scholarly journals An e-Delphi study to obtain expert consensus on the level of risk associated with preventable e-prescribing events.

2021 ◽  
Author(s):  
Sarah Slight ◽  
Jude Heed ◽  
Stephanie Klein ◽  
Neil Watson ◽  
Ann Slee ◽  
...  
Keyword(s):  
Decision Support ◽  
High Risk ◽  
Clinical Decision Support ◽  
Delphi Study ◽  
Antimicrobial Agents ◽  
Clinical Decision ◽  
Expert Consensus ◽  
Prescribing Error ◽  
Clinical Scenarios ◽  
Level Of Risk

Objectives We aim to seek expert opinion and gain consensus on the risks associated with a range of prescribing scenarios, preventable using e-prescribing systems, to inform the development of a simulation tool to evaluate the risk and safety of e-prescribing systems (ePRaSE). Methods We conducted a two-round eDelphi survey where expert participants were asked to score pre-designed prescribing scenarios using a five-point Likert scale to ascertain the likelihood of occurrence of the prescribing event, likelihood of occurrence of harm and the severity of the harm. Results Twenty four experts consented to participate with fifteen participants and thirteen participants completing rounds 1 and 2 respectively. Experts agreed on the level of risk associated with 136 out of 178 clinical scenarios with 131 scenarios categorised as high or extreme risk. Discussion We identified 131 extreme or high-risk prescribing scenarios that may be prevented using e-prescribing clinical decision support. The prescribing scenarios represent a variety of categories, with drug-disease contraindications, being the most frequent representing 37 (27%) scenarios and antimicrobial agents being the most common drug class representing 28 (21%) of the scenarios. Conclusion Our eDelphi study has achieved expert consensus on the risk associated with a range of clinical scenarios with most of the scenarios categorised as extreme or high risk. These prescribing scenarios represent the breadth of preventable prescribing error categories involving both basic and advanced clinical decision support. We will use the findings of this study to inform the development of the e-prescribing risk and safety evaluation tool.

scholarly journals Effectiveness of the clinical decision support tool ESR eGUIDE for teaching medical students the appropriate selection of imaging tests: randomized cross-over evaluation

2020 ◽  
Vol 30 (10) ◽  
pp. 5684-5689 ◽  
Author(s):  
Torsten Diekhoff ◽  
Franz Kainberger ◽  
Laura Oleaga ◽  
Marc Dewey ◽  
Elke Zimmermann
Keyword(s):  
Decision Support ◽  
Medical Students ◽  
Clinical Decision Support ◽  
Imaging Modality ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Scenarios ◽  
Group A ◽  
Clinical Decision Support Tool

Abstract Objectives To evaluate ESR eGUIDE—the European Society of Radiology (ESR) e-Learning tool for appropriate use of diagnostic imaging modalities—for learning purposes in different clinical scenarios. Methods This anonymized evaluation was performed after approval of ESR Education on Demand leadership. Forty clinical scenarios were developed in which at least one imaging modality was clinically most appropriate, and the scenarios were divided into sets 1 and 2. These sets were provided to medical students randomly assigned to group A or B to select the most appropriate imaging test for each scenario. Statistical comparisons were made within and across groups. Results Overall, 40 medical students participated, and 31 medical students (78%) answered both sets. The number of correctly chosen imaging methods per set in these 31 paired samples was significantly higher when answered with versus without use of ESR eGUIDE (13.7 ± 2.6 questions vs. 12.1 ± 3.2, p = 0.012). Among the students in group A, who first answered set 1 without ESR eGUIDE (11.1 ± 3.2), there was significant improvement when set 2 was answered with ESR eGUIDE (14.3 ± 2.5, p = 0.013). The number of correct answers in group B did not drop when set 2 was answered without ESR eGUIDE (12.4 ± 2.6) after having answered set 1 first with ESR eGUIDE (13.0 ± 2.7, p = 0.66). Conclusion The clinical decision support tool ESR eGUIDE is suitable for training medical students in choosing the best radiological imaging modality in typical scenarios, and its use in teaching radiology can thus be recommended. Key Points • ESR eGUIDE improved the number of appropriately selected imaging modalities among medical students. • This improvement was also seen in the group of students which first selected imaging tests without ESR eGUIDE. • In the student group which used ESR eGUIDE first, appropriate selection remained stable even without the teaching tool.

scholarly journals Clinical decision support system, using expert consensus-derived logic and natural language processing, decreased sedation-type order errors for patients undergoing endoscopy

2020 ◽  
Author(s):  
Lin Shen ◽  
Adam Wright ◽  
Linda S Lee ◽  
Kunal Jajoo ◽  
Jennifer Nayor ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Decision Support ◽  
Natural Language ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Expert Consensus ◽  
Order Error

