Analytical Requests Research Articles

Compliance with the clinical laboratory quality protocol in public primary healthcare centres.

The clinical and economic relevance of the clinical laboratories procedures in Andalusia (Spain) have led the Regional Department of Health to focus attention on their improvement. A unified laboratory protocol was implemented that consisted of the unification of criteria in the handling and processing of samples, and report of results.The objective of this study is to describe the degree of compliance with the clinical laboratory protocol in the preanalytical phase, which includes the analytical request and up to the delivery in the laboratory, as well as the influencing factors.Cross-sectional descriptive study with a sample of 214 healthcare professionals involved in the preanalytical phase of laboratory procedures in primary care. A self-reported questionnaire with 11 items was used for data collection. Each item was assessed separately with a scale from 0 to 10. A 5 points score was considered as the cutoff point. Descriptive analysis was conducted and Mann–Whitney U test was used to determine differences between subgroups. Internal consistency of the questionnaire was considered.The best rated item was verifying the correspondence between the request form and identity of the patient. Each item scored from 3 to 10, and the mean for each item ranged from 6.40 (standard deviation = 3.06) to 8.57 (standard deviation = 2.00). Values above or equal to 8 were obtained, for 63.6% of them. Statistically significant differences between accredited and nonaccredited centres were found. Differences were not noteworthy regarding centres with a teaching activity or those without it. All the items were measured separately. The compliance with the protocol was adequate among primary healthcare professionals, who have a strategic position in the sample collection and its transport during the preanalytical phase. Being so, standardisation should be a priority to reduce errors and improve clinical safety and results.

Аналіз особливостей використання спеціального програмного забезпечення оборонного планування в НАТО

The paper reviews the state and retrospective analysis of special software used to support defense planning processes in the Armed Forces of Ukraine, namely: the Defense Resources Management Model (DRMM) and the subsystem of the Unified Management System for Administrative and Economic Processes of the Armed Forces of Ukraine based on the SAP ERP software platform.The paper reviews the state and retrospective analysis of special software used to support defense planning processes in the Armed Forces of Ukraine, namely: the Defense Resources Management Model (DRMM) and the subsystem of the Unified Management System for Administrative and Economic Processes of the Armed Forces of Ukraine based on the SAP ERP software platform. In accordance with the provisions of the Strategic Defense Bulletin, the Armed Forces of Ukraine are moving to capability-based defense planning. This type of planning is a standard type for NATO countries. The NATO planning structure outlined in the paper is a prototype for the future planning of the Armed Forces of Ukraine. There is a need for defense planning software tools that meet the requirements of interoperability with NATO defense planning systems. To this end, a general analysis of the special software of NATO's military planning systems was conducted during the study.The authors of the article have identified the main trends in the construction of modern foreign military planning systems. Classification of planning systems according to their purpose, which is common for NATO countries, was carried out. These technologies not only include the Dynamic Analysis and Rescheduling Tool (DART) and the Joint Defense Planning Analytical Request Toolkit (J-DARTS) but also in terms of its functional purpose and content, best meets the tasks of defense planning at the national level. Therefore, a comprehensive software tool for planning and analyzing defense planning in NATO has been explored in more detail. Given the broad functionality of J-DARTS, this tool is proposed to be considered to test the needs of defense planning in the Armed Forces of Ukraine.

Preanalytical variables and its impact on total quality management of clinical biochemistry laboratory- A tertiary referral rural hospital study

Introduction: Advances in science and Technology have led to transformation of laboratory diagnostics from manual, cumbersome testing methods to fully automated science, ensuring accuracy and speed. Quality is the core issue for all laboratories and this requires total quality management in the laboratory process in the preanalytical, analytical and post analytical phases. Objectives: To study and evaluate the types, frequency and magnitude of errors in our tertiary health care clinical Biochemistry Laboratory in the preanalytical testing process and its overall impact on Total Quality Management System of our laboratory. Materials and Methods: A prospective observational study, samples received for analysis between Jan 2015 to Dec 2015 in the clinical biochemistry section was evaluated for various preanalytical errors. We evaluated the frequency and types of preanalytical errors found in our laboratory by monitoring specimes requested for laboratory analyses from both inpatient and outpatient divisions and statistically analyzed. Results and Discussion: Of the total 1,38,275 analytical requests, 6446 (4.66%) samples were rejected due to various errors in preanalytical variables. For the inpatient samples, preanalytical error rate was 4.20% (4107 samples), the preanalytical variable with highest frequency of occurrence was specimen hemolysis/ Lipemic sample (1.00%) 980 samples. For the outpatient sample preanalytical error rate was 5.75% (2339). The variable with highest frequency was requisitions with incomplete patient treating physician information 1.37% (559). Preanalytical errors in laboratories are very common and play a very important role in patient care and treatment. Lipemic/hemolytic samples are known to affect the parameter to be analysed and incomplete patient information leads to error in reporting the patient’s results, which means the post analytical error becomes unavoidable and incomplete treating physician information leads to delayed reporting

