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#FOAMed #EmergencyMedicine #MedEd #ClinicalDecisionSupport #AIinHealthcare

#Mentalhealth diagnosis + treatment decisions need to happen quickly. Clinicians need tools providing clear, evidence-informed guidance to act on. I invite Cdn GPs + NPs to Ori by #RapidsHealth for more efficient + informed decision-making: ow.ly/4P2b50YzrQj #PrimaryCare #ClinicalDecisionSupport

#DisabilityEquity #AYA #DigitalHealth #ClinicalDecisionSupport #SDOH

Utility of a Smartphone-Based Clinical Decision Support System for Pressure Ulcer Management by Physicians: Randomized Crossover Pilot Study Background: Clinical decision support systems (CDSSs) are widely used in various health care settings. In Japan, pressure ulcers are becoming a major concern in an aging society due to their increasing prevalence. However, management is often handled by nonspecialists in wound care due to regional disparities in specialist availability. Objective: To provide support for nonspecialists in wound care, we developed a prototype smartphone-based CDSS for pressure ulcer management. The system prompts users to answer questions about the wound's condition and recommends appropriate ointments and wound dressings by using a safety-first approach. This study aims to evaluate the utility of this system. Methods: We conducted a randomized crossover pilot study involving 28 general internal medicine (GIM) physicians. Participants were randomly assigned to group A (intervention-control) or group B (control-intervention). Participants evaluated 10 standardized pressure ulcer photographs and selected the most appropriate ointment and wound dressing for each. The unit of analysis was the individual response to each question (N=280 total observations). We used generalized estimating equations with an exchangeable correlation structure to account for within-subject clustering and adjust for potential period and sequence effects. Results: The overall correct response rate during the intervention phase was significantly higher than that during the control phase (49.3% vs 4.3%, respectively). After adjusting for clustering and crossover biases, the use of CDSS was associated with a 29.1-fold increase in the odds of a correct response (95% CI 8.2-103; P

JMIR Formative Res: Utility of a Smartphone-Based Clinical Decision Support System for Pressure Ulcer Management by Physicians: Randomized Crossover Pilot Study #SmartphoneHealth #ClinicalDecisionSupport #PressureUlcers #WoundCare #HealthcareInnovation

Barriers and Enablers for Sustaining #nurse-Led Use of Clinical Decision Support Tools for Antibiotic Stewardship: Qualitative Study Background: Clinical decision support (CDS) tools embedded in electronic health records in the form of integrated clinical prediction rules provide a potentially effective intervention to reduce inappropriate antibiotic prescribing for acute respiratory infections. However, their effectiveness has been limited by workflow barriers and low adoption by health care providers. #nurses are well positioned to implement evidence-based protocols using CDS tools. In a multicenter randomized controlled trial, a #nurse-led implementation strategy for acute respiratory infection integrated clinical prediction rules was evaluated for use in primary care and urgent care settings. Objective: This study aimed to examine #nurse and #nurse leader perspectives on the sustainability of an electronic health record–integrated CDS tool for antibiotic stewardship and explored factors influencing its potential long-term integration into ambulatory #nursing practice beyond the clinical trial. Methods: We interviewed 22 #nurses and #nurse leaders from 37 clinics across 3 academic medical centers that participated in the clinical trial. Two semistructured interview guides, one for #nurses and one for #nursing leadership, were developed to understand the barriers and facilitators to implementing a decision aid tool for #nurses and to elicit challenges specific to #nursing interactions with the CDS tool. Interviews were recorded and transcribed. Using thematic content analysis and iterative coding, our team collaboratively identified emerging themes related to sustainability and refined the results with consensus. Results: Five themes emerged: (1) importance of staffing stability and capacity, (2) impact of dedicated clinic resource availability, (3) variable #nurse readiness with CDS-guided clinical care, (4) influence of openness to change and a #nurse-supportive clinic culture, and (5) ongoing need for training and support. Specific recommendations for future actions were also noted. Conclusions: Our findings revealed specific barriers and facilitators to the sustainability of a CDS tool from the #nursing perspective that can inform further implementation of #nurse-led delegation protocols in the ambulatory setting. Future solutions should consider mapping physical workflows, scheduling specific to #nurse visits, continuing education, and treating cough and sore throat as 2 distinct processes.

