Guideline-based Decision Support Research Articles

My Journey Through the Field of Medical Informatics.

In this contribution some achievements and milestones in the field of medical informatics, especially concerning decision support, as perceived by the author, are presented. The author focuses on those topics with respect to decision support that during his career in medical informatics impressed him and triggered him to convince his PhD students to start research on related topics. Both some of these achievements and the related research of some of his PhD students will be presented. The contribution starts with signal classification. Both ECG classification and sleep EEG classification are discussed. Then the use of Bayes' theorem for diagnostic purposes is discussed and some early applications pass review, among which the AAPHelp system developed by de Dombal and colleagues. Attention is subsequently paid to the advent of expert systems and other knowledge-based systems such as MYCIN and INTERNIST and to guideline-based decision support systems. Finally, the author presents his ideas about challenges for the field.

Smartphone App in Stroke Management: A Narrative Updated Review.

The spread of smartphones and mobile-Health (m-health) has progressively changed clinical practice, implementing access to medical knowledge and communication between doctors and patients. Dedicated software called Applications (or Apps), assists the practitioners in the various phases of clinical practice, from diagnosis to follow-up and therapy management. The impact of this technology is even more important in diseases such as stroke, which are characterized by a complex management that includes several moments: primary prevention, acute phase management, rehabilitation, and secondary prevention. This review aims to evaluate and summarize the available literature on Apps for the clinical management of stroke. We described their potential and weaknesses, discussing potential room for improvement. Medline databases were interrogated for studies concerning guideline-based decision support Apps for stroke management and other medical scenarios from 2007 (introduction of the first iPhone) until January 2022. We found 551 studies. Forty-three papers were included because they fitted the scope of the review. Based on their purpose, Apps were classified into three groups: primary prevention Apps, acute stroke management Apps, and post-acute stroke Apps. We described the aim of each App and, when available, the results of clinical studies. For acute stroke, several Apps have been designed with the primary purpose of helping communication and sharing of patients’ clinical data among healthcare providers. However, interactive systems Apps aiming to assist clinicians are still lacking, and this field should be developed because it may improve stroke patients’ management.

System for Context-Specific Visualization of Clinical Practice Guidelines (GuLiNav): Concept and Software Implementation.

BackgroundClinical decision support systems often adopt and operationalize existing clinical practice guidelines leading to higher guideline availability, increased guideline adherence, and data integration. Most of these systems use an internal state-based model of a clinical practice guideline to derive recommendations but do not provide the user with comprehensive insight into the model.ObjectiveHere we present a novel approach based on dynamic guideline visualization that incorporates the individual patient’s current treatment context.MethodsWe derived multiple requirements to be fulfilled by such an enhanced guideline visualization. Using business process and model notation as the representation format for computer-interpretable guidelines, a combination of graph-based representation and logical inferences is adopted for guideline processing. A context-specific guideline visualization is inferred using a business rules engine.ResultsWe implemented and piloted an algorithmic approach for guideline interpretation and processing. As a result of this interpretation, a context-specific guideline is derived and visualized. Our implementation can be used as a software library but also provides a representational state transfer interface. Spring, Camunda, and Drools served as the main frameworks for implementation. A formative usability evaluation of a demonstrator tool that uses the visualization yielded high acceptance among clinicians.ConclusionsThe novel guideline processing and visualization concept proved to be technically feasible. The approach addresses known problems of guideline-based clinical decision support systems. Further research is necessary to evaluate the applicability of the approach in specific medical use cases.

Distributed application of guideline-based decision support through mobile devices: Implementation and evaluation

