User Modeling and User-Adapted Interaction

, Volume 27, Issue 2, pp 159–213 | Cite as

MobiGuide: a personalized and patient-centric decision-support system and its evaluation in the atrial fibrillation and gestational diabetes domains

  • Mor Peleg
  • Yuval Shahar
  • Silvana Quaglini
  • Adi Fux
  • Gema García-Sáez
  • Ayelet Goldstein
  • M. Elena Hernando
  • Denis Klimov
  • Iñaki Martínez-Sarriegui
  • Carlo Napolitano
  • Enea Parimbelli
  • Mercedes Rigla
  • Lucia Sacchi
  • Erez Shalom
  • Pnina Soffer
Article

Abstract

MobiGuide is a ubiquitous, distributed and personalized evidence-based decision-support system (DSS) used by patients and their care providers. Its central DSS applies computer-interpretable clinical guidelines (CIGs) to provide real-time patient-specific and personalized recommendations by matching CIG knowledge with a highly-adaptive patient model, the parameters of which are stored in a personal health record (PHR). The PHR integrates data from hospital medical records, mobile biosensors, data entered by patients, and recommendations and abstractions output by the DSS. CIGs are customized to consider the patients’ psycho-social context and their preferences; shared decision making is supported via decision trees instantiated with patient utilities. The central DSS “projects” personalized CIG-knowledge to a mobile DSS operating on the patients’ smart phones that applies that knowledge locally. In this paper we explain the knowledge elicitation and specification methodologies that we have developed for making CIGs patient-centered and enabling their personalization. We then demonstrate feasibility, in two very different clinical domains, and two different geographic sites, as part of a multi-national feasibility study, of the full architecture that we have designed and implemented. We analyze usage patterns and opinions collected via questionnaires of the 10 atrial fibrillation (AF) and 20 gestational diabetes mellitus (GDM) patients and their care providers. The analysis is guided by three hypotheses concerning the effect of the personal patient model on patients and clinicians’ behavior and on patients’ satisfaction. The results demonstrate the sustainable usage of the system by patients and their care providers and patients’ satisfaction, which stems mostly from their increased sense of safety. The system has affected the behavior of clinicians, which have inspected the patients’ models between scheduled visits, resulting in change of diagnosis for two of the ten AF patients and anticipated change in therapy for eleven of the twenty GDM patients.

Keywords

Computer-interpretable guidelines Decision-support system Clinical guidelines Patient centrality Personalization Mobile health 

