2014.09.010
      OpenUrl
    29. 29.↵
      2. Chukmaitov AS ,
      3. Tang A ,
      4. Carretta HJ , et al
      . Characteristics of all, occasional, and frequent emergency department visits due to ambulatory care-sensitive conditions in Florida. J Ambul Care Manage 2012;35:149–58.doi:10.1097/JAC.0b013e318244d222
      OpenUrlPubMed
    30. 30.↵
      2. Colligan EM ,
      3. Pines JM ,
      4. Colantuoni E , et al
      . Risk Factors for Persistent Frequent Emergency Department Use in Medicare Beneficiaries. Ann Emerg Med 2016;67:721–9.doi:10.1016/j.annemergmed.2016.01.033
      OpenUrl
    31. 31.↵
      2. Das LT ,
      3. Abramson EL ,
      4. Stone AE , et al
      . Predicting frequent emergency department visits among children with asthma using EHR data. Pediatr Pulmonol 2017;52:880–90.doi:10.1002/ppul.23735
      OpenUrl
    32. 32.↵
      2. Doran KM ,
      3. Colucci AC ,
      4. Wall SP , et al
      . Reasons for emergency department use: do frequent users differ? Am J Manag Care 2014;20:e506–e14.
      OpenUrl
    33. 33.↵
      2. Doran KM ,
      3. Raven MC ,
      4. Rosenheck RA
      . What drives frequent emergency department use in an integrated health system? National data from the Veterans Health Administration. Ann Emerg Med 2013;62:151–9.doi:10.1016/j.annemergmed.2013.02.016
      OpenUrlCrossRefPubMed
    34. 34.↵
      2. Fernandes AK ,
      3. Mallmann F ,
      4. Steinhorst AM , et al
      . Characteristics of acute asthma patients attended frequently compared with those attended only occasionally in an emergency department. J Asthma 2003;40:683–90.doi:10.1081/JAS-120023487
      OpenUrlCrossRefPubMedWeb of Science
    35. 35.↵
      2. Frost DW ,
      3. Vembu S ,
      4. Wang J , et al
      . Using the Electronic Medical Record to Identify Patients at High Risk for Frequent Emergency Department Visits and High System Costs. Am J Med 2017;130:601.e17–601.e22.doi:10.1016/j.amjmed.2016.12.008
      OpenUrl
    36. 36.↵
      2. Grinspan ZM ,
      3. Shapiro JS ,
      4. Abramson EL , et al
      . Predicting frequent ED use by people with epilepsy with health information exchange data. Neurology 2015;85:1031–8.doi:10.1212/WNL.0000000000001944
      OpenUrlPubMed
    37. 37.↵
      2. Hasegawa K ,
      3. Tsugawa Y ,
      4. Brown DF , et al
      . A population-based study of adults who frequently visit the emergency department for acute asthma. California and Florida, 2009-2010. Ann Am Thorac Soc 2014;11:158–66.doi:10.1513/AnnalsATS.201306-166OC
      OpenUrlCrossRefPubMed
    38. 38.↵
      2. Huang JA ,
      3. Tsai WC ,
      4. Chen YC , et al
      . Factors associated with frequent use of emergency services in a medical center. J Formos Med Assoc 2003;102:222–8.
