A novel method for causal structure discovery from EHR data and its application to type-2 diabetes mellitus

Differences in changes of data completeness after the implementation of an electronic medical record in three surgical departments of a German hospital-a longitudinal comparative document analysis

PURPOSE

The European health data space promises an efficient environment for research and policy-making. However, this data space is dependent on high data quality. The implementation of electronic medical record systems has a positive impact on data quality, but improvements are not consistent across empirical studies. This study aims to analyze differences in the changes of data quality and to discuss these against distinct stages of the electronic medical record's adoption process.

METHODS

Paper-based and electronic medical records from three surgical departments were compared, assessing changes in data quality after the implementation of an electronic medical record system. Data quality was operationalized as completeness of documentation. Ten information that must be documented in both record types (e.g. vital signs) were coded as 1 if they were documented, otherwise as 0. Chi-Square-Tests were used to compare percentage completeness of these ten information and t-tests to compare mean completeness per record type.

RESULTS

A total of N = 659 records were analyzed. Overall, the average completeness improved in the electronic medical record, with a change from 6.02 (SD = 1.88) to 7.2 (SD = 1.77). At the information level, eight information improved, one deteriorated and one remained unchanged. At the level of departments, changes in data quality show expected differences.

CONCLUSION

The study provides evidence that improvements in data quality could depend on the process how the electronic medical record is adopted in the affected department. Research is needed to further improve data quality through implementing new electronical medical record systems or updating existing ones.

Bayesian networks in modeling leucocyte interplay following brain irradiation: A comprehensive framework

BACKGROUND AND OBJECTIVE

Understanding the intricate interactions among leucocyte subpopulations following radiotherapy is crucial for advancing cancer research and immunology. Recently, interest in recent radiotherapy modalities, such as protons, has increased. Herein, we present a framework utilizing Bayesian networks to uncover these complex relationships via an illustrative example of brain irradiation in rodents.

METHODS

We utilized data from 96 healthy C57BL/6 adult mice subjected to either X-ray or proton brain irradiation. Leucocyte subpopulations in the blood collected 12 h after the final irradiated fraction were quantified. We employed Bayesian networks to detect causal interplay between physiological parameters, radiation variables and circulating leucocytes. The causal structure was learned via the use of the Bayesian information criterion as a scored criterion. Parameter estimation was performed to quantify the strength of the identified causal relationships. Cross-validation was used to validate our Bayesian network model's performance.

RESULTS

In the X-ray model, we discovered previously undisclosed interactions between NK-cells and neutrophils, and between monocytes and T-CD4+ cells. The proton model revealed an interplay involving T-CD4+ cells and neutrophils. Both X-rays and protons led to heightened interactions between T-CD8+ cells and B cells, indicating their significant role in orchestrating immune responses. Additionally, the proton model displayed strengthened interactions between T-CD4+ and T-CD8+ cells, emphasizing a dynamic and coordinated immune response post-irradiation. Cross-validation results demonstrated the robustness of the Bayesian network model in explaining data uncertainty.

CONCLUSION

The use of Bayesian networks as tools for causal structure discovery has revealed novel insights into the dynamics of immune responses to radiation exposure.

Mental health progress requires causal diagnostic nosology and scalable causal discovery

Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other medical disorders. Progress on outcome improvement for all medical disorders, including mental disorders, requires research capable of discovering causality at sufficient scale and speed, and a diagnostic nosology capable of encoding the causal knowledge that is discovered. Accordingly, the field's guiding paradigm limits progress by maintaining: (a) a diagnostic nosology (DSM-5) with a profound lack of causality; (b) a misalignment between mental health etiologic research and nosology; (c) an over-reliance on clinical trials beyond their capabilities; and (d) a limited adoption of newer methods capable of discovering the complex etiology of mental disorders. We detail feasible directions forward, to achieve greater levels of progress on improving outcomes for mental disorders, by: (a) the discovery of knowledge on the complex etiology of mental disorders with application of Causal Data Science methods; and (b) the encoding of the etiological knowledge that is discovered within a causal diagnostic system for mental disorders.

Multidimensional analysis of job advertisements for medical record information managers

Objective

The rapid growth of the medical industry has resulted in a tremendous increase in medical record data, which can be utilized for hospital management, aiding in diagnosis and treatment, medical research, and other purposes. For data management and analysis, medical institutions require more qualified medical record information managers. In light of this, we conducted an analysis of the qualifications, abilities, and job emphasis of medical record information managers in order to propose training recommendations.

Materials and methods

From online job posting sites, a sample of 241 job advertisements for medical record information management positions posted by Chinese healthcare institutions were collected. We conducted word frequency and keyword co-occurrence analysis to uncover overall demands at the macro level, and job analysis to investigate job-specific disparities at the micro level. Based on content analysis and job analysis, a competency framework was designed for medical record information managers.

Results

The most frequent keywords were "code," "job experience," and "coding certification," according to the word frequency analysis. The competency framework for managers of medical record information is comprised of seven domains: essential knowledge, medical knowledge, computer expertise, problem-solving skills, leadership, innovation, and attitude and literacy. One of the fundamental skills required of medical record information managers is coordination and communication. Similarly, knowledge and skill requirements emphasize theoretical knowledge, managerial techniques, performance enhancement, and innovation development.

Conclusion

According to organization type and job differences, the most crucial feature of the job duties of medical record information managers is cross-fertilization. The findings can be utilized by various healthcare organizations for strategic talent planning, by the field of education for medical record information managers for qualification and education emphasis adjustment, and by job seekers to enhance their grasp of the profession and self-evaluation.