Applied Clinical Informatics最新文献

{"title":"US Adults' Willingness to Use a Blockchain-Based Health Information Management Application: A Structural Equation Modeling Approach.","authors":"John Robert Bautista, Daniel T Harrell, Eric T Meyer, Anjum Khurshid","doi":"10.1055/a-2927-0791","DOIUrl":"10.1055/a-2927-0791","url":null,"abstract":"<p><strong>Background: </strong>MediLinker is a blockchain-based application for identity verification and personal health information management. To prepare for its clinical deployment, the perceptions of health consumers toward MediLinker should be examined.</p><p><strong>Objective: </strong>Guided by a conceptual model derived from the theory of reasoned action, privacy calculus, protection motivation theory, and regulatory uncertainty, the study identifies U.S. adults' willingness to use MediLinker for health information management and the factors associated with this willingness.</p><p><strong>Methods: </strong>We conducted a cross-sectional online survey in August 2022 among U.S. adults recruited on Amazon Mechanical Turk. The analytic sample includes data from 913 respondents who nearly reflect U.S. demographic characteristics by gender and race/ethnicity. Descriptive statistics were used to identify respondents' willingness to use MediLinker, while structural equation modeling (SEM) was used to identify direct and indirect factors associated with willingness.</p><p><strong>Results: </strong>Most respondents were willing to use MediLinker to store and manage health information (76.7%), share health information with providers (79.3%), and provide consent for clinical research (78.4%). SEM results show that attitude toward MediLinker has a direct positive association with willingness to use MediLinker in general. We also identified several indirect predictors of willingness. For instance, perceived liability issues and perceived vulnerability have a negative indirect effect on willingness through perceived risks and attitude. On the other hand, perceived vulnerability and perceived severity have a positive indirect effect on willingness via perceived benefits and attitude.</p><p><strong>Conclusion: </strong>Our sample of U.S. adults is willing to use MediLinker for health information management. Moreover, our conceptual model shows direct and indirect pathways influencing U.S. adults' willingness to use MediLinker. The results inform the deployment of blockchain-based health information management applications among health consumers. Overall, the study contributes to the literature on how healthcare consumers perceive blockchain technologies for medical decision making involving their online health information.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"691-700"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13476108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148632332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous Remote Patient Monitoring in Heart Failure Patients: The Heart Failure Cascade Study: Phase II and III Outcomes.","authors":"Sonia Sultan, Mukund Nakhate, Wei Ning Chi, Carolina Escobar, Urmila Ravichandran, Melissa Morton-Jost, Izabella Szum, Erica Sis, Sara Edwards, Regina Munoz, Safwan Gaznabi, Lilian Benck, Charu Gupta, Nitasha Sarswat, Robert Gordon, Karen Larimer, Anthony Solomonides, Rema Padman, Nirav S Shah","doi":"10.1055/a-2946-7234","DOIUrl":"10.1055/a-2946-7234","url":null,"abstract":"<p><strong>Abstract: </strong>OBJECTIVES: The objective of this study is to evaluate the effect of a continuous remote patient monitoring (CRPM) program on reducing 30-day readmissions in a case-versus-retrospective-propensity-matched-control study of heart failure (HF) patients.</p><p><strong>Abstract: </strong>METHODS: The study was conducted at Endeavor Health (formerly known as NorthShore University HealthSystem), Evanston, IL, in three post-soft launch phases: Phase IIa, IIb, and Phase III. HF patients were monitored for 30 days postdischarge with wearable biosensors to collect continuous ambulatory physiological data and a study phone for capturing patient-reported outcomes via daily surveys. Sensor data were analyzed by rules-based and machine learning algorithms to alert on physiologic perturbation. Platform alerts and survey data were monitored by home health nurses (HHNs) who assessed patients and, following a structured escalation pathway, referred them to HF advanced practice providers (APPs) or physicians for further management.</p><p><strong>Abstract: </strong>RESULTS: A total of 39 patients completed the study. Baseline characteristics were appropriately matched in all categories with the exception of the New York Heart Association (NYHA) classification (intervention vs. control, class II: 10.3 vs. 41.0%, class III: 76.9 vs. 53.8%, class IV: 12.8 vs. 5.1%; <i>p</i> = 0.006). The intervention group had more patients who received HF APP calls (66.7 vs. 7.7%; <i>p</i> ≤ 0.001), HF APP visits (43.6 vs. 5.1%; <i>p</i> < 0.001), HF physician visits (64.1 vs. 41.0%; <i>p</i> = 0.041), diuretic escalation (43.6 vs. 12.8%; <i>p</i> = 0.006), and laboratories within 30 days of discharge (76.9 vs. 43.6%; <i>p</i> = 0.005). There was no significant difference between the adjusted 30-day readmission rates for the intervention and control groups (0.31, 95% confidence interval [CI]: 0.06-1.38; <i>p</i> = 0.138). All-cause readmissions occurred, but none were due to HF in phases IIa, IIb, and III.