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Frei J, Auer FJ, Netzband S, Ignatenko Y, Kramer F. Web-based Prototype for Graphical Exploration of FHIR® Questionnaire Responses. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:351-358. [PMID: 38222405 PMCID: PMC10785863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
The evaluation of clinical questionnaires is an important part of gaining knowledge in empirical research. The electronically captured responses are encoded in a standard format such as HL7 FHIR® that facilitates data exchange and systems interoperability. However, this also complicates access of the information to explore and interpret the results without appropriate tools. In this work, we present the design of a web-based graphical exploration tool for categorical questionnaire response data that can interact with FHIR-conformant HTTP endpoints. The web app enables non-technical users with simplified, direct visual access to highly structured FHIR questionnaire response data and preserves the applicability in arbitrary data exploration tasks. We describe the abstract feature design with the derived technical implementation to allow a universal, user-configurable data subselection mechanism to generate conditional one- and two-data-dimensional charts. The applicability of our developed prototype is demonstrated on synthetic FHIR data with the source code available at https://github.com/frankkramer-lab/FHIR-QR-Explorer.
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Affiliation(s)
- Johann Frei
- IT Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany
| | - Florian J Auer
- IT Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany
| | - Steffen Netzband
- IT Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany
| | - Yevgeniia Ignatenko
- IT Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany
| | - Frank Kramer
- IT Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany
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Nolla K, Rasmussen LV, Rothrock NE, Butt Z, Bass M, Davis K, Cella D, Gershon R, Barnard C, Chmiel R, Almaraz F, Schachter M, Nelson T, Langer M, Starren J. Seamless Integration of Computer-Adaptive Patient Reported Outcomes into an Electronic Health Record. Appl Clin Inform 2024; 15:145-154. [PMID: 38154472 PMCID: PMC10881259 DOI: 10.1055/a-2235-9557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 12/06/2023] [Indexed: 12/30/2023] Open
Abstract
BACKGROUND Patient-reported outcome (PRO) measures have become an essential component of quality measurement, quality improvement, and capturing the voice of the patient in clinical care. In 2004, the National Institutes of Health endorsed the importance of PROs by initiating the Patient-Reported Outcomes Measurement Information System (PROMIS), which leverages computer-adaptive tests (CATs) to reduce patient burden while maintaining measurement precision. Historically, PROMIS CATs have been used in a large number of research studies outside the electronic health record (EHR), but growing demand for clinical use of PROs requires creative information technology solutions for integration into the EHR. OBJECTIVES This paper describes the introduction of PROMIS CATs into the Epic Systems EHR at a large academic medical center using a tight integration; we describe the process of creating a secure, automatic connection between the application programming interface (API) which scores and selects CAT items and Epic. METHODS The overarching strategy was to make CATs appear indistinguishable from conventional measures to clinical users, patients, and the EHR software itself. We implemented CATs in Epic without compromising patient data security by creating custom middleware software within the organization's existing middleware framework. This software communicated between the Assessment Center API for item selection and scoring and Epic for item presentation and results. The middleware software seamlessly administered CATs alongside fixed-length, conventional PROs while maintaining the display characteristics and functions of other Epic measures, including automatic display of PROMIS scores in the patient's chart. Pilot implementation revealed differing workflows for clinicians using the software. RESULTS The middleware software was adopted in 27 clinics across the hospital system. In the first 2 years of hospital-wide implementation, 793 providers collected 70,446 PROs from patients using this system. CONCLUSION This project demonstrated the importance of regular communication across interdisciplinary teams in the design and development of clinical software. It also demonstrated that implementation relies on buy-in from clinical partners as they integrate new tools into their existing clinical workflow.