Implementation of a protocol to increase the academic productivity of cardiothoracic surgery resident physicians

ObjectiveAcademic productivity during cardiothoracic surgery residency training is an important program metric, but is highly variable due to multiple factors. This study evaluated the influence of implementing a protocol to increase resident physicians' academic productivity in cardiac surgery. MethodsA comprehensive protocol for cardiac surgery was implemented at our institution that included active pairing of residents with academically productive faculty, regular research meetings, centralized data storage and analysis with a core team of biostatisticians, a formal peer-review protocol for analytic requests, and project prioritization and feedback. We compared cardiothoracic surgery residents’ academic productivity before implementation (July 2015-June 2017) versus after implementation (July 2017-June 2019). Academic productivity was measured by peer-reviewed articles, abstract presentations (oral or poster) at national cardiothoracic surgery meetings, and textbook chapters. ResultsThirty-four resident physicians (from traditional and integrated programs) trained at our institution during the study. A total of 122 peer-reviewed articles were produced over the course of the study: 74 (60.7%) cardiac- and 48 (39.3%) thoracic-focused. The number of cardiac-focused resident-produced articles increased from 10 preimplementation to 64 postimplementation (0.61 vs 2.03 articles per resident; P < .01). Abstract oral or poster presentations also increased, from 11 to 40 (0.61 vs 1.33 abstracts per resident; P = .01). Textbook chapters increased from 4 to 15 following the intervention (0.22 vs 0.5 chapters per resident; P = .01). ConclusionsImplementation of a dedicated protocol to facilitate faculty mentoring of resident research and streamline the data access, analysis, and publication process substantially improved cardiothoracic surgery residents’ academic productivity.

TiDB

Hybrid Transactional and Analytical Processing (HTAP) databases require processing transactional and analytical queries in isolation to remove the interference between them. To achieve this, it is necessary to maintain different replicas of data specified for the two types of queries. However, it is challenging to provide a consistent view for distributed replicas within a storage system, where analytical requests can efficiently read consistent and fresh data from transactional workloads at scale and with high availability. To meet this challenge, we propose extending replicated state machine-based consensus algorithms to provide consistent replicas for HTAP workloads. Based on this novel idea, we present a Raft-based HTAP database: TiDB. In the database, we design a multi-Raft storage system which consists of a row store and a column store. The row store is built based on the Raft algorithm. It is scalable to materialize updates from transactional requests with high availability. In particular, it asynchronously replicates Raft logs to learners which transform row format to column format for tuples, forming a real-time updatable column store. This column store allows analytical queries to efficiently read fresh and consistent data with strong isolation from transactions on the row store. Based on this storage system, we build an SQL engine to process large-scale distributed transactions and expensive analytical queries. The SQL engine optimally accesses row-format and column-format replicas of data. We also include a powerful analysis engine, TiSpark, to help TiDB connect to the Hadoop ecosystem. Comprehensive experiments show that TiDB achieves isolated high performance under CH-benCHmark, a benchmark focusing on HTAP workloads.

Системний аналіз ClickHouse - колоночної СУБД для online обробки аналітичних запитів

Системний аналіз ClickHouse - колоночної СУБД для online обробки аналітичних запитів

Emerging Databases for Next Generation Big Data Applications

This raised as quality into large scale periods into analytic application as real time inventor of pricing, mobile application in that offers we suggestion, fraud detections, risks as analyzed, etc. emphasis in the requirements with distributes knowledge’s management systems in which is into handle quickly transactions &amp; analytics simultaneous. Efficiently into processes into transactions &amp; analytically as requested, though, needed as complete into various optimization &amp; branching into knowledge selection as a systems. In the paper presented in the wildfire systems in that target Hybrid Transactional &amp; Analytical process (HTAP). This wildfire leverage in the Spark systems into modified as large scale process for different types as the difficult analytical request, &amp; columnar process to modify as quick transaction &amp; analytics simultaneous.

