Computer-based Clinical Decision Support Research Articles

Nursing Diagnosis Accuracy in Nursing Education: Clinical Decision Support System Compared With Paper-Based Documentation-A Before and After Study.

Computer-based technologies have been widely used in nursing education, although the best educational modality to improve documentation and nursing diagnostic accuracy using electronic health records is still under investigation. It is important to address this gap and seek an effective way to address increased accuracy around nursing diagnoses identification. Nursing diagnoses are judgments that represent a synthesis of data collected by the nurse and used to guide interventions and to achieve desirable patients' outcomes. This current investigation is aimed at comparing the nursing diagnostic accuracy, satisfaction, and usability of a computerized system versus a traditional paper-based approach. A total of 66 nursing students solved three validated clinical scenarios using the NANDA-International terminologies traditional paper-based approach and then the computer-based Clinical Decision Support System. Study findings indicated a significantly higher nursing diagnostic accuracy ( P < .001) in solving cancer and stroke clinical scenarios, whereas there was no significant difference in acute myocardial infarction scenario. The use of the electronic system increased the number of correct diagnostic indicators ( P < .05); however, the level of students' satisfaction was similar. The usability scores highlighted the need to make the electronic documentation systems more user-friendly.

The effects of pharmaceutical interventions on potentially inappropriate medications in older patients: a systematic review and meta-analysis.

Potentially inappropriate medications (PIMs) is a particular concern in older patients and is associated with negative health outcomes. As various interventions have been developed to manage it, we performed a systematic review and meta-analysis to evaluate the effect of pharmaceutical interventions on outcomes of PIMs in older patients. Meta-analysis of eligible randomized controlled trials (RCTs) was conducted to report the outcomes of pharmaceutical interventions in older patients searching from the databases of Cochrane Library, PubMed, Embase, Web of Science, Clinicaltrials.gov, SinoMed and Chinese Clinical Trial Registry (ChiCTR). The PRISMA guidelines were followed and the protocol was registered in PROSPERO (CRD42019134754). Cochrane bias risk assessment tool and the modified Jadad scale were used to assess the risk bias. RevMan software was used for data processing, analysis and graphical plotting. Sixty-five thousand, nine hundred seventy-one patients in 14 RCTs were included. Of the primary outcomes, pharmaceutical interventions could significantly reduce the incidence of PIMs in older patients (OR = 0.51, 95% CI: 0.42, 0.62; p < 0.001), and the number of PIMs per person (MD = -0.41, 95%CI: -0.51, -0.31; p < 0.001), accompanying by a low heterogeneity. Subgroup analysis showed that the application of computer-based clinical decision support for pharmacological interventions could remarkably decrease the incidence of PIMs and two assessment tools were more effective. Of the secondary outcomes, the meta-analysis showed that pharmacological interventions could reduce the number of drugs used per person (MD = -0.94, 95%CI: -1.51, -0.36; p = 0.001) and 30-day readmission rate (OR = 0.58, 95%CI: 0.36, 0.92; p = 0.02), accompanying by a low heterogeneity. However, the pharmaceutical interventions demonstrated no significant improvement on all-cause mortality and the number of falls. Our findings supported the efficacy of pharmaceutical interventions to optimize the use and management of drugs in older patients. https://clinicaltrials.gov/, CRD42019134754.

Survey of barriers to adherence to international inflammatory bowel disease guidelines: does gastroenterologists' confidence translate to high adherence?

Despite the availability of evidence-based inflammatory bowel disease (IBD) guidelines, suboptimal care persists. There is little published research assessing barriers to IBD guideline adherence. To identify barriers to IBD guideline adherence including gastroenterologists' knowledge and attitudes towards guidelines. An online cross-sectional survey of 824 Australian gastroenterologists was conducted from April to August 2018, with 198 (24%) responses. A novel survey was developed that was informed by the theoretical domain's framework. Confidence in guideline recommendations was high; however, referral to them was low. The European Crohn's and Colitis Organisation guidelines were referred to most commonly (43.6%). In multivariate analysis, significant predictors of frequent versus infrequent guideline referral were: high confidence in the guideline (odds ratio (OR) 7.70; 95% confidence interval (CI): 2.43-24.39; P = 0.001), and low (≤10 years) clinical experience (OR 3.62; 95% CI: 1.11-11.79; P = 0.03). The most common barriers to guideline adherence were not having time (62%), followed by guideline specifics being difficult to remember (61%). Low confidence was reported in managing pregnancy and IBD (34%) and loss of response to therapy (29%). High confidence was reported in managing immunomodulators; however, only 43% answered the associated knowledge question correctly. Although gastroenterologists have high confidence in guidelines, they use them infrequently, primarily due to specifics being difficult to remember and lack of time. Self-reported confidence in an area of IBD management does not always reflect knowledge. An intervention targeting these barriers, for example, computer-based clinical decision support tools, might improve adherence and standardise care.

