scholarly journals Clinical Decision Support Tool for Parental Tobacco Treatment in Hospitalized Children

2016 ◽  
Vol 07 (02) ◽  
pp. 399-411 ◽  
Author(s):  
Eric Shelov ◽  
Christopher Bonafide ◽  
Steven Bernstein ◽  
Alexander Fiks ◽  
Tyra Bryant-Stephens ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Hospitalized Children ◽  
Problem List ◽  
Discharge Instructions ◽  
Smoking Cessation Counseling ◽  
System Usability Scale ◽  
Shs Exposure

SummaryTo create and evaluate the feasibility, acceptability, and usability of a clinical decision support (CDS) tool within the electronic health record (EHR) to help pediatricians provide smoking cessation counseling and treatment to parents of hospitalized children exposed to secondhand smoke (SHS).Mixed method study of first-year pediatric residents on one inpatient unit. Residents received training in smoking cessation counseling, nicotine replacement therapy (NRT) prescribing, and use of a CDS tool to aid in this process. The tool, which alerted when a patient was identified as exposed to SHS based on the history taken on admission or during a prior encounter, had the following capabilities: adding SHS exposure to the patient’s problem list; referral to Free Quitline through discharge instructions; and linking to a printable NRT prescription form. We measured feasibility by EHR utilization data. We measured acceptability and usability of the tool by administering questionnaires to residents.From June-August 2015, the alert triggered for 106 patients, and the tool was used for 52 (49%) patients. 41 (39%) patients had SHS exposure added to the problem list, 34 (32%) parents were referred to the Quitline through discharge instructions, and 15 (14%) parents were prescribed NRT. 10 out of 15 (67%) eligible pediatricians used the tool. All clinicians surveyed (9 out of 10) found the tool acceptable and rated its usability good to excellent (average System Usability Scale score was 85 out of 100, 95% CI, 76-93).A non-interruptive CDS tool to help residents provide smoking cessation counseling in the hospital was feasible, acceptable, and usable. Future work will investigate impacts on patient outcomes.

scholarly journals Impact of a Web-Based Clinical Decision Support System to Assist Practitioners in Addressing Physical Activity and/or Healthy Eating for Smoking Cessation Treatment: Protocol for a Hybrid Type I Randomized Controlled Trial

10.2196/19157 ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. e19157
Author(s):  
Nadia Minian ◽  
Mathangee Lingam ◽  
Rahim Moineddin ◽  
Kevin E Thorpe ◽  
Scott Veldhuizen ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Smoking Cessation ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Smoking Cessation Program

Background Modifiable risk factors such as tobacco use, physical inactivity, and poor diet account for a significant proportion of the preventable deaths in Canada. These factors are also known to cluster together, thereby compounding the risks of morbidity and mortality. Given this association, smoking cessation programs appear to be well-suited for integration of health promotion activities for other modifiable risk factors. The Smoking Treatment for Ontario Patients (STOP) program is a province-wide smoking cessation program that currently encourages practitioners to deliver Screening, Brief Intervention, and Referral to treatment for patients who are experiencing depressive symptoms or consume excessive amounts of alcohol via a web-enabled clinical decision support system. However, there is no available clinical decision support system for physical inactivity and poor diet, which are among the leading modifiable risk factors for chronic diseases. Objective The aim of this study is to assess whether adding a computerized/web-enabled clinical decision support system for physical activity and diet to a smoking cessation program affects smoking cessation outcomes. Methods This study is designed as a hybrid type 1 effectiveness/implementation randomized controlled trial to evaluate a web-enabled clinical decision support system for supporting practitioners in addressing patients’ physical activity and diet as part of smoking cessation treatment in a primary care setting. This design was chosen as it allows for simultaneous testing of the intervention, its delivery in target settings, and the potential for implementation in real-world situations. Intervention effectiveness will be measured using a two-arm randomized controlled trial. Health care practitioners will be unblinded to their patients’ treatment allocation; however, patients will be blinded to whether their practitioner receives the clinical decision support system for physical activity and/or fruit/vegetable consumption. The evaluation of implementation will be guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Results Recruitment for the primary outcome of this study is ongoing and will be completed in November 2020. Results will be reported in March 2021. Conclusions The findings of the study will provide much needed insight into whether adding a computerized/web-enabled clinical decision support system for physical activity and diet to a smoking cessation program affects smoking cessation outcome. Furthermore, the implementation evaluation would provide insight into the feasibility of online-based interventions for physical activity and diet in a smoking cessation program. Addressing these risk factors simultaneously could have significant positive effects on chronic disease and cancer prevention. Trial Registration ClinicalTrials.gov NCT04223336; https://clinicaltrials.gov/ct2/show/NCT04223336 International Registered Report Identifier (IRRID) DERR1-10.2196/19157

