Distributed application of guideline-based decision support through mobile devices: Implementation and evaluation

Erez Shalom, Ayelet Goldstein, Elior Ariel, Moshe Sheinberger, Valerie Jones, Boris Van Schooten, Yuval Shahar

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Traditionally guideline (GL)-based Decision Support Systems (DSSs) use a centralized infrastructure to generate recommendations to care providers, rather than to patients at home. However, managing patients at home is often preferable, reducing costs and empowering patients. Thus, we wanted to explore an option in which patients, in particular chronic patients, might be assisted by a local DSS, which interacts as needed with the central DSS engine, to manage their disease outside the standard clinical settings. Objectives: To design, implement, and demonstrate the technical and clinical feasibility of a new architecture for a distributed DSS that provides patients with evidence-based guidance, offered through applications running on the patients' mobile devices, monitoring and reacting to changes in the patient's personal environment, and providing the patients with appropriate GL-based alerts and personalized recommendations; and increase the overall robustness of the distributed application of the GL. Methods: We have designed and implemented a novel projection–callback (PCB) model, in which small portions of the evidence-based guideline's procedural knowledge are projected from a projection engine within the central DSS server, to a local DSS that resides on each patient's mobile device. The local DSS applies the knowledge using the mobile device's local resources. The GL projections generated by the projection engine are adapted to the patient's previously defined preferences and, implicitly, to the patient's current context, in a manner that is embodied in the projected therapy plans. When appropriate, as defined by a temporal pattern within the projected plan, the local DSS calls back the central DSS, requesting further assistance, possibly another projection. To support the new model, the initial specification of the GL includes two levels: one for the central DSS, and one for the local DSS. We have implemented a distributed GL-based DSS using the projection–callback model within the MobiGuide EU project, which automatically manages chronic patients at home using sensors on the patients and their mobile phone. We assessed the new GL specification process, by specifying two very different, complex GLs: for Gestational Diabetes Mellitus, and for Atrial Fibrillation. Then, we evaluated the new computational architecture by applying the two GLs to the automated clinical management, at real time, of patients in two different countries: Spain and Italy, respectively. Results: The specification using the new projection-callback model was found to be quite feasible. We found significant differences between the distributed versions of the two GLs, suggesting further research directions and possibly additional ways to analyze and characterize GLs. Applying the two GLs to the two patient populations proved highly feasible as well. The mean time between the central and local interactions was quite different for the two GLs: 3.95 ± 1.95 days in the case of the gestational diabetes domain, and 23.80 ± 12.47 days, in the case of the atrial fibrillation domain, probably corresponding to the difference in the distributed specifications of the two GLs. Most of the interaction types were due to projections to the local DSS (83%); others were data notifications, mostly to change context (17%). Some of the data notifications were triggered due to technical errors. The robustness of the distributed architecture was demonstrated through the successful recovery from multiple crashes of the local DSS. Conclusions: The new projection-callback model has been demonstrated to be feasible, from specification to distributed application. Different GLs might significantly differ, however, in their distributed specification and application characteristics. Distributed medical DSSs can facilitate the remote management of chronic patients by enabling the central DSSs to delegate, in a dynamic fashion, determined by the patient's context, much of the monitoring and treatment management decisions to the mobile device. Patients can be kept in their home environment, while still maintaining, through the projection-callback mechanism, several of the advantages of a central DSS, such as access to the patient's longitudinal record, and to an up-to-date evidence-based GL repository.

Original languageEnglish
Article number102324
JournalArtificial Intelligence in Medicine
Volume129
DOIs
StatePublished - 1 Jul 2022

Keywords

  • Clinical decision support system
  • Clinical guidelines
  • Distributed computing
  • Knowledge engineering

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

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