Leveraging human factors in cybersecurity: an integrated methodological approach.

Computer and Information Security (CIS) is usually approached adopting a technology-centric viewpoint, where the human components of sociotechnical systems are generally considered as their weakest part, with little consideration for the end users' cognitive characteristics, needs and motivations. This paper presents a holistic/Human Factors (HF) approach, where the individual, organisational and technological factors are investigated in pilot healthcare organisations to show how HF vulnerabilities may impact on cybersecurity risks. An overview of current challenges in relation to cybersecurity is first provided, followed by the presentation of an integrated top-down and bottom-up methodology using qualitative and quantitative research methods to assess the level of maturity of the pilot organisations with respect to their capability to face and tackle cyber threats and attacks. This approach adopts a user-centred perspective, involving both the organisations' management and employees, The results show that a better cyber-security culture does not always correspond with more rule compliant behaviour. In addition, conflicts among cybersecurity rules and procedures may trigger human vulnerabilities. In conclusion, the integration of traditional technical solutions with guidelines to enhance CIS systems by leveraging HF in cybersecurity may lead to the adoption of non-technical countermeasures (such as user awareness) for a comprehensive and holistic way to manage cyber security in organisations.

A Mobile Computerized Decision Support System to Prevent Hypoglycemia in Hospitalized Patients With Type 2 Diabetes Mellitus.

BACKGROUND: Diabetes management requires complex and interdisciplinary cooperation of health care professionals (HCPs). To support this complex process, IT-support is recommended by clinical guidelines. The aim of this article is to report on results from a clinical feasibility study testing the prototype of a mobile, tablet-based client-server system for computerized decision and workflow support (GlucoTab®) and to discuss its impact on hypoglycemia prevention. METHODS: The system was tested in a monocentric, open, noncontrolled intervention study in 30 patients with type 2 diabetes mellitus (T2DM). The system supports HCPs in performing a basal-bolus insulin therapy. Diabetes therapy, adverse events, software errors and user feedback were documented. Safety, efficacy and user acceptance of the system were investigated. RESULTS: Only 1.3% of blood glucose (BG) measurements were <70 mg/dl and only 2.6% were >300 mg/dl. The availability of the system (97.3%) and the rate of treatment activities documented with the system (>93.5%) were high. Only few suggestions from the system were overruled by the users (>95.7% adherence). Evaluation of the 3 anonymous questionnaires showed that confidence in the system increased over time. The majority of users believed that treatment errors could be prevented by using this system. CONCLUSIONS: Data from our feasibility study show a significant reduction of hypoglycemia by implementing a computerized system for workflow and decision support for diabetes management, compared to a paper-based process. The system was well accepted by HCPs, which is shown in the user acceptance analysis and that users adhered to the insulin dose suggestions made by the system.

Realization of a service for the long-term risk assessment of diabetes-related complications.

AIM: We present a computerized system for the assessment of the long-term risk of developing diabetes-related complications. METHODS: The core of the system consists of a set of predictive models, developed through a data-mining/machine-learning approach, which are able to evaluate individual patient profiles and provide personalized risk assessments. Missing data is a common issue in (electronic) patient records, thus the models are paired with a module for the intelligent management of missing information. RESULTS: The system has been deployed and made publicly available as Web service, and it has been fully integrated within the diabetes-management platform developed by the European project REACTION. Preliminary usability tests showed that the clinicians judged the models useful for risk assessment and for communicating the risk to the patient. Furthermore, the system performs as well as the United Kingdom Prospective Diabetes Study (UKPDS) Risk Engine when both systems are tested on an independent cohort of UK diabetes patients. CONCLUSIONS: Our work provides a working example of risk-stratification tool that is (a) specific for diabetes patients, (b) able to handle several different diabetes related complications, (c) performing as well as the widely known UKPDS Risk Engine on an external validation cohort.

Development and validation of risk assessment models for diabetes-related complications based on the DCCT/EDIC data.

