Incidence and correlates of opioid-related psychiatric emergency care: A retrospective, multiyear cohort study

2020 ◽  
Vol 16 (3) ◽  
pp. 223-226 ◽  
Author(s):  
Anees Bahji, MD
Keyword(s):  
Cohort Study ◽  
Psychiatric Inpatient ◽  
Decision Support Tool ◽  
Relevant Information ◽  
Case Managers ◽  
Fiscal Year ◽  
Opioid Use ◽  
Psychiatric Admission ◽  
Support Tool ◽  
Opioid Intoxication

Background: In 2018, nearly 4,000 Canadian lives were claimed by the opioid epidemic. To date, only a few studies have reviewed shifts in emergency department (ED) utilization for opioid-related psychiatric presentations. Aims: To describe the characteristics of patients seeking ED care for opioid-related psychiatric presentations and to identify demographic and clinical characteristics that were associated with psychiatric inpatient admission for such presentations.Methods: Retrospective cohort study with multivariate logistic regression.Findings: Over a 4-year period, 555 opioid-related presentations were recorded (50 percent female, mean age 40.0 years). Time trend analysis showed a nonsignificant increase in the number of visits by fiscal year. The most common reason for ED presentation relevant to opioids was opioid withdrawal (49 percent). Nearly 20 percent of all visits required psychiatric admission; predictors of psychiatric admission were arrival by ambulance (adjusted odds ratio (AOR) = 2.03), older age (AOR = 1.05), longer length of ED stay (AOR = 1.10), and more severe triage score (AOR = 0.4). Sex and referring service were not associated with disposition in the ED. Admissions were more likely for opioid intoxication and withdrawal.Conclusion: EDs are serving increasing numbers of patients in psychiatric crisis related to opioid-use. A decision support tool could be developed and validated in the future to provide reliable, clinically relevant information to providers and case managers relevant to opioid-related ED presentations.

scholarly journals Implementing machine learning algorithms for suicide prevention: Designing a Clinical Decision Support tool in partnership with Native American care providers (Preprint)

2020 ◽  
Author(s):  
Emily Haroz ◽  
Fiona Grubin ◽  
Novalene Goklish ◽  
Shardai Pioche ◽  
Mary Cwik ◽  
...  
Keyword(s):  
Native American ◽  
Risk Prediction ◽  
Suicide Risk ◽  
Clinical Care ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Case Managers ◽  
Machine Learning Algorithms ◽  
Support Tool ◽  
Appropriate Care

BACKGROUND Machine learning algorithms for suicide risk prediction have been developed with notable improvements in accuracy. Implementing these algorithms to enhance clinical care and reduce suicide has not been well studied. OBJECTIVE Our study aimed to design a Clinical Decision Support tool (CDS) and appropriate care pathways for a community-based suicide surveillance and case management systems operating on Native American reservations. METHODS Participants included Native American case managers and supervisors (N = 9) who work on suicide surveillance and case management programs on two Native American reservations. We used in-depth interviews to understand how case managers think about and respond to suicide risk. Results from interviews informed a draft CDS tool, which was then reviewed with supervisors and combined with appropriate care pathways. RESULTS Case managers reported acceptance of risk flags based on a predictive algorithm in their surveillance system tools, particularly if the information was available in a timely way and used in conjunction with their clinical judgement. Implementation of risk flags needed to be programmed on a dichotomous basis so the algorithm could produce output indicating high vs. low risk. To dichotomize the continuous predicted probabilities, we developed a cutoff point that favored specificity, with the understanding that case managers’ clinical judgment would help increase sensitivity. CONCLUSIONS Suicide risk prediction algorithms show promise, but implementation to guide clinical care has remained relatively elusive. Our study demonstrates the utility of working with partners to develop and guide operationalization of risk prediction algorithms to enhance clinical care in a community setting.

scholarly journals Poseidon—Decision Support Tool for Water Reuse

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 153 ◽  
Author(s):  
Emmanuel Oertlé ◽  
Christoph Hugi ◽  
Thomas Wintgens ◽  
Christos Karavitis
Keyword(s):  
Decision Support ◽  
Water Reuse ◽  
Social Economic ◽  
Feasibility Studies ◽  
Decision Support Tool ◽  
Relevant Information ◽  
Decision Makers ◽  
Water Quality Standards ◽  
Support Tool ◽  
Quality Water

In an era when many water systems worldwide are experiencing water stress regarding water quantity and quality, water reuse has received growing attention as one of the most promising integrated mitigating solutions. Nevertheless, the plethora of technologies and their combinations available, as well as social, economic, and environmental constraints, often make it complex for stakeholders and especially decision makers to elicit relevant information. The scope of the current study is to develop a decision support tool that supports pre-feasibility studies and aims at promoting water reuse and building capacities in the field. The tool developed currently encompasses 37 unit processes combined into 70 benchmark treatment trains. It also contains information on water quality standards and typical wastewater qualities. It estimates the removal performances for 12 parameters and the lifecycle costs including distribution. The tool and all underlying data are open access and under continuous development. The underlying systemic approach of the tool makes it intuitive also for users with limited prior knowledge in the field to identify most adequate solutions based on a multi-criteria assessment. This should help to promote water reuse and spearhead initiates for more detailed feasibility and design commissioning for implementation of water reuse schemes.

A protocol for sampling pastures in hill country

2016 ◽  
Vol 78 ◽  
pp. 203-209 ◽  
Author(s):  
K.J. Hutchinson ◽  
D.R. Scobie ◽  
J. Beautrais ◽  
A.D. Mackay ◽  
G.M. Rennie ◽  
...  
Keyword(s):  
Random Sampling ◽  
Geographical Information Systems ◽  
Field Measurements ◽  
Decision Support Tool ◽  
Accurate Estimate ◽  
Ease Of Use ◽  
Geographical Information ◽  
Support Tool ◽  
Topographic Complexity ◽  
Hill Country

To develop a protocol to guide pasture sampling for estimation of paddock pasture mass in hill country, a range of pasture sampling strategies, including random sampling, transects and stratification based on slope and aspect, were evaluated using simulations in a Geographical Information Systems computer environment. The accuracy and efficiency of each strategy was tested by sampling data obtained from intensive field measurements across several farms, regions and seasons. The number of measurements required to obtain an accurate estimate was related to the overall pasture mass and the topographic complexity of a paddock, with more variable paddocks requiring more samples. Random sampling from average slopes provided the best balance between simplicity and reliability. A draft protocol was developed from the simulations, in the form of a decision support tool, where visual determination of the topographic complexity of the paddock, along with the required accuracy, were used to guide the number of measurements recommended. The protocol was field tested and evaluated by groups of users for efficacy and ease of use. This sampling protocol will offer farmers, consultants and researchers an efficient, reliable and simple way to determine pasture mass in New Zealand hill country settings. Keywords: hill country, feed budgeting, protocol pasture mass, slope

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