Clinical Decision Support Services Research Articles

Transforming evidence-based clinical guidelines into implementable clinical decision support services: the CAREPATH study for multimorbidity management.

The CAREPATH Project aims to develop a patient-centered integrated care platform tailored to older adults with multimorbidity, including mild cognitive impairment (MCI) or mild dementia. Our goal is to empower multidisciplinary care teams to craft personalized holistic care plans while adhering to evidence-based guidelines. This necessitates the creation of clear specifications for clinical decision support (CDS) services, consolidating guidance from multiple evidence-based clinical guidelines. Thus, a co-creation approach involving both clinical and technical experts is essential. This paper outlines a robust methodology for generating implementable specifications for CDS services to automate clinical guidelines. We have established a co-creation framework to facilitate collaborative exploration of clinical guidelines between clinical experts and software engineers. We have proposed an open, repeatable, and traceable method for translating evidence-based guideline narratives into implementable specifications of CDS services. Our approach, based on international standards such as CDS-Hooks and HL7 FHIR, enhances interoperability and potential adoption of CDS services across diverse healthcare systems. This methodology has been followed to create implementable specifications for 65 CDS services, automating CAREPATH consensus guideline consolidating guidance from 25 selected evidence-based guidelines. A total of 296 CDS rules have been formally defined, with input parameters defined as clinical concepts bound to FHIR resources and international code systems. Outputs include 346 well-defined CDS Cards, offering clear guidance for care plan activities and goal suggestions. These specifications have led to the implementation of 65 CDS services integrated into the CAREPATH Adaptive Integrated Care Platform. Our methodology offers a systematic, replicable process for generating CDS specifications, ensuring consistency and reliability across implementation. By fostering collaboration between clinical expertise and technical proficiency, we enhance the quality and relevance of generated specifications. Clear traceability enables stakeholders to track the development process and ensure adherence to guideline recommendations.

PillHarmonics: An Orchestrated Pharmacogenetics Medication Clinical Decision Support Service.

Pharmacogenetics (PGx) is increasingly important in individualizing therapeutic management plans, but is often implemented apart from other types of medication clinical decision support (CDS). The lack of integration of PGx into existing CDS may result in incomplete interaction information, which may pose patient safety concerns. We sought to develop a cloud-based orchestrated medication CDS service that integrates PGx with a broad set of drug screening alerts and evaluate it through a clinician utility study. We developed the PillHarmonics service for implementation per the CDS Hooks protocol, algorithmically integrating a wide range of drug interaction knowledge using cloud-based screening services from First Databank (drug-drug/allergy/condition), PharmGKB (drug-gene), and locally curated content (drug-renal/hepatic/race). We performed a user study, presenting 13 clinicians and pharmacists with a prototype of the system's usage in synthetic patient scenarios. We collected feedback via a standard questionnaire and structured interview. Clinician assessment of PillHarmonics via the Technology Acceptance Model questionnaire shows significant evidence of perceived utility. Thematic analysis of structured interviews revealed that aggregated knowledge, concise actionable summaries, and information accessibility were highly valued, and that clinicians would use the service in their practice. Medication safety and optimizing efficacy of therapy regimens remain significant issues. A comprehensive medication CDS system that leverages patient clinical and genomic data to perform a wide range of interaction checking and presents a concise and holistic view of medication knowledge back to the clinician is feasible and perceived as highly valuable for more informed decision-making. Such a system can potentially address many of the challenges identified with current medication-related CDS.

A Nationwide Chronic Disease Management Solution via Clinical Decision Support Services: Software Development and Real-Life Implementation Report.