      OpenUrlPubMedWeb of Science
    39. 39.↵
      2. Hudon C ,
      3. Sanche S ,
      4. Haggerty JL
      . Personal Characteristics and Experience of Primary Care Predicting Frequent Use of Emergency Department: A Prospective Cohort Study. PLoS One 2016;11:e0157489.doi:10.1371/journal.pone.0157489
    40. 40.↵
      2. Kerr T ,
      3. Wood E ,
      4. Grafstein E , et al
      . High rates of primary care and emergency department use among injection drug users in Vancouver. J Public Health 2005;27:62–6.doi:10.1093/pubmed/fdh189
      OpenUrlCrossRefPubMedWeb of Science
    41. 41.↵
      2. Kirby SE ,
      3. Dennis SM ,
      4. Jayasinghe UW , et al
      . Patient related factors in frequent readmissions: the influence of condition, access to services and patient choice. BMC Health Serv Res 2010;10:216.doi:10.1186/1472-6963-10-216
      OpenUrlCrossRefPubMed
    42. 42.↵
      2. Kirby SE ,
      3. Dennis SM ,
      4. Jayasinghe UW , et al
      . Frequent emergency attenders: is there a better way? Aust Health Rev 2011;35:462–7.doi:10.1071/AH10964
      OpenUrlPubMed
    43. 43.↵
      2. Ko M ,
      3. Lee Y ,
      4. Chen C , et al
      . Prevalence of and Predictors for Frequent Utilization of Emergency Department: A Population-Based Study. Medicine 2015;94:e1205.doi:10.1097/MD.0000000000001205
      OpenUrl
    44. 44.↵
      2. Ledoux Y ,
      3. Minner P
      . Occasional and frequent repeaters in a psychiatric emergency room. Soc Psychiatry Psychiatr Epidemiol 2006;41:115–21.doi:10.1007/s00127-005-0010-6
      OpenUrlCrossRefPubMed
    45. 45.↵
      2. Legramante JM ,
      3. Morciano L ,
      4. Lucaroni F , et al
      . Frequent use of emergency departments by the elderly population when continuing care is not well established. PLoS One 2016;11:e0165939.doi:10.1371/journal.pone.0165939
    46. 46.↵
      2. Leporatti L ,
      3. Ameri M ,
      4. Trinchero C , et al
      . Targeting frequent users of emergency departments: Prominent risk factors and policy implications. Health Policy 2016;120:462–70.doi:10.1016/j.healthpol.2016.03.005
      OpenUrl
    47. 47.↵
      2. Lim SF ,
      3. Wah W ,
      4. Pasupathi Y , et al
      . Frequent attenders to the ED: patients who present with repeated asthma exacerbations. Am J Emerg Med 2014;32:895–9.doi:10.1016/j.ajem.2014.04.052
      OpenUrl
    48. 48.↵
      2. Limsrivilai J ,
      3. Stidham RW ,
      4. Govani SM , et al
      . Factors That Predict High Health Care Utilization and Costs for Patients With Inflammatory Bowel Diseases. Clin Gastroenterol Hepatol 2017;15:385–92.doi:10.1016/j.cgh.2016.09.012
      OpenUrl
    49. 49.↵
      2. Liu SW ,
      3. Nagurney JT ,
      4. Chang Y , et al
      . Frequent ED users: are most visits for mental health, alcohol, and drug-related complaints? Am J Emerg Med 2013;31:1512–5.doi:10.1016/j.ajem.2013.08.006
      OpenUrlPubMed
    50. 50.↵
      2. Mandelberg JH ,
      3. Kuhn RE ,
      4. Kohn MA
      . Epidemiologic analysis of an urban, public emergency department’s frequent users. Acad Emerg Med 2000;7:637–46.doi:10.1111/j.1553-2712.2000.tb02037.x
      OpenUrlCrossRefPubMedWeb of Science
    51. 51.↵
      2. Mann EG ,
      3. Johnson A ,
      4. VanDenKerkhof EG
      . Frequency and characteristics of healthcare visits associated with chronic pain: results from a population-based Canadian study. Can J Anaesth 2016;63:411–41.doi:10.1007/s12630-015-0578-6
      OpenUrl
    52. 52.↵
      2. McMahon CG ,
      3. Power Foley M ,
      4. Robinson D , et al
      . High prevalence of frequent attendance in the over 65s. Eur J Emerg Med 2018;25:1.doi:10.1097/MEJ.0000000000000406
      OpenUrl
    53. 53.↵
      2. Milani SA ,
      3. Crooke H ,
      4. Cottler LB , et al
      . Sex differences in frequent ED use among those with multimorbid chronic diseases. Am J Emerg Med 2016;34:2127–31.doi:10.1016/j.ajem.2016.07.059
      OpenUrl
    54. 54.↵
      2. Mueller EL ,
      3. Hall M ,
      4. Carroll AE , et al
      . Frequent Emergency Department Utilizers Among Children with Cancer. Pediatr Blood Cancer 2016;63:859–64.doi:10.1002/pbc.25929
      OpenUrl
    55. 55.↵
      2. Nambiar D ,
      3. Stoové M ,
      4. Dietze P
      . Frequent emergency department presentations among people who inject drugs: A record linkage study. Int J Drug Policy 2017;44:115–20.doi:10.1016/j.drugpo.2017.03.010
      OpenUrl
    56. 56.↵
      2. Neufeld E ,
      3. Viau KA ,
      4. Hirdes JP , et al
      . Predictors of frequent emergency department visits among rural older adults in Ontario using the Resident Assessment Instrument-Home Care. Aust J Rural Health 2016;24:115–22.doi:10.1111/ajr.12213
      OpenUrl
    57. 57.↵
      2. Norman C ,
      3. Mello M ,
      4. Choi B
      . Identifying Frequent Users of an Urban Emergency Medical Service Using Descriptive Statistics and Regression Analyses. West J Emerg Med 2016;17:39–45.doi:10.5811/westjem.2015.10.28508
      OpenUrl
    58. 58.↵
      2. Palmer E ,
      3. Leblanc-Duchin D ,
      4. Murray J , et al
      . Emergency department use: is frequent use associated with a lack of primary care provider? Can Fam Physician 2014;60:e223–e9.
