scholarly journals Machine Learning-Based Predictive Analytics Marketed as Improving Health Care Efficiency: What Types of Products and Who is Marketing Them? (Preprint)

2020 ◽  
Author(s):  
Ariadne A Nichol ◽  
Jason N Batten ◽  
Meghan C Halley ◽  
Julia K Axelrod ◽  
Pamela L Sankar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Clinical Training ◽  
Predictive Analytics ◽  
Disease Onset ◽  
Academic Research ◽  
Cost Effective ◽  
Health Care Settings ◽  
Health Care Efficiency ◽  
Regulatory Challenges

BACKGROUND Considerable effort is devoted to development of artificial intelligence, including machine learning-based predictive analytics (MLPA), for use in health care settings. Growth of MLPA could be fueled by payment reforms that hold health care organizations responsible for providing high quality, cost-effective care. Policy analysts, ethicists and computer scientists have identified unique ethical and regulatory challenges from MLPA in health care. However, little is known about the types of MLPA health care products available on the market today or what their stated goals are. OBJECTIVE To better characterize available products, we identified and characterized claims about products currently in use in U.S. health care settings that are marketed as tools to improve health care efficiency by improving quality of care while reducing costs. METHODS We conducted systematic database searches of relevant business news and academic research to identify MLPA products for health care efficiency that met our inclusion and exclusion criteria. We used content analysis to generate MLPA product categories and to characterize the organizations marketing the products. RESULTS We identified 106 products and characterized them based on publicly available information in terms of the types of predictions made, and the size, type, and clinical training of the leadership of the companies marketing them. We identified five categories of predictions made by MLPA products based on the publicly available product marketing materials: disease onset and progression, treatment, cost and utilization, admissions and readmissions, and decompensation and adverse events. CONCLUSIONS Our findings provide a foundational reference to inform analysis of the specific ethical and regulatory challenges arising from the use of MLPA to improve healthcare efficiency.

scholarly journals Evaluation of the Balanced Menus Challenge: a healthy food and sustainability programme in hospitals in Maryland

2014 ◽  
Vol 18 (13) ◽  
pp. 2341-2349 ◽  
Author(s):  
Tarah D Ranke ◽  
C Louise Mitchell ◽  
Diane Marie St. George ◽  
Christopher R D’Adamo
Keyword(s):  
Health Care ◽  
Mixed Methods ◽  
Cost Effective ◽  
Washington Dc ◽  
Previous Survey ◽  
Structured Interviews ◽  
Health Care Settings ◽  
Retrospective Assessment ◽  
Good Nutrition ◽  
National Effort

AbstractObjectiveThe Balanced Menus Challenge (BMC) is a national effort to bring the healthiest, most sustainably produced meat available into health-care settings to preserve antibiotic effectiveness and promote good nutrition. The present study evaluated the outcomes of the BMC in the Maryland/Washington, DC region.DesignThe BMC is a cost-effective programme whereby participating hospitals reduce meat purchases by 20 % of their budget, then invest the savings into purchasing sustainably produced meat. A mixed-methods retrospective assessment was conducted to assess (i) utilization of the BMC ‘implementation toolkit’ and (ii) achievement of the 20 % reduction in meat purchases. Previous survey data were reviewed and semi-structured interviews were conducted.SettingHospitals located in the Maryland/Washington, DC region, USA, that adopted the BMC.SubjectsTwelve hospitals signed the BMC in the Maryland/Washington, DC region and six were available for interview.ResultsThree hospitals in the Maryland/Washington, DC region that signed the BMC tracked their progress and two achieved a reduction in meat procurement by ≥20 %. One hospital demonstrated that the final outcome goal of switching to a local and sustainable source for meat is possible to achieve, at least for a portion of the meal budget. The three hospitals that reduced meat purchases also received and used the highest number of BMC implementation tools. There was a positive correlation between receipt and usage of implementation tools (r=0·93, P=0·005).ConclusionsThe study demonstrates that hospitals in the Maryland/Washington, DC region that sign the BMC can increase the amount of sustainably produced meat purchased and served.

scholarly journals Occupational Screening for Latent Tuberculosis Infection

2021 ◽  
Vol 1 (9) ◽  
Author(s):  
Sara D. Khangura ◽  
Melissa Severn
Keyword(s):  
Health Care ◽  
Latent Tuberculosis Infection ◽  
Latent Tuberculosis ◽  
Cost Effective ◽  
Tuberculosis Infection ◽  
Economic Evaluations ◽  
Repeated Testing ◽  
Health Care Settings ◽  
Targeted Testing ◽  
The Cost

In people at risk of occupational exposure to tuberculosis, targeted testing for latent tuberculosis infection (e.g., testing for high-risk individuals, testing after tuberculosis exposure) appears to be more cost-effective than repeated testing, such as testing once a year or every 3 years (findings based on 2 economic evaluations that assessed the cost-effectiveness of repeated latent tuberculosis infection screening in workers of health care settings).

scholarly journals Optimal strategies to screen health care workers for COVID-19 in the US: a cost-effectiveness analysis

2022 ◽  
Vol 20 (1) ◽  
Author(s):  
Sigal Maya ◽  
Guntas Padda ◽  
Victoria Close ◽  
Trevor Wilson ◽  
Fareeda Ahmed ◽  
...  
Keyword(s):  
Health Care ◽  
Cost Effectiveness ◽  
Health Care Workers ◽  
Cost Effective ◽  
Health Care Settings ◽  
Testing Strategies ◽  
Effectiveness Analysis ◽  
Care Workers ◽  
Antigen Testing ◽  
Quality Adjusted Life

