Chi-square and Poissonian Data: Biases Even in the High-Count Regime and How to Avoid them

We demonstrate that two approximations to the chi^2 statistic as popularly employed by observational astronomers for fitting Poisson-distributed data can give rise to intrinsically biased model parameter estimates, even in the high counts regime, unless care is taken over the parameterization of the problem. For a small number of problems, previous studies have shown that the fractional bias introduced by these approximations is often small when the counts are high. However, we show that for a broad class of problem, unless the number of data bins is far smaller than \sqrt{N_c}, where N_c is the total number of counts in the dataset, the bias will still likely be comparable to, or even exceed, the statistical error. Conversely, we find that fits using Cash's C-statistic give comparatively unbiased parameter estimates when the counts are high. Taking into account their well-known problems in the low count regime, we conclude that these approximate chi^2 methods should not routinely be used for fitting an arbitrary, parameterized model to Poisson-distributed data, irrespective of the number of counts per bin, and instead the C-statistic should be adopted. We discuss several practical aspects of using the C-statistic in modelling real data. We illustrate the bias for two specific problems, measuring the count-rate from a lightcurve and obtaining the temperature of a thermal plasma from its X-ray spectrum measured with the Chandra X-ray observatory. In the context of X-ray astronomy, we argue the bias could give rise to systematically mis-calibrated satellites and a ~5-10% shift in galaxy cluster scaling relations.Comment: 9 pages, 2 figures. Accepted for publication in the Astrophysical Journa

An expandable approach for design and personalization of digital, just-in-time adaptive interventions

Objective: We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people's individual needs, momentary contexts, and psychosocial variables

ISURF: RFID Enabled Collaborative Supply Chain Planning Environment

To be able to cope with the requirements of today’s competitive and demanding digital world of business, companies, especially SMEs, need to be more agile, and be ready to react to the changing requirements of the sector. This requires a better view and a more comprehensive analysis of the whole marketplace which can be achieved through a knowledge oriented collaborative supply chain planning initiative. The parties also need to be capable of monitoring the supply chain visibility in a real time fashion, which can be enabled through the use of RFID devices. RFID enabled collaborative supply chain planning has been achieved by big industry players in well defined restricted business circumstances through some selected standard message schemes. However, SMEs are still far behind in this process due to their small IT budgets. In iSURF Project we address this problem by providing a set of open source tools to enable seamless collection of supply chain visibility, synchronizing this with master data, exchanging supply chain visibility and other planning data with each other through a service oriented supply chain planning environment which also handles the interoperability of the messages exchanged

Facilitating coordinated care for multi-morbidity patients through integrated preventive Clinical Decision Support (C3-Cloud)

Introduction: A growing share of the population in OECD countries is of age 65 and over, expected to reach 22% by 2030 (compared to 15% in 2010). Life expectancy has also significantly increased. People at age of 65 are expected to live for an average of 21 and 17 years for women and men; an almost 40% increase since 1960. The profound success in improving life expectancy has resulted in a new set of challenges. Challenge: Shift of resources was necessary, redirected to address the complex needs of multi-morbidity patients. Furthermore, patients’ needs are not effectively met by current care models, which tend to operate in isolation. This results in static services that patients need to wander. It is common for patients to revisit all levels of care discussing their needs, and reconciling potentially conflicting objectives amongst their conditions (e.g., incompatible lifestyle goals, adverse drug effects and side-effects, undetected conditions). Optimal collaboration and coordination between professionals in the delivery of integrated care have become essential requirements for the provision of high-quality care. Coordinated care aims for the orderly arrangement of individual and group efforts providing unity of action in pursuit of a common goal. Method: C3-Cloud is an e-health based ICT system, offering integrated, patient-centred care, considering all aspects of multi-morbidity and creating a collaborative environment, for all involved stakeholders. The navel of the system consists of the patient care plan, a digital shared picture of the patients’ needs and care regime. The care plan allows all professionals to review and understand the implications of one condition in the presence of others; this by its nature is complex, containing a considerable amount of diverse information. Navigating, understanding, and interpreting all the information can be confounding. The C3-Cloud Clinical Decision Support Service (CDS) offers an automated means of interpreting the available data. CDSS connects to the care plan repository, and continuously searches records for relevant data. The algorithms and integration of recommendations to the service were reviewed and validated by clinicians. Human computer interaction methods were employed to ensure optimal interaction between C3-Cloud and its users. Results: C3-Cloud offers CDSS for diabetes, renal failure, depression and congenital heart failure, with over 300 rules and checks that deliver four best practice guidelines in parallel; whilst reconciling their objectives, and monitoring their outcomes. It creates warnings or recommendations for the patient as well as for formal and informal carers. Discussion and Conclusions: C3-Cloud offers a powerful way to ensure that subtle, as well as critical, information about the patient, is presented to healthcare professionals, along with guideline based recommendations. The rules reconcile potential conflicts amongst conditions. Combined with a single patient and professionals interface, it provides a seamless experience throughout the health and care service. The C3-Cloud CDS service provides support to three pilot sites throughout Europe, currently undergoing evaluation. Acknowledgements: C3-Cloud is funded from the EU Horizon 2020 research and innovation project C3-Cloud, under grant agreement No 6891810. This abstract is based on the work and material of the entire C3-Cloud consortium

