The Impact of Big Data and Analytics on CIO Role

The joint consideration of big data and analytics is a relatively recent development, particularly, within the domain of Management of Information Systems (MIS). In recent years there is a dramatic increment of the data produced by digital devices and systems. This data and the various techniques applied on them have shaped the area of big data and analytics. The processing and analysis of big data has been proved of high importance for the organizations and help them to reform their strategies and practices. In this paper we investigate the rise of big data and big data analytics and their impact on CIO role. The results show the importance of the generation gap and the power of new entrants. It is also derived that there is an impact on the role of CIO

Big Data and Analytics Leaders

This article investigates the changing role of the CIO at organizational level with regard to the rise of big data and big data analytics as a potential source of innovation and competitive advantage. The paper aims to provide a theoretical contribution to the research stream on the topic, by further exploring the emergent properties and understandings related to the role of CIO as a consequence of the need to adopt advanced technologies, named to master the current unheard information growth for business innovation. To this end we present the results of a qualitative research based on grounded theory carried out on data concerning CIOs of medium and large companies from different industries in the Italian market. Finally, a substantive theory and categories are discussed, showing the role of generation gap and power of new entrants as well as of project and execution excellence on the making of identity and recognition of the CIO as relevant at the time of big data analytics

Phenomenology and the Social Study of Information Systems: Conversations with Kenneth Liberman

Liberman invites us to explore the depths of our ordinary social world, the primitive place of our experience. As an anthropologist who spent two years with some Australian Aboriginal tribes and three years in a Tibetan monastery, he encourages us to reflect on that world taken for granted that we call reality. Similarly, as philosopher, he sees the limits of reason and the difficulties we fall into when we overconceptualize our worldly relations, when we entrust entirely to what he calls "the formal analysis", when we don’t recognize the very carnal, practical and experiential character of social life. Starting from this basis, the paper offers grounds for reflection on the field of information systems

A relative risk assessment of the open burning of WEEE

Waste electric and electronic equipment (WEEE) represents a potential secondary source of valuable materials, whose recovery is a growing business activity worldwide. In low-income countries, recycling is carried out under poorly controlled conditions resulting in severe environmental pollution. High concentrations of both metallic and organic pollutants have been confirmed in air, soil, water, and sediments in countries with informal recycling areas. The release of these contaminants into the environment presents a risk to the health of the exposed population that has been widely acknowledged but still needs to be quantified. The aim of this work was to evaluate the relative risk from inhalation associated with the open burning of different kinds of WEEE. The shrinking core model was applied to estimate the concentration of the metals which would be released into the environment during the incineration of different types of WEEE. In addition, the potential generation of dioxins during the same informal practice was estimated, based on the plastic content of the WEEE. The results provided for the first time a comparative analysis of the risk posed from the open burning of WEEE components, proposing a methodology to address the absolute risk assessment to workers from the informal recycling of WEEE

Predicting all-cause mortality by means of a multisensor implantable defibrillator algorithm for heart failure monitoring

BACKGROUND The HeartLogic algorithm (Boston Scientific) has proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation. OBJECTIVE The purpose of this study was to determine whether remotely monitored data from this algorithm could be used to iden-tify patients at high risk for mortality. METHODS The algorithm combines implantable cardioverter-defibrillator (ICD)-measured accelerometer-based heart sounds, intrathoracic impedance, respiration rate, ratio of respiration rate to tidal volume, night heart rate, and patient activity into a single index. An alert is issued when the index crosses a programmable threshold. The feature was activated in 568 ICD patients from 26 centers. RESULTS During median follow-up of 26 months [25th-75th percentile 16-37], 1200 alerts were recorded in 370 patients (65%). Overall, the time IN-alert state was 13% of the total obser-vation period (151/1159 years) and 20% of the follow-up period of the 370 patients with alerts. During follow-up, 55 patients died (46 in the group with alerts). The rate of death was 0.25 per patient-year (95% confidence interval [CI] 0.17-0.34) IN-alert state and 0.02 per patient-year (95% CI 0.01-0.03) OUT of the alert state, with an incidence rate ratio of 13.72 (95% CI 7.62-25.60; P <.001). After multivariate correction for baseline confounders (age, ischemic cardiomyopathy, kidney disease, atrial fibrillation), the IN-alert state remained significantly associated with the occur-rence of death (hazard ratio 9.18; 95% CI 5.27-15.99; P <.001). CONCLUSION The HeartLogic algorithm provides an index that can be used to identify patients at higher risk for all-cause mortality. The index state identifies periods of significantly increased risk of death