scholarly journals Evaluation of a Clinical Decision Support System for Rare Diseases - A Qualitative Study

2020 ◽  
Author(s):  
Jannik Schaaf ◽  
Martin Sedlmayr ◽  
Brita Sedlmayr ◽  
Hans-Ulrich Prokosch ◽  
Holger Storf
Keyword(s):  
Decision Support ◽  
Qualitative Study ◽  
Clinical Decision Support ◽  
Rare Diseases ◽  
Qualitative Content Analysis ◽  
Clinical Decision ◽  
Similarity Analysis ◽  
University Hospitals ◽  
Difficult Patient ◽  
System Usability Scale

Abstract BackgroundRare Diseases (RDs) are difficult to diagnose. Clinical Decision Support Systems (CDSS) could support the diagnosis for RDs. The MIRACUM (Medical Informatics in Research and Medicine) consortium developed a CDSS for RDs based on distributed clinical data from ten German university hospitals. To support the diagnosis for difficult patient cases, the CDSS uses data from the different hospitals to perform a patient similarity analysis in order to obtain an indication of a diagnosis. To optimize our CDSS, we conducted this qualitative study to investigate the usability of the CDSS with its functionality and information included. Methods A Thinking Aloud Test (TA-Test) was performed with RDs experts recruited from Rare Diseases Centres (RDCs) at the MIRACUM locations which were specialized in the diagnosis and treatment of RDs.An instruction sheet with tasks was prepared that the participants should perform with the CDSS during the study. Participants were asked to share any thoughts about the CDSS. The TA-Test was recorded on audio and video. A questionnaire was handed out at the end of the study including the System Usability Scale (SUS). Afterwards, the data was analysed with the qualitative content analysis according to Mayring, which includes a category-guided deductive approach.ResultsA total of eight experts were included in the study since eight MIRACUM locations have established an RDC.The results show that more detailed information about the patients, such as descriptive attributes or findings, are needed. The given functionality of the CDSS was rated positively, such as the function for the overview of similar patients and medical history. However, there is a lack of transparency regarding the results of the CDSS patient similarity analysis. The participants stated that the system should present exactly which symptoms, diagnosis etc. have matched. Regarding usability, the CDSS received a score of 73.21 points according to the SUS, which is ranked as a good usability.ConclusionsThis qualitative study investigated the usability of a CDSS of RDs. Despite the promising results, the CDSS still needs some revisions before use in clinical practice, e.g. by improving the transparency of the patient similarity analysis.

scholarly journals 978. Assessing the Impact of a Mobile Device-Based Clinical Decision Support Tool on Guideline Adherence and Mental Workload

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S40-S40
Author(s):  
Katherine Richardson ◽  
Sarah Fouquet ◽  
Ellen Kerns ◽  
Russell Mcculloh
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Mental Workload ◽  
Clinical Decision ◽  
Medical Decision ◽  
Evidence Based ◽  
System Usability Scale ◽  
Nasa Tlx ◽  
Evidence Based Guidelines ◽  
The Impact