The increasing population of older adults has led to a rise in the demand for health care services, with chronic diseases being a major burden. Person-centered integrated care is required to address these challenges; hence, the Turkish Ministry of Health has initiated strategies to implement an integrated health care model for chronic disease management. We aim to present the design, development, nationwide implementation, and initial performance results of the national Disease Management Platform (DMP). This paper's objective is to present the design decisions taken and technical solutions provided to ensure successful nationwide implementation by addressing several challenges, including interoperability with existing IT systems, integration with clinical workflow, enabling transition of care, ease of use by health care professionals, scalability, high performance, and adaptability. The DMP is implemented as an integrated care solution that heavily uses clinical decision support services to coordinate effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines and, hence, to increase the quality of health care delivery. The DMP is designed and implemented to be easily integrated with the existing regional and national health IT systems via conformance to international health IT standards, such as Health Level Seven Fast Healthcare Interoperability Resources. A repeatable cocreation strategy has been used to design and develop new disease modules to ensure extensibility while ensuring ease of use and seamless integration into the regular clinical workflow during patient encounters. The DMP is horizontally scalable in case of high load to ensure high performance. As of September 2023, the DMP has been used by 25,568 health professionals to perform 73,715,269 encounters for 16,058,904 unique citizens. It has been used to screen and monitor chronic diseases such as obesity, cardiovascular risk, diabetes, and hypertension, resulting in the diagnosis of 3,545,573 patients with obesity, 534,423 patients with high cardiovascular risk, 490,346 patients with diabetes, and 144,768 patients with hypertension. It has been demonstrated that the platform can scale horizontally and efficiently provides services to thousands of family medicine practitioners without performance problems. The system seamlessly interoperates with existing health IT solutions and runs as a part of the clinical workflow of physicians at the point of care. By automatically accessing and processing patient data from various sources to provide personalized care plan guidance, it maximizes the effect of evidence-based decision support services by seamless integration with point-of-care electronic health record systems. As the system is built on international code systems and standards, adaptation and deployment to additional regional and national settings become easily possible. The nationwide DMP as an integrated care solution has been operational since January 2020, coordinating effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines.

Retrospective Analysis of Lipid-Lowering and Antiplatelet Therapy Regimen by Clinical Decision Support Service Based on Real-World Data from Electronic Medical Records “Intellect 3 Study”

Retrospective Analysis of Lipid-Lowering and Antiplatelet Therapy Regimen by Clinical Decision Support Service Based on Real-World Data from Electronic Medical Records “Intellect 3 Study”

Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology

Full Laboratory Automation is revolutionizing work habits in an increasing number of clinical microbiology facilities worldwide, generating huge streams of digital images for interpretation. Contextually, deep learning architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic bacterial culture plates, including presumptive pathogen identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony. Working on a large stream of clinical data and a complete set of 32 pathogens, the proposed system is capable of effectively assist plate interpretation with a surprising degree of accuracy in the widespread and demanding framework of Urinary Tract Infections. Moreover, thanks to the rich species-related generated information, DeepColony can be used for developing trustworthy clinical decision support services in laboratory automation ecosystems from local to global scale.

ELGA Terminology Server for Clinical Decision Support: A Case-Study Using an Existing Knowledge Base, CDS Hooks and FHIR.

There are many medical knowledge bases with potential for supporting medical professionals in their decision-making during routine care, yet usage of these sources remains low. Standardized linking of Clinical Decision Support (CDS) applications and existing medical knowledge bases is not a common practice. Using existing eHealth standards to increase the utilization of knowledge bases and implement a prototype. Linking an existing online knowledge base via a FHIR CodeSystem supplement to the Austrian national EHR (ELGA) terminology server and accessing these data using CDS Hooks and FHIR. We tested the approach by incorporating photosensitivity data of medications into a local copy of the Austrian terminology server. These data are directly used by a CDS Hooks compliant CDS service. The Austrian Terminology Server could be an important interface to access existing knowledge bases from within EHR systems. FHIR and CDS Hooks could lead the way for a simple and open integration of CDS services into EHR systems.

Shared Interoperable Clinical Decision Support Service for Drug-Allergy Interaction Checks: Implementation Study.

Clinical decision support (CDS) can improve health care with respect to the quality of care, patient safety, efficiency, and effectiveness. Establishing a CDS system in a health care setting remains a challenge. A few hospitals have used self-developed in-house CDS systems or commercial CDS solutions. Since these in-house CDS systems tend to be tightly coupled with a specific electronic health record system, the functionality and knowledge base are not easily shareable. A shared interoperable CDS system facilitates the sharing of the knowledge base and extension of CDS services. The study focuses on developing and deploying the national CDS service for the drug-allergy interaction (DAI) check for health care providers in Korea that need to introduce the service but lack the budget and expertise. To provide the shared interoperable CDS service, we designed and implemented the system based on the CDS Hooks specification and Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The study describes the CDS development process. The system development went through requirement analysis, design, implementation, and deployment. In particular, the concept architecture was designed based on the CDS Hooks structure. The MedicationRequest and AllergyIntolerance resources were profiled to exchange data using the FHIR standard. The discovery and DAI check application programming interfaces and rule engine were developed. The CDS service was deployed on G-Cloud, a government cloud service. In March 2021, the CDS service was launched, and 67 health care providers participated in the CDS service. The health care providers participated in the service with 1,008,357 DAI checks for 114,694 patients, of which 33,054 (3.32%) cases resulted in a "warning." Korea's Ministry of Health and Welfare has been trying to build an HL7 FHIR-based ecosystem in Korea. As one of these efforts, the CDS service initiative has been conducted. To promote the rapid adoption of the HL7 FHIR standard, it is necessary to accelerate practical service development and to appeal to policy makers regarding the benefits of FHIR standardization. With the development of various case-specific implementation guides using the Korea Core implementation guide, the FHIR standards will be distributed nationwide, and more shared interoperable health care services will be introduced in Korea.

