Comprehensive Clinical Decision Support for the Primary Care of Premature Infants

早产儿初级保健的综合临床决策支持

• 批准号: 7825848
• 负责人: Robert W Grundmeier
• 金额: $ 82.26万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2012-07-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7825848
• 关键词: Address; Affect; Age-Months; Area; Birth Weight; Caring; Clinical; Comorbidity; Complex; Custom; Data; Decision Making; Delaware; Development; Electronic Health Record; Ensure; Evaluation; Expert Systems; Funding; Future; Gestational Age; Guidelines; Health; Healthcare; Hospitals; Infant; Information Technology; Intensive Care; Intervention; Knowledge; Low Birth Weight Infant; Modeling; Neonatal; Neonatal Intensive Care Units; Nurseries; Occupations; Online Systems; Outcome; Patients; Pediatric Hospitals; Phase; Philadelphia; Premature Infant; Preventive; Primary Health Care; Process; Public Health; Publications; Quality of Care; Randomized; Recommendation; Recording of previous events; Research; Research Support; Risk; Schedule; Services; Stimulus; Time; United States; United States National Institutes of Health; Vendor; Very Low Birth Weight Infant; Vulnerable Populations; Work; base; cohort; data mining; design; economic impact; experience; follow-up; health information technology; high risk; high risk infant; improved; meetings; operation; post intervention; programs; prospective; standard care; success; tool; usability

DESCRIPTION (provided by applicant): Premature infants are a vulnerable population with multiple inter-related health problems that put them at risk for poor outcomes. Electronic health records capture large amounts of information that may help guide decisions, but existing alert and reminder-based clinical decision support (CDS) frameworks do not adequately apply multiple overlapping care guidelines to complex patient histories to produce coherent clinical recommendations. Our proposed study directly addresses the broad challenge area of 10 Information Technology for Processing Health Care Data for Research and the specific challenge topic 10-LM-102 Advanced Decision Support for Complex Clinical Decisions. This study will use a rules-based expert system embedded in an electronic health record (EHR) to extract, interpret, and present salient facts and recommendations related to the healthcare of premature infants. This proposal also meets the challenge of providing an immediate stimulus to the economy through the retention or creation of jobs and increased spending in the Delaware Valley region. The Children's Hospital of Philadelphia (CHOP) contributes substantially to the local economy. In 2008, CHOP's operations created and supported over 16,882 jobs in the region, and CHOP's total economic impact was over $2.01 billion. Moreover, through a combination of private donations, NIH funding, and allocations from its hospital operations, CHOP receives more total research support than any other children's hospital in the United States-$180 million in fiscal year 2007-2008. The current proposal will create or retain 5 jobs. The care of premature infants is a rapidly growing public health concern in the United States, with over 60,000 infants born in 2006 with a birth weight under 1500 grams1. With recent advances in neonatal care, more premature infants are surviving to discharge from the neonatal intensive care unit (NICU). Even though this high-risk group of patients is particularly sensitive to the care they receive after discharge, efforts to ensure high quality post-discharge care are haphazardly implemented. Electronic health records with embedded clinical decision support tools such as rules-based expert systems provide a natural opportunity to favorably affect the healthcare for these vulnerable infants. The complex decision-making for these infants must consider large numbers of variables that change over time. The matrices of rules required to cover all possible combinations of variables are huge. To date no such decision support tools have been implemented or evaluated to handle the complexity of decision-making required for the healthcare of premature infants. This project will use a prospective cluster randomized design to evaluate the success of a health information technology intervention to improve care quality for low birth weight (LBW) and very low birth weight (VLBW) premature infants from the time of intensive care nursery discharge through 24 months of age. The intervention will consist of (1) a real-time data mining tool functioning in the EHR that will organize large amounts of health information visually in a succinct time-line format; (2) a rules-based expert system that will forecast a schedule for upcoming preventive health assessments and interventions appropriate for LBW and VLBW infants, and (3) a suite of problem focused expert systems to guide decision-making related to the most prevalent co-morbidities and complications experienced by this vulnerable population. Evaluation will focus on three areas: (1) usability of the automated data-mining tool and expert system pre-implementation; (2) change in clinician knowledge of standard care process for LBW and VLBW infants pre- and post-intervention; and (3) change in care process outcomes in the intervention practices compared to the control practices. The proposed work will improve the capacity for delivering clinical decision support (CDS) in complex clinical domains. Rigorous evaluation of both the design phase and intervention phase will support publications regarding best practices for future CDS development. The approach of a standards-based web-service model with a modest custom programming "footprint" in the electronic health record (EHR) will facilitate the distribution of this and future CDS interventions to other healthcare organizations and EHR vendors. The proposed work will improve the capacity for delivering clinical decision support (CDS) in complex clinical domains. Rigorous evaluation of both the design phase and intervention phase will support publications regarding best practices for future CDS development. The approach of a standards-based web-service model with a modest custom programming "footprint" in the electronic health record (EHR) will facilitate the distribution of this and future CDS interventions to other healthcare organizations and EHR vendors.