Abnormal presentation of a bilateral, synchronous and plurimetastatic medium and large cell testicular lymphoma: A case report

Primary testicular lymphoma (PTL) accounts for 1-2% of all cases of non-Hodgkin's lymphoma, with a higher incidence in patients aged >60 years. The most common histological subtype is diffuse large-cell B lymphoma. By contrast, the bilateral synchronous and multimetastatic clinical presentation is a rare and unusual clinical presentation. In testicular masses, orchiectomy is essential for histopathological evaluation of the disease and definition of the immunophenotypic structure. The present study reported the case of a paucisymptomatic 54-year-old patient, who presented with erectile dysfunction and increasing testicular volume. Although clinical assessment and ultrasound examination showed an abnormal structure, highly suspicious for testicular cancer, the subsequent bilateral radical orchiectomy permitted the diagnosis of an unusual and rare PTL with multiple metastases reported at the PET/CT scan. In conclusion, the rare and aggressive disease represented by PTL requires a multidisciplinary approach and an aggressive treatment in order to provide the best care for patients affected

Performance of a multisensor implantable defibrillator algorithm for heart failure monitoring related to co-morbidities

Aims: The HeartLogic algorithm combines multiple implantable defibrillator (ICD) sensor data and has proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation in cardiac resynchronization therapy (CRT-D) patients. We evaluated the performance of this algorithm in non-CRT ICD patients and in the presence of co-morbidities. Methods and results: The HeartLogic feature was activated in 568 ICD patients (410 with CRT-D) from 26 centres. The median follow-up was 26 months [25th-75th percentile: 16-37]. During follow-up, 97 hospitalizations were reported (53 cardiovascular) and 55 patients died. We recorded 1200 HeartLogic alerts in 370 patients. Overall, the time IN the alert state was 13% of the total observation period. The rate of cardiovascular hospitalizations or death was 0.48/patient-year (95% CI: 0.37-0.60) with the HeartLogic IN the alert state and 0.04/patient-year (95% CI: 0.03-0.05) OUT of the alert state, with an incidence rate ratio of 13.35 (95% CI: 8.83-20.51, P < 0.001). Among patient characteristics, atrial fibrillation (AF) on implantation (HR: 1.62, 95% CI: 1.27-2.07, P < 0.001) and chronic kidney disease (CKD) (HR: 1.53, 95% CI: 1.21-1.93, P < 0.001) independently predicted alerts. HeartLogic alerts were not associated with CRT-D versus ICD implantation (HR: 1.03, 95% CI: 0.82-1.30, P = 0.775). Comparisons of the clinical event rates in the IN alert state with those in the OUT of alert state yielded incidence rate ratios ranging from 9.72 to 14.54 (all P < 0.001) in all groups of patients stratified by: CRT-D/ICD, AF/non-AF, and CKD/non-CKD. After multivariate correction, the occurrence of alerts was associated with cardiovascular hospitalization or death (HR: 1.92, 95% CI: 1.05-3.51, P = 0.036). Conclusions: The burden of HeartLogic alerts was similar between CRT-D and ICD patients, while patients with AF and CKD seemed more exposed to alerts. Nonetheless, the ability of the HeartLogic algorithm to identify periods of significantly increased risk of clinical events was confirmed, regardless of the type of device and the presence of AF or CKD

Performance of a multi-sensor implantable defibrillator algorithm for heart failure monitoring in the presence of atrial fibrillation

