scholarly journals The Promise for Reducing Healthcare Cost with Predictive Model: An Analysis with Quantized Evaluation Metric on Readmission

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Kareen Teo ◽  
Ching Wai Yong ◽  
Farina Muhamad ◽  
Hamidreza Mohafez ◽  
Khairunnisa Hasikin ◽  
...  
Keyword(s):  
Predictive Model ◽  
Healthcare Cost ◽  
Transitional Care ◽  
Predictive Analytics ◽  
Cost Effective ◽  
Digital Tool ◽  
Impact Machine ◽  
Targeted Interventions ◽  
Evaluation Metric ◽  
The Impact

Quality of care data has gained transparency captured through various measurements and reporting. Readmission measure is especially related to unfavorable patient outcomes that directly bends the curve of healthcare cost. Under the Hospital Readmission Reduction Program, payments to hospitals were reduced for those with excessive 30-day rehospitalization rates. These penalties have intensified efforts from hospital stakeholders to implement strategies to reduce readmission rates. One of the key strategies is the deployment of predictive analytics stratified by patient population. The recent research in readmission model is focused on making its prediction more accurate. As cost-saving improvements through artificial intelligent-based health solutions are expected, the broad economic impact of such digital tool remains unknown. Meanwhile, reducing readmission rate is associated with increased operating expenses due to targeted interventions. The increase in operating margin can surpass native readmission cost. In this paper, we propose a quantized evaluation metric to provide a methodological mean in assessing whether a predictive model represents cost-effective way of delivering healthcare. Herein, we evaluate the impact machine learning has had on transitional care and readmission with proposed metric. The final model was estimated to produce net healthcare savings at over $1 million given a 50% rate of successfully preventing a readmission.

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.

scholarly journals Impact of free cancer predisposition cascade genetic testing on uptake in Singapore

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Eliza Courtney ◽  
Amanda Kay-Lyn Chok ◽  
Zoe Li Ting Ang ◽  
Tarryn Shaw ◽  
Shao-Tzu Li ◽  
...  
Keyword(s):  
Cost Effective ◽  
Cancer Predisposition ◽  
Relationship Type ◽  
Targeted Interventions ◽  
Cascade Testing ◽  
Cost Effective Method ◽  
Increased Risk ◽  
Significant Barrier ◽  
Testing Uptake ◽  
The Impact

Abstract Cascade testing for cancer predisposition offers a highly efficient and cost-effective method for identifying individuals at increased risk for cancer, in whom targeted interventions can often improve survival. The aim of this study was to determine the impact of free cascade testing on uptake and identify other associated factors. Demographic and clinical data were gathered prospectively for 183 probands found to have a pathogenic variant associated with cancer predisposition and their 826 first-degree relatives (FDRs). The provision of free cascade testing was significantly associated with uptake (21.6% vs 6.1%; χ2, P < 0.001). Relationship type between FDR and proband and FDR age also demonstrated significant associations, suggesting greater engagement amongst younger generations. Overall, 29.0% (53/183) of families had at least 1 FDR who underwent cascade testing. Of these families, 67.9% (36/53) had an uptake rate of at least 40.0%. Cost is a significant barrier to cascade testing uptake in Singapore. Tailored interventions targeting underrepresented groups and genetic counseling approaches supporting family communication and decision-making are necessary.

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.

Regulatory and sustainability initiatives lead to improved polyaminopolyamide epichlorohydrin (PAE) wet-strength resins and paper products

TAPPI Journal ◽  
2018 ◽  
Vol 17 (09) ◽  
pp. 519-532 ◽  
Author(s):  
Mark Crisp ◽  
Richard Riehle
Keyword(s):  
Health And Safety ◽  
Cost Effective ◽  
Worker Safety ◽  
Regulatory Requirements ◽  
Wet Strength ◽  
The Past ◽  
Consumer Safety ◽  
Federal Institute ◽  
Sustainability Initiatives ◽  
The Impact

