Unlocking the Potential of Secondary Data for Public Health Research: Retrospective Study With a Novel Clinical Platform.

BACKGROUND: Clinical routine data derived from university hospitals hold immense value for health-related research on large cohorts. However, using secondary data for hypothesis testing necessitates adherence to scientific, legal (such as the General Data Protection Regulation, federal and state protection legislations), technical, and administrative requirements. This process is intricate, time-consuming, and susceptible to errors. OBJECTIVE: This study aims to develop a platform that enables clinicians to use current real-world data for testing research and evaluate advantages and limitations at a large university medical center (542,944 patients in 2022). METHODS: We identified requirements from clinical practitioners, conceptualized and implemented a platform based on the existing components, and assessed its applicability in clinical reality quantitatively and qualitatively. RESULTS: The proposed platform was established at the University Medical Center Hamburg-Eppendorf and made 639 forms encompassing 10,629 data elements accessible to all resident scientists and clinicians. Every day, the number of patients rises, and parts of their electronic health records are made accessible through the platform. Qualitatively, we were able to conduct a retrospective analysis of Parkinson disease over 777 patients, where we provide additional evidence for a significantly higher proportion of action tremors in patients with rest tremors (340/777, 43.8%) compared with those without rest tremors (255/777, 32.8%), as determined by a chi-square test (P<.001). Quantitatively, our findings demonstrate increased user engagement within the last 90 days, underscoring clinicians' increasing adoption of the platform in their regular research activities. Notably, the platform facilitated the retrieval of clinical data from 600,000 patients, emphasizing its substantial added value. CONCLUSIONS: This study demonstrates the feasibility of simplifying the use of clinical data to enhance exploration and sustainability in scientific research. The proposed platform emerges as a potential technological and legal framework for other medical centers, providing them with the means to unlock untapped potential within their routine data.

The FDA Sentinel Real World Evidence Data Enterprise (RWE-DE).

PURPOSE: The US Food and Drug Administration's Sentinel Innovation Center aimed to establish a query-ready, quality-checked distributed data network containing electronic health records (EHRs) linked with insurance claims data for at least 10 million individuals to expand the utility of real-world data for regulatory decision-making. METHODS: In this report, we describe the resulting network, the Real-World Evidence Data Enterprise (RWE-DE), including data from two commercial EHR-claims linked assets collectively termed the Commercial Network covering 21 million lives, and four academic partner institutions collectively termed the Development Network covering 4.5 million lives. RESULTS: We discuss provenance and completeness of the data converted in the Sentinel Common Data Model (SCDM), describe patient populations, and report on EHR-claims linkage characterization for all contributing data sources. Further, we introduce a standardized process to store free-text notes in the Development Network for efficient retrieval as needed. CONCLUSIONS: Finally, we outline typical use cases for the RWE-DE where it can broaden the reach of the types of questions that can be addressed by the Sentinel system.

Addressing Information Biases Within Electronic Health Record Data to Improve the Examination of Epidemiologic Associations With Diabetes Prevalence Among Young Adults: Cross-Sectional Study.

Background: Electronic health records (EHRs) are increasingly used for epidemiologic research to advance public health practice. However, key variables are susceptible to missing data or misclassification within EHRs, including demographic information or disease status, which could affect the estimation of disease prevalence or risk factor associations. Objective: In this paper, we applied methods from the literature on missing data and causal inference to assess whether we could mitigate information biases when estimating measures of association between potential risk factors and diabetes among a patient population of New York City young adults. Methods: We estimated the odds ratio (OR) for diabetes by race or ethnicity and asthma status using EHR data from NYU Langone Health. Methods from the missing data and causal inference literature were then applied to assess the ability to control for misclassification of health outcomes in the EHR data. We compared EHR-based associations with associations observed from 2 national health surveys, the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health and Nutrition Examination Survey, representing traditional public health surveillance systems. Results: Observed EHR-based associations between race or ethnicity and diabetes were comparable to health survey-based estimates, but the association between asthma and diabetes was significantly overestimated (OREHR 3.01, 95% CI 2.86-3.18 vs ORBRFSS 1.23, 95% CI 1.09-1.40). Missing data and causal inference methods reduced information biases in these estimates, yielding relative differences from traditional estimates below 50% (ORMissingData 1.79, 95% CI 1.67-1.92 and ORCausal 1.42, 95% CI 1.34-1.51). Conclusions: Findings suggest that without bias adjustment, EHR analyses may yield biased measures of association, driven in part by subgroup differences in health care use. However, applying missing data or causal inference frameworks can help control for and, importantly, characterize residual information biases in these estimates.

