How health leaders can benefit from predictive analytics

2017 ◽  
Vol 30 (6) ◽  
pp. 274-277 ◽  
Author(s):  
Aliyah Giga
Keyword(s):  
At Risk ◽  
Health System ◽  
Predictive Model ◽  
Population Health ◽  
Predictive Analytics ◽  
Productivity Costs ◽  
Targeted Interventions ◽  
Integrated Health ◽  
Patients At Risk

Predictive analytics can support a better integrated health system providing continuous, coordinated, and comprehensive person-centred care to those who could benefit most. In addition to dollars saved, using a predictive model in healthcare can generate opportunities for meaningful improvements in efficiency, productivity, costs, and better population health with targeted interventions toward patients at risk.

Performance of a Predictive Model versus Prescription-Based Thresholds in Identifying Patients at Risk of Fatal Opioid Overdose

2021 ◽  
Vol 56 (3) ◽  
pp. 396-403
Author(s):  
Lindsey M. Ferris ◽  
Brendan Saloner ◽  
Kate Jackson ◽  
B. Casey Lyons ◽  
Vijay Murthy ◽  
...  
Keyword(s):  
At Risk ◽  
Predictive Model ◽  
Opioid Overdose ◽  
Patients At Risk ◽  
Model Versus

scholarly journals Using Predictive Modeling and Supervised Machine Learning to Identify Patients at Risk for Venous Thromboembolism Following Posterior Lumbar Fusion

2021 ◽  
pp. 219256822110193
Author(s):  
Kevin Y. Wang ◽  
Ijezie Ikwuezunma ◽  
Varun Puvanesarajah ◽  
Jacob Babu ◽  
Adam Margalit ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Venous Thromboembolism ◽  
Risk Stratification ◽  
Predictive Model ◽  
Predictive Modeling ◽  
Lumbar Fusion ◽  
Posterior Lumbar Fusion ◽  
Single Level ◽  
Patients At Risk

Study Design: Retrospective review. Objective: To use predictive modeling and machine learning to identify patients at risk for venous thromboembolism (VTE) following posterior lumbar fusion (PLF) for degenerative spinal pathology. Methods: Patients undergoing single-level PLF in the inpatient setting were identified in the National Surgical Quality Improvement Program database. Our outcome measure of VTE included all patients who experienced a pulmonary embolism and/or deep venous thrombosis within 30-days of surgery. Two different methodologies were used to identify VTE risk: 1) a novel predictive model derived from multivariable logistic regression of significant risk factors, and 2) a tree-based extreme gradient boosting (XGBoost) algorithm using preoperative variables. The methods were compared against legacy risk-stratification measures: ASA and Charlson Comorbidity Index (CCI) using area-under-the-curve (AUC) statistic. Results: 13, 500 patients who underwent single-level PLF met the study criteria. Of these, 0.95% had a VTE within 30-days of surgery. The 5 clinical variables found to be significant in the multivariable predictive model were: age > 65, obesity grade II or above, coronary artery disease, functional status, and prolonged operative time. The predictive model exhibited an AUC of 0.716, which was significantly higher than the AUCs of ASA and CCI (all, P < 0.001), and comparable to that of the XGBoost algorithm ( P > 0.05). Conclusion: Predictive analytics and machine learning can be leveraged to aid in identification of patients at risk of VTE following PLF. Surgeons and perioperative teams may find these tools useful to augment clinical decision making risk stratification tool.

A Predictive Model of Intrinsic Factors Associated with Long-Stay Nursing Home Care After Hospitalization

2020 ◽  
pp. 105477382098527
Author(s):  
Jane Flanagan ◽  
Marie Boltz ◽  
Ming Ji
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Predictive Model ◽  
Operating Characteristic ◽  
Resource Planning ◽  
Term Care ◽  
Intrinsic Factors ◽  
Patients At Risk ◽  
Receiver Operating

We aimed to build a predictive model with intrinsic factors measured upon admission to skilled nursing facilities (SNFs) post-acute care (PAC) to identify older adults transferred from SNFs to long-term care (LTC) instead of home. We analyzed data from Massachusetts in 23,662 persons admitted to SNFs from PAC in 2013. Explanatory logistic regression analysis identified single “intrinsic predictors” related to LTC placement. To assess overfitting, the logistic regression predictive model was cross-validated and evaluated by its receiver operating characteristic (ROC) curve. A 12-variable predictive model with “intrinsic predictors” demonstrated both high in-sample and out-of-sample predictive accuracy in the receiver operating characteristic ROC and area under the ROC among patients at risk of LTC placement. This predictive model may be used for early identification of patients at risk for LTC after hospitalization in order to support targeted rehabilitative approaches and resource planning.

