Applied Clinical Informatics最新文献

Background Ensuring the security of nursing information holds substantial importance. The awareness of information security among nurses in China is generally inadequate, and there is a lack of standardized evaluation tools for nurse information security in nursing practice. The nursing sector necessitates the establishment of a robust culture surrounding information security. Objective The aim of this study was to construct a self-reporting instrument for evaluating nursing information security. Methods The research team utilized literature analysis and group discussions to draft the item pool. After two rounds of Delphi consultation by 15 experts and pilot testing, the initial questionnaire was formed. Item analysis was carried out on the questionnaire, and the validity and reliability of the instrument were statistically tested by computing the Keiser-Meier-Olkin (KMO) and Bartlett tests, an exploratory factor analysis, a confirmatory factor analysis, convergent and discriminative validity, descriptive statistics, Cronbach's α and test-retest reliability. Results A total of 501 nurses participated in the study, supplemented by the inclusion of five experts who were invited to contribute to the assessment of content validity. Four factors were formed using exploratory factor analysis (n=250), and the cumulative variance contribution rate was found to be 60.10%. The confirmatory factor analysis (n=251) showed the model fit was good. The overall Cronbach's α coefficient of the questionnaire was 0.948, and the test-retest reliability was 0.837. Conclusion Finally, the NIS-Q with 38 items and three dimensions of knowledge, attitude and practice were formed. A promising assessment instrument for gauging the degree of nursing information security was introduced. Further, a foundational platform was established for implementing specific enhancement strategies aimed at advancing nursing information security.

Objective: Duplicate patient records can increase cost and medical errors. We assessed the association between demographic factors, comorbidities, healthcare usage and duplicate electronic health records.

Materials and methods: We analyzed the association between duplicate patient records and multiple demographic variables (race, Hispanic ethnicity, sex and age) as well as Charlson comorbidity index (CCI), number of diagnoses, and number of healthcare encounters. The study population included 3,018,413 patients seen at a large urban academic medical center with at least one recorded diagnosis. Duplication of patient medical records was determined by using a previously validated enterprise Master Person Index.

Results: Unknown or missing demographic data, Black race when compared to White Race (OR 1.35, p < 0.001), Hispanic compared to non-Hispanic ethnicity (OR 1.48, p < 0.001), older age (OR 1.01, p < 0.001), and "Other" sex compared to female sex (OR 4.71, p < 0.001) were associated with higher odds of having a duplicate record. Comorbidities (CCI, OR 1.10, p < 0.001) and more encounters with the health care system (OR 1.01, p < 0.001) were also associated with higher odds of having a duplicate record. In contrast, male sex compared to female sex was associated with lower odds of having a duplicate record (OR 0.88, p < 0.001).

Discussion: The odds of duplications in medical records were higher in Black, Hispanic, older, non-male patients with more healthcare encounters, more comorbidities, and unknown demographic data. Understanding the epidemiology of duplicate records can help guide prevention and mitigation efforts for high-risk populations.

Conclusion: Duplicate records can contribute to disparities in health care outcomes in minority populations.

Objectives: This research study aims to advance the staging of Parkinson's disease (PD) by incorporating machine learning to assess and include a broader multi-functional spectrum of neurocognitive symptoms in the staging schemes beyond motor-centric assessments. Specifically, we provide a novel framework to modernize and personalize PD staging more objectively by proposing a hybrid feature scoring approach.

Methods: We recruited thirty-seven individuals diagnosed with PD, each of whom completed a series of tablet-based neurocognitive tests assessing motor, memory, speech, executive functions, and tasks ranging in complexity from single to multi-functional. Then, the collected data was used to develop a hybrid feature scoring system to calculate a weighted vector for each function. We evaluated current PD staging schemes and developed a new approach based on the features selected and extracted using Random Forest and Principal Component Analysis.

Results: Our findings indicate a substantial bias in current PD staging systems toward fine-motor skills, i.e., other neurological functions (memory, speech, executive function, etc.) do not map into current PD stages as well as fine-motor skills do. The results demonstrate that a more accurate and personalized assessment of PD severity could be achieved by including a more exhaustive range of neurocognitive functions in the staging systems either by involving multiple functions in a unified staging score or by designing a function-specific staging system.

Conclusions: The proposed hybrid feature score approach provides a comprehensive understanding of PD by highlighting the need for a staging system that covers various neurocognitive functions. This approach could potentially lead to more effective, objective, and personalized treatment strategies. Further, this proposed methodology could be adapted to other neurodegenerative conditions such as Alzheimer's disease or ALS.

Objectives This study aimed to pilot an application-based patient diagnostic questionnaire (PDQ) and assess the concordance of the admission diagnosis reported by the patient and entered by the clinician.

Methods Eligible patients completed the PDQ assessing patients' understanding of and confidence in the diagnosis 24 hours into hospitalization either independently or with assistance. Demographic data, the hospital principal problem upon admission, and International Classification of Diseases 10th Revision (ICD-10) codes were retrieved from the electronic health record (EHR). Two physicians independently rated concordance between patient-reported diagnosis and clinician-entered principal problem as full, partial, or no. Discrepancies were resolved by consensus. Descriptive statistics were used to report demographics for concordant (full) and nonconcordant (partial or no) outcome groups. Multivariable logistic regressions of PDQ questions and a priori selected EHR data as independent variables were conducted to predict nonconcordance.

