Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

On-site customer analytics and reporting (OSCAR):a portable clinical data warehouse for the in-house linking of hospital and telehealth data

This document conveys the results of the On-Site Customer Analytics and Reporting (OSCAR) project. This nine-month project started on January 2014 and was conducted at Philips Research in the Chronic Disease Management group as part of the H2H Analytics Project. Philips has access to telehealth data from their Philips Motiva tele-monitoring and other services. Previous projects within Philips Re-search provided a data warehouse for Motiva data and a proof-of-concept (DACTyL) solution that demonstrated the linking of hospital and Motiva data and subsequent reporting. Severe limitations with the DACTyL solution resulted in the initiation of OSCAR. A very important one was the unwillingness of hospitals to share personal patient data outside their premises due to stringent privacy policies, while at the same time patient personal data is required in order to link the hospital data with the Motiva data. Equally important is the fact that DACTyL considered the use of only Motiva as a telehealth source and only a single input interface for the hospitals. OSCAR was initiated to propose a suitable architecture and develop a prototype solution, in contrast to the proof-of-concept DACTyL, with the twofold aim to overcome the limitations of DACTyL in order to be deployed in a real-life hospital environment and to expand the scope to an extensible solution that can be used in the future for multiple telehealth services and multiple hospital environments. In the course of the project, a software solution was designed and consequently deployed in the form of a virtual machine. The solution implements a data warehouse that links and hosts the collected hospital and telehealth data. Hospital data are collected with the use of a modular service oriented data collection component by exposing web services described in WSDL that accept configurable XML data messages. ETL processes propagate the data, link, and load it on the OS-CAR data warehouse. Automated reporting is achieved using dash-boards that provide insight into the data stored in the data warehouse. Furthermore, the linked data is available for export to Philips Re-search in de-identified format

p-medicine: a medical informatics platform for integrated large scale heterogeneous patient data

Secure access to patient data is becoming of increasing importance, as medical informatics grows in significance, to both assist with population health studies, and patient specific medicine in support of treatment. However, assembling the many different types of data emanating from the clinic is in itself a difficulty, and doing so across national borders compounds the problem. In this paper we present our solution: an easy to use distributed informatics platform embedding a state of the art data warehouse incorporating a secure pseudonymisation system protecting access to personal healthcare data. Using this system, a whole range of patient derived data, from genomics to imaging to clinical records, can be assembled and linked, and then connected with analytics tools that help us to understand the data. Research performed in this environment will have immediate clinical impact for personalised patient healthcare

MiMiR - an integrated platform for microarray data sharing, mining and analysis

Background: Despite considerable efforts within the microarray community for standardising data format, content and description, microarray technologies present major challenges in managing, sharing, analysing and re-using the large amount of data generated locally or internationally. Additionally, it is recognised that inconsistent and low quality experimental annotation in public data repositories significantly compromises the re-use of microarray data for meta-analysis. MiMiR, the Microarray data Mining Resource was designed to tackle some of these limitations and challenges. Here we present new software components and enhancements to the original infrastructure that increase accessibility, utility and opportunities for large scale mining of experimental and clinical data.Results: A user friendly Online Annotation Tool allows researchers to submit detailed experimental information via the web at the time of data generation rather than at the time of publication. This ensures the easy access and high accuracy of meta-data collected. Experiments are programmatically built in the MiMiR database from the submitted information and details are systematically curated and further annotated by a team of trained annotators using a new Curation and Annotation Tool. Clinical information can be annotated and coded with a clinical Data Mapping Tool within an appropriate ethical framework. Users can visualise experimental annotation, assess data quality, download and share data via a web-based experiment browser called MiMiR Online. All requests to access data in MiMiR are routed through a sophisticated middleware security layer thereby allowing secure data access and sharing amongst MiMiR registered users prior to publication. Data in MiMiR can be mined and analysed using the integrated EMAAS open source analysis web portal or via export of data and meta-data into Rosetta Resolver data analysis package.Conclusion: The new MiMiR suite of software enables systematic and effective capture of extensive experimental and clinical information with the highest MIAME score, and secure data sharing prior to publication. MiMiR currently contains more than 150 experiments corresponding to over 3000 hybridisations and supports the Microarray Centre's large microarray user community and two international consortia. The MiMiR flexible and scalable hardware and software architecture enables secure warehousing of thousands of datasets, including clinical studies, from microarray and potentially other -omics technologies

