Crisis, Care, and the Terror of Uncertainty

ABSTRACT Successful management of an event where health-care needs exceed regional health-care capacity requires coordinated strategies for scarce resource allocation. Publications for rapid development, training, and coordination of regional hospital triage teams to manage the allocation of scarce resources during coronavirus disease 2019 (COVID-19) are lacking. Over a period of 3 weeks, over 100 clinicians, ethicists, leaders, and public health authorities convened virtually to achieve consensus on how best to save the most lives possible and share resources. This is referred to as population-based crisis management. The rapid regionalization of 22 acute care hospitals across 4500 square miles in the midst of a pandemic with a shifting regulatory landscape was challenging, but overcome by mutual trust, transparency, and confidence in the public health authority. Because many cities are facing COVID-19 surges, we share a process for successful rapid formation of health-care care coalitions, Crisis Standard of Care, and training of Triage Teams. Incorporation of continuous process improvement and methods for communication is essential for successful implementation. Use of our regional health-care coalition communications, incident command system, and the crisis care committee helped mitigate crisis care in the San Diego and Imperial County region as COVID-19 cases surged and scarce resource collaborative decisions were required.

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Mental health crisis resolution teams and crisis care systems in England: a national survey

BJPsych Bulletin ◽

10.1192/bjb.2018.19 ◽

2018 ◽

Vol 42 (4) ◽

pp. 146-151 ◽

Cited By ~ 3

Author(s):

Brynmor Lloyd-Evans ◽

Danielle Lamb ◽

Joseph Barnby ◽

Michelle Eskinazi ◽

Amelia Turner ◽

...

Keyword(s):

Mental Health ◽

Older Adults ◽

National Survey ◽

Online Survey ◽

Service Improvement ◽

Mental Health Crisis ◽

Crisis Resolution ◽

Health Crisis ◽

Crisis Resolution Teams ◽

Crisis Care

Aims and methodA national survey investigated the implementation of mental health crisis resolution teams (CRTs) in England. CRTs were mapped and team managers completed an online survey.ResultsNinety-five per cent of mapped CRTs (n = 233) completed the survey. Few CRTs adhered fully to national policy guidelines. CRT implementation and local acute care system contexts varied substantially. Access to CRTs for working-age adults appears to have improved, compared with a similar survey in 2012, despite no evidence of higher staffing levels. Specialist CRTs for children and for older adults with dementia have been implemented in some areas but are uncommon.Clinical implicationsA national mandate and policy guidelines have been insufficient to implement CRTs fully as planned. Programmes to support adherence to the CRT model and CRT service improvement are required. Clearer policy guidance is needed on requirements for crisis care for young people and older adults.Declaration of interestNone.

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MH providers urged to consider nontypical partners in crisis care

Mental Health Weekly ◽

10.1002/mhw.30161 ◽

2015 ◽

Vol 25 (17) ◽

pp. 1-7

Author(s):

Gary Enos

Keyword(s):

Crisis Care

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Patient Response Toward a Change in the System of Health Care Delivery

PEDIATRICS ◽

10.1542/peds.52.2.289 ◽

1973 ◽

Vol 52 (2) ◽

pp. 289-293

Author(s):

Kathleen J. Motil ◽

W. John Siar

Keyword(s):

Health Care ◽

Health Care Delivery ◽

Care Delivery ◽

Poor Quality ◽

Emergency Rooms ◽

Patient Response ◽

Number Of Patients ◽

Comprehensive Health ◽

Crisis Care

With the emphasis being placed on comprehensive health care, outpatient clinics in major city hospitals have found it necessary to reevaluate their methods of health care delivery. An increasing number of patients who fail to schedule or keep medical appointments appear for crisis care, resulting in a higher cost of hospital operation due to unnecessary utilization of emergency rooms and the wasting of time of clerical and professional personnel, as well as poor quality of health care due to See the Table in PDF File sporadic clinic attendance. When comparing behavior patterns and attitudes of clinic patients under different methods of health care delivery, patient preferences become apparent.