Agreement of Medication Information Derived From EHR Data Compared to Medicare Insurance Claims: An Analysis of Biologic Disease-Modifying Antirheumatic Drugs.

PURPOSE: Few studies have reported the agreement between medication information derived from ambulatory EHR data compared to administrative claims for high-cost specialty drugs. We used data from a national EHR-enabled registry, the Rheumatology Informatics System for Effectiveness (RISE), with linked Medicare claims in a population of patients with rheumatoid arthritis (RA) to investigate variations in agreement for different biologic disease-modifying agents (bDMARDs) between two data sources (RISE EHR data vs. Medicare claims), categorized by drug, route of administration, and patient insurance factors (dual eligibility). METHODS: Patients ≥ 65 years old, with ≥ 2 visits in RISE with RA ICD codes ≥ 30 days apart, and continuous enrollment in Medicare Parts B and D in 2017-2018 were included. We classified patients as bDMARD users or nonusers in Medicare claims or EHR data in 2018, and we calculated sensitivity, specificity, positive predicted value (PPV), and negative predicted value (NPV) of EHR data for identifying bDMARD users, using Medicare as the reference standard. We also calculated these metrics after stratifying by clinic-administered (Part B) versus. pharmacy-dispensed (Part D) bDMARDs and by patient dual-eligibility. RESULTS: A total of 26 097 patients were included in the study. Using Medicare claims as the reference standard, EHR data had a sensitivity of 75.0%-90.8% for identifying patients with the same medication and route. PPV for Part B bDMARDs was higher compared with Part D bDMARDs (range 94.3%-97.3% vs. 51.0%-69.6%). We observed higher PPVs for Part D bDMARDs among patients who were dual-eligible (range 82.4%-95.1%). CONCLUSION: The risk of misclassification of drug exposure based on EHR data sources alone is small for Medicare Part B bDMARDs but could be as high as 50% for Part D bDMARDs, in particular for patients who are not dually eligible for Medicare and Medicaid.

Decoding oxygen prescriptions: electronic health record documentation versus patient-reported use.

BACKGROUND: Long term oxygen therapy (LTOT) is prescribed for hypoxemia in pulmonary disease. Like other medical therapies, LTOT requires a prescription documenting the dosage (flow rate) and directions (at rest, with activity) which goes to a supplier. Communication with patients regarding oxygen prescription (flow rate, frequency, directions), monitoring (pulse oximetry) and dosage adjustment (oxygen titration) differs in comparison with medication prescriptions. We examined the communication of oxygen management plans in the electronic health record (EHR), and their consistency with patient-reported LTOT use. STUDY DESIGN AND METHODS: A cross-sectional study was conducted in 71 adults with chronic lung disease on LTOT. Physician communication regarding oxygen management was obtained from the EHR. Participants were interviewed on their LTOT management plan. The information from each source was compared. RESULTS: The study population was, on average, 64 years, two-thirds women, and most used oxygen for over 3 years. Only 45% of both at-rest and with-activity oxygen prescriptions were documented in the Electronic Health Record (EHR). Less than 20% of prescriptions were relayed to the patient in the after-visit summary. Of those with EHR-documented oxygen prescriptions, 44% of patients adhered to prescribed oxygen flow rates. Nearly all patients used a pulse oximeter (96%). INTERPRETATION: We identified significant gaps in communication of oxygen management plans from provider to patient. Even when the oxygen prescription was clearly documented, there were differences in patient-reported oxygen management. Critical gaps in oxygen therapy result from the lack of consistent documentation of oxygen prescriptions in the EHR and patient-facing documents. Addressing these issues systematically may improve home oxygen management.

Utilising primary care electronic health records to deliver the ALABAMA randomised controlled trial of penicillin allergy assessment.