Epilepsy Res

Objectives:The Epilepsy Learning Healthcare System (ELHS) was created in 2018 to address measurable improvements in outcomes for people with epilepsy. However, fragmentation of data systems has been a major barrier for reporting and participation. In this study, we aimed to test the feasibility of an open-source Data Integration (DI) method that connects real-life clinical data to national research and quality improvement (QI) systems.Methods:The ELHS case report forms were programmed as EPIC SmartPhrases at Mass General Brigham (MGB) in December 2018 and subsequently as EPIC SmartForms in June 2021 to collect actionable, standardized, structured epilepsy data in the electronic health record (EHR) for subsequent pull into the external national registry of the ELHS. Following the QI methodology in the Chronic Care Model, 39 providers, epileptologists and neurologists, incorporated the ELHS SmartPhrase into their clinical workflow, focusing on collecting diagnosis of epilepsy, seizure type according to the International League Against Epilepsy, seizure frequency, date of last seizure, medication adherence and side effects. The collected data was stored in the Enterprise Data Warehouse (EDW) without integration with external systems. We developed and validated a DI method that extracted the data from EDW using structured query language and later preprocessed using text mining. We used the ELHS data dictionary to match fields in the preprocessed notes to obtain the final structured dataset with seizure control information. For illustration, we described the data curated from the care period of 12/2018\u201312/2021.Results:The cohort comprised a total of 1806 patients with a mean age of 43 years old (SD: 17.0), where 57% were female, 80% were white, and 84% were non-Hispanic/Latino. Using our DI method, we automated the data mining, preprocessing, and exporting of the structured dataset into a local database, to be weekly accessible to clinicians and quality improvers. During the period of SmartPhrase implementation, there were 5168 clinic visits logged by providers documenting each patient\u2019s seizure type and frequency. During this period, providers documented 59% patients having focal seizures, 35% having generalized seizures and 6% patients having another type. Of the cohort, 45% patients had private insurance. The resulting structured dataset was bulk uploaded via web interface into the external national registry of the ELHS.Conclusions:Structured data can be feasibly extracted from text notes of epilepsy patients for weekly reporting to a national learning healthcare system.K08 AG053380/AG/NIA NIH HHSUnited States/R01 NS102190/NS/NINDS NIH HHSUnited States/RF1 NS120947/NS/NINDS NIH HHSUnited States/U48 DP006377/DP/NCCDPHP CDC HHSUnited States/R01 AG062282/AG/NIA NIH HHSUnited States/K08 NS118107/NS/NINDS NIH HHSUnited States/R01 AG073410/AG/NIA NIH HHSUnited States/P01 AG032952/AG/NIA NIH HHSUnited States/R01 NS107291/NS/NINDS NIH HHSUnited States

Methodology and model-based DSS to managing the reallocation of inventory to orders in LHP situations. Application to the ceramics sector