New in JMIR Nursing: Barriers and Enablers for Sustaining #nurse-Led Use of Clinical Decision Support Tools for Antibiotic Stewardship: Qualitative Study #Nurses #ClinicalDecisionSupport #AntibioticStewardship #Healthcare #DigitalHealth

How I Built FGRManager: A Physician-Developer’s Blueprint for Turning Clinical Protocols into Bedside Tools By Chukwuma Onyeije, MD, FACOG | Maternal-Fetal Medicine Specialist & Founder, lightslategray-turtle-256743.hostingersite.com Atlanta Perinatal Associates S

📖 www.doctorswhocode.blog/blog/how-i-b...

What protocol in your specialty is still living in a PDF? 👇

#doctorswhocode #ClinicalDecisionSupport #MaternalFetalMedicine

Optimizing the input: Can large language models standardize radiology requisitions? - European Radiology European Radiology -

#ArtificialIntelligence #RadiologyWorkflow #LLM #ClinicalDecisionSupport #Qualityandsafety

buff.ly/QBWvWDw (João Santinha & Helena Guerreiro)

Clinical Decision Support Tool for Early Pancreatic #Cancer Detection in Primary Care: Simulation Study Background: Early detection in primary care could improve pancreatic #Cancer survival, but diagnosis is often delayed due to the low prevalence of the disease, the nonspecific nature of early symptoms, and the broad range of conditions and volume of consultations managed by general practitioners (GPs). In Australia, improving pancreatic #Cancer outcomes, including via earlier diagnosis, is a priority being progressed under the National Pancreatic #Cancer Roadmap developed by #Cancer Australia. Computerized clinical decision support systems (CDSSs) have shown promise in aiding timely #Cancer diagnosis; however, barriers to adopting CDSS such as mistrust of the recommendations or not being embedded in the clinical workflow remain. Simulation techniques, which offer flexible and cost-effective ways to evaluate digital health interventions, can be used to test CDSS before real-world implementation. Objective: This study aims to assess the acceptability and #feasibility of identifying patients with symptoms associated with pancreatic #Cancer through a CDSS within a simulated environment. Methods: We developed a CDSS that interacted with an electronic health record used in general practice to identify patients with symptoms, which may indicate pancreatic #Cancer (unintended weight loss or new-onset diabetes), in a simulation laboratory for digital interventions. We tested it by inviting GPs (n=11) to use the CDSS, with patient actors simulating specific clinical scenarios. We then interviewed GPs about the interaction to assess the acceptability and #feasibility of the CDSS in their clinical practice. We used thematic analysis and 2 relevant frameworks to analyze the data. Results: GPs found the CDSS easy to use, unobstructive, and effective as a prompt to consider investigations for people with risk factors for pancreatic #Cancer. However, they expressed concerns about possible overtesting, financial costs, and the potential for anxiety in patients with a very low probability of having #Cancer. Conclusions: While GPs found the tool useful and compatible with their workflow, concerns about overtesting, lack of evidence, and cost-effectiveness were identified as barriers. GPs favored a stepwise approach to investigations rather than immediate imaging. Despite the overall acceptability of the tool, additional evidence to underpin clinical recommendations is necessary before implementing a CDSS with these specific recommendations for pancreatic #Cancer in primary care.

JMIR Formative Res: Clinical Decision Support Tool for Early Pancreatic #Cancer Detection in Primary Care: Simulation Study #PancreaticCancer #CancerAwareness #EarlyDetection #ClinicalDecisionSupport #DigitalHealth

On today's CAPES Meet the Faculty, we introduce Dr. Feifan Liu!

Learn more about Dr. Liu: profiles.umassmed.edu/display/1550...

#MeetTheFaculty #DataScience #ArtificialIntelligence #MachineLearning #HealthInformatics #ClinicalDecisionSupport #SuicidePrevention #CAPES

On today's CAPES Meet the Faculty, we introduce Dr. Sharon A. Johnson!

Learn more about Dr. Johnson: www.wpi.edu/people/facul...