BACKGROUND: Use of electronic health records (EHR) to provide real-world data for research is established, but using EHR to deliver randomised controlled trials (RCTs) more efficiently is less developed. The Allergy AntiBiotics And Microbial resistAnce (ALABAMA) RCT evaluated a penicillin allergy assessment pathway versus usual clinical care in a UK primary care setting. The aim of this paper is to describe how EHRs were used to facilitate efficient delivery of a large-scale randomised trial of a complex intervention embracing efficient participant identification, supporting minimising GP workload, providing accurate post-intervention EHR updates of allergy status, and facilitating participant follow up and outcome data collection. The generalisability of the EHR approach and health economic implications of EHR in clinical trials will be reported in the main ALABAMA trial cost-effectiveness analysis. METHODS: A descriptive account of the adaptation of functionality within SystmOne used to deliver/facilitate multiple trial processes from participant identification to outcome data collection. RESULTS: An ALABAMA organisation group within SystmOne was established which allowed sharing of trial functions/materials developed centrally by the research team. The 'ALABAMA unit' within SystmOne was also created and provided a secure efficient environment to access participants' EHR data. Processes of referring consented participants, allocating them to a trial arm, and assigning specific functions to the intervention arm were developed by adapting tools such as templates, reports, and protocols which were already available in SystmOne as well as pathways to facilitate allergy de-labelling processes and data retrieval for trial outcome analysis. CONCLUSIONS: ALABAMA is one of the first RCTs to utilise SystmOne EHR functionality and data across the RCT delivery, demonstrating feasibility and applicability to other primary care RCTs. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04108637, registered 05/03/2019. ISRCTN: ISRCTN20579216.

A Texting- and Internet-Based Self-Reporting System for Enhanced Vaccine Safety Surveillance With Insights From a Large Integrated Health Care System in the United States: Prospective Cohort Study.

BACKGROUND: SMS text messaging- and internet-based self-reporting systems can supplement existing vaccine safety surveillance systems, but real-world participation patterns have not been assessed at scale. OBJECTIVE: This study aimed to describe the participation rates of a new SMS text messaging- and internet-based self-reporting system called the Kaiser Permanente Side Effect Monitor (KPSEM) within a large integrated health care system. METHODS: We conducted a prospective cohort study of Kaiser Permanente Southern California (KPSC) patients receiving a COVID-19 vaccination from April 23, 2021, to July 31, 2023. Patients received invitations through flyers, SMS text messages, emails, or patient health care portals. After consenting, patients received regular surveys to assess adverse events up to 5 weeks after each dose. Linkage with medical records provided demographic and clinical data. In this study, we describe KPSEM participation rates, defined as providing consent and completing at least 1 survey within 35 days of COVID-19 vaccination. RESULTS: Approximately, 8% (164,636/2,091,975) of all vaccinated patients provided consent and completed at least 1 survey within 35 days. The lowest participation rates were observed for parents of children aged 12-17 years (1349/152,928, 0.9% participation rate), and the highest participation was observed among older adults aged 61-70 years (39,844/329,487, 12.1%). Persons of non-Hispanic White race were more likely to participate compared with other races and ethnicities (13.1% vs 3.9%-7.5%, respectively; P<.001). In addition, patients residing in areas with a higher neighborhood deprivation index were less likely to participate (5.1%, 16,503/323,122 vs 10.8%, 38,084/352,939 in the highest vs lowest deprivation quintiles, respectively; P<.001). Invitations through the individual's Kaiser Permanente health care portal account and by SMS text message were associated with the highest participation rate (19.2%, 70,248/366,377 and 10.5%, 96,169/914,793, respectively), followed by email (19,464/396,912, 4.9%) and then QR codes on flyers (25,882/2,091,975, 1.2%). SMS text messaging-based surveys demonstrated the highest sustained daily response rates compared with internet-based surveys. CONCLUSIONS: This real-world prospective study demonstrated that a novel digital vaccine safety self-reporting system implemented through an integrated health care system can achieve high participation rates. Linkage with participants' electronic health records is another unique benefit of this surveillance system. We also identified lower participation among selected vulnerable populations, which may have implications when interpreting data collected from similar digital systems.