Abstract Background Fever in infants <90 days old can indicate a serious bacterial infection (SBI) such as urinary tract infection, bacteremia, or meningitis. Clinical management of febrile infants varies widely. Implementing clinical practice guidelines (CPGs) can help standardize care, and electronic clinical decision support (eCDS) tools are a potential means of distributing CPGs. Little is known regarding the individual-level impact of eCDS tool use on medical decision-making. Children’s Mercy Kansas City developed a mobile eCDS tool (CMPeDS: Pediatric Decision Support) that was used internationally in a practice standardization project focused on the management of febrile infants. Methods We conducted a prospective cross-over simulation study amongst pediatric healthcare providers. Attending and resident physicians performed simulated patient scenarios using either CMPeDS or a standard text reference (the Harriet Lane Handbook). Participants’ responses in the simulation were evaluated based on adherence to evidence-based guidelines. Participants’ mental workload was assessed using the NASA Task Load Index survey (NASA-TLX, in which lower scores are optimal) to assesses mental, physical, and temporal demand, as well as performance, effort, and frustration when completing a series of tasks. Paired t-test and ANOVA were used to determine significance for case performance scores and NASA-TLX scores, respectively. A System Usability Scale (SUS) was used to determine usability of the CMPeDS app. Results A total 28 of 32 planned participants have completed trial procedures to date. Mean performance scores on the cases were significantly higher with CMPeDS vs. standard reference, (87.7% vs. 72.4% [t(27) 3.22, P = 0.003]). Participants reported lower scores on the NASA-TLX when using CMPeDS compared with standard reference tool (Figure 1). Mean score on SUS was 88.2 (scale 0–100) indicating excellent tool usability (Figure 2). Conclusion Using the eCDS tool CMPeDS was associated with significantly increased adherence to evidence-based guidelines for febrile infant management and decreased mental workload in simulation. Our findings highlight the potential value of eCDS deployment as part of CPG implementation projects. Disclosures All authors: No reported disclosures.

scholarly journals A system uptake analysis and GUIDES checklist evaluation of the Electronic Asthma Management System: A point-of-care computerized clinical decision support system

2020 ◽  
Vol 27 (5) ◽  
pp. 726-737 ◽  
Author(s):  
Jeffrey Lam Shin Cheung ◽  
Natalie Paolucci ◽  
Courtney Price ◽  
Jenna Sykes ◽  
Samir Gupta ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Management System ◽  
Point Of Care ◽  
Action Plan ◽  
Asthma Management ◽  
Clinical Decision ◽  
User Feedback ◽  
Care Quality ◽  
System Usability Scale

Abstract Objective Computerized clinical decision support systems (CCDSSs) promise improvements in care quality; however, uptake is often suboptimal. We sought to characterize system use, its predictors, and user feedback for the Electronic Asthma Management System (eAMS)—an electronic medical record system–integrated, point-of-care CCDSS for asthma—and applied the GUIDES checklist as a framework to identify areas for improvement. Materials and Methods The eAMS was tested in a 1-year prospective cohort study across 3 Ontario primary care sites. We recorded system usage by clinicians and patient characteristics through system logs and chart reviews. We created multivariable models to identify predictors of (1) CCDSS opening and (2) creation of a self-management asthma action plan (AAP) (final CCDSS step). Electronic questionnaires captured user feedback. Results Over 1 year, 490 asthma patients saw 121 clinicians. The CCDSS was opened in 205 of 1033 (19.8%) visits and an AAP created in 121 of 1033 (11.7%) visits. Multivariable predictors of opening the CCDSS and producing an AAP included clinic site, having physician-diagnosed asthma, and presenting with an asthma- or respiratory-related complaint. The system usability scale score was 66.3 ± 16.5 (maximum 100). Reported usage barriers included time and system accessibility. Discussion The eAMS was used in a minority of asthma patient visits. Varying workflows and cultures across clinics, physician beliefs regarding asthma diagnosis, and relevance of the clinical complaint influenced uptake. Conclusions Considering our findings in the context of the GUIDES checklist helped to identify improvements to drive uptake and provides lessons relevant to CCDSS design across diseases.

