A pilot ontology for a large, diverse set of national health service healthcare quality indicators

Objectives: This project seeks to reduce duplication of effort in finding data for NHS healthcare quality indicators, to resolve issues identified in previous efforts to develop quality-monitoring ontologies and to identify areas for future computer-interpretable quality indicator development for the United Kingdom’s Department of Health and National Health Service (NHS). Outcomes will include specification of inclusion and exclusion criteria for a set of healthcare quality indicators, along with categorisation beyond screening and prevention and identification of levels of indicator relationships Methodology: Following an exploration of potential methods for ontology development, Methontology was the method chosen to develop the ontology. This involved a conceptual analysis to inform the development of an ontology for a 2009 set of healthcare quality indicators made available on the NHS Information Centre website. Indicators were categorised by NHS Dimension, NHS-specified clinical pathway and by United States Institute of Medicine purpose. Relationships between indicators were identified, as well as an initial set of inclusion and exclusion criteria. Protégé 3.4.1 was the platform used to develop a pilot ontology. Results: NHS quality indicators that share some of the same criteria were made searchable, along with broader and narrower related criteria. Up to six layers of inclusion and exclusion criteria were specified and incorporated into the ontology. Search capabilities were created for indicators originating from the same source and from more than one source, along with indicators assigned to specific care pathways. It was shown that indicators have purposes other than prevention and screening, rendering Arden Syntax, intended for computer-interpretable guidelines and previously tested on a specialised set of healthcare quality indicators, unsuitable for a large, diverse set of quality indicators. A large number, 222, of quality indicators with different purposes justified the development of a separate ontology. Conclusions: This ontology could reduce duplication of effort in finding data for NHS healthcare quality indicators. There is potential to link to components of queries currently in use in the NHS, as an interim step away from the need to develop separate queries for each indicator. Areas for future computer- interpretable quality indicator development include resolving Electronic Health Record compatibility issues and improved indicator metadata quality. The ontology could be useful to NHS indicator developers, NHS data xtractors and vendors of electronic health records who supply to the NHS

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Query Health: standards-based, cross-platform population health surveillance

Objective: Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects. Materials and methods Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language. Results: We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed. Discussions This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC's Data Access Framework initiative. Conclusions: Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites

Evaluation of an integrated system for classification, assessment and comparison of services for long-term care in Europe: the eDESDE-LTC study

The harmonization of European health systems brings with it a need for tools to allow the standardized collection of information about medical care. A common coding system and standards for the description of services are needed to allow local data to be incorporated into evidence-informed policy, and to permit equity and mobility to be assessed. The aim of this project has been to design such a classification and a related tool for the coding of services for Long Term Care (DESDE-LTC), based on the European Service Mapping Schedule (ESMS). Methods. The development of DESDE-LTC followed an iterative process using nominal groups in 6 European countries. 54 researchers and stakeholders in health and social services contributed to this process. In order to classify services, we use the minimal organization unit or "Basic Stable Input of Care" (BSIC), coded by its principal function or "Main Type of Care" (MTC). The evaluation of the tool included an analysis of feasibility, consistency, ontology, inter-rater reliability, Boolean Factor Analysis, and a preliminary impact analysis (screening, scoping and appraisal). Results: DESDE-LTC includes an alpha-numerical coding system, a glossary and an assessment instrument for mapping and counting LTC. It shows high feasibility, consistency, inter-rater reliability and face, content and construct validity. DESDE-LTC is ontologically consistent. It is regarded by experts as useful and relevant for evidence-informed decision making. Conclusion: DESDE-LTC contributes to establishing a common terminology, taxonomy and coding of LTC services in a European context, and a standard procedure for data collection and international comparison

Capturing data for emergency department performance monitoring purposes.

Background: Good-quality data is required for valid and reliable key performance indicators. Little is known of the facilitators and barriers of capturing the required data for emergency department key performance indicators. This study aimed to explore and understand how current emergency department data collection systems relevant to emergency department key performance indicators are integrated into routine service delivery, and to identify the resources required to capture these data elements. Methods: Following pilot testing, we conducted two focus groups with a multi-disciplinary panel of 14 emergency department stakeholders drawn from urban and rural emergency departments, respectively. Focus groups were analyzed using Attride-Stirling's framework for thematic network analysis. Results: The global theme "Understanding facilitators and barriers for emergency department data collection systems" emerged from three organizing themes: "understanding current emergency department data collection systems"; "achieving the ideal emergency department data capture system for the implementation of emergency department key performance indicators"; and "emergency department data capture systems for performance monitoring purposes within the wider context". Conclusion: The pathways to improving emergency department data capture systems for emergency department key performance indicators include upgrading emergency department information systems and investment in hardware technology and data managers. Educating stakeholders outside the emergency department regarding the importance of emergency department key performance indicators as hospital-wide performance indicators underpins the successful implementation of valid and reliable emergency department key performance indicators

