Corruption in the Middle East and the Limits of Conventional Approaches

Die Unzufriedenheit mit der verbreiteten Korruption war 2011/2012 eine wesentliche Ursache für die arabischen Unruhen und weitere Aufstände weltweit. Der Fall Jordanien zeigt allerdings, dass konventionelle Ansätze zur Bekämpfung von Korruption nicht ausreichen. Eine angemessene Strategie gegen Korruption muss diese als ein Problem der Verteilungsgerechtigkeit und nicht des Strafrechts verstehen. Wie in allen anderen arabischen Staaten ist die Unzufriedenheit in der Bevölkerung über die offensichtliche Korruption auch in Jordanien beträchtlich. Allerdings wird im Allgemeinen nicht über Fälle von Bestechung und Erpressung geklagt, die weniger häufig vorkommen, sondern über lokale Praktiken politischer Patronage und Begünstigung, die unter dem Begriff "Wasta" zusammengefasst werden. "Wasta" wurde bislang als Form der Korruption und strafrechtliches Problem angesehen, weshalb Versuche zur Eindämmung überwiegend ineffizient blieben: "Wasta"-Praktiken werden in der Regel nicht mit Rechtsverstößen verbunden, sondern bewegen sich innerhalb formal legaler Verfahren. Konventionelle Ansätze zur Bekämpfung von Korruption, die sich an rechtsstaatlichen Grundsätzen und Transparenz orientieren, sind deshalb nicht zielführend. Demokratisierung allein ist ebenfalls ungeeignet, das Problem „Wasta” zu lösen. In der parlamentarischen Praxis macht "Wasta" den Großteil der Aktivitäten aller Parlamentsmitglieder aus. Diese werden deshalb als persönliche Dienstleister für ihre Wahlbezirke und nicht als Mitglieder einer gesetzgebenden Körperschaft wahrgenommen. Gleichzeitig hält die Bevölkerung das Parlament für eine zutiefst korrupte Institution. "Wasta" wird problematisch, wenn diese Praxis zu einem ungleichen Zugang der Bürger zu öffentlichen Ressourcen führt. Statt sich nur auf politische und administrative Reformen zu konzentrieren, muss der Fokus der Bekämpfung auf den (Wieder-)Aufbau wohlfahrtsstaatlicher Strukturen gelegt werden, zu denen alle Bürger gleichermaßen Zugang haben

Telemonitoring for Patients With COVID-19:Recommendations for Design and Implementation

Despite significant efforts, the COVID-19 pandemic has put enormous pressure on health care systems around the world, threatening the quality of patient care. Telemonitoring offers the opportunity to carefully monitor patients with a confirmed or suspected case of COVID-19 from home and allows for the timely identification of worsening symptoms. Additionally, it may decrease the number of hospital visits and admissions, thereby reducing the use of scarce resources, optimizing health care capacity, and minimizing the risk of viral transmission. In this paper, we present a COVID-19 telemonitoring care pathway developed at a tertiary care hospital in the Netherlands, which combined the monitoring of vital parameters with video consultations for adequate clinical assessment. Additionally, we report a series of medical, scientific, organizational, and ethical recommendations that may be used as a guide for the design and implementation of telemonitoring pathways for COVID-19 and other diseases worldwide

ESC Working Group on e-Cardiology Position Paper: Use of Commercially Available Wearable Technology for Heart Rate and Activity Tracking in Primary and Secondary Cardiovascular Prevention

Commercially available health technologies such as smartphones and smartwatches, activity trackers and eHealth applications, commonly referred to as wearables, are increasingly available and used both in the leisure and healthcare sector for pulse and fitness/ activity tracking. The aim of the Position Paper is to identify specific barriers and knowledge gaps for the use of wearables, in particular for heart rate and activity tracking, in clinical cardiovascular healthcare to support their implementation into clinical care. The widespread use of heart rate and fitness tracking technologies provides unparalleled opportunities for capturing physiological information from large populations in the community, which has previously only been available in patient populations in the setting of healthcare provision. The availability of low-cost and high-volume physiological data from the community also provides unique challenges. While the number of patients meeting healthcare providers with data from wearables is rapidly growing, there are at present no clinical guidelines on how and when to use data from wearables in primary and secondary prevention. Technical aspects of heart rate tracking especially during activity need to be further validated. How to analyze, translate, and interpret large datasets of information into clinically applicable recommendations needs further consideration. While the current users of wearable technologies tend to be young, healthy and in the higher sociodemographic strata, wearables could potentially have a greater utility in the elderly and higher risk population. Wearables may also provide a benefit through increased health awareness, democratization of health data and patient engagement. Use of continuous monitoring may provide opportunities for detection of risk factors and disease development earlier in the causal pathway, which may provide novel applications in both prevention and clinical research. However, wearables may also have potential adverse consequences due to unintended modification of behaviour, uncertain use and interpretation of large physiological data, a possible increase in social inequality due to differential access and technological literacy, challenges with regulatory bodies and privacy issues. In the present position paper, current applications as well as specific barriers and gaps in knowledge are identified and discussed in order to support the implementation of wearable technologies from gadget-ology into clinical cardiology