Abstract Background Transmission of SARS-CoV-2 in health care facilities poses a challenge against pandemic control. Health care workers (HCWs) have frequent and high-risk interactions with COVID-19 patients. We undertook a cost-effectiveness analysis to determine optimal testing strategies for screening HCWs to inform strategic decision-making in health care settings. Methods We modeled the number of new infections, quality-adjusted life years lost, and net costs related to six testing strategies including no test. We applied our model to four strata of HCWs, defined by the presence and timing of symptoms. We conducted sensitivity analyses to account for uncertainty in inputs. Results When screening recently symptomatic HCWs, conducting only a PCR test is preferable; it saves costs and improves health outcomes in the first week post-symptom onset, and costs $83,000 per quality-adjusted life year gained in the second week post-symptom onset. When screening HCWs in the late clinical disease stage, none of the testing approaches is cost-effective and thus no testing is preferable, yielding $11 and 0.003 new infections per 10 HCWs. For screening asymptomatic HCWs, antigen testing is preferable to PCR testing due to its lower cost. Conclusions Both PCR and antigen testing are beneficial strategies to identify infected HCWs and reduce transmission of SARS-CoV-2 in health care settings. IgG tests’ value depends on test timing and immunity characteristics, however it is not cost-effective in a low prevalence setting. As the context of the pandemic evolves, our study provides insight to health-care decision makers to keep the health care workforce safe and transmissions low.

Kernel-Based Machine Learning Techniques

2018 ◽  
pp. 260-283
Author(s):  
Kumar Vijay ◽  
Saxena Arti ◽  
Kumar Suresh
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Principal Component ◽  
Cost Effective ◽  
Good Health ◽  
Developed Countries ◽  
Health Care Facilities ◽  
Machine Learning Techniques ◽  
Kernel Principal Component Analysis ◽  
Learning Techniques

Health care is considered as the fundamental right of every citizen and it is principle duty of every country to provide good health care facilities. Many developed countries spend substantial amount of gross domestic product (GDP) on healthcare. In this chapter, we discuss kernel based machine learning techniques, i.e., k-PCA (Kernel principal component analysis) and its related properties with a aim to prescribe cost effective treatments and easy diagnosis of diseases. This objective could be met only by the serious collaboration between physician and data scientist. We discussed that how we could construct a kernel and exact features based on the given dataset. Also, we compared the proposed method with the other methods. For the sake of easy understanding, applications of the proposed method are included in the text.

scholarly journals Prediction Model in Medical Science and Health Care

2019 ◽  
Vol 8 (6S3) ◽  
pp. 815-818
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Prediction Model ◽  
Early Treatment ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Medical Science ◽  
Useful Knowledge

The healthcare part has seen an incredible advancement following the improvement of new computer innovations, and that pushed this region to deliver increasingly restorative information, that which brought forth different fields of research. Numerous endeavors are done to adapt to the blast of therapeutic information on one hand, and to acquire valuable learning from it then again. To help in making decisions and to extract useful knowledge this incited specialists to apply all the specialized developments like predictive analytics, learning algorithms, machine learning and predictive analytics. In medical science to determine the risk of building up a disease the prediction models are used so that it can enable early treatment or prevention of that disease. To markers of future disposition to a disease multiple or single analyses are used.

scholarly journals Optimal strategies to screen health care workers for COVID-19 in the US: a cost-effectiveness analysis

2021 ◽  
Author(s):  
Sigal Maya ◽  
Guntas Padda ◽  
Victoria Close ◽  
Trevor Wilson ◽  
Fareeda Ahmed ◽  
...  
Keyword(s):  
Health Care ◽  
Cost Effectiveness ◽  
Health Care Workers ◽  
Cost Effective ◽  
Health Care Settings ◽  
Testing Strategies ◽  
Effectiveness Analysis ◽  
Care Workers ◽  
Antigen Testing ◽  
Quality Adjusted Life

Abstract Background: Transmission of SARS-CoV-2 in health care facilities poses a challenge against pandemic control. Health care workers (HCWs) have frequent and high-risk interactions with COVID-19 patients. We undertook a cost-effectiveness analysis to determine optimal testing strategies for screening HCWs to inform strategic decision-making in health care settings. Methods: We modeled the number of new infections, quality-adjusted life years lost, and net costs related to six testing strategies including no tests. We applied our model to four strata of HCWs, defined by the presence and timing of symptoms. We conducted sensitivity analyses to account for uncertainty in inputs. Results: When screening recently symptomatic HCWs, conducting only a PCR test is preferable; it saves costs and improves health outcomes in the first week post-symptom onset, and costs $83,000 per quality-adjusted life year gained in the second week post-symptom onset. When screening HCWs in the late clinical disease stage, none of the testing approaches is cost-effective and thus no testing is preferable, yielding $11 and 0.003 new infections per 10 HCWs. For screening asymptomatic HCWs, antigen testing is preferable to PCR testing due to its lower cost. Conclusions: Both PCR and antigen testing are beneficial strategies to identify infected HCWs and reduce transmission of SARS-CoV-2 in health care settings. IgG testing clinical value depends on test timing and immunity characteristics, however is not cost-effective in a low prevalence setting. As the context of the pandemic evolves, our study provides insight to health-care decision makers to keep the health care workforce safe and transmissions low.

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