ACI 是第三本涉及生物医学和健康信息学的 SCHATTAUER 期刊。它完美地补充了我们的其他期刊 ÖFFNET INTERNEN LINK IM AKTUELLEN FENSTERMETHODS OF INFORMATION IN MEDICINE 和 ÖFFNET INTERNEN LINK IM AKTUELLEN FENSTERYEARBOOK OF MEDICAL INFORMATICS。 THE YEARBOOK OF MEDICAL INFORMATICS 是 IMIA 的“里程碑”或最先进的期刊，METHOD OF INFORMATION IN MEDICINE 是 IMIA 的“SCIENCE AND RESEARCH”期刊，ACI 打算成为 IMIA 的“PRACTICAL”期刊。

Applied Clinical Informatics is the third journal published by SCHATTAUER that focuses on biomedical and health informatics. It complements our other journals, Methods of Information in Medicine and Yearbook of Medical Informatics. The Yearbook of Medical Informatics is considered IMIA's 'milestone' or leading journal, while Methods of Information in Medicine serves as IMIA's 'science and research' journal. Applied Clinical Informatics aims to be IMIA's 'practical' journal.

《Applied Clinical Informatics》是第三本涉及生物医学和健康信息学的SCHATTAUER期刊。它完美地补充了我们的其他期刊《Methods of Information in Medicine》和《Yearbook of Medical Informatics》。其中，《Yearbook of Medical Informatics》是IMIA的“里程碑”或最先进的期刊，而《Methods of Information in Medicine》则是IMIA的“科学与研究”期刊。《Applied Clinical Informatics》旨在成为IMIA的“实用”期刊。

Applied Clinical Informatics

The telemedicine industry has been experiencing fast growth in recent years. The outbreak of coronavirus disease 2019 (COVID-19) further accelerated the deployment and utilization of telemedicine services. An analysis of the socioeconomic characteristics of telemedicine users to understand potential socioeconomic gaps and disparities is critical for improving the adoption of telemedicine services among patients.This study aims to measure the correlation of socioeconomic determinants with the use of telemedicine services in Milwaukee metropolitan area.Electronic health record review of patients using telemedicine services compared with those not using telemedicine services within an academic-community health system: patient demographics (e.g., age, gender, race, and ethnicity), insurance status, and socioeconomic determinants obtained through block-level census data in Milwaukee area. The telemedicine users were compared with all other patients using regression analysis. The telemedicine adoption rates were calculated across regional ZIP codes to analyze the geographic patterns of telemedicine adoption.A total of 104,139 patients used telemedicine services during the study period. Patients who used video visits were younger (median age 48.12), more likely to be White (odds ratio [OR] 1.34; 95% confidence interval [CI], 1.31-1.37), and have private insurance (OR 1.43; CI, 1.41-1.46); patients who used telephone visits were older (median age 57.58), more likely to be Black (OR 1.31; CI 1.28-1.35), and have public insurance (OR 1.30; CI 1.27-1.32). In general, Latino and Asian populations were less likely to use telemedicine; women used more telemedicine services in general than men. In the multiple regression analysis of social determinant factors across 126 ZIP codes, college education (coefficient 1.41, p = 0.01) had a strong correlation to video telemedicine adoption rate.Adoption of telemedicine services was significantly impacted by the social determinant factors of health, such as income, education level, race, and insurance type. The study reveals the potential inequities and disparities in telemedicine adoption.

