Improving the Continuum of Care by Bridging the Gap between Prehospital and Hospital Discharge Data through Stepwise Deterministic Linkage

Chikani, Vatsal; Blust, Robyn N.; Vossbrink, Anne; Wightman, Patrick; Bissell, Shelley; Graw, Joe; Martinez, Rogelio; Fisher, Benjamin

doi:10.1080/10903127.2019.1604925

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Researchers actively studied how to merge patient records in healthcare databases [ [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] ]. These studies can be roughly grouped into three approaches: a deterministic approach [ [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] ], a probabilistic approach [ [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] , [55] , [56] , [57] , [58] ], and a hybrid approach [ [59] , [60] , [61] , [62] , [63] ]. Our method can be categorized into the deterministic approach, but it can be extended by incorporatin...…”

Section: Discussionmentioning

confidence: 99%

Virtual patient identifier (vPID): Improving patient traceability using anonymized identifiers in Japanese healthcare insurance claims database

Sato

Mitsutake

Yamada

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Virtual patient identifier (vPID): Improving patient traceability using anonymized identifiers in Japanese healthcare insurance claims database

Sato

Mitsutake

Yamada

et al. 2023

Heliyon

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“…Without unique identifiers (e.g., ISAN), deterministic and probabilistic methods apply. Deterministic linkage uses a predefined algorithm to combine variables and generate a unique identifier, namely a linkage key [37]. A pair of data records are linked once their linkage keys match.…”

Section: Data Linkage Across Systemsmentioning

confidence: 99%

Automatic Information Exchange in the Early Rescue Chain Using the International Standard Accident Number (ISAN)

et al. 2021

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Thus far, emergency calls are answered by human operators who interview the calling person in order to obtain all relevant information. In the near future—based on the Internet of (Medical) Things (IoT, IoMT)—accidents, emergencies, or adverse health events will be reported automatically by smart homes, smart vehicles, or smart wearables, without any human in the loop. Several parties are involved in this communication: the alerting system, the rescue service (responding system), and the emergency department in the hospital (curing system). In many countries, these parties use isolated information and communication technology (ICT) systems. Previously, the International Standard Accident Number (ISAN) has been proposed to securely link the data in these systems. In this work, we propose an ISAN-based communication platform that allows semantically interoperable information exchange. Our aims are threefold: (i) to enable data exchange between the isolated systems, (ii) to avoid data misinterpretation, and (iii) to integrate additional data sources. The suggested platform is composed of an alerting, responding, and curing system manager, a workflow manager, and a communication manager. First, the ICT systems of all parties in the early rescue chain register with their according system manager, which tracks the keep-alive. In case of emergency, the alerting system sends an ISAN to the platform. The responsible rescue services and hospitals are determined and interconnected for platform-based communication. Next to the conceptual design of the platform, we evaluate a proof-of-concept implementation according to (1) the registration, (2) channel establishment, (3) data encryption, (4) event alert, and (5) information exchange. Our concept meets the requirements for scalability, error handling, and information security. In the future, it will be used to implement a virtual accident registry.

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“…When patients' journey data are pieced together by a unique patient identification (ID), it creates a more complete picture of prehospital and in-hospital emergency care. 5,6 This study will explore whether a previously developed air ambulance quality framework 7,8 is useful when applied to existing air ambulance patients' journeys using outcomes from linked data analysis. It is hypothesized that the framework will help guide prehospital and in-hospital performance reporting and improve the planning and delivery of aeromedical services.…”

Section: Introductionmentioning

confidence: 99%

Using a Quality Framework to Explore Air Ambulance Patients’ Journey Outcomes in Central Queensland, Australia

Edwards

Franklin

Jones

et al. 2022

Prehosp. Disaster med.

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Introduction: In Australia, aeromedical retrieval provides a vital link for rural communities with limited health services to definitive care in urban centers. Yet, there are few studies of aeromedical patient experiences and outcomes, or clear measures of the service quality provided to these patients. Study Objective: This study explores whether a previously developed quality framework could usefully be applied to existing air ambulance patient journeys (ie, the sequences of care that span multiple settings; prehospital and hospital-based pre-flight, flight transport, after-flight hospital in-patient, and disposition). The study aimed to use linked data from aeromedical, emergency department (ED), and hospital sources, and from death registries, to document and analyze patient journeys. Methods: A previously developed air ambulance quality framework was used to place patient, prehospital, and in-hospital service outcomes in relevant quality domains identified from the Institutes of Medicine (IOM) and Dr. Donabedian models. To understand the aeromedical patients’ journeys, data from all relevant data sources were linked by unique patient identifiers and the outcomes of the resulting analyses were applied to the air ambulance quality framework. Results: Overall, air ambulance referral pathways could be classified into three categories: Intraregional (those retrievals which stayed within the region), Out of Region, and Into Region. Patient journeys and service outcomes varied markedly between referral pathways. Prehospital and in-hospital service variables and patient outcomes showed that the framework could be used to explore air ambulance service quality. Conclusion: The air ambulance quality framework can usefully be applied to air ambulance patient experiences and outcomes using linked data analysis. The framework can help guide prehospital and in-hospital performance reporting. With variations between regional referral pathways, this knowledge will aid with planning within the local service. The study successfully linked data from aeromedical, ED, in-hospital, and death sources and explored the aeromedical patients’ journeys.

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Improving the Continuum of Care by Bridging the Gap between Prehospital and Hospital Discharge Data through Stepwise Deterministic Linkage

Cited by 7 publications

References 17 publications

Virtual patient identifier (vPID): Improving patient traceability using anonymized identifiers in Japanese healthcare insurance claims database

Virtual patient identifier (vPID): Improving patient traceability using anonymized identifiers in Japanese healthcare insurance claims database

Automatic Information Exchange in the Early Rescue Chain Using the International Standard Accident Number (ISAN)

Using a Quality Framework to Explore Air Ambulance Patients’ Journey Outcomes in Central Queensland, Australia

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