Clinical decision support with automated text processing for cervical cancer screening

Objective

To develop a computerized clinical decision support system (CDSS) for cervical cancer screening that can interpret free-text Papanicolaou (Pap) reports.

Materials and Methods

The CDSS was constituted by two rulebases: the free-text rulebase for interpreting Pap reports and a guideline rulebase. The free-text rulebase was developed by analyzing a corpus of 49 293 Pap reports. The guideline rulebase was constructed using national cervical cancer screening guidelines. The CDSS accesses the electronic medical record (EMR) system to generate patient-specific recommendations. For evaluation, the screening recommendations made by the CDSS for 74 patients were reviewed by a physician.

Results and Discussion

Evaluation revealed that the CDSS outputs the optimal screening recommendations for 73 out of 74 test patients and it identified two cases for gynecology referral that were missed by the physician. The CDSS aided the physician to amend recommendations in six cases. The failure case was because human papillomavirus (HPV) testing was sometimes performed separately from the Pap test and these results were reported by a laboratory system that was not queried by the CDSS. Subsequently, the CDSS was upgraded to look up the HPV results missed earlier and it generated the optimal recommendations for all 74 test cases.

Limitations

Single institution and single expert study.

Conclusion

An accurate CDSS system could be constructed for cervical cancer screening given the standardized reporting of Pap tests and the availability of explicit guidelines. Overall, the study demonstrates that free text in the EMR can be effectively utilized through natural language processing to develop clinical decision support tools.     

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.     

Computerized clinical decision support for medication prescribing and utilization in pediatrics

Background and objective

Accurate and informed prescribing is essential to ensure the safe and effective use of medications in pediatric patients. Computerized clinical decision support (CCDS) functionalities have been embedded into computerized physician order entry systems with the aim of ensuring accurate and informed medication prescribing. Owing to a lack of comprehensive analysis of the existing literature, this review was undertaken to analyze the effect of CCDS implementation on medication prescribing and use in pediatrics.

Materials and methods

A literature search was performed using keywords in PubMed to identify research studies with outcomes related to the implementation of medication-related CCDS functionalities.

Results and discussion

Various CCDS functionalities have been implemented in pediatric patients leading to different results. Medication dosing calculators have decreased calculation errors. Alert-based CCDS functionalities, such as duplicate therapy and medication allergy checking, may generate excessive alerts. Medication interaction CCDS has been minimally studied in pediatrics. Medication dosing support has decreased adverse drug events, but has also been associated with high override rates. Use of medication order sets have improved guideline adherence. Guideline-based treatment recommendations generated by CCDS functionalities have had variable influence on appropriate medication use, with few studies available demonstrating improved patient outcomes due to CCDS use.

Conclusion

Although certain medication-related CCDS functionalities have shown benefit in medication prescribing for pediatric patients, others have resulted in high override rates and inconsistent or unknown impact on patient care. Further studies analyzing the effect of individual CCDS functionalities on safe and effective prescribing and medication use are required.     

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.     

Electronic decision support for diagnostic imaging in a primary care setting

Methods

Clinical guideline adherence for diagnostic imaging (DI) and acceptance of electronic decision support in a rural community family practice clinic was assessed over 36 weeks. Physicians wrote 904 DI orders, 58% of which were addressed by the Canadian Association of Radiologists guidelines.

Results

Of those orders with guidelines, 76% were ordered correctly; 24% were inappropriate or unnecessary resulting in a prompt from clinical decision support. Physicians followed suggestions from decision support to improve their DI order on 25% of the initially inappropriate orders. The use of decision support was not mandatory, and there were significant variations in use rate. Initially, 40% reported decision support disruptive in their work flow, which dropped to 16% as physicians gained experience with the software.

Conclusions

Physicians supported the concept of clinical decision support but were reluctant to change clinical habits to incorporate decision support into routine work flow.     

Effects of computer-aided clinical decision support systems in improving antibiotic prescribing by primary care providers: a systematic review

Objective To assess the effectiveness of computer-aided clinical decision support systems (CDSS) in improving antibiotic prescribing in primary care.Methods A literature search utilizing Medline (via PubMed) and Embase (via Embase) was conducted up to November 2013. Randomized controlled trials (RCTs) and cluster randomized trials (CRTs) that evaluated the effects of CDSS aiming at improving antibiotic prescribing practice in an ambulatory primary care setting were included for review. Two investigators independently extracted data about study design and quality, participant characteristics, interventions, and outcomes.Results Seven studies (4 CRTs, 3 RCTs) met our inclusion criteria. All studies were performed in the USA. Proportions of eligible patient visits that triggered CDSS use varied substantially between intervention arms of studies (range 2.8–62.8%). Five out of seven trials showed marginal to moderate statistically significant effects of CDSS in improving antibiotic prescribing behavior. CDSS that automatically provided decision support were more likely to improve prescribing practice in contrast to systems that had to be actively initiated by healthcare providers.Conclusions CDSS show promising effectiveness in improving antibiotic prescribing behavior in primary care. Magnitude of effects compared to no intervention, appeared to be similar to other moderately effective single interventions directed at primary care providers. Additional research is warranted to determine CDSS characteristics crucial to triggering high adoption by providers as a perquisite of clinically relevant improvement of antibiotic prescribing.     