Ethereum blockchain for electronic health records: securing and streamlining patient management.

Electronic health records (EHRs) are increasingly replacing traditional paper-based medical records due to their speed, security, and ability to eliminate redundant data. However, challenges such as EHR interoperability and privacy concerns remain unresolved. Blockchain, a distributed ledger technology comprising connected, encrypted data blocks, presents a promising solution. This study explores how blockchain technology can revolutionize hospital EHR management. Our proposed solution securely transfers medical records between patients and doctors using the InterPlanetary File System (IPFS) and the Ethereum platform. Utilizing smart contracts automates data transfers, ensuring patient anonymity and reducing computational complexity while securely storing patient data on the network. Patient records are stored locally on the Ganache server, with the front end managed using HTML, CSS, ReactJS, and JavaScript, and the backend developed in Solidity. Blockchain technologies combined with Role- Based access control instead of attribute -based access control. The system's throughput increases linearly with the number of users and requests, enhancing the framework's efficiency and scalability. The minimum recorded latency is 14 ms.

MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation.

Health risk prediction aims to forecast the potential health risks that patients may face using their historical Electronic Health Records (EHR). Although several effective models have developed, data insufficiency is a key issue undermining their effectiveness. Various data generation and augmentation methods have been introduced to mitigate this issue by expanding the size of the training data set through learning underlying data distributions. However, the performance of these methods is often limited due to their task-unrelated design. To address these shortcomings, this paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. Furthermore, MedDiffusion discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data. Experimental evaluation on four real-world medical datasets demonstrates that MedDiffusion outperforms 14 cutting-edge baselines in terms of PR-AUC, F1, and Cohen's Kappa. We also conduct ablation studies and benchmark our model against GAN-based alternatives to further validate the rationality and adaptability of our model design. Additionally, we analyze generated data to offer fresh insights into the model's interpretability. The source code is available via https://shorturl.at/aerT0.

Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study.

BACKGROUND: Studies have shown that patients have difficulty understanding medical jargon in electronic health record (EHR) notes, particularly patients with low health literacy. In creating the NoteAid dictionary of medical jargon for patients, a panel of medical experts selected terms they perceived as needing definitions for patients. OBJECTIVE: This study aims to determine whether experts and laypeople agree on what constitutes medical jargon. METHODS: Using an observational study design, we compared the ability of medical experts and laypeople to identify medical jargon in EHR notes. The laypeople were recruited from Amazon Mechanical Turk. Participants were shown 20 sentences from EHR notes, which contained 325 potential jargon terms as identified by the medical experts. We collected demographic information about the laypeople's age, sex, race or ethnicity, education, native language, and health literacy. Health literacy was measured with the Single Item Literacy Screener. Our evaluation metrics were the proportion of terms rated as jargon, sensitivity, specificity, Fleiss κ for agreement among medical experts and among laypeople, and the Kendall rank correlation statistic between the medical experts and laypeople. We performed subgroup analyses by layperson characteristics. We fit a beta regression model with a logit link to examine the association between layperson characteristics and whether a term was classified as jargon. RESULTS: The average proportion of terms identified as jargon by the medical experts was 59% (1150/1950, 95% CI 56.1%-61.8%), and the average proportion of terms identified as jargon by the laypeople overall was 25.6% (22,480/87,750, 95% CI 25%-26.2%). There was good agreement among medical experts (Fleiss κ=0.781, 95% CI 0.753-0.809) and fair agreement among laypeople (Fleiss κ=0.590, 95% CI 0.589-0.591). The beta regression model had a pseudo-R2 of 0.071, indicating that demographic characteristics explained very little of the variability in the proportion of terms identified as jargon by laypeople. Using laypeople's identification of jargon as the gold standard, the medical experts had high sensitivity (91.7%, 95% CI 90.1%-93.3%) and specificity (88.2%, 95% CI 86%-90.5%) in identifying jargon terms. CONCLUSIONS: To ensure coverage of possible jargon terms, the medical experts were loose in selecting terms for inclusion. Fair agreement among laypersons shows that this is needed, as there is a variety of opinions among laypersons about what is considered jargon. We showed that medical experts could accurately identify jargon terms for annotation that would be useful for laypeople.