Optimal provisioning and scheduling of analytics as a service in cloud computing

AbstractWe propose an analytic request‐centric multiobjective provisioning and scheduling method based on hybrid particle swarm optimization, called analytics‐as‐a‐service (AaaS) provisioning and resource scheduling (APARS) system, to optimally provision analytics service and schedule cloud resources by taking into account of user specified constraints. Analytics‐as‐a‐service provisioning and resource scheduling is employed to maximize accuracy of the analytic service and minimize the execution time and cost of the analytic request by combining multiobjectives with the constraint of minimal deadline and budget. Provisioning AaaS and scheduling cloud resources for analytic request is challenging as the analytic request requires scaling up and down of cloud resources dynamically. Thus, this domain invites a rigid system that can provide optimal analytics using cloud services for specified quality of service parameters. Our proposed system can handle diverse set of analytic requests with the goal of minimizing execution time, cost, critical performance, load balancing level and maximizing resource utilization, AaaS utilization, and success rate. The proposed APARS system is tested and evaluated under different use cases and compared with other metaheuristic algorithms, and we found that the results to be convincing our claims. The experimentation is carried out for various scenarios and the proposed APARS is evaluated with other metaheuristic algorithm. Simulation results validate the APARS system and show that the proposed method performs better than other existing methods with various use cases.

ReHumanizing Hospital Satisfaction Data: Text Analysis, the Lifeworld, and Contesting Stakeholders’ Beliefs in Evidence

Declining clinician engagement, increasing rates of burnout, and stagnant patient and family experience scores have led hospital leadership at Seattle Children's Hospital to submit requests to a data scientist and an anthropologist to identify key themes of survey comments and provide recommendations to improve experience and satisfaction. This study explored ways of understanding satisfaction as well as analytic approaches to textual data, and found that various modes of evidence, while seemingly ideal to leaders, are hard pressed to meet their expectations. Examining satisfaction survey comments via text mining, content analysis, and ethnographic investigation uncovered several specific challenges to stakeholder requests for actionable insights. Despite its hype, text mining struggled to identify actionable themes, accurate sentiment, or group distinctions that are readily identified by both content analysis and end users, while more insightful ethnographic results were sometimes discounted for lack of quantitative results or perceived implementation difficulty. Unfortunately, institutional contexts and preferences for specific types of data can lead to unnecessary requests and wasted efforts. Through including the subjective lifeworld, how an individual's lived experiences impact interactions with others, the authors were able re‐humanize satisfaction. Cross‐discipline collaboration can enhance the quality, validation, and advocacy of evidence from both qualitative and quantitative data. Co‐developing a “Return on Method” (ROM) of satisfaction data can help improve analytic requests and expectations by end users. Ultimately, a lifeworld‐informed combination of data science and ethnography can provide contextual and culturally situated insights that are both meaningful and actionable.

Modeling Analytical Streams for Social Business Intelligence

Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network’s contents and the company’s analytical requests, as well as the enormous amount of noisy data. Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. In this paper, we propose a multidimensional formalism to represent and evaluate social indicators directly from fact streams derived in turn from social network data. This formalism relies on two main aspects: the semantic representation of facts via Linked Open Data and the support of OLAP-like multidimensional analysis models. Contrary to traditional BI formalisms, we start the process by modeling the required social indicators according to the strategic goals of the company. From these specifications, all the required fact streams are modeled and deployed to trace the indicators. The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. We demonstrate its usefulness by introducing a real scenario user case in the automotive sector.

Janus: A Hybrid Scalable Multi-Representation Cloud Datastore

Cloud-based data-intensive applications have to process high volumes of transactional and analytical requests on large-scale data. Businesses base their decisions on the results of analytical requests, creating a need for real-time analytical processing. We propose Janus, a hybrid scalable cloud datastore, which enables the efficient execution of diverse workloads by storing data in different representations. Janus manages big datasets in the context of datacenters, thus supporting scaling out by partitioning the data across multiple servers. This requires Janus to efficiently support distributed transactions. In order to support the different datacenter requirements, Janus also allows diverse partitioning strategies for the different representations. Janus proposes a novel data movement pipeline to continuously ensure up to date data between the different representations. Unlike existing multi-representation storage systems and Change Data Capture (CDC) pipelines, the data movement pipeline in Janus supports partitioning and handles both distributed transactions and diverse partitioning strategies. In this paper, we focus on supporting Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads, and hence use row and column-oriented representations, which are the most efficient representations for these workloads. Our evaluations over Amazon AWS illustrate that Janus can provide real-time analytical results, in addition to processing high-throughput transactional workloads.