临床决策系统助力提高脓毒症休克集束化治疗依从性

目的 提高医护人员对脓毒症休克集束化治疗的依从性和执行率。 方法 选择脓毒症休克患者144例随机分为对照组和试验组。对照组按指南要求在脓毒症休克诊断1 h、3 h、6 h内分别完成既定的集束化治疗措施;试验组利用重症智能临床决策系统脓毒症休克质量控制模块,建立脓毒症休克诊断后1 h、3 h、6 h内的集束化治疗路径流程,系统在每个特定的时间段及时、反复对医护人员进行提醒,直到脓毒症休克集束化治疗措施各指标完成。比较两组集束化治疗效果,包括1 h、3 h、6 h目标完成率、平均动脉压（mean arterial pressure,MAP）、中心静脉压（central venous pressure,CVP）、中心静脉血氧饱和度（central venous oxygen saturation,ScvO2）、6 h乳酸清除率（lactate clearance,LCR）、尿量、去甲肾上腺素剂量、结局指标。 结果 试验组集束化治疗1 h、3 h、6 h目标完成率均高于对照组（P＜0.05）,两组MAP、CVP、尿量比较差异无统计学意义（P＞0.05）,试验组ScvO2、6 h LCR低于对照组,去甲肾上腺素高于对照组（P＜0.05）,试验组ICU住院时间短于对照组（P＜0.05）,两组28 d病死率比较差异无统计学意义（P＞0.05）。 结论 重症智能临床决策系统通过对数据汇总分析可及时预警、诊断脓毒症休克,通过特定时间段内及时、反复提醒,可以促进脓毒症休克集束化治疗各项目的有效实施,改善脓毒症休克患者的治疗效果,提高脓毒症休克治疗效率和医疗护理质量。     

Comparison of a basic and an advanced pharmacotherapy-related clinical decision support system in a hospital care setting in the Netherlands

Objective

To compare the clinical relevance of medication alerts in a basic and in an advanced clinical decision support system (CDSS).

Design

A prospective observational study.

Materials and methods

We collected 4023 medication orders in a hospital for independent evaluation in two pharmacotherapy-related decision support systems. Only the more advanced system considered patient characteristics and laboratory test results in its algorithms. Two pharmacists assessed the clinical relevance of the medication alerts produced. The alert was considered relevant if the pharmacist would undertake action (eg, contact the physician or the nurse). The primary analysis concerned the positive predictive value (PPV) for clinically relevant medication alerts in both systems.

Results

The PPV was significantly higher in the advanced system (5.8% vs 17.0%; p<0.05). Significant differences were found in the alert categories: drug–(drug) interaction (9.9% vs 14.8%; p<0.05), drug–age interaction (2.9% vs 73.3%; p<0.05), and dosing guidance (5.6% vs 16.9%; p<0.05). Including laboratory values and other patient characteristics resulted in a significantly higher PPV for the advanced CDSS compared to the basic medication alerts (12.2% vs 23.3%; p<0.05).

Conclusion

The advanced CDSS produced a higher proportion of clinically relevant medication alerts, but the number of irrelevant alerts remained high. To improve the PPV of the advanced CDSS, the algorithms should be optimized by identifying additional risk modifiers and more data should be made electronically available to improve the performance of the algorithms. Our study illustrates and corroborates the need for cyclic testing of technical improvements in information technology in circumstances representative of daily clinical practice.     

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.     

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.     

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.     

Presence of key findings in the medical record prior to a documented high-risk diagnosis

Background

Failure or delay in diagnosis is a common preventable source of error. The authors sought to determine the frequency with which high-information clinical findings (HIFs) suggestive of a high-risk diagnosis (HRD) appear in the medical record before HRD documentation.

Methods

A knowledge base from a diagnostic decision support system was used to identify HIFs for selected HRDs: lumbar disc disease, myocardial infarction, appendicitis, and colon, breast, lung, ovarian and bladder carcinomas. Two physicians reviewed at least 20 patient records retrieved from a research patient data registry for each of these eight HRDs and for age- and gender-compatible controls. Records were searched for HIFs in visit notes that were created before the HRD was established in the electronic record and in general medical visit notes for controls.