The user-centered design and development of a childhood and adolescent obesity Electronic Health Record tool, a mixed-methods study.

Background: Childhood and adolescent obesity are persistent public health issues in the United States. Childhood obesity Electronic Health Record (EHR) tools strengthen provider-patient relationships and improve outcomes, but there are currently limited EHR tools that are linked to adolescent mHealth apps. This study is part of a larger study entitled, CommitFit, which features both an adolescent-targeted mobile health application (mHealth app) and an ambulatory EHR tool. The CommitFit mHealth app was designed to be paired with the CommitFit EHR tool for integration into clinical spaces for shared decision-making with patients and clinicians. Objectives: The objective of this sub-study was to identify the functional and design needs and preferences of healthcare clinicians and professionals for the development of the CommitFit EHR tool, specifically as it relates to childhood and adolescent obesity management. Methods: We utilized a user-centered design process with a mixed-method approach. Focus groups were used to assess current in-clinic practices, deficits, and general beliefs and preferences regarding the management of childhood and adolescent obesity. A pre- and post-focus group survey helped assess the perception of the design and functionality of the CommitFit EHR tool and other obesity clinic needs. Iterative design development of the CommitFit EHR tool occurred throughout the process. Results: A total of 12 healthcare providers participated throughout the three focus group sessions. Two themes emerged regarding EHR design: (1) Functional Needs, including Enhancing Clinical Practices and Workflow, and (2) Visualization, including Colors and Graphs. Responses from the surveys (n = 52) further reflect the need for Functionality and User-Interface Design by clinicians. Clinicians want the CommitFit EHR tool to enhance in-clinic adolescent lifestyle counseling, be easy to use, and presentable to adolescent patients and their caregivers. Additionally, we found that clinicians preferred colors and graphs that improved readability and usability. During each step of feedback from focus group sessions and the survey, the design of the CommitFit EHR tool was updated and co-developed by clinicians in an iterative user-centered design process. Conclusion: More research is needed to explore clinician actual user analytics for the CommitFit EHR tool to evaluate real-time workflow, design, and function needs. The effectiveness of the CommitFit mHealth and EHR tool as a weight management intervention needs to be evaluated in the future.

Role of nurse informaticists in the implementation of Electronic Health Records (EHRs) at resource-limited settings.

Electronic health records (EHRs) play a critical role in the management of patient information and timely decision making in health facilities. In resource-limited settings, especially low- and middle-income countries (LMICs), nurse informaticists play a pivotal role in the implementation of EHRs. This article underscores their multifaceted responsibilities, emphasising critical contributions in vendor selection, system evaluation, workflow analysis, content development, end-user device assessment, training, and post-implementation stability support. By providing nurse informaticists in lower middle-income countries with a clear understanding of their responsibilities and tailored strategies, this article aims to enhance EHR implementation success in these unique contexts.

A step-by-step roadmap for the development and deployment of an electronic health record sidecar application that tracks patient outcomes: The RA PRO dashboard.

Objective: Despite interest in optimizing the electronic health record (EHR) to facilitate chronic disease care for conditions like rheumatoid arthritis (RA), progress in this area has been slow. EHR sidecar applications offer one solution, but little guidance exists to facilitate their successful development, deployment, and maintenance in the healthcare setting. We aimed to provide a roadmap for how to develop and deploy an EHR sidecar application based on our experience building a new EHR-integrated, patient-facing visualization tool that displayed disease outcomes to RA patients during a clinical visit (the "RA PRO dashboard") in a large academic health center. Methods: We describe the technical design and implementation of the RA PRO dashboard; report clinic workflow adaptations to incorporate this new technology; and discuss the resources required and challenges encountered in maintaining this application. Results: The RA PRO dashboard required extensive human-centered design work, regulatory approvals, software development, user testing, integration with Epic-based workflows, and maintenance. Key requirements were prioritized based on the anticipated effects on usefulness and ease of use. Implementation science strategies were used to improve use of the dashboard in clinic and included education for patients, staff, and clinicians; reports of actual use of the dashboard and data quality; and regular meetings between the research team and clinicians to discuss and address barriers to use. Conclusion: Successful development and deployment of an EHR-integrated application are resource-intensive and require technical, operational, and educational innovations. The roadmap presented in this study can serve as a resource for future developers.