Developing a Clinical Decision Support Tool for Appropriate Antibiotic Prescribing in Australian General Practice: A Simulation Study

2020 ◽  
Vol 40 (4) ◽  
pp. 428-437 ◽  
Author(s):  
Jo-Anne Manski-Nankervis ◽  
Ruby Biezen ◽  
Karin Thursky ◽  
Douglas Boyle ◽  
Malcolm Clark ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Video Data ◽  
Consumer Information ◽  
Antibiotic Prescribing ◽  
Clinical Workflow ◽  
System Usability Scale ◽  
System Usability ◽  
The Impact

Background. Inappropriate antibiotic prescribing can lead to antimicrobial resistance and drug side effects. Tools that assist general practitioners (GPs) in prescribing decisions may help to optimize prescribing. The aim of this study was to explore the use, acceptability, and feasibility of a clinical decision support (CDS) tool that incorporates evidence-based guidelines and consumer information that integrates with the electronic medical record (EMR). Methods. Eight GPs completed an interview and brief survey after participating in 2 simulated consultations. The survey consisted of demographic questions, perception of realism and representativeness of consultations, Post-Study System Usability Questionnaire, and System Usability Scale. Qualitative data were analyzed using framework analysis. Video data were reviewed, with length of consultation and time spent using the CDS tool documented. Results. Survey responses indicated that all GPs thought the consultations were “real” and representative of real-life consultations; 7 of 8 GPs were satisfied with usability of the tool. Key qualitative findings included that the tool assisted with clinical decision making and informed appropriate antibiotic prescribing. Accessibility and ease of use, including content (guideline and patient education resources), layout, and format, were key factors that determined whether GPs said that they would access the tool in everyday practice. Integration of the tool at multiple sites within the EMR facilitated access to guidelines and assisted in ensuring that the tool fit the clinical workflow. Conclusion. Our CDS tool was acceptable to GPs. Key features required for the tool were easy navigation, clear and useful guideline content, ability to fit into the clinical workflow, and incorporation into the EMR. Piloting of the tool in general practices to assess the impact and feasibility of use in real-world consultations will now be undertaken.

scholarly journals 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

2015 ◽  
Vol 22 (5) ◽  
pp. 1081-1088 ◽  
Author(s):  
Allison B McCoy ◽  
Adam Wright ◽  
Dean F Sittig
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Thyroid Stimulating Hormone ◽  
Boolean Logic ◽  
Problem List ◽  
Medication List ◽  
Active Medication ◽  
Laboratory Test Results ◽  
Electronic Health

Abstract 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.

scholarly journals Evaluation of a clinical decision support system for rare diseases: a qualitative study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jannik Schaaf ◽  
Martin Sedlmayr ◽  
Brita Sedlmayr ◽  
Hans-Ulrich Prokosch ◽  
Holger Storf
Keyword(s):  
Decision Support ◽  
Qualitative Study ◽  
Clinical Decision Support ◽  
Rare Diseases ◽  
Qualitative Content Analysis ◽  
Clinical Decision ◽  
Similarity Analysis ◽  
Difficult Patient ◽  
Study Results ◽  
System Usability Scale

