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Objective: Electronic medical records (EMRs) have streamlined workflows for health care professionals, yet their full potential is not always actualized. Modern EMRs are often capable of generating automated prepopulated inpatient lists, however if these capabilities are not made available to inpatient teams or not designed with the end user in mind, resident physicians may be left to create alternative, manual solutions to ensure reliable and efficient care. The purpose of the current study was to longitudinally compare the impact of both manual and automated inpatient lists on resident education, wellness, and patient safety., Design: Retrospective standardized surveys were administered to resident physicians in the orthopedic surgery department at a level I trauma center over a 3-year period to evaluate the impact of various automated and manual list iterations coinciding with changes to the EMR. Data collected included post graduate year (PGY) status, arrival time to work, daily time spent preparing and updating the list, perceived impact on patient safety, resident workload, education, and sleep. We compared manual versus automated list data with unpaired t-tests and chi-squared tests., Setting: The study was conducted at Brooke Army Medical Center, a level 1 trauma center in San Antonio, Texas. It is an Academic Medical Center and the Department of Defense's largest medical facility., Participants: A total of 71 surveys were collected from 33 orthopedic surgery residents in all levels of training., Results: Intern list prep time in the morning was 27 ± 16 minutes for the automated list (n = 17) vs 72 ± 21 minutes for the manual lists (n = 23) (p < 0.0001). Total time spent by interns updating the list for the entire day was on average 83 minutes for the automated list (n = 17) vs 196 minutes for the manual lists (n = 23) (p < 0.0001). In addition, 86% of interns felt the time spent on the manual list impacted their education, with 96% stating that it directly impacted the amount of time they had to study and 100% agreed that it negatively impacted their sleep (n = 23). Only 48% of interns (n = 23) were satisfied with the performance of the manual lists compared to 94% satisfaction (n = 17) with the automated list. Further, 87% of interns felt the manual list negatively impacted patient care and negatively affected their job satisfaction. In comparison, 59% of interns felt the automated list improved their job satisfaction. Ultimately, for an intern an automated list versus a manual list affords them an extra 106 minutes per day for education, sleep, or other activities. PGY2 residents and above noted similar trends regarding their experience with the lists., Conclusions: The benefits of utilizing automated inpatient lists as determined by our study are improved efficiency in the morning with less preparation and maintenance throughout the day. Additionally, with automated lists there was more perceived time for education and sleep, with improved job satisfaction. Most importantly, respondents felt that automated lists were safer for patient care when compared to manual lists. This should compel further research and efforts into improving automated EMR tracking lists in hospitals. In summary, as compared to the automated electronic medical record inpatient list, manual lists resulted in substantially more preparation time and maintenance throughout the day thereby negatively impacting resident education and quality of life, while raising concerns for patient safety., (Copyright © 2024 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.)

Background: Clinical trials are essential for medical research and medical progress. Nevertheless, trials often fail to reach their recruitment goals. Patient recruitment systems aim to support clinical trials by providing an automated search for eligible patients in the databases of health care institutions like university hospitals. To integrate patient recruitment systems into existing workflows, previous works have assessed user requirements for these tools. In this study, we tested patient recruitment systems KAS+ and recruIT as part of the MIRACUM (Medical Informatics in Research and Care in University Medicine) project., Objective: Our goal was to investigate whether and to what extent the 2 different evaluated tools can meet the requirements resulting from the first requirements analysis, which was performed in 2018-2019. A user survey was conducted to determine whether the tools are usable in practice and helpful for the trial staff. Furthermore, we investigated whether the test phase revealed further requirements for recruitment tools that were not considered in the first place., Methods: We performed semistructured interviews with 10 participants in 3 German university hospitals who used the patient recruitment tools KAS+ or recruIT for at least 1 month with currently recruiting trials. Thereafter, the interviews were transcribed and analyzed by Meyring method. The identified statements of the interviewees were categorized into 5 groups of requirements and sorted by their frequency., Results: The evaluated recruIT and KAS+ tools fulfilled 7 and 11 requirements of the 12 previously identified requirements, respectively. The interviewed participants mentioned the need for different notification schedules, integration into their workflow, different patient characteristics, and pseudonymized screening lists. This resulted in a list of new requirements for the implementation or enhancement of patient recruitment systems., Conclusions: Trial staff report a huge need of support for the identification of eligible trial participants. Moreover, the workflows in patient recruitment differ across trials. For better suitability of the recruitment systems in the workflow of different kinds of trials, we recommend the implementation of an adjustable notification schedule for screening lists, a detailed workflow analysis, broad patient filtering options, and the display of all information needed to identify the persons on the list. Despite criticisms, all participants confirmed to use the patient recruitment systems again., (©Alexandra Stein, Romina Blasini, Cosima Strantz, Kai Fitzer, Christian Gulden, Torsten Leddig, Wolfgang Hoffmann. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 27.09.2024.)