      OpenUrlAbstract/FREE Full Text
    59. 59.↵
      2. Panopalis P ,
      3. Gillis JZ ,
      4. Yazdany J , et al
      . Frequent use of the emergency department among persons with systemic lupus erythematosus. Arthritis Care Res 2010;62:401–8.doi:10.1002/acr.20107
      OpenUrlCrossRef
    60. 60.↵
      2. Paul P ,
      3. Heng BH ,
      4. Seow E , et al
      . Predictors of frequent attenders of emergency department at an acute general hospital in Singapore. Emerg Med J 2010;27:843–8.doi:10.1136/emj.2009.079160
      OpenUrlAbstract/FREE Full Text
    61. 61.↵
      2. Pereira M ,
      3. Singh V ,
      4. Hon CP ,
      5. Greg McKelvey T ,
      6. Sushmita S ,
      7. De Cock M
      , et al . eds. Predicting future frequent users of emergency departments in California state2016: Association for Computing Machinery, Inc.
    62. 62.↵
      2. Pines JM ,
      3. Buford K
      . Predictors of frequent emergency department utilization in Southeastern Pennsylvania. J Asthma 2006;43:219–23.doi:10.1080/02770900600567015
      OpenUrlCrossRefPubMedWeb of Science
    63. 63.↵
      2. Quilty S ,
      3. Shannon G ,
      4. Yao A , et al
      . Factors contributing to frequent attendance to the emergency department of a remote Northern Territory hospital. Med J Aust 2016;204:111–7.doi:10.5694/mja15.00648
      OpenUrl
    64. 64.↵
      2. Samuels-Kalow ME ,
      3. Bryan MW ,
      4. Shaw KN , et al
      . Low-Acuity Utilization of the Pediatric Emergency Department. Acad Pediatr 2017;17:256–60.
      OpenUrlPubMed
    65. 65.↵
      2. Schmoll S ,
      3. Boyer L ,
      4. Henry JM , et al
      . [Frequent visitors to psychiatric emergency service: Demographical and clinical analysis]. Encephale 2015;41:123–9.doi:10.1016/j.encep.2013.01.002
      OpenUrl
    66. 66.↵
      2. Soler JJ ,
      3. Sánchez L ,
      4. Román P , et al
      . Risk factors of emergency care and admissions in COPD patients with high consumption of health resources. Respir Med 2004;98:318–29.doi:10.1016/j.rmed.2003.04.001
      OpenUrlCrossRefPubMedWeb of Science
    67. 67.↵
      2. Sun BC ,
      3. Burstin HR ,
      4. Brennan TA
      . Predictors and outcomes of frequent emergency department users. Acad Emerg Med 2003;10:320–8.doi:10.1197/aemj.10.4.320
      OpenUrlCrossRefPubMedWeb of Science
    68. 68.↵
      2. Tangherlini N ,
      3. Pletcher MJ ,
      4. Covec MA , et al
      . Frequent use of emergency medical services by the elderly: a case-control study using paramedic records. Prehosp Disaster Med 2010;25:258–64.doi:10.1017/S1049023X0000813X
      OpenUrlPubMed
    69. 69.↵
      2. Thakarar K ,
      3. Morgan JR ,
      4. Gaeta JM , et al
      . Predictors of Frequent Emergency Room Visits among a Homeless Population. PLoS One 2015;10:e0124552.doi:10.1371/journal.pone.0124552
    70. 70.↵
      2. Vandyk AD ,
      3. VanDenKerkhof EG ,
      4. Graham ID , et al
      . Profiling frequent presenters to the emergency department for mental health complaints: socio-demographic, clinical, and service use characteristics. Arch Psychiatr Nurs 2014;28:420–5.doi:10.1016/j.apnu.2014.09.001
      OpenUrl
    71. 71.↵
      2. Vinton DT ,
      3. Capp R ,
      4. Rooks SP , et al
      . Frequent users of US emergency departments: characteristics and opportunities for intervention. Emerg Med J 2014;31:526–32.doi:10.1136/emermed-2013-202407
      OpenUrlAbstract/FREE Full Text
    72. 72.↵
      2. Vu F ,
      3. Daeppen JB ,
      4. Hugli O , et al
      . Screening of mental health and substance users in frequent users of a general Swiss emergency department. BMC Emerg Med 2015;15:27.doi:10.1186/s12873-015-0053-2
      OpenUrl
    73. 73.↵
      2. Watase H ,
      3. Hagiwara Y ,
      4. Chiba T , et al