AIM: To derive and validate a set of computational models able to assess the risk of developing complications and experiencing adverse events for patients with diabetes. The models are developed on data from the Diabetes Control and Complications Trial (DCCT) and the Epidemiology of Diabetes Interventions and Complications (EDIC) studies, and are validated on an external, retrospectively collected cohort. METHODS: We selected fifty-one clinical parameters measured at baseline during the DCCT as potential risk factors for the following adverse outcomes: Cardiovascular Diseases (CVD), Hypoglycemia, Ketoacidosis, Microalbuminuria, Proteinuria, Neuropathy and Retinopathy. For each outcome we applied a data-mining analysis protocol in order to identify the best-performing signature, i.e., the smallest set of clinical parameters that, considered jointly, are maximally predictive for the selected outcome. The predictive models built on the selected signatures underwent both an interval validation on the DCCT/EDIC data and an external validation on a retrospective cohort of 393 diabetes patients (49 Type I and 344 Type II) from the Chorleywood Medical Center, UK. RESULTS: The selected predictive signatures contain five to fifteen risk factors, depending on the specific outcome. Internal validation performances, as measured by the Concordance Index (CI), range from 0.62 to 0.83, indicating good predictive power. The models achieved comparable performances for the Type I and, quite surprisingly, Type II external cohort. CONCLUSIONS: Data-mining analyses of the DCCT/EDIC data allow the identification of accurate predictive models for diabetes-related complications. We also present initial evidences that these models can be applied on a more recent, European population.

A systematic review of predictive risk models for diabetes complications based on large scale clinical studies.

This work presents a systematic review of long-term risk assessment models for evaluating the probability of developing complications in diabetes patients. Diabetes mellitus can cause many complications if not adequately controlled; risk assessment models can help physicians and patients in identifying the complications most likely to arise and in taking the necessary countermeasures. We identified six large medical studies related to diabetes mellitus upon which current available risk assessment models are built on; all these studies had duration over 5 years and most of them included some common demographic and clinical data strongly related to diabetic complications. The most common predictions for long term diabetes complications are related to cardiovascular diseases and diabetic retinopathy. Our analysis of the literature led us to the conclusion that researchers and medical practitioners should take in account that some limitations undermine the applicability of risk assessment models; for example, it is hard to judge whether results obtained on a specific cohort can be effectively translated to other populations. Nevertheless, all these studies have significantly contributed to identify significant risk factors associated with the major diabetes complications.

Diabetes management using modern information and communication technologies and new care models.

BACKGROUND: Diabetes, a metabolic disorder, has reached epidemic proportions in developed countries. The disease has two main forms: type 1 and type 2. Disease management entails administration of insulin in combination with careful blood glucose monitoring (type 1) or involves the adjustment of diet and exercise level, the use of oral anti-diabetic drugs, and insulin administration to control blood sugar (type 2). OBJECTIVE: State-of-the-art technologies have the potential to assist healthcare professionals, patients, and informal carers to better manage diabetes insulin therapy, help patients understand their disease, support self-management, and provide a safe environment by monitoring adverse and potentially life-threatening situations with appropriate crisis management. METHODS: New care models incorporating advanced information and communication technologies have the potential to provide service platforms able to improve health care, personalization, inclusion, and empowerment of the patient, and to support diverse user preferences and needs in different countries. The REACTION project proposes to create a service-oriented architectural platform based on numerous individual services and implementing novel care models that can be deployed in different settings to perform patient monitoring, distributed decision support, health care workflow management, and clinical feedback provision. RESULTS: This paper presents the work performed in the context of the REACTION project focusing on the development of a health care service platform able to support diabetes management in different healthcare regimes, through clinical applications, such as monitoring of vital signs, feedback provision to the point of care, integrative risk assessment, and event and alarm handling. While moving towards the full implementation of the platform, three major areas of research and development have been identified and consequently approached: the first one is related to the glucose sensor technology and wearability, the second is related to the platform architecture, and the third to the implementation of the end-user services. The Glucose Management System, already developed within the REACTION project, is able to monitor a range of parameters from various sources including glucose levels, nutritional intakes, administered drugs, and patient's insulin sensitivity, offering decision support for insulin dosing to professional caregivers on a mobile tablet platform that fulfills the need of the users and supports medical workflow procedures in compliance with the Medical Device Directive requirements. CONCLUSIONS: Good control of diabetes, as well as increased emphasis on control of lifestyle factors, may reduce the risk profile of most complications and contribute to health improvement. The REACTION project aims to respond to these challenges by providing integrated, professional, management, and therapy services to diabetic patients in different health care regimes across Europe in an interoperable communication platform.

