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

Glachs, Dietmar; Namlı, Tuncay; Strohmeier, Felix; Suárez, Gustavo Rodríguez; Sluis, M.T. van der; Delgado-Lista, Javier; Sont, Jacob K.; Graaf, Albert A. de; Salzsieder, Eckhard; Vogt, Lutz

doi:10.3233/shti210321

Cited by 6 publications

(5 citation statements)

References 12 publications

(12 reference statements)

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“…The system has two components: the web-based shared decision-making dashboard (SDMD) (ESM Fig. 1 ), used by individuals together with healthcare professionals during clinical consultations, and a self-management support system (SMSS) [ 23 ] that is available as a mobile application (ESM Fig. 2 ) and webpage (ESM Fig.…”

Section: Methodsmentioning

confidence: 99%

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

Ruissen,

Torres-Peña,

Uitbeijerse

et al. 2023

Diabetologia

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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). Graphical Abstract

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Section: Methodsmentioning

confidence: 99%

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

Ruissen,

Torres-Peña,

Uitbeijerse

et al. 2023

Diabetologia

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“…A team of experts from the Netherlands Organization for Applied Scientific Research (TNO), representing multidisciplinary research fields, and health care professionals acted as an advisory committee in the iterative process of the development of the tool. For each domain, the potential list of parameters and elements was drafted by 3 experts in the field using previously developed tools in the Primus project [ 18 ] and Power2DM project [ 17 ], a literature review, and expert consultation. Thereafter, 3 other experts from the advisory committee reviewed and complemented the list.…”

Section: Methodsmentioning

confidence: 99%

“…To develop a tool that encompasses a holistic approach, including comprehensive health status assessments that are subsequently discussed between patient and health care professionals and followed up by interventions, if necessary, we were inspired by the web-based tools of the Power2DM project [ 17 ] and the Primus project [ 18 ]. The goal of the Power2DM project [ 17 ] was to support patients with diabetes by developing a self-management support system and a shared decision-making app for health care professionals and patients. The goal of the Primus project [ 18 ] was to improve the health and quality of life of people aged 55-74 years.…”

Section: Introductionmentioning

confidence: 99%

360° Diagnostic Tool to Personalize Lifestyle Advice in Primary Care for People With Type 2 Diabetes: Development and Usability Study

Harakeh¹,

Hoogh²,

Keulen³

et al. 2023

JMIR Form Res

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Background Various multifaceted factors need to be addressed to improve the health and quality of life of people with type 2 diabetes (T2D). Therefore, we developed a web-based decision support tool that comprises a more holistic diagnosis (including 4 domains: body, thinking and feeling, behavior, and environment) and personalized advice. This 360° diagnostic tool enables people with T2D and health care professionals at the general practice to obtain an overview of the most important T2D-related issues and, subsequently, determine the most suitable intervention for the person with T2D. Objective This study aimed to describe the systematic and iterative development and evaluation of the web-based 360° diagnostic tool. Methods We defined the requirements for the web-based 360° diagnostic tool based on previously developed tools, a literature review, and inputs from a multidisciplinary team of experts. As part of the conceptualization, we defined 3 requirements: diagnostics; feedback; and advice, consultation, and follow-up. Next, we developed and designed the content for each of these requirements. We evaluated the diagnostic part of the tool (ie, measurement instruments and visualization) with a qualitative design, in a usability study with a think-aloud strategy and interview questions, among 8 people with T2D at a Dutch general practice. Results For each of the 4 domains, specific parameters and underlying elements were selected, and measurement instruments (including clinical data and questionnaires) were chosen. Cutoff values were defined to identify high-, middle-, and low-ranking scores, and decision rules were developed and implemented using R scripts and algorithms. A traffic light color visual design was created (profile wheel) to provide an overview of the scores per domain. We mapped the interventions that could be added to the tool and developed a protocol designed as a card deck with motivational interview steps. Furthermore, the usability study showed that people with T2D perceived the tool as easy to use, useful, easy to understand, and insightful. Conclusions Preliminary evaluation of the 360° diagnostic tool by experts, health care professionals, and people with T2D showed that the tool was considered relevant, clear, and practical. The iterative process provided insights into the areas of improvement, which were implemented. The strengths, shortcomings, future use, and challenges are also discussed.

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“…Table 1 lists the typical profile definitions and the corresponding FHIR foundational resources discussed in the reviewed articles. As shown, these articles cover a wide range of categories, including genomics [10][11][12][13][14], imaging [15][16][17], cancer [18][19][20], diabetes [21,22], COVID-19 [23,24], infections [25], electrocardiography [26], screening [27], and allergy [28].…”

Section: Data Standardizationmentioning

confidence: 99%

Designing Interoperable Health Care Services Based on Fast Healthcare Interoperability Resources: Literature Review

Nan¹,

Xu²

2023

JMIR Med Inform

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Background With the advent of the digital economy and the aging population, the demand for diversified health care services and innovative care delivery models has been overwhelming. This trend has accelerated the urgency to implement effective and efficient data exchange and service interoperability, which underpins coordinated care services among tiered health care institutions, improves the quality of oversight of regulators, and provides vast and comprehensive data collection to support clinical medicine and health economics research, thus improving the overall service quality and patient satisfaction. To meet this demand and facilitate the interoperability of IT systems of stakeholders, after years of preparation, Health Level 7 formally introduced, in 2014, the Fast Healthcare Interoperability Resources (FHIR) standard. It has since continued to evolve. FHIR depends on the Implementation Guide (IG) to ensure feasibility and consistency while developing an interoperable health care service. The IG defines rules with associated documentation on how FHIR resources are used to tackle a particular problem. However, a gap remains between IGs and the process of building actual services because IGs are rules without specifying concrete methods, procedures, or tools. Thus, stakeholders may feel it nontrivial to participate in the ecosystem, giving rise to the need for a more actionable practice guideline (PG) for promoting FHIR’s fast adoption. Objective This study aimed to propose a general FHIR PG to facilitate stakeholders in the health care ecosystem to understand FHIR and quickly develop interoperable health care services. Methods We selected a collection of FHIR-related papers about the latest studies or use cases on designing and building FHIR-based interoperable health care services and tagged each use case as belonging to 1 of the 3 dominant innovation feature groups that are also associated with practice stages, that is, data standardization, data management, and data integration. Next, we reviewed each group’s detailed process and key techniques to build respective care services and collate a complete FHIR PG. Finally, as an example, we arbitrarily selected a use case outside the scope of the reviewed papers and mapped it back to the FHIR PG to demonstrate the effectiveness and generalizability of the PG. Results The FHIR PG includes 2 core elements: one is a practice design that defines the responsibilities of stakeholders and outlines the complete procedure from data to services, and the other is a development architecture for practice design, which lists the available tools for each practice step and provides direct and actionable recommendations. Conclusions The FHIR PG can bridge the gap between IGs and the process of building actual services by proposing actionable methods, procedures, and tools. It assists stakeholders in identifying participants’ roles, managing the scope of responsibilities, and developing relevant modules, thus helping promote FHIR-based interoperable health care services.

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

Cited by 6 publications

References 12 publications

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

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

360° Diagnostic Tool to Personalize Lifestyle Advice in Primary Care for People With Type 2 Diabetes: Development and Usability Study

Designing Interoperable Health Care Services Based on Fast Healthcare Interoperability Resources: Literature Review

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