References

  1. Boaz, D., Shahar, Y.: A framework for distributed mediation of temporal-abstraction queries to clinical databases. Artif. Intell. Med. 34(1), 3–24 (2005)CrossRefGoogle Scholar
  2. Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(suppl 1), D267–D270 (2004)CrossRefGoogle Scholar
  3. Camerini, L., Giacobazzi, M., Boneschi, M., Schulz, P.J., Rubinell, S.: Design and implementation of a web-based tailored gymnasium to enhance self-management of fibromyalgia. User Model. User-Adap. Inter. 21, 485–511 (2011)CrossRefGoogle Scholar
  4. Chittaro, L., Carchietti, E., De Marco, L., Zampa, A.: Personalized emergency medical assistance for disabled people. User Model. User-Adap. Inter. 21(4), 407–440 (2011)CrossRefGoogle Scholar
  5. Consumer Health Information Corporation Motivating Patients to Use Smartphone Health Apps. http://www.consumer-health.com/motivating-patients-to-use-smartphone-health-apps/ (2012)
  6. Fux, A., Peleg, M., Soffer, P.: How does personal information affect clinical decision making? Eliciting categories of personal context and effects. AMIA Symposium, 1741 (2012)Google Scholar
  7. García-Sáez, G., Rigla, M., Martínez-Sarriegui, I., Shalom, E., Peleg, M., Broens, T., Pons, B., Caballero-Ruíz, E., Gómez, E.J.: Elena Hernando, M.: Patient-oriented computerized clinical guidelines for mobile decision support in gestational diabetes. J. Diabetes Sci. Technol. 8(2), 238–246 (2014)CrossRefGoogle Scholar
  8. García-Sáez, G., Rigla, M., Shalom, E., Peleg, M., Caballero, E., Gómez, E J., Hernando, ME.: Parallel workflows to personalize clinical guidelines recommendations: application to gestational diabetes mellitus. 13th Mediterranean Conf on Medical and Biological Engineering and Computing, pp. 1409–1412 (2013)Google Scholar
  9. González-Ferrer, A., Peleg, M., Marcos, M., Maldonado, J.A.: Analysis of the process of representing clinical statements for decision-support applications: a comparison of openEHR archetypes and HL7 virtual medical record. J. Med. Syst. 40(7), 163–172 (2016)CrossRefGoogle Scholar
  10. Grandi, F.: Dynamic multi-version ontology-based personalization. J. Comput. Syst. Sci. 82(1), 69–90 (2016)MathSciNetCrossRefMATHGoogle Scholar
  11. Grandi, F., Mandreoli, F., Martoglia, R.: Efficient management of multi-version clinical guidelines. J. Biomed. Inform. 45(6), 1120–1136 (2012)CrossRefGoogle Scholar
  12. Kahneman, D., Tversky, A.: The simulation heuristic. In: Kahneman, A.J., Slovic, D., Tversky, P. (eds.) Judgment Under Uncertainty: Heuristics and Biases, pp. 201–208. Cambridge University Press, Cambridge (1982)CrossRefGoogle Scholar
  13. Lanzola, G., Parimbelli, E., Micieli, G., Cavallini, A., Quaglini, S.: Data quality and completeness in a web stroke registry as the basis for data and process mining. J. Healthc. Eng. 5(2), 163–184 (2014)CrossRefGoogle Scholar
  14. Lasierra, N., Alesanco, A., Guillén, S., García, J.: A three stage ontology-driven solution to provide personalized care to chronic patients at home. J. Biomed. Inform. 46(3), 516–529 (2013)CrossRefGoogle Scholar
  15. Lindgren, H.: Towards Personalized Decision Support in the Dementia Domain Based on Clinical Practice Guidelines. User Model. User-Adap. Inter. 21(4), 377–406 (2011)CrossRefGoogle Scholar
  16. Marcos, C., González-Ferrer, A., Peleg, M., Cavero, C.: Solving the interoperability challenge of a distributed complex patient guidance system: a data integrator based on HL7’s virtual medical record standard. J. Am. Med. Inform. Assoc. 22(3), 587–599 (2015)Google Scholar
  17. Martins, S., Shahar, Y., Goren-Bar, D., Galperin, M., Kaizer, H., et al.: Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data. Artif. Intell. Med. 43(1), 17–34 (2008)CrossRefGoogle Scholar
  18. Miksch, S., Shahar, Y., Johnson, P.: Asbru: A Task-Specific, Intention-Based, and Time-Oriented Language for Representing Skeletal Plans. In 7th Workshop on Knowledge Engineering: Methods & Languages, 1–25 (1997)Google Scholar
  19. MobiGuide Consorium.: Monitored Patterns, Notifications and Recommendations Used in the AF and GDM CIGs of MobiGuide. http://mis.hevra.haifa.ac.il/~morpeleg/MobiGuide_Patterns.pdf (2016)
  20. Parimbelli, E., Sacchi, L., Rubrichi, S., Mazzanti, A., Quaglini, S.: UceWeb: a web-based collaborative tool for collecting and sharing quality of life data. Methods Inf. Med. 54(2), 156–163 (2015)CrossRefGoogle Scholar
  21. Peleg, M.: Computer-interpretable clinical guidelines: a methodological review. J. Biomed. Inform. 46(4), 744–763 (2013)CrossRefGoogle Scholar
  22. Peleg, M., Gonzalez-Ferrer, A.: Chapter 16: guidelines and workflow models. In: Greenes, R.A. (ed.) Clinical Decision Support The Road to Broad Adoption, 2nd edn, pp. 435–464. Academic Press, New York (2014)CrossRefGoogle Scholar
