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Background: Hospitals are required to have rapid response (RR) systems in place to respond to acute changes in a patient's condition. In high-stress situations like RR, medical residents face decision-making challenges due to time constraints and perceived pressure. Instituting order panels (OPs) can facilitate clinical decision making and improve residents' and nurses' satisfaction and patient safety. Objective: This quality improvement (QI) project aimed to create and institute standardized OPs for common RR clinical scenarios to improve satisfaction of internal medicine residents and nurses with the RR process. Methods: This was a single tertiary care center QI project that developed OPs for 10 common RR scenarios. Resident and nursing satisfaction with RR was assessed before and after OP implementation via survey and qualitative data collection. Results: Residents and nurses expressed high levels of satisfaction across various aspects of the RR process before and after OP implementation in both quantitative and qualitative analysis. Increased satisfaction was observed among residents regarding time spent placing orders (94%; P = 0.02) and time spent correcting wrong orders (87%; P = 0.03) after OP implementation. The nurses' survey revealed no statistically significant differences in satisfaction before and after the implementation of OPs regarding communication, collaboration, efficiency, and organization of the team. Conclusion: The introduction of standardized OPs for RRs resulted in increased satisfaction among internal medicine residents in terms of order placement and correcting wrong orders. Nurse satisfaction based on survey responses remained neutral. Qualitative data from both groups demonstrated a positive impact on communication, efficiency, and teamwork.

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This retrospective cohort study found that implementing source-specific antibiotic order sets for sepsis in the emergency department increased appropriate empiric antibiotic selection from 51% to 74% (P = .01).

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Background: Glycemic control is crucial in managing hospitalized patients with type II diabetes (T2DM), and it presents as a clinical challenge in the cardiac population. Therefore, we aimed to evaluate the impact of computerized insulin order sets in T2DM hospitalized cardiac patients. Methods: A quasi-experimental, pre- and post-study design. We included T2DM patients who were hospitalized for at least 3 days. Patients undergoing cardiac surgery were excluded. The primary endpoint was the mean difference in random blood glucose level (BGL) before and after the implementation of insulin order sets. While the secondary endpoints were to compare the median differences in fasting BGLs and the number of hyperglycemic and hypoglycemic episodes during the first 7 days. The study consisted of three phases: pre-implementation, intervention and post-phase. In the intervention phase, insulin order sets were integrated into the electronic prescribing system, and education was provided to the cardiology department. The post-phase included the patient's post-implementations. Results: A total of 194 patients were enrolled during the study period. The mean random BGL was 11.17 mmol/L, 95% CI, 10.6-11.7 in the pre-phase and 9.5 mmol/L, 95% CI, 9-1 -9.9 mmol/L in the post-phase (P < 0.001). The median fasting BGL was 9.2 mmol/L (7.4-11.8, IQR) in the pre-phase and 8.5 mmol/L (6.6-10.3, IQR) in the post-phase (P = 0.027). The number of hypoglycemic episodes was 24 in pre-phase and 33 in post-phase (P = 0.13). Conclusion: The use of computerized insulin order sets was associated with potential improvements in random and fasting glycemic control without increasing the risk of hyperglycemia or hypoglycemia.

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INTRODUCTION: Due to a national policy change in the management of unused platelet units from September 2018, there was a drastic increase in the number of platelet units wasted in our institution. METHODS: Using Quality Improvement (QI) tools, platelet wastages from pediatric heart surgeries was identified as a priority area to work on. An intervention based on the creation of 'Order Sets' for pediatric open-heart surgeries was implemented, standardizing standby platelet orders based on type of surgery and patient weight. RESULTS: This intervention led to a dramatic improvement in the number of platelets ordered on standby, and consequently a decrease in platelet wastage from 47.6% to 16.9% for pediatric open-heart surgeries, without any reported adverse events. CONCLUSION: With the creation of Order Sets and continuous education, it was possible to eradicate the practice of requesting unnecessary standby platelets for surgeries. This is an effective patient blood management (PBM) strategy resulting in a significant decrease in platelet wastage rate and substantial cost savings.