A Coding Framework for Usability Evaluation of Digital Health Technologies.

Several studies have reported low adherence and high resistance from clinicians to adopt digital health technologies into clinical practice, particularly the use of computer-based clinical decision support systems. Poor usability and lack of integration with the clinical workflow have been identified as primary issues. Few guidelines exist on how to analyze the collected data associated with the usability of digital health technologies. In this study, we aimed to develop a coding framework for the systematic evaluation of users' feedback generated during focus groups and interview sessions with clinicians, underpinned by fundamental usability principles and design components. This codebook also included a coding category to capture the user's clinical role associated with each specific piece of feedback, providing a better understanding of role-specific challenges and perspectives, as well as the level of shared understanding across the multiple clinical roles. Furthermore, a voting system was created to quantitatively inform modifications of the digital system based on usability data. As a use case, we applied this method to an electronic cognitive aid designed to improve coordination and communication in the cardiac operating room, showing that this framework is feasible and useful not only to better understand suboptimal usability aspects, but also to recommend relevant modifications in the design and development of the system from different perspectives, including clinical, technical, and usability teams. The framework described herein may be applied in other highly complex clinical settings, in which digital health systems may play an important role in improving patient care and enhancing patient safety.

Elderly Patients in Primary Care are Still at Risks of Receiving Potentially Inappropriate Medications.

BackgroundMost older adults with comorbidities in primary care clinics use multiple medications and are at risk of potentially inappropriate medications (PIMs) prescription.ObjectiveThis study examined the prevalence of polypharmacy and PIMs using Thai criteria for PIMs.MethodsThis study was a retrospective cross-sectional study. Data were collected from electronic medical records in a primary care clinic in 2018. Samples were patients aged ≥65 years old with at least 1 prescription. Variables included age, gender, comorbidities, and medications. The list of risk drugs for Thai elderly version 2 was the criteria for PIMs. The prevalence of polypharmacy and PIMs were calculated, and multiple logistic regression was conducted to examine associations between variables and PIMs.ResultsOf 2806 patients, 27.5% and 43.7% used ≥5 medications and PIMs, respectively. Of 10 290 prescriptions, 47% had at least 1 PIM. The top 3 PIMs were anticholinergics, proton-pump inhibitors, and nonsteroidal anti-inflammatory drugs (NSAIDs). Polypharmacy and dyspepsia were associated with PIM prescriptions (adjusted odds ratio 2.48 [95% confident interval or 95% CI 2.07-2.96] and 3.88 [95% CI 2.65-5.68], respectively).ConclusionPrescriptions with PIMs were high in the primary care clinic. Describing unnecessary medications is crucial to prevent negative health outcomes from PIMs. Computer-based clinical decision support, pharmacy-led interventions, and patient-specific drug recommendations are promising interventions to reduce PIMs in a primary care setting.

Designing, Conducting, and Reporting Clinical Decision Support Studies: Recommendations and Call to Action.

By enabling more efficient and effective medical decision making, computer-based clinical decision support (CDS) could unlock widespread benefits from the significant investment in electronic health record (EHR) systems in the United States. Evidence from high-quality CDS studies is needed to enable and support this vision of CDS-facilitated care optimization, but limited guidance is available in the literature for designing and reporting CDS studies. To address this research gap, this article provides recommendations for designing, conducting, and reporting CDS studies to: 1) ensure that EHR data to inform the CDS are available; 2) choose decision rules that are consistent with local care processes; 3) target the right users and workflows; 4) make the CDS easy to access and use; 5) minimize the burden placed on users; 6) incorporate CDS success factors identified in the literature, in particular the automatic provision of CDS as a part of clinician workflow; 7) ensure that the CDS rules are adequately tested; 8) select meaningful evaluation measures; 9) use as rigorous a study design as is feasible; 10) think about how to deploy the CDS beyond the original host organization; 11) report the study in context; 12) help the audience understand why the intervention succeeded or failed; and 13) consider the financial implications. If adopted, these recommendations should help advance the vision of more efficient, effective care facilitated by useful and widely available CDS.