      . Japanese Emergency Medicine Network Investigators. Multicentre observational study of adults with asthma exacerbations: who are the frequent users of the emergency department in Japan? BMJ Open 2015;5:e007435.doi:10.1136/bmjopen-2014-007435
    74. 74.↵
      2. Woo JH ,
      3. Grinspan Z ,
      4. Shapiro J , et al
      . Frequent Users of Hospital Emergency Departments in Korea Characterized by Claims Data from the National Health Insurance: A Cross Sectional Study. PLoS One 2016;11:e0147450.doi:10.1371/journal.pone.0147450
    75. 75.↵
      2. Wu J ,
      3. Grannis SJ ,
      4. Xu H , et al
      . A practical method for predicting frequent use of emergency department care using routinely available electronic registration data. BMC Emerg Med 2016;16:12.doi:10.1186/s12873-016-0076-3
      OpenUrl
    76. 76.↵
      2. Hasegawa K ,
      3. Tsugawa Y ,
      4. Tsai CL , et al
      . Frequent utilization of the emergency department for acute exacerbation of chronic obstructive pulmonary disease. Respir Res 2014;15:40.doi:10.1186/1465-9921-15-40
      OpenUrlCrossRefPubMed
    77. 77.↵
      2. Freitag FG ,
      3. Kozma CM ,
      4. Slaton T , et al
      . Characterization and prediction of emergency department use in chronic daily headache patients. Headache 2005;45:891–8.doi:10.1111/j.1526-4610.2005.05157.x
      OpenUrlCrossRefPubMedWeb of Science
    78. 78.↵
      2. Friedman BW ,
      3. Serrano D ,
      4. Reed M , et al
      . Use of the emergency department for severe headache. A population-based study. Headache 2009;49:21–30.doi:10.1111/j.1526-4610.2008.01282.x
      OpenUrlCrossRefPubMedWeb of Science
    79. 79.↵
      2. O’Toole TP ,
      3. Pollini R ,
      4. Gray P , et al
      . Factors identifying high-frequency and low-frequency health service utilization among substance-using adults. J Subst Abuse Treat 2007;33:51–9.doi:10.1016/j.jsat.2006.12.002
      OpenUrlCrossRefPubMedWeb of Science
    80. 80.↵
      2. Pasic J ,
      3. Russo J ,
      4. Roy-Byrne P
      . High utilizers of psychiatric emergency services. Psychiatr Serv 2005;56:678–84.doi:10.1176/appi.ps.56.6.678
      OpenUrlCrossRefPubMedWeb of Science
    81. 81.↵
      2. Rask KJ ,
      3. Williams MV ,
      4. McNagny SE , et al
      . Ambulatory health care use by patients in a public hospital emergency department. J Gen Intern Med 1998;13:614–20.doi:10.1046/j.1525-1497.1998.00184.x
      OpenUrlCrossRefPubMedWeb of Science
    82. 82.↵
      2. Blonigen DM ,
      3. Macia KS ,
      4. Bi X , et al
      . Factors associated with emergency department useamong veteran psychiatric patients. Psychiatr Q 2017;88:721–32.doi:10.1007/s11126-017-9490-2
      OpenUrl
    83. 83.↵
      2. Batra P ,
      3. Fridman M ,
      4. Leng M , et al
      . Emergency Department Care in the Postpartum Period: California Births, 2009-2011. Obstet Gynecol 2017;130:1073–81.doi:10.1097/AOG.0000000000002269
      OpenUrl
    84. 84.↵
      2. Burner E ,
      3. Ruiz A ,
      4. Sanchez A , et al
      . 155 Insulin Use Predicts High Emergency Department Utilization Among Patients With Poorly Controlled Diabetes. Ann Emerg Med 2018;72:S65–S.doi:10.1016/j.annemergmed.2018.08.160
      OpenUrl
    85. 85.↵
      2. Flood C ,
      3. Sheehan K ,
      4. Crandall M
      . Predictors of Emergency Department Utilization Among Children in Vulnerable Families. Pediatr Emerg Care 2017;33:765–9.doi:10.1097/PEC.0000000000000658
      OpenUrl
    86. 86.↵
      2. Kanzaria HK ,
      3. Niedzwiecki MJ ,
      4. Montoy JC , et al
      . Persistent Frequent Emergency Department Use: Core Group Exhibits Extreme Levels Of Use For More Than A Decade. Health Aff 2017;36:1720–8.doi:10.1377/hlthaff.2017.0658
      OpenUrlAbstract/FREE Full Text
    87. 87.↵
      2. Naseer M ,
      3. Dahlberg L ,
      4. Fagerström C