#MeetTheFaculty #ImplementationScience #HealthSystemsEngineering #EmergencyMedicine #ClinicalDecisionSupport #CAPES #WPI

NEW: Effect of Clinical Decision Support Alerts on #Anticoagulation Management in #AtrialFibrillation

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#MedSky #CardioSky #CDS #ClinicalDecisionSupport #DigitalHealth

AI enables real-time, personalized healthcare decisions

Edition-2: Artificial Intelligence (AI) in Medicine – AIIM 2026
May 04–05, 2026
Boston, Massachusetts, USA
🌐 ai-medicalcongress.com

#AIIM2026 #AIinMedicine #MedicalAI #DigitalHealth #ClinicalDecisionSupport #WearableAI #HealthcareInnovation

Evaluating a Clinical Decision Support Tool for #Cancer Risk Assessment in Primary Care: Simulation Study of Unintended Weight Loss Background: Early #Cancer detection is crucial, but recognising the significance of associated symptoms such as unintended weight loss in primary care remains challenging. Clinical Decision Support Systems (CDSS) can aid #Cancer detection, but face implementation barriers and low uptake in real-world settings. To address these issues, simulation environments offer a controlled setting to study CDSS usage and improve their design for better adoption in clinical practice. Objective: To evaluate a CDSS integrated within general practice electronic health records aimed at identifying patients at risk of undiagnosed #Cancer. Methods: The evaluation of CDSS to identify patients with unintended weight loss was conducted in a simulated primary care environment where GPs interacted with the CDSS in simulated clinical consultations. There were four possible clinical scenarios based on patient gender and risk of #Cancer. Data collection included interviews with GPs, #Cancer survivors (lived-experience community advocates), and patient actors, as well as video analysis of GP-CDSS interactions. Two theoretical frameworks were employed for thematic interpretation of the data. Results: We recruited 10 GPs and 6 community advocates, conducting 20 simulated consultations with two patient actors (two consultations per GP, one high-risk and one low-risk). All participants found the CDSS acceptable and unobtrusive. GPs utilised CDSS recommendations in three distinct ways: as a communication aid when discussing follow up with the patient, as a reminder for differential diagnoses and recommended investigations, and as an aid to diagnostic decision-making without sharing with patients. The CDSS's impact on patient-doctor communication varied, both facilitating and hindering interactions depending on the GP's communication style. Conclusions: We developed and evaluated a CDSS for identifying #Cancer risk in patients with unintended weight loss in a simulated environment, revealing its potential to aid clinical decision-making and communication, while highlighting implementation challenges and the need for context-sensitive application. Clinical Trial: NA

JMIR Formative Res: Evaluating a Clinical Decision Support Tool for #Cancer Risk Assessment in Primary Care: Simulation Study of Unintended Weight Loss #Cancer #HealthTech #ClinicalDecisionSupport #PrimaryCare #WeightLoss

An Expert Knowledge Algorithm and Model Predicting Wound Healing Trends for a Decision Support System for Pressure Injury Management in Home Care Nursing: Development and Validation Study Background: Home-visiting #nurses have difficulty selecting appropriate pressure injury (PI) management despite using clinical practice guidelines in various home-visiting settings. Clinical decision support systems (CDSS) can help home-visiting #nurses’ decision-making. Objective: This study aimed to develop a care algorithm reflecting the expertise of a wound expert #nurse and a predictive model for the change of PI severity to inform home-visiting #nurses to receive actual consultation. Methods: First, an existing algorithm was modified by semi-structured interviews with a certified wound expert #nurse. Case information was input into both base and high-expertise algorithms, which provided care recommendations across nine fields: 1) pressure relief, 2) nutritional management, 3) shear relief, 4) moisture management, 5) wound dressing use, 6) care for physical factors including bone prominence, obesity, joint contractures, and periwound edema, 7) care for systemic disorder, 8) selection of wound dressings, ointments, and negative pressure wound therapy, and 9) wound cleansing. An expert interviewee assessed the high-expertise algorithm’s recommendations on a five-point scale, comparing them to the base algorithm and their own clinical judgment. To measure the algorithm’s applicability, agreement proportions were calculated as the number of vignettes where the care recommendation was considered appropriate/total number of vignettes. To measure the algorithm’s alignment, improvement proportions were calculated as the number of vignettes where the care recommendation improved/total number of vignettes excluding vignettes when the existing and high-expertise algorithm both showed an appropriate recommendation. Expected healing levels were evaluated by a 4-point scale where four indicates the high-expertise algorithm can “much improve” the case. Second, predictive distributions of changes of DESIGN-R®2020 score, PI severity score, were estimated with a hierarchical Bayesian model. The best model determined using training data (n=42) calculated coverage probabilities of 90% prediction interval in test data (n=34). The coverage probability of 90% prediction interval was defined as follows: the number of times when actual scores were within the 90% prediction interval/the number of assessments when the prediction was conducted. Results: The agreement proportions were 0.92, 0.75, and 0.89, respectively. The improvement proportions were 0.73, 0.25, and 0.76, respectively. The expected healing level was 2.67, 3.00, and 3.25, respectively. Coverage probabilities of 90% prediction interval in the test data were 0.67, 0.83, 0.86, and 0.80, respectively. Conclusions: This study developed an algorithm reflecting the expertise and a model to estimate predictive distributions of changes of DESIGN-R®2020 score for developing clinically applicable CDSS for home-visiting #nurses providing appropriate PI management. Clinical Trial: Not applicable.

