Applied Clinical Informatics最新文献

Background:  Hospital settings provide a unique opportunity to screen for intimate partner violence (IPV) and sexual assault (SA) yet often lack health information technology (IT) solutions for generating reliable and valid medicolegal documentation via forensic reports.

Objectives:  The objective of the project was to evaluate a pilot, technology "tool" for documenting cases of IPV and SA that could support forensic nurse examiners and related stakeholders in generating high-quality documentation and coordinating victim support services.

Methods:  The tool was a digital health intervention implemented for use among forensic nurse examiners, law enforcement, victim support organizations, and more within four counties of California. We conducted a mixed-methods pilot study that captured data around the adoption, use, and impact of having access to the newly implemented tool.

Results:  The tool successfully went live in all four pilot counties at different time points with different proportions of use by county and form type: exams, referrals, addenda, risk assessments, and other. Participants were motivated to use the tool out of a perceived need for data handling functionalities that went beyond traditional manual (paper) means. Key functionalities included body mapping, data quality controls within validated forms, attaching addenda to already existing case reports, and the means to distribute data to external recipients. Further study and development are needed on functions to incorporate into body maps and forms and understanding the information needs of law enforcement and victim support organizations.

Conclusion:  Our evaluation demonstrated the feasibility and acceptability of a health IT tool to support forensic nurse documentation of IPV and SA and direct information to multiple legal and support-related stakeholders. Areas of future development include integrating IPV- and SA-related data standards for digitized forms, enhancements to the body mapping feature, and understanding the needs of those who receive digital data from forensic nurse examiners within the tool.

Background:  The integration of patient-reported outcomes (PROs) into clinical care, particularly in the context of cancer and multimorbidity, is crucial. While PROs have the potential to enhance patient-centered care and improve health outcomes through improved symptom assessment, they are not always adequately documented by the health care team.

Objectives:  This study aimed to explore the concordance between patient-reported symptom occurrence and symptoms documented in electronic health records (EHRs) in people undergoing treatment for cancer in the context of multimorbidity.

Methods:  We analyzed concordance between patient-reported symptom occurrence of 13 symptoms from the Memorial Symptom Assessment Scale and provider-documented symptoms extracted using NimbleMiner, a machine learning tool, from EHRs for 99 patients with various cancer diagnoses. Logistic regression guided with the Akaike Information Criterion was used to identify significant predictors of symptom concordance.

Results:  Our findings revealed discrepancies in patient and provider reports, with itching showing the highest concordance (66%) and swelling showing the lowest concordance (40%). There was no statistically significant association between multimorbidity and high concordance, while lower concordance was observed for women, patients with advanced cancer stages, individuals with lower education levels, those who had partners, and patients undergoing highly emetogenic chemotherapy.

Conclusion:  These results highlight the challenges in achieving accurate and complete symptom documentation in EHRs and the necessity for targeted interventions to improve the precision of clinical documentation. By addressing these gaps, health care providers can better understand and manage patient symptoms, ultimately contributing to more personalized and effective cancer care.

Background:  Choice architecture refers to the design of decision environments, which can influence decision-making in health care. Nudges are subtle adjustments in these environments that guide decisions toward desired outcomes. For example, computerized provider order entry within electronic health records (EHRs) recommends frequencies for interventions such as nursing assessments and medication administrations, but these can link to around-the-clock schedules without clinical necessity.

Objectives:  This study aimed to evaluate an intervention to promote sleep-friendly practices by optimizing choice architecture and employing targeted nudges on inpatient order frequencies.

Methods:  We employed a quasi-experimental interrupted time series analysis of a multifaceted, multiphase intervention to reduce overnight interventions in a hospital system. Our intervention featured EHR modifications to optimize the scheduling of vital sign checks, neurological checks, and medication administrations. Additionally, we used targeted secure messaging reminders and education on an inpatient neurology unit (INU) to supplement the initiative.

Results:  Significant increases in sleep-friendly medication orders were observed at the academic medical center (AMC) and community hospital affiliate (CHA), particularly for acetaminophen and heparin at the AMC. This led to a reduction in overnight medication administrations, with the most substantial impact observed with heparin at all locations (CHA: 18%, AMC: 10%, INU: 10%, p < 0.05). Sleep-friendly vital sign orders increased significantly at all sites (AMC: 6.7%, CHA: 4.3%, INU: 14%, p < 0.05), and sleep-friendly neuro check orders increased significantly at the AMC (8.1%, p < 0.05). There was also a significant immediate reduction in overnight neurological checks performed at the AMC.

Discussion:  Tailoring EHR modifications and employing multifaceted nudging strategies emerged as promising approaches for reducing unnecessary overnight interventions. The observed shifts in sleep-friendly ordering translated into decreases in overnight interventions.