      . Health related quality of life and emergency department visits in adults of age ≥ 66 years: a prospective cohort study. Health Qual Life Outcomes 2018;16:144.doi:10.1186/s12955-018-0967-y
      OpenUrl
    88. 88.↵
      2. Samuels-Kalow M ,
      3. Peltz A ,
      4. Rodean J , et al
      . Predicting Low-Resource-Intensity Emergency Department Visits in Children. Acad Pediatr 2018;18:297–304.doi:10.1016/j.acap.2017.12.012
      OpenUrl
    89. 89.↵
      2. Weidner TK ,
      3. Kidwell JT ,
      4. Etzioni DA , et al
      . Factors Associated with Emergency Department Utilization and Admission in Patients with Colorectal Cancer. J Gastrointest Surg 2018;22:913–20.doi:10.1007/s11605-018-3707-z
      OpenUrl
    90. 90.↵
      2. Zook HG ,
      3. Kharbanda AB ,
      4. Puumala SE , et al
      . Emergency Department Utilization by Native American Children. Pediatr Emerg Care 2018;34:802–9.doi:10.1097/PEC.0000000000001289
      OpenUrl
    91. 91.↵
      2. Ahn E ,
      3. Kim J ,
      4. Rahman K , et al
      . Development of a risk predictive scoring system to identify patients at risk of representation to emergency department: a retrospective population-based analysis in Australia. BMJ Open 2018;8:e021323.doi:10.1136/bmjopen-2017-021323
    92. 92.↵
      2. Andrews CM ,
      3. Westlake M ,
      4. Wooten N
      . Availability of Outpatient Addiction Treatment and Use of Emergency Department Services Among Medicaid Enrollees. Psychiatr Serv 2018;69:729–32.doi:10.1176/appi.ps.201700413
      OpenUrl
    93. 93.↵
      2. Gruneir A ,
      3. Cigsar C ,
      4. Wang X , et al
      . Repeat emergency department visits by nursing home residents: a cohort study using health administrative data. BMC Geriatr 2018;18:157.doi:10.1186/s12877-018-0854-8
      OpenUrl
    94. 94.↵
      2. Lee J ,
      3. Lin J ,
      4. Suter LG , et al
      . Persistently Frequent Emergency Department Utilization among Persons with Systemic Lupus Erythematosus. Arthritis Care Res 2018 (Epub 2018/10/09).doi:10.1002/acr.23777
    95. 95.↵
      2. Mann EG ,
      3. Johnson A ,
      4. Gilron I , et al
      . Pain Management Strategies and Health Care Use in Community-Dwelling Individuals Living with Chronic Pain. Pain Med 2017;18:2267–79.doi:10.1093/pm/pnw341
      OpenUrl
    96. 96.↵
      2. Colligan EM ,
      3. Pines JM ,
      4. Colantuoni E , et al
      . Factors Associated With Frequent Emergency Department Use in the Medicare Population. Med Care Res Rev 2017;74:311–27.doi:10.1177/1077558716641826
      OpenUrl
    97. 97.↵
      2. Cunningham A ,
      3. Mautner D ,
      4. Ku B , et al
      . Frequent emergency department visitors are frequent primary care visitors and report unmet primary care needs. J Eval Clin Pract 2017;23:567–73.doi:10.1111/jep.12672
      OpenUrl
    98. 98.↵
      2. Kidane B ,
      3. Jacob B ,
      4. Gupta V , et al
      . Medium and long-term emergency department utilization after oesophagectomy: a population-based analysis. Eur J Cardiothorac Surg 2018;54:683–8.doi:10.1093/ejcts/ezy155
      OpenUrl
    99. 99.↵
      2. Schlichting LE ,
      3. Rogers ML ,
      4. Gjelsvik A , et al
      . Pediatric Emergency Department Utilization and Reliance by Insurance Coverage in the United States. Acad Emerg Med 2017;24:1483–90.doi:10.1111/acem.13281
      OpenUrl
    100. 100.↵
      2. Supat B ,
      3. Brennan JJ ,
      4. Vilke GM , et al
      . Characterizing pediatric high frequency users of California emergency departments. Am J Emerg Med 2018.doi:10.1016/j.ajem.2018.12.015
    101. 101.↵
      2. Peltz A ,
      3. Samuels-Kalow ME ,
      4. Rodean J , et al
      . Characteristics of Children Enrolled in Medicaid With High-Frequency Emergency Department Use. Pediatrics 2017;140:e20170962.doi:10.1542/peds.2017-0962
      OpenUrlAbstract/FREE Full Text
    102. 102.↵
      2. Hasegawa K ,
      3. Tsugawa Y ,
