CONECT-6: a case-finding tool to identify patients with complex health needs

Hudon, Catherine; Bisson, Mathieu; Dubois, Marie‐France; Chiu, Yohann; Chouinard, Maud‐Christine; Dubuc, Nicole; Elazhary, Nicolas; Sabourin, Véronique; Vanasse, Alain

doi:10.1186/s12913-021-06154-4

Cited by 13 publications

(15 citation statements)

References 51 publications

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“…We know that patients with complex needs frequently use many health and social services, and that ED visits are a good proxy of this use [44]. For screening, we will thus use our COmplex NEeds Case-finding Tool -6 (CONECT-6) [45]. We validated this 6-question tool among patients at their third or more visit to the ED within 12 months, to screen those with complex needs (INTERMED Self-Assessment positive), with a sensitivity of 90% and a specificity of 66% with a threshold of two or more positive answers [45].…”

Section: Sampling Of Patients With Complex Needsmentioning

confidence: 99%

“…For screening, we will thus use our COmplex NEeds Case-finding Tool -6 (CONECT-6) [45]. We validated this 6-question tool among patients at their third or more visit to the ED within 12 months, to screen those with complex needs (INTERMED Self-Assessment positive), with a sensitivity of 90% and a specificity of 66% with a threshold of two or more positive answers [45]. Patients screened as having complex needs with CONECT-6 will be invited to confirm their complex needs with INTERMED Self-Assessment (IMSA) [46].…”

Section: Sampling Of Patients With Complex Needsmentioning

confidence: 99%

“…During the first year of the study, research assistants will be present in each ED four days a week to identify adults (≥ 18 years) at their third or more visit to the ED within 12 months, using the information system of the ED. Relying on a previous study [45], we estimate this number of patients at six per day in each ED (more in bigger EDs and fewer less in smaller EDs). Research assistants will invite those patients to answer CONECT-6 (two minutes).…”

Section: Sampling Of Patients With Complex Needsmentioning

confidence: 99%

“…Research assistants will invite those patients to answer CONECT-6 (two minutes). Approximately 30% of these patients will score positive on CONECT-6 [45] (n = 2 per day in each ED), and the research assistants will administer the IMSA questionnaire to those patients. CONECT-6 has a positive predictive value of 50% [45]; therefore, one patient will score complex on IMSA per day in each ED.…”

Section: Sampling Of Patients With Complex Needsmentioning

confidence: 99%

“…Approximately 30% of these patients will score positive on CONECT-6 [45] (n = 2 per day in each ED), and the research assistants will administer the IMSA questionnaire to those patients. CONECT-6 has a positive predictive value of 50% [45]; therefore, one patient will score complex on IMSA per day in each ED. Estimating an acceptance rate of 50%, we will recruit two patients per week for the project in each ED.…”

Section: Sampling Of Patients With Complex Needsmentioning

confidence: 99%

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Better understanding care transitions of adults with complex health and social care needs: a study protocol

Hudon

Aubrey‐Bassler

Chouinard

et al. 2022

BMC Health Serv Res

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Background Adults with chronic conditions who also suffer from mental health comorbidities and/or social vulnerability require services from many providers across different sectors. They may have complex health and social care needs and experience poorer health indicators and high mortality rates while generating considerable costs to the health and social services system. In response, the literature has stressed the need for a collaborative approach amongst providers to facilitate the care transition process. A better understanding of care transitions is the next step towards the improvement of integrated care models. The aim of the study is to better understand care transitions of adults with complex health and social care needs across community, primary care, and hospital settings, combining the experiences of patients and their families, providers, and health managers. Methods/design We will conduct a two-phase mixed methods multiple case study (quantitative and qualitative). We will work with six cases in three Canadian provinces, each case being the actual care transitions across community, primary care, and hospital settings. Adult patients with complex needs will be identified by having visited the emergency department at least three times over the previous 12 months. To ensure they have complex needs, they will be invited to complete INTERMED Self-Assessment and invited to enroll if positive. For the quantitative phase, data will be obtained through questionnaires and multi-level regression analyses will be conducted. For the qualitative phase, semi-structured interviews and focus groups will be conducted with patients, family members, care providers, and managers, and thematic analysis will be performed. Quantitative and qualitative results will be compared and then merged. Discussion This study is one of the first to examine care transitions of adults with complex needs by adopting a comprehensive vision of care transitions and bringing together the experiences of patients and family members, providers, and health managers. By using an integrated knowledge translation approach with key knowledge users, the study’s findings have the potential to inform the optimization of integrated care, to positively impact the health of adults with complex needs, and reduce the economic burden to the health and social care systems.

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Section: Sampling Of Patients With Complex Needsmentioning

confidence: 99%

Section: Sampling Of Patients With Complex Needsmentioning

confidence: 99%

Section: Sampling Of Patients With Complex Needsmentioning

confidence: 99%

Section: Sampling Of Patients With Complex Needsmentioning

confidence: 99%

Section: Sampling Of Patients With Complex Needsmentioning

confidence: 99%

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Better understanding care transitions of adults with complex health and social care needs: a study protocol

Hudon

Aubrey‐Bassler

Chouinard

et al. 2022

BMC Health Serv Res

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First Evidence on the Validity of the Complexity Index Derived from the Resident Assessment Instrument for Home Care in Home Care Patients

Vallet,

Ludwig,

Ashikali

et al. 2024

Journal of the American Medical Directors Association

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Machine learning to improve frequent emergency department use prediction: a retrospective cohort study

Chiu

Courteau

Dufour

et al. 2023

Sci Rep

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Frequent emergency department use is associated with many adverse events, such as increased risk for hospitalization and mortality. Frequent users have complex needs and associated factors are commonly evaluated using logistic regression. However, other machine learning models, especially those exploiting the potential of large databases, have been less explored. This study aims at comparing the performance of logistic regression to four machine learning models for predicting frequent emergency department use in an adult population with chronic diseases, in the province of Quebec (Canada). This is a retrospective population-based study using medical and administrative databases from the Régie de l’assurance maladie du Québec. Two definitions were used for frequent emergency department use (outcome to predict): having at least three and five visits during a year period. Independent variables included sociodemographic characteristics, healthcare service use, and chronic diseases. We compared the performance of logistic regression with gradient boosting machine, naïve Bayes, neural networks, and random forests (binary and continuous outcome) using Area under the ROC curve, sensibility, specificity, positive predictive value, and negative predictive value. Out of 451,775 ED users, 43,151 (9.5%) and 13,676 (3.0%) were frequent users with at least three and five visits per year, respectively. Random forests with a binary outcome had the lowest performances (ROC curve: 53.8 [95% confidence interval 53.5–54.0] and 51.4 [95% confidence interval 51.1–51.8] for frequent users 3 and 5, respectively) while the other models had superior and overall similar performance. The most important variable in prediction was the number of emergency department visits in the previous year. No model outperformed the others. Innovations in algorithms may slightly refine current predictions, but access to other variables may be more helpful in the case of frequent emergency department use prediction.

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CONECT-6: a case-finding tool to identify patients with complex health needs

Cited by 13 publications

References 51 publications

Better understanding care transitions of adults with complex health and social care needs: a study protocol

Better understanding care transitions of adults with complex health and social care needs: a study protocol

First Evidence on the Validity of the Complexity Index Derived from the Resident Assessment Instrument for Home Care in Home Care Patients

Machine learning to improve frequent emergency department use prediction: a retrospective cohort study

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