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Background. Emergency department (ED) overcrowding represents a significant public health problem in developed countries. Frequent users of the emergency departments (FUEDs; reporting 5 or more ED visits in the past year) are often affected by medical, psychological, social, and substance use problems and account for a disproportionately high number of ED visits. Past research indicates that the case management (CM) intervention is a promising way to reduce ED overcrowding and improve FUEDs' quality of life. There is, however, very limited knowledge about how to disseminate and implement this intervention on a large scale to diverse clinical settings, including community hospitals and non-academic centers. This research project aims to implement a CM intervention tailored to FUEDs in the public hospitals with ED in the French-speaking region of Switzerland and to evaluate both the implementation process and effectiveness of the CM intervention. Methods. This research project will examine both implementation and clinical outcomes. The implementation part of the study will describe quantitatively and qualitatively factors that influence the implementation process; the investigators will also examine implementation effectiveness (i.e., whether the implementation of the CM intervention in the ED was successful or not). The clinical part of the study will evaluate participants' trajectories on clinical variables (e.g., quality of life, ED use) after receiving the CM intervention. Discussion. This research project will contribute to implementation science by providing key insights into the processes for implementing CM into broader practice. This research project is also likely to have both clinical and public health implications.
Frequent users of the emergency departments (FUEDs) and other health care services are of much interest to clinicians, administrators and researchers. Emergency department (ED) overuse is linked to ED overcrowding, which in turn has a negative impact on patients and health system outcomes. There are over 1.4 million annual ED visits in Switzerland, with 84% of EDs reporting overcrowding. FUEDs made up 4.4% of the patient population and made 12.1% of all visits at the Lausanne University Hospital ED. Driving this high use of health care services is the fact that FUEDs often suffer from chronic medical diseases, including heart disease, pulmonary disease and cancer, at high rates, in addition to mental illness and substance use disorders. CM has been shown to be a promising intervention to reduce ED overuse by frequent users in research settings; however, there is limited knowledge about how to disseminate and implement a CM intervention for FUEDs on a large scale to diverse clinical settings, including community hospitals and non-academic centers.
The research project has the following three specific aims:
This research project is an observational study with a hybrid study design, measuring both implementation variables and clinical outcomes relating to the dissemination and implementation of the CM intervention. The implementation part of the study uses a mixed methods design (i.e., using both qualitative and quantitative analyses) to describe both qualitatively and quantitatively factors that may influence implementation process. The clinical part of the study uses a within-subject (pre-post intervention) design to evaluate participants' trajectories after receiving the CM intervention.
The whole research project will take place over five phases.
Phases 1-2. Development and exploration Procedures. The I-CaM research team will develop the CM Toolkit, the implementation program, the team member selection-support materials and the informational announcement to be disseminated to hospitals in the French-speaking region of Switzerland. Specifically, the research team will develop and send by email a survey aiming to gauge interest and needs regarding the CM intervention to all eligible hospitals (to key staff, Chief of Emergency Department). The I-CaM research team will follow up with more information about the CM intervention and the study procedures. Then, interested parties will participate in a one-day workshop at Lausanne University Hospital, during which key staff will receive training on the CM intervention, on the implementation science and on the study procedures in general. Following the workshop, all sites agreeing to participate will a) be included in the study and b) complete a questionnaire and participate in a semi-structured interview (assessing implementation outcomes). Further, the same assessment will be conducted with hospitals not interested in participating in the study, in face-to-face or by phone and mail depending upon possibilities (i.e., disinterest analysis).
Phase 3. Preparation Procedures. Included sites will then prepare for implementation of the CM intervention; sites will first identify CM intervention team members, including strategical and operational champions and clinicians. Whereas the strategical champion (i.e., leader) will promote and hold the implementation project, the operational champion will support its implementation and application and supervise clinicians who will be in charge of the CM intervention administration. Next, available resources will be established at each source and data collection and storage systems will be finalized to gather data on health service and implementation outcomes by the research team. The I-CaM research team will also conduct trainings for the CM team to relevant local staff. Finally, at the end of the preparation phase, champions and clinicians involved in the project on-site will complete a questionnaire.
