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Digital Health and New Technologies
Published in Connie White Delaney, Charlotte A. Weaver, Joyce Sensmeier, Lisiane Pruinelli, Patrick Weber, Deborah Trautman, Kedar Mate, Howard Catton, Nursing and Informatics for the 21st Century – Embracing a Digital World, 3rd Edition, Book 1, 2022
Making disparate healthcare data sources and repositories interoperable has been a challenge. However, interoperability is vital to operationalize new technologies core to digital health. Connecting data sources for successful use in new technologies include Application Programming Interfaces (APIs), as they are communication points between systems. With APIs, healthcare IT developers are simplifying interoperability to provide healthcare professionals and users' data more efficiently (O'Dowd n.d.). APIs are pieces of code that expose data from underlying information systems in industry-standard formats, including FHIR (Fast Healthcare Interoperability Resources) specification, along with the now-common use of HL7 v2. APIs enable uniform, scalable and repeatable integrations that accelerate development cycles through standardization and reuse (Padmanabhan, 2020). The dependence on data sharing to use new technologies in digital health, such as AI and IoMT, enabled by APIs, will forge a way for health systems, providers, payers and patient care teams to connect siloed technology systems and digital health sources of data. It is necessary to realize the use and value of predictive analytics to further proactive, personalized risk-focused care to track, trend and trace health outcomes across the patient care journey of care to identify risk and develop strategies for care quality and safety (Snowden, 2020).
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Published in Christopher M. Hayre, Dave J. Muller, Marcia J. Scherer, Everyday Technologies in Healthcare, 2019
Michelle A. Meade, Marisa J. Perera
EHRs were introduced in the early seventies, but were not widely adopted until the Health Information Technology for Economic and Clinical Health (HITECH) Act was passed in 2009 and federal funding became available to hospitals in 2011 to support their adoption and set standards for their maintenance (Mann, 2017; Washington et al., 2017). Later policies, including Fast Healthcare Interoperability Resources (FHIR) and Meaningful Use II, supported better health data transfer between various computers and apps and improved data transfer related to patient portals (HL7.org, 2018; Kitsiou et al., 2006). Today, EHRs are virtually universal, FHIR is improved, many patent portals and mobile health apps exist and smartphones are ubiquitous. Accessibility and engagement is increasing, and there is a flood of data being processed. These resources, however, are not available to all patients, and how to facilitate access to these technologies to allow for the most effective use of the data is not well developed. The future of integrating mHealth and EHRs is focused on improving clinical outcomes and delivering value. To do that, barriers for technical integration must be reduced or eliminated, which requires shared patient/clinician cooperative development that is agile and human centred. Moreover, policy must continue to support the development of accessible health records, patient portals and apps to promote their integration into the healthcare practice (Farris et al., 2017).
Cancer registry and big data exchange
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
Zhenwei Shi, Leonard Wee, Andre Dekker
HL7 (Dolin et al. 2001, 2006) refers to a widely accepted standard-setting organization that provides standards to define the protocol, language, and data type used for information communication among different systems. The most used version of HL7 is version 2 with which only a limited and not semantically rich data can be exchanged. HL7 version 3 had a much wider scope but is generally considered a failed standard due to its complexity and limited uptake. HL7 FHIR is the most recent standard and is receiving a lot of positive attention from the community and has resulted in real-world implementations by medical vendors.
Pragmatic Research and Clinical Duties: Solutions Through Precision AI-Enabled Clinically Embedded Research
Published in The American Journal of Bioethics, 2023
Emma Friedman, Matthew John Baumann, Shruti Sehgal, Justin Starren, Russell Steans, Amanda Venables, Kelly Michelson
PACER leverages technology to integrate minimal risk research into the clinical setting, minimizing time commitment, while maximizing study enrollment across diverse patient populations. Electronic health records (EHRs) are nearly ubiquitous. The Cures Act mandates electronic interfaces to EHRs through the Fast Healthcare Interoperability Resource (FHIR) (HL7 FHIR Release 5 Overview 2023). The Clinical Decision Support (CDS) Hooks standard uses FHIR to support integration of pop-up alerts across institutions, independent of their EHR vendor (CDS Hooks 2023). To enable rapid, pragmatic research, PACER uses CDS alerts generated from a central location to request that clinicians across multiple health care systems ask specific patients an additional or atypical question or check for a physical finding. For example, with PACER, the initially unrecognized COVID-19 symptom of anosmia might have been identified earlier if clinicians ordering COVID-19 viral tests had been automatically prompted to ask patients about anosmia symptoms—facilitating diagnosis and mitigating disease spread.