  23. Peleg, M., Shahar, Y., Quaglini, S.: Making healthcare more accessible, better, faster, and cheaper: The mobiguide project. Eur. J. ePract. 20, 5–20 (2013)Google Scholar
  24. Peleg, M., Shahar, Y., Quaglini, S., Broens, T., Budasu, R., Fung, N., Fux, A., García-Sáez, G., Goldstein, A., González-Ferrer, A., Hermens, H., Elena Hernando, M., Jones, V., Klebanov, G., Klimov, D., Knoppel, D., Larburu, N., Marcos, C., Martínez-Sarriegui, I., Napolitano, C., Pallás, Á., Palomares, A., Parimbelli, E., Pons, B., Rigla, M., Sacchi, L., Shalom, E., Soffer, P., van Schooten, B.: Assessment of a personalized and distributed patient guidance system. Int. J. Med. Inform. (2017). doi:10.1016/j.ijmedinf.2017.02.010
  25. Peleg, M., Tu, S.W., Bury, J., Ciccarese, P., Fox, J., et al.: Comparing computer-interpretable guideline models: A case-study approach. J. Am. Med. Inform. Assoc. 10(1), 52–68 (2003)CrossRefGoogle Scholar
  26. Pitts, M.G., Browne, G.J.: Improving requirements elicitation: an empirical investigation of procedural prompts. Inform. Syst. J. 17(1), 89–110 (2007)CrossRefGoogle Scholar
  27. Quaglini, S., Miksch, S., Shahar, Y., Peleg, M., Peleg, M., Rigla, M., Napolitano, C., Pallàs, A., Parimbelli, E., Sacchi, L.: Supporting shared decision making within the MobiGuide Project. In AMIA Symposium, pp. 1175–1184 (2013)Google Scholar
  28. Riaño, D., Real, F., López-Vallverdú, J.A., Campana, F., Ercolani, S., et al.: An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients. J. Biomed. Inform. 45(3), 429–446 (2012)CrossRefGoogle Scholar
  29. Rubrichi, S., Rognoni, C., Sacchi, L., Parimbelli, E., Napolitano, C., Mazzanti, A., Quaglini, S.: Graphical representation of life paths to better convey results of decision models to patients. Med. Decis. Making 35(3), 398–402 (2015)CrossRefGoogle Scholar
  30. Sacchi, L., Fux, A., Napolitano, C., Panzarasa, S., Peleg, M., et al.: Patient-tailored workflow patterns from clinical practice guidelines recommendations. Stud. Health Technol. Inform. 192, 392–396 (2013)Google Scholar
  31. Shahar, Y.: A framework for knowledge-based temporal abstraction. Artif. Intell. 90(1–2), 79–133 (1997)CrossRefMATHGoogle Scholar
  32. Shahar, Y.: Dynamic temporal interpretation contexts for temporal abstraction. Ann. Math. Artif. Intell. 22(1–2), 159–192 (1998)CrossRefMATHGoogle Scholar
  33. Shahar, Y., Miksch, S., Johnson, P.: The asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artif. Intell. Med. 14(1–2), 29–51 (1998)CrossRefGoogle Scholar
  34. Shahar, Y., Musen, M.A.: Knowledge-based temporal abstraction in clinical domains. Artif. Intell. Med. 8(3), 267–298 (1996)CrossRefGoogle Scholar
  35. Shalom, E., Shahar, Y., Parmet, Y., Lunenfeld, E.: A multiple-scenario assessment of the effect of a continuous-care, guideline-based decision support system on clinicians’ compliance to clinical guidelines. Int. J. Med. Inform. 84(4), 248–262 (2015)CrossRefGoogle Scholar
  36. Shalom, E., Shahar, Y., Lunenfeld, E.: An architecture for a continuous, user-driven, and data-driven application of clinical guidelines and its evaluation. J. Biomed. Inform. (2016). doi:10.1016/j.jbi.2015.11.006 Google Scholar
  37. Shalom, E., Shahar, Y., Taieb, M., Goren-Bar, D., Yarkoni, A., et al.: A quantitative evaluation of a methodology for collaborative specification of clinical guidelines at multiple representation levels. J. Biomed. Inform. 41(6), 889–903 (2008)CrossRefGoogle Scholar
  38. Villaplana, M., Pons, B., Morillo, M., Aguilar, A., Mendez, A., Tirado, R., et al.: Early introduction of insulin in gestational diabetes seems to prevent from birth weight abnormalities. Metabolic Syndrome & Pregnancy Symposium, Diabetes, Hypertension (2015)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Mor Peleg
    • 1
  • Yuval Shahar
    • 2
  • Silvana Quaglini
    • 3
  • Adi Fux
    • 1
  • Gema García-Sáez
    • 4
  • Ayelet Goldstein
    • 2
  • M. Elena Hernando
    • 4
  • Denis Klimov
    • 2
  • Iñaki Martínez-Sarriegui
    • 4
  • Carlo Napolitano
    • 5
  • Enea Parimbelli
    • 3
  • Mercedes Rigla
    • 6
  • Lucia Sacchi
    • 3
  • Erez Shalom
    • 2
  • Pnina Soffer
    • 1
  1. 1.Department of Information SystemsUniversity of HaifaHaifaIsrael
  2. 2.Department of Information Systems EngineeringBen Gurion University of the NegevBeer-ShevaIsrael
  3. 3.Dipartimento di Ingegneria Industriale e dell’InformazioneUniversity of PaviaPaviaItaly
  4. 4.Centre for Biomedical Technology (CTB), ETSITUniversidad Politécnica de Madrid, and Networking Research Centre for Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)MadridSpain
  5. 5.IRCCS Foundation “Salvatore Maugeri”PaviaItaly
  6. 6.Endocrinology and Nutrition DepartmentHospital de SabadellSabadellSpain

Personalised recommendations