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Saudi Arabia's ambitious Vision 2030 project was launched in 2016 as a strategy for economic development and national growth, with 11 Vision Realization Programs put in charge of its implementation. The backbone of its Transformation Program for the Health Sector has been the definition of a new Model of Care aiming to deliver 42 coordinated interventions across 6 Systems of Care, with the development of clinical guidelines identified as a key cross-cutting intervention to foster the use of national, evidence-based practices across KSA, reduce care variation, and promote accountable care. This article provides an overview of the history, progress to date, and future outlook of the recently initiated National Guidelines Center in Saudi Arabia, established in collaboration between the Health Holding Company and the Saudi Health Council represented by its National Center for Evidence-based Medicine. The lessons learnt from previous guideline initiatives are grouped under the Center's design principles of high quality, relevance, practical implementation, and sustainability. Aspects setting the project apart from previous endeavors have been its focus on extensive engagement with key stakeholders in the Saudi guideline ecosystem, the co-development of evidence-based recommendations with aligned key performance measures, and the implementation of guideline recommendations in the clinical workflow via integrated electronic order sets. Nine activity streams aim to enable the Center to take its place among the leading regional and global guideline developing organizations and to optimally support clinicians and patients, Saudi Arabia's health sector transformation, and the work of guideline communities worldwide.

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PURPOSE OF REVIEW: This review discusses ways in which the electronic health record (EHR) can offer clinical decision support (CDS) tools for management of inpatient diabetes and hyperglycemia. RECENT FINDINGS: The use of electronic order sets can help providers order comprehensive basal bolus insulin regimens that are consistent with current guidelines. Order sets have been shown to reduce insulin errors and hypoglycemia rates. They can also help set glycemic targets, give hemoglobin A1C reminders, guide weight-based dosing, and match insulin regimen to nutritional profile. Glycemic management dashboards allow multiple variables affecting blood glucose to be shown in a single view, which allows for efficient evaluation of glucose trends and adjustment of insulin regimen. With the use glycemic management dashboards, active surveillance and remote management also become feasible. Hypoglycemia prevention and management are another part of inpatient diabetes management that is enhanced by EHR CDS tools. Furthermore, diagnosis and management of diabetic ketoacidosis and hyperglycemia hyperosmolar state are improved with the aid of EHR CDS tools. The use of EHR CDS tools helps improve the care of patients with diabetes and hyperglycemia in the inpatient hospital setting.

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Order sets that adhere to disease-specific guidelines have been shown to increase clinician efficiency and patient safety but curating these order sets, particularly for consistency across multiple sites, is difficult and time consuming. We created software called CDS-Compare to alleviate the burden on expert reviewers in rapidly and effectively curating large databases of order sets. We applied our clustering-based software to a database of NLP-processed order sets extracted from VA's Electronic Health Record, then had subject-matter experts review the web application version of our software for clustering validity.

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BACKGROUND: Knowledge translation and dissemination are some of the main challenges that affect evidence-based medicine. Web 2.0 platforms promote the sharing and collaborative development of content. Executable knowledge tools, such as order sets, are a knowledge translation tool whose localization is critical to its effectiveness but a challenge for organizations to develop independently. OBJECTIVE: This paper describes a Web 2.0 resource, referred to as the collaborative network (TCN), for order set development designed to share executable knowledge (order sets). This paper also analyzes the scope of its use, describes its use through network analysis, and examines the provision and use of order sets in the platform by organizational size. METHODS: Data were collected from Think Research's TxConnect platform. We measured interorganization sharing across Canadian hospitals using descriptive statistics. A weighted chi-square analysis was used to evaluate institutional size to share volumes based on institution size, with post hoc Cramer V score to measure the strength of association. RESULTS: TCN consisted of 12,495 order sets across 683 diagnoses or processes. Between January 2010 and March 2015, a total of 131 health care organizations representing 360 hospitals in Canada downloaded order sets 105,496 times. Order sets related to acute coronary syndrome, analgesia, and venous thromboembolism were most commonly shared. COVID-19 order sets were among the most actively shared, adjusting for order set lifetime. A weighted chi-square analysis showed nonrandom downloading behavior (P<.001), with medium-sized institutions downloading content from larger institutions acting as the most significant driver of this variance (chi-gram=124.70). CONCLUSIONS: In this paper, we have described and analyzed a Web 2.0 platform for the sharing of order set content with significant network activity. The robust use of TCN to access customized order sets reflects its value as a resource for health care organizations when they develop or update their own order sets.