</p><p><strong>Abstract: </strong>CONCLUSION: Integrating CRPM directly into clinical workflows is feasible but challenging. Further research with larger patient cohorts and an adequately powered randomized trial is required to robustly evaluate the role of CRPM, machine learning algorithms, and cascading workflows in reducing 30-day readmissions.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 4","pages":"789-801"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13593373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Canadian Paramedic User Requirements for Speech Recognition-Aided Documentation.","authors":"Desmond Hedderson, Karen L Courtney, Helen Monkman, Ian E Blanchard","doi":"10.1055/a-2969-2253","DOIUrl":"10.1055/a-2969-2253","url":null,"abstract":"<p><strong>Abstract: </strong>OBJECTIVE: Developing technology may assist paramedics in the competing tasks of providing life-saving care and accurately documenting that care. The objective of this study was to identify and classify paramedic user requirements for speech recognition (SR)-aided documentation.</p><p><strong>Abstract: </strong>METHODS: This was a two-phase, mixed-methods study. Phase 1 involved a web-based questionnaire distributed to paramedics across Canada. Phase 2 consisted of opt-in one-on-one interviews to gain further insight into the survey results. Descriptive and associative statistics were used to identify which features respondents deemed most important, which were classified into five categories based on questionnaire results. Thematic content analysis of the qualitative data was used to understand the quantitative rankings.</p><p><strong>Abstract: </strong>RESULTS: A total of 323 respondents completed the questionnaire. Overall, 86% of respondents (<i>n</i> = 277) had a positive perception of SR. Respondents rated the following 10 features as most important: Patient Interaction, Compliance, Support, Durability, Training, Accuracy, Continuous Improvement, Training Support, Dictation, and Editing. Themes from 14 interviews described the role of technology in the future of paramedicine, adoption enablers and barriers, and SR benefits. Joint displays were used to gain further insight into the quantitative ratings, including the possibility that the presence or absence of these features could affect adoption.</p><p><strong>Abstract: </strong>CONCLUSION: These prioritized features highlight the unique practice context of paramedicine. While this was a Canadian study, the results may be applicable to other similar paramedicine practice systems. This work provides a robust list and classification of SR user requirements for Canadian paramedics.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"873-881"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149082547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a Large Language Model Discharge Summary Hospital Course Tool: Improved Quality but Longer Documentation Time.","authors":"Alexander J Dobek, Fawaz Naeem, Christina DeBenedictus, Sayari Patel, Maritza Suarez, Sebastian Suarez","doi":"10.1055/a-2962-8004","DOIUrl":"10.1055/a-2962-8004","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: No studies have examined the effect of artificial intelligence (AI)-generated hospital courses on discharge summary documentation time.</p><p><strong>Abstract: </strong>OBJECTIVE: This study aimed to evaluate the effect of an AI-generated hospital course tool on discharge summary edit time, quality, and clinician perceptions.</p><p><strong>Abstract: </strong>METHODS: Observational study between a \"pre-AI period\" (March 16, 2025-November 19, 2025) and a \"post-AI period\" (November 20, 2025-February 11, 2026). An AI hospital course tool using a GPT-4.1 model to generate a draft hospital course integrated into mandatory discharge summary templates on November 20, 2025. We included 8298 hospitalized adults cared for by hospital medicine clinicians. The primary outcome was discharge summary edit time, defined as time from note creation to signature across all sessions. A subset of 27 encounters was reviewed for cohesiveness, comprehensiveness, conciseness, potential for harm, and overall quality. Thirty-six clinicians completed a survey assessing usability, efficiency, trustworthiness, and quality.