描述(由申请人提供)： 早产儿是一个脆弱的群体，有多种相互关联的健康问题，使他们面临不良结局的风险。电子健康记录捕获了大量有助于指导决策的信息，但现有的基于警报和提醒的临床决策支持(CDS)框架不能充分地将多个重叠的护理指南应用于复杂的患者病历，以产生连贯的临床建议。我们建议的研究直接涉及10信息技术为研究处理医疗保健数据的广泛挑战领域，以及特定挑战主题10-LM-102复杂临床决策的高级决策支持。这项研究将使用嵌入电子健康记录(EHR)中的基于规则的专家系统来提取、解释和提出与早产儿保健相关的显著事实和建议。这项提议还应对通过在特拉华河谷地区保留或创造就业机会和增加支出来立即刺激经济的挑战。费城儿童医院(CHOP)为当地经济做出了巨大贡献。2008年，CHOP的业务在该地区创造和支持了超过16,882个工作岗位，CHOP的总经济影响超过20.1亿美元。此外，通过私人捐款、NIH资助和医院运营拨款，CHOP获得的研究资助总额超过美国其他任何一家儿童医院--2007-2008财年为1.8亿美元。目前的提案将创造或保留5个就业机会。在美国，早产儿的护理是一个迅速增长的公共卫生问题，2006年出生的早产儿有60,000多名，出生体重低于1500克1。随着新生儿护理的最新进展，越来越多的早产儿从新生儿重症监护病房(NICU)存活到出院。尽管这群高危患者对他们出院后接受的护理特别敏感，但确保高质量出院后护理的努力却被随意实施。带有嵌入式临床决策支持工具的电子健康记录，如基于规则的专家系统，提供了一个自然的机会，可以有利地影响这些脆弱婴儿的医疗保健。这些婴儿的复杂决策必须考虑到随着时间的推移而变化的大量变量。涵盖所有可能的变量组合所需的规则矩阵非常庞大。到目前为止，还没有实施或评估这种决策支持工具来处理早产儿保健所需决策的复杂性。该项目将使用前瞻性整群随机设计来评估健康信息技术干预的成功，以改善从重症监护托儿所出院到24个月大的低出生体重(LBW)和极低出生体重(VLBW)早产儿的护理质量。干预措施将包括(1)在电子健康记录中发挥作用的实时数据挖掘工具，将以简洁的时间线格式可视化地组织大量健康信息；(2)基于规则的专家系统，将预测适用于LBW和VLBW婴儿的即将到来的预防性健康评估和干预的时间表，以及(3)一套专注于问题的专家系统，以指导与这一弱势群体经历的最普遍的共病和并发症相关的决策。评估将集中在三个方面：(1)自动化数据挖掘工具和专家系统实施前的可用性；(2)临床医生在干预前后对低出生体重儿和极低出生体重儿标准护理流程的了解的变化；以及(3)干预实践与对照实践相比护理过程结果的变化。拟议的工作将提高在复杂临床领域提供临床决策支持(CDS)的能力。对设计阶段和干预阶段的严格评估将支持有关未来CDS开发最佳做法的出版物。采用基于标准的网络服务模式，在电子健康记录(EHR)中保留适度的定制编程“足迹”，将有助于将这一干预措施和未来的CDS干预措施分发给其他医疗保健组织和电子健康记录供应商。拟议的工作将提高在复杂临床领域提供临床决策支持(CDS)的能力。对设计阶段和干预阶段的严格评估将支持有关未来CDS开发最佳做法的出版物。采用基于标准的网络服务模式，在电子健康记录(EHR)中保留适度的定制编程“足迹”，将有助于将这一干预措施和未来的CDS干预措施分发给其他医疗保健组织和电子健康记录供应商。