SMART Markers: collecting patient-generated health data as a standardized property of health information technology

Sayeed, Raheel; Gottlieb, Daniel; Mandl, Kenneth D

doi:10.1038/s41746-020-0218-6

Cited by 66 publications

(48 citation statements)

References 15 publications

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“…Future opportunities for cross-linking the real-world dataset with electronic medical records (EMRs) or electronic health record (EHR) datasets would make the assessment of outcomes much more insightful, given the greater availability of treatment plans and outcome data now available. Recent developments in computing interfaces focused on supporting seamless data transfer demonstrate potential for approaching integration into EMRs and EHRs [ 47 ]. However, direct integration of patient data presents additional challenges such as legal and ethical considerations [ 48 ] and the fragmentation of the technological landscape, in which each country appears to have its own specific subset of EHR systems and vendor which are specific to local practice.…”

Section: Discussionmentioning

confidence: 99%

Analysis of real-world data on growth hormone therapy adherence using a connected injection device

Koledova

Tornincasa

Dommelen

2020

BMC Med Inform Decis Mak

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Background Poor adherence to long-term recombinant human growth hormone (r-hGH) treatment can lead to suboptimal clinical outcomes; consequently, supporting and monitoring adherence is a crucial part of patient management. We assessed adherence to r-hGH treatment in children with growth disorders over 48 months using a connected monitoring device (easypod™), which automatically transmits adherence data via an online portal (easypod™ connect); both sit within an adherence decision support system (ADSS). We also investigated the effect of age and sex on adherence. Methods Data from children transmitting over 10 injections between January 2007 and February 2019 were analyzed. Adherence (mg injected/mg prescribed) was categorized as high (≥85%), intermediate (> 56–84%) or low (≤56%) and assessed at seven time points from the start of treatment up to 48 months. Adherence was investigated over time and stratified by puberty status and sex. Mean transmission rate in each adherence category (total number of transmissions/total number of children) at each time point was calculated as a proxy measure of engagement in disease and treatment management. Descriptive analyses were performed. Results Longitudinal records were available for 13,553 children. Overall, 71% ( n = 9578) had high adherence, 22% ( n = 2989) intermediate and 7% ( n = 986) low. The proportion of children with high adherence decreased over time from 87% ( n = 12,964) to 65% ( n = 957) and was higher in pre-pubertal than pubertal children (girls: 80% [ n = 1270] vs 70% [ n = 4496]; boys 79% [ n = 2573] vs 65% [ n = 5214]). Children with high adherence had a higher mean number of transmissions (12.5 [SD 24.9]) than children with intermediate (7.2 [SD 15.3]) or low (3.5 [SD 5.7]) adherence. Conclusions High adherence was seen in patients administering r-hGH using the connected device. Children with high adherence were most likely to regularly transmit data. Pubertal children showed lower adherence. We show the potential to develop an ADSS to analyze trends in real-world adherence data. This may prove useful to direct interventions to improve adherence while the ability to readily share data with healthcare professionals may itself improve adherence.

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Section: Discussionmentioning

confidence: 99%

Analysis of real-world data on growth hormone therapy adherence using a connected injection device

Koledova

Tornincasa

Dommelen

2020

BMC Med Inform Decis Mak

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“…For example, the SMART (Substitutable Medical Applications, Reusable Technologies) on FHIR (Fast Healthcare Interoperability Resources) API enables medical researchers, clinical providers, and patients to connect apps to the health system across EHR platforms 47 . As part of such an automated real-world data system, data from sensors, mobile devices, patient-generated data and patient-reported outcomes could become more routine trial markers and endpoints 48 . The SMART/HL7 Flat FHIR/Bulk Data Export API 49 provides a standardized approach to readily create population-level datasets from EHR.…”

