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.
Unlabelled: The EyeMatics project, embedded as a clinical use case in Germany's Medical Informatics Initiative, is a large digital health initiative in ophthalmology. The objective is to improve the understanding of the treatment effects of intravitreal injections, the most frequent procedure to treat eye diseases. To achieve this, valuable patient data will be meaningfully integrated and visualized from different IT systems and hospital sites. EyeMatics emphasizes a governance framework that actively involves patient representatives, strictly implements interoperability standards, and employs artificial intelligence methods to extract biomarkers from tabular and clinical data as well as raw retinal scans. In this perspective paper, we delineate the strategies for user-centered implementation and health care-based evaluation in a multisite observational technology study.
Unlabelled: The paper reviews digital solutions for health services management in Brazil, focusing on certified software features. It reveals the integration of various functionalities in operational, financial, and clinical needs simultaneously, which are critical for enhancing operational efficiency and patient care. This study highlights the integration of critical features like interoperability, compliance management, and data-driven decision support, although advancing innovation and integration remains essential for broader impact.
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