BackgroundTraditionally guideline (GL)-based Decision Support Systems (DSSs) use a centralized infrastructure to generate recommendations to care providers, rather than to patients at home. However, managing patients at home is often preferable, reducing costs and empowering patients. Thus, we wanted to explore an option in which patients, in particular chronic patients, might be assisted by a local DSS, which interacts as needed with the central DSS engine, to manage their disease outside the standard clinical settings. ObjectivesTo design, implement, and demonstrate the technical and clinical feasibility of a new architecture for a distributed DSS that provides patients with evidence-based guidance, offered through applications running on the patients' mobile devices, monitoring and reacting to changes in the patient's personal environment, and providing the patients with appropriate GL-based alerts and personalized recommendations; and increase the overall robustness of the distributed application of the GL. MethodsWe have designed and implemented a novel projection–callback (PCB) model, in which small portions of the evidence-based guideline's procedural knowledge are projected from a projection engine within the central DSS server, to a local DSS that resides on each patient's mobile device. The local DSS applies the knowledge using the mobile device's local resources. The GL projections generated by the projection engine are adapted to the patient's previously defined preferences and, implicitly, to the patient's current context, in a manner that is embodied in the projected therapy plans. When appropriate, as defined by a temporal pattern within the projected plan, the local DSS calls back the central DSS, requesting further assistance, possibly another projection. To support the new model, the initial specification of the GL includes two levels: one for the central DSS, and one for the local DSS. We have implemented a distributed GL-based DSS using the projection–callback model within the MobiGuide EU project, which automatically manages chronic patients at home using sensors on the patients and their mobile phone.We assessed the new GL specification process, by specifying two very different, complex GLs: for Gestational Diabetes Mellitus, and for Atrial Fibrillation. Then, we evaluated the new computational architecture by applying the two GLs to the automated clinical management, at real time, of patients in two different countries: Spain and Italy, respectively. ResultsThe specification using the new projection-callback model was found to be quite feasible. We found significant differences between the distributed versions of the two GLs, suggesting further research directions and possibly additional ways to analyze and characterize GLs. Applying the two GLs to the two patient populations proved highly feasible as well. The mean time between the central and local interactions was quite different for the two GLs: 3.95 ± 1.95 days in the case of the gestational diabetes domain, and 23.80 ± 12.47 days, in the case of the atrial fibrillation domain, probably corresponding to the difference in the distributed specifications of the two GLs. Most of the interaction types were due to projections to the local DSS (83%); others were data notifications, mostly to change context (17%). Some of the data notifications were triggered due to technical errors. The robustness of the distributed architecture was demonstrated through the successful recovery from multiple crashes of the local DSS. ConclusionsThe new projection-callback model has been demonstrated to be feasible, from specification to distributed application. Different GLs might significantly differ, however, in their distributed specification and application characteristics. Distributed medical DSSs can facilitate the remote management of chronic patients by enabling the central DSSs to delegate, in a dynamic fashion, determined by the patient's context, much of the monitoring and treatment management decisions to the mobile device. Patients can be kept in their home environment, while still maintaining, through the projection-callback mechanism, several of the advantages of a central DSS, such as access to the patient's longitudinal record, and to an up-to-date evidence-based GL repository.

Effectiveness of a clinical decision support system for hypertension management in primary care: study protocol for a pragmatic cluster-randomized controlled trial

BackgroundClinical decision support systems (CDSS) are low-cost, scalable tools with the potential to improve guideline-based antihypertensive treatment in primary care, but their effectiveness needs to be tested, especially in low- and middle-income countries such as China.MethodsThe Learning Implementation of Guideline-based decision support system for Hypertension Treatment (LIGHT) trial is a pragmatic, four-stage, cluster-randomized trial conducted in 94 primary care sites in China. For each city-based stage, sites are randomly assigned to either implementation of the CDSS for hypertension management (which guides doctors’ treatment recommendations based on measured blood pressure and patient characteristics), or usual care. Patients are enrolled during the first 3 months after site randomization and followed for 9 months. The primary outcome is the proportion of hypertension management visits at which guideline-based treatment is provided.In a nested trial conducted within the CDSS, with the patient as the unit of randomization, the LIGHT-ACD trial, patients are randomized to receive different initial mono- or dual-antihypertensive therapy. The primary outcome of the LIGHT-ACD trial is the changes in blood pressure.DiscussionThe LIGHT trial will provide evidence on the effectiveness of a CDSS for improving guideline adherence for hypertension management in primary care in China. The nested trial, the LIGHT-ACD trial, will provide data on the effect of different initial antihypertensive regimens for blood pressure management in this setting.Trial registrationClinicalTrials.gov, identifier: LIGHT (NCT03636334) and LIGHT-ACD (NCT03587103). Registered on 3 July 2018.