Aims: The HeartLogic Index combines data from multiple implantable cardioverter defibrillators (ICDs) sensors and has been shown to accurately stratify patients at risk of heart failure (HF) events. We evaluated and compared the performance of this algorithm during sinus rhythm and during long-lasting atrial fibrillation (AF). Methods and results: HeartLogic was activated in 568 ICD patients from 26 centres. We found periods of ≥30 consecutive days with an atrial high-rate episode (AHRE) burden <1 h/day and periods with an AHRE burden ≥20 h/day. We then identified patients who met both criteria during the follow-up (AHRE group, n = 53), to allow pairwise comparison of periods. For control purposes, we identified patients with an AHRE burden <1 h throughout their follow-up and implemented 2:1 propensity score matching vs. the AHRE group (matched non-AHRE group, n = 106). In the AHRE group, the rate of alerts was 1.2 [95% confidence interval (CI): 1.0-1.5]/patient-year during periods with an AHRE burden <1 h/day and 2.0 (95% CI: 1.5-2.6)/patient-year during periods with an AHRE-burden ≥20 h/day (P = 0.004). The rate of HF hospitalizations was 0.34 (95% CI: 0.15-0.69)/patient-year during IN-alert periods and 0.06 (95% CI: 0.02-0.14)/patient-year during OUT-of-alert periods (P < 0.001). The IN/OUT-of-alert state incidence rate ratio of HF hospitalizations was 8.59 (95% CI: 1.67-55.31) during periods with an AHRE burden <1 h/day and 2.70 (95% CI: 1.01-28.33) during periods with an AHRE burden ≥20 h/day. In the matched non-AHRE group, the rate of HF hospitalizations was 0.29 (95% CI: 0.12-0.60)/patient-year during IN-alert periods and 0.04 (95% CI: 0.02-0.08)/patient-year during OUT-of-alert periods (P < 0.001). The incidence rate ratio was 7.11 (95% CI: 2.19-22.44). Conclusion: Patients received more alerts during periods of AF. The ability of the algorithm to identify increased risk of HF events was confirmed during AF, despite a lower IN/OUT-of-alert incidence rate ratio in comparison with non-AF periods and non-AF patients. Clinical trial registration: http://clinicaltrials.gov/Identifier: NCT02275637

Multiparametric Implantable Cardioverter-Defibrillator Algorithm for Heart Failure Risk Stratification and Management: An Analysis in Clinical Practice

BACKGROUND: The HeartLogic algorithm combines multiple implantable cardioverter-defibrillator sensors to identify patients at risk of heart failure (HF) events. We sought to evaluate the risk stratification ability of this algorithm in clinical practice. We also analyzed the alert management strategies adopted in the study group and their association with the occurrence of HF events.METHODS: The HeartLogic feature was activated in 366 implantable cardioverter-defibrillator and cardiac resynchronization therapy implantable cardioverter-defibrillator patients at 22 centers. The median follow-up was 11 months [25th-75th percentile: 6-16]. The HeartLogic algorithm calculates a daily HF index and identifies periods IN alert state on the basis of a configurable threshold.RESULTS: The HeartLogic index crossed the threshold value 273 times (0.76 alerts/patient-year) in 150 patients. The time IN alert state was 11% of the total observation period. Patients experienced 36 HF hospitalizations, and 8 patients died of HF during the observation period. Thirty-five events were associated with the IN alert state (0.92 events/patient-year versus 0.03 events/patient-year in the OUT of alert state). The hazard ratio in the IN/OUT of alert state comparison was (hazard ratio, 24.53 [95% CI, 8.55-70.38], P<0.001), after adjustment for baseline clinical confounders. Alerts followed by clinical actions were associated with less HF events (hazard ratio, 0.37 [95% CI, 0.14-0.99], P=0.047). No differences in event rates were observed between in-office and remote alert management.CONCLUSIONS: This multiparametric algorithm identifies patients during periods of significantly increased risk of HF events. The rate of HF events seemed lower when clinical actions were undertaken in response to alerts. Extra in-office visits did not seem to be required to effectively manage HeartLogic alerts. Registration: URL: ; Unique identifier: NCT02275637