JMIR Medical Informatics最新文献

Background: Health and clinical activity data are a vital resource for research, improving patient care and service efficiency. Health care data are inherently complex, and their acquisition, storage, retrieval, and subsequent analysis require a thorough understanding of the clinical pathways underpinning such data. Better use of health care data could lead to improvements in patient care and service delivery. However, this depends on the identification of relevant datasets.

Objective: We aimed to demonstrate the application of business process modeling notation (BPMN) to represent clinical pathways at a UK neurosciences center and map the clinical activity to corresponding data flows into electronic health records and other nonstandard data repositories.

Methods: We used BPMN to map and visualize a patient journey and the subsequent movement and storage of patient data. After identifying several datasets that were being held outside of the standard applications, we collected information about these datasets using a questionnaire.

Results: We identified 13 standard applications where neurology clinical activity was captured as part of the patient's electronic health record including applications and databases for managing referrals, outpatient activity, laboratory data, imaging data, and clinic letters. We also identified 22 distinct datasets not within standard applications that were created and managed within the neurosciences department, either by individuals or teams. These were being used to deliver direct patient care and included datasets for tracking patient blood results, recording home visits, and tracking triage status.

Conclusions: Mapping patient data flows and repositories allowed us to identify areas wherein the current electronic health record does not fulfill the needs of day-to-day patient care. Data that are being stored outside of standard applications represent a potential duplication in the effort and risks being overlooked. Future work should identify unmet data needs to inform correct data capture and centralization within appropriate data architectures.

Background: Data quality is fundamental to maintaining the trust and reliability of health data for both primary and secondary purposes. However, before the secondary use of health data, it is essential to assess the quality at the source and to develop systematic methods for the assessment of important data quality dimensions.

Objective: This case study aims to offer a dual aim-to assess the data quality of height and weight measurements across 7 Belgian hospitals, focusing on the dimensions of completeness and consistency, and to outline the obstacles these hospitals face in sharing and improving data quality standards.

Methods: Focusing on data quality dimensions completeness and consistency, this study examined height and weight data collected from 2021 to 2022 within 3 distinct departments-surgical, geriatrics, and pediatrics-in each of the 7 hospitals.

Results: Variability was observed in the completeness scores for height across hospitals and departments, especially within surgical and geriatric wards. In contrast, weight data uniformly achieved high completeness scores. Notably, the consistency of height and weight data recording was uniformly high across all departments.

Conclusions: A collective collaboration among Belgian hospitals, transcending network affiliations, was formed to conduct this data quality assessment. This study demonstrates the potential for improving data quality across health care organizations by sharing knowledge and good practices, establishing a foundation for future, similar research.

Background: Traditional rule-based natural language processing approaches in electronic health record systems are effective but are often time-consuming and prone to errors when handling unstructured data. This is primarily due to the substantial manual effort required to parse and extract information from diverse types of documentation. Recent advancements in large language model (LLM) technology have made it possible to automatically interpret medical context and support pathologic staging. However, existing LLMs encounter challenges in rapidly adapting to specialized guideline updates. In this study, we fine-tuned an LLM specifically for lung cancer pathologic staging, enabling it to incorporate the latest guidelines for pathologic TN classification.

Objective: This study aims to evaluate the performance of fine-tuned generative language models in automatically inferring pathologic TN classifications and extracting their rationale from lung cancer surgical pathology reports. By addressing the inefficiencies and extensive parsing efforts associated with rule-based methods, this approach seeks to enable rapid and accurate reclassification aligned with the latest cancer staging guidelines.

Methods: We conducted a comparative performance evaluation of 6 open-source LLMs for automated TN classification and rationale generation, using 3216 deidentified lung cancer surgical pathology reports based on the American Joint Committee on Cancer (AJCC) Cancer Staging Manual8th edition, collected from a tertiary hospital. The dataset was preprocessed by segmenting each report according to lesion location and morphological diagnosis. Performance was assessed using exact match ratio (EMR) and semantic match ratio (SMR) as evaluation metrics, which measure classification accuracy and the contextual alignment of the generated rationales, respectively.