Phase 4. Operation The operation phase. The CM intervention will be implemented at all sites included in the study. At each site, patients fulfilling the inclusion criteria will be contacted by the case managers. The number of participants included will depend upon each site resources. If possible (depending upon resources on each site), eligible patients having been treated in the ED in the 10 days prior the recruitment window will be contacted by phone and proposed to come back to the ED to participate in the study. Remaining FUEDs (i.e., the FUEDs not invited to participate in the study) will receive usual care. When first meeting the participant, the case managers will present the study and examine the exclusion criterion. If the patient does not have any exclusion criteria and is interested in participating, the case managers will conduct the informed consent process. After providing written consent, participants will receive the CM intervention. After each inclusion on site, the case managers will up-date the I-CaM research team in charge of the clinical assessment (i.e., baseline and follow-up assessments of the clinical variables to describe participants' trajectories). The I-CaM research team will contact participants and conduct the baseline assessment within 10 days following the inclusion. Participants will then complete follow-up assessments at 3, 6 and 12 months post-baseline. Other clinical variables will be directly extracted from medical records on-site by the I-CaM research team. At the end of the operation phase, the research team will conduct semi-structured interviews with case managers and champions. Both champions and case managers will also complete a questionnaire.
Phase 5. Sustainability Procedures. Following the implementation of the CM intervention, the sites will continue the CM programs at their discretion. The I-CaM research team will monitor the activity at each site and will be available for as-needed support to all sites. Clinical outcomes (i.e., ED use, quality of life, etc.) will not be measured over this finale phase. At the end of the sustainability phase, the research team will conduct a semi -structured interview with clinicians and champions. Both champions and clinicians will also complete a survey.
Data Analysis Plan Implementation measures analysis. Descriptive statistics will be used to describe participants' characteristics and to report implementation outcomes. The investigators will also test implementation measure changes over time. Given the expected small sample size regarding implementation outcomes, these analyses will be triangulated with qualitative data. Specifically, interview contents will be transcribed and explored to identify participants' recurring codes and categories; the investigators will use conventional content analysis (i.e., a systematic process of coding and classification) [1] using a qualitative software (i.e., Atlas.ti or NVivo) to examine qualitative data.
Clinical outcomes. Data will be screened for missing cases, outliers, and normality of distributions using descriptive statistics and plots. The investigators will take appropriate steps to deal with missing data. First, they will conduct analyses to detect missingness patterns and test whether they may be considered "ignorable."[2] If more than 5% of outcome data are missing,[3] the sample will be divided into 2 groups (i.e., missing, not missing), and fully observed variables to predict missingness on the affected outcome will be used. If fully observed variables are not significant predictors, missingness may be considered as "observed at random" and fulfilling some criteria for "missing completely at random" (MCAR) assumptions. In that case, the investigators will use multiple imputations procedures for measured outcomes and direct maximum likelihood estimation for structural models. If data missingness is non ignorable (MNAR), pattern-mixture models with multiple imputation to model the missingness mechanism will be used.
Main analyses will comprise multilevel models (i.e., MLM; mixed effects model) [4] utilizing appropriate distributions for the outcome variables (e.g., Poisson, negative binomial, normal). MLM examine the effect of time (after receiving the CM intervention) on clinical outcomes (e.g., quality of life, self-efficacy). MLM is appropriate to handle nonindependence data. Specifically, data will be clustered by participants (i.e., repeated measures) and by hospitals. MLM does not assume independence of observations. Dependence is modeled through random effects (representing different sources of variability in the data). The investigators will include sources of random variability at the group level accounting for between-group differences and another random effects for the individual accounting for within-person differences in the repeated measures. MLM will be adjusted for demographic variables (i.e., age and gender), health-care utilization (including ED use), at-risk behaviors (e.g., alcohol use disorders) and health status. Descriptive statistics will be conducted on SPSS and MLM on STATA. The significance level will be set at p = .05.