APERITIF – Automatic Patient Recruiting for Clinical Trials Based on HL7 FHIR

Banach, Alexandra; Ulrich, Hans; Kroll, Björn; Kiel, Alexander; Ingenerf, Josef; Kock-Schoppenhauer, Ann-Kristin

doi:10.3233/shti210120

Cited by 10 publications

(11 citation statements)

References 6 publications

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“…Of the 49 studies, most (73%, n=36) studies covered the research domain of clinical research, of which 10 (20%) studies were clinical trials [ 22 , 29 - 31 , 36 , 39 , 43 , 56 , 65 , 66 ]; 3 (6%) studies focused on solutions in public health and epidemiology [ 38 , 40 , 64 ], and the remaining studies did not specify their research domain (20%, n=10; Figure 4 ) [ 24 , 32 , 41 , 42 , 45 - 47 , 50 , 63 , 69 ]. The included studies used FHIR for the standardization of data (41%, n=20) [ 23 , 26 , 30 , 34 , 41 , 45 - 48 , 51 - 53 , 57 - 60 , 63 , 66 , 67 , 70 ], data capture (29%, n=14) [ 1 , 12 , 22 , 24 , 27 , 35 - 37 , 43 , 44 , 55 , 61 , 64 , 65 ], recruitment (14%, n=7) [ 28 , 29 , 31 , 32 , 49 , 56 , 62 ], analysis (12%, n=6) [ 25 , 38 , 42 , 50 , 68 , ...…”

Section: Resultsmentioning

confidence: 99%

“…Among the 49 studies, 37% (n=18) did not report on or use additional standards or terminologies [ 12 , 22 - 24 , 27 , 28 , 30 - 32 , 38 , 39 , 48 , 50 , 55 , 57 , 64 , 66 , 69 ]. SNOMED CT [ 1 , 25 , 26 , 35 , 37 , 43 , 45 - 47 , 51 , 55 , 56 , 65 , 70 ] and LOINC [ 25 , 26 , 35 , 37 , 42 - 45 , 47 , 49 , 51 , 55 , 56 , 58 , 61 , 65 , 68 , 70 ] were reported to be used by 29% (n=14) and 37% (n=18) of the studies, respectively; 18% (n=9) of the studies used International Classification of Diseases 10th Revision [ 25 , 26 , 35 , 37 , 49 , 51 , 58 , 65 , 68 ] and 12% (n=6) used OMOP CDM [ 26 , 29 , 35 , 47 , 52 , 60 ]; and 43% (n=21) of the studies used additional standards which were categorized under “Other” ( Table 1 ) [ 26 , 34 - …”

Section: Resultsmentioning

confidence: 99%

“…SNOMED CT [ 1 , 25 , 26 , 35 , 37 , 43 , 45 - 47 , 51 , 55 , 56 , 65 , 70 ] and LOINC [ 25 , 26 , 35 , 37 , 42 - 45 , 47 , 49 , 51 , 55 , 56 , 58 , 61 , 65 , 68 , 70 ] were reported to be used by 29% (n=14) and 37% (n=18) of the studies, respectively; 18% (n=9) of the studies used International Classification of Diseases 10th Revision [ 25 , 26 , 35 , 37 , 49 , 51 , 58 , 65 , 68 ] and 12% (n=6) used OMOP CDM [ 26 , 29 , 35 , 47 , 52 , 60 ]; and 43% (n=21) of the studies used additional standards which were categorized under “Other” ( Table 1 ) [ 26 , 34 - 37 , 40 , 42 , 43 , 45 - 47 , 49 , 51 , 56 , 58 - 60 , 62 , 63 , 67 , 70 ]. The implemented FHIR resources by each study are listed in Table 2 ; 5 (10%) studies did not precisely list thei...…”

Section: Resultsmentioning

confidence: 99%

“…Of the 49 studies, the majority were conducted in Germany (47%, n=23) [12,26,[28][29][30][31]34,35,[40][41][42][45][46][47]52,53,[56][57][58]60,62,63,69], the United States (27%, n=13) [22,25,36,44,[48][49][50]61,[64][65][66]68,70], and Australia (6%, n=3) [1,43,67]. The remaining studies were performed in Austria (2%, n=1) [32], Canada (2%, n=1) [24], France (2%, n=1) [51], Greece (2%, n=1) [59], Japan (2%, n=1) [27], Pakistan (2%, n=1) [38], Spain (2%, n=1) [55], Switzerland (2%, n=1) [39], Taiwan (2%, n=1) [23], and the United Kingdom (2%, n=1)…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…In terms of medical specialty, most (55%, 27/49) of the studies [24,[27][28][29][30][31][32]34,[36][37][38][39][40][41][42]44,46,48,49,53,56,[60][61][62][63]67,70] were using a generic approach-implementable in any kind of specialty (Table 2). Of the remaining studies, 16% (8/49) use cases focused on infectious disease [1,22,23,26,45,47,50,59], whereas 12% (6/49) focused on oncology [25,55,57,58,65,66] and 8% (4/49) on genomics [43,52,68,69].…”