The development and implementation of a guideline-based clinical decision support system to improve empirical antibiotic prescribing

BackgroundTo describe and evaluate a clinical decision support system (CDSS) for empirical antibiotic therapy using a systematic framework.MethodsA reporting framework for behavior change intervention implementation was used, which includes several domains: development, evaluation and implementation. Within the development domain a description is given of the engagement of stakeholders, a rationale for how the CDSS may influence antibiotic prescribing and a detailed outline of how the system was developed. Within the evaluation domain a technical validation is performed and the interaction between potential users and the CDSS is analyzed. Within the domain of implementation a description is given on how the CDSS was tested in the real world and the strategies that were used for implementation and adoption of the CDSS.ResultsDevelopment: a CDSS was developed, with the involvement of stakeholders, to assist empirical antibiotic prescribing by physicians. Evaluation: Technical problems were determined during the validation process and corrected in a new CDSS version. A usability study was performed to assess problems in the system-user interaction. Implementation: In 114 patients the antibiotic advice that was generated by the CDSS was followed. For 54 patients the recommendations were not adhered to.ConclusionsThis study describes the development and validation of a CDSS for empirical antibiotic therapy and shows the usefulness of the systematic framework for reporting CDSS interventions. In addition it shows that CDSS recommendations are not always adhered to which is associated with incorrect use of the system.

The Impact of an Inpatient Pancreatitis Service and Educational Intervention Program on the Outcome of Acute Pancreatitis

BackgroundWe introduced an inpatient pancreatitis consultative service aimed to 1) provide guideline-based recommendations to acute pancreatitis inpatients and 2) educate inpatient teams on best practices for acute pancreatitis management. We assessed the impact of pancreatitis service on acute pancreatitis outcomes. MethodsInpatients with acute pancreatitis (2008-2018) were included in this cohort study. Primary outcomes included length of stay and refeeding time. The educational intervention was a guideline-based decision support tool, reinforced at hospital-wide educational forums. In Part A (n = 965), we compared outcomes pre-service (2008-2010) to post-service (2012-2018), excluding 2011, when the pancreatitis service was introduced. In Part B (n = 720, 2012-2018), we divided patients into 2 groups based on if co-managed with the pancreatitis service, and compared outcomes, including subgroup analysis based on severity, focusing on mild acute pancreatitis. ResultsIn Part A, for mild acute pancreatitis, length of stay (111 vs 88.4 h, P = .001), refeeding time (61.8 vs 47.4 h, P = .002), and infections (10.0% vs 1.87%, P < .001) were significantly improved after the pancreatitis service was introduced, with multivariable analysis showing reduced length of stay (odds ratio 0.83; 95% confidence interval, 0.82-0.84; P < .001) and refeeding time (odds ratio 0.75; 95% confidence interval, 0.74-0.77; P < .001). In Part B, for mild acute pancreatitis, refeeding time (44.2 vs 50.3 h, P = .123) and infections (5.58% vs 4.70%, P = .80) were similar in patients cared for without and with the service. Length of stay was higher in the pancreatitis service group (93.3 vs 81.2 h, P = .05), as they saw more gallstone acute pancreatitis patients who had greater length of stay and magnetic resonance cholangiopancreatography. In the post-service period, a majority of patients with moderate/severe acute pancreatitis and nearly all intensive care unit admits received care from the pancreatitis service. ConclusionsImplementation of an inpatient pancreatitis service was associated with improved outcomes in mild acute pancreatitis. Guideline-based educational interventions have a beneficial impact on management of mild acute pancreatitis by admitting teams even without pancreatitis consultation.

Users' Assessement of the Visualization with Gauges of a Guideline-Based Decision Support System Output: A Qualitative Study of Focus Groups.

Computerized decision support systems (CDSSs) are still poorly routinely implemented in clinical practices mainly because of usability problems related to the technology interface. We previously proposed to use gauges to visualize the output of a guideline-based CDSS applied to malnutrition and pressure ulcer management in nursing homes. This interface was assessed by four focus groups including 16 healthcare professionals with expertise in geriatrics. A USE-like questionnaire was distributed. Participants considered the dashboard-with-gauges visualization was useful (94%), easy to use (63%), easy to learn (88%), and 88% thought they could be satisfied with it. However, concerns were expressed about the difficulty to follow up multiple healthcare problems.

Reusing Decisions Made with One Decision Support System to Assess a Second Decision Support System: Introducing the Notion of Complex Cases.