Developing a medication adherence technologies repository: proposed structure and protocol for an online real-time Delphi study

Introduction An online interactive repository of available medication adherence technologies may facilitate their selection and adoption by different stakeholders. Developing a repository is among the main objectives of the European Network to Advance Best practices and technoLogy on medication adherencE (ENABLE) COST Action (CA19132). However, meeting the needs of diverse stakeholders requires careful consideration of the repository structure. Methods and analysis A real-time online Delphi study by stakeholders from 39 countries with research, practice, policy, patient representation and technology development backgrounds will be conducted. Eleven ENABLE members from 9 European countries formed an interdisciplinary steering committee to develop the repository structure, prepare study protocol and perform it. Definitions of medication adherence technologies and their attributes were developed iteratively through literature review, discussions within the steering committee and ENABLE Action members, following ontology development recommendations. Three domains (product and provider information (D1), medication adherence descriptors (D2) and evaluation and implementation (D3)) branching in 13 attribute groups are proposed: product and provider information, target use scenarios, target health conditions, medication regimen, medication adherence management components, monitoring/measurement methods and targets, intervention modes of delivery, target behaviour determinants, behaviour change techniques, intervention providers, intervention settings, quality indicators and implementation indicators. Stakeholders will evaluate the proposed definition and attributes’ relevance, clarity and completeness and have multiple opportunities to reconsider their evaluations based on aggregated feedback in real-time. Data collection will stop when the predetermined response rate will be achieved. We will quantify agreement and perform analyses of process indicators on the whole sample and per stakeholder group. Ethics and dissemination Ethical approval for the COST ENABLE activities was granted by the Malaga Regional Research Ethics Committee. The Delphi protocol was considered compliant regarding data protection and security by the Data Protection Officer from University of Basel. Findings from the Delphi study will form the basis for the ENABLE repository structure and related activities

Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

[EN] Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.The authors wish to acknowledge the consortium of the MOSAIC project (funded by the European Commission, Grant No. FP7-ICT 600914) for their commitment during concept development, which led to the development of the research reported in this manuscriptMartinez-Millana, A.; Bayo-Monton, JL.; Argente-Pla, M.; Fernández Llatas, C.; Merino-Torres, JF.; Traver Salcedo, V. (2018). Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes. Sensors. 18 (1)(79):1-26. https://doi.org/10.3390/s18010079S12618 (1)79Thomas, C. C., & Philipson, L. H. (2015). Update on Diabetes Classification. Medical Clinics of North America, 99(1), 1-16. doi:10.1016/j.mcna.2014.08.015Kahn, S. E., Hull, R. L., & Utzschneider, K. M. (2006). Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature, 444(7121), 840-846. doi:10.1038/nature05482Guariguata, L., Whiting, D. R., Hambleton, I., Beagley, J., Linnenkamp, U., & Shaw, J. E. (2014). Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Research and Clinical Practice, 103(2), 137-149. doi:10.1016/j.diabres.2013.11.002Beagley, J., Guariguata, L., Weil, C., & Motala, A. A. (2014). Global estimates of undiagnosed diabetes in adults. Diabetes Research and Clinical Practice, 103(2), 150-160. doi:10.1016/j.diabres.2013.11.001Hippisley-Cox, J., Coupland, C., Robson, J., Sheikh, A., & Brindle, P. (2009). Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ, 338(mar17 2), b880-b880. doi:10.1136/bmj.b880Meigs, J. B., Shrader, P., Sullivan, L. M., McAteer, J. B., Fox, C. S., Dupuis, J., … Cupples, L. A. (2008). Genotype Score in Addition to Common Risk Factors for Prediction of Type 2 Diabetes. New England Journal of Medicine, 359(21), 2208-2219. doi:10.1056/nejmoa0804742Gillies, C. L., Abrams, K. R., Lambert, P. C., Cooper, N. J., Sutton, A. J., Hsu, R. T., & Khunti, K. (2007). Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ, 334(7588), 299. doi:10.1136/bmj.39063.689375.55Noble, D., Mathur, R., Dent, T., Meads, C., & Greenhalgh, T. (2011). Risk models and scores for type 2 diabetes: systematic review. BMJ, 343(nov28 1), d7163-d7163. doi:10.1136/bmj.d7163Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015). Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement. Annals of Internal Medicine, 162(1), 55. doi:10.7326/m14-0697Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., … Schroter, S. (2013). Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research. PLoS Medicine, 10(2), e1001381. doi:10.1371/journal.pmed.1001381Collins, G. S., & Moons, K. G. M. (2012). Comparing risk prediction models. BMJ, 344(may24 2), e3186-e3186. doi:10.1136/bmj.e3186Riley, R. D., Ensor, J., Snell, K. I. E., Debray, T. P. A., Altman, D. G., Moons, K. G. M., & Collins, G. S. (2016). External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ, i3140. doi:10.1136/bmj.i3140Reilly, B. M., & Evans, A. T. (2006). Translating Clinical Research into Clinical Practice: Impact of Using Prediction Rules To Make Decisions. Annals of Internal Medicine, 144(3), 201. doi:10.7326/0003-4819-144-3-200602070-00009Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009). Prognosis and prognostic research: validating a prognostic model. BMJ, 338(may28 1), b605-b605. doi:10.1136/bmj.b605Moons, K. G. M., Royston, P., Vergouwe, Y., Grobbee, D. E., & Altman, D. G. (2009). Prognosis and prognostic research: what, why, and how? BMJ, 338(feb23 1), b375-b375. doi:10.1136/bmj.b375Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., … Kattan, M. W. (2010). Assessing the Performance of Prediction Models. Epidemiology, 21(1), 128-138. doi:10.1097/ede.0b013e3181c30fb2Kayacan, E., Ulutas, B., & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert Systems with Applications, 37(2), 1784-1789. doi:10.1016/j.eswa.2009.07.064Schmidt, M. I., Duncan, B. B., Bang, H., Pankow, J. S., Ballantyne, C. M., … Golden, S. H. (2005). Identifying Individuals at High Risk for Diabetes: The Atherosclerosis Risk in Communities study. Diabetes Care, 28(8), 2013-2018. doi:10.2337/diacare.28.8.2013Talmud, P. J., Hingorani, A. D., Cooper, J. A., Marmot, M. G., Brunner, E. J., Kumari, M., … Humphries, S. E. (2010). Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ, 340(jan14 1), b4838-b4838. doi:10.1136/bmj.b4838Sackett, D. L. (1997). Evidence-based medicine. Seminars in Perinatology, 21(1), 3-5. doi:10.1016/s0146-0005(97)80013-4Segagni, D., Ferrazzi, F., Larizza, C., Tibollo, V., Napolitano, C., Priori, S. G., & Bellazzi, R. (2011). R Engine Cell: integrating R into the i2b2 software infrastructure. Journal of the American Medical Informatics Association, 18(3), 314-317. doi:10.1136/jamia.2010.007914Semantic Webhttp://www.w3.org/2001/sw/Murphy, S. N., Weber, G., Mendis, M., Gainer, V., Chueh, H. C., Churchill, S., & Kohane, I. (2010). Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2), 124-130. doi:10.1136/jamia.2009.000893Murphy, S., Churchill, S., Bry, L., Chueh, H., Weiss, S., Lazarus, R., … Kohane, I. (2009). Instrumenting the health care enterprise for discovery research in the genomic era. Genome Research, 19(9), 1675-1681. doi:10.1101/gr.094615.109Lindstrom, J., & Tuomilehto, J. (2003). The Diabetes Risk Score: A practical tool to predict type 2 diabetes risk. Diabetes Care, 26(3), 725-731. doi:10.2337/diacare.26.3.725Alssema, M., Vistisen, D., Heymans, M. W., Nijpels, G., Glümer, C., … Dekker, J. M. (2010). The Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes. Diabetologia, 54(5), 1004-1012. doi:10.1007/s00125-010-1990-7Mann, D. M., Bertoni, A. G., Shimbo, D., Carnethon, M. R., Chen, H., Jenny, N. S., & Muntner, P. (2010). Comparative Validity of 3 Diabetes Mellitus Risk Prediction Scoring Models in a Multiethnic US Cohort: The Multi-Ethnic Study of Atherosclerosis. American Journal of Epidemiology, 171(9), 980-988. doi:10.1093/aje/kwq030Stern, M. P., Williams, K., & Haffner, S. M. (2002). Identification of Persons at High Risk for Type 2 Diabetes Mellitus: Do We Need the Oral Glucose Tolerance Test? Annals of Internal Medicine, 136(8), 575. doi:10.7326/0003-4819-136-8-200204160-00006Abdul-Ghani, M. A., Abdul-Ghani, T., Stern, M. P., Karavic, J., Tuomi, T., Bo, I., … Groop, L. (2011). Two-Step Approach for the Prediction of Future Type 2 Diabetes Risk. Diabetes Care, 34(9), 2108-2112. doi:10.2337/dc10-2201Rahman, M., Simmons, R. K., Harding, A.-H., Wareham, N. J., & Griffin, S. J. (2008). A simple risk score identifies individuals at high risk of developing Type 2 diabetes: a prospective cohort study. Family Practice, 25(3), 191-196. doi:10.1093/fampra/cmn024Guasch-Ferré, M., Bulló, M., Costa, B., Martínez-Gonzalez, M. Á., Ibarrola-Jurado, N., … Estruch, R. (2012). A Risk Score to Predict Type 2 Diabetes Mellitus in an Elderly Spanish Mediterranean Population at High Cardiovascular Risk. PLoS ONE, 7(3), e33437. doi:10.1371/journal.pone.0033437Wilson, P. W. F. (2007). Prediction of Incident Diabetes Mellitus in Middle-aged Adults. Archives of Internal Medicine, 167(10), 1068. doi:10.1001/archinte.167.10.1068Franzin, A., Sambo, F., & Di Camillo, B. (2016). bnstruct: an R package for Bayesian Network structure learning in the presence of missing data. Bioinformatics, btw807. doi:10.1093/bioinformatics/btw807Rood, B., & Lewis, M. J. (2009). Grid Resource Availability Prediction-Based Scheduling and Task Replication. Journal of Grid Computing, 7(4), 479-500. doi:10.1007/s10723-009-9135-2Ramakrishnan, L., & Reed, D. A. (2009). Predictable quality of service atop degradable distributed systems. Cluster Computing, 16(2), 321-334. doi:10.1007/s10586-009-0078-yKianpisheh, S., Kargahi, M., & Charkari, N. M. (2017). Resource Availability Prediction in Distributed Systems: An Approach for Modeling Non-Stationary Transition Probabilities. IEEE Transactions on Parallel and Distributed Systems, 28(8), 2357-2372. doi:10.1109/tpds.2017.2659746Weber, G. M., Murphy, S. N., McMurry, A. J., MacFadden, D., Nigrin, D. J., Churchill, S., & Kohane, I. S. (2009). The Shared Health Research Information Network (SHRINE): A Prototype Federated Query Tool for Clinical Data Repositories. Journal of the American Medical Informatics Association, 16(5), 624-630. doi:10.1197/jamia.m3191Martinez-Millana, A., Fico, G., Fernández-Llatas, C., & Traver, V. (2015). Performance assessment of a closed-loop system for diabetes management. Medical & Biological Engineering & Computing, 53(12), 1295-1303. doi:10.1007/s11517-015-1245-3Foundation for Intelligent Physical Agentshttp://www.pa.org/González-Vélez, H., Mier, M., Julià-Sapé, M., Arvanitis, T. N., García-Gómez, J. M., Robles, M., … Lluch-Ariet, M. (2007). HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis. Applied Intelligence, 30(3), 191-202. doi:10.1007/s10489-007-0085-8Bellazzi, R. (2014). Big Data and Biomedical Informatics: A Challenging Opportunity. Yearbook of Medical Informatics, 23(01), 08-13. doi:10.15265/iy-2014-0024Maximilien, E. M., & Singh, M. P. (2004). A framework and ontology for dynamic Web services selection. IEEE Internet Computing, 8(5), 84-93. doi:10.1109/mic.2004.2

The National Transport Data Framework

Report by Professor Peter Landshoff (Cambridge University) and Professor John Polak (Imperial College London) on a project for the Department for Transport. emails: [email protected] [email protected] NTDF is designed to be a resource for data owners to deposit descriptions into a central catalogue, so that people can search for data and find data and understand their characteristics. The value of this is to individuals, to commercial organizations, and to public bodies. For example, services that provide better information to travellers will help to make their journey less stressful and persuade them to make more use of public transport. Transport operators need very diverse information to help them plan developments to their services: demographic, geographical, economic etc. And policy makers need a similar range of information to help them decide how to divide their budget and afterwards to evaluate how valuable it has been.This work was supported by the Department for Transport (DfT)