A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources

ObjectiveThe Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled “Developing a Clinical Genomic Informatics Research Agenda”. The meeting’s goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings.Materials and MethodsExperts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting''s goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy.ResultsOutcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address.DiscussionDiscussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.     

Retinal image registration and comparison for clinical decision support

Background

For eye diseases, such as glaucoma and age-related macular degeneration (ARMD), involved in long-term degeneration procedure, longitudinal comparison of retinal images is a common step for reliable diagnosis of these kinds of diseases.

Aims

To provide a retinal image registration approach for longitudinal retinal image alignment and comparison.

Method

Two image registration solutions were proposed for facing different image qualities of retinal images to make the registration methods more robust and feasible in a clinical application system.

Results

Thirty pairs of longitudinal retinal images were used for the registration test. The experiments showed both solutions provided good performance for the accurate image registrations with efficiency.

Conclusion

We proposed a set of retinal image registration solutions for longitudinal retinal image observation and comparison targeting a clinical application environment.     

Key principles for a national clinical decision support knowledge sharing framework: synthesis of insights from leading subject matter experts

Objective

To identify key principles for establishing a national clinical decision support (CDS) knowledge sharing framework.

Materials and methods

As part of an initiative by the US Office of the National Coordinator for Health IT (ONC) to establish a framework for national CDS knowledge sharing, key stakeholders were identified. Stakeholders'' viewpoints were obtained through surveys and in-depth interviews, and findings and relevant insights were summarized. Based on these insights, key principles were formulated for establishing a national CDS knowledge sharing framework.

Results

Nineteen key stakeholders were recruited, including six executives from electronic health record system vendors, seven executives from knowledge content producers, three executives from healthcare provider organizations, and three additional experts in clinical informatics. Based on these stakeholders'' insights, five key principles were identified for effectively sharing CDS knowledge nationally. These principles are (1) prioritize and support the creation and maintenance of a national CDS knowledge sharing framework; (2) facilitate the development of high-value content and tooling, preferably in an open-source manner; (3) accelerate the development or licensing of required, pragmatic standards; (4) acknowledge and address medicolegal liability concerns; and (5) establish a self-sustaining business model.

Discussion

Based on the principles identified, a roadmap for national CDS knowledge sharing was developed through the ONC''s Advancing CDS initiative.

Conclusion

The study findings may serve as a useful guide for ongoing activities by the ONC and others to establish a national framework for sharing CDS knowledge and improving clinical care.     

基于社会认知理论的在线健康社区用户持续使用行为影响因素

基于社会认知理论,在借鉴已有研究成果的基础上,对影响我国在线健康社区用户持续使用意愿的因素进行了深入分析,用问卷调查方式进行的模型检验与实证研究表明,自我效能、结果期望、信息质量、系统质量和服务质量对持续使用在线健康社区的意愿产生显著正向影响,自我效能显著正向影响结果期望,持续使用在线健康社区的意愿对持续使用行为存在显著正向影响。     

Standards for reporting randomized controlled trials in medical informatics: a systematic review of CONSORT adherence in RCTs on clinical decision support

Introduction

The Consolidated Standards for Reporting Trials (CONSORT) were published to standardize reporting and improve the quality of clinical trials. The objective of this study is to assess CONSORT adherence in randomized clinical trials (RCT) of disease specific clinical decision support (CDS).

Methods

A systematic search was conducted of the Medline, EMBASE, and Cochrane databases. RCTs on CDS were assessed against CONSORT guidelines and the Jadad score.

Result

32 of 3784 papers identified in the primary search were included in the final review. 181 702 patients and 7315 physicians participated in the selected trials. Most trials were performed in primary care (22), including 897 general practitioner offices. RCTs assessing CDS for asthma (4), diabetes (4), and hyperlipidemia (3) were the most common. Thirteen CDS systems (40%) were implemented in electronic medical records, and 14 (43%) provided automatic alerts. CONSORT and Jadad scores were generally low; the mean CONSORT score was 30.75 (95% CI 27.0 to 34.5), median score 32, range 21–38. Fourteen trials (43%) did not clearly define the study objective, and 11 studies (34%) did not include a sample size calculation. Outcome measures were adequately identified and defined in 23 (71%) trials; adverse events or side effects were not reported in 20 trials (62%). Thirteen trials (40%) were of superior quality according to the Jadad score (≥3 points). Six trials (18%) reported on long-term implementation of CDS.

Conclusion

The overall quality of reporting RCTs was low. There is a need to develop standards for reporting RCTs in medical informatics.     