</p><p><strong>Abstract: </strong>RESULTS: Implementation of the AI tool was not associated with a significant change in discharge summary edit time between study periods (6.60 [IQR: 3.4-13.5] vs. 6.28 [IQR: 3.2-13.0] min, <i>p</i> = 0.11). Among 2206 encounters in the post-AI period, edit time was longer when the AI tool was used (9.20 [IQR: 4.9-16.3] vs. 4.93 [IQR: 2.5-9.7] min, <i>p</i> < 0.001). In multivariable analysis, the AI tool was associated with a 32% increase in edit time (95% CI: 24-41, <i>p</i> < 0.001; adjusted <i>R</i> ² = 0.34). Faculty review found AI-generated summaries had higher quality and lower harm scores but were less concise. Thirty-one (86.1%) clinicians agreed the tool improved efficiency.</p><p><strong>Abstract: </strong>CONCLUSION: Discharge summary edit time did not differ between study periods but was longer when the AI tool was used. However, there was increased perception of efficiency. The tool improved summary quality and reduced potential harm but may increase documentation time.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"825-833"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13619331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149002319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ambient AI's Value for Nursing: Will \"Saving Time\" Be a \"Staffing Trap\"?","authors":"Seo Yoon Lee, Ransom J Wyse, Alvin D Jeffery","doi":"10.1055/a-2947-2383","DOIUrl":"10.1055/a-2947-2383","url":null,"abstract":"","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 4","pages":"763-766"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13549733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148907721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effects of Artificial Intelligence in Quality Assessment of Outpatient Mental Health Services on Human Auditors.","authors":"Hayden T McLeod, Joshua L Cohen, Dena M Bravata, Roma M Bajaria, Kelvin Tran, Neha P Chaudhary, Nicole M Benson, Jeff Gould","doi":"10.1055/a-2961-0302","DOIUrl":"https://doi.org/10.1055/a-2961-0302","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: The use of large language model (LLM)-based artificial intelligence (AI) to assess the quality of outpatient mental health documentation represents an understudied application. Critical questions remain about the effects of exposing human auditors to AI-generated recommendations on auditor efficiency, agreement, and patterns of decision-making.</p><p><strong>Abstract: </strong>OBJECTIVES: The objective of this study is to examine how exposure to LLM-generated quality assessments influences auditor performance during reviews of documents of outpatient mental health intake sessions.</p><p><strong>Abstract: </strong>METHODS: We conducted a pre-post observational evaluation of 10 trained human auditors who reviewed outpatient mental health intake notes and assigned quality ratings using a standardized 13-item chart audit rubric before and after implementation of LLM support. The AI system used Anthropic Claude Sonnet 4 with a fixed chain-prompting strategy. We assessed item-level pass frequency for each auditor across the pre-AI and post-AI periods, AI-human agreement (the alignment between auditor pass/fail determinations and AI-generated recommendations), and auditing time. The pre-AI period included 9,711 notes (126,243 item-level observations) and the post-AI period included 2,677 notes (34,801 item-level observations).</p><p><strong>Abstract: </strong>RESULTS: Following implementation of AI-supported auditing, the mean item-level pass rate increased by approximately 2%. The distribution of auditor-level changes was broad (range: -35 to +35%, standard deviation [SD]: 10%) indicating substantial heterogeneity in individual auditor responses to AI support. High auditor agreement with AI recommendations was associated with larger pass-rate changes for selected rubric items, with some auditors increasing and others decreasing pass rates relative to the pre-AI period. The average time for an auditor to review notes decreased from 8.77 min (SD: 9.64) per review during the pre-AI period to 8.06 min (SD: 10.28) during the post-AI period (<i>p</i> = 0.002), an 8% improvement equivalent to 71 min saved per 100 notes reviewed.</p><p><strong>Abstract: </strong>CONCLUSION: LLM-based AI tools may improve the efficiency of clinical quality assurance workflows, but exposure to AI recommendations may meaningfully change human auditor behavior.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 4","pages":"834-843"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626751/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing Order Friction in Pediatric Electronic Health Record Systems: A Quasi-experimental Pre-Post Analysis.","authors":"Bayley Bennett, Brittany Brennan, Courtney Byrd, Vedant Gupta, Sunita A Hemani, Swaminathan Kandaswamy, Gargi Mukherjee, Colin Packard, Ryosuke Takei, Sarah A Thompson, Julia K W Yarahuan","doi":"10.1055/a-2939-3171","DOIUrl":"10.1055/a-2939-3171","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: Excessive alerts, difficult-to-use interfaces, and inefficient workflows increase cognitive load and time in the electronic health record (EHR), contributing to clinician burnout. Ordering workflows are a high-friction component of the EHR experience. Order friction (OF) is a vendor-derived metric quantifying ordering burden as a count of required manipulations and interruptions.