Ethics, Legal and Privacy Concerns for the Next Generation of Insurance Policies

Tékhne ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 31-38
Author(s):  
Klaus-Georg Deck ◽  
Reinhard Riedl ◽  
Adamantios Koumpis
Keyword(s):  
Prediction Models ◽  
Risk Mitigation ◽  
Personal Information ◽  
Insurance Companies ◽  
Negative Consequences ◽  
Risk Prediction Models ◽  
Privacy Concerns ◽  
Individual Health ◽  
Heart Infarct

Abstract We present a set of hypothetical scenarios and cases where the need for access, sharing and processing of sensitive personal information increases the transparency of the customer to buyer relationship, although it may irreversibly damage the customer’s sphere of privacy. Highly personalised early risk prediction models for use by insurance companies to estimate the probability that a specific event (heart infarct) or a disease (diabetes) occurs in a given individual over a predefined time can enable earlier and better intervention, prevent negative consequences on a person’s quality of life and thus result in improved individual health outcomes. The challenge is to design, develop and validate new generations of comprehensive models that will be the result of a consensual process with the customers and will be based on artificial intelligence and other state-of-the-art technologies using multiple available data resources and will integrate them in personalised insurance policy pathways that empower the customers to actively contribute to their own individual health-risk mitigation and prevention.

scholarly journals Risk prediction models for atherosclerotic cardiovascular disease: A systematic assessment with particular reference to Qatar

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Aziz Sheikh ◽  
Ulugbek Nurmatov ◽  
Huda Amer Al-Katheeri ◽  
Rasmeh Ali Al Huneiti
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Ethnically Diverse ◽  
Risk Of Bias ◽  
Low Risk ◽  
Atherosclerotic Cardiovascular Disease ◽  
Eligibility Criteria ◽  
Risk Prediction Models ◽  
Ascvd Risk

Background: Atherosclerotic cardiovascular disease (ASCVD) is a common disease in the State of Qatar and results in considerable morbidity, impairment of quality of life and mortality. The American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) is currently used in Qatar to identify those at high risk of ASCVD. However, it is unclear if this is the optimal ASCVD risk prediction model for use in Qatar's ethnically diverse population. Aims: This systematic review aimed to identify, assess the methodological quality of and compare the properties of established ASCVD risk prediction models for the Qatari population. Methods: Two reviewers performed head-to-head comparisons of established ASCVD risk calculators systematically. Studies were independently screened according to predefined eligibility criteria and critically appraised using Prediction Model Risk Of Bias Assessment Tool. Data were descriptively summarized and narratively synthesized with reporting of key statistical properties of the models. Results: We identified 20,487 studies, of which 41 studies met our eligibility criteria. We identified 16 unique risk prediction models. Overall, 50% (n = 8) of the risk prediction models were judged to be at low risk of bias. Only 13% of the studies (n = 2) were judged at low risk of bias for applicability, namely, PREDICT and QRISK3.Only the PREDICT risk calculator scored low risk in both domains. Conclusions: There is no existing ASCVD risk calculator particularly well suited for use in Qatar's ethnically diverse population. Of the available models, PREDICT and QRISK3 appear most appropriate because of their inclusion of ethnicity. In the absence of a locally derived ASCVD for Qatar, there is merit in a formal head-to-head comparison between PCE, which is currently in use, and PREDICT and QRISK3.

scholarly journals Conventional risk prediction models fail to accurately predict mortality risk among patients with coronavirus disease 2019 in intensive care units: a difficult time to assess clinical severity and quality of care

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Hideki Endo ◽  
Hiroyuki Ohbe ◽  
Junji Kumasawa ◽  
Shigehiko Uchino ◽  
Satoru Hashimoto ◽  
...  
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Risk Prediction ◽  
Prediction Models ◽  
Clinical Severity ◽  
Risk Prediction Models ◽  
Prediction Tools ◽  
Difficult Time ◽  
Care Database

AbstractSince the start of the coronavirus disease 2019 (COVID-19) pandemic, it has remained unknown whether conventional risk prediction tools used in intensive care units are applicable to patients with COVID-19. Therefore, we assessed the performance of established risk prediction models using the Japanese Intensive Care database. Discrimination and calibration of the models were poor. Revised risk prediction models are needed to assess the clinical severity of COVID-19 patients and monitor healthcare quality in ICUs overwhelmed by patients with COVID-19.