Tasks as needs: reframing the paradigm of clinical natural language processing research for real-world decision support

Electronic medical records are increasingly used to store patient information in hospitals and other clinical settings. There has been a corresponding proliferation of clinical natural language processing (cNLP) systems aimed at using text data in these records to improve clinical decision-making, in comparison to manual clinician search and clinical judgment alone. However, these systems have delivered marginal practical utility and are rarely deployed into healthcare settings, leading to proposals for technical and structural improvements. In this paper, we argue that this reflects a violation of Friedman’s “Fundamental Theorem of Biomedical Informatics,” and that a deeper epistemological change must occur in the cNLP field, as a parallel step alongside any technical or structural improvements. We propose that researchers shift away from designing cNLP systems independent of clinical needs, in which cNLP tasks are ends in themselves—“tasks as decisions”—and toward systems that are directly guided by the needs of clinicians in realistic decision-making contexts—“tasks as needs.” A case study example illustrates the potential benefits of developing cNLP systems that are designed to more directly support clinical needs.     

Automation in nursing decision support systems: A systematic review of effects on decision making,care delivery,and patient outcomes

ObjectiveThe study sought to summarize research literature on nursing decision support systems (DSSs ); understand which steps of the nursing care process (NCP) are supported by DSSs, and analyze effects of automated information processing on decision making, care delivery, and patient outcomes.Materials and MethodsWe conducted a systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, CINAHL, Cochrane, Embase, Scopus, and Web of Science were searched from January 2014 to April 2020 for studies focusing on DSSs used exclusively by nurses and their effects. Information about the stages of automation (information acquisition, information analysis, decision and action selection, and action implementation), NCP, and effects was assessed.ResultsOf 1019 articles retrieved, 28 met the inclusion criteria, each studying a unique DSS. Most DSSs were concerned with two NCP steps: assessment (82%) and intervention (86%). In terms of automation, all included DSSs automated information analysis and decision selection. Five DSSs automated information acquisition and only one automated action implementation. Effects on decision making, care delivery, and patient outcome were mixed. DSSs improved compliance with recommendations and reduced decision time, but impacts were not always sustainable. There were improvements in evidence-based practice, but impact on patient outcomes was mixed.ConclusionsCurrent nursing DSSs do not adequately support the NCP and have limited automation. There remain many opportunities to enhance automation, especially at the stage of information acquisition. Further research is needed to understand how automation within the NCP can improve nurses’ decision making, care delivery, and patient outcomes.     

Cross-vendor evaluation of key user-defined clinical decision support capabilities: a scenario-based assessment of certified electronic health records with guidelines for future development

Objective Clinical decision support (CDS) is essential for delivery of high-quality, cost-effective, and safe healthcare. The authors sought to evaluate the CDS capabilities across electronic health record (EHR) systems.Methods We evaluated the CDS implementation capabilities of 8 Office of the National Coordinator for Health Information Technology Authorized Certification Body (ONC-ACB)-certified EHRs. Within each EHR, the authors attempted to implement 3 user-defined rules that utilized the various data and logic elements expected of typical EHRs and that represented clinically important evidenced-based care. The rules were: 1) if a patient has amiodarone on his or her active medication list and does not have a thyroid-stimulating hormone (TSH) result recorded in the last 12 months, suggest ordering a TSH; 2) if a patient has a hemoglobin A1c result >7% and does not have diabetes on his or her problem list, suggest adding diabetes to the problem list; and 3) if a patient has coronary artery disease on his or her problem list and does not have aspirin on the active medication list, suggest ordering aspirin.Results Most evaluated EHRs lacked some CDS capabilities; 5 EHRs were able to implement all 3 rules, and the remaining 3 EHRs were unable to implement any of the rules. One of these did not allow users to customize CDS rules at all. The most frequently found shortcomings included the inability to use laboratory test results in rules, limit rules by time, use advanced Boolean logic, perform actions from the alert interface, and adequately test rules.Conclusion Significant improvements in the EHR certification and implementation procedures are necessary.     

人工智能技术的应用:罕见病临床决策系统的需求、现状与挑战

罕见病的发病率极低,但其总体患病人数不少。罕见病引起的病变后果严重,对患者及其家庭和社会造成了沉重负担。虽然当前利用基因测序技术、临床决策支持系统结合人工智能技术辅助临床进行遗传病诊断的研究火热,但临床诊断罕见病仍是非常大的技术挑战。本文简要综述了罕见病临床决策系统,旨在分析人工智能技术在罕见病中的发展现状和挑战。     

面向决策支持的临床指南知识表示方法研究

目的：更好地表示临床指南知识,以支持临床决策支持系统的构建。方法：以缺血性卒中为例,在系统梳理临床指南知识表示方法的基础上,提出使用解释节点、数据获取节点、动作节点、复合节点,以及有向边和无向边对指南诊疗流程进行表示,同时考虑实际的电子病历数据格式,设计自然语言处理模块,提高指南与电子病历系统的数据交互能力。结果：在实证研究中,使用8个节点、7条边,以及节点和边中的4个逻辑推理式表示了缺血性卒中患者临床类型判断流程,同时使用9个节点、8条边,以及节点和边中的6个逻辑推理式表示了缺血性卒中急性期的血糖管理,详细说明了临床指南知识表示建模过程。结论：本研究提出的临床指南知识表示方法可以使用节点、边、数据、逻辑推理算法等表示临床指南知识及推理逻辑,以支撑临床决策支持系统的构建。     

电子病历的临床决策支持

目前电子病历正向智能化和知识化发展,其核心价值是满足临床诊疗现场的信息需求及能够有效地改善医生的临床决策支持.电子病历的开放式结构化数据录入使临床描述信息结构化,并使临床医疗和科研活动充分利用这些数据成为可能.     