Design of a mobile, safety-critical in-patient glucose management system.

Diabetes mellitus is one of the most widespread diseases in the world. People with diabetes usually have long stays in hospitals and need specific treatment. In order to support in-patient care, we designed a prototypical mobile in-patient glucose management system with decision support for insulin dosing. In this paper we discuss the engineering process and the lessons learned from the iterative design and development phases of the prototype. We followed a user-centered development process, including real-life usability testing from the outset. Paper mock-ups in particular proved to be very valuable in gaining insight into the workflows and processes, with the result that user interfaces could be designed exactly to the specific needs of the hospital personnel in their daily routine.

Decision support in heart failure through processing of electro- and echocardiograms.

OBJECTIVE: Signal and imaging investigations are currently key components in the diagnosis, prognosis and follow up of heart diseases. Nowadays, the need for more efficient, cost-effective and personalised care has led to a renaissance of clinical decision support systems (CDSSs). The purpose of this paper is to present an effective way of achieving a high-level integration of signal and image processing methods in the general process of care, by means of a clinical decision support system, and to discuss the advantages of such an approach. From the wide range of heart diseases, heart failure, whose complexity best highlights the benefits of this integration, has been selected. METHODS: After an analysis of users' needs and expectations, significant and suitably designed image and signal processing algorithms are introduced to objectively and reliably evaluate important features involved in decisional problems in the heart failure domain. Then, a CDSS is conceived so as to combine the domain knowledge with advanced analytical tools for data processing. In particular, the relevant and significant medical knowledge and experts' knowhow are formalised according to an ontological formalism, suitably augmented with a base of rules for inferential reasoning. RESULTS: The proposed methods were tested and evaluated in the daily practice of the physicians operating at the Department of Cardiology, University Magna Graecia, Catanzaro, Italy, on a population of 79 patients. Different scenarios, involving decisional problems based on the analysis of biomedical signals and images, were considered. In these scenarios, after some training and 3 months of use, the CDSS was able to provide important and useful suggestions in routine workflows, by integrating the clinical parameters computed through the developed methods for echocardiographic image segmentation and the algorithms for electrocardiography processing. CONCLUSIONS: The CDSS allows the integration of signal and image processing algorithms into the general process of care. Feedback from end-users has been positive.

Interoperability in digital electrocardiography: harmonization of ISO/IEEE x73-PHD and SCP-ECG.

The ISO/IEEE 11073 (x73) family of standards is a reference frame for medical device interoperability. A draft for an ECG device specialization (ISO/IEEE 11073-10406-d02) has already been presented to the Personal Health Device (PHD) Working Group, and the Standard Communications Protocol for Computer-Assisted ElectroCardioGraphy (SCP-ECG) Standard for short-term diagnostic ECGs (EN1064:2005+A1:2007) has recently been approved as part of the x73 family (ISO 11073-91064:2009). These factors suggest the coordinated use of these two standards in foreseeable telecardiology environments, and hence the need to harmonize them. Such harmonization is the subject of this paper. Thus, a mapping of the mandatory attributes defined in the second draft of the ISO/IEEE 11073-10406-d02 and the minimum SCP-ECG fields is presented, and various other capabilities of the SCP-ECG Standard (such as the messaging part) are also analyzed from an x73-PHD point of view. As a result, this paper addresses and analyzes the implications of some inconsistencies in the coordinated use of these two standards. Finally, a proof-of-concept implementation of the draft x73-PHD ECG device specialization is presented, along with the conversion from x73-PHD to SCP-ECG. This paper, therefore, provides recommendations for future implementations of telecardiology systems that are compliant with both x73-PHD and SCP-ECG.