Jake Luo

Ling Tong

Bradley H. Crotty

Melek Somai

Bradley Taylor

Kristen Osinski

Ben George

Telemedicine Adoption during the COVID-19 Pandemic: Gaps and Inequalities

COVID-19疫情期间的远程医疗采用：差距与不平等

Abstract Background Patient cohorts generated by machine learning can be enhanced with clinical knowledge to increase translational value and provide a practical approach to patient segmentation based on a mix of medical, behavioral, and social factors. Objectives This study aimed to generate a pragmatic example of how machine learning could be used to quickly and meaningfully cohort patients using unsupervised classification methods. Additionally, to demonstrate increased translational value of machine learning models through the integration of nursing knowledge. Methods A primary care practice dataset (N = 3,438) of high-need patients defined by practice criteria was parsed to a subset population of patients with diabetes (n = 1233). Three expert nurses selected variables for k-means cluster analysis using knowledge of critical factors for care coordination. Nursing knowledge was again applied to describe the psychosocial phenotypes in four prominent clusters, aligned with social and medical care plans. Results Four distinct clusters interpreted and mapped to psychosocial need profiles, allowing for immediate translation to clinical practice through the creation of actionable social and medical care plans. (1) A large cluster of racially diverse female, non-English speakers with low medical complexity, and history of childhood illness; (2) a large cluster of English speakers with significant comorbidities (obesity and respiratory disease); (3) a small cluster of males with substance use disorder and significant comorbidities (mental health, liver and cardiovascular disease) who frequently visit the hospital; and (4) a moderate cluster of older, racially diverse patients with renal failure. Conclusion This manuscript provides a practical method for analysis of primary care practice data using machine learning in tandem with expert clinical knowledge.

Sharon Hewner

Erica Smith

Suzanne S. Sullivan

Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods

利用护理知识和机器学习方法识别高需求初级保健患者

Interruptive alerts are known to be associated with clinician alert fatigue, and poorly performing alerts should be evaluated for alternative solutions. An interruptive alert to remind clinicians about a required peripherally inserted central catheter (PICC) dressing change within the first 48-hours after placement resulted in 617 firings in a 6-month period with only 11 (1.7%) actions taken from the alert.

Lindsey A Knake

Rachel Asbury

Shannon Penisten

Nathan Meyer

Keith Burrell

Rebecca Chuffo Davila

Adam Wright

James M Blum

Successfully transitioning an interruptive alert into a non-interruptive alert for central line dressing changes in the Neonatal Intensive Care Unit

成功将新生儿重症监护室中心静脉导管敷料更换的中断性警报转变为非中断性警报

Abstract Objectives This study aimed to highlight the necessity of developing and implementing appropriate reference ranges for transgender and nonbinary (TGNB) patient populations to minimize misinterpretation of laboratory results and ensure equitable health care. Case Report We describe a situation where a TGNB patient's abnormal laboratory values were not flagged due to undefined reference ranges for gender “X” in the Laboratory Information System (LIS). Implementation of additional reference ranges mapped to sex label “X” showed significant improvement in flagging abnormal lab results, utilizing sex-invariant reporting as an interim solution while monitoring developments on TGNB-specific reference ranges. Conclusion Informatics professionals should assess their institution's policies for registration and lab reporting on TGNB patients as nonimplementation poses significant patient safety risks. Best practices include using TGNB-specific reference ranges emerging in the literature, reporting both male and female reference ranges for clinical interpretation and sex-invariant reporting.

Anthony B. Cardillo

Dan Chen

Nina Haghi

Luke O'Donnell

Jeffrey Jhang

Paul A. Testa

Nicholas Genes

Reference Ranges for All: Implementing Reference Ranges for Transgender and Nonbinary Patients