Design,effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis

ObjectivesElectronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases.Material and MethodsWe conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted.ResultsThe review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY.ConclusionWe summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies.RegistrationPROSPERO (CRD42020203716)     

医技科室对临床路径的影响和支撑作用简

临床路径是针对患者从入院到出院的线路所制订的不同疾病的标准化诊疗方法，主要目的是控制医疗成本和缩短诊疗时间，其中任何一个环节出现问题都会使临床路径产生变异。在实施临床路径过程中，医技科室的工作质量和完成时间等均会直接影响临床路径的顺利进行。作者旨在分析目前医改形势下医技科室的工作现状对临床路径的影响，探讨在实施临床路径过程中医技科室的支撑作用。     

Expected clinical utility of automatable prediction models for improving palliative and end-of-life care outcomes: Toward routine decision analysis before implementation

ObjectiveThe study sought to evaluate the expected clinical utility of automatable prediction models for increasing goals-of-care discussions (GOCDs) among hospitalized patients at the end of life (EOL).Materials and MethodsWe built a decision model from the perspective of clinicians who aim to increase GOCDs at the EOL using an automated alert system. The alternative strategies were 4 prediction models—3 random forest models and the Modified Hospital One-year Mortality Risk model—to generate alerts for patients at a high risk of 1-year mortality. They were trained on admissions from 2011 to 2016 (70 788 patients) and tested with admissions from 2017-2018 (16 490 patients). GOCDs occurring in usual care were measured with code status orders. We calculated the expected risk difference (beneficial outcomes with alerts minus beneficial outcomes without alerts among those at the EOL), the number needed to benefit (number of alerts needed to increase benefit over usual care by 1 outcome), and the net benefit (benefit minus cost) of each strategy.ResultsModels had a C-statistic between 0.79 and 0.86. A code status order occurred during 2599 of 3773 (69%) hospitalizations at the EOL. At a risk threshold corresponding to an alert prevalence of 10%, the expected risk difference ranged from 5.4% to 10.7% and the number needed to benefit ranged from 5.4 to 10.9 alerts. Using revealed preferences, only 2 models improved net benefit over usual care. A random forest model with diagnostic predictors had the highest expected value, including in sensitivity analyses.DiscussionPrediction models with acceptable predictive validity differed meaningfully in their ability to improve over usual decision making.ConclusionsAn evaluation of clinical utility, such as by using decision curve analysis, is recommended after validating a prediction model because metrics of model predictiveness, such as the C-statistic, are not informative of clinical value.     

Optimizing gender-affirming medical care through anatomical inventories,clinical decision support,and population health management in electronic health record systems

Recent advances in electronic health records and health information technology are providing new opportunities to improve the quality of care for transgender and gender diverse people, a population that experiences significant health disparities. This article recommends changes to electronic health record systems that have the potential to optimize gender-affirming care. Specifically, we discuss the importance of creating an anatomical inventory form that captures organ diversity, and of developing clinical decision support tools and population health management systems that consider each patient’s gender identity, sex assigned at birth, and anatomy.     

OrderRex clinical user testing: a randomized trial of recommender system decision support on simulated cases

ObjectiveTo assess usability and usefulness of a machine learning-based order recommender system applied to simulated clinical cases.Materials and Methods43 physicians entered orders for 5 simulated clinical cases using a clinical order entry interface with or without access to a previously developed automated order recommender system. Cases were randomly allocated to the recommender system in a 3:2 ratio. A panel of clinicians scored whether the orders placed were clinically appropriate. Our primary outcome included the difference in clinical appropriateness scores. Secondary outcomes included total number of orders, case time, and survey responses.ResultsClinical appropriateness scores per order were comparable for cases randomized to the order recommender system (mean difference -0.11 order per score, 95% CI: [-0.41, 0.20]). Physicians using the recommender placed more orders (median 16 vs 15 orders, incidence rate ratio 1.09, 95%CI: [1.01-1.17]). Case times were comparable with the recommender system. Order suggestions generated from the recommender system were more likely to match physician needs than standard manual search options. Physicians used recommender suggestions in 98% of available cases. Approximately 95% of participants agreed the system would be useful for their workflows.DiscussionUser testing with a simulated electronic medical record interface can assess the value of machine learning and clinical decision support tools for clinician usability and acceptance before live deployments.ConclusionsClinicians can use and accept machine learned clinical order recommendations integrated into an electronic order entry interface in a simulated setting. The clinical appropriateness of orders entered was comparable even when supported by automated recommendations.