scholarly journals Risk prediction models for acute kidney injury in adults: An overview of systematic reviews

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0248899
Author(s):  
Paulien Van Acker ◽  
Wim Van Biesen ◽  
Evi V. Nagler ◽  
Muguet Koobasi ◽  
Nic Veys ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
High Risk ◽  
Risk Prediction ◽  
Methodological Quality ◽  
Prediction Models ◽  
Kidney Injury ◽  
Risk Of Bias ◽  
Bias Assessment ◽  
Risk Prediction Models

Background The incidence of Acute Kidney Injury (AKI) and its human and economic cost is increasing steadily. One way to reduce the burden associated with AKI is to prevent the event altogether. An important step in prevention lies in AKI risk prediction. Due to the increasing number of available risk prediction models (RPMs) clinicians need to be able to rely on systematic reviews (SRs) to provide an objective assessment on which RPM can be used in a specific setting. Our aim was to assess the quality of SRs of RPMs in AKI. Methods The protocol for this overview was registered in PROSPERO. MEDLINE and Embase were searched for SRs of RPMs of AKI in any setting from 2003 till August 2020. We used the ROBIS tool to assess the methodological quality of the retrieved SRs. Results Eight SRs were retrieved. All studies were assessed as being at high risk for bias using the ROBIS tool. Eight reviews had a high risk of bias in study eligibility criteria (domain 1), five for study identification and selection (domain 2), seven for data collection and appraisal (domain 3) and seven for synthesis and findings (domain 4). Five reviews were scored at high risk of bias across all four domains. Risk of bias assessment with a formal risk of bias tool was only performed in five reviews. Primary studies were heterogeneous and used a wide range of AKI definitions. Only 19 unique RPM were externally validated, of which 11 had only 1 external validation report. Conclusion The methodological quality of SRs of RPMs of AKI is inconsistent. Most SRs lack a formal risk of bias assessment. SRs ought to adhere to certain standard quality criteria so that clinicians can rely on them to select a RPM for use in an individual patient. Trial registration PROSPERO registration number is CRD 42020204236, available at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=204236.

The Quality of Online Privacy Policies

2010 ◽  
pp. 2046-2065
Author(s):  
Veda C. Storey ◽  
Gerald C. Kane ◽  
Kathy Stewart Schwaig
Keyword(s):  
Private Information ◽  
Social Issues ◽  
Personal Information ◽  
Information Age ◽  
Online Privacy ◽  
Privacy Policies ◽  
High Quality ◽  
Privacy Concerns ◽  
Privacy Issues

Privacy concerns and practices, especially those dealing with the acquisition and use of consumer personal information by corporations, are at the forefront of business and social issues associated with the information age. This research examines the privacy policies of large U.S. companies to assess the substance and quality of their stated information practices. Six factors are identified that indicate the extent to which a firm is dependent upon consumer personal information, and therefore more likely to develop high quality privacy statements. The study’s findings provide practical and theoretical implications for information privacy issues, particularly for consumers who need to determine whether or not to disclose their personal identifying information to firms. The results illustrate the complexity involved in managing personal private information.

scholarly journals Reporting and Performance of Hepatocellular Carcinoma Risk Prediction Models: Based on TRIPOD Statement and Meta-Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Liuqing Yang ◽  
Qiang Wang ◽  
Tingting Cui ◽  
Jinxin Huang ◽  
Hui Jin
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Risk Prediction ◽  
Missing Values ◽  
Prediction Models ◽  
Meta Analysis ◽  
External Validation ◽  
Discrimination Performance ◽  
Risk Prediction Models

Background. The performance of risk prediction models for hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) was uncertain. The aim of the study was to critically evaluate the reports of transparent and external validation performances of these prediction models based on system review and meta-analysis. Methods. A systematic search of the Web of Science and PubMed was performed for studies published until October 17, 2020. The transparent reporting of a multivariable prediction model for the individual prognosis or diagnosis (TRIPOD) tool was used to critically evaluate the quality of external validation reports for six models (CU-HCC, GAG-HCC, PAGE-B, mPAGE-B, REACH-B, and mREACH-B). The area under the receiver operator characteristic curve (AUC) values was to estimate the pooled external validating performance based on meta-analysis. Subgroup analysis and metaregression were also performed to explore heterogeneity. Results. Our meta-analysis included 22 studies published between 2011 and 2020. The compliance of the included studies to TRIPOD ranged from 59% to 90% (median, 74%; interquartile range (IQR), 70%, 79%). The AUC values of the six models ranged from 0.715 to 0.778. In the antiviral therapy subgroups, the AUC values of mREACH-B, GAG-HCC, and mPAGE-B were 0.785, 0.760, and 0.778, respectively. In the cirrhosis subgroup, all models had poor discrimination performance (AUC < 0.7). Conclusions. A full report of calibration and handling of missing values would contribute to a greater improvement in the quality of external validation reports for CHB-related HCC risk prediction. It was necessary to develop a specific HCC risk prediction model for patients with cirrhosis.

