An Innovative Risk Assessment Tool for Prospective Risk Analysis to Improve the Quality in Health Care: The Bow-Tie

Purpose The growing importance of risk management programmes and practices in different industries has given rise to a new risk management approach, i.e. enterprise risk management. The purpose of this paper is to better understand the necessity, benefit, approaches and methodologies of managing risks in healthcare. It compares and contrasts between the traditional and enterprise risk management approaches within the healthcare context. In addition, it introduces bow tie methodology, a prospective risk assessment tool proposed by the American Society for Healthcare Risk Management as a visual risk management tool used in enterprise risk management. Design/methodology/approach This is a critical review of published literature on the topics of governance, patient safety, risk management, enterprise risk management and bow tie, which aims to draw a link between them and find the benefits behind their adoption. Findings Enterprise risk management is a generic holistic approach that extends the benefits of risk management programme beyond the traditional insurable hazards and/or losses. In addition, the bow tie methodology is a barrier-based risk analysis and management tool used in enterprise risk management for critical events related to the relevant day-to-day operations. It is a visual risk assessment tool which is used in many higher reliability industries. Nevertheless, enterprise risk management and bow ties are reported with limited use in healthcare. Originality/value The paper suggests the applicability and usefulness of enterprise risk management to healthcare, and proposes the bow tie methodology as a proactive barrier-based risk management tool valid for enterprise risk management implementation in healthcare.

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Validity and reliability of the modified John Hopkins Fall Risk Assessment Tool for elderly patients in home health care

Geriatric Nursing ◽

10.1016/j.gerinurse.2013.05.011 ◽

2013 ◽

Vol 34 (5) ◽

pp. 423-427 ◽

Cited By ~ 16

Author(s):

Sandra Hnizdo ◽

Raquel A. Archuleta ◽

Barbara Taylor ◽

Son Chae Kim

Keyword(s):

Risk Assessment ◽

Health Care ◽

Elderly Patients ◽

Home Health Care ◽

Fall Risk ◽

Assessment Tool ◽

Home Health ◽

Risk Assessment Tool ◽

Validity And Reliability ◽

Fall Risk Assessment

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Validation of a risk screening tool for pediatric type 1 diabetes patients: a predictor of increased acute health care utilization

Journal of Pediatric Endocrinology and Metabolism ◽

10.1515/jpem-2019-0156 ◽

2019 ◽

Vol 32 (10) ◽

pp. 1155-1162

Author(s):

Bethany A. Glick ◽

K. Ming Chan Hong ◽

Don Buckingham ◽

Melissa Moore-Clingenpeel ◽

Ann Salvator ◽

...

Keyword(s):

Risk Factors ◽

Risk Assessment ◽

Health Care ◽

Type 1 Diabetes ◽

Assessment Tool ◽

Risk Assessment Tool ◽

Diabetes Diagnosis ◽

Treatment Pathways ◽

Acute Health Care

Abstract Background Both psychosocial and socioeconomic risk factors contribute to poor glycemic control (GC). Previous research has identified that diabetes care behaviors are generally ‘set’ by late childhood, further highlighting the importance of psychosocial screening and intervention in the early course of disease management. The purpose of the current study was to determine whether this brief risk assessment tool is associated with GC and acute health care (HC) utilization, and to evaluate the discriminatory utility of the tool for predicting poor outcomes. Methods This was a retrospective cohort design in which we compared risk assessment scores with health outcomes at 6, 12, and 18 months after new-onset type 1 diabetes diagnosis for 158 patients between 2015 and 2017. The two primary outcome variables were GC and acute HC utilization. Results Our data demonstrate that the greatest utility of the tool is for predicting increased acute HC utilization. It was most useful in differentiating between patients with vs. without any acute HC utilization, with excellent discriminatory ability (area under the receiver operator characteristic curve [AUC] = 0.93), sensitivity (90%), and specificity (97%). Conclusions Knowledge of the risk category in addition to identification of individual risk factors within each domain allows for not only clear treatment pathways but also individualized interventions. The risk assessment tool was less effective at differentiating patients with poor GC; however, the tool did have high specificity (83%) for predicting poor GC at 18 months which suggests that the tool may also be useful for predicting patients at risk for poor GC.

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The Validity of a Brief Risk Assessment Tool for Predicting Suicidal Behavior in Veterans Utilizing VHA Mental Health Care

Suicide and Life-Threatening Behavior ◽

10.1111/sltb.12229 ◽

2016 ◽

Vol 46 (4) ◽

pp. 471-485 ◽

Cited By ~ 2

Author(s):

Neal Doran ◽

Sharon De Peralta ◽

Colin Depp ◽

Ben Dishman ◽

Lindsay Gold ◽

...

Keyword(s):

Mental Health ◽

Risk Assessment ◽

Health Care ◽

Mental Health Care ◽

Suicidal Behavior ◽

Assessment Tool ◽

Risk Assessment Tool

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Use of a Telemedicine Risk Assessment Tool to Predict the Risk of Hospitalization of 496 Outpatients With COVID-19: Retrospective Analysis (Preprint)

10.2196/preprints.25075 ◽

2020 ◽

Author(s):

James B O'Keefe ◽

Elizabeth J Tong ◽

Thomas H Taylor Jr ◽

Ghazala A Datoo O’Keefe ◽

David C Tong

Keyword(s):

Risk Assessment ◽

Health Care ◽

High Risk ◽

Assessment Tool ◽

Cox Regression ◽

Monitoring Program ◽

Risk Assessment Tool ◽

Tier 2 ◽

Tier 1 ◽

Tier 3

BACKGROUND Risk assessment of patients with acute COVID-19 in a telemedicine context is not well described. In settings of large numbers of patients, a risk assessment tool may guide resource allocation not only for patient care but also for maximum health care and public health benefit. OBJECTIVE The goal of this study was to determine whether a COVID-19 telemedicine risk assessment tool accurately predicts hospitalizations. METHODS We conducted a retrospective study of a COVID-19 telemedicine home monitoring program serving health care workers and the community in Atlanta, Georgia, with enrollment from March 24 to May 26, 2020; the final call range was from March 27 to June 19, 2020. All patients were assessed by medical providers using an institutional COVID-19 risk assessment tool designating patients as Tier 1 (low risk for hospitalization), Tier 2 (intermediate risk for hospitalization), or Tier 3 (high risk for hospitalization). Patients were followed with regular telephone calls to an endpoint of improvement or hospitalization. Using survival analysis by Cox regression with days to hospitalization as the metric, we analyzed the performance of the risk tiers and explored individual patient factors associated with risk of hospitalization. RESULTS Providers using the risk assessment rubric assigned 496 outpatients to tiers: Tier 1, 237 out of 496 (47.8%); Tier 2, 185 out of 496 (37.3%); and Tier 3, 74 out of 496 (14.9%). Subsequent hospitalizations numbered 3 out of 237 (1.3%) for Tier 1, 15 out of 185 (8.1%) for Tier 2, and 17 out of 74 (23%) for Tier 3. From a Cox regression model with age of 60 years or older, gender, and reported obesity as covariates, the adjusted hazard ratios for hospitalization using Tier 1 as reference were 3.74 (95% CI 1.06-13.27; <i>P</i>=.04) for Tier 2 and 10.87 (95% CI 3.09-38.27; <i>P</i><.001) for Tier 3. CONCLUSIONS A telemedicine risk assessment tool prospectively applied to an outpatient population with COVID-19 identified populations with low, intermediate, and high risk of hospitalization.

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