</p><p><strong>Abstract: </strong>OBJECTIVES: The objective of this study is to (1) evaluate and characterize OF across inpatient medication orders within a large multihospital pediatric health system and (2) develop, implement, and assess a targeted intervention to reduce OF.</p><p><strong>Abstract: </strong>METHODS: This quasi-experimental pre-post analysis took place in a three-hospital pediatric health system using Epic Systems. We analyzed vendor-supplied OF data from January to December 2024 for all inpatient medication orders. A multidisciplinary workgroup used total order changes (TOC = changes per order [CPO] × order volume) to identify high-volume, high-friction medications, and then implemented a pediatric hospital medicine preference list, available to all inpatient providers. We prepopulated dosing, frequency, and \"as needed\" indications for 15 medications, built as 45 order variants mapping to 25 orderables. Using an uncontrolled pre-post design, we compared pre- and postintervention CPO at the orderable level (22 paired orderables; 25 in a sensitivity analysis), using a paired Wilcoxon signed-rank test.</p><p><strong>Abstract: </strong>RESULTS: Among 22 paired orderables, median CPO decreased from 3.20 (interquartile range [IQR]: 2.41-3.68) to 0.39 (IQR: 0.18-1.04; mean: 3.15-0.79; <i>W</i> = 253, <i>n</i> = 22, <i>p</i> < 0.001). Sensitivity analysis including 25 orderables yielded similar results (median: 3.21-0.40). At the system level, combining institutional and preference-list ordering for these medications, volume-weighted CPO decreased from 3.06 to 2.72 as adoption of the optimized list rose from 9.6% to 15.8%.</p><p><strong>Abstract: </strong>CONCLUSION: Vendor-derived OF metrics can identify and optimize high-friction pediatric medication orders. Prepopulating clinically appropriate defaults substantially reduced CPO, suggesting a feasible approach to reducing EHR-related burden in pediatric inpatient care.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 4","pages":"711-718"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13557842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148851651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Decision Support and Prescribing Analytics to Improve Empiric Antibiotic Selection for Pediatric Urinary Tract Infections.","authors":"Hani Abou Hatab, Michael J Smith, Michael E Yarrington","doi":"10.1055/a-2976-8504","DOIUrl":"https://doi.org/10.1055/a-2976-8504","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: Outpatient antibiotic prescribing often deviates from local guidelines despite the availability of institutional pathways and antibiograms. Limited integration of these resources into clinical workflow may reduce their effect on point-of-care decision-making.</p><p><strong>Abstract: </strong>OBJECTIVE: This study aimed to evaluate whether a multicomponent intervention integrating electronic health record (EHR)-embedded clinical decision support (CDS), provider education, and practice-level prescribing analytics could increase use of the recommended first-line empiric antibiotic selection for pediatric urinary tract infections (UTIs).</p><p><strong>Abstract: </strong>METHODS: We conducted a 12-month quality improvement initiative across pediatric outpatient clinics within a large academic health system using iterative Plan-Do-Study-Act cycles. Empiric therapy was defined as antibiotic prescribing at the initial visit before culture results. Encounters were identified using the International Classification of Diseases, Tenth Revision (ICD-10) codes with concurrent antibiotic prescribing. The intervention combined sequential components: provider education, passive CDS embedded within urinalysis results linking to institutional guidance, practice-level prescribing reports with audit and feedback, and targeted case-based emails discouraging broader-spectrum cephalosporin use. Prescribing trends were evaluated using run charts.</p><p><strong>Abstract: </strong>RESULTS: Among 3312 eligible encounters, empiric prescribing of the recommended first-line increased from 13.9% at baseline to 41.9% within 12 months. A shift in prescribing patterns followed the deployment of the EHR-embedded CDS and dissemination of practice-level prescribing reports. Concurrently, the use of broad-spectrum second- and third-generation cephalosporins declined.</p><p><strong>Abstract: </strong>CONCLUSION: This initiative brought stewardship guidance into the EHR workflow and reinforced it with provider education, prescribing analytics, and audit and feedback. We observed improved empiric antibiotic selection for outpatient pediatric UTIs after implementation. These findings suggest that informatics strategies may support outpatient antimicrobial stewardship when they fit naturally into clinical decision-making and make prescribing patterns visible over time.