Polyaminopolyamide-epichlorohydrin (PAE) resins are the predominant commercial products used to manufacture wet-strengthened paper products for grades requiring wet-strength permanence. Since their development in the late 1950s, the first generation (G1) resins have proven to be one of the most cost-effective technologies available to provide wet strength to paper. Throughout the past three decades, regulatory directives and sustainability initiatives from various organizations have driven the development of cleaner and safer PAE resins and paper products. Early efforts in this area focused on improving worker safety and reducing the impact of PAE resins on the environment. These efforts led to the development of resins containing significantly reduced levels of 1,3-dichloro-2-propanol (1,3-DCP) and 3-monochloropropane-1,2-diol (3-MCPD), potentially carcinogenic byproducts formed during the manufacturing process of PAE resins. As the levels of these byproducts decreased, the environmental, health, and safety (EH&S) profile of PAE resins and paper products improved. Recent initiatives from major retailers are focusing on product ingredient transparency and quality, thus encouraging the development of safer product formulations while maintaining performance. PAE resin research over the past 20 years has been directed toward regulatory requirements to improve consumer safety and minimize exposure to potentially carcinogenic materials found in various paper products. One of the best known regulatory requirements is the recommendations of the German Federal Institute for Risk Assessment (BfR), which defines the levels of 1,3-DCP and 3-MCPD that can be extracted by water from various food contact grades of paper. These criteria led to the development of third generation (G3) products that contain very low levels of 1,3-DCP (typically <10 parts per million in the as-received/delivered resin). This paper outlines the PAE resin chemical contributors to adsorbable organic halogens and 3-MCPD in paper and provides recommendations for the use of each PAE resin product generation (G1, G1.5, G2, G2.5, and G3).

Exploring the Benefits of Early Contractor Involvement (ECI) and the Mechanism for Automated 5D BIM Quantification Process in Offsite Modular Construction

2016 ◽  
Author(s):  
Tochukwu Moses ◽  
David Heesom ◽  
David Oloke ◽  
Martin Crouch
Keyword(s):  
Effective Means ◽  
Cost Effective ◽  
Modular Construction ◽  
Automated Quantification ◽  
Building Information ◽  
Project Risks ◽  
Value Add ◽  
The Uk ◽  
The Impact ◽  
Level 2

The UK Construction Industry through its Government Construction Strategy has recently been mandated to implement Level 2 Building Information Modelling (BIM) on public sector projects. This move, along with other initiatives is key to driving a requirement for 25% cost reduction (establishing the most cost-effective means) on. Other key deliverables within the strategy include reduction in overall project time, early contractor involvement, improved sustainability and enhanced product quality. Collaboration and integrated project delivery is central to the level 2 implementation strategy yet the key protocols or standards relative to cost within BIM processes is not well defined. As offsite construction becomes more prolific within the UK construction sector, this construction approach coupled with BIM, particularly 5D automated quantification process, and early contractor involvement provides significant opportunities for the sector to meet government targets. Early contractor involvement is supported by both the industry and the successive Governments as a credible means to avoid and manage project risks, encourage innovation and value add, making cost and project time predictable, and improving outcomes. The contractor is seen as an expert in construction and could be counter intuitive to exclude such valuable expertise from the pre-construction phase especially with the BIM intent of äóÖbuild it twiceäó», once virtually and once physically. In particular when offsite construction is used, the contractoräó»s construction expertise should be leveraged for the virtual build in BIM-designed projects to ensure a fully streamlined process. Building in a layer of automated costing through 5D BIM will bring about a more robust method of quantification and can help to deliver the 25% reduction in overall cost of a project. Using a literature review and a case study, this paper will look into the benefits of Early Contractor Involvement (ECI) and the impact of 5D BIM on the offsite construction process.