OSABIDE IN RESIDENCES: Continuity of care, through the Integration of Systems

Policy developments : There is consensus among experts assert that the system of social health provision is fragmented, users will be those who suffer the consequences, and that we must provide better coordinated and integrated socio-services model. In Euskadi, with great institutional experience in geriatric care, there is a Basque Council of geriatric care, whose purpose is the guidance and monitoring of policy decisions, regulatory, economic, organizational and care, in terms of care coordination. In July 2013, the Basque Council of geriatric care (CVASS) approved five Strategic Lines (L.E.) for 2013-2016. The document that collects, includes a review of the concept of geriatric care, calling complex construct. The L.E. reinforce the strategic approach model of coordination between sectors, which does not prevent progress, when necessary, to integrated health and social care provision. Also the strategic lines of the Basque health service OSAKIDETZA, provide Advance the Implementation and Consolidation of Clinical Information Unified with actions to integrate the systems of healthcare information among different health care settings and social partners, to promote continuity of care under the same process. Introduction : For our project, we focus on L.E-2: System Implementation Information and Communication Sociosanitario. This line aims to avoid the fragmentation of information and the difficulties of accessibility to enable more equitable benefits and improve continuity of care and attention, people integrated into the Sociosanitario space. In 2012, Euskadi had a population> 65 years of 431,460 people, there 17746 residential places. Of these, 3735 with Osakidetza doctor and squares 14011 (80.48%), with other medical institutions without access to clinical data Osabide history. We also find residences that have different clinical information systems not compatible with H. C. Osabide, Osakidetza and offline with the electronic prescription. Objective : Piloting and deploy the Osabide medical history, Residences, responding to an approach focused on three elements: Person: Security, Privacy, comfort, satisfaction and continuity of care. Professional: Agility, Communication, Collaboration, Coordination and Feedback. Institutions: Coordination, agreements, consensus, communication and interoperability. METHODOLOGY: Project Management Methodology was used. The project was divided into several phases, with different solutions: - Phase I: CLINIC (medical history viewer) - Phase II: CLINIC + PRESBIDE (universal electronic prescribing system) - Phase III: GLOBAL Osabide (unified medical record) + PRESBIDE - Pass IV: GLOBAL Osabide + PRESBIDE + OSANAIA (clinical nursing station) Public Residences, concerted Private residences, both with / without medical Osakidetza: Inclusion criteria were defined. the type of units of each residence, number and profile of residents, professionals, existing healthcare information system was assessed. proposal for piloting, which approved the CVASS was developed. Results : - Piloting in 19 Residences (07-2014_06-2015): 4 in Araba, Bizkaia 9 and 6 in Gipuzkoa - Professional Training. - Obtaining indicators - Impact: reach 2122 residential places (12%) - Detection enhancements for subsequent deployment - Approach deployment. Target 2015: exceed 30% range in residential places. Lessons learned / improvements to mainstream :PRESBIDE achieve interoperability with information systems Residences: Resiplus, SIGECA, Others, ... Residences allow the connection to the public computer network EUSKAL SAREA. Implement other features on-line (Interconsultations, RX and analytical requests, ...). Improve the involvement of professionals and Institutions Enable access to other professionals involved in the P.A.I. Objectives Integration incorporate clinical information systems, through concerts and Contracts Program. Conclusions : - The pilot of this project has detected improvements over Information and Inter-agency coordination (Meso level). - Access to the HC Osabide, an improvement of Integrated Management, both in diagnostic activity, treatment and monitoring of institutionalized Residential users, achieving improvements in safety. - Improved interagency coordination, information sharing systems. - Allows benefit from synergies and opportunities for coordination and improved health integration. - Progress was objectified in the development of geriatric space, at the micro level.

Identifying risk in the use of tumor markers to improve patient safety.