Section: Study Objectivesmentioning

confidence: 99%

See 4 more Smart Citations

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review

Vorisek¹,

Lehne²,

Klopfenstein³

et al. 2022

JMIR Med Inform

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Background:The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as "Public Health & Research" and "Evidence-Based Medicine" while using already established web technologies. Therefore, FHIR could help standardize data across different data sources and improve interoperability in health research. Objective:The aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions. Methods:We searched the PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore, and Cochrane Library databases for studies published from 2011 to 2022. Studies investigating the use of FHIR in health research were included. Articles published before 2011, abstracts, reviews, editorials, and expert opinions were excluded. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered this study with PROSPERO (CRD42021235393). Data synthesis was done in tables and figures. Results:We identified a total of 998 studies, of which 49 studies were eligible for inclusion. Of the 49 studies, most (73%, n=36) covered the domain of clinical research, whereas the remaining studies focused on public health or epidemiology (6%, n=3) or did not specify their research domain (20%, n=10). Studies used FHIR for data capture (29%, n=14), standardization of data (41%, n=20), analysis (12%, n=6), recruitment (14%, n=7), and consent management (4%, n=2). Most (55%, 27/49) of the studies had a generic approach, and 55% (12/22) of the studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, and pulmonary hypertension) reported their solutions to be conferrable to other use cases. Most (63%, 31/49) of the studies reported using additional data models or terminologies: Systematized Nomenclature of Medicine Clinical Terms (29%, n=14), Logical Observation Identifiers Names and Codes (37%, n=18), International Classification of Diseases 10th Revision (18%, n=9), Observational Medical Outcomes Partnership common data model (12%, n=6), and others (43%, n=21). Only 4 (8%) studies used a FHIR resource from the domain "Public Health & Research." Limitations using FHIR included the possible change in the content of FHIR resources, safety, legal matters, and the need for a FHIR server. Conclusions:Our review found that FHIR can be implemented in health research, and the areas of application are broad and generalizable in most use cases. The implementation of international terminologies was common, and other standards such as the Observational Medical Outcomes Partnership common data model could be used as a complement to FHIR. Limitations such...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Section: Study Objectivesmentioning

confidence: 99%

See 3 more Smart Citations

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review

Vorisek¹,

Lehne²,

Klopfenstein³

et al. 2022

JMIR Med Inform

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Striking a match between FHIR‐based patient data and FHIR‐based eligibility criteria

Alper¹,

Dehnbostel²,

Shahin³

et al. 2023

Learning Health Systems

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Inputs and OutputsThe Strike‐a‐Match Function, written in JavaScript version ES6+, accepts the input of two datasets (one dataset defining eligibility criteria for research studies or clinical decision support, and one dataset defining characteristics for an individual patient). It returns an output signaling whether the patient characteristics are a match for the eligibility criteria.PurposeUltimately, such a system will play a “matchmaker” role in facilitating point‐of‐care recognition of patient‐specific clinical decision support.SpecificationsThe eligibility criteria are defined in HL7 FHIR (version R5) EvidenceVariable Resource JSON structure. The patient characteristics are provided in an FHIR Bundle Resource JSON including one Patient Resource and one or more Observation and Condition Resources which could be obtained from the patient's electronic health record.ApplicationThe Strike‐a‐Match Function determines whether or not the patient is a match to the eligibility criteria and an Eligibility Criteria Matching Software Demonstration interface provides a human‐readable display of matching results by criteria for the clinician or patient to consider. This is the first software application, serving as proof of principle, that compares patient characteristics and eligibility criteria with all data exchanged using HL7 FHIR JSON. An Eligibility Criteria Matching Software Library at https://fevir.net/110192 provides a method for sharing functions using the same information model.

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The Fast Health Interoperability Resources (FHIR) and Clinical Research, a Scoping Review

Pavão,

Bastardo,

Rocha

2024

Lecture Notes in Networks and Systems

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APERITIF – Automatic Patient Recruiting for Clinical Trials Based on HL7 FHIR

Cited by 10 publications

References 6 publications

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review

Striking a match between FHIR‐based patient data and FHIR‐based eligibility criteria

The Fast Health Interoperability Resources (FHIR) and Clinical Research, a Scoping Review

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