      4. Camargo CA , et al
      . Frequent utilization of the emergency department for acute heart failure syndrome: a population-based study. Circ Cardiovasc Qual Outcomes 2014;7:735–42.doi:10.1161/CIRCOUTCOMES.114.000949
      OpenUrlAbstract/FREE Full Text
    103. 103.↵
      2. Huynh C ,
      3. Ferland F ,
      4. Blanchette-Martin N , et al
      . Factors Influencing the Frequency of Emergency Department Utilization by Individuals with Substance Use Disorders. Psychiatr Q 2016;87:713–28.doi:10.1007/s11126-016-9422-6
      OpenUrl
    104. 104.↵
      2. Lin WC ,
      3. Bharel M ,
      4. Zhang J , et al
      . Frequent Emergency Department Visits and Hospitalizations Among Homeless People With Medicaid: Implications for Medicaid Expansion. Am J Public Health 2015;105:S716–22.doi:10.2105/AJPH.2015.302693
      OpenUrlPubMed
    105. 105.↵
      2. Beck A ,
      3. Sanchez-Walker E ,
      4. Evans LJ , et al
      . Characteristics of people who rapidly and frequently reattend the emergency department for mental health needs. Eur J Emerg Med 2016;23:351–5.doi:10.1097/MEJ.0000000000000349
      OpenUrl
    106. 106.↵
      2. Nambiar D ,
      3. Spelman T ,
      4. Stoové M , et al
      . Are People Who Inject Drugs Frequent Users of Emergency Department Services? A Cohort Study (2008-2013). Subst Use Misuse 2018;53:457–65.doi:10.1080/10826084.2017.1341921
      OpenUrl
    107. 107.↵
      2. Christensen EW ,
      3. Kharbanda AB ,
      4. Velden HV , et al
      . Predicting Frequent Emergency Department Use by Pediatric Medicaid Patients. Popul Health Manag 2017;20:208–15.doi:10.1089/pop.2016.0051
      OpenUrl
    108. 108.↵
      2. Hardie TL ,
      3. Polek C ,
      4. Wheeler E , et al
      . Characterising emergency department high-frequency users in a rural hospital. Emerg Med J 2015;32:21–5.doi:10.1136/emermed-2013-202369
      OpenUrlAbstract/FREE Full Text
    109. 109.↵
      2. Meyer JP ,
      3. Qiu J ,
      4. Chen NE , et al
      . Frequent emergency department use among released prisoners with human immunodeficiency virus: characterization including a novel multimorbidity index. Acad Emerg Med 2013;20:79–88.doi:10.1111/acem.12054
      OpenUrlCrossRefPubMedWeb of Science
    110. 110.↵
      2. Milbrett P ,
      3. Halm M
      . Characteristics and predictors of frequent utilization of emergency services. J Emerg Nurs 2009;35:191–8.doi:10.1016/j.jen.2008.04.032
      OpenUrlCrossRefPubMed
    111. 111.↵
      2. Sacamano P ,
      3. Krawczyk N ,
      4. Latkin C
      . Emergency Department Visits in a Cohort of Persons with Substance Use: Incorporating the Role of Social Networks. Subst Use Misuse 2018;53:2265–9.doi:10.1080/10826084.2018.1461225
      OpenUrl
    112. 112.↵
      2. Blair M ,
      3. Poots AJ ,
      4. Lim V , et al
      . Preschool children who are frequent attenders in emergency departments: an observational study of associated demographics and clinical characteristics. Arch Dis Child 2018;103.doi:10.1136/archdischild-2016-311952
    113. 113.↵
      2. Genell Andrén K ,
      3. Rosenqvist U
      . Heavy users of an emergency department--a two year follow-up study. Soc Sci Med 1987;25:825–31.doi:10.1016/0277-9536(87)90040-2
      OpenUrlCrossRefPubMedWeb of Science
    114. 114.↵
      2. Girts TK ,
      3. Crawford AG ,
      4. Goldfarb NI , et al
      . Predicting High Utilization of Emergency Department Services Among Patients with a Diagnosis of Psychosis in a Medicaid Managed Care Organization. Disease Management 2002;5:189–96.doi:10.1089/10935070260474967
      OpenUrl
    115. 115.↵
      2. Rauch J ,
      3. Hüsers J ,
      4. Babitsch B , et al
      . Understanding the Characteristics of Frequent Users of Emergency Departments: What Role Do Medical Conditions Play? Stud Health Technol Inform 2018;253:175–9.