全民参考区间：实施跨性别和非二元性别患者的参考区间

Abstract Background Health-related social needs (HRSNs) are the unmet social and economic needs (e.g., housing instability) that affect individuals' health and well-being. HRSNs are associated with more emergency department (ED) visits, longer stays, and worse health outcomes. More than a third of ED patients have at least one HRSN, yet patients are rarely screened for HRSNs in the ED. A clinical decision support (CDS) system with predictive modeling offers a promising approach to identifying patients systematically and efficiently with HRSNs in the ED. Objective This study aimed to identify ED clinician and staff preferences for designing and implementing an HRSN-related CDS system. Methods A multistep, user-centered design study involving qualitative semistructured interviews, observations of ED workflows, and a multidisciplinary design workshop. Results We conducted 16 semistructured interviews with ED clinicians and staff. Following the interviews, three research team members observed ED workflows, focusing on patient entry and clinician and staff usage of the electronic health record (EHR) system. Finally, we conducted a 3-hour multidisciplinary design workshop. An HRSN-related CDS system should be visually appealing, color-coordinated, and easily accessible in the EHR. An HRSN-related CDS system should target a select group of ED patients (to be discharged from the ED) and highlight a select set of critical HRSN issues early in the workflow to adjust clinical care adequately. An HRSN-related CDS system should provide a list of actions and the ability to notify the clinical team if the patient's HRSNs were addressed. Conclusion The user-centered design identified a set of specific preferences for an HRSN-related CDS system to be implemented in the ED. Future work will focus on implementing and refining the CDS system and assessing the rates of changes in clinical care (e.g., rates of referrals) to address patient HRSNs in the ED.

Olena Mazurenko

Adam T. Hirsh

Christopher A. Harle

Cassidy McNamee

Joshua R. Vest

A Clinical Decision Support System for Addressing Health-Related Social Needs in Emergency Department: Defining End User Needs and Preferences

急诊部门应对健康相关社会需求的临床决策支持系统：界定终端用户需求与偏好

Background: The COVID pandemic accelerated the adoption of telehealth. Long-term implications for continued telehealth use on a large scale in primary care are still emerging, and the key to this understanding is how telehealth adoption impacts the delivery of health care. Objectives: Our objective was to quantify how telehealth adoption during the pandemic impacted patients’ access to healthcare and the usage patterns of different access modalities (in-office, audio, and video). Methods: This study analyzed two years of de-identified EHR data from the ambulatory clinics at a Midwest medical school from 2020 and 2021. We focused on patient demographics, primary diagnoses, and patient preferences for receiving healthcare throughout the COVID-19 pandemic. Results: Of the 105,362 patient visits analyzed across in-office, audio, and video visits, demographic data varied by gender, age, and racial composition. During the early pandemic, telehealth usage peaked in April 2020, with audio and video visits accounting for 45.8% and 18.07% of the total visits, respectively. These rates declined to averages of 12.47% and 3.58%, respectively, over the following months. Primary diagnoses during telehealth visits are often related to COVID-19 exposure or mental health or behavioral issues. Lastly, statistically significant associations exist between the visit modality and primary diagnoses. Discussion: The COVID-19 pandemic initially saw a surge in audio telehealth visits; however, as safety measures were established, the mix of visit modalities returned to pre-lockdown levels. Video and audio telehealth maintained increased usage relative to pre-pandemic levels, potentially owing to increased awareness of the technology as a means for accessing healthcare and a learning curve associated with adoption. Patients preferred remote care during high COVID transmission. Video telehealth has shown significant associations with psychiatric, behavioral, and neurodevelopmental diagnoses. Disparities in video telehealth use among racial groups have highlighted potential access issues.

Ali Shammout

Shamsi Daneshvari Berry

Peter Ziemkowski

Phillip Kroth

The Rise, Fall, and Readjustment of Telehealth: Effect of COVID-19 on its Use in an Academic Health Clinic