scholarly journals IMPROVING THE QUALITY OF TRANSPORT SERVICES FOR LONG-DISTANCE ROAD PASSENGER TRANSPORTATION

2017 ◽  
Vol 11 (6) ◽  
pp. 54-66
Author(s):  
Наталья Москалева ◽  
Natal'ya Moskaleva
Keyword(s):  
Social Services ◽  
Technical Condition ◽  
Practical Significance ◽  
Insurance Companies ◽  
Negative Consequences ◽  
Road Transportation ◽  
Long Distance ◽  
Travel Agents ◽  
Transport Services

The article justifies the role of self-control by transportation organizations and monitoring by tour operators and insurance companies of the behavior of drivers, their health and technical condition of vehicles. The article reveals the methodology of improving the quality of social services for the public by monitoring the behavior, health of drivers of vehicles and the technical state of vehicles in longdistance, international transport of the population (including tourists). The methodology is aimed at identifying possible dangers, discomfort in the process of providing services to the population and proactive purposeful actions to eliminate or minimize the negative consequences. The methodology contains a set of measures and recommendations that improve the quality of tourism road transportation, and allow car carriers, tour operators and travel agents significantly to reduce financial costs by avoiding traffic accidents through the fault of drivers of car carriers. The author develops and analyzes the concepts of behavior, the health status of drivers of the vehicle, the signs of their manifestation. The methodology includes recommendations for transportation organizations, tour operators (travel agents) for tests of the behavior of vehicle drivers through a «mysterious passenger». The author suggests ways to organize quality control of pre-trip medical examination of drivers. The author recommends for auto carriers introduce measures aimed at maintaining the normal technical condition of vehicles. The practical significance of the study lies in the possibility of implementing the methodology in the work of auto carriers, tour operators, travel agents not only in the Russian Federation, but also the host party abroad.

scholarly journals Risk prediction models for intensive care unit-acquired weakness in intensive care unit patients: A systematic review

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257768
Author(s):  
Wei Zhang ◽  
Yun Tang ◽  
Huan Liu ◽  
Li ping Yuan ◽  
Chu chu Wang ◽  
...  
Keyword(s):  
Systematic Review ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Groups ◽  
Icu Patients ◽  
Risk Prediction Models ◽  
Verification Methods

Background and objectives Intensive care unit-acquired weakness (ICU-AW) commonly occurs among intensive care unit (ICU) patients and seriously affects the survival rate and long-term quality of life for patients. In this systematic review, we synthesized the findings of previous studies in order to analyze predictors of ICU-AW and evaluate the discrimination and validity of ICU-AW risk prediction models for ICU patients. Methods We searched seven databases published in English and Chinese language to identify studies regarding ICU-AW risk prediction models. Two reviewers independently screened the literature, evaluated the quality of the included literature, extracted data, and performed a systematic review. Results Ultimately, 11 studies were considered for this review. For the verification of prediction models, internal verification methods had been used in three studies, and a combination of internal and external verification had been used in one study. The value for the area under the ROC curve for eight models was 0.7–0.923. The predictor most commonly included in the models were age and the administration of corticosteroids. All the models have good applicability, but most of the models are biased due to the lack of blindness, lack of reporting, insufficient sample size, missing data, and lack of performance evaluation and calibration of the models. Conclusions The efficacy of most models for the risk prediction of ICU-AW among high-risk groups is good, but there was a certain bias in the development and verification of the models. Thus, ICU medical staff should select existing models based on actual clinical conditions and verify them before applying them in clinical practice. In order to provide a reliable basis for the risk prediction of ICU-AW, it is necessary that large-sample, multi-center studies be conducted in the future, in which ICU-AW risk prediction models are verified.

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