Structure and Funding of Clinical Informatics Fellowships: A National Survey of Program Directors.

BACKGROUND: In 2011, the American Board of Medical Specialties established clinical informatics (CI) as a subspecialty in medicine, jointly administered by the American Board of Pathology and the American Board of Preventive Medicine. Subsequently, many institutions created CI fellowship training programs to meet the growing need for informaticists. Although many programs share similar features, there is considerable variation in program funding and administrative structures. OBJECTIVES: The aim of our study was to characterize CI fellowship program features, including governance structures, funding sources, and expenses. METHODS: We created a cross-sectional online REDCap survey with 44 items requesting information on program administration, fellows, administrative support, funding sources, and expenses. We surveyed program directors of programs accredited by the Accreditation Council for Graduate Medical Education between 2014 and 2021. RESULTS: We invited 54 program directors, of which 41 (76%) completed the survey. The average administrative support received was $27,732/year. Most programs (85.4%) were accredited to have two or more fellows per year. Programs were administratively housed under six departments: Internal Medicine (17; 41.5%), Pediatrics (7; 17.1%), Pathology (6; 14.6%), Family Medicine (6; 14.6%), Emergency Medicine (4; 9.8%), and Anesthesiology (1; 2.4%). Funding sources for CI fellowship program directors included: hospital or health systems (28.3%), clinical departments (28.3%), graduate medical education office (13.2%), biomedical informatics department (9.4%), hospital information technology (9.4%), research and grants (7.5%), and other sources (3.8%) that included philanthropy and external entities. CONCLUSION: CI fellowships have been established in leading academic and community health care systems across the country. Due to their unique training requirements, these programs require significant resources for education, administration, and recruitment. There continues to be considerable heterogeneity in funding models between programs. Our survey findings reinforce the need for reformed federal funding models for informatics practice and training.

Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients.

Aims: There are no comprehensive machine learning (ML) tools used by oncologists to assist with risk identification and referrals to cardio-oncology. This study applies ML algorithms to identify oncology patients at risk for cardiovascular disease for referrals to cardio-oncology and to generate risk scores to support quality of care. Methods and results: De-identified patient data were obtained from Vanderbilt University Medical Center. Patients with breast, kidney, and B-cell lymphoma cancers were targeted. Additionally, the study included patients who received immunotherapy drugs for treatment of melanoma, lung cancer, or kidney cancer. Random forest (RF) and artificial neural network (ANN) ML models were applied to analyse each cohort: A total of 20 023 records were analysed (breast cancer, 6299; B-cell lymphoma, 9227; kidney cancer, 2047; and immunotherapy for three covered cancers, 2450). Data were divided randomly into training (80%) and test (20%) data sets. Random forest and ANN performed over 90% for accuracy and area under the curve (AUC). All ANN models performed better than RF models and produced accurate referrals. Conclusion: Predictive models are ready for translation into oncology practice to identify and care for patients who are at risk of cardiovascular disease. The models are being integrated with electronic health record application as a report of patients who should be referred to cardio-oncology for monitoring and/or tailored treatments. Models operationally support cardio-oncology practice. Limited validation identified 86% of the lymphoma and 58% of the kidney cancer patients with major risk for cardiotoxicity who were not referred to cardio-oncology.

Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution.

Background/Objective: The University of Illinois at Chicago (UIC), along with many academic institutions worldwide, made significant efforts to address the many challenges presented during the COVID-19 pandemic by developing clinical staging and predictive models. Data from patients with a clinical encounter at UIC from July 1, 2019 to March 30, 2022 were abstracted from the electronic health record and stored in the UIC Center for Clinical and Translational Science Clinical Research Data Warehouse, prior to data analysis. While we saw some success, there were many failures along the way. For this paper, we wanted to discuss some of these obstacles and many of the lessons learned from the journey. Methods: Principle investigators, research staff, and other project team members were invited to complete an anonymous Qualtrics survey to reflect on the project. The survey included open-ended questions centering on participants' opinions about the project, including whether project goals were met, project successes, project failures, and areas that could have been improved. We then identified themes among the results. Results: Nine project team members (out of 30 members contacted) completed the survey. The responders were anonymous. The survey responses were grouped into four key themes: Collaboration, Infrastructure, Data Acquisition/Validation, and Model Building. Conclusion: Through our COVID-19 research efforts, the team learned about our strengths and deficiencies. We continue to work to improve our research and data translation capabilities.

Effect of Electronic Health Record Clinical Decision Support on Contextualization of Care: A Randomized Clinical Trial.

