An Interoperable HL7 FHIR-based Medical Device Data System (MDDS) For Accessing And Integrating Live Point-Of-Care Data From High-Acuity Bedside Patient Monitoring Equipment

摘要 该提案的总体目标是将生物医学工程和关键技术的联合收割机专业知识和方法结合起来， 设计一种通用的床边生理信号采集、记录和传输系统 监测患者。病人监护仪每小时产生超过一百万个数据点的信息，然而， 这些数据的一小部分被传输或记录。为了改善医疗保健中的数据交换， 医疗保健互操作性资源（FHIR）标准于2014年发布。然而，它还没有做出一个 对医疗器械集成（MDI）产生重大影响，MDI是临床流程自动化的过程， 从床边医疗设备（如患者监护仪）到外部系统（如电子医疗设备）的数据 记录（EMR）。此外，MDI是一项复杂的任务，因为来自这些设备的数据是高频和高- 因为大多数设备使用专有协议和过时的接口，如串行电缆。 因此，医院和研究人员除了使用昂贵的和供应商特定的药物外， MDI解决方案，用于访问这些数据或使用手动数据输入到患者EMR中，这将导致数据输入 错误、数据输入延迟和临床护理时间损失。手动数据输入只能捕获 可用的数据，以及对人工智能（AI）和机器学习的研究兴趣的增加， 越来越需要一种标准化的方式来访问来自床边设备的大量数据。这个项目 将开发一个供应商中立的基于软件的医疗器械数据系统（MDDS）， 来自整个医院网络的床边设备的数据，并使用 FHIR应用编程接口（API）。拟议的概念验证将包括三个要素： [i]一个传输器，其加密并通过网络传输患者信号，[ii]一个聚合器， 接收、转换和记录信号到中央位置，以及[iii]具有API的FHIR服务器，用于允许 外部系统访问实时数据作为FHIR资源。该提案旨在创造一种新颖的设计， 克服了医疗保健、医学和研究领域的一个关键障碍。拟议的MDDS将对以下方面有价值： 医院，临床医生，研究人员和应用程序开发人员，因为它使数据可访问， 只有临床医生才能实时看到。

Abstract The overall goal of this proposal is to combine expertise and approaches from biomedical engineering and critical care medicine to design a universal system to acquire, record and transmit physiological signals from bedside monitored patients. Patient monitors generate over a million datapoints of information per hour, however, only a tiny fraction of those data are transmitted or recorded. In order to improve data exchange in healthcare, the Fast Healthcare Interoperability Resources (FHIR) standard was published in 2014. However, it has yet to make a significant impact on Medical Device Integration (MDI), which is the process of automating the flow of clinical data from bedside medical devices, such as patient monitors, to external systems such as Electronic Medical Records (EMR). Also, MDI is a complex task because data from these devices are high-frequency and high- volume and because most devices use proprietary protocols and outdated interfaces such as serial cables. Hospitals and researchers have therefore been left with few options except to use expensive and vendor-specific MDI solutions to access these data or to use manual data entry into the patient EMR, which leads to data entry errors, late entry of data, and lost time for clinical care. Manual data entry only captures a tiny fraction of the available data, and with increased research interest in Artificial Intelligence (AI) and Machine Learning, there is a growing need for a standardized way to access the vast amounts of data from bedside devices. This project will develop a vendor-neutral software-based Medical Device Data System (MDDS) that acquires and records data from bedside devices across a hospital network and makes live data available to 3rd party systems using a FHIR application programming interface (API). The proposed proof-of-concept will consist of three elements: [i] a transmitter which encrypts and transmits patient signals across the network, [ii] an aggregator which receives, translates and records the signals to a central location, and [iii] a FHIR Server with API for allowing external systems to access live data as FHIR resources. This proposal seeks to create a novel design that will overcome a critical barrier in healthcare, medicine and research. The proposed MDDS will be valuable to hospitals, clinicians, researchers and app developers because it makes data accessible which were previously only available to clinicians at the bedside in real-time.