Effect on thromboprophylaxis among hospitalized patients using a system-wide multifaceted quality improvement intervention: Rationale and design for a multicenter cluster randomized clinical trial in China

BackgroundVenous thromboembolism (VTE) is a life-threatening disease that can affect each hospitalized patient. But the current in-hospital thromboprophylaxis remains suboptimal and there exists a large gap between clinical practice and guideline-recommended care in China.MethodsTo facilitate implementation of guideline recommendations, we conduct a multicenter, adjudicator-blinded, cluster-randomized clinical trial, aiming to assess the effectiveness of a system-wide multifaceted quality improvement (QI) strategy on VTE prophylaxis improvement and thromboembolism reduction in clinical setting. Hospitals are randomized into intervention or control group. In intervention group, hospitals receive the concept of appropriate in-hospital thromboprophylaxis plus a multifaceted QI which encompasses four components: (1) an electronic alert combining computer-based clinical decision support system and electronic reminders, (2) appropriate prophylaxis based on dynamic VTE and bleeding risk assessments, (3) periodical audit and interactive feedback on performance, (4) strengthened training and patient education. In control, hospitals receive the concept of recommended prophylaxis alone without QI. Thromboprophylaxis will be at the discretion of hospitals and conducted as usual. With a final sample size of 5760 hospitalized patients in 32 hospitals on mainland China, this trial will examine the effect of QI on improvement in thromboprophylaxis and patient-centered outcomes. This is an open-label trial that patients and healthcare professionals will know group allocation after enrollment, but endpoint adjudicators and statisticians will be blinded. RCT# NCT04211181ConclusionsThe system-wide multifaceted QI intervention is expected to facilitate implementation of recommended VTE prophylaxis in hospital, thereafter reducing VTE incidence and relevant adverse events among hospitalized patients in China.

The role of computer-based clinical decision support systems to deliver protective mechanical ventilation.

Mechanical ventilation of adults and children with acute respiratory failure necessitates balancing lung and diaphragm protective ventilation. Computerized decision support (CDS) offers advantages in circumstances where complex decisions need to be made to weigh potentially competing risks, depending on the physiologic state of the patient. Significant variability in how ventilator protocols are applied still exists and clinical data show that there continues to be wide variability in ventilator management. We have developed a CDS, which we are currently testing in a Phase II randomized controlled trial. The CDS is called Real-time Effort Driven ventilator management (REDvent). We will describe the rationale and methods for development of CDS for lung and diaphragm protective ventilation, using the REDvent CDS as an exemplar. Goals for achieving compliance and physiologic objectives can be met when CDS instructions are simple and explicit, provide the clinician with the underlying rule set, permit acceptable reasons for declining and allow for iterative adjustments.

Computational modeling of bicuspid aortopathy: Towards personalized risk strategies

This paper describes current advances on the application of in-silico for the understanding of bicuspid aortopathy and future perspectives of this technology on routine clinical care. This includes the impact that artificial intelligence can provide to develop computer-based clinical decision support system and that wearable sensors can offer to remotely monitor high-risk bicuspid aortic valve (BAV) patients. First, we discussed the benefit of computational modeling by providing tangible examples of in-silico software products based on computational fluid-dynamic (CFD) and finite-element method (FEM) that are currently transforming the way we diagnose and treat cardiovascular diseases. Then, we presented recent findings on computational hemodynamic and structural mechanics of BAV to highlight the potentiality of patient-specific metrics (not-based on aortic size) to support the clinical-decision making process of BAV-associated aneurysms. Examples of BAV-related personalized healthcare solutions are illustrated.