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Affiliation(s)
- Kyle Nolla
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
| | - Luke V. Rasmussen
- Department of Preventative Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
| | - Nan E. Rothrock
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
| | - Zeeshan Butt
- Phreesia, Inc, Clinical Content, Wilmington, DE, USA
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
| | - Michael Bass
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
| | - Kristina Davis
- Department of Nursing Quality, Stanford Health Care, Stanford, California, United States
| | - David Cella
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
| | - Richard Gershon
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
| | - Cynthia Barnard
- Department of General Internal Medicine, Feinberg School of Medicine, Northwestern University and Northwestern Memorial HealthCare, Chicago, Illinois, United States
| | - Ryan Chmiel
- Department of Information Services, Northwestern Memorial HealthCare, Chicago, Illinois, United States
| | - Federico Almaraz
- Department of Information Services, Northwestern Memorial HealthCare, Chicago, Illinois, United States
| | - Michael Schachter
- Department of Information Services, Northwestern Memorial HealthCare, Chicago, Illinois, United States
| | - Therese Nelson
- Clinical and Translational Sciences Institute, Northwestern University, Chicago, Illinois, United States
| | - Michelle Langer
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
| | - Justin Starren
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
- Department of Preventative Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
- Clinical and Translational Sciences Institute, Northwestern University, Chicago, Illinois, United States
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Nan J, Xu LQ. Designing Interoperable Health Care Services Based on Fast Healthcare Interoperability Resources: Literature Review. JMIR Med Inform 2023; 11:e44842. [PMID: 37603388 PMCID: PMC10477925 DOI: 10.2196/44842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 04/07/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023] Open
Abstract
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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Affiliation(s)
- Jingwen Nan
- Health IT Research, China Mobile (Chengdu) Industrial Research Institute, Chengdu, China
| | - Li-Qun Xu
- Health IT Research, China Mobile (Chengdu) Industrial Research Institute, Chengdu, China
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Wolfien M, Ahmadi N, Fitzer K, Grummt S, Heine KL, Jung IC, Krefting D, Kühn A, Peng Y, Reinecke I, Scheel J, Schmidt T, Schmücker P, Schüttler C, Waltemath D, Zoch M, Sedlmayr M. Ten Topics to Get Started in Medical Informatics Research. J Med Internet Res 2023; 25:e45948. [PMID: 37486754 PMCID: PMC10407648 DOI: 10.2196/45948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/29/2023] [Accepted: 04/11/2023] [Indexed: 07/25/2023] Open
Abstract
The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.
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Affiliation(s)
- Markus Wolfien
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany
| | - Najia Ahmadi
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Kai Fitzer
- Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany
| | - Sophia Grummt
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Kilian-Ludwig Heine
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ian-C Jung
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center, Goettingen, Germany
| | - Andreas Kühn
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Yuan Peng
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ines Reinecke
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Julia Scheel
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Tobias Schmidt
- Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany
| | - Paul Schmücker
- Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany
| | - Christina Schüttler
- Central Biobank Erlangen, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Dagmar Waltemath
- Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany
- Department of Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - Michele Zoch
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany
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Duda SN, Kennedy N, Conway D, Cheng AC, Nguyen V, Zayas-Cabán T, Harris PA. HL7 FHIR-based tools and initiatives to support clinical research: a scoping review. J Am Med Inform Assoc 2022; 29:1642-1653. [PMID: 35818340 DOI: 10.1093/jamia/ocac105] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 05/23/2022] [Accepted: 06/20/2022] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES The HL7® fast healthcare interoperability resources (FHIR®) specification has emerged as the leading interoperability standard for the exchange of healthcare data. We conducted a scoping review to identify trends and gaps in the use of FHIR for clinical research. MATERIALS AND METHODS We reviewed published literature, federally funded project databases, application websites, and other sources to discover FHIR-based papers, projects, and tools (collectively, "FHIR projects") available to support clinical research activities. RESULTS Our search identified 203 different FHIR projects applicable to clinical research. Most were associated with preparations to conduct research, such as data mapping to and from FHIR formats (n = 66, 32.5%) and managing ontologies with FHIR (n = 30, 14.8%), or post-study data activities, such as sharing data using repositories or registries (n = 24, 11.8%), general research data sharing (n = 23, 11.3%), and management of genomic data (n = 21, 10.3%). With the exception of phenotyping (n = 19, 9.4%), fewer FHIR-based projects focused on needs within the clinical research process itself. DISCUSSION Funding and usage of FHIR-enabled solutions for research are expanding, but most projects appear focused on establishing data pipelines and linking clinical systems such as electronic health records, patient-facing data systems, and registries, possibly due to the relative newness of FHIR and the incentives for FHIR integration in health information systems. Fewer FHIR projects were associated with research-only activities. CONCLUSION The FHIR standard is becoming an essential component of the clinical research enterprise. To develop FHIR's full potential for clinical research, funding and operational stakeholders should address gaps in FHIR-based research tools and methods.
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Affiliation(s)
- Stephany N Duda
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Nan Kennedy
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Douglas Conway
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Alex C Cheng
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Viet Nguyen
- Stratametrics LLC, Salt Lake City, Utah, USA.,HL7 Da Vinci Project, Ann Arbor, Michigan, USA
| | - Teresa Zayas-Cabán
- National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Paul A Harris
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
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