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Background: Pharmacist-led clinical pathways/order sets (PLCOs) were first applied for designated diseases and surgical operations, such as cancer. They were not used in pharmacotherapy until recently. After screening a large number of publications, we found that PLCOs were rarely accessible. Objective: To evaluate the effects and the changes of relevant medical outcomes of PLCOs. Methods: Articles from PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Wanfang database, and China Biology Medicine disc (CBM) were systematically retrieved. Clinical research comparing cancer patients' clinical effects with or without clinical pathway/order sets was performed. Two reviewers performed quality assessment, and the data were abstracted independently. A narrative synthesis of the extracted data was performed due to heterogeneity. Results: Nine studies were identified, including six uncontrolled before-after studies and three case-series studies. The scopes of PLCOs of included research can be divided into two types, one focusing on chemotherapy agents and the other on the managements of chemotherapy-induced complications. The PLCOs shortened hospital length of stay, decreased initial antibiotic time intervals in patients with febrile neutropenia, reduced medication error incidence, and increased physicians' adherence rate to clinical pathway/order sets. Moreover, three articles included economic effects showing positive savings on medication costs through PLCOs. Conclusion: PLCOs can have beneficial effects on medication effectiveness, safety, and economic outcomes. Nevertheless, clinical pathway/order sets need to be further optimized and expanded to other clinical areas.

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BACKGROUND: Computerized clinical decision support systems (CDSS) have shown promising effectiveness in improving outpatient antibiotic prescribing. METHODS: We developed an intervention in the form of EPIC (Verona, WI, USA) order sets comprised of outpatient treatment pathways for 3 pediatric bacterial acute respiratory infections (ARIs) coupled with educational sessions. Four pediatric clinics were randomized into intervention and control arms over pre- and postimplementation study periods. In the intervention clinics, education was provided in between the 2 study periods and EPIC order sets became available at the beginning of the postimplementation period. The primary end point was the percentage of first-line antibiotic prescribing, and the secondary end points included antibiotic duration and antibiotic prescription modification within 14 days. RESULTS: A total of 2690 antibiotic prescriptions were included. During the pre-implementation phase, there was no difference in first-line antibiotic prescribing (74.9% vs 77.7%; P = .211) or antibiotic duration (9.69 ± 0.96 days vs 9.63 ± 1.07 days; P > .999) between the study arms. Following implementation, the intervention clinics had a higher percentage of first-line antibiotic prescribing (83.1% vs 77.7%; P = .024) and shorter antibiotic duration (9.28 ± 1.56 days vs 9.79 ± 0.75 days; P < .001) compared with the control clinics. The percentage of modified antibiotics was small in all clinics (1.1%-1.6%) and did not differ before and after the intervention (for all statistical comparisons, P ≤ .354). CONCLUSIONS: A computerized CDSS involving treatment pathways in the form of order sets coupled with educational sessions was associated with a higher percentage of first-line antibiotic prescribing and shorter antibiotic duration for the outpatient treatment of pediatric bacterial ARIs.