Impact of Interventional Policies Including Vaccine on COVID-19 Propagation and Socio-Economic Factors: Predictive Model Enabling Simulations Using Machine Learning and Big Data (Preprint)

2020 ◽  
Author(s):  
Haonan Wu ◽  
Rajarshi Banerjee ◽  
Indhumathi V ◽  
Daniel Percy-Hughes ◽  
Praveen Chougale
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Economic Impact ◽  
Predictive Model ◽  
Predictive Analytics ◽  
Scenario Planning ◽  
Economic Factors ◽  
Targeted Interventions ◽  
Controllable Factors ◽  
Forward Looking

BACKGROUND A novel coronavirus disease has emerged (later named COVID-19) and caused the world to enter a new reality, with many direct and indirect factors influencing it. Some are human-controllable (e.g. interventional policies, mobility and the vaccine); some are not (e.g. the weather). We have sought to test how a change in these human-controllable factors might influence two measures: the number of daily cases against economic impact. If applied at the right level and with up-to-date data to measure, policymakers would be able to make targeted interventions and measure their cost. OBJECTIVE The study aimed to provide a predictive analytics framework to model, predict and simulate COVID-19 propagation and the socio-economic impact of interventions intended to reduce the spread of the disease such as policy and/or vaccine. It allows policymakers, government representatives and business leaders to make better-informed decisions about the potential effect of various interventions with forward-looking views via scenario planning. METHODS We leveraged a recently launched opensource COVID-19 big data platform and used published research to find potentially relevant variables (features), completing feature selection and engineering via in-depth data quality checks and analytics. An advanced machine learning pipeline has been developed. It contains the ensemble models, auto/semi-auto hyperparameter tuning and customized interpretability functions. And It is self-evolving as always learned from the most recent data. The output predicts daily cases and economic factors (e.g. small business revenue) to allow simulation of interventions including a vaccine (proxied by an influenza vaccination efficacy model). This framework is built using an open-source technology stack and we make the source code being publicly available as well. RESULTS This model is self-evolving and deployed on modern machine learning architecture. It has high accuracy for trend prediction (back-tested with r-squared). We bring simulation and interpretability in the framework. It models not just daily-cases, but also socio-economic demographics. CONCLUSIONS Human behaviour and extreme natural disasters are hard to measure with data points. No model can provide an answer that is correct 100% of the time; however, with high-quality model and big data, a forward-looking view can be inferred or at least noted. This predictive model can help the policymakers to test scenarios, plan proactive actions, optimize logistics, measure the cost and create an open dialogue with the general public.

A predictive model to identify cancer patients at risk for in-hospital mortality based on admission criteria.

2011 ◽  
Vol 29 (15_suppl) ◽  
pp. 6045-6045
Author(s):  
K. Ramchandran ◽  
J. Shega ◽  
M. Schumacher ◽  
A. Rademaker ◽  
b. B. Weitner ◽  
...  
Keyword(s):  
At Risk ◽  
Hospital Mortality ◽  
Predictive Model ◽  
Cancer Patients ◽  
Admission Criteria ◽  
Patients At Risk

scholarly journals A predictive model for identifying patients at risk of delayed transfer of care: a retrospective, cross-sectional study of routinely collected data

2021 ◽  
Vol 33 (3) ◽  
Author(s):  
Andrew Davy ◽  
Thomas Hill ◽  
Sarahjane Jones ◽  
Alisen Dube ◽  
Simon c Lea ◽  
...  
Keyword(s):  
At Risk ◽  
Predictive Model ◽  
Cross Sectional Study ◽  
Early Warning Score ◽  
Sectional Study ◽  
Cross Sectional ◽  
Transfer Of Care ◽  
Routinely Collected Data ◽  
Patients At Risk ◽  
Transfers Of Care