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Konferenz »Covid, Crisis, Care and Change« am 18./19. März 2021 an der TU Dresden

Feministische Studien ◽

10.1515/fs-2021-0037 ◽

2021 ◽

Vol 39 (2) ◽

pp. 373-376

Author(s):

Annika Reinke

Keyword(s):

Crisis Care

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Predicting hospitalization following psychiatric crisis care using machine learning

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01361-1 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Matthijs Blankers ◽

Louk F. M. van der Post ◽

Jack J. M. Dekker

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Prediction Models ◽

Learning Algorithms ◽

Nearest Neighbors ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Ensemble Model ◽

K Nearest Neighbors ◽

Crisis Care

Abstract Background Accurate prediction models for whether patients on the verge of a psychiatric criseis need hospitalization are lacking and machine learning methods may help improve the accuracy of psychiatric hospitalization prediction models. In this paper we evaluate the accuracy of ten machine learning algorithms, including the generalized linear model (GLM/logistic regression) to predict psychiatric hospitalization in the first 12 months after a psychiatric crisis care contact. We also evaluate an ensemble model to optimize the accuracy and we explore individual predictors of hospitalization. Methods Data from 2084 patients included in the longitudinal Amsterdam Study of Acute Psychiatry with at least one reported psychiatric crisis care contact were included. Target variable for the prediction models was whether the patient was hospitalized in the 12 months following inclusion. The predictive power of 39 variables related to patients’ socio-demographics, clinical characteristics and previous mental health care contacts was evaluated. The accuracy and area under the receiver operating characteristic curve (AUC) of the machine learning algorithms were compared and we also estimated the relative importance of each predictor variable. The best and least performing algorithms were compared with GLM/logistic regression using net reclassification improvement analysis and the five best performing algorithms were combined in an ensemble model using stacking. Results All models performed above chance level. We found Gradient Boosting to be the best performing algorithm (AUC = 0.774) and K-Nearest Neighbors to be the least performing (AUC = 0.702). The performance of GLM/logistic regression (AUC = 0.76) was slightly above average among the tested algorithms. In a Net Reclassification Improvement analysis Gradient Boosting outperformed GLM/logistic regression by 2.9% and K-Nearest Neighbors by 11.3%. GLM/logistic regression outperformed K-Nearest Neighbors by 8.7%. Nine of the top-10 most important predictor variables were related to previous mental health care use. Conclusions Gradient Boosting led to the highest predictive accuracy and AUC while GLM/logistic regression performed average among the tested algorithms. Although statistically significant, the magnitude of the differences between the machine learning algorithms was in most cases modest. The results show that a predictive accuracy similar to the best performing model can be achieved when combining multiple algorithms in an ensemble model.

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Predicting Hospitalization following Psychiatric Crisis Care using Machine Learning

10.21203/rs.2.12338/v1 ◽

2019 ◽

Author(s):

Matthijs Blankers ◽

Louk F. M. van der Post ◽

Jack J. M. Dekker

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Learning Algorithms ◽

Nearest Neighbors ◽

Machine Learning Algorithms ◽

Predictor Variables ◽

Gradient Boosting ◽

K Nearest Neighbors ◽

Psychiatric Crisis ◽

Crisis Care

Abstract Background: It is difficult to accurately predict whether a patient on the verge of a potential psychiatric crisis will need to be hospitalized. Machine learning may be helpful to improve the accuracy of psychiatric hospitalization prediction models. In this paper we evaluate and compare the accuracy of ten machine learning algorithms including the commonly used generalized linear model (GLM/logistic regression) to predict psychiatric hospitalization in the first 12 months after a psychiatric crisis care contact, and explore the most important predictor variables of hospitalization. Methods: Data from 2,084 patients with at least one reported psychiatric crisis care contact included in the longitudinal Amsterdam Study of Acute Psychiatry were used. The accuracy and area under the receiver operating characteristic curve (AUC) of the machine learning algorithms were compared. We also estimated the relative importance of each predictor variable. The best and least performing algorithms were compared with GLM/logistic regression using net reclassification improvement analysis. Target variable for the prediction models was whether or not the patient was hospitalized in the 12 months following inclusion in the study. The 39 predictor variables were related to patients’ socio-demographics, clinical characteristics and previous mental health care contacts. Results: We found Gradient Boosting to perform the best (AUC=0.774) and K-Nearest Neighbors performing the least (AUC=0.702). The performance of GLM/logistic regression (AUC=0.76) was above average among the tested algorithms. Gradient Boosting outperformed GLM/logistic regression and K-Nearest Neighbors, and GLM outperformed K-Nearest Neighbors in a Net Reclassification Improvement analysis, although the differences between Gradient Boosting and GLM/logistic regression were small. Nine of the top-10 most important predictor variables were related to previous mental health care use. Conclusions: Gradient Boosting led to the highest predictive accuracy and AUC while GLM/logistic regression performed average among the tested algorithms. Although statistically significant, the magnitude of the differences between the machine learning algorithms was modest. Future studies may consider to combine multiple algorithms in an ensemble model for optimal performance and to mitigate the risk of choosing suboptimal performing algorithms.

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