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Frequent users of emergency departement | FUEDs receiving the CM intervention in sites participating in the research project will be assessed over time on clinical variables (see inclusion and exclusion criteria) |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Case-management intervention for frequent users of the emergency department | Other | The CM intervention to be implemented is an evidence-based practice. It consists of individualized services based on a detailed baseline evaluation using validated scales to identify all of the social and medical needs of each FUED. The CM team provides counseling and education on health care utilization, substance abuse and the social determinants of health directly to the FUEDs, using skills such as motivational interviewing and cross-cultural competences. Additionally, the CM team provides concrete services including, referral to psychiatric or substance abuse treatment, or medical services (such as a GP or medical specialist), on a case-by-case basis. Another key element of the CM intervention is to connect all health care or social service providers within the hospital or in the community, promoting continuity of care and improving the FUEDs' ability to navigate the complex health care system. |
| Measure | Description | Time Frame |
|---|---|---|
| Adoption rate as assessed by the number of hospitals included in the research project divided by the number of hospitals invited to participate | In total, 22 hospitals will be invited to participate therefore the number of hospitals included will be divided by 22 | An average of 6 months |
| Reach as assessed by the number of patients receiving the CM intervention divided by the total number of eligible patients at the operation phase | An average of 18 months | |
| Reach as assessed by the number of patients receiving the CM intervention divided by the total number of eligible patients at the sustainability phase | An average of 36 months | |
| Level of the CM intervention integration as assessed with the Normalization MeAsure Development survey (NoMad) at the operation phase [5] | The NoMad survey includes 20 items organized around four subscales (coherence, cognitive participation, collective action and reflexive monitoring). Participants (i.e., staff involved in the CM intervention implementation) are asked to indicate the degree to which they agree or disagree with 20 statements related to the CM intervention integration, using a 5-point Likert-scale, where 1 = strongly disagree and 5 = strongly agree. Descriptive statistics (means and percentages) will be computed for each item. Higher scores will indicate higher agreement from the sample. | An average of 18 months |
| Level of the CM intervention integration as assessed with the Normalization MeAsure Development survey (NoMad) at the sustainability phase [5] | The NoMad survey includes 20 items organized in four subscales (coherence, cognitive participation, collective action and reflexive monitoring). Participants (i.e., staff involved in the CM intervention implementation) are asked to indicate the degree to which they agree or disagree with 20 statements related to the CM intervention integration, using a 5-point Likert-scale, where 1 = strongly disagree and 5 = strongly agree. Descriptive statistics (means and percentages) will be computed for each item. Higher scores will indicate higher agreement from the sample. |
| Measure | Description | Time Frame |
|---|---|---|
| Number of implementation stages completed as assessed with an adapted version of the Stages of Implementation Completion tool [7] (in hospitals included in the study) | The SIC is a quasi-quantitative questionnaire measuring progression of implementation activities organized in eight stages by recording the dates implementation activities were completed. | An average of 36 months |
| Measure | Description | Time Frame |
|---|---|---|
| Change in levels of acceptability of the CM intervention in staff involved with the implementation between the preparation and the operation phase as assessed with the Acceptability Intervention Measure (AIM) [13] | The four-item AIM will assess the extent to which staff evaluate the intervention as acceptable using a 5-point Likert scale, where 1 = strongly disagree and 5 = strongly agree. Mean scores will be computed and higher scores will indicate higher levels of acceptability of the intervention. |
Implementation part of the study:
Inclusion Criteria:
No exclusion Criteria:
Clinical part of the study:
Inclusion Criteria:
Exclusion Criteria:
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Frequent users of the emergency departments (i.e., FUEDs; reporting 5 or more ED visits in the past year) [17] are in need of targeted attention from clinicians, public health advisors and researchers. FUEDs disproportionately access ED services, and contribute to ED overcrowding (i.e., FUEDs account for 3 to 8% of all patients and 12 to 28% of all ED visits) [18-20]. Driving this high use of health care services is the fact that FUEDs are often affected by multiple chronic medical diseases (e.g., heart disease, cancer) [21-23], as well as psychiatric, substance use and social problems (e.g., unemployment or social isolation) [18, 24-26].