New in JMIR Nursing: An Expert Knowledge Algorithm and Model Predicting Wound Healing Trends for a Decision Support System for Pressure Injury Management in Home Care Nursing: Development and Validation Study #Nursing #Healthcare #WoundCare #PressureInjury #ClinicalDecisionSupport

Guideline Adherence and Subjective Effects of a Mobile Clinical Decision Support System on Physicians' Practice: A Nationwide Survey-Based Within-Subject Study
E. C. d., J. G. V. d. et al.
Paper
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#ClinicalDecisionSupport #PhysicianPractice #HealthTechResearch

NEW: A Two-Phase Framework Leveraging User Feedback and Systemic Validation to Improve Post-Live #ClinicalDecisionSupport

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#MedSky #CDS #DigitalHealth

NEW: Effect of a #ClinicalDecisionSupport Tool for Identifying Patients Benefiting from #End-of-Life Discussions on #EmergencyDepartment Clinician Behavior www.thieme-connect.de/products/ejo...

#MedSky #CDS #PalliativeCare #DigitalHealth

#healthcareAI #genAI #clinicaldecisionsupport #healthAI

3➡️Une revue axée sur l’utilisation de l’IA dans les maladies infectieuses synthétisant ses applications actuelles et les défis à résoudre.

www.sciencedirect.com/science/arti...

#IDSky #IDSkyFr #InfectiousDiseases #AI #GenerativeArtificialIntelligence #MachineLearning #ClinicalDecisionSupport

Evaluation of an AI-Based Clinical Decision Support System for Perioperative Care of Older Patients: Ethical Analysis of Focus Groups With Older Adults Background: The development and introduction of an AI-based Clinical Decision Support System (CDSS) in surgical departments within the SURGE-Ahead project responds to the increasing aging population. Thereby, digital geriatric co-management with an evidence-based evaluation of the patient’s health condition and corresponding medical recommendations is aimed to improve the perioperative geriatric patient care. Objective: The use of an AI-based CDSS in patient care raises ethical challenges. The collection of opinions, expectations and concerns of older people as potential geriatric patients on the CDSS enables the identification of ethical chances, concerns and limitations that arise after implementing the technical device in hospitals. Methods: In total, five focus groups with participants aged 65 years and above were conducted. The transcripts were evaluated according to the qualitative content analysis and ethically analyzed: First, an inductive formation of categories was implemented, followed by a thematic classification of the participants’ statements. Thereby, we disproved technical understanding to impact the older people’s opinions. Results: Ethical chances and concerns were detected: The diagnosis and therapy could speed up, changes in the patient-AI-physician-interaction could improve medical treatment and enhance the coordination in hospitals. However, the quality of the CDSS depends on an adequate data basis and cyber security. A habituation effect and loss of a second medical option could develop, and the severity of an illness could be considered as an impact factor on the patient’s attitude towards medical suggestions. The risk of overdiagnosis and overtherapy was discussed controversially, and the range of therapy options could be influenced by interests and finances. Saving time resources would remain challenging, medical skills could decrease and the extent of the patient’s hospital stay could be affected. Conclusions: To respond to the ethical challenges, we recommend a time-sufficient use and emphasize an individual revision of the CDSS’s results. Furthermore, we suggest a limitation of private financial sponsoring.

New in JMIR Aging: Evaluation of an AI-Based Clinical Decision Support System for Perioperative Care of Older Patients: Ethical Analysis of Focus Groups With Older Adults #AIHealthcare #ClinicalDecisionSupport #GeriatricCare #PerioperativeCare #EthicalAI

ICE Advisory Group Reviews Evolving Immunization Guidelines | HLN The Immunization Calculation Engine (ICE) Advisory Group met on September 29, 2025, bringing together over 30 experts to address evolving immunization guidelines, ACIP recommendations, and challenges ...

The #ICE Advisory Group convened 30+ experts to address shifting #ClinicalGuidelines and their impact on #ClinicalDecisionSupport. @HLNConsulting shares key takeaways from this vital discussion: hln.com//ice-advisory-group-immunization-guidelines-2025.