[EN] Lack of homogeneity in the product (LHP) is a problem when customers require homogeneous units of a single product. In such cases, the optimal allocation of inventory to orders becomes much more complex. Furthermore, in an MTS environment, an optimal initial allocation may become less than ideal over time, due to different circumstances. This problem occurs in the ceramics sector, where the final product varies in tone and calibre. This paper proposes a methodology for the reallocation of inventory to orders in LHP situation (MERIO-LHP) and a model-based decision-support system (DSS) to support the methodology, which enables an optimal reallocation of inventory to order lines to be carried out in real businesses environments in which LHP is inherent. The proposed methodology and model-based DSS were validated by applying it to a real case at a ceramics company. The analysis of the results indicates that considerable improvements can be obtained with regard to the quantity of orders fulfilled and sales turnover.Oltra Badenes, RF.; Gil Gómez, H.; Merigó, JM.; Palacios Marqués, D. (2019). Methodology and model-based DSS to managing the reallocation of inventory to orders in LHP situations. Application to the ceramics sector. PLoS ONE. 14(7):1-19. https://doi.org/10.1371/journal.pone.0219433S119147Alarcón, F., Alemany, M. M. E., Lario, F. C., & Oltra, R. F. (2011). La falta de homogeneidad del producto (FHP) en las empresas cerámicas y su impacto en la reasignación del inventario. Boletín de la Sociedad Española de Cerámica y Vidrio, 50(1), 49-58. doi:10.3989/cyv.072011Wanke, P., Alvarenga, H., Correa, H., Hadi-Vencheh, A., & Azad, M. A. K. (2017). Fuzzy inference systems and inventory allocation decisions: Exploring the impact of priority rules on total costs and service levels. Expert Systems with Applications, 85, 182-193. doi:10.1016/j.eswa.2017.05.043JÖNSSON, H., & SILVER, E. A. (1987). Stock allocation among a central warehouse and identical regional warehouses in a particular push inventory control system. International Journal of Production Research, 25(2), 191-205. doi:10.1080/00207548708919833Wu, H. H., & Yeh, C. S. (2014). A Study of the Bin Inventory Allocation Model for LED-CM Plants. Applied Mechanics and Materials, 543-547, 4440-4443. doi:10.4028/www.scientific.net/amm.543-547.4440Wu, H.-H., & Jiang, X.-Y. (2017). Improved genetic algorithms for optimization of inventory allocation in LED chip manufacturing plants. Journal of Interdisciplinary Mathematics, 20(3), 727-738. doi:10.1080/09720502.2017.1357328Kristianto, Y., Gunasekaran, A., Helo, P., & Hao, Y. (2014). A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path. Expert Systems with Applications, 41(1), 39-49. doi:10.1016/j.eswa.2013.07.009Protopappa-Sieke, M., Sieke, M. A., & Thonemann, U. W. (2016). Optimal two-period inventory allocation under multiple service level contracts. European Journal of Operational Research, 252(1), 145-155. doi:10.1016/j.ejor.2016.01.013Luo, K., Bollapragada, R., & Kerbache, L. (2017). Inventory allocation models for a two-stage, two-product, capacitated supplier and retailer problem with random demand. International Journal of Production Economics, 187, 168-181. doi:10.1016/j.ijpe.2016.12.014Zhao, H., Huang, E., Dou, R., & Wu, K. (2019). A multi-objective production planning problem with the consideration of time and cost in clinical trials. Expert Systems with Applications, 124, 25-38. doi:10.1016/j.eswa.2019.01.038Kang, K., Pu, W., Ma, Y., & Wang, X. (2018). Bi-objective inventory allocation planning problem with supplier selection and carbon trading under uncertainty. PLOS ONE, 13(11), e0206282. doi:10.1371/journal.pone.0206282Esmaeili-Najafabadi, E., Fallah Nezhad, M. S., Pourmohammadi, H., Honarvar, M., & Vahdatzad, M. A. (2019). A joint supplier selection and order allocation model with disruption risks in centralized supply chain. Computers & Industrial Engineering, 127, 734-748. doi:10.1016/j.cie.2018.11.017Chen, C.-M. J., & Thomas, D. J. (2017). Inventory Allocation in the Presence of Service-Level Agreements. Production and Operations Management, 27(3), 553-577. doi:10.1111/poms.12814Chen, C.-Y., Zhao, Z.-Y., & Ball, M. O. (2001). Information Systems Frontiers, 3(4), 477-488. doi:10.1023/a:1012837207691CHEN, C.-Y., ZHAO, Z., & BALL, M. O. (2009). A MODEL FOR BATCH ADVANCED AVAILABLE-TO-PROMISE. Production and Operations Management, 11(4), 424-440. doi:10.1111/j.1937-5956.2002.tb00470.xPibernik, R. (2005). Advanced available-to-promise: Classification, selected methods and requirements for operations and inventory management. International Journal of Production Economics, 93-94, 239-252. doi:10.1016/j.ijpe.2004.06.023Pibernik, R. (2006). Managing stock‐outs effectively with order fulfilment systems. Journal of Manufacturing Technology Management, 17(6), 721-736. doi:10.1108/17410380610678765Meyr, H. (2008). Customer segmentation, allocation planning and order promising in make-to-stock production. OR Spectrum, 31(1), 229-256. doi:10.1007/s00291-008-0123-xPibernik, R., & Yadav, P. (2008). Inventory reservation and real-time order promising in a Make-to-Stock system. OR Spectrum, 31(1), 281-307. doi:10.1007/s00291-007-0121-4Venkatadri, U., Srinivasan, A., Montreuil, B., & Saraswat, A. (2006). Optimization-based decision support for order promising in supply chain networks. International Journal of Production Economics, 103(1), 117-130. doi:10.1016/j.ijpe.2005.05.019Xiong, M. H., Tor, S. B., Bhatnagar, R., Khoo, L. P., & Venkat, S. (2006). A DSS approach to managing customer enquiries for SMEs at the customer enquiry stage. International Journal of Production Economics, 103(1), 332-346. doi:10.1016/j.ijpe.2005.08.008Mahdavi Pajouh, F., Xing, D., Zhou, Y., Hariharan, S., Balasundaram, B., Liu, T., & Sharda, R. (2013). A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates. Interfaces, 43(6), 503-517. doi:10.1287/inte.2013.0693Yang, W., & Fung, R. Y. K. (2014). An available-to-promise decision support system for a multi-site make-to-order production system. International Journal of Production Research, 52(14), 4253-4266. doi:10.1080/00207543.2013.877612Castiglione, C., Alfieri, A., & Pastore, E. (2018). Decision Support System to balance inventory in customer-driven demand. IFAC-PapersOnLine, 51(11), 1499-1504. doi:10.1016/j.ifacol.2018.08.288Mhiri, E., Jacomino, M., Mangione, F., Vialletelle, P., & Lepelletier, G. (2015). Finite capacity planning algorithm for semiconductor industry considering lots priority. IFAC-PapersOnLine, 48(3), 1598-1603. doi:10.1016/j.ifacol.2015.06.314Alemany, M. M. E., Lario, F.-C., Ortiz, A., & Gómez, F. (2013). Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: An illustration of a ceramic case. Applied Mathematical Modelling, 37(5), 3380-3398. doi:10.1016/j.apm.2012.07.022ALEMANY, M. M. E., A., A., BOZA, A., & FUERTES-MIQUEL, V. S. (2015). A MODEL-DRIVEN DECISION SUPPORT SYSTEM FOR REALLOCATION OF SUPPLY TO ORDERS UNDER UNCERTAINTY IN CERAMIC COMPANIES. Technological and Economic Development of Economy, 21(4), 596-625. doi:10.3846/20294913.2015.1055613Grillo, H., Alemany, M. M. E., & Ortiz, A. (2016). A review of mathematical models for supporting the order promising process under Lack of Homogeneity in Product and other sources of uncertainty. 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Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting

Background: Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems.Methods: Case report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project.Results: The analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records.Conclusions: Common data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.</p

Design of VR app applied to cognitive training

L’objectiu principal d’aquest projecte és el disseny d’una aplicació de realitat virtual per millorar el tractament dels pacients amb deteriorament cognitiu lleu, així com estudiar els possibles avantatges que aquesta tecnologia pot proporcionar en aquest camp. Es va escollir la realitat virtual perquè permet augmentar la sensació d’immersió pel que fa a les tecnologies actuals. Actualment la realitat virtual s’està utilitzant amb aquest tipus de tractament i està aconseguint gran resultats amb els pacients. A més, mitjançant l’ús d’aquesta tècnica d’immersió visual, s’espera que ajudi a millorar la capacitat dels pacients davant nous problemes, com pot ser la iniciació a la realitat virtual, una qüestió fonamental que ajuda a la millora dels pacients que es troben en les primeres etapes de la malaltia. L’aplicació consisteix en un entorn de supermercat virtual on el pacient pot realitzar diverses proves. En aquesta hi haurà diferents nivells amb diverses complexitats, sempre després d’haver realitzat un tutorial previ. L’aplicació s’ha realitzat en dues fases diferents: primer es va crear el guió, amb col·laboració amb la unitat d’Alzheimer de l’Hospital Clínic. Els nivells de l’aplicació es van definir aquí. El següent va ser la realització de l’aplicació amb col·laboració amb la companyia Vysion 360. Per a la seva utilització per la unitat d’Alzheimer de l’Hospital Clínic, l’aplicació tenia que complir diferents criteris. En primer lloc, els nivells de dificultat tenen que ser suficients per realitzar un tractament a llarg termini. En segon lloc, per crear una bona experiència de immersió, l’entorn creat té que ser el més realista possible. Finalment, s’ha creat una base de dades local per guardar la informació de totes les sessions, utilitzat posteriorment en l’anàlisi de evolució dels pacients. Amb aquesta aplicació, s’espera que els resultats en els pacients amb deteriorament cognitiu lleu milloren respecte a les tècniques anteriors. Especialment gràcies a la gran experiència d’immersió aconseguida amb la realitat virtual, la qual ajuda a la concentració dels pacients durant el tractament