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Patrick Bodenmann, MD, MSc | Contact | 0041213144937 | patrick.bodenmann@hospvd.ch | |
| Veronique S Grazioli, PhD | Contact | 1141 21 314 23 72 | veronique.grazioli@chuv.ch |
| Name | Affiliation | Role |
|---|---|---|
| Patrick Bodenmann, MD MSc | University of Lausanne Hospitals | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hôpital du Jura Bernois | Recruiting | Saint-Imier | Canton of Bern | 2610 | Switzerland |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 16204405 | Background | Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005 Nov;15(9):1277-88. doi: 10.1177/1049732305276687. | |
| Background | 2. Allison PD: Missing data. Thousand Oaks, CA: Sage; 2001. | ||
| Background | 3. Tabachnick BG, Fidell LS: Using Multivariate Statistics. 5th edn. Boston: Allyn and Bacon; 2007. | ||
| 19649151 |
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|
| An average of 36 months |
| Level of normalization of the CM intervention as assessed with the Measure of Inner Context Sustainment (MICS; in development and testing) at the operation phase | Participants (i.e., staff involved in the CM intervention implementation) are asked to indicate to which extent they agree with 22 statements related to the CM intervention normalization using a 5-point Likert scale, where 0 = not at all and 4 = to a very great extent. Descriptive statistics (means, percentages) will be computed for each item. Higher scores will indicate higher levels of agreement from the sample. | An average of 18 months |
| Level of normalization of the CM intervention as assessed with the Measure of Inner Context Sustainment (MICS; in development and testing) at the sustainability phase | Participants (i.e., staff involved in the CM intervention implementation) are asked to indicate to which extent they agree with 22 statements related to the CM intervention normalization using a 5-point Likert scale, where 0 = not at all and 4 = to a very great extent. Descriptive statistics (means, percentages) will be computed for each item. Higher scores will indicate higher levels of agreement from the sample. | An average of 36 months |
| Change in the number of emergency department visits between baseline and 12 months follow-up assessments over the 12 months study period among FUEDs receiving the CM intervention | Number of ED visits over the past 12 months will be extracted from medical records. | Baseline and 12-month follow-up assessments |
| Trajectories in quality of life over the 12 months study period among FUEDs receiving the CM intervention as assessed with the World Health Organization Quality of Life - Bref scale (WHOQOL-BREF) [6] (among FUEDs receiving the CM intervention). | The WHOQOL-BREF includes 22 items assessing four domains of quality of life (4 subscales): physical health, psychological health, social relationships and environment. Each question refers to the last past two week and uses a 5-point Likert scale (where 1 = very unsatisfied and 5 = very satisfied, 1= never and 5 = always or 1 = strongly disagree and 5 = strongly agree depending on item content. Following the instrument guideline, percentage ratings within each domain will be computed ranging from 0 to 100, where 0 = lowest quality of life and 100 = highest quality of life | Baseline, 3-, 6- and 12-month follow-up assessments |
| Time spent in each implementation stage as assessed with an adapted version of the Stages of Implementation Completion Tool (SIC) | The SIC is a quasi-quantitative questionnaire measuring progression of implementation activities organized in eight stages by recording the dates implementation activities were completed. | An average of 36 months |
| Proportion of activities completed on each stage as assessed with an adapted version of the Stages of Implementation Completion Tool (SIC) | The SIC is a quasi-quantitative questionnaire measuring progression of implementation activities organized in eight stages by recording the dates implementation activities were completed. | An average of 36 months |
| Costs salary required for staff implementing the CM intervention as assessed with an adapted version of the Stages of Implementation Completion Tool (SIC) [8] | The SIC will be used to track hours put forth among staff involved with the CM intervention implementation on sites. Full time equivalent and averaged salary costs scores will be computed based on these data. | An average of 36 months |
