Evaluation of a Digital Decision Support System to Integrate Type 2 Diabetes Mellitus and Periodontitis Care: Case-Vignette Study in Simulated Environments.

BACKGROUND: As highlighted by the recent World Health Organization Oral Health Resolution, there is an urgent need to better integrate primary and oral health care. Despite evidence and guidelines substantiating the relevance of integrating type 2 diabetes mellitus (T2DM) and periodontitis care, the fragmentation of primary and oral health care persists. OBJECTIVE: This paper reports on the evaluation of a prototype digital decision support system (DSS) that was developed to enhance the integration of T2DM and periodontitis care. METHODS: The effects of the prototype DSS were assessed in web-based simulated environments, using 2 different sets of case vignettes in combination with evaluation surveys among 202 general dental practitioners (GDPs) and 206 general practitioners (GPs). Each participant evaluated 3 vignettes, one of which, chosen at random, was assisted by the DSS. Logistic regression analyses were conducted at the participant and case levels. RESULTS: Under DSS assistance, GPs had 8.3 (95% CI 4.32-16.03) times higher odds of recommending a GDP visit. There was no significant impact of DSS assistance on GP advice about common risk factors for T2DM and periodontal disease. GDPs had 4.3 (95% CI 2.08-9.04) times higher odds of recommending a GP visit, 1.6 (95% CI 1.03-2.33) times higher odds of giving advice on disease correlations, and 3.2 (95% CI 1.63-6.35) times higher odds of asking patients about their glycated hemoglobin value. CONCLUSIONS: The findings of this study provide a proof of concept for a digital DSS to integrate T2DM and periodontal care. Future updating and testing is warranted to continuously enhance the functionalities of the DSS in terms of interoperability with various types of data sources and diagnostic devices; incorporation of other (oral) health dimensions; application in various settings, including via telemedicine; and further customization of end-user interfaces.

Development and evaluation of a mobile patient application to enhance medical-dental integration for the treatment of periodontitis and diabetes.

INTRODUCTION: People around the world are increasingly affected by multimorbidity, where conditions in different medical specialties can correlate in complex ways. This increases the relevance of multidisciplinary integrated care pathways. Modern software solutions provide vast opportunities to enhance information exchange between patients and various healthcare professionals, thereby improving patient-centered and inter-professional care. This paper describes the development and validation of a mobile patient application which exploits Patient Reported Outcomes to enhance patient-centered medical-dental integration with a focus on integrated management of periodontitis and diabetes. METHODS: This study was part of a multidisciplinary project for enhancement of medical-dental integration. The Intervention Mapping Protocol was supplemented by the RAND/UCLA Appropriateness Method, including literature reviews, focus group discussions and a Delphi panel in cooperation with various stakeholders. A mobile application was developed in close collaboration with patients, physicians and dentists. The usability of the application's core components was validated in two medical and two dental practices using the System Usability Scale (SUS). RESULTS: 39 questions were identified to provide relevant patient-reported information which can be collected via a mobile application to enhance integrated management of periodontitis and diabetes. Usability testing of the application's core components (14 questions) among 137 participants in medical and dental practices indicated a good SUS score of 77.88 (±12.17). DISCUSSION: The systematically developed mobile application offers the potential to provide physicians and dentists with treatment-relevant information to enhance medical-dental integration, thereby reducing the workload of medical staff, improving the quality of routinely collected data, and enabling automated data processing. This unique, novel, and validated approach can serve as an open framework for the development and evaluation of interdisciplinary healthcare software.

Current Knowledge on Correlations Between Highly Prevalent Dental Conditions and Chronic Diseases: An Umbrella Review.