远程医疗的兴衰与再调整：COVID-19对学术医疗诊所使用的影响

Background: Large language models (LLMs) have shown promise in various professional fields, including medicine and law. However, their performance in highly specialized tasks, such as extracting ICD-10-CM codes from patient notes, remains underexplored. Objective: The primary objective was to evaluate and compare the performance of ICD-10-CM code extraction by different LLMs with that of human coder. Methods: We evaluated performance of six LLMs (GPT-3.5, GPT-4, Claude 2.1, Claude 3, Gemini Advanced, and Llama 2-70b) in extracting ICD-10-CM codes against human coder. We used deidentified inpatient notes from American Health Information Management Association Vlab authentic patient cases for this study. We calculated percent agreement and Cohen’s kappa values to assess the agreement between LLMs and human coder. We then identified reasons for discrepancies in code extraction by LLMs in a 10% random subset. Results: Among 50 inpatient notes, human coder extracted 165 unique ICD-10-CM codes. LLMs extracted significantly higher number of unique ICD-10-CM codes than human coder, with Llama 2-70b extracting most (658) and Gemini Advanced the least (221). GPT-4 achieved highest percent agreement with human coder at 15.2%, followed by Claude 3 (12.7%) and GPT-3.5 (12.4%). Cohen’s kappa values indicated minimal to no agreement, ranging from -0.02 to 0.01. When focusing on primary diagnosis, Claude 3 achieved highest percent agreement (26%) and kappa value (0.25). Reasons for discrepancies in extraction of codes varied amongst LLMs and included extraction of codes for diagnoses not confirmed by providers (60% with GPT-4), extraction of non-specific codes (25% with GPT-3.5), extraction of codes for signs and symptoms despite presence of more specific diagnosis (22% with Claude-2.1) and hallucinations (35% with Claude-2.1). Conclusions: Current LLMs have poor performance in extraction of ICD-10-CM codes from inpatient notes when compared against the human coder.

Ashley Simmons

Kullaya Takkavatakarn

Megan McDougal

Brian Dilcher

Jami Pincavitch

Lukas Meadows

Justin Kauffman

Eyal Klang

Rebecca Wig

Gordon Stephen Smith

Ali Soroush

Robert Freeman

Donald Apakama

Alexander Charney

Roopa Kohli-Seth

Girish Nadkarni

Ankit Sakhuja

Extracting International Classification of Diseases Codes from Clinical Documentation using Large Language Models

使用大型语言模型从临床文档中提取国际疾病分类代码

Background: To keep pace with the developments in the medical informatics field, the curriculum of the Heidelberg/Heilbronn Medical Informatics Master of Science program is continuously updated. In its latest revision we restructured our program to allow more flexibility to accommodate updates and include current topics and to enable students’ choices. Objectives: To present our new concepts for graduate medical informatics education, share our experiences, and provide insights into the perception of these concepts by advanced students and graduates. Methods: Our new curriculum consists of three core components: Areas of Concentration that bundle elective courses in an important domain of medical informatics, a large catalog of elective courses, introductory/alignment courses for students without a bachelor’s degree in medical informatics. We conducted an online survey of graduates and students with at least 75 credits to assess their opinion on the program’s effectiveness and attractiveness. Results: Mandatory courses include clinical medicine, project management, research, and practical training in biomedical informatics. Five areas of concentration bundle elective courses for 30 credits to provide a solid foundation in an important domain in medical informatics. These are bioinformatics, data science, computer-aided diagnosis and therapy systems, information management, and software engineering in medicine. The catalog of electives offers a total of 67 courses. About 75% of them are assigned to more than one area of concentration. Our survey demonstrates that the participants highly appreciate the flexibility of the electives and the opportunity to develop an area of expertise. Conclusion: Offering a high degree of flexibility to our students has motivated them to join our program and resulted in a high level of student satisfaction. By designing the curriculum with areas of concentration and providing an infrastructure that permits courses on emerging topics to be added easily to the curriculum, we were able to meet our students’ expectations.

Petra Knaup

Rolf Bendl

Urs Eisenmann

Marc Hastenteufel

Alexandra Reichenbach

Special_Issue_Teaching_and_Training_Future_Health_Informaticians: Managing the transition from tradition to innovation of the Heidelberg/Heilbronn Medical Informatics Master’s Program