Background: Clinical trials (CTs) are foundational to the advancement of evidence-based medicine and recruiting a sufficient number of participants is one of the crucial steps to their successful conduct. Yet, poor recruitment remains the most frequent reason for premature discontinuation or costly extension of clinical trials., Methods: We designed and implemented a novel, open-source software system to support the recruitment process in clinical trials by generating automatic recruitment recommendations. The development is guided by modern, cloud-native design principles and based on Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) as an interoperability standard with the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) being used as a source of patient data. We evaluated the usability using the system usability scale (SUS) after deploying the application for use by study personnel., Results: The implementation is based on the OMOP CDM as a repository of patient data that is continuously queried for possible trial candidates based on given clinical trial eligibility criteria. A web-based screening list can be used to display the candidates and email notifications about possible new trial participants can be sent automatically. All interactions between services use HL7 FHIR as the communication standard. The system can be installed using standard container technology and supports more sophisticated deployments on Kubernetes clusters. End-users (n = 19) rated the system with a SUS score of 79.9/100., Conclusion: We contribute a novel, open-source implementation to support the patient recruitment process in clinical trials that can be deployed using state-of-the art technologies. According to the SUS score, the system provides good usability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: As part of the German Medical Informatics Initiative, the MIRACUM project establishes data integration centers across ten German university hospitals. The embedded MIRACUM Use Case "Alerting in Care - IT Support for Patient Recruitment", aims to support the recruitment into clinical trials by automatically querying the repositories for patients satisfying eligibility criteria and presenting them as screening candidates. The objective of this study is to investigate whether the developed recruitment tool has a positive effect on study recruitment within a multi-center environment by increasing the number of participants. Its secondary objective is the measurement of organizational burden and user satisfaction of the provided IT solution., Methods: The study uses an Interrupted Time Series Design with a duration of 15 months. All trials start in the control phase of randomized length with regular recruitment and change to the intervention phase with additional IT support. The intervention consists of the application of a recruitment-support system which uses patient data collected in general care for screening according to specific criteria. The inclusion and exclusion criteria of all selected trials are translated into a machine-readable format using the OHDSI ATLAS tool. All patient data from the data integration centers is regularly checked against these criteria. The primary outcome is the number of participants recruited per trial and week standardized by the targeted number of participants per week and the expected recruitment duration of the specific trial. Secondary outcomes are usability, usefulness, and efficacy of the recruitment support. Sample size calculation based on simple parallel group assumption can demonstrate an effect size of d=0.57 on a significance level of 5% and a power of 80% with a total number of 100 trials (10 per site). Data describing the included trials and the recruitment process is collected at each site. The primary analysis will be conducted using linear mixed models with the actual recruitment number per week and trial standardized by the expected recruitment number per week and trial as the dependent variable., Discussion: The application of an IT-supported recruitment solution developed in the MIRACUM consortium leads to an increased number of recruited participants in studies at German university hospitals. It supports employees engaged in the recruitment of trial participants and is easy to integrate in their daily work., (© 2024. The Author(s).)

Background: Clinical trials (CTs) are crucial for medical research; however, they frequently fall short of the requisite number of participants who meet all eligibility criteria (EC). A clinical trial recruitment support system (CTRSS) is developed to help identify potential participants by performing a search on a specific data pool. The accuracy of the search results is directly related to the quality of the data used for comparison. Data accessibility can present challenges, making it crucial to identify the necessary data for a CTRSS to query. Prior research has examined the data elements frequently used in CT EC but has not evaluated which criteria are actually used to search for participants. Although all EC must be met to enroll a person in a CT, not all criteria have the same importance when searching for potential participants in an existing data pool, such as an electronic health record, because some of the criteria are only relevant at the time of enrollment., Objective: In this study, we investigated which groups of data elements are relevant in practice for finding suitable participants and whether there are typical elements that are not relevant and can therefore be omitted., Methods: We asked trial experts and CTRSS developers to first categorize the EC of their CTs according to data element groups and then to classify them into 1 of 3 categories: necessary, complementary, and irrelevant. In addition, the experts assessed whether a criterion was documented (on paper or digitally) or whether it was information known only to the treating physicians or patients., Results: We reviewed 82 CTs with 1132 unique EC. Of these 1132 EC, 350 (30.9%) were considered necessary, 224 (19.8%) complementary, and 341 (30.1%) total irrelevant. To identify the most relevant data elements, we introduced the data element relevance index (DERI). This describes the percentage of studies in which the corresponding data element occurs and is also classified as necessary or supplementary. We found that the query of "diagnosis" was relevant for finding participants in 79 (96.3%) of the CTs. This group was followed by "date of birth/age" with a DERI of 85.4% (n=70) and "procedure" with a DERI of 35.4% (n=29)., Conclusions: The distribution of data element groups in CTs has been heterogeneously described in previous works. Therefore, we recommend identifying the percentage of CTs in which data element groups can be found as a more reliable way to determine the relevance of EC. Only necessary and complementary criteria should be included in this DERI., (©Romina Blasini, Cosima Strantz, Christian Gulden, Sven Helfer, Jakub Lidke, Hans-Ulrich Prokosch, Keywan Sohrabi, Henning Schneider. Originally published in JMIR Formative Research (https://formative.jmir.org), 31.01.2024.)