INTRODUCTION: Studies have investigated the relationships between chronic systemic and dental conditions, but it remains unclear how such knowledge can be used in clinical practice. In this article, we provide an overview of existing systematic reviews, identifying and evaluating the most frequently reported dental-chronic disease correlations and common risk factors. METHODS: We conducted a systematic review of existing systematic reviews (umbrella review) published between 1995 and 2017 and indexed in 4 databases. We focused on the 3 most prevalent dental conditions and 10 chronic systemic diseases with the highest burden of disease in Germany. Two independent reviewers assessed all articles for eligibility and methodologic quality using the AMSTAR criteria and extracted data from the included studies. RESULTS: Of the initially identified 1,249 systematic reviews, 32 were included for qualitative synthesis. The dental condition with most frequently observed correlations to chronic systemic diseases was periodontitis. The chronic systemic disease with the most frequently observed correlations with a dental condition was type 2 diabetes mellitus (T2DM). Most dental-chronic disease correlations were found between periodontitis and T2DM and periodontitis and cardiovascular disease. Frequently reported common risk factors were smoking, age, sex, and overweight. Using the AMSTAR criteria, 2 studies were assessed as low quality, 26 studies as moderate quality, and 4 studies as high quality. CONCLUSION: The quality of included systematic reviews was heterogeneous. The most frequently reported correlations were found for periodontitis with T2DM and for periodontitis with cardiovascular disease. However, the strength of evidence for these and other disease correlations is limited, and the evidence to assess the causality of these disease correlations remains unclear. Future research should focus on the causality of disease links in order to provide more decisive evidence with respect to the design of intersectoral care processes.

A Mobile Application for Patient Engagement to Support Interdisciplinary Care.

There is evidence on interrelationships between several dental and chronic diseases. However, dentists and general practitioners often lack information when treating such multimorbid patients. Engagement of the patient in the decision making process may help to fill this gap and improve intersectoral care. The Dent@Prevent project therefore aims to develop a mobile application that can be used by patients to report information about their health status for interdisciplinary care. In this paper, the user interface concept and evaluation of the prototype of this mobile application is presented.

Using FAIR Metadata for Secondary Use of Administrative Claims Data.

The FAIR principles require the reporting of rich metadata. However, when researchers use data for secondary use from external data owners, the FAIR principles require a different implementation as if the researchers would describe their own data. In this paper, we specify how FAIR metadata can be implemented for secondary data analyses and provide a suggestion for relevant metadata.

Detecting and Resolving Data Conflicts when Using International Claims Data for Research.

Using claims data for research is well established. However, most claims data analyses are focused on single countries. Multi-national approaches are scarce. The application of different anonymization techniques before data are shared for research as well as differences in the reimbursement systems hamper the use of claims data from multiple countries. This paper analyses data conflicts that occur when international claims data sets are used for research and develops a generic process to detect and resolve these conflicts. The approach was successfully applied in the EU-funded ADVOCATE (Added Value for Oral Care) project that acquired data from health insurance providers, health funds or health authorities in six European countries.

Approach Towards an Evidence-Oriented Knowledge and Data Acquisition for the Optimization of Interdisciplinary Care in Dentistry and General Medicine.

Associations between dental and chronic-systemic diseases were observed frequently in medical research, however the findings of this research have so far found little relevance in everyday clinical treatment. Major problems are the assessment of evidence for correlations between such diseases and how to integrate current medical knowledge into the intersectoral care of dentists and general practitioners. On the example of dental and chronic-systemic diseases, the Dent@Prevent project develops an interdisciplinary decision support system (DSS), which provides the specialists with information relevant for the treatment of such cases. To provide the physicians with relevant medical knowledge, a mixed-methods approach is developed to acquire the knowledge in an evidence-oriented way. This procedure includes a literature review, routine data analyses, focus groups of dentists and general practitioners as well as the identification and integration of applicable guidelines and Patient Reported Measures (PRMs) into the treatment process. The developed mixed methods approach for an evidence-oriented knowledge acquisition indicates to be applicable and supportable for interdisciplinary projects. It can raise the systematic quality of the knowledge-acquisition process and can be applicable for an evidence-based system development. Further research is necessary to assess the impact on patient care and to evaluate possible applicability in other interdisciplinary areas.

Systems Medicine for Multiple Myeloma: A Review on Decision Support Systems.

Systems medicine is a current approach trying to improve treatment for patients with complex diseases by analyzing as much phenotype and genotype data as possible for the disease in question. For individualized treatment decisions in clinical practice, this task has to be supported by an application system with decision support component. For a research project on systems medicine we reviewed methods for decision support. Criteria for selecting a method are derived from characteristics of the data and the diseases. They include, among others: dimensionality of data and existence of a priori models for diseases. As a result we decided to implement a prototype system with a case-based reasoning component for systems medicine on multiple myeloma.