Blockchain-enabled EHR access auditing: Enhancing healthcare data security.

In the realm of modern healthcare, Electronic Health Records EHR serve as invaluable assets, yet they also pose significant security challenges. The absence of EHR access auditing mechanisms, which includes the EHR audit trails, results in accountability gaps and magnifies security vulnerabilities. This situation effectively paves the way for unauthorized data alterations to occur without detection or consequences. Inadequate EHR compliance auditing procedures, particularly in verifying and validating access control policies, expose healthcare organizations to risks such as data breaches, and unauthorized data usage. These vulnerabilities result from unchecked unauthorized access activities. Additionally, the absence of EHR audit logs complicates investigations, weakens proactive security measures, and raises concerns to put healthcare institutions at risk. This study addresses the pressing need for robust EHR auditing systems designed to scrutinize access to EHR data, encompassing who accesses it, when, and for what purpose. Our research delves into the complex field of EHR auditing, which includes establishing an immutable audit trail to enhance data security through blockchain technology. We also integrate Purpose-Based Access Control (PBAC) alongside smart contracts to strengthen compliance auditing by validating access legitimacy and reducing unauthorized entries. Our contributions encompass the creation of audit trail of EHR access, compliance auditing via PBAC policy verification, the generation of audit logs, and the derivation of data-driven insights, fortifying EHR access security.

Languages other than English in a pediatric ED: Documentation, extended stays, and revisits.

OBJECTIVE: Given the increasing proportion of patients and caregivers who use languages other than English (LOE) at our institution and across the U.S, we evaluated key workflow and outcome measures in our emergency department (ED) for patients and caregivers who use LOE. METHODS: This was a retrospective, cross-sectional study of patients and caregivers who presented to a free-standing urban pediatric facility. We used electronic health record data (EHR) and interpreter usage log data for our analysis of language documentation, length of stay, and ED revisits. We assessed ED revisits within 72-h using a multivariable logistic regression model adjusting for whether a primary care provider (PCP) was listed in the EHR, whether discharge was close to or on the weekend, and insurance status. We restricted our analysis to low-acuity patient encounters (Emergency Severity Index (ESI) scores of 4 and 5) to limit confounding factors related to higher ESI scores. RESULTS: We found that one in five patients and caregivers who use LOE had incorrect documentation of their language needs in the EHR. Using interpreter usage data to most accurately capture encounters using LOE, we found that patient encounters using LOE had a 38-min longer length of stay (LOS) and twice the odds of a 72-h ED revisit compared to encounters using English. CONCLUSION: These results highlight the need for better language documentation and understanding of factors contributing to extended stays and increased revisits for pediatric patients and caregivers who use LOE.

Scalable and successful patient portal lifestyle coaching training for primary care clinical staff.

Maintaining a healthy weight postintentional weight loss is crucial for preventing chronic health conditions, yet many regain weight postintervention. Electronic health record (EHR) portals offer a promising avenue for weight management interventions, leveraging patient-primary care relationships. Our previous research demonstrated that coaching alongside self-monitoring improves weight maintenance compared to monitoring alone. Integrating weight management into routine clinical practice by training existing staff could enhance scalability and sustainability. However, challenges such as inconsistent staff qualifications and high coach turnover rates could affect intervention effectiveness. Standardizing services, training, and coaching continuity seem crucial for success. To report on developing, testing, and evaluating an EHR-based coaching training program for clinical staff, guided by an implementation tool for the MAINTAIN PRIME study. Conducted across 14 University of Utah primary care sites, we developed, tested, and evaluated a coaching training for clinical staff. Guided by a planning model and the Predisposing, Enabling, and Reinforcing (PER) tool, stakeholders actively participated in planning, ensuring alignment with clinic priorities. All clinical staff were invited to participate voluntarily. Evaluation measures included staff interest, training effectiveness, confidence, and readiness. Data collection utilized REDCap, with survey results analyzed using descriptive statistics. Despite increased clinical workload and reassignments posed by coronavirus disease 2019, we were able to train 39 clinical staff, with 34 successfully coaching patients. Feedback indicated high readiness and positive perceptions of coaching feasibility. Coaches reported satisfaction with training, support, and enjoyed establishing connections with patients. The PER strategies allowed us to implement a well-received training program found effective by primary care coaches.