Simple and Elaborated Clinician Reminder Prompts for Human Papillomavirus Vaccination: A Randomized Clinical Trial

ObjectiveTo evaluate the effects of simple and elaborated health care provider (HCP) reminder prompts on human papillomavirus (HPV) vaccine initiation rates. MethodsTwenty-nine pediatric HCPs serving 5 pediatric clinics were randomized to 1 of 3 arms: 1) usual practice control, 2) simple reminder prompt, and 3) elaborated reminder prompt, which included suggested language for recommending the early adolescent platform vaccines. Prompts were delivered via a computer-based clinical decision support system deployed in the 5 clinics. Eligible patients were ages 11 to 13 years, had not received HPV vaccine, and were due for meningococcal ACWY (MenACWY) vaccine and/or the tetanus, diphtheria, and pertussis booster (Tdap). Receipt of HPV vaccine was determined via automated queries sent to the Indiana immunization registry. Data were analyzed via logistic regression models, with generalized estimating equations used to account for the clustering of patients within HCPs. ResultsTen HCPs in the control group saw 301 patients, 8 HCPs in the simple prompt group saw 124, and 11 HCPs in the elaborated prompt group saw 223. The elaborated prompt arm had a higher rate of HPV vaccination (62%) than the control arm (45%): adjusted odds ratio, 2.76; 95% confidence interval, 1.07 to 7.14. The simple prompt arm did not differ significantly from the control arm with respect to HPV vaccine initiation, which might have been because of the small sample size for this arm. MenACWY and Tdap rates did not vary across the 3 arms. ConclusionsResults suggest that an elaborated HCP-targeted reminder prompt, with suggested recommendation language, might improve rates of HPV vaccine initiation.

Clinical decision support models and frameworks: Seeking to address research issues underlying implementation successes and failures

Computer-based clinical decision support (CDS) has been pursued for more than five decades. Despite notable accomplishments and successes, wide adoption and broad use of CDS in clinical practice has not been achieved. Many issues have been identified as being partially responsible for the relatively slow adoption and lack of impact, including deficiencies in leadership, recognition of purpose, understanding of human interaction and workflow implications of CDS, cognitive models of the role of CDS, and proprietary implementations with limited interoperability and sharing.To address limitations, many approaches have been proposed and evaluated, drawing on theoretical frameworks, as well as management, technical and other disciplines and experiences. It seems clear, because of the multiple perspectives involved, that no single model or framework is adequate to encompass these challenges. This Viewpoint paper seeks to review the various foci of CDS and to identify aspects in which theoretical models and frameworks for CDS have been explored or could be explored and where they might be expected to be most useful.

A Health Decision Support System for Disease Diagnosis Based on Wearable Medical Sensors and Machine Learning Ensembles

Even with an annual expenditure of more than $3 trillion, the U.S. healthcare system is far from optimal. For example, the third leading cause of death in the U.S. is preventable medical error, immediately after heart disease and cancer. Computer-based clinical decision support systems (CDSSs) have been proposed to address such deficiencies and have significantly improved clinical practice over the past decade. However, they remain limited to clinics and hospitals, and do not take advantage of patient data that are obtained on a daily basis using wearable medical sensors (WMSs) that have the ability to bridge this information gap. WMSs can collect physiological signals from anyone anywhere anytime. Thus, they have the potential to usher in an era of pervasive healthcare. However, most prior work on WMSs only focuses on hardware and protocol design, and not on an information system that can fully utilize the collected signals for efficient disease diagnosis. In this paper, for the first time, we introduce a hierarchical health decision support system for disease diagnosis that integrates health data from WMSs into CDSSs. The proposed system has a multi-tier structure, starting with a WMS tier, backed by robust machine learning, that enables diseases to be tracked individually by a disease diagnosis module. We demonstrate the feasibility of such a system through six disease diagnosis modules aimed at four ICD-10-CM disease categories. We show that the system is scalable using five more disease categories. Just the WMS tier offers impressive diagnostic accuracies for various diseases: arrhythmia (86 percent), type-2 diabetes (78 percent), urinary bladder disorder (99 percent), renal pelvis nephritis (94 percent), and hypothyroid (95 percent). We estimate that the disease diagnosis modules of all known 69,000 human diseases would require just 62 GB of storage space in the WMS tier. This is practical even in today’s cloud or base station oriented WMS systems.