Results

25% of records reviewed (61/243) contained HIFs in notes before the HRD was established. The mean duration between HIFs first occurring in the record and time of diagnosis ranged from 19 days for breast cancer to 2 years for bladder cancer. In three of the eight HRDs, HIFs were much less likely in control patients without the HRD.

Conclusions

In many records of patients with an HRD, HIFs were present before the HRD was established. Reasons for delay include non-compliance with recommended follow-up, unusual presentation of a disease, and system errors (eg, lack of laboratory follow-up). The presence of HIFs in clinical records suggests a potential role for the integration of diagnostic decision support into the clinical workflow to provide reminder alerts to improve the diagnostic focus.     

Understanding the acceptability of a computer decision support system in pediatric primary care

Objective

Individual users’ attitudes and opinions help predict successful adoption of health information technology (HIT) into practice; however, little is known about pediatric users’ acceptance of HIT for medical decision-making at the point of care.

Materials and methods

We wished to examine the attitudes and opinions of pediatric users’ toward the Child Health Improvement through Computer Automation (CHICA) system, a computer decision support system linked to an electronic health record in four community pediatric clinics. Surveys were administered in 2011 and 2012 to all users to measure CHICA''s acceptability and users’ satisfaction with it. Free text comments were analyzed for themes to understand areas of potential technical refinement.

Results

70 participants completed the survey in 2011 (100% response rate) and 64 of 66 (97% response rate) in 2012. Initially, satisfaction with CHICA was mixed. In general, users felt the system held promise; however various critiques reflected difficulties understanding integrated technical aspects of how CHICA worked, as well as concern with the format and wording on generated forms for families and users. In the subsequent year, users’ ratings reflected improved satisfaction and acceptance. Comments also reflected a deeper understanding of the system''s logic, often accompanied by suggestions on potential refinements to make CHICA more useful at the point of care.

Conclusions

Pediatric users appreciate the system''s automation and enhancements that allow relevant and meaningful clinical data to be accessible at point of care. Understanding users’ acceptability and satisfaction is critical for ongoing refinement of HIT to ensure successful adoption into practice.     

面向心律失常疾病的临床决策支持系统交互性研究与实现

目的/意义 针对当前临床决策支持系统使用过程中存在的问题,提出界面设计原则并将其应用于心律失常疾病临床决策支持系统建设中。方法/过程 从交互设计角度,针对可解释性、时效性、可用性、相关性、尊重性和循证性6个维度,提出界面设计原则。选取心律失常疾病决策支持作为临床场景,设计临床决策支持系统交互界面原型,阐述界面功能、信息功能、交互功能设计与实现过程。结果/结论 本研究提出的界面设计原则可有效缓解6类问题,经论证具有应用于心律失常疾病临床决策支持原型系统的可行性以及可泛化性,可用于指导多种疾病辅助诊疗工具的交互设计。     

Evaluating the impact of an integrated computer-based decision support with person-centered analytics for the management of asthma in primary care: a randomized controlled trial

Background Computer-based decision support has been effective in providing alerts for preventive care. Our objective was to determine whether a personalized asthma management computer-based decision support increases the quality of asthma management and reduces the rate of out-of-control episodes.Methods A cluster-randomized trial was conducted in Quebec, Canada among 81 primary care physicians and 4447 of their asthmatic patients. Patients were followed from the first visit for 3–33 months. The physician control group used the Medical Office of the 21st century (MOXXI) system, an integrated electronic health record. A custom-developed asthma decision support system was integrated within MOXXI and was activated for physicians in the intervention group.Results At the first visit, 9.8% (intervention) to 12.9% (control) of patients had out-of-control asthma, which was defined as a patient having had an emergency room visit or hospitalization for respiratory-related problems and/or more than 250 doses of fast-acting β-agonist (FABA) dispensed in the past 3 months. By the end of the trial, there was a significant increase in the ratio of doses of inhaled corticosteroid use to fast-acting β-agonist (0.93 vs. 0.69: difference: 0.27; 95% CI: 0.02–0.51; P = 0.03) in the intervention group. The overall out-of-control asthma rate was 54.7 (control) and 46.2 (intervention) per 100 patients per year (100 PY), a non-significant rate difference of −8.7 (95% CI: −24.7, 7.3; P = 0.29). The intervention’s effect was greater for patients with out-of-control asthma at the beginning of the study, a group who accounted for 44.7% of the 5597 out-of-control asthma events during follow-up, as there was a reduction in the event rate of −28.4 per 100 PY (95% CI: −55.6, −1.2; P = 0.04) compared to patients with in-control asthma at the beginning of the study (−0.08 [95% CI: −10.3, 8.6; P = 0.86]).Discussion This study evaluated the effectiveness of a novel computer-assisted ADS system that facilitates systematic monitoring of asthma control status, follow-up of patients with out of control asthma, and evidence-based, patient-specific treatment recommendations. We found that physicians were more likely to use ADS for out-of-control patients, that in the majority of these patients, they were advised to add an inhaled corticosteroid or a leukotriene inhibitor to the patient s treatment regimen, and the intervention significantly increased the mean ratio of inhaled corticosteroids to FABA during follow-up. It also reduced the rate of out-of-control episodes during follow up among patients whose asthma was out-of-control at the time of study entry. Future research should assess whether coupling patient-specific treatment recommendations, automated follow-up, and home care with comparative feedback on quality and outcomes of care can improve guideline adoption and care outcomes.Conclusions A primary care-personalized asthma management system reduced the rate of out-of-control asthma episodes among patients whose asthma was poorly controlled at the study’s onset.Trial Registration Clinicaltrials.gov Identifier: {"type":"clinical-trial","attrs":{"text":"NCT00170248","term_id":"NCT00170248"}}NCT00170248 http://clinicaltrials.gov/ct2/show/{"type":"clinical-trial","attrs":{"text":"NCT00170248","term_id":"NCT00170248"}}NCT00170248?term=Asthma&spons=McGill+University&state1=NA%3ACA%3AQC&rank=2     