The guideline-based decision support system (GL-DSS) of the DESIREE project and OncoDoc are two clinical decision support systems applied to the management of breast cancer. In order to evaluate the DESIREE GL-DSS, we decided to reuse a sample of clinical cases previously resolved by the multidisciplinary tumor board (MTB) of the Tenon Hospital (Paris, France) when using OncoDoc. Since we had two different knowledge representation models to represent clinical parameters and decisions, and two formalisms to represent guidelines, we developed a transformation sequence, involving the creation of synthetic patients, the enrichment of DESIREE ontology, and the translation of clinical cases and their decisions, to transform OncoDoc data into the DESIREE representation. Considering MTB decisions as the gold standard, the 84% compliance rate of DESIREE recommendations was rather satisfactory. Some situations (0.7%) concerned clinical cases that were compliant neither with OncoDoc nor with DESIREE that we defined as complex cases, not handled by guidelines, which necessitate effective MTB discussions.

Visualization of Guideline-Based Decision Support for the Management of Pressure Ulcers in Nursing Homes.

Though a preventable risk, the management of pressure ulcers (PUs) in nursing homes is not satisfactory due to inadequate prevention and complex care plans. PUs early detection and wound assessment require to know the patient condition and risk factors and to have a good knowledge of best practices. We built a guideline-based clinical decision support system (CDSS) for the prevention, the assessment, and the management of PUs. Clinical practice guidelines have been modeled as decision trees and formalized as IF-THEN rules to be triggered by electronic health record (EHR) data. From PU assessment yielded by the CDSS, we propose a synthetic visualization of PU current and previous stages as a gauge that illustrates the different stages of PU continuous evolution. This allows to display PU current and previous stages to inform health care professionals of PU updated assessment and support their evaluation of previously delivered care efficiency. The CDSS will be integrated in NETSoins nursing homes EHR where gauges for several health problems constitute a patient dashboard.

From Atomic Guideline-Based Recommendations to Complete Therapeutic Care Plans: A Knowledge-Based Approach Applied to Breast Cancer Management.

How textual clinical practice guidelines are written may have an impact on how they are formalized and on the kind of recommendations issued by the clinical decision support systems (CDSSs) that implement them. Breast cancer guidelines are mostly centered on the description of the different recommended therapeutic modalities, represented as atomic recommendations, but seldom provide comprehensive plans that drive care delivery. The objective of this work is to implement a knowledge-based approach to develop a care plan builder (CPB) that works on atomic recommendations to build patient-centered care plans as sequences of chronologically ordered therapeutic steps. The CPB uses the atomic recommendations issued by the guideline-based decision support system (GL-DSS) of the DESIREE project. The domain knowledge is represented as the list of all care plans that apply to breast cancer patients. Scenarios are introduced to locate the patient on these theoretical care plans. The CPB has been evaluated on a sample of 99 solved clinical cases leading to an overall performance of 89,8%.

Evaluation of a National Comprehensive Cancer Network Guidelines–Based Decision Support Tool in Patients With Non–Small Cell Lung Cancer

The association of guideline-based decision support with the quality of care in patients with non-small cell lung cancer (NSCLC) is not known. To evaluate the association of exposure to the National Comprehensive Cancer Center (NCCN) guidelines with guideline-concordant care and patients' decisional conflict. A nonrandomized clinical trial, conducted at a tertiary care academic institution, enrolled patients from February 23, 2015, to September 28, 2017. Data analysis was conducted from July 19, 2019, to April 22, 2020. A cohort of 76 patients with NSCLC seen at diagnosis or disease progression and a retrospective cohort of 157 patients treated before the trial were included. Adherence to 6 NCCN recommendations were evaluated: (1) smoking cessation counseling, (2) adjuvant chemotherapy for patients with stage IB to IIB NSCLC after surgery, (3) pathologic mediastinal staging in patients with stage III NSCLC before surgery, (4) pathologic mediastinal staging in patients with stage III NSCLC before nonsurgical treatment, (5) definitive chemoradiotherapy for patients with stage III NSCLC not having surgery, and (6) molecular testing for epidermal growth factor receptor and anaplastic lymphoma kinase alterations for patients with stage IV NSCLC. Subgroup analysis was conducted to compare the rates of guideline concordance between the prospective and retrospective cohorts. Secondary end points included decisional conflict and satisfaction. An online tool customizing the NCCN guidelines to patients' clinical and pathologic features was used during consultation, facilitated by a trained coordinator. Concordance of practice with 6 NCCN treatment recommendations on NSCLC and patients' decisional conflict. Of the 76 patients with NSCLC, 44 were men (57.9%), median age at diagnosis was 68 years (interquartile range [IQR], 41-87 years), and 59 patients (77.6%) had adenocarcinoma. In the retrospective cohort, 91 of 157 patients (58.0%) were men, median age at diagnosis was 66 years (IQR, 61-65 years), and 105 patients (66.9%) had adenocarcinoma. After the intervention, patients received more smoking cessation counseling (4 of 5 [80.0%] vs 1 of 24 [4.2%], P < .001) and less adjuvant chemotherapy (0 of 7 vs 7 of 11 [63.6%]; P = .012). There was no significant change in mutation testing of non-squamous cell stage IV disease (20 of 20 [100%] vs 48 of 57 [84.2%]; P = .10). There was no significant change in pathologic mediastinal staging or initial chemoradiotherapy for patients with stage III disease. After consultation with the tool, decisional conflict scores improved by a median of 20 points (IQR, 3-34; P < .001). The findings of this study suggest that exposure to the NCCN guidelines is associated with increased guideline-concordant care for 2 of 6 preselected recommendations and improvement in decisional conflict. ClinicalTrials.gov Identifier: NCT03982459.