Pound Wise and Penny Foolish? Weight Loss and The Dynamics of Health Care Spending

2011 ◽  
Vol 14 (2) ◽  
Author(s):  
Thomas G Koch
Keyword(s):  
Weight Loss ◽  
Cost Savings ◽  
Cost Effective ◽  
Economic Consequences ◽  
Health Care Spending ◽  
Order Of Magnitude ◽  
Dynamic Measures ◽  
The Cost ◽  
The Impact ◽  
The Relationship

Current estimates of obesity costs ignore the impact of future weight loss and gain, and may either over or underestimate economic consequences of weight loss. In light of this, I construct static and dynamic measures of medical costs associated with body mass index (BMI), to be balanced against the cost of one-time interventions. This study finds that ignoring the implications of weight loss and gain over time overstates the medical-cost savings of such interventions by an order of magnitude. When the relationship between spending and age is allowed to vary, weight-loss attempts appear to be cost-effective starting and ending with middle age. Some interventions recently proven to decrease weight may also be cost-effective.

Motivating Compliance: Firm Response to Mandatory Disclosure Policies

2018 ◽  
Vol 32 (2) ◽  
pp. 103-119
Author(s):  
Colleen M. Boland ◽  
Chris E. Hogan ◽  
Marilyn F. Johnson
Keyword(s):  
Conflict Of Interest ◽  
Low Cost ◽  
Cost Effective ◽  
Data Availability ◽  
Policy Adoption ◽  
Future Research ◽  
Regulated Firms ◽  
Firm Response ◽  
Disclosure Rules ◽  
The Impact

SYNOPSIS Mandatory existence disclosure rules require an organization to disclose a policy's existence, but not its content. We examine policy adoption frequencies in the year immediately after the IRS required mandatory existence disclosure by nonprofits of various governance policies. We also examine adoption frequencies in the year of the subsequent change from mandatory existence disclosure to a disclose-and-explain regime that required supplemental disclosures about the content and implementation of conflict of interest policies. Our results suggest that in areas where there is unclear regulatory authority, mandatory existence disclosure is an effective and low cost regulatory device for encouraging the adoption of policies desired by regulators, provided those policies are cost-effective for regulated firms to implement. In addition, we find that disclose-and-explain regulatory regimes provide stronger incentives for policy adoption than do mandatory existence disclosure regimes and also discourage “check the box” behavior. Future research should examine the impact of mandatory existence disclosure rules in the year that the regulation is implemented. Data Availability: Data are available from sources cited in the text.

scholarly journals The Impact of Big Data on Electronic Commerce in Profit Organisations in Saudi Arabia

2019 ◽  
Vol 10 (4) ◽  
pp. 106
Author(s):  
Bader A. Alyoubi
Keyword(s):  
Saudi Arabia ◽  
Big Data ◽  
Inventory Management ◽  
New Technologies ◽  
Predictive Analytics ◽  
Business Environment ◽  
Primary Data ◽  
Research Approach ◽  
Multiple Sources ◽  
The Impact

Big Data is gaining rapid popularity in e-commerce sector across the globe. There is a general consensus among experts that Saudi organisations are late in adopting new technologies. It is generally believed that the lack of research in latest technologies that are specific to Saudi Arabia that is culturally, socially, and economically different from the West, is one of the key factors for the delay in technology adoption in Saudi Arabia. Hence, to fill this gap to a certain extent and create awareness about Big Data technology, the primary goal of this research was to identify the impact of Big Data on e-commerce organisations in Saudi Arabia. Internet has changed the business environment of Saudi Arabia too. E-commerce is set for achieving new heights due to latest technological advancements. A qualitative research approach was used by conducting interviews with highly experienced professional to gather primary data. Using multiple sources of evidence, this research found out that traditional databases are not capable of handling massive data. Big Data is a promising technology that can be adopted by e-commerce companies in Saudi Arabia. Big Data’s predictive analytics will certainly help e-commerce companies to gain better insight of the consumer behaviour and thus offer customised products and services. The key finding of this research is that Big Data has a significant impact in e-commerce organisations in Saudi Arabia on various verticals like customer retention, inventory management, product customisation, and fraud detection.

scholarly journals Control of Oxygen Impurities in a Continuous-Feeding Czochralski-Silicon Crystal Growth by the Double-Crucible Method

