Doctors, Patients, and Pills--A System Popping Under Too Much Physician Discretion? A Law-Policy Prescription to Make Drug Approval More Meaningful in the Delivery of Health Care

This article challenges the scope of physician discretion to engage in off-label use of prescription drugs. The discretion to prescribe dimensions beyond the clinical research that puts new drugs on pharmacy shelves has been shaped by two historic influences: a legacy of physician paternalism, solidarity, autonomy, and self-determination that predates the contemporary commercialization of medicine by more than half a century, and regulatory necessity due to the limits of science and innate crudeness of pharmaceuticals prior to the genomics revolution (drug development and delivery based upon genetic expression). Although both factors have changed immensely, the standard for drug approval has lingered. This article proposes that doctor discretion to prescribe off label must be modified and the regulatory standard for new drug approvals raised given the proliferation of adverse events, drug ineffectiveness, the need to make choices among treatment options under time pressures, the increasing complexity of biopharmaceuticals, health care cost pressures, and the vulnerability of patients—seekers of health care, not research subjects protected under the scrutiny of regulations to protect human subjects. The article concludes that, although some physician discretion to prescribe off label still is necessary, law-policy reforms to shift more of the drug discovery process from the clinical care of patients to clinical research in drug development are long overdue. Proposals to accomplish this, drawn from recent legislation and ongoing health care reform, include heightening the regulatory standards for new drug approvals and drug reimbursement

2nd International Consensus Report on Gaps & Opportunities for the Clinical Translation of Precision Diabetes Medicine

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for the heterogeneous etiology, clinical presentation, and pathogenesis of common forms of diabetes and risk of complications. This 2nd International Consensus Report on Precision Diabetes Medicine summarize the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; further, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability, and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine

Inhalation therapy in the next decade : Determinants of adherence to treatment in asthma and COPD

Peer reviewedPublisher PD

Prioritizing research challenges and funding for allergy and asthma and the need for translational research-The European Strategic Forum on Allergic Diseases

The European Academy of Allergy and Clinical Immunology (EAACI) organized the first European Strategic Forum on Allergic Diseases and Asthma. The main aim was to bring together all relevant stakeholders and decision-makers in the field of allergy, asthma and clinical Immunology around an open debate on contemporary challenges and potential solutions for the next decade. The Strategic Forum was an upscaling of the EAACI White Paper aiming to integrate the Academy's output with the perspective offered by EAACI's partners. This collaboration is fundamental for adapting and integrating allergy and asthma care into the context of real-world problems. The Strategic Forum on Allergic Diseases brought together all partners who have the drive and the influence to make positive change: national and international societies, patients' organizations, regulatory bodies and industry representatives. An open debate with a special focus on drug development and biomedical engineering, big data and information technology and allergic diseases and asthma in the context of environmental health concluded that connecting science with the transformation of care and a joint agreement between all partners on priorities and needs are essential to ensure a better management of allergic diseases and asthma in the advent of precision medicine together with global access to innovative and affordable diagnostics and therapeutics.Peer reviewe

Servant Leadership Characteristics and Empathic Care: Developing a Culture of Empathy in the Healthcare Setting

The purpose of this study was to assess the degree to which servant leadership characteristics are exhibited in medical group practices, and the degree to which servant leadership characteristics correlated with measures of empathic care. This study featured an explanatory mixed methods research design embedded in appreciative inquiry. A total of 189 mid-level practitioners consisting of nurse practitioners, physician assistants, and practice mangers responded to a 32-item scale survey that featured a six-point Likert scale to measure servant leadership items and a 10-point continuous scale to assess measures of empathic care. The servant leadership items were based on the seven pillars of servant leadership. Data analyses included assessing means, standard deviations, and percentage distributions for servant leadership statements and empathic care statements. Additionally, bivariate correlation analysis and standard multiple regression analysis were conducted to assess the degree of influence of servant leadership characteristics on measures of empathic care. Findings from this study identified Pillar 1 (Persons of Character) as the servant leadership pillar most strongly exhibited in the medical group practices. Furthermore, Pillar 5 (Has Foresight) was the strongest correlate of reported empathic care within medical group practices as well as team members’ proclivity to practice servant leadership behaviors with patients more than with each other. The study also found that clinicians and non-clinicians significantly differed in their endorsement of all of the servant leadership pillars except Pillar 1 (Persons of Character). The findings of this dissertation point to strategies for promoting an environment of empathic care, and team building and organizational development and training in the medical group practices. This dissertation is available in open access at AURA: Antioch University Repository and Archive, http://aura.antioch.edu/ and OhioLINK ETD Center, https://etd.ohiolink.edu

Technofixing the Future: Ethical Side Effects of Using AI and Big Data to meet the SDGs

While the use of smart information systems (the combination of AI and Big Data) offer great potential for meeting many of the UN’s Sustainable Development Goals (SDGs), they also raise a number of ethical challenges in their implementation. Through the use of six empirical case studies, this paper will examine potential ethical issues relating to use of SIS to meet the challenges in six of the SDGs (2, 3, 7, 8, 11, and 12). The paper will show that often a simple “technofix”, such as through the use of SIS, is not sufficient and may exacerbate, or create new, issues for the development community using SIS

ELSI issues of Precision medicine - Comparison of US, South Korea, China and Mongolia focusing on informed consent and privacy