Importance: Contextualizing care is a process of incorporating information about the life circumstances and behavior of individual patients, termed contextual factors, into their plan of care. In 4 steps, clinicians recognize clues (termed contextual red flags), clinicians ask about them (probe for context), patients disclose contextual factors, and clinicians adapt care accordingly. The process is associated with a desired outcome resolution of the presenting contextual red flag. Objective: To determine whether contextualized clinical decision support (CDS) tools in the electronic health record (EHR) improve clinician contextual probing, attention to contextual factors in care planning, and the presentation of contextual red flags. Design, Setting, and Participants: This randomized clinical trial was performed at the primary care clinics of 2 academic medical centers with different EHR systems. Participants were adults 18 years or older consenting to audio record their visits and their physicians between September 6, 2018, and March 4, 2021. Patients were randomized to an intervention or a control group. Analyses were performed on an intention-to-treat basis. Interventions: Patients completed a previsit questionnaire that elicited contextual red flags and factors and appeared in the clinician's note template in a contextual care box. The EHR also culled red flags from the medical record, included them in the contextual care box, used passive and interruptive alerts, and proposed relevant orders. Main Outcomes and Measures: Proportion of contextual red flags noted at the index visit that resolved 6 months later (primary outcome), proportion of red flags probed (secondary outcome), and proportion of contextual factors addressed in the care plan by clinicians (secondary outcome), adjusted for study site and for multiple red flags and factors within a visit. Results: Four hundred fifty-two patients (291 women [65.1%]; mean [SD] age, 55.6 [15.1] years) completed encounters with 39 clinicians (23 women [59.0%]). Contextual red flags were not more likely to resolve in the intervention vs control group (adjusted odds ratio [aOR], 0.96 [95% CI, 0.57-1.63]). However, the intervention increased both contextual probing (aOR, 2.12 [95% CI, 1.14-3.93]) and contextualization of the care plan (aOR, 2.67 [95% CI, 1.32-5.41]), controlling for whether a factor was identified by probing or otherwise. Across study groups, contextualized care plans were more likely than noncontextualized plans to result in improvement in the presenting red flag (aOR, 2.13 [95% CI, 1.38-3.28]). Conclusions and Relevance: This randomized clinical trial found that contextualized CDS did not improve patients' outcomes but did increase contextualization of their care, suggesting that use of this technology could ultimately help improve outcomes. Trial Registration: ClinicalTrials.gov Identifier: NCT03244033.

Examining perspectives on the adoption and use of computer-based patient-reported outcomes among clinicians and health professionals: a Q methodology study.

OBJECTIVE: To determine factors that influence the adoption and use of patient-reported outcomes (PROs) in the electronic health record (EHR) among users. MATERIALS AND METHODS: Q methodology, supported by focus groups, semistructured interviews, and a review of the literature was used for data collection about opinions on PROs in the EHR. An iterative thematic analysis resulted in 49 statements that study participants sorted, from most unimportant to most important, under the following condition of instruction: "What issues are most important or most unimportant to you when you think about the adoption and use of patient-reported outcomes within the electronic health record in routine clinical care?" Using purposive sampling, 50 participants were recruited to rank and sort the 49 statements online, using HTMLQ software. Principal component analysis and Varimax rotation were used for data analysis using the PQMethod software. RESULTS: Participants were mostly physicians (24%) or physician/researchers (20%). Eight factors were identified. Factors included the ability of PROs in the EHR to enable: efficient and reliable use; care process improvement and accountability; effective and better symptom assessment; patient involvement for care quality; actionable and practical clinical decisions; graphical review and interpretation of results; use for holistic care planning to reflect patients' needs; and seamless use for all users. DISCUSSION: The success of PROs in the EHR in clinical settings is not dependent on a "one size fits all" strategy, demonstrated by the diversity of viewpoints identified in this study. A sociotechnical approach for implementing PROs in the EHR may help improve its success and sustainability. CONCLUSIONS: PROs in the EHR are most important to users when the technology is used to improve patient outcomes. Future research must focus on the impact of embedding this EHR functionality on care processes.

Cloud Computing Synthetic Syndromic Surveillance Systems: Opioid Epidemic in Illinois.

With an increasing number of overdose cases yearly, the city of Chicago is facing an opioid epidemic. Many of these overdose cases lead to 911 calls that necessitate timely response from our limited emergency medicine services. This paper demonstrates how data from these calls along with synthetic and geospatial data can help create a syndromic surveillance system to combat this opioid crisis. Chicago EMS data is obtained from the Illinois Department of Public Health with a database structure using the NEMSIS standard. This information is combined with information from the RTI U.S. Household Population database, before being transferred to an Azure Data Lake. Afterwards, the data is integrated with Azure Synapse before being refined in another data lake and filtered with ICD-10 codes. Afterwards, we moved the data to ArcGIS Enterprise to apply spatial statistics and geospatial analytics to create our surveillance system.

Predicting clinical outcomes among hospitalized COVID-19 patients using both local and published models.