Abstract Background Delays to the transfer of care from hospital to other settings represent a significant human and financial cost. This delay occurs when a patient is clinically ready to leave the inpatient setting but is unable to because other necessary care, support or accommodation is unavailable. The aim of this study was to interrogate administrative and clinical data routinely collected when a patient is admitted to hospital following attendance at the emergency department (ED), to identify factors related to delayed transfer of care (DTOC) when the patient is discharged. We then used these factors to develop a predictive model for identifying patients at risk for delayed discharge of care. Objective To identify risk factors related to the delayed transfer of care and develop a prediction model using routinely collected data. Methods This is a single centre, retrospective, cross-sectional study of patients admitted to an English National Health Service university hospital following attendance at the ED between January 2018 and December 2020. Clinical information (e.g. national early warning score (NEWS)), as well as administrative data that had significant associations with admissions that resulted in delayed transfers of care, were used to develop a predictive model using a mixed-effects logistic model. Detailed model diagnostics and statistical significance, including receiver operating characteristic analysis, were performed. Results Three-year (2018–20) data were used; a total of 92 444 admissions (70%) were used for model development and 39 877 (30%) admissions for model validation. Age, gender, ethnicity, NEWS, Glasgow admission prediction score, Index of Multiple Deprivation decile, arrival by ambulance and admission within the last year were found to have a statistically significant association with delayed transfers of care. The proposed eight-variable predictive model showed good discrimination with 79% sensitivity (95% confidence intervals (CIs): 79%, 81%), 69% specificity (95% CI: 68%, 69%) and 70% (95% CIs: 69%, 70%) overall accuracy of identifying patients who experienced a DTOC. Conclusion Several demographic, socio-economic and clinical factors were found to be significantly associated with whether a patient experiences a DTOC or not following an admission via the ED. An eight-variable model has been proposed, which is capable of identifying patients who experience delayed transfers of care with 70% accuracy. The eight-variable predictive tool calculates the probability of a patient experiencing a delayed transfer accurately at the time of admission.

scholarly journals A multinational observational study identifying primary care patients at risk of overestimation of asthma control

2019 ◽  
Vol 29 (1) ◽  
Author(s):  
Vicky Kritikos ◽  
David Price ◽  
Alberto Papi ◽  
Antonio Infantino ◽  
Bjorn Ställberg ◽  
...  
Keyword(s):  
At Risk ◽  
Disease Control ◽  
Asthma Control ◽  
Real Life ◽  
Fixed Dose ◽  
Targeted Interventions ◽  
Factors Associated ◽  
Patient Reported ◽  
Patients At Risk ◽  
Respiratory Specialist

AbstractFactors related to the discrepancy between patient-perceived and actual disease control remain unclear. Identifying patients at risk of overestimation of asthma control remains elusive. This study aimed to (i) investigate the relationship between patient-reported and actual level of asthma control (ii), compare the characteristics between patients who believe their asthma is well controlled that accurately report ‘well-controlled’ asthma with those that do not, and (iii) identify factors associated with inaccurately reported ‘well-controlled’ asthma. A historical, multinational, cross-sectional study using data from the iHARP (initiative Helping Asthma in Real-life Patients) review service for adults with asthma prescribed fixed-dose combination therapy. Data from 4274 patients were analysed. A major discrepancy between patient-reported and Global Initiative for Asthma defined asthma control was detected; 71.1% of patients who reported ‘well-controlled’ asthma were inaccurate in their perception despite receiving regular maintenance therapy. Significant differences were noted in age, gender, body mass index, education level, medication use, side effects, attitudes to preventer inhaler use, inhaler technique review and respiratory specialist review between patients who accurately reported ‘well-controlled’ asthma and those who did not. Independent risk factors associated with inaccurately reported ‘well-controlled’ asthma were: having taken a maximum of 5–12 puffs or more of reliever inhaler on at least one day within the previous 4 weeks; being female; having seen a respiratory specialist more than a year ago (rather than in the previous year); and having required oral corticosteroids for worsening asthma in the previous year. The study highlighted the significant hidden burden associated with under-recognition of poor asthma control, on the part of the patient and the need for targeted interventions designed to address the continuing discrepancy between perceived and actual disease control.

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