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

A collaborative platform for management of chronic diseases via guideline-driven individualized care plans

Older age is associated with an increased accumulation of multiple chronic conditions. The clinical management of patients suffering from multiple chronic conditions is very complex, disconnected and time-consuming with the traditional care settings. Integrated care is a means to address the growing demand for improved patient experience and health outcomes of multimorbid and long-term care patients. Care planning is a prevalent approach of integrated care, where the aim is to deliver more personalized and targeted care creating shared care plans by clearly articulating the role of each provider and patient in the care process. In this paper, we present a method and corresponding implementation of a semi-automatic care plan management tool, integrated with clinical decision support services which can seamlessly access and assess the electronic health records (EHRs) of the patient in comparison with evidence based clinical guidelines to suggest personalized recommendations for goals and interventions to be added to the individualized care plans. We also report the results of usability studies carried out in four pilot sites by patients and clinicians

Localisation, personalisation and delivery of best practice guidelines on an integrated care and cure cloud architecture : the C3-cloud approach to managing multimorbidity

Background: C3-Cloud is an integrated care ICT infrastructure offering seamless patient-centered approach to managing multimorbidity, deployed in three European pilot sites. Challenge: The digital delivery of best practice guidelines unified for multimorbidity, customized to local practice, offering the capability to improve patient personalization and benefit. Method: C3-Cloud has adopted a co-production approach to developing unified multimorbidity guidelines, by collating and reconciling best practice guidelines for each condition. Clinical and technical teams at pilot sites and the C3-Cloud consortium worked in tandem to create the specification and technical implementation. 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, customized for each pilot site. Conclusions: The process provided a traceable, maintainable and audited digitally delivered collated and reconciled guidelines

Koneoppimisen regressioalgoritmin soveltaminen Azure Machine Learning-palvelussa

Tämän tutkimuksen aiheena oli selvittää miten koneoppimisen tekniikoita voi käyttää laskurin tuloksen laskemiseksi ilman työlästä perinteisten algoritmien kehittämistä manuaalisesti. Koneoppiminen laajempana ilmiönä tarjoaa mahdollisuuden löytää merkityksiä laajoista tietomassoista, joihin perinteisin menetelmin vastaava on vaatinut hyvin usein useamman vuoden kestävän kehitysprosessin. Pitkään nämä koneoppimisen menetelmät ovat olleet ainoastaan pidempään koneoppimista opiskelleiden asiantuntijoiden käytettävissä. Viimeisten vuosien aikana teknologiatoimittajat ja pilvipalvelujen tarjoajat ovat kuitenkin tuoneet markkinoille palveluja, joiden avulla koneoppimisen menetelmät ja teknologiat ovat helpommin kaikkien saatavilla. Tutkimuksen ensimmäisessä osassa selvitetään mistä koneoppimisessa on kyse ja miten koneoppimista käyttävät tietotekniset ratkaisut toimivat käytännössä. Tässä osassa esitellään koneoppimisen vahvuuksia perinteisiin menetelmiin verrattuna, siihen liittyvä yleinen toimintaperiaate, ja pureudutaan syvemmin piirteiden suunnitteluun ja algoritmien käyttöön koneoppimisessa. Tutkimuksen toisessa osassa tutustutaan CRISP-DM-menetelmään, joka on yleisin tiedonlouhinnan ja koneoppimisen prosessimalli. CRISP-DM-menetelmään kuuluu 6 vaihetta, joissa liiketoiminnan ymmärtämisen ja käytettävän datan ymmärtämisen kautta päästään julkaisemaan toimiva koneoppimisen malli sitä hyödyntäviin palveluihin ja sovelluksiin. Tutkimuksen kokeet toteutettiin Azure Machine Learning palvelun avulla. Azure Machine Learning on MLaaS-tyyppinen pilvipalvelu, jossa ilman omille koneille asentamista voi suoraan kehittää koneoppimisen malleja ja toteuttaa erilaisia siihen liittyviä kokeita. Kappaleen alussa esitellään hyvän MLaaS-palvelun arviointikriteerejä ja verrataan miten hyvin tämä palvelu vastaa näihin kriteereihin. Kappaleen toisessa osassa esitellään itse palvelu ja sen koneoppimisen mallin kehittämisen kannalta olennaisimmat moduulit. Työn käytännön osassa suoritin käytännön kokeena yleisesti tunnetun FINRISKI-laskurin toteuttamisen koneoppimisen keinoin CRISP-DM-menetelmän mukaisesti. Ensimmäisenä vaiheena tässä osuudessa oli selvittää ja kuvata laskurin toiminta, jonka jälkeen hain avoimista tietolähteistä tutkimukseen soveltuvaa aineistoa. Tutkimuksen aineistolle tein laadunvarmistuksen kahdessa vaiheessa tutkimuksessa kuvattujen menetelmien mukaisesti ja tällä aineistolla vertailin ja arvioin palvelun regressio-algoritmeja. Laatuvarmistetun datan ja soveltuvimman algoritmin avulla toteutin FINRISKI-laskurin ja varmistin sovellettavan menetelmän keinoin mallin toimivuuden. Lopuksi julkaisin opetetun mallin ulkopuolisten sovellusten käytettäväksi palvelun Web Services-rajapintojen avulla