Results: Among the 6 models, the Orca2_13b model achieved the highest performance with an EMR of 0.934 and an SMR of 0.864. The Orca2_7b model also demonstrated strong performance, recording an EMR of 0.914 and an SMR of 0.854. In contrast, the Llama2_7b model achieved an EMR of 0.864 and an SMR of 0.771, while the Llama2_13b model showed an EMR of 0.762 and an SMR of 0.690. The Mistral_7b and Llama3_8b models, on the other hand, showed lower performance, with EMRs of 0.572 and 0.489, and SMRs of 0.377 and 0.456, respectively. Overall, the Orca2 models consistently outperformed the others in both TN stage classification and rationale generation.

Conclusions: The generative language model approach presented in this study has the potential to enhance and automate TN classification in complex cancer staging, supporting both clinical practice and oncology data curation. With additional fine-tuning based on cancer-specific guidelines, this approach can be effectively adapted to other cancer types.

Background: Elderly care physicians (ECPs) in nursing homes document patients' health, medical conditions, and the care provided in electronic health records (EHRs). However, much of these health data currently lack structure and standardization, limiting their potential for health information exchange across care providers and reuse for quality improvement, policy development, and scientific research. Enhancing this potential requires insight into the attitudes and behaviors of ECPs toward standardized and structured recording in EHRs.

Objective: This study aims to answer why and how ECPs record their findings in EHRs and what factors influence them to record in a standardized and structured manner. The findings will be used to formulate recommendations aimed at enhancing standardized and structured data recording for the reuse of EHR data.

Methods: Semistructured interviews were conducted with 13 ECPs working in Dutch nursing homes. We recruited participants through purposive sampling, aiming for diversity in age, gender, health care organization, and use of EHR systems. Interviews continued until we reached data saturation. Analysis was performed using inductive thematic analysis.

Results: ECPs primarily use EHRs to document daily patient care, ensure continuity of care, and fulfill their obligation to record specific information for accountability purposes. The EHR serves as a record to justify their actions in the event of a complaint. In addition, some respondents also mentioned recording information for secondary purposes, such as research and quality improvement. Several factors were found to influence standardized and structured recording. At a personal level, it is crucial to experience the added value of standardized and structured recording. At the organizational level, clear internal guidelines and a focus on their implementation can have a substantial impact. At the level of the EHR system, user-friendliness, interoperability, and guidance were most frequently mentioned as being important. At a national level, the alignment of internal guidelines with overarching standards plays a pivotal role in encouraging standardized and structured recording.

Conclusions: The results of our study are similar to the findings of previous research in hospital care and general practice. Therefore, long-term care can learn from solutions regarding standardized and structured recording in other health care sectors. The main motives for ECPs to record in EHRs are the daily patient care and ensuring continuity of care. Standardized and structured recording can be improved by aligning the recording method in EHRs with the primary care process. In addition, there are incentives for motivating ECPs to record in a standardized and structured way, mainly at the personal, organizational, EHR system, and national levels.

Background: Prediction models based on machine learning (ML) methods are being increasingly developed and adopted in health care. However, these models may be prone to bias and considered unfair if they demonstrate variable performance in population subgroups. An unfair model is of particular concern in bladder cancer, where disparities have been identified in sex and racial subgroups.

Objective: This study aims (1) to develop a ML model to predict survival after radical cystectomy for bladder cancer and evaluate for potential model bias in sex and racial subgroups; and (2) to compare algorithm unfairness mitigation techniques to improve model fairness.

Methods: We trained and compared various ML classification algorithms to predict 5-year survival after radical cystectomy using the National Cancer Database. The primary model performance metric was the F₁-score. The primary metric for model fairness was the equalized odds ratio (eOR). We compared 3 algorithm unfairness mitigation techniques to improve eOR.

