Status and recommendations of technological and data-driven innovations in cancer care:Focus group study

Background: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)-funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. Objective: This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020-funded projects. Methods: Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. Results: Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. Conclusions: Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations

Creating hospital-specific customized clinical pathways by applying semantic reasoning to clinical data

AbstractObjectiveClinical pathways (CPs) are widely studied methods to standardize clinical intervention and improve medical quality. However, standard care plans defined in current CPs are too general to execute in a practical healthcare environment. The purpose of this study was to create hospital-specific personalized CPs by explicitly expressing and replenishing the general knowledge of CPs by applying semantic analysis and reasoning to historical clinical data.MethodsA semantic data model was constructed to semantically store clinical data. After querying semantic clinical data, treatment procedures were extracted. Four properties were self-defined for local ontology construction and semantic transformation, and three Jena rules were proposed to achieve error correction and pathway order recognition. Semantic reasoning was utilized to establish the relationship between data orders and pathway orders.ResultsA clinical pathway for deviated nasal septum was used as an example to illustrate how to combine standard care plans and practical treatment procedures. A group of 224 patients with 11,473 orders was transformed to a semantic data model, which was stored in RDF format. Long term order processing and error correction made the treatment procedures more consistent with clinical practice. The percentage of each pathway order with different probabilities was calculated to declare the commonality between the standard care plans and practical treatment procedures. Detailed treatment procedures with pathway orders, deduced pathway orders, and orders with probability greater than 80% were provided to efficiently customize the CPs.ConclusionsThis study contributes to the practical application of pathway specifications recommended by the Ministry of Health of China and provides a generic framework for the hospital-specific customization of standard care plans defined by CPs or clinical guidelines

Bioinformaatika meetodid personaalses farmakoteraapias

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKogutavate terviseandmete hulk kasvab kiiresti. Tänu neile andmetele on meditsiinilise ravi pakkumisel võimalik senisest enam arvesse võtta individuaalseid bioloogilisi andmeid. See doktoritöö käsitleb mitmeid personaalses meditsiinis esinevaid probleeme ja näitab, et ravi individualiseerimiseks kasutatavad andmed tulevad väga erinevatest allikatest. Inimestevahelised erinevused teevad ravimite metabolismi ennustamise keerukaks, siiski on ravi käigus kogutavad kontsentratsioonimõõtmised ravimiefekti hindamisel heaks allikaks. Me arendasime välja täppisdoseerimise tööriista, mis võimaldab vankomütsiini ravil vastsündinutele määrata ravi tõhustavat personaalseid doose kasutades selleks nende endi ravi käigus kogutud kontsentratsioone. Suurema osa ravimiteraapiate puhul ei ole võimalik pidevalt ravimi kontsentratsioone koguda. Nende ülejäänud ravimite puhul on heaks informatsiooniallikaks geneetika. Paljude ravimimetabolismiga seotud geneetiliste variantide mõju on piisav, et tingida muutuseid ravi läbiviimisel. Me uurisime geneetika ja ravimite kõrvalmõjude omavahelisi seoseid kasutades rahvastikupõhist lähenemist. See toetus Eesti Geenivaramu geeniandmetele ja teistele laiapõhjalistele terviseandmete registritele. Me leidsime ja valideerisime seose, et CTNNA3 geenis olev geenivariant tõstab oksikaamide ravil olevate inimeste jaoks kõrvalmõjude sagedust. Arvutuslik geneetika toetub kvantitatiivsetele meetoditele, millest kõige levinum on ülegenoomne assotsiatsiooni analüüs (GWAS). Sagedasti kasutatav GWASi järelsamm on aega nõudev GWASist ilmnenud p-väärtuste visuaalne hindamine teiste samas genoomi piirkonnas olevate geneetiliste variantide kontekstis. Selle sammu automatiseerimiseks arendasime me kaks tööriista, Manhattan Harvester ja Cropper, mis võimaldavad automaatselt huvipakkuvaid piirkondi tuvastada ja nende headust hinnata.The amount of collected health data is growing fast. Insights from these data allow using biological patient specifics to improve therapy management with further individualization. This thesis addresses problems in multiple sub-fields of personalised medicine and aims to illustrate that data for precision medicine emerges from different sources. Drug metabolism is difficult to predict because individual biological differences. Fortunately, drug concentrations are a good proxy for drug effect. To address the growing need for tools that allow on-line therapy adjustment based on individual concentrations we have developed and externally evaluated a precision dosing tool that allows individualised dosing of vancomycin in neonates. Other than drugs used in therapeutic drug monitoring, most pharmacotherapies can not rely on continuous concentration measurements but for such drugs genetics provides a valuable source of information for individualization. Effects of many genetic variants in drug metabolism pathways are often large enough to require changes in drug prescriptions or schedules. We have applied a population-based approach in testing relations between drug related adverse effects and genomic loci, and found and validated a novel variant in CTNNA3 gene that increases adverse drug effects in patients with oxicam prescriptions. This was done by leveraging the data in Estonian Genome Center and linking these to nation-wide electronic health data registries. Computational genetics relies on quantitative methods for which the most common is the genome-wide association analysis (GWAS). A common GWAS downstream step involves time-consuming visual assessment of the association study p-values in context with other variants in genomic vicinity. In order to streamline this step, we developed, Manhattan Harvester and Cropper, that allow for automated detection of peak areas and assign scores by emulating human evaluators.https://www.ester.ee/record=b524282