      OpenUrl
    116. 116.↵
      2. Wong TH ,
      3. Lau ZY ,
      4. Ong WS , et al
      . Cancer patients as frequent attenders in emergency departments: A national cohort study. Cancer Med 2018;7:4434–46.doi:10.1002/cam4.1728
      OpenUrl
    117. 117.↵
      2. Neuman MI ,
      3. Alpern ER ,
      4. Hall M , et al
      . Characteristics of recurrent utilization in pediatric emergency departments. Pediatrics 2014;134:e1025–e31.doi:10.1542/peds.2014-1362
      OpenUrlAbstract/FREE Full Text
    118. 118.↵
      2. Ngamini-Ngui A ,
      3. Fleury MJ ,
      4. Moisan J , et al
      . High users of emergency departments in Quebec among patients with both schizophrenia and a substance use disorder. Psychiatr Serv 2014;65:1389–91.doi:10.1176/appi.ps.201300474
      OpenUrlPubMed
    119. 119.↵
      2. Samuels-Kalow ME ,
      3. Faridi MK ,
      4. Espinola JA , et al
      . Comparing Statewide and Single-center Data to Predict High-frequency Emergency Department Utilization Among Patients With Asthma Exacerbation. Acad Emerg Med 2018;25:657–67.doi:10.1111/acem.13342
      OpenUrl
    120. 120.↵
      2. Altman DG
      . Practical statistics for medical research. London: CRC press, 1990.
    121. 121.↵
      2. Moe J ,
      3. Bailey AL ,
      4. Oland R , et al
      . Defining, quantifying, and characterizing adult frequent users of a suburban Canadian emergency department. CJEM 2013;15:214–26.doi:10.2310/8000.2013.130936
      OpenUrlPubMed
    122. 122.↵
      2. Wajnberg A ,
      3. Hwang U ,
      4. Torres L , et al
      . Characteristics of frequent geriatric users of an urban emergency department. J Emerg Med 2012;43:376–81.doi:10.1016/j.jemermed.2011.06.056
      OpenUrlCrossRefPubMed
    123. 123.↵
      2. Street M ,
      3. Berry D ,
      4. Considine J
      . Frequent use of emergency departments by older people: a comparative cohort study of characteristics and outcomes. Int J Qual Health Care 2018;30:624–9.doi:10.1093/intqhc/mzy062
      OpenUrl
    124. 124.↵
      2. Birmingham LE ,
      3. Cochran T ,
      4. Frey JA , et al
      . Emergency department use and barriers to wellness: a survey of emergency department frequent users. BMC Emerg Med 2017;17:16.doi:10.1186/s12873-017-0126-5
      OpenUrl
    125. 125.↵
      2. Kim JJ ,
      3. Kwok ESH ,
      4. Cook OG , et al
      . Characterizing Highly Frequent Users of a Large Canadian Urban Emergency Department. West J Emerg Med 2018;19:926–33.doi:10.5811/westjem.2018.9.39369
      OpenUrl
    126. 126.↵
      2. Klein LR ,
      3. Martel ML ,
      4. Driver BE , et al
      . Emergency Department Frequent Users for Acute Alcohol Intoxication. West J Emerg Med 2018;19:398–402.doi:10.5811/westjem.2017.10.35052
      OpenUrl
    127. 127.↵
      2. Kononenko I
      . Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 2001;23:89–109.doi:10.1016/S0933-3657(01)00077-X
      OpenUrlCrossRefPubMedWeb of Science
    128. 128.↵
      2. Hu X ,
      3. Barnes S ,
      4. Bjarnadóttir M , et al
      . Intelligent selection of frequent emergency department patients for case management: A machine learning framework based on claims data. IISE Trans Healthc Syst Eng 2017;7:130–43.doi:10.1080/24725579.2017.1351502
      OpenUrl
    129. 129.↵
      2. Liao S-H ,
      3. Chu P-H ,
      4. Hsiao P-Y
      . Data mining techniques and applications – A decade review from 2000 to 2011. Expert Syst Appl 2012;39:11303–11.doi:10.1016/j.eswa.2012.02.063
      OpenUrlCrossRef
    130. 130.↵
      2. Wang S ,
      3. Summers RM
      . Machine learning and radiology. Med Image Anal 2012;16:933–51.doi:10.1016/j.media.2012.02.005
      OpenUrlCrossRefPubMedWeb of Science
    131. 131.↵
      2. Kourou K ,
      3. Exarchos TP ,
      4. Exarchos KP , et al
      . Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 2015;13:8–17.doi:10.1016/j.csbj.2014.11.005
      OpenUrl
    132. 132.↵
      2. Ramos-Pollán R ,
      3. Guevara-López MA ,
      4. Suárez-Ortega C , et al
      . Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J Med Syst 2012;36:2259–69.doi:10.1007/s10916-011-9693-2
      OpenUrlCrossRefPubMed
    133. 133.↵
      2. Murdoch TB ,
      3. Detsky AS
      . The inevitable application of big data to health care. JAMA 2013;309:1351–2.doi:10.1001/jama.2013.393
      OpenUrlCrossRefPubMedWeb of Science
    134. 134.↵
      2. Churpek MM ,
      3. Yuen TC ,
      4. Winslow C , et al
      . Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards. Crit Care Med 2016;44:368–74.doi:10.1097/CCM.0000000000001571
      OpenUrlCrossRef
    135. 135.↵
      2. Hastie T ,
      3. Tibshirani R ,
      4. Friedman J
      . The Elements of Statistical Learning. New York: Springer, 2009.