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 4","pages":"945-949"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661140/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149185344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing Incomplete Mobile Health Application Registration in Oncology.","authors":"Barbara Kitzan, Marshall Pitz, Pascal Lambert, Kim Macatangay, Jason Berry, Jinxin Yuan, Kun Liu, Kathleen M Decker","doi":"10.1055/a-2939-3111","DOIUrl":"10.1055/a-2939-3111","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: Noona is a patient-facing app used by CancerCare Manitoba to connect patients to their electronic health record and communicate with their care team. Since the implementation of Noona, staff have been responsible for registering patients with Noona. Registration rates have been consistently low. With competing clinic priorities and workload demands on front-line staff, an alternative solution was needed.</p><p><strong>Abstract: </strong>OBJECTIVE: The objective of this study was to improve Noona registration amongst cancer patients in Manitoba, Canada.</p><p><strong>Abstract: </strong>MATERIALS: We used the business process management lifecycle approach to explore reasons for low Noona registration and identify strategies to address the issue. The process problem was identified and documented, process models were developed, and semi-structured interviews with 25 front-line staff were conducted. Noona registration was redesigned, tested, and implemented in October 2024.</p><p><strong>Abstract: </strong>RESULTS: Front-line staff had limited time to discuss Noona with patients and register them to use Noona when the patient attended a clinic appointment or for treatment. Staff communication tools and support for patients were limited. Based on the process analysis, a centralized, patient-driven registration process was designed, tested, and implemented. The percentage of new accounts that were patient-activated increased over time.</p><p><strong>Abstract: </strong>CONCLUSION: The patient-driven, centralized process increased the percentage of patient-activated Noona registrations.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"17 4","pages":"719-726"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13518062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148833832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Usability and Acceptability of a Symptom Checker App by Patients Seeking Urgent Care.","authors":"Hamish S F Fraser, Ian Bacher, Ishaani Khatri, Sasha Raman, Drew Nagle, Daven Crossland, Tracy Madsen, Ross Hilliard","doi":"10.1055/a-2962-9261","DOIUrl":"10.1055/a-2962-9261","url":null,"abstract":"<p><strong>Abstract: </strong>BACKGROUND: Diagnostic errors are common in medical care and can significantly affect patient outcomes. Symptom checker apps promise to help patients understand their symptoms, possible diagnoses, and actions to take, but lack evidence on accuracy, safety, and usability.</p><p><strong>Abstract: </strong>OBJECTIVES: This study aimed to evaluate the usability and acceptability of the Ada symptom checker app for patients in urgent primary care (PC) settings, and extended it with more diverse data from an earlier emergency department (ED) study.</p><p><strong>Abstract: </strong>METHODS: Patients entered their symptoms into Ada before assessment by a physician. Both the PC study and the ED study compared Ada's diagnoses to the treating physicians' diagnoses. Usability and acceptability were evaluated using a questionnaire based on the Technology Acceptance Model (TAM). Factors influencing usability were analyzed using linear and logistic regression. Quality of clinical history was also evaluated.</p><p><strong>Abstract: </strong>RESULTS: The study included 214 urgent PC patients and 40 ED patients. Usability scores were generally high; 73% of PC patients agreed or strongly agreed with the seven TAM usability questions, and ED patients scored higher at 84% (with more direct assistance). Predictors of lower usability scores were age over 50 and limited technology experience. Sex, race, ethnicity, socioeconomic status, and educational level were not significant. In the PC study, Ada's top three diagnoses matched at least one treating physician diagnosis in 109/172 (63.3%) of cases with clear final diagnoses (sensitivity); three independent physicians had median matching of 124/172 (72.1%) on Ada summaries. Clinical history was rated complete in two-thirds of cases.</p><p><strong>Abstract: </strong>CONCLUSION: The usability and acceptance of Ada were rated highly except by people older than 50 or with lower technology experience. Extensive research is needed to validate the usability of different diagnostic tools, including symptom checkers and large language models (used widely for symptom checking), across diverse populations, and their effects on patient decision-making and care-seeking behavior.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"863-872"},"PeriodicalIF":2.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645476/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}