Interoperable E-Health System Using Structural and Semantic Interoperability Approaches in CAREPATH

Technical and semantic interoperability are broadly used components of interoperability technology in healthcare. Technical Interoperability provides interoperability interfaces to enable data exchange within different healthcare systems, despite any underlying heterogeneity. Semantic interoperability make different healthcare systems understand and interpret the meaning of the data that is exchanged, by using and mapping standardized terminologies, coding systems, and data models to describe the concept and structure of data. We propose a solution using Semantic and Structural Mapping techniques within CAREPATH; a research project designed to develop ICT solutions for the care management of elderly multimorbid patients with mild cognitive impairment or mild dementia. Our technical interoperability solution supplies a standard-based data exchange protocol to enable information exchange between local care systems and CAREPATH components. Our semantic interoperability solution supplies programmable interfaces, in order to semantically mediate different clinical data representation formats and incorporating data format and terminology mapping features. The solution offers a more reliable, flexible and resource efficient method across EHRs.</p

Management of personalised guideline-driven care plans addressing the needs of multi-morbidity via clinical decision support services

Introduction: The clinical management of patients suffering from multiple chronic conditions is very complex, disconnected and time-consuming with the traditional care settings. C3-Cloud project aims to build an integrated care platform for addressing the growing demand for improved health outcomes of multimorbid and long-term care patients. Theory/Methods: C3-Cloud has established an ICT infrastructure enabling continuous coordination of patient-centred care activities by a multidisciplinary care team MDT and patients/informal care givers. The Coordinated Care and Cure Delivery Platform C3DP allows, collaborative creation and execution of personalised care plans for multi-morbid patients through systematic and semi-automatic reconciliation of clinical guidelines. Clinical decision support CDS systems implementing flowcharts from evidence based clinical guidelines are integrated to present suggestions for treatment goal and activities e.g. medications, follow-up appointments, diet, exercise, lab tests. Pilot site local care systems are integrated with the C3DP via the technical and semantic interoperability platform to facilitate informed decision making. Active patient involvement is realized through a Patient Empowerment Platform presenting personalized care plan to the patient and establishing a continuous bi-way communication with the patient to collect patient observations, questionnaire responses, symptoms and feedback about care plan goals and activities. Results: The following research results have been achieved to enable guideline enabled personalised care plan management for addressing the needs of multi-morbidity: 43 logical flowcharts were designed out of 4 disease guidelines Type 2 Diabetes, Heart Failure, Renal Failure and Depression. 181 CDS rules assessing 166 patient criteria and recommending 154 goal/activity suggestions were implemented as CDS services in GDL covering T2D and RF. 52 reconciliation rules were designed for eliminating contradicting guideline recommendations due to multi-morbidity. 23 HL7 FHIR profiles were defined for representing care plan and patient data. C3DP has been integrated with these CDS services via CDS-Hooks specification to recommend personalised care plan goals and activities. Discussions: In this research, we have successfully implemented an ICT infrastructure enabling guideline-driven integrated care for multi-morbid patients. Although our ICT solution covers all the technical requirements identified by clinical partners, effective implementation of integrated care in real-life care setting requires major changes in organisational responsibilities and care pathways. Conclusions: User-centred design and usability testing have successfully been completed. C3-Cloud pilot application will now be operated in 3 European pilot sites with the participation of 62 MDT members and 1200 multi-morbid patients for 15 months. Lessons learned: There are two main research lines for reconciliation of contradicting guideline recommendations: 1 fully-automated reconciliation via ontology reasoning, 2 manually-crafted reconciliation rules by clinical expert groups. Although first approach is more dynamic, research results are still for very primitive cases and not clinically validated. As we are targeting an industry-ready solution after piloting in real-life settings, we have opted for the second option. Limitations: When a new chronic disease is to be addressed within our platform, reconciliation rules covering all disease combinations have to be re-assessed by the clinical expert group. Suggestions for future research: Fully-automated reconciliation approaches need to be further studied and validated in real-life settings