An Artificial Intelligence-Driven Digital Health Solution to Support Clinical Management of Patients With Long COVID-19: Protocol for a Prospective Multicenter Observational Study.

BackgroundCOVID-19 pandemic has revealed the weaknesses of most health systems around the world, collapsing them and depleting their available health care resources. Fortunately, the development and enforcement of specific public health policies, such as vaccination, mask wearing, and social distancing, among others, has reduced the prevalence and complications associated with COVID-19 in its acute phase. However, the aftermath of the global pandemic has called for an efficient approach to manage patients with long COVID-19. This is a great opportunity to leverage on innovative digital health solutions to provide exhausted health care systems with the most cost-effective and efficient tools available to support the clinical management of this population. In this context, the SENSING-AI project is focused on the research toward the implementation of an artificial intelligence–driven digital health solution that supports both the adaptive self-management of people living with long COVID-19 and the health care staff in charge of the management and follow-up of this population.ObjectiveThe objective of this protocol is the prospective collection of psychometric and biometric data from 10 patients for training algorithms and prediction models to complement the SENSING-AI cohort.MethodsPublicly available health and lifestyle data registries will be consulted and complemented with a retrospective cohort of anonymized data collected from clinical information of patients diagnosed with long COVID-19. Furthermore, a prospective patient-generated data set will be captured using wearable devices and validated patient-reported outcomes questionnaires to complement the retrospective cohort. Finally, the ‘Findability, Accessibility, Interoperability, and Reuse’ guiding principles for scientific data management and stewardship will be applied to the resulting data set to encourage the continuous process of discovery, evaluation, and reuse of information for the research community at large.ResultsThe SENSING-AI cohort is expected to be completed during 2022. It is expected that sufficient data will be obtained to generate artificial intelligence models based on behavior change and mental well-being techniques to improve patients’ self-management, while providing useful and timely clinical decision support services to health care professionals based on risk stratification models and early detection of exacerbations.ConclusionsSENSING-AI focuses on obtaining high-quality data of patients with long COVID-19 during their daily life. Supporting these patients is of paramount importance in the current pandemic situation, including supporting their health care professionals in a cost-effective and efficient management of long COVID-19.Trial RegistrationClinicaltrials.gov NCT05204615; https://clinicaltrials.gov/ct2/show/NCT05204615International Registered Report Identifier (IRRID)DERR1-10.2196/37704

NHS Scotland tenders for £66m pharmacogenomics decision support service

NHS Scotland is tendering for a £66m pharmacogenomics and pharmaceutical clinical decision support service to safely identify patients at greatest risk of harm from polypharmacy. The aim of the service is to enable prescribers to develop an efficient, safe and effective medicines regimen that is tailored to meet the patient’s changing needs, in order to […]

Challenges of Applying AI in Healthcare in India

With the advent of digitalization, upcoming technologies like Artificial Intelligence (AI) are being utilized by healthcare services to manage various healthcare services to mimic human cognitive functions. This technology is expected to bring about a massive change in healthcare. Patient management, clinical decision support, patient tracking, and health care services are the four main AI-enabled fields of the healthcare industry. The method carrying out the study was based on secondary research by the themes of the studies performed earlier using Artificial intelligence in healthcare sector, through observations, interviews and valid documentations from prominent databases, by means of challenges and its analysis and the last by the issues associated with the study and the target groups are the front line workers in healthcare sectors. The AI applications in health care have gathered much attention, but AI's adoption issues have not been significantly tended. There are several challenges of its implementation, such as resolving the unequal relationship between trained physicians and patients and increasing physicians' efficiency to be more effective in their work; providing AI-enabled healthcare equipment in rural communities; and educating physicians or doctors in handling it. AI technologies have the potential to enhance patient outcomes. Still, they may also pose significant risks in terms of inadequate patient risk assessment, medical error, and suggestions for treatment, privacy violations, and others.

Web Services and Cloud Computing in Pediatric Care.