Section: Data Analyticsmentioning

confidence: 99%

Digitizing clinical trials

et al. 2020

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Clinical trials are a fundamental tool used to evaluate the efficacy and safety of new drugs and medical devices and other health system interventions. The traditional clinical trials system acts as a quality funnel for the development and implementation of new drugs, devices and health system interventions. The concept of a "digital clinical trial" involves leveraging digital technology to improve participant access, engagement, trial-related measurements, and/or interventions, enable concealed randomized intervention allocation, and has the potential to transform clinical trials and to lower their cost. In April 2019, the US National Institutes of Health (NIH) and the National Science Foundation (NSF) held a workshop bringing together experts in clinical trials, digital technology, and digital analytics to discuss strategies to implement the use of digital technologies in clinical trials while considering potential challenges. This position paper builds on this workshop to describe the current state of the art for digital clinical trials including (1) defining and outlining the composition and elements of digital trials; (2) describing recruitment and retention using digital technology; (3) outlining data collection elements including mobile health, wearable technologies, application programming interfaces (APIs), digital transmission of data, and consideration of regulatory oversight and guidance for data security, privacy, and remotely provided informed consent; (4) elucidating digital analytics and data science approaches leveraging artificial intelligence and machine learning algorithms; and (5) setting future priorities and strategies that should be addressed to successfully harness digital methods and the myriad benefits of such technologies for clinical research.

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“…The successful practice of precision medicine depends on clinical genomic data sharing and knowledge-based interpretations of genomic variant data at the point of care [ 11 ]. To improve interoperability as part of precision medicine, health care stakeholders encourage the use of application programming interfaces (APIs) and app-based ecosystems such as SMART on FHIR, CDS Hooks, and SMART Markers [ 22 - 26 ]. These platforms enable easy implementation for health care use cases and facilitate functional extensibility.…”

Section: Introductionmentioning

confidence: 99%

Fast Healthcare Interoperability Resources (FHIR)–Based Quality Information Exchange for Clinical Next-Generation Sequencing Genomic Testing: Implementation Study

Seong¹,

Jung²,

Bae³

et al. 2021

J Med Internet Res

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Background Next-generation sequencing (NGS) technology has been rapidly adopted in clinical practice, with the scope extended to early diagnosis, disease classification, and treatment planning. As the number of requests for NGS genomic testing increases, substantial efforts have been made to deliver the testing results clearly and unambiguously. For the legitimacy of clinical NGS genomic testing, quality information from the process of producing genomic data should be included within the results. However, most reports provide insufficient quality information to confirm the reliability of genomic testing owing to the complexity of the NGS process. Objective The goal of this study was to develop a Fast Healthcare Interoperability Resources (FHIR)–based web app, NGS Quality Reporting (NGS-QR), to report and manage the quality of the information obtained from clinical NGS genomic tests. Methods We defined data elements for the exchange of quality information from clinical NGS genomic tests, and profiled a FHIR genomic resource to enable information exchange in a standardized format. We then developed the FHIR-based web app and FHIR server to exchange quality information, along with statistical analysis tools implemented with the R Shiny server. Results Approximately 1000 experimental data entries collected from the targeted sequencing pipeline CancerSCAN designed by Samsung Medical Center were used to validate implementation of the NGS-QR app using real-world data. The user can share the quality information of NGS genomic testing and verify the quality status of individual samples in the overall distribution. Conclusions This study successfully demonstrated how quality information of clinical NGS genomic testing can be exchanged in a standardized format. As the demand for NGS genomic testing in clinical settings increases and genomic data accumulate, quality information can be used as reference material to improve the quality of testing. This app could also motivate laboratories to perform diagnostic tests to provide high-quality genomic data.

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SMART Markers: collecting patient-generated health data as a standardized property of health information technology

Cited by 66 publications

References 15 publications

Analysis of real-world data on growth hormone therapy adherence using a connected injection device

Analysis of real-world data on growth hormone therapy adherence using a connected injection device

Digitizing clinical trials

Fast Healthcare Interoperability Resources (FHIR)–Based Quality Information Exchange for Clinical Next-Generation Sequencing Genomic Testing: Implementation Study

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