Implementation of an ontological reasoning to support the guideline-based management of primary breast cancer patients in the DESIREE project.

The DESIREE project has developed a platform offering several complementary therapeutic decision support modules to improve the quality of care for breast cancer patients. All modules are operating consistently with a common breast cancer knowledge model (BCKM) following the generic entity-attribute-value model. The BCKM is formalized as an ontology including both the data model to represent clinical patient information and the termino-ontological model to represent the application domain concepts. This ontological model is used to describe data semantics and to allow for reasoning at different levels of abstraction. We present the guideline-based decision support module (GL-DSS). Three breast cancer clinical practice guidelines have been formalized as decision rules including evidence levels, conformance levels, and two types of dependency, "refinement" and "complement", used to build complete care plans from the reconciliation of atomic recommendations. The system has been assessed on 138 decisions previously made without the system and re-played with the system after a washout period on simulated tumor boards (TBs) in three pilot sites. When TB clinicians changed their decision after using the GL-DSS, it was for a better decision than the decision made without the system in 75 % of the cases.

Guideline-Based Decision Support System for Nursing Homes: A Case Study with the Management of Malnutrition.

Nursing home (NH) residents are known to be at risk of preventable adverse events due to inadequate monitoring or failure to provide necessary treatments. Missed care has been partially explained by nurses' lack of knowledge. We describe a guideline-based decision support system for the management of malnutrition in NHs. Three steps are distinguished: screening, therapeutic management, and follow-up. Clinical practice guidelines have been modeled as decision trees and formalized as IF-THEN rules to be triggered by electronic health records data (e.g., weight, albuminemia). We propose a visualization of recommendations as a dashboard of gauges displaying both current and previous levels of malnutrition to inform on the effect of therapeutic management and facilitate a correct follow-up.

Development of an Guideline-Based Decision Support System for Effective Diagnostic Workflow for Oncologic Pathologists.

Accurate and rapid differential diagnosis are required for personalized cancer treatment. However, owing to the numerous molecular tests used for establishing a diagnosis, pathologists need time to investigate and confirm necessary test items. We present a guideline-based decision support system for effective workflow with regards to the molecular tests for pathological differential diagnosis.

Differing Guidance from Public Health Guidelines: Identifying Challenges to Developing Guideline-Based Decision Support for Lead Screening &amp; Management

Differing Guidance from Public Health Guidelines: Identifying Challenges to Developing Guideline-Based Decision Support for Lead Screening &amp; Management

Gathering Real World Evidence Through the Evaluation of Decision History.

Clinical Practice Guidelines (CPGs) gather latest evidence-based results to guide and support clinicians over the decision-making process to provide best care. Nevertheless, clinical cases may be subject to some biases (understood as non-compliance with CPGs) that can lead to adapt care delivery. In this work an experience-based decision support leaning on the structuration of the Decisional Event concept for tracking and storing each clinical decision is presented. Moreover, a visual analytics tool is provided in order to facilitate the visualization of biases from guideline-based decision support and the identification and inclusion of real-world evidence into the reasoning process by augmenting the knowledge formalized in the implemented guidelines.