Electronic health record (EHR) systems do not uniformly implement pediatric-supportive functionalities. One method of adding these capabilities across EHR platforms is to integrate Web services and Web applications that may perform decision support and store data in the cloud when the EHR platform is able to integrate Web services. Specific examples of these services are described, such as immunization clinical decision support services, consumer health resources, and bilirubin nomograms. Health care providers, EHR vendors, and developers share responsibilities in the appropriate development, integration, and use of Web services and Web applications as they relate to best practices in the areas of data security and confidentiality, technical availability, audit trails, terminology and messaging standards, compliance with the Health Insurance Portability and Accountability Act, testing, usability, and other considerations. It is desirable for health care providers to have knowledge of Web services and Web applications that can improve pediatric capabilities in their own EHRs because this will naturally inform discussions concerning EHR features and facilitate implementation and subsequent use of these capabilities by clinicians caring for children.

Exploring Methods of Implementing Arden Syntax for CDS Hooks.

Specifications for Arden Syntax lack provisions for the standardized access of clinical decision support (CDS) services. The CDS Hooks standard provides such access. To extend an ArdenSuite reference implementation of the Arden Syntax by providing a CDS-Hooks-compatible interface. With the use case Hepaxpert, an Arden-Syntax-based expert system for the interpretation of hepatitis serology test results, a needs analysis was performed to identify changes required in the ArdenSuite reference implementation to support the CDS Hooks API. Arden Syntax language support for CDS Hooks was also assessed. The needs assessment was performed in three phases: hook assessment, hook context definition, and Card definition. For the use case, the ArdenSuite was modified to include a new hook and hook context, which defines the type of CDS service as well its input parameters. Card definitions were created in the ArdenSuite. Examples of Arden Syntax support for the use case are presented for all three phases. Minor changes in the ArdenSuite made it compatible with the CDS Hooks specification.

Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support.

This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models.

Responses of Staff Nurses to an EMR-Based Clinical Decision Support Service for Predicting Inpatient Fall Risk.

Wide spread of electronic medical records provide an opportunity to use time-variant longuitudinal data near real time. Hospital nurses would benefit greatly from the ability to use such data to predict adverse event risks of individual patient. We have developed an clinical decision support service to predict inpatient falling using machine learning and clinical big data approach. This study reports the initial responses of nurses to the service in an acute care setting.

Strategies for Clinical Decision Support for Electronic Case Reporting

ObjectiveTo discuss how clinical decision support (CDS) for electronic case reporting (eCR) will evolve over time to provide multiple deployment modelsIntroductionAs the knowledge required to support case reporting evolves from unstructured to more structured and standardized formats, it becomes suitable for electronic clinical decision support (CDS). CDS for case reporting confronts two challenges: a) While EHRs are moving toward local CDS capabilities, it will take several years for EHR systems to consistently support this capability; and b) public health-related CDS knowledge, such as Zika infection detection and reporting rules, may differ from jurisdiction to jurisdiction. Therefore, there is an ongoing need to manage reporting rules in a distributed manner. Similarly, there is a need for more decentralized models of CDS execution to overcome some of the disadvantages of centralized deployment and to leverage local CDS capabilities as they become available over the next several years.MethodsThe Reportable Condition Knowledge Management System (RCKMS) is a project funded by the CDC, through the Council of State and Territorial Epidemiologists (CSTE), to develop a tool that allows jurisdictions to author rules that define whether a patient is reportable for certain conditions. RCKMS includes a Decision Support Service (DSS) that runs the jurisdictions’ rules and determines if a patient is reportable, for which condition(s) and to which jurisdiction(s). RCKMS currently plays a significant role in the broader Digital Bridge project that has been working to provide structure and governance around the national planning and implementation effort of eCR. RCKMS is currently a centralized CDS service that can be accessed by EHRs until they all have local CDS capabilities; and a knowledge authoring environment that allows ongoing distributed rule authoring. RCKMS supports the strategy for public health knowledge management, and it will evolve over time to provide the systems and services to satisfy short-, mid-, and long-term public health CDS requirements. In addition, RCKMS will comply with emerging technical standards that support this work.ResultsRCKMS is currently being deployed as a single, central, national service on the APHL Informatics Messaging Services (AIMS) platform, which is operated and maintained by Association of Public Health Laboratories (APHL). The AIMS platform connects directly with reporters and provides a routing and validation service for incoming and outgoing messages. Two distributed CDS scenarios for decentralized eCR models have been identified. In the first scenario, the Decision Support Service component of the RCKMS software is installed within a clinical organization (as it would be in a centralized service) and executed locally. The second distributed CDS scenario for eCR involves the distribution of the Reporting Specification rules without the software. In this scenario, local electronic health record (EHR) implementations would be required to consume the Reporting Specifications and utilize them in a CDS capability within their EHR.ConclusionsIt is expected that given the diversity of organizations, systems, and architectures in the United States that multiple deployment scenarios for CDS for eCR will be simultaneously deployed for the foreseeable future. It cannot be stressed enough, however, that in all scenarios – centralized and distributed – there must be a centralized and uniform authoring of the Reporting Specification rules, since the specifications themselves originate from public health through a centralized process and must be administered nationally through a well-established process. It is also essential that all sites have all the rules available to them, since there may be multiple jurisdictions whose rules and reporting is required, with the determination of jurisdiction(s) based on where a patient lives and where they receive care.