Tumor markers (TM) are a routine test that are not always used well, and can lead to unnecessary additional tests, which are not without risks for the patients. So, to implement appropriate strategies to improve the adequate use of TM and, therefore, improve patient safety, is required to analyze the use of TM, identifying risks and establishing if there are differences in their use as a function of their utility. The study was a descriptive, longitudinal, retrospective and systematic study in the area covered by the University Hospital of Padua. In the follow-up 2-year study, 23,059 analytical requests of TM, corresponding to 14,728 patients, were analyzed. For the level of statistical significance it applies an approximation of the normal law (Z statistic) and χ2-test. Only 9196 requests (39.88%) out of a total of 23,059 on 5080 patients with neoplastic diseases have been classified as adecuate according to current guidelines. The number of requests per patient was variable (1.57±1.35). In patients with neoplastic diseases this increased to 1.80±1.68. The mean of number of TM per request was 2.4±1.73. The analysis showed an association between the number of requests and the type of marker used. The use of TM is variable, mainly of the follow-up markers, when they are used as screening. This inappropriate use, minimizes their utility favoring erroneous interpretations and increases the risk of damage to the patient. So it is essential to implement safe practices in the use of TM.

Errores en la identificación del paciente y en muestras biológicas en el proceso analítico: ¿es posible la mejora de la seguridad de los pacientes?

BackgroundPatient identification errors and biological samples are one of the problems with the highest risk factor in causing an adverse event in the patient. ObjectiveTo detect and analyse the causes of patient identification errors in analytical requests (PIEAR) from emergency departments, and to develop improvement strategies. Material and methodsA process and protocol was designed, to be followed by all professionals involved in the requesting and performing of laboratory tests. Evaluation and monitoring indicators of PIEAR were determined, before and after the implementation of these improvement measures (years 2010-2014). ResultsA total of 316 PIEAR were detected in a total of 483,254 emergency service requests during the study period, representing a mean of 6.80/10,000 requests. Patient identification failure was the most frequent in all the 6-monthly periods assessed, with a significant difference (P<.0001). ConclusionsThe improvement strategies applied showed to be effective in detecting PIEAR, as well as the prevention of such errors. However, we must continue working with this strategy, promoting a culture of safety for all the professionals involved, and trying to achieve the goal that 100% of the analytical and samples are properly identified.

Diferencias de género en el uso de marcadores tumorales

Introduction and objectiveGender is one of the factors that can influence the use of health resources. The use of tumour markers is widespread, due to the importance of these in monitoring cancer development. The aim of this study is to analyse the influence of gender on the use of tumour markers, and to investigate whether there are differences in their use. Material and methodsA longitudinal, retrospective and descriptive study, with a 2-year follow-up, was conducted in the catchment area of the University Hospital of Padua. An analysis was performed on 23,059 analytical requests for tumour markers. A descriptive and frequency analysis was performed on all variables. The statistical analysis was performed using Chi squared, Student t and Mann-Whitney U to test for significance. ResultsThe number of requests for women (1.5) was lower than men (1.6). In patients with tumour pathology, the number of requests was higher than in patients without tumour disease. In the analysis by disease and gender, the difference remained significant. As regards the number of tumour markers per request, the difference between genders was also significant: 2.13 in males versus 2.85 in women. Similar results were obtained when requests for tumour markers linked to gender-related diseases were eliminated. ConclusionsThere are differences in the use of tumour markers by gender with the number of requests for male patients being higher than for females. However, the number of tumour markers per request is greater in women than in men.

PanDA Workload Management System Meta-data Segmentation

Abstract The PanDA (Production and Distributed Analysis) workload management system (WMS) was developed to meet the scale and complexity of LHC distributed computing for the ATLAS experiment. PanDA currently distributes jobs among more than 100,000 cores at well over 120 Grid sites, supercomputing centers, commercial and academic clouds. ATLAS physicists submit more than 1.5 M data processing, simulation and analysis PanDA jobs per day, and the system keeps all meta-information about job submissions and execution events in Oracle RDBMS. The above information is used for monitoring and accounting purposes. One of the most challenging monitoring issues is tracking errors that has occurred during the execution of the jobs. Current meta-data storage technology doesn’t support inner tools for data aggregation, needed to build error summary tables, charts and graphs. Delegating these tasks to the monitor slows down the execution of requests. We will describe a project aimed at optimizing interaction between PanDA front-end and back-end, by meta-data storage segmentation into two parts – operational and archived. Active meta-data are remained in Oracle database (operational part), due to the high requirements for data integrity. Historical (read-only) meta-data used for the system analysis and accounting are exported to NoSQL storage (archived part). New data model based on usage of Cassandra as the NoSQL backend has been designed as a set of query-specific data structures. This allowed to remove most of data preparation workload from PanDA Monitor and improve its scalability and performance. Segmentation and synchronization between operational and archived parts of jobs meta-data is provided by a Hybrid Meta-data Storage Framework (HMSF). PanDA monitor was partly adopted to interact with HMSF. The operational data queries are forwarded to the primary SQL-based repository and the analytic data requests are processed by NoSQL database. The results of performance and scalability tests of HMSF-adopted part of PanDA Monitor shows that presented method of optimization, in conjunction with a properly configured NoSQL database and reasonable data model, provides performance improvements and scalability.

