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

BACKGROUND: 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). OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Clinical impact of an integrated e-health system for diabetes self-management support and shared decision making (POWER2DM): a randomised controlled trial.

AIMS/HYPOTHESIS: There is a lack of e-health systems that integrate the complex variety of aspects relevant for diabetes self-management. We developed and field-tested an e-health system (POWER2DM) that integrates medical, psychological and behavioural aspects and connected wearables to support patients and healthcare professionals in shared decision making and diabetes self-management. METHODS: Participants with type 1 or type 2 diabetes (aged >18 years) from hospital outpatient diabetes clinics in the Netherlands and Spain were randomised using randomisation software to POWER2DM or usual care for 37 weeks. This RCT assessed the change in HbA1c between the POWER2DM and usual care groups at the end of the study (37 weeks) as a primary outcome measure. Participants and clinicians were not blinded to the intervention. Changes in quality of life (QoL) (WHO-5 Well-Being Index [WHO-5]), diabetes self-management (Diabetes Self-Management Questionnaire - Revised [DSMQ-R]), glycaemic profiles from continuous glucose monitoring devices, awareness of hypoglycaemia (Clarke hypoglycaemia unawareness instrument), incidence of hypoglycaemic episodes and technology acceptance were secondary outcome measures. Additionally, sub-analyses were performed for participants with type 1 and type 2 diabetes separately. RESULTS: A total of 226 participants participated in the trial (108 with type 1 diabetes; 118 with type 2 diabetes). In the POWER2DM group (n=111), HbA1c decreased from 60.6±14.7 mmol/mol (7.7±1.3%) to 56.7±12.1 mmol/mol (7.3±1.1%) (means ± SD, p<0.001), compared with no change in the usual care group (n=115) (baseline: 61.7±13.7 mmol/mol, 7.8±1.3%; end of study: 61.0±12.4 mmol/mol, 7.7±1.1%; p=0.19) (between-group difference 0.24%, p=0.008). In the sub-analyses in the POWER2DM group, HbA1c in participants with type 2 diabetes decreased from 62.3±17.3 mmol/mol (7.9±1.6%) to 54.3±11.1 mmol/mol (7.1±1.0%) (p<0.001) compared with no change in HbA1c in participants with type 1 diabetes (baseline: 58.8±11.2 mmol/mol [7.5±1.0%]; end of study: 59.2±12.7 mmol/mol [7.6±1.2%]; p=0.84). There was an increase in the time during which interstitial glucose levels were between 3.0 and 3.9 mmol/l in the POWER2DM group, but no increase in clinically relevant hypoglycaemia (interstitial glucose level below 3.0 mmol/l). QoL improved in participants with type 1 diabetes in the POWER2DM group compared with the usual care group (baseline: 15.7±3.8; end of study: 16.3±3.5; p=0.047 for between-group difference). Diabetes self-management improved in both participants with type 1 diabetes (from 7.3±1.2 to 7.7±1.2; p=0.002) and those with type 2 diabetes (from 6.5±1.3 to 6.7±1.3; p=0.003) within the POWER2DM group. The POWER2DM integrated e-health support was well accepted in daily life and no important adverse (or unexpected) effects or side effects were observed. CONCLUSIONS/INTERPRETATION: POWER2DM improves HbA1c levels compared with usual care in those with type 2 diabetes, improves QoL in those with type 1 diabetes, improves diabetes self-management in those with type 1 and type 2 diabetes, and is well accepted in daily life. TRIAL REGISTRATION: ClinicalTrials.gov NCT03588104. FUNDING: This study was funded by the European Union's Horizon 2020 Research and Innovation Programme (grant agreement number 689444).

A Predictive Model-Based Decision Support System for Diabetes Patient Empowerment.

The main objective of POWER2DM is to develop and validate a personalized self-management support system (SMSS) for T1 and T2 diabetes patients that combines and integrates i) a decision support system (DSS) based on leading European predictive personalized models for diabetes interlinked with predictive computer models, ii) automated e-coaching functionalities based on Behavioral Change Theories, and iii) real-time Personal Data processing and interpretation. The SMSS offers a guided workflow based on treatment goals and activities where a periodic review evaluates the patients progress and provides detailed feedback on how to improve towards a healthier, diabetes appropriate lifestyle.