Conclusion:  Multifaceted nudges can effectively influence clinician decision-making and patient care. The varied impacts across nudge types and settings emphasizes the importance of thoughtful nudge design and understanding local workflows.

Background:  The electronic health record (EHR) has been associated with provider burnout, exacerbated by increasing In-Basket burden.

Objectives:  We sought to study the impact of implementing a team-based approach to In-Basket management on a series of primary care ambulatory sites.

Methods:  We performed a workflow analysis of the transition to the Advanced In-Basket Management (AIM) nurse team triage for six family medicine clinic locations in a large health system. We abstracted and analyzed associated provider workflow metrics from our EHR. We conducted a postintervention provider survey on satisfaction with the AIM project and provider burnout.

Results:  The AIM project was implemented in six family medicine clinics after provider townhalls and workgroup development. A nurse team curriculum was created using the principles of "maturing the message" before sending it to a provider and "only handle it once" to improve response efficiency. Provider workload metrics abstracted from the EHR demonstrated 12.2 fewer In-Basket messages per provider per day (p < 0.05), 6.3 fewer minutes per provider per day worked outside scheduled hours (p < 0.05), 3.5 fewer minutes spent in the In-Basket per provider per day (p < 0.05), but 13.7 more seconds spent per completed message per provider (p = 0.017), likely attributable to increased message complexity. Sixty-four percent of providers reported no burnout symptoms in a postintervention survey, 56% agreed that the AIM project reduced their burnout, and approximately 70% of providers agreed that the AIM project was acceptable and appropriate for their clinic.

Conclusion:  The AIM project demonstrates team-based nurse In-Basket triage is possible to implement across multiple primary care sites, is an acceptable intervention for providers, can reduce provider workload burden and self-reported provider burnout.

Background:  Leaders in Informatics, Quality, and Systems (LInQS) is a non-ACGME (Accreditation Council for Graduate Medical Education)-accredited 2-year training program developed to enhance training in the fields of health care delivery, quality improvement (QI), clinical informatics, and leadership.

Methods:  This single-institution 2-year longitudinal training program grounded in QI and informed by leadership and clinical informatics includes didactics, coaching, and mentorship, all centered around individualized QI projects. The program has been available to sub-specialty fellows, advanced practice providers, and physicians.

Results:  From 2019 to 2023, 32 fellows have been accepted into the program with 13 graduates and 16 currently enrolled. Fellows have been predominately female, physicians, and from multiple specialties but predominantly hospital medicine. Fellows' evaluations of the fellowship are highly positive, rating the didactics and mentorship aspects of the curriculum most favorably. Most fellows' projects utilized informatics solutions including clinical decision support tools to increase quality of care, improve patient outcomes, and reduce costs of care resulting in manuscript publications, national presentations, and a national specialty society award. Since matriculation, 50% of fellows received certification as Epic Physician Builders and 34% received leadership positions in clinical informatics, quality, and education.

Conclusion:  Our experience supports the need to provide health care providers more expansive training in the areas of QI, clinical informatics, and leadership for improving health care delivery. Additional in-depth knowledge and experience in these fields may produce and benefit leaders in these fields.

Background:  There is growing recognition of the need to incorporate informatics education in U.S. residencies. Medical residency training programs are critical in shaping system change and can play a pivotal role by incorporating clinical informatics (CI) based learning into their training requirements.

Objectives:  We searched the Accreditation Council for Graduate Medical Education (ACGME) Residency Program Requirements effective July 1, 2023, to assess the inclusion of CI topics for all medical residency specialties to assess the relative levels of CI knowledge expected by graduates.

Methods:  We performed independent full-text search queries of 23 informatics-related keywords (e.g., electronic health record, innovation, database) in the ACGME Residency Program Requirements of 24 medical specialties.

Results:  All specialties' requirements contained at least five different keywords, with the total count ranging from 25 to 42 (mean: 32.00; standard deviation: 5.09). Pathology contained the highest counts with 42, followed by internal medicine and family medicine with 41 each. Pathology included the most distinct keywords (11). The most common keywords were "leadership" (62%) and "electronic health record" (10%). There were no specific mentions of several keywords-including "analytics," "artificial intelligence," and "machine learning"-within any program requirements. Although the ACGME Residency Program Requirements state that residents must demonstrate competence in using information technology to optimize learning, the extent is not fully specified; only 10 programs mention the keyword "information technology" within their specialty guidelines.

Conclusion:  The integration of CI education varies across specialties and may be even more variable across programs. Our study highlights potential opportunities for further standardization and integration of CI into resident curriculum requirements in order to better prepare future physician workforces for a changing medical landscape. We encourage educators, residency review committees, and national specialty organizations to consider further exploring the incorporation of CI content into residency training program requirements.

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.