Exporting data from an openEHR repository to standard formats

With the healthcare sector computerization, a large amount of data is produced from medical encounters, therapeutic outcomes and other aspects of healthcare provider’s organizations current activity. Decision support systems cover several methodologies and approaches that may be applied to the healthcare sector and, since that they store and analyze data in a tabular format, it becomes necessary to assure that data sources with different data representations can be used to feed these systems. The present work focuses on the development of a methodology to export data from an openEHR repository to standard formats through a software tool which adapts itself to different data sources for later exploration in statistical and decision support systems. From use case and requirements analysis to the efective development of the tool, several steps were performed to document progress and to ground conclusions regarding operational test data. Obtained results indicate that this data export is feasible, but also highlight the need to define parameters so that the tool may function.Com a informatização do sector da saúde, uma grande quantidade de dados é produzida a partir de encontros médicos, resultados terapêuticos e outros aspectos da actividade corrente dos prestadores de cuidados. Os sistemas de apoio à decisão englobam várias metodologias e abordagens que se podem aplicar ao sector da saúde e, sendo que esses sistemas armazenam e analisam dados em formato tabular, torna-se necessário assegurar que fontes de dados com diferentes representações de informação podem ser utilizadas para alimentar estes sistemas. O presente trabalho debruça-se no desenvolvimento de uma metodologia para a exportação de dados de um repositório openEHR para formatos standard através de uma ferramenta de software que se adapte às diferentes fontes de dados para posterior análise em sistemas estatísticos e de apoio à decisão. Desde a análise de casos de uso e requerimentos até ao efectivo desenvolvimento da ferramenta, vários passos foram dados para documentar o progresso e fundamentar conclusões respeitantes aos dados dos testes operacionais. Os resultados obtidos indicam que esta exportação é exequível, mas evidenciam também a necessidade de definir parâmetros para que a ferramenta possa funcionar

Proposal to Strenghern Health Information System [HIS]

\ud The HMIS Program described in this document aims at improving and strengthening the current Health Management Information System (HMIS) in Tanzania, known as MTUHA. The consortium behind the HMIS Program is headed by the Ministry of Health & Social Welfare (MOHSW) and consists of the following additional partners; Ifakara Health Research and Development Centre, University of Dar es Salaam and the University of Oslo, representing national and international capacity in HMIS. The HMIS Program is linked to the Payment for performance (P4P) funding scheme which is initiated by the Norway Tanzania Partnership Initiative. The P4P has a focus on maternal and child health and relies upon quality indicators on performance in these areas from health facilities and districts. The provision of quality data and indicators on MDG 4 & 5 is therefore a key target for the HMIS Program. The chosen approach is, however, to derive these data from the HMIS and not to establish a separate data collection structure, hence the HMIS Program. Quality information by way of essential indicators, such as for monitoring the Millennium Development Goals 4 & 5, are crucial for health services delivery and program management as well as for M&E. Currently, however, the HMIS is not providing such needed data of sufficient completeness, timeliness and quality, leading health programs and funding agencies to establish their own structures for data collection, and thus creating fragmentation and adding to the problem. The HMIS Program aims at changing this negative trend and turning the HMIS into the key source of shared essential quality information in Tanzania by; focusing on action oriented use of information for management at each level of the health services and by providing timely quality information to all stakeholders, including all health programs and funding agencies in the HMIS strengthening process – making it an all-inclusive national process, focusing on capacity development; on-site support and facilitation, short courses and continuous education, building capacity in the MOHSW and establishing a national network of HMIS support, and by building on experience, methods and tools from Africa’s “best practices” HMIS, such as South Africa – and Zanzibar Within this proposal the aim is to carry out the HMIS strengthening process in 1/3 of the districts in the country, 7 regions, during the first 3 years. The objective, however, is to cover the entire country during the 5 years duration of the NTPI. By aiming at quick and tangible results, the expectation is that other funding agencies will join forces and thereby ensuring national coverage.\ud \u