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OBJECTIVE: To evaluate the effect of discharge order sets on prescribing patterns of opioids after pediatric tonsillectomy. Secondary outcomes included encounters for postoperative pain, dehydration, and bleeding. METHODS: Retrospective chart review of pre- and post-intervention in pediatric post-tonsillectomy patients, 0-18 years old (n = 1486). Order sets were installed with age-specific analgesic medication defaults and recommendation of concurrent alternating scheduled ibuprofen and acetaminophen. Time-balanced pre- and post-intervention cohorts were established. Opioid outcomes calculated in morphine milligram equivalents per kilogram (MME/kg) per dosage and total prescribed. RESULTS: Discharge order set intervention resulted in 17% reduction of opioid dose prescribed (0.095 MME/kg [95% CI, 0.092-0.099] vs. 0.079 [95% CI, 0.076-0.083], P < .001). Total number of opioid doses prescribed was reduced after order set implementation (46.4 [95% CI, 43.6-49.1] to 20.3 [95% CI, 19.1-21.5], P < .001). Patients <7 years old prescribed opioids remained rare in pre- and post-intervention groups (1.6% and 1.8%, respectively, P = .86). Admissions and emergency department visits for postoperative dehydration and pain were significantly reduced. Post-intervention group showed an increase in readmissions for post-tonsillectomy hemorrhage (9.2% vs. 5.2%, P = .003) which was isolated to an increase in the older post-intervention group after stratification by age. CONCLUSION: Utilization of order sets with standardized analgesic medication regimen of acetaminophen, ibuprofen, and opioid helped effectively reduce opioid amount per dose, total opioid amount dispensed, and variability in the total opioid amount dispensed while maintaining pain control. An increase in post-tonsillectomy hemorrhage was recognized following this implementation which did not persist after the study period despite continuation of intervention. LEVEL OF EVIDENCE: 4 Laryngoscope, 131:1386-1391, 2021.

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OBJECTIVE: To assess usability and usefulness of a machine learning-based order recommender system applied to simulated clinical cases. MATERIALS AND METHODS: 43 physicians entered orders for 5 simulated clinical cases using a clinical order entry interface with or without access to a previously developed automated order recommender system. Cases were randomly allocated to the recommender system in a 3:2 ratio. A panel of clinicians scored whether the orders placed were clinically appropriate. Our primary outcome included the difference in clinical appropriateness scores. Secondary outcomes included total number of orders, case time, and survey responses. RESULTS: Clinical appropriateness scores per order were comparable for cases randomized to the order recommender system (mean difference -0.11 order per score, 95% CI: [-0.41, 0.20]). Physicians using the recommender placed more orders (median 16 vs 15 orders, incidence rate ratio 1.09, 95%CI: [1.01-1.17]). Case times were comparable with the recommender system. Order suggestions generated from the recommender system were more likely to match physician needs than standard manual search options. Physicians used recommender suggestions in 98% of available cases. Approximately 95% of participants agreed the system would be useful for their workflows. DISCUSSION: User testing with a simulated electronic medical record interface can assess the value of machine learning and clinical decision support tools for clinician usability and acceptance before live deployments. CONCLUSIONS: Clinicians can use and accept machine learned clinical order recommendations integrated into an electronic order entry interface in a simulated setting. The clinical appropriateness of orders entered was comparable even when supported by automated recommendations.