Abstract Background Rare Diseases (RDs) are difficult to diagnose. Clinical Decision Support Systems (CDSS) could support the diagnosis for RDs. The Medical Informatics in Research and Medicine (MIRACUM) consortium developed a CDSS for RDs based on distributed clinical data from eight German university hospitals. To support the diagnosis for difficult patient cases, the CDSS uses data from the different hospitals to perform a patient similarity analysis to obtain an indication of a diagnosis. To optimize our CDSS, we conducted a qualitative study to investigate usability and functionality of our designed CDSS. Methods We performed a Thinking Aloud Test (TA-Test) with RDs experts working in Rare Diseases Centers (RDCs) at MIRACUM locations which are specialized in diagnosis and treatment of RDs. An instruction sheet with tasks was prepared that the participants should perform with the CDSS during the study. The TA-Test was recorded on audio and video, whereas the resulting transcripts were analysed with a qualitative content analysis, as a ruled-guided fixed procedure to analyse text-based data. Furthermore, a questionnaire was handed out at the end of the study including the System Usability Scale (SUS). Results A total of eight experts from eight MIRACUM locations with an established RDC were included in the study. Results indicate that more detailed information about patients, such as descriptive attributes or findings, can help the system perform better. The system was rated positively in terms of functionality, such as functions that enable the user to obtain an overview of similar patients or medical history of a patient. However, there is a lack of transparency in the results of the CDSS patient similarity analysis. The study participants often stated that the system should present the user with an overview of exact symptoms, diagnosis, and other characteristics that define two patients as similar. In the usability section, the CDSS received a score of 73.21 points, which is ranked as good usability. Conclusions This qualitative study investigated the usability and functionality of a CDSS of RDs. Despite positive feedback about functionality of system, the CDSS still requires some revisions and improvement in transparency of the patient similarity analysis.

scholarly journals Impact of a Web-Based Clinical Decision Support System to Assist Practitioners in Addressing Physical Activity and/or Healthy Eating for Smoking Cessation Treatment: Protocol for a Hybrid Type I Randomized Controlled Trial (Preprint)

2020 ◽  
Author(s):  
Nadia Minian ◽  
Mathangee Lingam ◽  
Rahim Moineddin ◽  
Kevin E Thorpe ◽  
Scott Veldhuizen ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Smoking Cessation ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Smoking Cessation Program

BACKGROUND Modifiable risk factors such as tobacco use, physical inactivity, and poor diet account for a significant proportion of the preventable deaths in Canada. These factors are also known to cluster together, thereby compounding the risks of morbidity and mortality. Given this association, smoking cessation programs appear to be well-suited for integration of health promotion activities for other modifiable risk factors. The Smoking Treatment for Ontario Patients (STOP) program is a province-wide smoking cessation program that currently encourages practitioners to deliver Screening, Brief Intervention, and Referral to treatment for patients who are experiencing depressive symptoms or consume excessive amounts of alcohol via a web-enabled clinical decision support system. However, there is no available clinical decision support system for physical inactivity and poor diet, which are among the leading modifiable risk factors for chronic diseases. OBJECTIVE The aim of this study is to assess whether adding a computerized/web-enabled clinical decision support system for physical activity and diet to a smoking cessation program affects smoking cessation outcomes. METHODS This study is designed as a hybrid type 1 effectiveness/implementation randomized controlled trial to evaluate a web-enabled clinical decision support system for supporting practitioners in addressing patients’ physical activity and diet as part of smoking cessation treatment in a primary care setting. This design was chosen as it allows for simultaneous testing of the intervention, its delivery in target settings, and the potential for implementation in real-world situations. Intervention effectiveness will be measured using a two-arm randomized controlled trial. Health care practitioners will be unblinded to their patients’ treatment allocation; however, patients will be blinded to whether their practitioner receives the clinical decision support system for physical activity and/or fruit/vegetable consumption. The evaluation of implementation will be guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. RESULTS Recruitment for the primary outcome of this study is ongoing and will be completed in November 2020. Results will be reported in March 2021. CONCLUSIONS The findings of the study will provide much needed insight into whether adding a computerized/web-enabled clinical decision support system for physical activity and diet to a smoking cessation program affects smoking cessation outcome. Furthermore, the implementation evaluation would provide insight into the feasibility of online-based interventions for physical activity and diet in a smoking cessation program. Addressing these risk factors simultaneously could have significant positive effects on chronic disease and cancer prevention. CLINICALTRIAL ClinicalTrials.gov NCT04223336; https://clinicaltrials.gov/ct2/show/NCT04223336 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/19157

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