| Costs salary required for the research team to assist the CM intervention implementation as assessed with an adapted version of the Stages of Implementation Completion Tool (SIC) [8] | The SIC will be used to track hours put forth among research staff involved with the CM intervention implementation. Full time equivalent and averaged salary costs scores will be computed based on these data. | An average of 36 months |
| Trajectories in health-care reorientations over the 12 months study period among FUEDs receiving the CM intervention | Health-care reorientations will be extracted from medical records. | Baseline, 3, 6 and 12-months follow-up assessments |
| Trajectories in empowerment over the 12 months study period among FUEDs receiving the CM intervention as assessed with the Health Care Empowerment Informed, Committed, Collaborative and Engaged subscales of the Health Care Empowerment Inventory [9]. | The Health Care Empowerment Informed, Committed, Collaborative and Engaged subscales of the Health Care Empowerment Inventory includes 4 items assessing empowerment. Participants are asked to indicate to what extent they agree with 4 statements using a 5-point Likert scale where 1=strongly disagree and 5 strongly agree. Means scores will be computed. Higher scores indicate higher levels of empowerment. | Baseline, 3, 6 and 12-months follow-up assessments |
| Trajectories in self-efficacy over the 12 months study period among FUEDs receiving the CM intervention as assessed with the General Self-Efficacy scale [10]. | The General Self-Efficacy scale includes 10 items assessing self-efficacy. Participants are asked to indicate to what extent they agree with 10 statements, using a 4-point Likert scale where 1=not at all true and 4=completely true. Means scores will be computed. Higher scores indicate higher levels of general self-efficacy. | Baseline, 3, 6 and 12-months follow-up assessments |
| Trajectories in health literacy over the 12 months study period among FUEDs receiving the CM intervention using the European Health Literacy Project Questionnaire [11] | The European Health Literacy Project Questionnaire is a 16-item questionnaire assessing health-literacy. Participants are asked to indicate to what extent they find easy or difficult to access, understand and appraise health-related information using a 4-point Likert scale where 1 = very difficult and 4 = very easy. Mean scores will be computed. Higher scores indicate higher health literacy. | Baseline, 3, 6 and 12-months follow-up assessments |
| Trajectories in precursors of alcohol use changes over the 12-months study period among FUEDs receiving the CM intervention as assessed with four single-item visual analog rulers [12]. | The four single-item visual analog rulers will assess importance, intentions, readiness and confidence regarding alcohol use changes. Participants are asked to indicate how important it is to change their alcohol use, to what extend the intend to change their alcohol use, to what extent they are ready and self-confident to do so, using a visual analog rulers ranging from 1 to 10, where 1= not at all and 10 = completely. Each item will provide a score, and higher scores will indicate higher levels of readiness to change alcohol use. | Baseline, 3, 6 and 12-months follow-up assessments |
| An average of 12 and 18 months |
| Change in levels of the CM intervention appropriateness perception in staff involved with the implementation between the preparation and the operation phase as assessed with the Appropriateness Measure (IAM) [13]. | The four-item IAM will assess the extent to which staff evaluate the intervention as appropriate using a 5-point Likert scale, where 1 = strongly disagree and 5 = strongly agree. Mean scores will be computed and higher scores will indicate higher levels of appropriateness perception of the intervention. | An average of 12 and 18 months |
| Change in levels of the CM intervention feasibility perception in staff involved with the implementation between the preparation and the operation phase as assessed with the Feasibility of the Intervention Measure (FIM) [13] | The four-item FIM will assess the extent to which staff evaluate the intervention as feasible using a 5-point Likert scale, where 1 = strongly disagree and 5 = strongly agree. Mean scores will be computed and higher scores will indicate higher levels of feasibility perception of the intervention. | An average of 12 and 18 months |