This report describes a training program for medical staff like nurses and medical assistants. The goal is to teach them how to coach patients through an online portal to help them keep their weight off after making healthy lifestyle changes. We worked with different clinic groups and used a planning tool called PER worksheet (predisposing, enabling, and reinforcing) to set up the training program. From September 2021 to March 2023, we offered the training in 14 clinics, and most interested staff completed it. The results showed that the training worked well. People who took part felt they learned enough to coach patients and felt ready to coach. They liked the training and found it helpful. This study suggests that we can teach coaching skills in just four hours of training and that ongoing support and mentorship are important to the trained coaches. Furthermore, this training set-up allows new staff to be trained as they join, which is especially important in places where staff changes frequently. Overall, using the PER tool enabled us to create a training program that staff can use in outpatient clinics to help patients improve their weight management.

Standardizing Corneal Transplantation Records Using openEHR: Case Study.

BACKGROUND: Corneal transplantation, also known as keratoplasty, is a widely performed surgical procedure that aims to restore vision in patients with corneal damage. The success of corneal transplantation relies on the accurate and timely management of patient information, which can be enhanced using electronic health records (EHRs). However, conventional EHRs are often fragmented and lack standardization, leading to difficulties in information access and sharing, increased medical errors, and decreased patient safety. In the wake of these problems, there is a growing demand for standardized EHRs that can ensure the accuracy and consistency of patient data across health care organizations. OBJECTIVE: This paper proposes the use of openEHR structures for standardizing corneal transplantation records. The main objective of this research was to improve the quality and interoperability of EHRs in corneal transplantation, making it easier for health care providers to capture, share, and analyze clinical information. METHODS: A series of sequential steps were carried out in this study to implement standardized clinical records using openEHR specifications. These specifications furnish a methodical approach that ascertains the development of high-quality clinical records. In broad terms, the methodology followed encompasses the conduction of meetings with health care professionals and the modeling of archetypes, templates, forms, decision rules, and work plans. RESULTS: This research resulted in a tailored solution that streamlines health care delivery and meets the needs of medical professionals involved in the corneal transplantation process while seamlessly aligning with contemporary clinical practices. The proposed solution culminated in the successful integration within a Portuguese hospital of 3 key components of openEHR specifications: forms, Decision Logic Modules, and Work Plans. A statistical analysis of data collected from May 1, 2022, to March 31, 2023, allowed for the perception of the use of the new technologies within the corneal transplantation workflow. Despite the completion rate being only 63.9% (530/830), which can be explained by external factors such as patient health and availability of donor organs, there was an overall improvement in terms of task control and follow-up of the patients' clinical process. CONCLUSIONS: This study shows that the adoption of openEHR structures represents a significant step forward in the standardization and optimization of corneal transplantation records. It offers a detailed demonstration of how to implement openEHR specifications and highlights the different advantages of standardizing EHRs in the field of corneal transplantation. Furthermore, it serves as a valuable reference for researchers and practitioners who are interested in advancing and improving the exploitation of EHRs in health care.

Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review.

BACKGROUND: Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice. OBJECTIVE: This scoping review aimed to explore methods for identifying patients with postoperative infections using electronic health record (EHR) data to go beyond the reference standard of manual chart review. METHODS: We performed a systematic search strategy across PubMed, Embase, Web of Science (Core Collection), the Cochrane Library, and Emcare (Ovid), targeting studies addressing the prediction and fully automated surveillance (ie, without manual check) of diverse bacterial infections in the postoperative setting. For prediction modeling studies, we assessed the labeling methods used, categorizing them as either manual or automated. We evaluated the different types of EHR data needed for the surveillance and labeling of postoperative infections, as well as the performance of fully automated surveillance systems compared with manual chart review. RESULTS: We identified 75 different methods and definitions used to identify patients with postoperative infections in studies published between 2003 and 2023. Manual labeling was the predominant method in prediction modeling research, 65% (49/75) of the identified methods use structured data, and 45% (34/75) use free text and clinical notes as one of their data sources. Fully automated surveillance systems should be used with caution because the reported positive predictive values are between 0.31 and 0.76. CONCLUSIONS: There is currently no evidence to support fully automated labeling and identification of patients with infections based solely on structured EHR data. Future research should focus on defining uniform definitions, as well as prioritizing the development of more scalable, automated methods for infection detection using structured EHR data.