    136. 136.↵
      2. Sanchez-Pinto LN ,
      3. Luo Y ,
      4. Churpek MM
      . Big Data and Data Science in Critical Care. Chest 2018;154:1239–48.doi:10.1016/j.chest.2018.04.037
      OpenUrl
    137. 137.↵
      2. Johnson KW ,
      3. Torres Soto J ,
      4. Glicksberg BS , et al
      . Artificial Intelligence in Cardiology. J Am Coll Cardiol 2018;71:2668–79.doi:10.1016/j.jacc.2018.03.521
      OpenUrlFREE Full Text
    138. 138.↵
      2. Taylor RA ,
      3. Pare JR ,
      4. Venkatesh AK , et al
      . Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach. Acad Emerg Med 2016;23:269–78.doi:10.1111/acem.12876
      OpenUrl
    139. 139.↵
      2. Jolliffe IT
      . Principal Component Analysis and Factor Analysis. Principal component analysis: Springer, 1986:115–28.
    140. 140.↵
      2. Burgel PR ,
      3. Paillasseur JL ,
      4. Caillaud D , et al
      . Initiatives BPCO Scientific Committee. Clinical COPD phenotypes: a novel approach using principal component and cluster analyses. Eur Respir J 2010;36:531–9.doi:10.1183/09031936.00175109
      OpenUrlAbstract/FREE Full Text
    141. 141.↵
      2. Gordon DB ,
      3. Polomano RC ,
      4. Pellino TA , et al
      . Revised American Pain Society Patient Outcome Questionnaire (APS-POQ-R) for quality improvement of pain management in hospitalized adults: preliminary psychometric evaluation. J Pain 2010;11:1172–86.doi:10.1016/j.jpain.2010.02.012
      OpenUrlCrossRefPubMed
    142. 142.↵
      2. Gasquet I ,
      3. Villeminot S ,
      4. Estaquio C , et al
      . Construction of a questionnaire measuring outpatients' opinion of quality of hospital consultation departments. Health Qual Life Outcomes 2004;2:43.doi:10.1186/1477-7525-2-43
      OpenUrlCrossRefPubMed
    143. 143.↵
      2. Koenker R
      . Quantile regression: Cambridge university press, 2005.
    144. 144.↵
      2. Ding R ,
      3. McCarthy ML ,
      4. Desmond JS , et al
      . Characterizing waiting room time, treatment time, and boarding time in the emergency department using quantile regression. Acad Emerg Med 2010;17:813–23.doi:10.1111/j.1553-2712.2010.00812.x
      OpenUrlCrossRefPubMed
    145. 145.↵
      2. Bottai M ,
      3. Cai B ,
      4. McKeown RE
      . Logistic quantile regression for bounded outcomes. Stat Med 2010;29:309–17.doi:10.1002/sim.3781
      OpenUrlPubMed

    Footnotes

    • Contributors YC and CH designed the study with FR-H, ID and AV. YC, ID, CH and MB collected and analysed the data. YC and CH wrote the first draft of the manuscript. FR-H, ID, AV, M-CC and MB contributed to the writing of the manuscript. All authors read and approved the final manuscript.

    • Funding This work was financed by grants from the Fonds de recherche du Québec – Santé and the Centre de recherche du Centre hospitalier universitaire de Sherbrooke. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement There are no unpublished additional data from the study.

    • Patient consent for publication Not required.