Development A Guideline-based Decision Support System to Diagnosis of Primary Immunodeficiency Diseases

Introduction: Primary immunodeficiency diseases (PID) are generally rare genetic disorders affecting the immune system. Overlapping PIDs symptoms and signs is a challenge to diagnosis and treatment. On the other hand, remembering of all diagnosis criteria is difficult for practitioners. The purpose of this research is developing guideline-based clinical decision support system for diagnosis of primary immune deficiency diseases, to assist practitioner in order to diagnose of disease in early stage and to minimize complications of such diseases.Material and Methods: To provide data a checklist was used and most important demographic information, symptoms, family history, physical findings and laboratory findings to diagnose eight common PIDs extracted from guidelines and literature under specialists opinion. The diagnosis inference model design and develop in Protégé (version 3.4.8) frame based ontology modeling using "Noy and McGuinness" method. Then the mobile based inference model in Eclipse (SDK version 3.7.1) software has been developed and clinical decision support system of primary immunodeficiency has been created.Results: To design the diagnosis inference model in Protégé software, data were classified in 5 main classes and 24 subclasses as hierarchical. Then, specific properties of each class, and determine the value of each property. Then define Instances of each class and initialized instance properties. Then use this model to develop CDSS based on mobile in Eclipse software. At the end, the inference model and the CDSS test with 110 patient’s record data and both of them recognized all 110 patient correctly such as specialist recognition.Conclusion:Guideline-based decision support systems help to detect diseases correctly, quickly and early. Guideline-based decision support systems are very reliable to practitioner, because guidelines are accepted to their. These systems reduce the forget probability of diagnosis stages and percentage error of diagnosis by practitioner and increase the accuracy of diagnosis.

Development A Guideline-based Decision Support System to Diagnosis of Primary Immunodeficiency Diseases

Introduction: Primary immunodeficiency diseases (PID) are generally rare genetic disorders affecting the immune system. Overlapping PIDs symptoms and signs is a challenge to diagnosis and treatment. On the other hand, remembering of all diagnosis criteria is difficult for practitioners. The purpose of this research is developing guideline-based clinical decision support system for diagnosis of primary immune deficiency diseases, to assist practitioner in order to diagnose of disease in early stage and to minimize complications of such diseases. Material and Methods: To provide data a checklist was used and most important demographic information, symptoms, family history, physical findings and laboratory findings to diagnose eight common PIDs extracted from guidelines and literature under specialists opinion. The diagnosis inference model design and develop in Protege (version 3.4.8) frame based ontology modeling using "Noy and McGuinness" method. Then the mobile based inference model in Eclipse (SDK version 3.7.1) software has been developed and clinical decision support system of primary immunodeficiency has been created. Results: To design the diagnosis inference model in Protege software, data were classified in 5 main classes and 24 subclasses as hierarchical. Then, specific properties of each class, and determine the value of each property. Then define Instances of each class and initialized instance properties. Then use this model to develop CDSS based on mobile in Eclipse software. At the end, the inference model and the CDSS test with 110 patient’s record data and both of them recognized all 110 patient correctly such as specialist recognition. Conclusion:Guideline-based decision support systems help to detect diseases correctly, quickly and early. Guideline-based decision support systems are very reliable to practitioner, because guidelines are accepted to their. These systems reduce the forget probability of diagnosis stages and percentage error of diagnosis by practitioner and increase the accuracy of diagnosis.

Not a magic pill: a qualitative exploration of provider perspectives on antibiotic prescribing in the outpatient setting

BackgroundInappropriate prescribing of antibiotics poses an urgent public health threat. Limited research has examined factors associated with antibiotic prescribing practices in outpatient settings. The goals of this study were to explore elements influencing provider decisions to prescribe antibiotics, identify provider recommendations for interventions to reduce inappropriate antibiotic use, and inform the clinical management of patients in the outpatient environment for infections that do not require antibiotics.MethodsThis was a qualitative study using semi-structured interviews with key informants. Seventeen outpatient providers (10 medical doctors and 7 advanced care practitioners) within a large healthcare system in Charlotte, North Carolina, participated. Interviews were audio recorded, transcribed, and analyzed for themes.ResultsPrimary barriers to reducing inappropriate antibiotic prescribing included patient education and expectations, system-level factors, and time constraints. Providers indicated they would be interested in having system-wide, evidence-based guidelines to inform their prescribing decisions and that they would also be receptive to efforts to improve their awareness of their own prescribing practices. Results further suggested that providers experience a high demand for antibiotic prescriptions; consequently, patient education around appropriate use would be beneficial.ConclusionsFindings suggest that antibiotic prescribing in the outpatient setting is influenced by many pressures, including patient demand and patient satisfaction. Training on appropriate antibiotic prescribing, guideline-based decision support, feedback on prescribing practices, and patient education are recommended interventions to improve levels of appropriate prescribing.