The morphological classification of heartbeats as dominant and non-dominant in ECG signals.

Surface electrocardiography (ECG) is the art of analyzing the heart's electrical activity by applying electrodes to certain positions on the body and measuring potentials at the body surface resulting from this electrical activity. Usually, significant clinical information can be obtained from analysis of the dominant beat morphology. In this respect, identification of the dominant beats and their averaging can be very helpful, allowing clinicians to carry out the measurement of amplitudes and intervals on a beat much cleaner from noise than a generic beat selected from the entire ECG recording. In this paper a standard clustering algorithm for the morphological grouping of heartbeats has been analyzed based on K-means, different signal representations, distance metrics and validity indices. The algorithm has been tested on all the records of the MIT-BIH Arrhythmia Database (MIT-BIH AD) obtaining satisfying performances in terms of averaged dominant beat estimation, but the results have not been fully satisfactory in terms of sensitivity and specificity. In order to improve the clustering accuracy, an ad hoc algorithm based on a two-phase decision tree, which integrates additional specific knowledge related to the ECG domain, has been implemented. Similarity features extracted from every beat have been used in the decision trees for the identification of different morphological classes of ECG beats. The results, in terms of dominant beat discrimination, have been evaluated on all annotated beats of the MIT-BIH AD with sensitivity = 99.05%, specificity = 93.94%, positive predictive value = 99.32% and negative predictive value = 91.69%. Further tests have shown a very slight decrement of the performances on all detected beats of the same database using an already published QRS detector, demonstrating the validity of the algorithm in real unsupervised clustering situations where annotated beat positions are not available but beats are detected with a high-performance beat detector.

Standard-compliant real-time transmission of ECGs: harmonization of ISO/IEEE 11073-PHD and SCP-ECG.

Ambient assisted living and integrated care in an aging society is based on the vision of the lifelong Electronic Health Record calling for HealthCare Information Systems and medical device interoperability. For medical devices this aim can be achieved by the consistent implementation of harmonized international interoperability standards. The ISO/IEEE 11073 (x73) family of standards is a reference standard for medical device interoperability. In its Personal Health Device (PHD) version several devices have been included, but an ECG device specialization is not yet available. On the other hand, the SCP-ECG standard for short-term diagnostic ECGs (EN1064) has been recently approved as an international standard ISO/IEEE 11073-91064:2009. In this paper, the relationships between a proposed x73-PHD model for an ECG device and the fields of the SCP-ECG standard are investigated. A proof-of-concept implementation of the proposed x73-PHD ECG model is also presented, identifying open issues to be addressed by standards development for the wider interoperability adoption of x73-PHD standards.

Noninvasive ECG as a tool for predicting termination of paroxysmal atrial fibrillation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and entails an increased risk of thromboembolic events. Prediction of the termination of an AF episode, based on noninvasive techniques, can benefit patients, doctors and health systems. The method described in this paper is based on two-lead surface electrocardiograms (ECGs): 1-min ECG recordings of AF episodes including N-type (not terminating within an hour after the end of the record), S-type (terminating 1 min after the end of the record) and T-type (terminating immediately after the end of the record). These records are organised into three learning sets (N, S and T) and two test sets (A and B). Starting from these ECGs, the atrial and ventricular activities were separated using beat classification and class averaged beat subtraction, followed by the evaluation of seven parameters representing atrial or ventricular activity. Stepwise discriminant analysis selected the set including dominant atrial frequency (DAF, index of atrial activity) and average HR (HRmean, index of ventricular activity) as optimal for discrimination between N/T-type episodes. The linear classifier, estimated on the 20 cases of the N and T learning sets, provided a performance of 90% on the 30 cases of a test set for the N/T-type discrimination. The same classifier led to correct classification in 89% of the 46 cases for N/S-type discrimination. The method has shown good results and seems to be suitable for clinical application, although a larger dataset would be very useful for improvement and validation of the algorithms and the development of an earlier predictor of paroxysmal AF spontaneous termination time.

HEARTFAID: A knowledge based platform of services for supporting medical-clinical management of heart failure within elderly population.