| Change in intentions to use the CM intervention in staff involved with the implementation between the preparation and the operation phase as assessed with the Measure of Innovation Specific Implementation Intentions (MISII) [14] | The three-item MISII will assess intentions to use the CM intervention. Participants are asked to indicate to which intent they intent to use the CM intervention with a 5-point Likert scale, where 1 = not at all and 5 = to a very great extent. Higher scores will indicate higher intentions to use the CM intervention. | An average of 12 and 18 months |
| Change in implementation climate in hospitals implementing the CM intervention between the preparation and operation phase as assessed with the implementation climate scale [15] | The implementation climate scale includes 6 items assessing to which intent staff perceive that CM intervention use is expected, supported and rewarded by the institution. Participants answered to each item with a 5-point Likert scale, where 0 = disagree and 4 = agree. Higher scores indicate more supportive implementation climates. | An average of 12 and 18 months |
| Change in organizational readiness for change assessed in staff involved with the implementation between the preparation and operation phase as assessed with the Organizational Readiness for Implementation Change (ORIC) [16] | The ORIC includes 12 items assessing organizational readiness to change using a 5-point Likert scale where 1 = strongly disagree and 5 = strongly agree. Mean scores will be computed and higher scores will indicate higher perceived organizational readiness to change. | An average of 12 and 18 months |
| Hôpital du Jura | Recruiting | Delémont | Canton of Jura | 2800 | Switzerland |
|
| Hôpital Intercantonal de la Broye (HIB) | Recruiting | Payerne | Canton of Vaud | 1530 | Switzerland |
|
| Etablissements hospitaliers du Nord Vaudois (eHnv) | Recruiting | Yverdon-les-Bains | Canton of Vaud | 1400 | Switzerland |
|
| Hôpital du Valais | Recruiting | Sion | Valais | 1951 | Switzerland |
|
| Hôpital Fribourgeois | Recruiting | Fribourg | 1708 | Switzerland |
|
| Hôpitaux Universitaires de Genève (HUG) | Recruiting | Geneva | 1205 | Switzerland |
|
| Hôpitaux Neuchâtelois (HNE) | Recruiting | Neuchâtel | 2000 | Switzerland |
|
| Background |
| Kwok OM, Underhill AT, Berry JW, Luo W, Elliott TR, Yoon M. Analyzing Longitudinal Data with Multilevel Models: An Example with Individuals Living with Lower Extremity Intra-articular Fractures. Rehabil Psychol. 2008 Aug;53(3):370-386. doi: 10.1037/a0012765. |
| Background | 5. Normalization Process Theory On-line Users' Manual, Toolkt and NoMad instrument [http://www.normalizationprocess.org] |
| 15085902 | Background | Skevington SM, Lotfy M, O'Connell KA; WHOQOL Group. The World Health Organization's WHOQOL-BREF quality of life assessment: psychometric properties and results of the international field trial. A report from the WHOQOL group. Qual Life Res. 2004 Mar;13(2):299-310. doi: 10.1023/B:QURE.0000018486.91360.00. |
| 21974914 | Background | Chamberlain P, Brown CH, Saldana L. Observational measure of implementation progress in community based settings: the Stages of Implementation Completion (SIC). Implement Sci. 2011 Oct 6;6:116. doi: 10.1186/1748-5908-6-116. |
| 24729650 | Background | Saldana L, Chamberlain P, Bradford WD, Campbell M, Landsverk J. The Cost of Implementing New Strategies (COINS): A Method for Mapping Implementation Resources Using the Stages of Implementation Completion. Child Youth Serv Rev. 2014 Apr 1;39:177-182. doi: 10.1016/j.childyouth.2013.10.006. |
| 23029184 | Background | Johnson MO, Rose CD, Dilworth SE, Neilands TB. Advances in the conceptualization and measurement of Health Care Empowerment: development and validation of the Health Care Empowerment inventory. PLoS One. 2012;7(9):e45692. doi: 10.1371/journal.pone.0045692. Epub 2012 Sep 19. |
| Background | 10. French adaptation of the general self- efficacy scale - Auto-efficacité généralisée [http://userpage.fu-berlin.de/~health/french.htm] |
| Background | 11. Consortium H-E: Comparative report of health literacy in eight EU member states. The European health literacy survey HLS-EU. 2012. |
| 28930060 | Background | Walton MA, Ngo QM, Chermack ST, Blow FC, Ehrlich PF, Bonar EE, Cunningham RM. Understanding Mechanisms of Change for Brief Alcohol Interventions Among Youth: Examination of Within-Session Interactions. J Stud Alcohol Drugs. 2017 Sep;78(5):725-734. doi: 10.15288/jsad.2017.78.725. |
