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INNOVATIONS IN DIGITAL HEALTH TO IMPROVE CARE DELIVERY: THE BJC HEALTHCARE/WASHINGTON UNIVERSITY SCHOOL OF MEDICINE HEALTHCARE INNOVATION LAB.数字医疗创新改善医疗服务:圣路易斯华盛顿大学医学院/巴恩斯-犹太医院医疗创新实验室。
Trans Am Clin Climatol Assoc. 2024;134:239-251.
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本文引用的文献

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Cardiovascular diseases prediction by machine learning incorporation with deep learning.结合深度学习的机器学习用于心血管疾病预测
Front Med (Lausanne). 2023 Apr 17;10:1150933. doi: 10.3389/fmed.2023.1150933. eCollection 2023.
2
Advanced Care Planning for Hospitalized Patients Following Clinician Notification of Patient Mortality by a Machine Learning Algorithm.临床医生使用机器学习算法通知患者死亡后,对住院患者进行高级医疗照护计划。
JAMA Netw Open. 2023 Apr 3;6(4):e238795. doi: 10.1001/jamanetworkopen.2023.8795.
3
Effect of interventions for non-emergent medical transportation: a systematic review and meta-analysis.非紧急医疗转运干预措施的效果:系统评价和荟萃分析。
BMC Public Health. 2022 Apr 21;22(1):799. doi: 10.1186/s12889-022-13149-1.
4
The Current State and Validity of Digital Assessment Tools for Psychiatry: Systematic Review.精神病学数字评估工具的现状与有效性:系统评价
JMIR Ment Health. 2022 Mar 30;9(3):e32824. doi: 10.2196/32824.
5
Association of Race and Neighborhood Disadvantage with Patient Engagement in a Home-Based COVID-19 Remote Monitoring Program.种族和邻里劣势与基于家庭的 COVID-19 远程监测计划中患者参与的关联。
J Gen Intern Med. 2022 Mar;37(4):838-846. doi: 10.1007/s11606-021-07207-4. Epub 2022 Jan 6.
6
Implementation of a non-emergent medical transportation programme at an integrated health system.在综合卫生系统中实施非紧急医疗运输计划。
BMJ Health Care Inform. 2021 Sep;28(1). doi: 10.1136/bmjhci-2021-100417.
7
Factors influencing the effectiveness of remote patient monitoring interventions: a realist review.影响远程患者监护干预效果的因素:一个现实主义综述。
BMJ Open. 2021 Aug 25;11(8):e051844. doi: 10.1136/bmjopen-2021-051844.
8
Using electronic health records and claims data to identify high-risk patients likely to benefit from palliative care.利用电子健康记录和索赔数据来识别可能从姑息治疗中受益的高危患者。
Am J Manag Care. 2021 Jan 1;27(1):e7-e15. doi: 10.37765/ajmc.2021.88578.
9
Predictive analytics in health care: how can we know it works?医疗保健中的预测分析:我们如何知道它是否有效?
J Am Med Inform Assoc. 2019 Dec 1;26(12):1651-1654. doi: 10.1093/jamia/ocz130.
10
Knowledge of Palliative Care Among American Adults: 2018 Health Information National Trends Survey.美国成年人对姑息治疗的认知:2018 年健康信息国家趋势调查。
J Pain Symptom Manage. 2019 Jul;58(1):39-47.e3. doi: 10.1016/j.jpainsymman.2019.03.014. Epub 2019 Mar 26.

数字医疗创新改善医疗服务:圣路易斯华盛顿大学医学院/巴恩斯-犹太医院医疗创新实验室。

INNOVATIONS IN DIGITAL HEALTH TO IMPROVE CARE DELIVERY: THE BJC HEALTHCARE/WASHINGTON UNIVERSITY SCHOOL OF MEDICINE HEALTHCARE INNOVATION LAB.

机构信息

St. Louis, Missouri.

出版信息

Trans Am Clin Climatol Assoc. 2024;134:239-251.

PMID:39135571
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11316891/
Abstract

The Healthcare Innovation Lab, established by BJC HealthCare and Washington University School of Medicine, has catalyzed care delivery innovations since 2017. Focusing on digital health to enhance care delivery and patient outcomes, the Lab emphasizes predictive analytics, digital point-of-care tools, and remote patient monitoring. The Lab identifies innovative ideas that align with the health system mission and deliver empiric value to its patients and care teams. Since its inception, the Lab has vetted 507 ideas, piloting 98, with a success rate of 40%. Examples include a predictive model to improve palliative care referrals and goal-of-care discussions, a digital approach to non-emergent medical transportation that enhances access and equity, and a COVID-19 home monitoring program that proved essential during the pandemic. These initiatives underscore the importance of integrating digital technology with health care, balancing innovation with practical application, and using a data-informed approach to innovation selection and assessment.

摘要

BJC 医疗保健公司和华盛顿大学医学院成立的医疗保健创新实验室自 2017 年以来推动了医疗保健创新。该实验室专注于数字医疗,以提高医疗服务和患者的治疗效果,重点关注预测分析、数字即时工具和远程患者监测。该实验室确定了与医疗系统使命一致的创新理念,并为患者和护理团队提供经验价值。自成立以来,该实验室已经审查了 507 个创意,并进行了 98 个试点,成功率为 40%。其中的一些例子包括一个预测模型,用于改善姑息治疗转介和治疗目标讨论;一种用于非紧急医疗运输的数字方法,提高了可及性和公平性;以及一个 COVID-19 家庭监测计划,在大流行期间非常重要。这些举措强调了将数字技术与医疗保健相结合的重要性,平衡创新与实际应用,以及使用数据驱动的方法进行创新选择和评估。