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.

A Pharmacogenomics Clinical Decision Support Service Based on FHIR and CDS Hooks.

Pharmacogenomics (PGx) is often considered a low-hanging fruit for genomics-electronic health record (EHR) integrations, and many have expressed the notion that drug-gene interaction checking might one day become as much a commodity in EHRs as drug-drug and drug-allergy checking. In addition, the U.S. Office of the National Coordinator has recognized the trend toward storing complete sequencing data outside the EHR in a Genomic Archiving and Communication System (GACS) and has emphasized the need for "pilots that test Fast Healthcare Interoperability Resources (FHIR) Genomics for GACS integration with EHRs." We sought to develop a PGx clinical decision support (CDS) service, leveraging the emerging FHIR and CDS Hooks standards, and based on an assumption that pharmacogene sequencing data would be stored alongside the EHR in a GACS. We developed a PGx CDS service as a functional prototype. The service is triggered by a medication order in the EHR. When evoked, the service looks for relevant genetic data in a GACS and returns corresponding recommendations back to the ordering clinician. Where the patient has no genetic data on file, the service can recommend pretreatment genetic testing where applicable. Overall, we were able to meet our objectives and deploy a functional prototype, interfaced with a commercial EHR. We identified several areas where FHIR or CDS Hooks lacked necessary semantics or have implementation ambiguity. Primary FHIR challenges included multiple ways to say the same thing, which exacerbated the complexity of variant to allele conversion and lack of representation of deoxyribonucleic acid region(s) studied. Primary CDS Hooks challenges included the complexity of executing an authenticated query against one system (GACS) upon being triggered by a different system (the EHR), and limitations in the types of actionable recommendations that can be returned to the EHR. In conclusion, we have found that PGx CDS based on FHIR and CDS Hooks appears to represent a promising means of genomics-EHR integration. More real-world testing along with a set of use-case driven GACS interface requirements will push us closer to the U.S. National Human Genome Research Institute vision of a plug-in PGx app.

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.

Personalised Care Plan Management Utilizing Guideline-Driven Clinical Decision Support Systems.

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.

How to integrate drug Information: Korea Drug Utilization Review System

Introduction: Korea adopts a National Health Insurance System (NHIS). Under the NHIS, one of the biggest challenges is to find a way to provide affordable, yet high-quality healthcare services to citizens. This report introduces the process of establishing Korea's drug utilization review service and discusses lessons learned from the achievements. Short description of practice change implemented: The NHIS of Korea where there is no concept of family doctor separates doctor's prescription and pharmacist's dispensing. In the clinical setting, the doctor should confirm patient's medication history during consultation of the patient. But, it is difficult to confirm the characteristics and precautions of medicines with statements of patients (non-experts) only. Aim and theory of change: By integrating the medication history of the patient, physicians receive warnings in real time, upon entry of drug orders, if a risk factor to the patient is detected. It is a form of online clinical decision support. Target population and stakeholders: The whole population and approximately 80,000 healthcare providers. Timeline: - 2008: Safety checks on medicines in a single prescription - 2009: Safety checks on medicines in two or more prescriptions issued by a single provider - 2010: Safety checks of medicines in two or more prescriptions issued by multiple providers. - 2015: Enforcement through an amendment of related law Highlights: Saved over 17.96 million USD dollars per year and prevented 5.4 million cases of unsafe use of drugs in prescription. Comments on sustainability: Patient safety improvements and NHIS budget savings Comments on transferability: Reliable broadband network communication and stakeholders’ cooperation are needed. Conclusions: If information technology is used appropriately to realize integrated medical care, clinical decision support services can achieve better outcomes in more cost effective manner. Discussion: More applicable fields should be identified.