Abstract Objective Determination of appropriate endoscopy sedation strategy is an important preprocedural consideration. To address manual workflow gaps that lead to sedation-type order errors at our institution, we designed and implemented a clinical decision support system (CDSS) to review orders for patients undergoing outpatient endoscopy. Materials and Methods The CDSS was developed and implemented by an expert panel using an agile approach. The CDSS queried patient-specific historical endoscopy records and applied expert consensus-derived logic and natural language processing to identify possible sedation order errors for human review. A retrospective analysis was conducted to evaluate impact, comparing 4-month pre-pilot and 12-month pilot periods. Results 22 755 endoscopy cases were included (pre-pilot 6434 cases, pilot 16 321 cases). The CDSS decreased the sedation-type order error rate on day of endoscopy (pre-pilot 0.39%, pilot 0.037%, Odds Ratio = 0.094, P-value < 1e-8). There was no difference in background prevalence of erroneous orders (pre-pilot 0.39%, pilot 0.34%, P = .54). Discussion At our institution, low prevalence and high volume of cases prevented routine manual review to verify sedation order appropriateness. Using a cohort-enrichment strategy, a CDSS was able to reduce number of chart reviews needed per sedation-order error from 296.7 to 3.5, allowing for integration into the existing workflow to intercept rare but important ordering errors. Conclusion A workflow-integrated CDSS with expert consensus-derived logic rules and natural language processing significantly reduced endoscopy sedation-type order errors on day of endoscopy at our institution.

scholarly journals Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital

2020 ◽  
Vol 11 (04) ◽  
pp. 570-577
Author(s):  
Santiago Romero-Brufau ◽  
Kirk D. Wyatt ◽  
Patricia Boyum ◽  
Mindy Mickelson ◽  
Matthew Moore ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
High Risk ◽  
Clinical Decision Support ◽  
Readmission Rate ◽  
Hospital Readmissions ◽  
Clinical Decision ◽  
Regional Hospital ◽  
Patient Centered ◽  
Readmission Rates

Abstract Background Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. Objective The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. Methods A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. Results Among 2,460 hospitalizations assessed using the tool, 611 were designated by the tool as high risk. Sensitivity and specificity for risk assignment were 65% and 89%, respectively. Over 6 months following implementation, readmission rates decreased from 11.4% during the comparison period to 8.1% (p < 0.001). After accounting for the 0.5% decrease in readmission rates (from 9.3 to 8.8%) at control hospitals, the relative reduction in readmission rate was 25% (p < 0.001). Among patients designated as high risk, the number needed to treat to avoid one readmission was 11. Conclusion We observed a decrease in hospital readmission after implementing artificial intelligence-based clinical decision support. Our experience suggests that use of artificial intelligence to identify patients at the highest risk for readmission can reduce quality gaps when coupled with patient-centered interventions.

Clinical Decision Support for High-Risk Stage II Colon Cancer: A Real-World Study of Treatment Concordance and Survival

2020 ◽  
Vol 63 (10) ◽  
pp. 1383-1392 ◽  
Author(s):  
Peng-ju Chen ◽  
Tian-le Li ◽  
Ting-ting Sun ◽  
Van C. Willis ◽  
M. Christopher Roebuck ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Decision Support ◽  
High Risk ◽  
Clinical Decision Support ◽  
Real World ◽  
Clinical Decision ◽  
Stage Ii ◽  
Stage Ii Colon Cancer

Time-dependent Estimates of Recurrence and Survival in Colon Cancer: Clinical Decision Support System Tool Development for Adjuvant Therapy and Oncological Outcome Assessment

2014 ◽  
Vol 80 (5) ◽  
pp. 441-453 ◽  
Author(s):  
Scott R. Steele ◽  
Anton Bilchik ◽  
Eric K. Johnson ◽  
Aviram Nissan ◽  
George E. Peoples ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Decision Support ◽  
High Risk ◽  
Adjuvant Therapy ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Predictive Values

Unanswered questions remain in determining which high-risk node-negative colon cancer (CC) cohorts benefit from adjuvant therapy and how it may differ in an equal access population. Machine-learned Bayesian Belief Networks (ml-BBNs) accurately estimate outcomes in CC, providing clinicians with Clinical Decision Support System (CDSS) tools to facilitate treatment planning. We evaluated ml-BBNs ability to estimate survival and recurrence in CC. We performed a retrospective analysis of registry data of patients with CC to train–test–crossvalidate ml-BBNs using the Department of Defense Automated Central Tumor Registry (January 1993 to December 2004). Cases with events or follow-up that passed quality control were stratified into 1-, 2-, 3-, and 5-year survival cohorts. ml-BBNs were trained using machine-learning algorithms and k-fold crossvalidation and receiver operating characteristic curve analysis used for validation. BBNs were comprised of 5301 patients and areas under the curve ranged from 0.85 to 0.90. Positive predictive values for recurrence and mortality ranged from 78 to 84 per cent and negative predictive values from 74 to 90 per cent by survival cohort. In the 12-month model alone, 1,132,462,080 unique rule sets allow physicians to predict individual recurrence/mortality estimates. Patients with Stage II (N0M0) CC benefit from chemotherapy at different rates. At one year, all patients older than 73 years of age with T2–4 tumors and abnormal carcinoembryonic antigen levels benefited, whereas at five years, all had relative reduction in mortality with the largest benefit amongst elderly, highest T-stage patients. ml-BBN can readily predict which high-risk patients benefit from adjuvant therapy. CDSS tools yield individualized, clinically relevant estimates of outcomes to assist clinicians in treatment planning.

