Patients Decision Aid System Based on FHIR Profiles
- 291 Downloads
Patients are becoming more and more involved in clinical decision-making process. Several factors support this process. Advances in omics allows individualization of diagnosis and treatment. Patient awareness and easy availability of data on the Internet allows patients to become informed decision makers when it comes even to disease management. Mass media emphasize the issue of medical errors, making patients demanding for quality in medical care. In some healthcare settings, patents face a problem of interpreting medical data and making decisions on treatment tactics without having a doctor, who could potentially support them. Delegating this task to a Patient Decision Aide system can add automatically generated recommendations to result reports without adding significant workload on the doctors, increase patients’ motivation and support their decisions. We have implemented a patient decision aid system based on the productions rules, which: Collects data from available sources; Automatically analyses and interprets laboratory test results; Recommends running additional tests for a more precise diagnostic; Delivers automatically generated reports to doctors and patients in a natural language. To achieve semantic interoperability with other systems we have implemented a FHIR engine. The knowledge base has been organized as a graph structure. The application is structured as a set of lightly coupled services, which implement the logic of the decision support system. In total, we have modelled 365 nodes of test components, 5084 nodes of inference rules, 49932 connections and 3072 blocks of text for medical certificates. The findings of the research provide a deep understanding of how the semantically interoperable clinical decision support systems are implemented. Advances in notification the patients with the elements of patient decision aid is important for clinical data management, and for patients’ empowerment and protection. We suppose that the system empowering patients in such way can play a meaningful role in helping patients to make informed decisions during the process of diagnostics and treatment.
KeywordsDecision support Laboratory information system Telemedicine First order predicates
The research is funded from Russian Science Foundation (RSF), The research was at Tomsk Polytechnic University within the framework of Tomsk Polytechnic University Competitiveness Enhancement Program grant.
Compliance with Ethical Standards
Conflict of Interest
Ilia Semenov declares that he has no conflict of interest; Georgy Kopanitsa declares that he has no conflict of interest; Dmitry Denisov declares that he has no conflict of interest; Yakovenko Alexandr declares that he has no conflict of interest; Osenev Roman declares that he has no conflict of interest; Andreychuk Yury declares that he has no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.
- 4.Barros Castro, J., Lamelo Alfonsin, A., Prieto Cebreiro, J., Rimada Mora, D., Carrajo Garcia, L., and Vazquez Gonzalez, G., Development of ISO 13606 archetypes for the standardisation of data registration in the primary care environment. Stud Health Technol Inform 210:877–881, 2015.PubMedGoogle Scholar
- 9.Ceriello, A., Barkai, L., Christiansen, J. S., Czupryniak, L., Gomis, R., Harno, K., Kulzer, B., Ludvigsson, J., Nemethyova, Z., Owens, D., Schnell, O., Tankova, T., Taskinen, M. R., Verges, B., Weitgasser, R., and Wens, J., Diabetes as a case study of chronic disease management with a personalized approach: The role of a structured feedback loop. Diabetes Res Clin Pract 98:5–10, 2012.CrossRefPubMedGoogle Scholar
- 10.Chi, C. L., Nick Street, W., Robinson, J. G., and Crawford, M. A., Individualized patient-centered lifestyle recommendations: An expert system for communicating patient specific cardiovascular risk information and prioritizing lifestyle options. J Biomed Inform 45:1164–1174, 2012.CrossRefPubMedGoogle Scholar
- 13.Dexheimer, J. W., Abramo, T. J., Arnold, D. H., Johnson, K., Shyr, Y., Ye, F., Fan, K. H., Patel, N., and Aronsky, D., Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: A randomized clinical trial. Int J Med Inform 83:805–813, 2014.CrossRefPubMedPubMedCentralGoogle Scholar
- 17.Herrick, D. B., Nakhasi, A., Nelson, B., Rice, S., Abbott, P. A., Saber Tehrani, A. S., Rothman, R. E., Lehmann, H. P., and Newman-Toker, D. E., Usability characteristics of self-administered computer-assisted interviewing in the emergency department: Factors affecting ease of use, efficiency, and entry error. Appl Clin Inform 4:276–292, 2013.CrossRefPubMedPubMedCentralGoogle Scholar
- 18.Herwehe, J., Wilbright, W., Abrams, A., Bergson, S., Foxhood, J., Kaiser, M., Smith, L., Xiao, K., Zapata, A., and Magnus, M., Implementation of an innovative, integrated electronic medical record (EMR) and public health information exchange for HIV/AIDS. J Am Med Inform Assoc 19:448–452, 2012.CrossRefPubMedGoogle Scholar
- 27.LeBlanc, A., Ruud, K. L., Branda, M. E., Tiedje, K., Boehmer, K. R., Pencille, L. J., Van Houten, H., Matthews, M., Shah, N. D., May, C. R., Yawn, B. P., and Montori, V. M., The impact of decision aids to enhance shared decision making for diabetes (the DAD study): Protocol of a cluster randomized trial. BMC Health Serv Res 12:130, 2012.CrossRefPubMedPubMedCentralGoogle Scholar
- 30.Lindblom, K., Gregory, T., Wilson, C., Flight, I. H., and Zajac, I., The impact of computer self-efficacy, computer anxiety, and perceived usability and acceptability on the efficacy of a decision support tool for colorectal cancer screening. J Am Med Inform Assoc 19:407–412, 2012.CrossRefPubMedGoogle Scholar
- 37.Pulley, J. M., Denny, J. C., Peterson, J. F., Bernard, G. R., Vnencak-Jones, C. L., Ramirez, A. H., Delaney, J. T., Bowton, E., Brothers, K., Johnson, K., Crawford, D. C., Schildcrout, J., Masys, D. R., Dilks, H. H., Wilke, R. A., Clayton, E. W., Shultz, E., Laposata, M., McPherson, J., Jirjis, J. N., and Roden, D. M., Operational implementation of prospective genotyping for personalized medicine: The design of the Vanderbilt PREDICT project. Clin Pharmacol Ther 92:87–95, 2012.CrossRefPubMedPubMedCentralGoogle Scholar
- 39.Riano, D., Real, F., Lopez-Vallverdu, J. A., Campana, F., Ercolani, S., Mecocci, P., Annicchiarico, R., and Caltagirone, C., An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients. J Biomed Inform 45:429–446, 2012.CrossRefPubMedGoogle Scholar
- 44.Simon, D., Kriston, L., von Wolff, A., Buchholz, A., Vietor, C., Hecke, T., Loh, A., Zenker, M., Weiss, M., and Harter, M., Effectiveness of a web-based, individually tailored decision aid for depression or acute low back pain: A randomized controlled trial. Patient Educ Couns 87:360–368, 2012.CrossRefPubMedGoogle Scholar
- 45.Stein, B. D., Kogan, J. N., Mihalyo, M. J., Schuster, J., Deegan, P. E., Sorbero, M. J., and Drake, R. E., Use of a computerized medication shared decision making tool in community mental health settings: Impact on psychotropic medication adherence. Community Ment Health J 49:185–192, 2013.CrossRefPubMedGoogle Scholar
- 49.Yu, H. J., Lai, H. S., Chen, K. H., Chou, H. C., Wu, J. M., Dorjgochoo, S., Mendjargal, A., Altangerel, E., Tien, Y. W., Hsueh, C. W., and Lai, F., A sharable cloud-based pancreaticoduodenectomy collaborative database for physicians: Emphasis on security and clinical rule supporting. Comput Methods Programs Biomed 111:488–497, 2013.CrossRefPubMedGoogle Scholar