Preanalytical errors: a preliminary approach to the point of view of primary health care givers.

The presence of errors in the preanalytical phase is a widely studied topic. However, information regarding the perspective of those professionals involved is rather scant. Two focus groups of professionals from Primary Care involved in the preanalytical phase (general practitioners [GP], community nurses [CN], and other auxiliary health workers, including administrative personnel [AHW]) were convened. A qualitative analysis with a phenomenological approach was performed by using the structure of SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis as a guide, and results were categorized by grouping the resultant dimensions according to this structure. Overall, 12 professionals (3 GP, 6 CN, and 3 AHW) were distributed in two groups. Age and gender distribution were similar between groups. The most commented strengths were organizational capability and teamwork. The main weakness was the workload increase (compared to the short time spent on sample collection). Opportunities were related to workload optimization through on-line analytical requests. Threats were related to the long time elapsed between sample drawing at Primary Care and delivery to the Central Laboratory. The phenomenological approach allows revealing those aspects that cannot be entirely elucidated by objective data measurement. Attitudes considered as positive can be exploited by the institution, whereas those considered as negative alert us to possible future problems. Primary Care professionals offered a different point of view to laboratory staff, but both recognized high workload as the main threat and on-line analytical request as the best opportunity. These perspectives may help to improve detection and decrease the number of errors.

Pre-analytical errors management in the clinical laboratory: a five-year study.

Introduction:This study describes quality indicators for the pre-analytical process, grouping errors according to patient risk as critical or major, and assesses their evaluation over a five-year period.Materials and methods:A descriptive study was made of the temporal evolution of quality indicators, with a study population of 751,441 analytical requests made during the period 2007–2011. The Runs Test for randomness was calculated to assess changes in the trend of the series, and the degree of control over the process was estimated by the Six Sigma scale.Results:The overall rate of critical pre-analytical errors was 0.047%, with a Six Sigma value of 4.9. The total rate of sampling errors in the study period was 13.54% (P = 0.003). The highest rates were found for the indicators “haemolysed sample” (8.76%), “urine sample not submitted” (1.66%) and “clotted sample” (1.41%), with Six Sigma values of 3.7, 3.7 and 2.9, respectively.Conclusions:The magnitude of pre-analytical errors was accurately valued. While processes that triggered critical errors are well controlled, the results obtained for those regarding specimen collection are borderline unacceptable; this is particularly so for the indicator “haemolysed sample”.

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining, and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level.Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network.

La seguridad del paciente en el laboratorio clínico. Implantación de un protocolo de identificación inequívoca de paciente

Errors in patient identification represent one of the problems with risk factor index in causing an adverse event in the patient. ObjectiveDevelopment of Improvement Strategies for the identification of patients and their biological samples. Material and methodsDesign of the Procedure and Protocol to follow by all the professionals involved in the request process and production of analytical tests. Preparation of Safety Guidelines for the identification of patients and their biological samples. Evaluation of Indicators before and after the introduction of these improvement strategies. ResultsDecreases in the number of incidents were observed after introducing the improved procedures; 5.70% versus 0.97% in emergencies, and 21.5% versus 2.09% in the hospital patients. However, no significant decreases were observed in cases where patient identification was made manually; 6.9% versus 5.35% in emergencies and 8.3% versus 9.5% in hospital patients. ConclusionsThe improvement strategies applied have demonstrated to be effective in improving the identification of the patient in the analytical requests. However, we must continue working with this strategy, encouraging the safety culture in all the professionals involved, and try to achieve objective of 100% of the requests being identified correctly.