HEARTFAID is a research and development project aimed at devising, developing and validating an innovative knowledge based platform of services, able to improve early diagnosis and to make more effective the medical-clinical management of heart diseases within elderly population. Chronic Heart Failure is one of the most remarkable health problems for prevalence and morbidity, especially in the developed western countries, with a strong impact in terms of social and economic effects. All these aspects are typically emphasized within the elderly population, with very frequent hospital admissions and a significant increase of medical costs. Recent studies and experiences have demonstrated that accurate heart failure management programs, based on a suitable integration of inpatient and outpatient clinical procedures, might prevent and reduce hospital admissions, improving clinical status and reducing costs. HEARTFAID aims at defining efficient and effective health care delivery organization and management models for the "optimal" management of the care in the field of cardiovascular diseases. The HEARTFAID innovative computerized system will improve the processes of diagnosis, prognosis and therapy provision, providing the following services: * electronic health record for easy and ubiquitous access to heterogeneous patients data;* integrated services for healthcare professionals, including patient telemonitoring, signal and image processing, alert and alarm system;* clinical decision support in the heart failure domain, based on pattern recognition in historical data, knowledge discovery analysis and inferences on patients' clinical data.The formalization of the pre-existing clinical knowledge and the discovery of new elicited knowledge represent the core of the HEARTFAID platform.

Interoperability as a quality label for portable & wearable health monitoring systems.

Advances in ICT promising universal access to high quality care, reduction of medical errors, and containment of health care costs, have renewed interest in electronic health records (EHR) standards and resulted in comprehensive EHR adoption programs in many European states. Health cards, and in particular the European health insurance card, present an opportunity for instant cross-border access to emergency health data including allergies, medication, even a reference ECG. At the same time, research and development in miniaturized medical devices and wearable medical sensors promise continuous health monitoring in a comfortable, flexible, and fashionable way. These trends call for the seamless integration of medical devices and intelligent wearables into an active EHR exploiting the vast information available to increase medical knowledge and establish personal wellness profiles. In a mobile connected world with empowered health consumers and fading barriers between health and healthcare, interoperability has a strong impact on consumer trust. As a result, current interoperability initiatives are extending the traditional standardization process to embrace implementation, validation, and conformance testing. In this paper, starting from the OpenECG initiative, which promotes the consistent implementation of interoperability standards in electrocardiography and supports a worldwide community with data sets, open source tools, specifications, and online conformance testing, we discuss EHR interoperability as a quality label for personalized health monitoring systems. Such a quality label would support big players and small enterprises in creating interoperable eHealth products, while opening the way for pervasive healthcare and the take-up of the eHealth market.

Support of daily ECG procedures in a cardiology department via the integration of an existing clinical database and a commercial ECG management system.

BACKGROUND: In the context of HYGEIAnet, the regional health telematics network of Crete, a clinical cardiology database (CARDIS) has been installed in several hospitals. The large number of resting ECGs recorded daily made it a priority to have computerized support for the entire ECG procedure. METHODS: Starting in late 2000, ICS-FORTH and Mortara Instrument, Inc., collaborated to integrate the Mortara E-Scribe/NT ECG management system with CARDIS in order to support daily ECG procedures. CARDIS was extended to allow automatic ordering of daily ECGs via E-Scribe/NT. The ECG order list is downloaded to the electrocardiographs and executed, the recorded ECGs are transmitted to E-Scribe/NT, where confirmed ECG records are linked back to CARDIS. A thorough testing period was used to identify and correct problems. An ECG viewer/printer was extended to read ECG files in E-Scribe/NT format. RESULTS: The integration of E-Scribe/NT and CARDIS, enabling automatic scheduling of ECG orders and immediate availability of confirmed ECGs records for viewing and printing in the clinical database, took approximately 4 man months. The performance of the system is highly satisfactory and it is now ready for deployment in the hospital. CONCLUSIONS: Integration of a commercially available ECG management system with an existing clinical database can provide a rapid, practical solution that requires no major modifications to either software component. The success of this project makes us optimistic about extending CARDIS to support additional examination procedures such as digital coronary angiography and ultrasound examinations.