An integrated care platform system (C3-Cloud) for care planning, decision support, and empowerment of patients With multimorbidity : protocol for a technology trial

Background: There is an increasing need to organize the care around the patient and not the disease, while considering the complex realities of multiple physical and psychosocial conditions, and polypharmacy. Integrated patient-centered care delivery platforms have been developed for both patients and clinicians. These platforms could provide a promising way to achieve a collaborative environment that improves the provision of integrated care for patients via enhanced information and communication technology solutions for semiautomated clinical decision support. Objective: The Collaborative Care and Cure Cloud project (C3-Cloud) has developed 2 collaborative computer platforms for patients and members of the multidisciplinary team (MDT) and deployed these in 3 different European settings. The objective of this study is to pilot test the platforms and evaluate their impact on patients with 2 or more chronic conditions (diabetes mellitus type 2, heart failure, kidney failure, depression), their informal caregivers, health care professionals, and, to some extent, health care systems. Methods: This paper describes the protocol for conducting an evaluation of user experience, acceptability, and usefulness of the platforms. For this, 2 “testing and evaluation” phases have been defined, involving multiple qualitative methods (focus groups and surveys) and advanced impact modeling (predictive modeling and cost-benefit analysis). Patients and health care professionals were identified and recruited from 3 partnering regions in Spain, Sweden, and the United Kingdom via electronic health record screening. Results: The technology trial in this 4-year funded project (2016-2020) concluded in April 2020. The pilot technology trial for evaluation phases 3 and 4 was launched in November 2019 and carried out until April 2020. Data collection for these phases is completed with promising results on platform acceptance and socioeconomic impact. We believe that the phased, iterative approach taken is useful as it involves relevant stakeholders at crucial stages in the platform development and allows for a sound user acceptance assessment of the final product. Conclusions: Patients with multiple chronic conditions often experience shortcomings in the care they receive. It is hoped that personalized care plan platforms for patients and collaboration platforms for members of MDTs can help tackle the specific challenges of clinical guideline reconciliation for patients with multimorbidity and improve the management of polypharmacy. The initial evaluative phases have indicated promising results of platform usability. Results of phases 3 and 4 were methodologically useful, yet limited due to the COVID-19 pandemic

An Integrated Care Platform System (C3-Cloud) for Care Planning, Decision Support, and Empowerment of Patients With Multimorbidity: Protocol for a Technology Trial

Background: There is an increasing need to organize the care around the patient and not the disease, while considering the complex realities of multiple physical and psychosocial conditions, and polypharmacy. Integrated patient-centered care delivery platforms have been developed for both patients and clinicians. These platforms could provide a promising way to achieve a collaborative environment that improves the provision of integrated care for patients via enhanced information and communication technology solutions for semiautomated clinical decision support. Objective: The Collaborative Care and Cure Cloud project (C3-Cloud) has developed 2 collaborative computer platforms for patients and members of the multidisciplinary team (MDT) and deployed these in 3 different European settings. The objective of this study is to pilot test the platforms and evaluate their impact on patients with 2 or more chronic conditions (diabetes mellitus type 2, heart failure, kidney failure, depression), their informal caregivers, health care professionals, and, to some extent, health care systems. Methods: This paper describes the protocol for conducting an evaluation of user experience, acceptability, and usefulness of the platforms. For this, 2 “testing and evaluation” phases have been defined, involving multiple qualitative methods (focus groups and surveys) and advanced impact modeling (predictive modeling and cost-benefit analysis). Patients and health care professionals were identified and recruited from 3 partnering regions in Spain, Sweden, and the United Kingdom via electronic health record screening. Results: The technology trial in this 4-year funded project (2016-2020) concluded in April 2020. The pilot technology trial for evaluation phases 3 and 4 was launched in November 2019 and carried out until April 2020. Data collection for these phases is completed with promising results on platform acceptance and socioeconomic impact. We believe that the phased, iterative approach taken is useful as it involves relevant stakeholders at crucial stages in the platform development and allows for a sound user acceptance assessment of the final product. Conclusions: Patients with multiple chronic conditions often experience shortcomings in the care they receive. It is hoped that personalized care plan platforms for patients and collaboration platforms for members of MDTs can help tackle the specific challenges of clinical guideline reconciliation for patients with multimorbidity and improve the management of polypharmacy. The initial evaluative phases have indicated promising results of platform usability. Results of phases 3 and 4 were methodologically useful, yet limited due to the COVID-19 pandemic