Results: We identified 16,481 patients; 23.1% (n=3800) were female, and 91.5% (n=15,080) were "White," 5% (n=832) were "Black," 2.3% (n=373) were "Hispanic," and 1.2% (n=196) were "Asian." The 5-year mortality rate was 75% (n=12,290). The best naive model was extreme gradient boosting (XGBoost), which had an F₁-score of 0.860 and eOR of 0.619. All unfairness mitigation techniques increased the eOR, with correlation remover showing the highest increase and resulting in a final eOR of 0.750. This mitigated model had F₁-scores of 0.86, 0.904, and 0.824 in the full, Black male, and Asian female test sets, respectively.

Conclusions: The ML model predicting survival after radical cystectomy exhibited bias across sex and racial subgroups. By using algorithm unfairness mitigation techniques, we improved algorithmic fairness as measured by the eOR. Our study highlights the role of not only evaluating for model bias but also actively mitigating such disparities to ensure equitable health care delivery. We also deployed the first web-based fair ML model for predicting survival after radical cystectomy.

Background: During the COVID-19 pandemic, in the period of preventing and controlling the spread of the virus, a large amount of personal data was collected in China, and privacy leakage incidents occurred.

Objective: We aimed to examine the information source characteristics of personal data leakage during the COVID-19 pandemic in China.

Methods: We extracted information source characteristics of 40 personal data leakage cases using open coding and analyzed the data with 1D and 2D matrices.

Results: In terms of organizational characteristics, data leakage cases mainly occurred in government agencies below the prefecture level, while few occurred in the medical system or in high-level government organizations. The majority of leakers were regular employees or junior staff members rather than temporary workers or senior managers. Family WeChat groups were the primary route for disclosure; the forwarding of documents was the main method of divulgence, while taking screenshots and pictures made up a comparatively smaller portion.

Conclusions: We propose the following suggestions: restricting the authority of nonmedical institutions and low-level government agencies to collect data, strengthening training for low-level employees on privacy protection, and restricting the flow of data on social media through technical measures.

Background: Dashboards have become ubiquitous in health care settings, but to achieve their goals, they must be developed, implemented, and evaluated using methods that help ensure they meet the needs of end users and are suited to the barriers and facilitators of the local context.

Objective: This scoping review aimed to explore published literature on health care dashboards to characterize the methods used to identify factors affecting uptake, strategies used to increase dashboard uptake, and evaluation methods, as well as dashboard characteristics and context.

Methods: MEDLINE, Embase, Web of Science, and the Cochrane Library were searched from inception through July 2020. Studies were included if they described the development or evaluation of a health care dashboard with publication from 2018-2020. Clinical setting, purpose (categorized as clinical, administrative, or both), end user, design characteristics, methods used to identify factors affecting uptake, strategies to increase uptake, and evaluation methods were extracted.

Results: From 116 publications, we extracted data for 118 dashboards. Inpatient (45/118, 38.1%) and outpatient (42/118, 35.6%) settings were most common. Most dashboards had ≥2 stated purposes (84/118, 71.2%); of these, 54 of 118 (45.8%) were administrative, 43 of 118 (36.4%) were clinical, and 20 of 118 (16.9%) had both purposes. Most dashboards included frontline clinical staff as end users (97/118, 82.2%). To identify factors affecting dashboard uptake, half involved end users in the design process (59/118, 50%); fewer described formative usability testing (26/118, 22%) or use of any theory or framework to guide development, implementation, or evaluation (24/118, 20.3%). The most common strategies used to increase uptake included education (60/118, 50.8%); audit and feedback (59/118, 50%); and advisory boards (54/118, 45.8%). Evaluations of dashboards (84/118, 71.2%) were mostly quantitative (60/118, 50.8%), with fewer using only qualitative methods (6/118, 5.1%) or a combination of quantitative and qualitative methods (18/118, 15.2%).

Conclusions: Most dashboards forego steps during development to ensure they suit the needs of end users and the clinical context; qualitative evaluation-which can provide insight into ways to improve dashboard effectiveness-is uncommon. Education and audit and feedback are frequently used to increase uptake. These findings illustrate the need for promulgation of best practices in dashboard development and will be useful to dashboard planners.