Early Detection of Fluid Retention in Patients with Advanced Heart Failure: A Review of a Novel Multisensory Algorithm, HeartLogicTM

Heart failure (HF) hospitalisations due to decompensation are associated with shorter life expectancy and lower quality of life. These hospitalisations pose a significant burden on the patients, doctors and healthcare resources. Early detection of an upcoming episode of decompensation may facilitate timely optimisation of the ambulatory medical treatment and thereby prevent heart-failure-related hospitalisations. The HeartLogicTM algorithm combines data from five sensors of cardiac implantable electronic devices into a cumulative index value. It has been developed for early detection of fluid retention in heart failure patients. This review aims to provide an overview of the current literature and experience with the HeartLogicTM algorithm, illustrate how the index can be implemented in daily clinical practice and discuss ongoing studies and potential future developments of interest

eHealth to improve patient outcome in rehabilitating myocardial infarction patients

Introduction: Cardiac rehabilitation is aimed at risk factor modification and improving quality of life. eHealth has a couple of potential benefits to improve this aim. The primary purpose of this review is to summarize available literature for eHealth strategies that have been investigated in randomized controlled trials in post-myocardial infarction (MI) patients. The second purpose of this review is to investigate the clinical effectiveness in post-MI patients. Areas covered: The literature was searched using PubMed. Randomized controlled trials (RCTs) describing interventions in patients that had experienced an ST-elevation myocardial infarction or non-ST acute coronary syndrome were eligible for inclusion. Fifteen full-texts were included and their results are described in this review. These RCTs described interventions that used remote coaching or remote monitoring in post-MI patients. Most interventions resulted in an improved cardiovascular risk profile. Remote coaching had a positive effect on activity and dietary intake. Expert opinion: eHealth might be clinically beneficial in post-MI patients, particularly for risk estimation. Moreover, eHealth as a tool for remote coaching on activity is a good addition to traditional cardiac rehabilitation programs. Further research needs to corroborate these findings

Serial ECG Analysis: Absolute Rather Than Signed Changes in the Spatial QRS-T Angle Should Be Used to Detect Emerging Cardiac Pathology

Background. Larger one-time values of spatial QRS-T angle (SA) are associated with risk. However, experience how serial changes in SA (ΔSA) should be interpreted is lacking. Even within normal limits, any ΔSA likely signifies electrical remodeling. This study aimed to assess the impact of choosing either ΔSA or |ΔSA| as one of a set of serial ECG difference features that constitute the input for our deep learning serial-ECG classifier (DLSEC). Methods. DLSEC was trained and tested to detect emerging pathology in two serial ECG databases: a heart failure database and an acute ischemia database. Either ΔSA or |ΔSA| were among 13 features of serial-ECG differences. DLSEC was dynamically generated during learning, and testing area under the curve (AUC) of the receiver operating characteristic was computed. Results. The DLSECs performed well in emerging heart failure as well as in acute ischemia: testing AUCs were 72% and 84% for the heart failure database and 77% and 83% for the ischemia database, for ΔSA or |ΔSA| among the features, respectively. Conclusion. |ΔSA| among the features was superior to ΔSA in discriminating cases and controls. Our study supports the concept that any ΔSA, irrespective of its sign, indicates a worsening clinical condition. Further corroboration requires studies in other clinical situations

Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach

Abstract Background Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we present a novel algorithm to construct dedicated deep-learning neural networks (NNs) that are specialized in detecting newly emerging or aggravating existing cardiac pathology in serial ECGs. Methods We developed a novel deep-learning method for serial ECG analysis and tested its performance in detection of heart failure in post-infarction patients, and in the detection of ischemia in patients who underwent elective percutaneous coronary intervention. Core of the method is the repeated structuring and learning procedure that, when fed with 13 serial ECG difference features (intra-individual differences in: QRS duration; QT interval; QRS maximum; T-wave maximum; QRS integral; T-wave integral; QRS complexity; T-wave complexity; ventricular gradient; QRS-T spatial angle; heart rate; J-point amplitude; and T-wave symmetry), dynamically creates a NN of at most three hidden layers. An optimization process reduces the possibility of obtaining an inefficient NN due to adverse initialization. Results Application of our method to the two clinical ECG databases yielded 3-layer NN architectures, both showing high testing performances (areas under the receiver operating curves were 84% and 83%, respectively). Conclusions Our method was successful in two different clinical serial ECG applications. Further studies will investigate if other problem-specific NNs can successfully be constructed, and even if it will be possible to construct a universal NN to detect any pathologic ECG change

Implementation of smart technology to improve medication adherence in patients with cardiovascular disease: is it effective?

Cardiolog