A reinforcement learning based algorithm for personalization of digital, just-in-time, adaptive interventions.

Suboptimal health related behaviors and habits; and resulting chronic diseases are responsible for majority of deaths globally. Studies show that providing personalized support to patients yield improved results by preventing and/or timely treatment of these problems. Digital, just-in-time and adaptive interventions are mobile phone-based notifications that are being utilized to support people wherever and whenever necessary in coping with their health problems. In this research, we propose a reinforcement learning-based mechanism to personalize interventions in terms of timing, frequency and preferred type(s). We simultaneously employ two reinforcement learning models, namely intervention-selection and opportune-moment-identification; capturing and exploiting changes in people's long-term and momentary contexts respectively. While the intervention-selection model adapts the intervention delivery with respect to type and frequency, the opportune-moment-identification model tries to find the most opportune moments to deliver interventions throughout a day. We propose two accelerator techniques over the standard reinforcement learning algorithms to boost learning performance. First, we propose a customized version of eligibility traces for rewarding past actions throughout an agent's trajectory. Second, we utilize the transfer learning method to reuse knowledge across multiple learning environments. We validate the proposed approach in a simulated experiment where we simulate four personas differing in their daily activities, preferences on specific intervention types and attitudes towards the targeted behavior. Our experiments show that the proposed approach yields better results compared to the standard reinforcement learning algorithms and successfully capture the simulated variations associated with the personas.

FHIR Driven Self-Management Support System for Diabetes.

The number of people with diabetes is increasing in every European country and as with all chronic diseases, coping with diabetes is a long-term process. Self-Management and supporting behaviour change are aspects when dealing with diabetes. The POWER2DM (Funded by the EU Horizon 2020 research and innovation programme under grant agreement No 689444) system combines treatment planning and self-management activities by providing interventions to change a patient's lifestyle towards a healthier, diabetes-appropriate life. FHIR3 allows for data exchange.

An expandable approach for design and personalization of digital, just-in-time adaptive interventions.

Objective: We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people's individual needs, momentary contexts, and psychosocial variables. Materials and Methods: We propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions. Results: We evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns. Conclusion: While the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.

An interoperability test framework for HL7-based systems.

Health Level Seven (HL7) is a prominent messaging standard in the eHealth domain, and with HL7 v2, it addresses only the messaging layer. However, HL7 implementations also deal with the other layers of interoperability, namely the business process layer and the communication layer. This need is addressed in HL7 v3 by providing a number of normative transport specification profiles. Furthermore, there are storyboards describing HL7 v3 message choreographies between specific roles in specific events. Having alternative transport protocols and descriptive message choreographies introduces great flexibility in implementing HL7 standards, yet, this brings in the need for test frameworks that can accommodate different protocols and permit the dynamic definition of test scenarios. In this paper, we describe a complete test execution framework for HL7-based systems that provides high-level constructs allowing dynamic set up of test scenarios involving all the layers in the interoperability stack. The computer-interpretable test description language developed offers a configurable system with pluggable adaptors. The Web-based GUIs make it possible to test systems over the Web anytime, anywhere, and with any party willing to do so.

Collaborative business process support in eHealth: integrating IHE profiles through ebXML business process specification language.

Integrating healthcare enterprise (IHE) specifies integration profiles describing selected real world use cases to facilitate the interoperability of healthcare information resources. While realizing a complex real-world scenario, IHE profiles are combined by grouping the related IHE actors. Grouping IHE actors implies that the associated business processes (IHE profiles) that the actors are involved must be combined, that is, the choreography of the resulting collaborative business process must be determined by deciding on the execution sequence of transactions coming from different profiles. There are many IHE profiles and each user or vendor may support a different set of IHE profiles that fits to its business need. However, determining the precedence of all the involved transactions manually for each possible combination of the profiles is a very tedious task. In this paper, we describe how to obtain the overall business process automatically when IHE actors are grouped. For this purpose, we represent the IHE profiles through a standard, machine-processable language, namely, Organization for the Advancement of Structured Information Standards (OASIS) ebusiness eXtensible Markup Language (ebXML) Business Process Specification (ebBP) Language. We define the precedence rules among the transactions of the IHE profiles, again, in a machine-processable way. Then, through a graphical tool, we allow users to select the actors to be grouped and automatically produce the overall business process in a machine-processable format.