This study aims to review current precision medicine, analyze ELSI issues and compare legal and regulatory framework of informed consent and privacy issues in countries, namely US, South Korea, China and Mongolia. This has been conducted through analysis of current situation and challenges that four countries are facing in terms of informed consent and privacy issues in precision medicine. The purpose of this study is to develop recommendation for Mongolia based on three countries experiences and pros during development of precision medicine regarding informed consent and privacy. In order to carry out this study, mainly two study methods are applied. First, the literature review was performed with academic articles and reports on precision medicine and its ELSI research including informed consent and privacy issues, and officual documents from each government website. Secondly, the comparative analysis of the legal and regulatory frameworks that relate to informed consent and privacy on prevision medicine was conducted in four countries. Through the analysis, it has clearly revealed that Mongolia need to improve regulation related to informed consent, including appropriate language and terms, evaluation questions and approval of electronic version. But in Mongolia, special contemplation should be discussed in order to develop electronic informed consent due to nomadic life, lack of infrastructure, like internet, computer in remote areas, and low computer and health literacy, especially in non-capital areas. Since Mongolia is taking first step in privacy protection in context of personal information and sensitive information including genetic and biometric, several updates and recommendation could be proposed based on described approaches and solution ways from respective countries. This includes official implementation of PIPL, development of guideline for de-identification of personal information and building capacity for human resources and technology. Moreover, experience from developed countries can help improvement but approach need to be naturalized accordance with Mongolian background. Even though Mongolian government is started to focus on biomedical researches and related issues to enhance quality of research field and open more gates to researchers, infrastructure preparedness has developed very slowly. Recommendations arise from this study, may provide some opinions in building better frameworks targeting informed consent and privacy issues in Mongolian situation. Furthermore, detailed analysis from expert’s perspective will need to be conducted for achieving successful results with improvement from international experts’ experiences and support.open석

Drug Development--Stuck in a State of Puberty?: Regulatory Reform of Human Clinical Research to Raise Responsiveness to the Reality of Human Variability

Scathing critiques of the Food and Drug Administration\u27s (“FDA”) performance by the Government Accountability Office and Institutes of Medicine, a plummet in innovative new drug approvals in spite of significant annual investment increases in biopharmaceutical research and development (“R&D”), and market controversies such as the painkiller Vioxx and the diabetes drug Avandia (both associated with significantly escalated risks of heart attacks and strokes) have raised doubts about the sufficiency of FDA *364 regulation. This Article questions how prescription medicines reach the market and proposes law-policy reforms to enhance the FDA\u27s science standard for human clinical trials and new drug approvals. The core message is that relying too heavily on clinical research data generated through the global “gold standard” of group experimental design--reliance on statistical analysis to compile and compare group averages--risks predicting little about the actual impact of prescription medicines on individuals, including members of the groups under study. This Article introduces a law-policy methodology based upon commercial incentives and intervention by Congress and the FDA to raise the science standard for human clinical research, and to make drug development more closely parallel the reality of drug delivery in the practice of medicine. The objectives of this proposal are to promote several pressing needs: maximize drug performance and minimize adverse events; end the pattern of putting new prescription medications on the market with too much dependence on the medical profession to introduce meaningful clinical understanding of drugs through patient use over time; improve biopharmaceutical R&D decision making; align the regulatory standard with the infusion of added precision associated with contemporary genetics-based R&D; and realize more sound scientific information directly through the regulatory process to support the integrity of science in an age of academia-industry integration, aggressive commercialization, secrecy in science, and constantly, rapidly evolving technology

Precision Clinical Medicine Through Machine Learning: Using High and Low Quantile Ranges of Vital Signs for Risk Stratification of ICU Patients

Remote monitoring of patients in the intensive care unit (ICU) is a crucial observation and assessment task that is necessary for precision medicine. We have recently built a cloud-based intelligent remote patient monitoring (IRPM) framework in which we follow the state-of-the-art in risk stratification through machine learning-based prediction, but with minimal features that rely on vital signs, the most commonly used physiological variables obtained inside and outside hospitals. In this work, we significantly improve the functionality of the initial IRPM framework by building three machine learning models for readmission, abnormality, and next-day vital sign measurements. We provide a formal representation of a feature engineering algorithm and report the development and validation of three reproducible machine learning prediction models: ICU patient readmission, abnormality, and next-day vital sign measurements. For the readmission model, we proposed two solutions for data with imbalanced classes and applied five binary classification algorithms to each solution. For the abnormality model, we applied the same five algorithms to predict whether a patient will show abnormal health conditions. Our findings indicate that we can still achieve a reasonable performance using these machine learning models by focusing on low and high quantile ranges of vital signs. The best accuracy achieved in the readmission model was around 67.53%, with an area under the receiver operating characteristic (AUROC) of 0.7376. The highest accuracy achieved in the abnormality model was around 67.40%, with an AUROC of 0.7379. For the next-day vital sign measurements model, we provide three approaches for selecting model predictors and apply the eXtreme Gradient Boosting (XGB) and Random Forest Regression (RFR) algorithms to each solution. We found that, in general, the use of the most recent vital sign measurements achieves the least prediction error. Considering the large investment from the medical industry in patient monitoring devices, the developed models will be incorporated into an Intelligent ICU Patient Monitoring (IICUPM) module that can potentially facilitate the delivery of high quality care by implementing cost-efficient policies for handling the patients who utilize ICU resources the most