Signaling Sepsis Scenario Development & Validation

2018 ◽  
Vol 62 (1) ◽  
pp. 615-619
Author(s):  
Danielle L.M. Weldon ◽  
Rebecca Kowalski ◽  
Laura Schubel ◽  
Brett Schuchardt ◽  
Ryan Arnold ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Vital Signs ◽  
Usability Testing ◽  
Clinical Decision ◽  
Scenario Development ◽  
Real Patient ◽  
Patient Disposition ◽  
Usability Tests ◽  
Clinical Scenarios

Patient-based scenario-driven usability tests are routinely created for health information technology and clinical decision support evaluations. Due to low clinician awareness of sepsis, a study was undertaken to understand clinician performance and preference of different display types for sepsis clinical decision support through multi-centered usability testing. Patient-based clinical scenarios were created to mimic the environment in which providers would interact with clinical decision support. The data provided in the scenarios were drawn from real patient cases from two sepsis databases, including: demographics, visit/operational details, medical history (comorbidities, assessments, vital signs, laboratory values, clinician documentation), and patient disposition/outcomes. The purpose of this work is to inform electronic health record alert optimization and clinical practice workflow to support the effective and timely delivery of high quality sepsis care. This paper discusses the methodology, selection, and validation of patient-based cases used as the clinical scenarios in usability testing.

scholarly journals Impact of Patient Reminders on Papanicolaou Test Completion for High-Risk Patients Identified by a Clinical Decision Support System

2018 ◽  
Vol 27 (5) ◽  
pp. 569-574 ◽  
Author(s):  
Kathy L. MacLaughlin ◽  
Maya E. Kessler ◽  
Ravikumar Komandur Elayavilli ◽  
Branden C. Hickey ◽  
Marianne R. Scheitel ◽  
...  
Keyword(s):  
Decision Support ◽  
High Risk ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Papanicolaou Test ◽  
Risk Patients ◽  
Test Completion

MISSION Syncope App: Developing An Evidence-based Clinical Support Tool (Preprint)

2020 ◽  
Author(s):  
Shiraz Amin ◽  
Vedant Gupta ◽  
Gaixin Du ◽  
Colleen McMullen ◽  
Matthew Sirrine ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Differential Diagnosis ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Mobile App ◽  
Evidence Based ◽  
Tool Selection ◽  
Diagnosis And Prognosis ◽  
Clinical Scenarios

BACKGROUND Syncope is a prevalent and recurrent condition. Because of concerns that patients presenting with syncope are at risk for an impending catastrophic event, overuse of testing and admission are common. Though clinical decision support (CDS) tool is an effective strategy, CDS tools in syncope are lacking and limited. OBJECTIVE Develop and demonstrate the viability of a clinical decision support for syncope diagnosis and prognosis. METHODS With input from clinicians representing diverse health systems, we developed the MISSION Syncope OptimalCare Pathway based on 2017 Syncope ACC/AHA Guidelines. This protocol provides flexible decision-making and tailoring of treatment to patient’s clinical presentation, medical history and needs while emphasizing the care continuum. We designed a mobile App for the implementation of this protocol. Following human-centered design principles and applying research evidence, development of the App was a multi-step process including: 1) ideation; 2) assessment variable decision; 3) mathematical model development for differential diagnosis; 4) clinical risk tool selection; 5) recommendation decisions; and 6) technical buildout of the application. We used baseline prevalence of different etiologies of syncope, pre-test odds ratio (OR) and likelihood ratios (LR), for each indicator in available literature to predict each etiology. Multiple binary regression models were used to calculate post-test ORs. Clinicians guided refinement of the model where LRs were lacking or clinical indicators warranted assessment. RESULTS The App product is consistent with guideline recommendations. It walks through clinical assessment in a concise manner that is consistent with evidence-based practice and provides recommendations for diagnosis and prognosis based on user input. Though a ranked differential diagnosis is displayed, the user can pick their most likely differential. The same set of questions also determines a Canadian Syncope Score, a validated clinical scoring system to identify those at higher risk for cardiovascular events after a syncopal episode. CONCLUSIONS Our App demonstrates the viability of using evidenced-based literature in developing a CDS tool, and the importance of applying clinical experience to fill gaps in research. It is essential for a successful App to be deliberate in pursuing a practical clinical model instead of striving for a perfect mathematical model. Our experience can be applied to similar CDS tool development, especially in clinical scenarios without definitive research.

Sign in / Sign up

Export Citation Format

Share Document