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OBJECTIVE: This study assesses whether neural networks trained on electronic health record (EHR) data can anticipate what individual clinical orders and existing institutional order set templates clinicians will use more accurately than existing decision support tools. MATERIALS AND METHODS: We process 57 624 patients worth of clinical event EHR data from 2008 to 2014. We train a feed-forward neural network (ClinicNet) and logistic regression applied to the traditional problem structure of predicting individual clinical items as well as our proposed workflow of predicting existing institutional order set template usage. RESULTS: ClinicNet predicts individual clinical orders (precision = 0.32, recall = 0.47) better than existing institutional order sets (precision = 0.15, recall = 0.46). The ClinicNet model predicts clinician usage of existing institutional order sets (avg. precision = 0.31) with higher average precision than a baseline of order set usage frequencies (avg. precision = 0.20) or a logistic regression model (avg. precision = 0.12). DISCUSSION: Machine learning methods can predict clinical decision-making patterns with greater accuracy and less manual effort than existing static order set templates. This can streamline existing clinical workflows, but may not fit if historical clinical ordering practices are incorrect. For this reason, manually authored content such as order set templates remain valuable for the purposeful design of care pathways. ClinicNet's capability of predicting such personalized order set templates illustrates the potential of combining both top-down and bottom-up approaches to delivering clinical decision support content. CONCLUSION: ClinicNet illustrates the capability for machine learning methods applied to the EHR to anticipate both individual clinical orders and existing order set templates, which has the potential to improve upon current standards of practice in clinical order entry.

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OBJECTIVE: To evaluate the efficacy of disease-based standard order sets in reducing time of order entry, order processing and medication dispensation in emergency department of a tertiary care hospital. METHOD: The pilot study was conducted as part of a retrospective clinical audit using pre- and post-intervention design comprising data from July to September 2013 of the emergency department of a tertiary care hospital in Karachi. Data collected related to the reduction in medicine order entry, processing and dispensing time of eight common emergency conditions with standard order set. Subsequently, standard medication orders for the selected medical conditions were developed together with physicians of emergency and other specialties. Post-intervention data was collected and the two data sets were compared using SPSS version 23.0. RESULTS: Mean medication order entry and processing time from the physician end reduced from 67.7±22.7 seconds to 20.5±7.1 seconds. Mean medication order processing and dispensing time at pharmacist end reduced from 70.0±22.4 to 20.6±8.8 seconds. The difference between pre- and post intervention values was significant (p<0.001). CONCLUSIONS: Implementation of disease-based standard order set significantly improved efficiency.

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Laboratory tests are an integral part of the electronic health record (EHR). Providing clinical decision support (CDS) for the ordering, collection, reporting, viewing, and interpretation of laboratory testing is a fundamental function of the EHR. The implementation of a sustainable, effective laboratory CDS program requires a commitment to standardization and harmonization of the laboratory dictionaries that are the foundation of laboratory-based CDS. In this review, the authors provide an overview of the tools available within the EHR to improve decision making throughout the entire laboratory testing process, from test order to clinical action.

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Given current and evolving medical expectations from patients and ever-increasing complexities and demands placed upon clinical breast radiologists, to include patient access, workflow, communications, and timely medical reports, we need support systems to help advance professional productivity and efficiency. Likewise, advancing technological support with integrated systems should help support the objectives of increased professional productivity, career satisfaction, and quality patient care. In this report, we review important elements that help advance the mission of clinical breast radiologists, while meeting or exceeding patient expectations.

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BACKGROUND: Standardized order sets (SOSs) are clinical tools derived from clinical care pathways that have shown improved patient-recovery and economic benefits. The primary objective was to examine the effect of SOSs on adherence to evidence-based postoperative guidelines for laryngectomy patients. METHODS: A retrospective chart review comparing handwritten and SOS-based postoperative physician orders was conducted for consecutive laryngectomies performed (n=70) within a 3-year time period. Orders were analyzed for errors and deviations from evidence-based guidelines. Secondary outcome included complications such as thromboembolic disease, return to operating room, fistula formation, salivary bypass tube, length of hospital stay and death. RESULTS: Approximately 81% of cases utilizing handwritten orders had at least one error (n=36) compared to 38% in the group that used an SOS (n=34) (P<0.0001). Subgroup analyses demonstrated that errors in mechanical deep vein thrombosis prophylaxis (P<0.0001) and antibiotic prophylaxis (P=0.0173) orders were significantly reduced in the SOS group compared to the handwritten group. No significant differences were observed between the two groups for measured postoperative complications (P>0.05) and length of hospital stay (18.6 days in both SOS and handwritten orders groups). CONCLUSIONS: SOSs are associated with reduced errors in postoperative orders. They are important tools to improve adherence to standardized guidelines for surgeries requiring complex postoperative management. Clinical care pathways and Enhanced Recovery After Surgery protocols can use SOSs to ensure appropriate orders are being made.