| 28851459 | Background | Weiner BJ, Lewis CC, Stanick C, Powell BJ, Dorsey CN, Clary AS, Boynton MH, Halko H. Psychometric assessment of three newly developed implementation outcome measures. Implement Sci. 2017 Aug 29;12(1):108. doi: 10.1186/s13012-017-0635-3. |
| Background | 14. Moullin JC, Erhart MG, Torres EM, Aarons GA: 'Development and testing of a brief EBP implementation intentions scale using Rasch analysis. In Society for Implementation Research Collaboration (SIRC); Seattle, USA. 2017 |
| 24742308 | Background | Jacobs SR, Weiner BJ, Bunger AC. Context matters: measuring implementation climate among individuals and groups. Implement Sci. 2014 Apr 17;9:46. doi: 10.1186/1748-5908-9-46. |
| 24410955 | Background | Shea CM, Jacobs SR, Esserman DA, Bruce K, Weiner BJ. Organizational readiness for implementing change: a psychometric assessment of a new measure. Implement Sci. 2014 Jan 10;9:7. doi: 10.1186/1748-5908-9-7. |
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| 27400922 | Background | Bodenmann P, Velonaki VS, Griffin JL, Baggio S, Iglesias K, Moschetti K, Ruggeri O, Burnand B, Wasserfallen JB, Vu F, Schupbach J, Hugli O, Daeppen JB. Case Management may Reduce Emergency Department Frequent use in a Universal Health Coverage System: a Randomized Controlled Trial. J Gen Intern Med. 2017 May;32(5):508-515. doi: 10.1007/s11606-016-3789-9. Epub 2016 Jul 11. |
| 22221292 | Background | Bieler G, Paroz S, Faouzi M, Trueb L, Vaucher P, Althaus F, Daeppen JB, Bodenmann P. Social and medical vulnerability factors of emergency department frequent users in a universal health insurance system. Acad Emerg Med. 2012 Jan;19(1):63-8. doi: 10.1111/j.1553-2712.2011.01246.x. Epub 2012 Jan 5. |
| 17513534 | Background | Locker TE, Baston S, Mason SM, Nicholl J. Defining frequent use of an urban emergency department. Emerg Med J. 2007 Jun;24(6):398-401. doi: 10.1136/emj.2006.043844. |
| 25647038 | Background | van Tiel S, Rood PP, Bertoli-Avella AM, Erasmus V, Haagsma J, van Beeck E, Patka P, Polinder S. Systematic review of frequent users of emergency departments in non-US hospitals: state of the art. Eur J Emerg Med. 2015 Oct;22(5):306-15. doi: 10.1097/MEJ.0000000000000242. |
| 12833184 | Background | Huang JA, Tsai WC, Chen YC, Hu WH, Yang DY. Factors associated with frequent use of emergency services in a medical center. J Formos Med Assoc. 2003 Apr;102(4):222-8. |
| 20346540 | Background | LaCalle E, Rabin E. Frequent users of emergency departments: the myths, the data, and the policy implications. Ann Emerg Med. 2010 Jul;56(1):42-8. doi: 10.1016/j.annemergmed.2010.01.032. Epub 2010 Mar 26. |
| 26505061 | Background | Bergeron P, Courteau J, Vanasse A. Proximity and emergency department use: Multilevel analysis using administrative data from patients with cardiovascular risk factors. Can Fam Physician. 2015 Aug;61(8):e391-7. |
| 12605196 | Background | Byrne M, Murphy AW, Plunkett PK, McGee HM, Murray A, Bury G. Frequent attenders to an emergency department: a study of primary health care use, medical profile, and psychosocial characteristics. Ann Emerg Med. 2003 Mar;41(3):309-18. doi: 10.1067/mem.2003.68. |
| 26452550 | Background | Vu F, Daeppen JB, Hugli O, Iglesias K, Stucki S, Paroz S, Canepa Allen M, Bodenmann P. Screening of mental health and substance users in frequent users of a general Swiss emergency department. BMC Emerg Med. 2015 Oct 9;15:27. doi: 10.1186/s12873-015-0053-2. |
| 21689565 | Background | Althaus F, Paroz S, Hugli O, Ghali WA, Daeppen JB, Peytremann-Bridevaux I, Bodenmann P. Effectiveness of interventions targeting frequent users of emergency departments: a systematic review. Ann Emerg Med. 2011 Jul;58(1):41-52.e42. doi: 10.1016/j.annemergmed.2011.03.007. |
| 30634955 | Derived | Grazioli VS, Moullin JC, Kasztura M, Canepa-Allen M, Hugli O, Griffin J, Vu F, Hudon C, Jackson Y, Wolff H, Burnand B, Daeppen JB, Bodenmann P. Implementing a case management intervention for frequent users of the emergency department (I-CaM): an effectiveness-implementation hybrid trial study protocol. BMC Health Serv Res. 2019 Jan 11;19(1):28. doi: 10.1186/s12913-018-3852-9. |
| ID | Term |
|---|---|
| D004630 | Emergencies |
| ID | Term |
|---|---|
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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