31 Performance of a Novel Computer-Based Clinical Decision Support Alert and the Impact of Patient Partitioning and Optimization to Identify Septic Patients in an Urban Emergency Department

31 Performance of a Novel Computer-Based Clinical Decision Support Alert and the Impact of Patient Partitioning and Optimization to Identify Septic Patients in an Urban Emergency Department

Health care providers\u2019 perceptions of use and influence of clinical decision support reminders: qualitative study following a randomized trial to improve HPV vaccination rates

BackgroundHuman Papillomavirus (HPV) leads to serious health issues and remains the most common sexually transmitted infection. Despite availability of effective vaccines, HPV vaccination rates are suboptimal. Furthermore, providers recommend the HPV vaccine less than half the time for eligible patients. Prior informatics research has demonstrated the effectiveness of computer-based clinical decision support (CDS) in changing provider behavior, especially in the area of preventative services.MethodsFollowing a randomized clinical trial to test the effect of a CDS intervention on HPV vaccination rates, we conducted semi-structured interviews with health care providers to understand whether they noticed the CDS reminders and why providers did or did not respond to the prompts. Eighteen providers, a mix of medical doctors and nurse practitioners, were interviewed from five publicly-funded, urban health clinics. Interview data were qualitatively analyzed by two independent researchers using inductive content analysis.ResultsWhile most providers recalled seeing the CDS reminders, few of them perceived the intervention as effective in changing their behavior. Providers stated many reasons for why they did not perceive a change in their behavior, yet the results of the trial showed HPV vaccination rates increased as a result of the intervention.ConclusionsCDS reminders may be effective at changing provider behavior even if providers perceive them to be of little use.Trial registrationClinicalTrials.gov Identifier: NCT02551887Registered on September 15, 2015

Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology

IntroductionWe have developed a new knowledge base intelligent system for obstetrics and gynecology ultrasound imaging, based on an ontology and a reference image collection. This study evaluates the new system to support accurate annotations of ultrasound images. We have used the early ultrasound diagnosis of ectopic pregnancies as a model clinical issue. Material and methodsThe ectopic pregnancy ontology was derived from medical texts (4260 ultrasound reports of ectopic pregnancy from a specialist center in the UK and 2795 Pubmed abstracts indexed with the MeSH term “Pregnancy, Ectopic”) and the reference image collection was built on a selection from 106 publications. We conducted a retrospective analysis of the signs in 35 scans of ectopic pregnancy by six observers using the new system. ResultsThe resulting ectopic pregnancy ontology consisted of 1395 terms, and 80 images were collected for the reference collection. The observers used the knowledge base intelligent system to provide a total of 1486 sign annotations. The precision, recall and F-measure for the annotations were 0.83, 0.62 and 0.71, respectively. The global proportion of agreement was 40.35% 95% CI [38.64–42.05]. DiscussionThe ontology-based intelligent system provides accurate annotations of ultrasound images and suggests that it may benefit non-expert operators. The precision rate is appropriate for accurate input of a computer-based clinical decision support and could be used to support medical imaging diagnosis of complex conditions in obstetrics and gynecology.

Computer-Based Clinical Decision Support Systems and Patient-Reported Outcomes: A Systematic Review.

Evidence-based treatment guidelines embedded in computer-based clinical decision support systems (CCDSS) may improve patient-reported outcomes (PRO). We systematically reviewed the literature for content and application of CCDSS, and their effects on PRO. A systematic review in MEDLINE and EMBASE was conducted according to PRISMA standards. Searches were limited to the publication period 1996-May 2014 and the English language. The search terms covered "computerized clinical decision systems" and "patient-reported outcomes". Screening and extraction was done independently by two reviewers according to predefined inclusion (computer and guideline) and exclusion criteria (no trial, no PRO). Study and CCDSS quality was rated according to predefined criteria. The database searches identified 1,331 references. Eighty-seven full-text articles were analyzed. The main reason for exclusion was no PRO as a study outcome measure. Fifteen studies met the inclusion criteria, representing 13,480 patients. Nine studies used a computerized device to fill in data; in four studies, this was used by the patients themselves. Most of the studies presented the data to the clinician at point of care and incorporated international guidelines. Three studies showed a positive effect on PRO, but only on symptoms. Overall, no negative effects were reported. There was no association with study quality or year of study publication. There are marginal positive effects of CCDSS on specific PRO. Factors that facilitate the use and effect are identified. Easy to use systems with difficult to ignore evidence-based advice need to be developed and tested.