Results A total of 157 (77.7%) questionnaires were completed by 202 participants; 77 (49.0%), 46 (29.3%), and 34 (21.7%) were rated fully concordant, partially concordant, and not concordant, respectively. Cohen's kappa for agreement on preconsensus ratings by independent reviewers was 0.81 (0.74, 0.88). In multivariable analyses, patient-reported lack of confidence and undifferentiated symptoms (ICD-10 “R-code”) for the principal problem were significantly associated with nonconcordance (partial or no concordance ratings) after adjusting for other PDQ questions (3.43 [1.30, 10.39], p = 0.02) and in a model using selected variables (4.02 [1.80, 9.55], p < 0.01), respectively.

Conclusion About one-half of patient-reported diagnoses were concordant with the clinician-entered diagnosis on admission. An ICD-10 “R-code” entered as the principal problem and patient-reported lack of confidence may predict patient–clinician nonconcordance early during hospitalization via this approach.

Background Imaging and Clinical Informatics are domains of biomedical informatics. Imaging Informatics topics are often not covered in depth in most Clinical Informatics fellowships. While dedicated Imaging Informatics fellowships exist, they may not have the same rigor as ACGME (Accreditation Council for Graduate Medical Education) accredited Clinical Informatics fellowships and they do not provide a direct path toward subspecialty board certification.

Objectives We compared published curricula and test content between Clinical and Imaging Informatics fellowship programs. We then highlighted differences between training programs and identified overlapping topics and opportunities for additional education for each type of trainee.

Methods Published consensus curricula and topics were extracted for each specialty. Two informaticists compared topics as shared or not shared between specialties. Next, test content outlines were compared for each specialty exam, extracted, and classified as shared or not shared content. A Venn diagram was created to highlight areas unique to each specialty as well as areas of overlap.

Results There were 139 Clinical Informatics topics compared with 97 Imaging Informatics topics. Of the 139 Clinical Informatics topics, 115 (83%) were covered in the Imaging Informatics curriculum. Of the 97 Imaging Informatics topics, 74 (76%) were covered in the Clinical Informatics curriculum. When using test content outline data, 170 out of 397 (43%) Imaging Informatics topics matched to 64 out of 139 (46%) Clinical Informatics topics. We describe examples of overlapping topics and those unique to each program to identify potential areas to expand.

Conclusion Imaging Informatics and Clinical Informatics fellowship programs have some overlap with areas unique to each. Our review may help guide those seeking informatics education and potential certification. As enterprise imaging evolves, these differences may become more important and create knowledge gaps, if not systematically evaluated.

Background Multicenter precision oncology real-world evidence requires a substantial long-term investment by hospitals to prepare their data and align on common Clinical Research processes and medical definitions. Our team has developed a self-assessment framework to support hospitals and hospital networks to measure their digital maturity and better plan and coordinate those investments. From that framework, we developed PRISM for Cancer Outcomes: PRagmatic Institutional Survey and benchMarking.

Objectives The primary objective was to develop PRISM as a tool for self-assessment of digital maturity in oncology hospitals and research networks; a secondary objective was to create an initial benchmarking cohort of >25 hospitals using the tool as input for future development.

Methods PRISM is a 25-question semiquantitative self-assessment survey developed iteratively from expert knowledge in oncology real-world study delivery. It covers four digital maturity dimensions: (1) Precision oncology, (2) Clinical digital data, (3) Routine outcomes, and (4) Information governance and delivery. These reflect the four main data types and critical enablers for precision oncology research from routine electronic health records.

Results During piloting with 26 hospitals from 19 European countries, PRISM was found to be easy to use and its semiquantitative questions to be understood in a wide diversity of hospitals. Results within the initial benchmarking cohort aligned well with internal perspectives. We found statistically significant differences in digital maturity, with Precision oncology being the most mature dimension, and Information governance and delivery the least mature.

Conclusion PRISM is a light footprint benchmarking tool to support the planning of large-scale real-world research networks. It can be used to (i) help an individual hospital identify areas most in need of investment and improvement, (ii) help a network of hospitals identify sources of best practice and expertise, and (iii) help research networks plan research. With further testing, policymakers could use PRISM to better plan digital investments around the Cancer Mission and European Digital Health Space.

Background: In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians.

Objective: Addressing the limited exposure of healthcare providers to leverage and critique data science methods, this interactive workshop aims to provide clinicians with foundational knowledge in data science, enabling them to contribute effectively to teams focused on improving care quality.

Methods: The workshop focuses on meaningful topics for clinicians, such as model performance evaluation and introduces machine learning through hands-on exercises using free, interactive python notebooks. Clinical case studies on sepsis recognition and opioid overdose death provide relatable contexts for applying data science concepts.

Results: Positive feedback from over 300 participants across various settings highlights the workshop's effectiveness in making complex topics accessible to clinicians.

Conclusions: Our approach prioritizes engaging content delivery and practical application over extensive programming instruction, aligning with adult learning principles. This initiative underscores the importance of equipping clinicians with data science knowledge to navigate today's data-driven healthcare landscape, offering a template for integrating data science education into healthcare informatics programs or continuing professional development.