人工肝支持系统治疗慢性乙型重型肝炎的疗效观察

[目的 ]观察人工肝支持系统治疗慢性乙型重型肝炎的疗效。 [方法 ]177名患者分成治疗组和对照组 ,治疗组在综合药物治疗的基础上加用人工肝支持系统 ,而对照组只采用药物治疗。观察2组患者临床症状、肝功能、凝血酶原时间 (PT)、凝血酶原活动度 (PTA)及各期存活率。 [结果 ]治疗组在临床症状、肝功能、PT、PTA等指标的改善上明显优于对照组 (P >0 .0 5 ) ;治疗组早、中期存活率明显优于对照组 (P >0 .0 5 )。 [结论 ]联合人工肝支持系统能明显提高药物治疗慢性乙型重型肝炎的疗效。     

人工肝支持系统应用简介

本文回顾了人工肝的发展及在临床急慢性肝脏功能衰竭中的应用。目前非生物型人工肝仍在临床广泛应用并取得了较好效果。非生物型人工肝目前国内应用最多的方法为血浆置换法。一般血浆置换法设备是由多组泵、阀、管路、传感器以及过滤器(分离器)组合而成,并由计算机控制的系统。因为血浆置换法是侵人性的,设备的特点是安全性必须好,但由于肝脏功能的复杂性,以体外培养活性细胞为中心的生物型或混合型人工肝在肝衰时的支持治疗作用会更加明显和接近自然肝脏的功能。生物型人工肝发展迅速,但还有一系列问题需要解决。     

重症SARS患者的临床营养支持及血糖水平和胰岛素用量与结局的关系

目的观察重症SARS患者接受临床营养支持前后部分与营养有关指标的变化,并分析血糖水平/胰岛素用量与结局的关系。方法以我所英东重症医学监护中心收治的21例临床诊断重症SARS患者为研究对象,转入ICU后均给予呼吸支持及临床营养支持。经肠内营养接受的热量约4184kJ/d(1000kcal/d),蛋白质约38g/d;经肠外营养接受的热量约3347.2kJ/d(800kcal/d)。监测给予患者肠内、肠外营养支持前后的血糖、血清白蛋白、血淋巴细胞总数及谷丙转氨酶等指标的变化。所有患者均接受甲基强的松龙约200mg/d。为将血糖保持在4.44～7.78mmol/L(80～140mg/dl)的较低水平,应用静脉泵入胰岛素,记录血糖及胰岛素用量,再分析其与结局的关系。结果在发病(11.0±2.8)d后转入ICU的21例患者中,16例(76.2%)伴有营养不良。经过平均12d肠内与肠外营养支持后,患者血清白蛋白显著升高犤(28.5±2.2)g/Lvs(37.0±4.1)g/L犦(P=0.0001);血淋巴细胞总数升高犤(0.74±0.47)×109/Lvs(1.22±0.73)×109/L犦(P=0.02);血谷丙转氨酶升高的病例有所下降,但差异不显著(81.0%vs57.1%,P=0.18);生存组血糖降至较低水平犤(9.5±2.3)mmol/Lvs(6.3±1.8)mmol/L犦(P=0.0002);死亡组血糖也有下降犤(13.0±3.3)mmol/Lvs(9.5±1.3)mmol/L犦(P=0.04);生存组血糖水平低于