Exploring a clinically friendly web-based approach to clinical decision support linked to the electronic health record: design philosophy, prototype implementation, and framework for assessment.

BackgroundComputer-based clinical decision support (CDS) is an important component of the electronic health record (EHR). As an increasing amount of CDS is implemented, it will be important that this be accomplished in a fashion that assists in clinical decision making without imposing unacceptable demands and burdens upon the provider’s practice.ObjectiveThe objective of our study was to explore an approach that allows CDS to be clinician-friendly from a variety of perspectives, to build a prototype implementation that illustrates features of the approach, and to gain experience with a pilot framework for assessment.MethodsThe paper first discusses the project’s design philosophy and goals. It then describes a prototype implementation (Neuropath/CDS) that explores the approach in the domain of neuropathic pain and in the context of the US Veterans Administration EHR. Finally, the paper discusses a framework for assessing the approach, illustrated by a pilot assessment of Neuropath/CDS.ResultsThe paper describes the operation and technical design of Neuropath/CDS, as well as the results of the pilot assessment, which emphasize the four areas of focus, scope, content, and presentation.ConclusionsThe work to date has allowed us to explore various design and implementation issues relating to the approach illustrated in Neuropath/CDS, as well as the development and pilot application of a framework for assessment.

Barriers and Facilitators to Clinical Decision Support Systems Adoption: A Systematic Review

The objective of the study was to identify potential barriers and facilitators to improve clinical practice using computer-based Clinical Decision Support System (CDSS). Studies published since 2000 were found using PubMed database, PsychInfo, CINAHL, EBSCO host database, and Google scholar. Twenty-six relevant publications were examined. Thirty-five unique barriers and twenty-five unique facilitators were identified in the literature as important determinants of CDSS’s adoption in clinical practice. The list of barriers and facilitators collected from each study were then organized under the four dimensions of The Unified Theory of Acceptance and Use of Technology (UTAUT) model: performance expectancy, effort expectancy, social influence, and facilitating conditions. Some of the important barriers to CDSS use include; lack of time or time constraints, economic constraints (e.g., finance and resources), lack of knowledge of system or content, reluctance to use system in front of patients, obscure workflow issues, less authenticity or reliability of information, lack of agreement with the system, and physician or user attitude toward the system. The study contributes immensely to the literature by identifying the important barriers and facilitators of CDSS.

Developing an international register of clinical prediction rules for use in primary care: a descriptive analysis.

We describe the methodology used to create a register of clinical prediction rules relevant to primary care. We also summarize the rules included in the register according to various characteristics. To identify relevant articles, we searched the MEDLINE database (PubMed) for the years 1980 to 2009 and supplemented the results with searches of secondary sources (books on clinical prediction rules) and personal resources (eg, experts in the field). The rules described in relevant articles were classified according to their clinical domain, the stage of development, and the clinical setting in which they were studied. Our search identified clinical prediction rules reported between 1965 and 2009. The largest share of rules (37.2%) were retrieved from PubMed. The number of published rules increased substantially over the study decades. We included 745 articles in the register; many contained more than 1 clinical prediction rule study (eg, both a derivation study and a validation study), resulting in 989 individual studies. In all, 434 unique rules had gone through derivation; however, only 54.8% had been validated and merely 2.8% had undergone analysis of their impact on either the process or outcome of clinical care. The rules most commonly pertained to cardiovascular disease, respiratory, and musculoskeletal conditions. They had most often been studied in the primary care or emergency department settings. Many clinical prediction rules have been derived, but only about half have been validated and few have been assessed for clinical impact. This lack of thorough evaluation for many rules makes it difficult to retrieve and identify those that are ready for use at the point of patient care. We plan to develop an international web-based register of clinical prediction rules and computer-based clinical decision support systems.

Section I. Computer-based Clinical Decision Support: Overview, Status, and Challenges

Section I. Computer-based Clinical Decision Support: Overview, Status, and Challenges