𝗜𝗻 𝗖𝗮𝘀𝗲 𝗬𝗼𝘂 𝗠𝗶𝘀𝘀𝗲𝗱 𝗜𝘁: #OncoscopeAI Founder & CEO Anna Forsythe new article in Forbes, "𝘍𝘳𝘰𝘮 𝘌𝘷𝘪𝘥𝘦𝘯𝘤𝘦 𝘛𝘰 #𝘈𝘐: 𝘞𝘩𝘺 𝘛𝘩𝘦 𝘍𝘶𝘵𝘶𝘳𝘦 𝘖𝘧 #𝘖𝘯𝘤𝘰𝘭𝘰𝘨𝘺 𝘋𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘚𝘶𝘱𝘱𝘰𝘳𝘵 𝘔𝘶𝘴𝘵 𝘉𝘦 𝘉𝘶𝘪𝘭𝘵 𝘖𝘯 𝘓𝘪𝘷𝘪𝘯𝘨 𝘌𝘷𝘪𝘥𝘦𝘯𝘤𝘦"

Read the full article at the "News & Press" #LinkInBio.

#Cancer #ClinicalDecisionSupport #PrecisionMedicine

Health Care Providers’ Perspectives of Clinical Decision Support Tools for Pediatric Sepsis in Bangladesh: Qualitative Study Background: Sepsis, a life-threatening condition resulting from a dysregulated immune response to infection, disproportionately affects children in low- and middle-income countries (LMICs). Children with sepsis in LMICs face high mortality rates, with early detection and clinical monitoring posing significant challenges to effective management. There is great potential for digital technologies, such as wearable biosensor devices and mobile health (mHealth) clinical decision support (CDS) tools, together referred to as clinical decision support systems (CDSSs), to enable closer monitoring and more prompt recognition of children at risk of advanced sepsis and death. However, little is known about the perceptions of health care providers (HCPs) regarding the introduction of new digital health tools for pediatric sepsis care in LMICs. Objective: The objective of this study was to assess HCPs’ understanding, perceptions, and recommendations regarding the design and implementation of digital CDSSs for pediatric sepsis care in Bangladesh. Methods: Between February and May 2024, 18 individual semistructured in-depth interviews were conducted with HCPs (nurses and physicians) at 3 urban hospitals in Bangladesh. The data were transcribed, translated from Bangla to English, and analyzed using a framework matrix analysis approach. Participants were asked about familiarity with digital health tools, feedback on CDSS design, perceptions of the system’s utility, and barriers and facilitators to use of similar tools in clinical settings in Bangladesh. Results: Participants reported overall positive perceptions toward the potential implementation of a CDSS for pediatric sepsis care in Bangladesh. Some key priorities for the design of a CDSS were durability, re#usability, cost considerations, reliability, and accuracy. Clinicians desired the CDS tool to also have customizable alarm parameters and include additional functions such as glucose monitoring. Many favored audio (ringtone) or visual (light) alarms to alert about changes in captured vital signs. HCPs believed that a CDSS could enhance patient care by allowing greater staff capacity to monitor patients, reducing management time, and aiding in faster clinical decision-making, with some suggesting it could lower mortality rates. Concerns regarding implementation included internet availability, affordability of the wearable devices, and trust in the CDSS outputs compared to expert clinician judgement. Conclusions: The findings of this study highlight HCPs’ perceptions toward the potential of wearable biosensor devices and CDS tools (CDSSs) for improving pediatric sepsis outcomes in LMICs and highlight the need to address implementation challenges to ensure the effective integration of CDSSs into health care systems.

JMIR Formative Res: Health Care Providers’ Perspectives of Clinical Decision Support Tools for Pediatric Sepsis in Bangladesh: Qualitative Study #Sepsis #PediatricHealthcare #DigitalHealth #mHealth #ClinicalDecisionSupport

A figure showing disappearing text in a red box nudging providers to document an anticoagulation plan. Data presented in the figure are imaginary. Copyright 2025 Epic Systems Corporation

NEW: Disappearing Text as a Clinical Decision Support Layer: A Case Series www.thieme-connect.de/products/ejo...

#MedSky #CDS #ClinicalDecisionSupport #DigitalHealth

A conceptual framework of how fatigue affects decision-making in emergency departments. The numbers correspond to the themes and subthemes in the Results section of the manuscript.

