Disrupting Electronic Health Records Systems: The Next Generation

Celi, Leo Anthony; Marshall, Jeffrey D.; Lai, Yuan; Stone, David J.

doi:10.2196/medinform.4192

Cited by 31 publications

(26 citation statements)

References 14 publications

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“…Although progress is being made towards a digital infrastructure for the learning health system [95], it is, in our opinion, not likely that EASS of HAI will be implemented globally within the next decade. A data-driven and decisionsupported healthcare system, including infection control surveillance, requires next generation electronic health records systems [96], clinical ownership and a good and close working relationship between infection control professionals and medical information specialists [97].…”

Section: Discussionmentioning

confidence: 99%

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

et al. 2020

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Background: Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives: To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods: In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results: A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37-1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions: Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency.

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Section: Discussionmentioning

confidence: 99%

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

et al. 2020

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“…Translational science represents the next challenge for the realisation of actual health promotion with personalised medicine 37 . In the context of clinico-genomics, translational approaches ultimately target the syntactic and semantic interoperability between genomics and clinical practice, to ensure business continuity in terms of knowledge management [37][38][39] . Previous approaches have stressed a need for structural transformation to overcome the currently low adaptation of genomic information for clinical decision-making.…”

Section: Discussionmentioning

confidence: 99%

Clinical Genome Data Model (cGDM) provides Interactive Clinical Decision Support for Precision Medicine

Kim

Park

et al. 2020

Sci Rep

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in light of recent developments in genomic technology and the rapid accumulation of genomic information, a major transition toward precision medicine is anticipated. However, the clinical applications of genomic information remain limited. This lag can be attributed to several complex factors, including the knowledge gap between medical experts and bioinformaticians, the distance between bioinformatics workflows and clinical practice, and the unique characteristics of genomic data, which can make interpretation difficult. Here we present a novel genomic data model that allows for more interactive support in clinical decision-making. Informational modelling was used as a basis to design a communication scheme between sophisticated bioinformatics predictions and the representative data relevant to a clinical decision. This study was conducted by a multidisciplinary working group who carried out clinico-genomic workflow analysis and attribute extraction, through Failure Mode and Effects Analysis (FMEA). Based on those results, a clinical genome data model (cGDM) was developed with 8 entities and 46 attributes. The cGDM integrates reliability-related factors that enable clinicians to access the reliability problem of each individual genetic test result as clinical evidence. The proposed cGDM provides a data-layer infrastructure supporting the intellectual interplay between medical experts and informed decision-making.

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“…We will progressively need to introduce important note information from other sources (eg, personal device and patient-entered data, population databases, even genomics) that supplement what is now available to the clinicians creating or reviewing the note at a later time [ 6 ]. In a previous publication, we described an engineered system that would support electronic note writing but did not specifically suggest how this might be done in a technical sense [ 7 ]. We suggest that the now increasingly familiar tools provided by AI provide a potential means by which to “de-bin” the process of data element selection and assist in the assembly of the data pieces with the goal of improved and more efficient electronic note creation.…”

Section: Introductionmentioning

confidence: 99%

Clinical Note Creation, Binning, and Artificial Intelligence

2017

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The creation of medical notes in software applications poses an intrinsic problem in workflow as the technology inherently intervenes in the processes of collecting and assembling information, as well as the production of a data-driven note that meets both individual and healthcare system requirements. In addition, the note writing applications in currently available electronic health records (EHRs) do not function to support decision making to any substantial degree. We suggest that artificial intelligence (AI) could be utilized to facilitate the workflows of the data collection and assembly processes, as well as to support the development of personalized, yet data-driven assessments and plans.

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Disrupting Electronic Health Records Systems: The Next Generation

Cited by 31 publications

References 14 publications

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

Clinical Genome Data Model (cGDM) provides Interactive Clinical Decision Support for Precision Medicine

Clinical Note Creation, Binning, and Artificial Intelligence

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