Crystals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 264
Author(s):  
Wenhan Zhao ◽  
Jiancheng Li ◽  
Lijun Liu
Keyword(s):  
Oxygen Concentration ◽  
Silicon Crystal ◽  
Cost Effective ◽  
Czochralski Method ◽  
Crucible Wall ◽  
Oxygen Impurity ◽  
Continuous Feeding ◽  
The Impact ◽  
Oxygen Impurities ◽  
Crystal Interface

The continuous-feeding Czochralski method is a cost-effective method to grow single silicon crystals. An inner crucible is used to prevent the un-melted silicon feedstock from transferring to the melt-crystal interface in this method. A series of global simulations were carried out to investigate the impact of the inner crucible on the oxygen impurity distributions at the melt-crystal interface. The results indicate that, the inner crucible plays a more important role in affecting the O concentration at the melt-crystal interface than the outer crucible. It can prevent the oxygen impurities from being transported from the outer crucible wall effectively. Meanwhile, it also introduces as a new source of oxygen impurity in the melt, likely resulting in a high oxygen concentration zone under the melt-crystal interface. We proposed to enlarge the inner crucible diameter so that the oxygen concentration at the melt-crystal interface can be controlled at low levels.

scholarly journals Cost-effectiveness and feasibility of conditional economic incentives and motivational interviewing to improve HIV health outcomes of adolescents living with HIV in Anambra State, Nigeria

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Obinna Ikechukwu Ekwunife ◽  
Chinelo Janefrances Ofomata ◽  
Charles Ebuka Okafor ◽  
Maureen Ugonwa Anetoh ◽  
Stephen Okorafor Kalu ◽  
...  
Keyword(s):  
Viral Load ◽  
Cost Effectiveness ◽  
Service Delivery ◽  
Motivational Interviewing ◽  
Cost Effective ◽  
Economic Incentives ◽  
Additional Patient ◽  
Incentive Scheme ◽  
The Cost ◽  
The Impact

Abstract Background In sub-Saharan Africa, there is increasing mortality and morbidity of adolescents due to poor linkage, retention in HIV care and adherence to antiretroviral therapy (ART). This is a result of limited adolescent-centred service delivery interventions. This cost-effectiveness and feasibility study were piggybacked on a cluster-randomized trial that assessed the impact of an adolescent-centred service delivery intervention. The service delivery intervention examined the impact of an incentive scheme consisting of conditional economic incentives and motivational interviewing on the health outcomes of adolescents living with HIV in Nigeria. Method A cost-effectiveness analysis from the healthcare provider’s perspective was performed to assess the cost per additional patient achieving undetected viral load through the proposed intervention. The cost-effectiveness of the incentive scheme over routine care was estimated using the incremental cost-effectiveness ratio (ICER), expressed as cost/patient who achieved an undetectable viral load. We performed a univariate sensitivity analysis to examine the effect of key parameters on the ICER. An in-depth interview was conducted on the healthcare personnel in the intervention arm to explore the feasibility of implementing the service delivery intervention in HIV treatment hospitals in Nigeria. Result The ICER of the Incentive Scheme intervention compared to routine care was US$1419 per additional patient with undetectable viral load. Going by the cost-effectiveness threshold of US$1137 per quality-adjusted life-years suggested by Woods et al., 2016, the intervention was not cost-effective. The sensitivity test showed that the intervention will be cost-effective if the frequency of CD4 count and viral load tests are reduced from quarterly to triannually. Healthcare professionals reported that patients’ acceptance of the intervention was very high. Conclusion The conditional economic incentives and motivational interviewing was not cost-effective, but can become cost-effective if the frequency of HIV quality of life indicator tests are performed 1–3 times per annum. Patients’ acceptance of the intervention was very high. However, healthcare professionals believed that sustaining the intervention may be difficult unless factors such as government commitment and healthcare provider diligence are duly addressed. Trial registration This trial is registered in the WHO International Clinical Trials Registry through the WHO International Registry Network (PACTR201806003040425).

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