ICYMI: Clinical Decision-Making and Use of Clinical Decision Support When Clinicians are Fatigued in an #EmergencyDepartment: A Qualitative Study www.thieme-connect.de/products/ejo...

#MedSky #CDS #DigitalHealth #ClinicalDecisionSupport @mozkaynak.bsky.social

In the last three months, 𝗻𝗲𝗮𝗿𝗹𝘆 𝟭𝟬% 𝗼𝗳 𝗨.𝗦. 𝗼𝗻𝗰𝗼𝗹𝗼𝗴𝗶𝘀𝘁𝘀 signed up for our real-time Oncology library.

Hear what Dr. Peter Kaufman, Oncologist at the University of Vermont Cancer Center, had to say about Oncoscope Edge: bit.ly/41LEK6U

#Oncology #ClinicalDecisionSupport #PrecisionMedicine

Over half of cancers in relatives go undocumented in EHRs—risking missed chances to identify familial cancer risk and reduce preventable mortality. bit.ly/41GriRS #GIMO #FamilyHistory #CancerScreening #ElectronicHealthRecord #ClinicalDecisionSupport #Kinship

A figure showing a screen capture of interruptive CDS alert for venous thromboembolism prophylaxis

NEW: Sisyphus' Alert: The Uphill Struggle to Improve Venous #Thromboembolism Prophylaxis #ClinicalDecisionSupport www.thieme-connect.de/products/ejo...

#MedSky #CDS #DigitalHealth

Evaluating the Prototype of a Clinical Decision Support System in Primary Care: Qualitative Study Background: General practitioners are confronted with a wide variety of diseases and sometimes diagnostic uncertainty. Clinical decision support systems could be valuable to improve diagnosis, but existing tools are not adapted to the requirements and workflow in the primary setting. In the project SATURN, the prototype of a clinical decision support system based on artificial intelligence (#AI) is being developed together with users specifically for primary care in Germany. It aims to reduce diagnostic uncertainty in cases of unclear and rare diseases and focuses on three medical fields. A user-centered design approach is applied for prototype development and evaluation. Objective: This study evaluates the #usability of a high-fidelity prototype and explores aspects of user experience like the subjective impression, satisfaction and areas of improvement. Methods: Five general practitioners participated in the evaluation which consisted of (1) a remote think-aloud test, (2) a post-session interview, and (3) a survey with the System #usability Scale. During the think-aloud tests, the participants verbalized their thoughts and actions and solved several vignette based tasks. Remarkable observations were logged, transcribed with quotes, and analyzed for #usability problems and positive findings. All observations and interview responses were deductively assigned to the following categories: (1) Content, (2) Comprehensibility, (3) User-friendliness, (4) Layout, (5) Feedback, (6) Navigation. #usability problems were described in detail and solutions for improvement proposed. Median and total scores were calculated for all questionnaire items. Results: The evaluation detected both strengths and areas for improvement. The participants particularly liked the clear and well-structured layout of the prototype. Key issues identified were content-related limitations, such as the inability to enter unlisted symptoms, medications, and examination findings. Also, participants found the terminology for laboratory not suitable to their needs. Another key issue was a lack of user-friendliness concerning the time required to input medication plans and lab values. Participants expressed a need for faster data entry, potentially through direct imports from practice management systems or laboratory files. Adding symptom duration, weighting symptoms, and incorporating hereditary factors were suggestions made for improvement. Overall, the SATURN prototype was deemed useful and promising for future clinical use, despite the need for further refinements, particularly in the areas of data entry, as this is a key obstacle to its use. Conclusions: The #usability evaluation methods combined proved to be location independent and easy to use. They provided important findings on #usability issues and improvements that will be implemented in a second high-fidelity prototype, which will also be tested by users. Technically demanding user requirements, such as direct data transfer from the practice management system and entry options that require complex data models were beyond the scope of this project, but should be considered in future development projects.

JMIR Formative Res: Evaluating the Prototype of a Clinical Decision Support System in Primary Care: Qualitative Study #ClinicalDecisionSupport #PrimaryCare #HealthcareInnovation #ArtificialIntelligence #UserExperience

Comparison of OneChoice(R) AI-based clinical decision support recommendations with infectious disease specialists and non-specialists for bacteremia treatment in Lima, Peru
Caceres, J., Chavez-Lencinas, C. et al.
Paper
Details
#OneChoiceAI #ClinicalDecisionSupport #BacteremiaTreatmentPeru