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Surgical site infections, readmissions, and extended hospital stays are risks for patients undergoing colon surgeries. These procedures are often urgent, and patients may have multiple comorbidities. Preoperative and postoperative steps to reduce the number of complications provide substantial benefits clinically, economically, and psychologically. We used a multidisciplinary, collaborative approach to identify best practices when developing and implementing a standardized approach to the management of patients undergoing elective colon surgery. Interventions included nutrition supplements and preoperative and postoperative protocols. Our management project showed a 74.6% reduction in readmissions, a 22.73% reduction in length of stay, an 85% reduction in colon surgical site infections measured by incidence (84.5% reduction) and standard infection ratio (54.55% reduction), and 95% compliance with the use of both order sets during an 18-month period. Applying standardized order sets for assessing and addressing patient comorbidities before colorectal surgery can result in a substantial and sustainable reduction in complications.

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BACKGROUND: Standardized protocols have been previously shown to be helpful in managing end-of-life (EOL) care in hospital. The comfort measures order set (CMOS), a standardized framework for assessing imminently dying patients' symptoms and needs, was implemented at a tertiary academic hospital. OBJECTIVE: We assessed whether there were comparable differences in the care of a dying patient when the CMOS was utilized and when it was not. METHODS: A retrospective chart review was completed on patients admitted under oncology and general internal medicine, who were referred to the inpatient palliative care team for "EOL care" between February 2015 and March 2016. RESULTS: Of 83 patients, 56 (67%) received intiation of the CMOS and 27 (33%) did not for EOL care. There was significant involvement of spiritual care with the CMOS (66%), as compared to the group without CMOS (19%), P < .05. The use of CMOS resulted in 1.7 adjustments to symptom management per patient by palliative care, which was significantly less than the number of symptom management adjustments per patient when CMOS was not used (3.3), P < .05. However, initiating CMOS did not result in a signficant difference in patient distress around the time of death ( P = .11). Dyspnea was the most frequently identified symptom causing distress in actively dying patients. CONCLUSIONS: Implementation of the CMOS is helpful in providing a foundation to a comfort approach in imminently dying patients. However, more education on its utility as a framework for EOL care and assessment across the organization is still required.

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PURPOSE: To assess the success of order set and pharmacist training improvement (OSPTI) in improving prescription of antiretroviral therapy (ART) in a tertiary care, public, teaching hospital. METHODS: In this pre-OSPTI (January 2012 through June 2013) and post-OSPTI study (July 2013 through September 2014), an infectious disease pharmacist reviewed all patients on ART. A review of intervention data in July 2013 led to order-set changes in the hospital's computerized order entry system for frequently intervened on antiretrovirals: ritonavir, tenofovir, emtricitabine/tenofovir disoproxil fumarate (FTC/TDF), and lamivudine. Concurrently, case-based education modules were conducted to help pharmacists identify ART errors. The number of patients on ART, number of interventions, and types of ritonavir interventions were compared between pre- and post-OSPTI periods. RESULTS: In the pre-OSPTI period, an average of 239 patients were reviewed per quarter compared to an average of 216 per quarter in the post-OSPTI period. After implementing enhanced order sets, the number of interventions decreased by approximately 34% ( P < .0001). The number of ritonavir interventions decreased on average by 45% ( P < .0001), although the types of ritonavir interventions were similar. CONCLUSION: Enhanced antiretroviral order sets and pharmacy education modules improved ART prescription by reducing the overall number of antiretroviral interventions required per quarter. This modality was effective in improving prescribing of ART and reducing the need for pharmacist interventions.

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