特刊：培养未来健康信息学家的教学与培训：海德堡/海尔布隆医学信息学硕士项目的传统与创新转型管理

Background The Centers for Medicare &amp; Medicaid Services (CMS) introduced changes in outpatient and inpatient evaluation and management (E/M) current procedural terminology (CPT) codes in 2021 and 2023, which were intended to streamline providers’ clinical documentation. Objectives To study the effects of aligning inpatient and outpatient note templates with updated CMS guidelines on character length and documentation time per note at an internal medicine residency program in the southeastern United States. Methods In April 2023, the Atrium Health Wake Forest Baptist Internal Medicine Residency Program’s inpatient and outpatient note templates were updated according to the most recent CMS guidelines. A pre-post analysis of resident documentation time and length was conducted comparing notes written with the residency note templates from May 1, 2022 to August 31, 2022 (6439 notes) to notes written with the residency note templates from May 1, 2023 to August 31, 2023 (8828 notes). Interns were surveyed regarding their perceptions of the updated note templates. Results After the note template updates, when adjusted for differing percentages of note types in the pre- and post-periods and accounting for multiple notes written by each resident, notes written with the residency note templates decreased by a mean character length of -882 characters (95% CI: -953, -811, p&lt;.0001), while time spent writing notes did not significantly decrease. 17/17 respondents had favorable perceptions of the note templates. Conclusions The internal medicine residency inpatient and outpatient note templates were updated to align with the most recent CMS E/M documentation requirement changes. These note template changes were associated with a meaningful decrease in documentation length but no overall significant reduction in mean documentation time when adjusted for differing percentages of note types in the pre- and post-periods and multiple notes written by the same author. The interns perceived the note template changes positively.

Sarah Stern

William Childs Lippert

Joseph Rigdon

Corey Obermiller

Lauren Witek

Matthew Anderson

Sneha Chebrolu

Adam Moses

Ted Xiao

Ajay Dharod

Joseph Cristiano

Effects of Aligning Residency Note Templates with CMS Evaluation and Management Documentation Requirements

住院病历模板与CMS评估和管理文档要求一致性的影响

Background: Adolescents are at high risk for sexually transmitted infections (STIs) and frequently present to emergency departments (EDs) for care. Screening for STIs using confidential patient-reported outcomes represents an ideal use of electronic screening methodology. Objectives: The objectives of this study were to implement a patient-facing, confidential electronic survey to assess adolescent risk for STIs and consent for testing with integrated provider facing electronic clinical decision support (CDS) across six geographically dispersed pediatric EDs and evaluate implementation based on survey and CDS usage metrics. Methods: A pilot site provided code for the electronic survey, data query, and CDS templates to six EDs. Institutions identified necessary information technology (IT) personnel, completed local build, and made modifications to suit individual site workflow variations with all sites successfully deploying the electronic survey with electronic health record (EHR) -embedded CDS. Results: 6,165 adolescents completed the confidential health survey between April 12, 2021 – September 25, 2022 out of 79,780 eligible adolescents. The CDS was triggered indicating the patient was at-risk or consented to STI testing across all six sites 2,058 times. The average percentage of time the CDS was acknowledged by a provider was 81.6% (range 45.7% - 97.6%). The median number of providers who acknowledged each instance of the CDS was 2.0. STI testing was ordered from the CDS on average 47.3% of the time. CDS acknowledge selections of “other” and “[testing] already ordered” were the most frequent indications STI testing was not ordered from the CDS. Conclusions: Successful deployment of patient-facing screeners with integrated electronic CDS across multiple healthcare institutions is feasible. A combination of different types of IT and informatics expertise combined with local knowledge of clinical workflows is essential to success.

Sarah Kathleen Schmidt

Judith Dexheimer

Joe Zorc

Chella Palmer

Theron Charles Casper

Kristin Stukus

Michelle Pickett

Cynthia Mollen

Cara Elsholz

Andrea T. Cruz

Erin Augustine

Monika Goyal

Jennifer L. Reed

Multisite implementation of a sexual health survey and clinical decision support to promote adolescent sexually transmitted infection screening

多站点实施性健康调查及临床决策支持以促进青少年性传播感染筛查

Abbreviation	Appl. Clin. Inform.
Journal Impact	2.16
Quartiles(Global)	MEDICAL INFORMATICS(Q4)

ISSN	1869-0327
h-index	43

Publication Information	Publisher: Georg Thieme Verlag，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2009，Proportion of original research papers: 88.50%，Self Citation Rate:28.60%， Gold OA Rate: 26.90%

Applied Clinical Informatics

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