The Development of an Electronic Medical Record System to Improve Quality of Care for Individuals With Type 1 Diabetes in Rwanda: Qualitative Study.

BACKGROUND: Electronic medical record (EMR) systems have the potential to improve the quality of care and clinical outcomes for individuals with chronic and complex diseases. However, studies on the development and use of EMR systems for type 1 (T1) diabetes management in sub-Saharan Africa are few. OBJECTIVE: The aim of this study is to analyze the need for improvements in the care processes that can be facilitated by an EMR system and to develop an EMR system for increasing quality of care and clinical outcomes for individuals with T1 diabetes in Rwanda. METHODS: A qualitative, cocreative, and multidisciplinary approach involving local stakeholders, guided by the framework for complex public health interventions, was applied. Participant observation and the patient's personal experiences were used as case studies to understand the clinical care context. A focus group discussion and workshops were conducted to define the features and content of an EMR. The data were analyzed using thematic analysis. RESULTS: The identified themes related to feature requirements were (1) ease of use, (2) automatic report preparation, (3) clinical decision support tool, (4) data validity, (5) patient follow-up, (6) data protection, and (7) training. The identified themes related to content requirements were (1) treatment regimen, (2) mental health, and (3) socioeconomic and demographic conditions. A theory of change was developed based on the defined feature and content requirements to demonstrate how these requirements could strengthen the quality of care and improve clinical outcomes for people with T1 diabetes. CONCLUSIONS: The EMR system, including its functionalities and content, can be developed through an inclusive and cocreative process, which improves the design phase of the EMR. The development process of the EMR system is replicable, but the solution needs to be customized to the local context.

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.

BACKGROUND: Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse drug event (ADE) signals. As different aspects of a patient's status are stored in different types of documents, we propose an NLP system capable of processing 6 types of documents: physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. OBJECTIVE: This study aimed to investigate the system's performance in detecting ADEs by evaluating the results from multitype texts. The main objective is to detect adverse events accurately using an NLP system. METHODS: We used data written in Japanese from 2289 patients with breast cancer, including medication data, physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. Our system performs 3 processes: named entity recognition, normalization of symptoms, and aggregation of multiple types of documents from multiple patients. Among all patients with breast cancer, 103 and 112 with peripheral neuropathy (PN) received paclitaxel or docetaxel, respectively. We evaluate the utility of using multiple types of documents by correlation coefficient and regression analysis to compare their performance with each single type of document. All evaluations of detection rates with our system are performed 30 days after drug administration. RESULTS: Our system underestimates by 13.3 percentage points (74.0%-60.7%), as the incidence of paclitaxel-induced PN was 60.7%, compared with 74.0% in the previous research based on manual extraction. The Pearson correlation coefficient between the manual extraction and system results was 0.87 Although the pharmacist progress notes had the highest detection rate among each type of document, the rate did not match the performance using all documents. The estimated median duration of PN with paclitaxel was 92 days, whereas the previously reported median duration of PN with paclitaxel was 727 days. The number of events detected in each document was highest in the physician's progress notes, followed by the pharmacist's and nursing records. CONCLUSIONS: Considering the inherent cost that requires constant monitoring of the patient's condition, such as the treatment of PN, our system has a significant advantage in that it can immediately estimate the treatment duration without fine-tuning a new NLP model. Leveraging multitype documents is better than using single-type documents to improve detection performance. Although the onset time estimation was relatively accurate, the duration might have been influenced by the length of the data follow-up period. The results suggest that our method using various types of data can detect more ADEs from clinical documents.