BACKGROUND: Many models are published which predict outcomes in hospitalized COVID-19 patients. The generalizability of many is unknown. We evaluated the performance of selected models from the literature and our own models to predict outcomes in patients at our institution. METHODS: We searched the literature for models predicting outcomes in inpatients with COVID-19. We produced models of mortality or criticality (mortality or ICU admission) in a development cohort. We tested external models which provided sufficient information and our models using a test cohort of our most recent patients. The performance of models was compared using the area under the receiver operator curve (AUC). RESULTS: Our literature review yielded 41 papers. Of those, 8 were found to have sufficient documentation and concordance with features available in our cohort to implement in our test cohort. All models were from Chinese patients. One model predicted criticality and seven mortality. Tested against the test cohort, internal models had an AUC of 0.84 (0.74-0.94) for mortality and 0.83 (0.76-0.90) for criticality. The best external model had an AUC of 0.89 (0.82-0.96) using three variables, another an AUC of 0.84 (0.78-0.91) using ten variables. AUC's ranged from 0.68 to 0.89. On average, models tested were unable to produce predictions in 27% of patients due to missing lab data. CONCLUSION: Despite differences in pandemic timeline, race, and socio-cultural healthcare context some models derived in China performed well. For healthcare organizations considering implementation of an external model, concordance between the features used in the model and features available in their own patients may be important. Analysis of both local and external models should be done to help decide on what prediction method is used to provide clinical decision support to clinicians treating COVID-19 patients as well as what lab tests should be included in order sets.

Required data elements for interprofessional rounds through the lens of multiple professions.

The lack of a proper system for ongoing open interprofessional communication among care providers increases miscommunications and medical errors. Seamless access to patient information is important for care providers to prevent miscommunication and improve patient safety. A shared understanding of the information needs of different care providers in an interprofessional team is lacking. Our purpose is to identify care providers' information needs from the perspective of different professions for communication, shared understanding about the patient, and decision-making. We conducted semi-structured interviews with 10 subject matter experts representing eight professions, including dentistry, dietetics, medicine, nursing, occupational therapy, pharmacy, physical therapy, and social work in a 465-bed academic hospital at a large urban Midwestern city. We used an in-house rounding tool presenting physicians' information needs and a hypothetical patient scenario to collect participants' feedback. Interview notes were coded using direct content analysis. We identified 22 additional essential data elements for an interprofessional rounding tool. We categorized those into six domains: discharge-related, social determinants of health, hospital safety, nutrition, interprofessional situation awareness, and patient history. A well-designed validated rounding tool that includes an interprofessional team of care providers' information needs could improve communication, care planning, and decision-making among them.

Planning for patient-reported outcome implementation: Development of decision tools and practical experience across four clinics.

INTRODUCTION: Many institutions are attempting to implement patient-reported outcome (PRO) measures. Because PROs often change clinical workflows significantly for patients and providers, implementation choices can have major impact. While various implementation guides exist, a stepwise list of decision points covering the full implementation process and drawing explicitly on a sociotechnical conceptual framework does not exist. METHODS: To facilitate real-world implementation of PROs in electronic health records (EHRs) for use in clinical practice, members of the EHR Access to Seamless Integration of Patient-Reported Outcomes Measurement Information System (PROMIS) Consortium developed structured PRO implementation planning tools. Each institution pilot tested the tools. Joint meetings led to the identification of critical sociotechnical success factors. RESULTS: Three tools were developed and tested: (1) a PRO Planning Guide summarizes the empirical knowledge and guidance about PRO implementation in routine clinical care; (2) a Decision Log allows decision tracking; and (3) an Implementation Plan Template simplifies creation of a sharable implementation plan. Seven lessons learned during implementation underscore the iterative nature of planning and the importance of the clinician champion, as well as the need to understand aims, manage implementation barriers, minimize disruption, provide ample discussion time, and continuously engage key stakeholders. CONCLUSIONS: Highly structured planning tools, informed by a sociotechnical perspective, enabled the construction of clear, clinic-specific plans. By developing and testing three reusable tools (freely available for immediate use), our project addressed the need for consolidated guidance and created new materials for PRO implementation planning. We identified seven important lessons that, while common to technology implementation, are especially critical in PRO implementation.

Impact of EHR-based rounding tools on interactive communication: A prospective observational study.

OBJECTIVE: Structured rounding tools have shown to improve the overall efficiency and perceived satisfaction with the rounding process. However, little is known about how EHR-integrated rounding tools impact the content, structure and interactivity of communication during rounds. METHOD: We conducted a prospective pre-post evaluation with two rounding tools: a Microsoft Word-based fillable rounding tool (usual tool), and an EHR-integrated rounding report tool (RRT). 27 clinicians across two teams participated in rounds for 169 patients (nusual=84, nRRT=85). We audio-recorded and coded communication during rounds using conversational analysis methods. Using the coded communication interactions, we investigated differences between the two tools on: clinical content discussed, questions raised, and breakdowns in interactive communication. Additionally, we gathered clinician perspectives on the rounding tools through follow-up interviews. RESULTS: We found that the use of RRT was associated with significantly more discussion of patient identifiers (e.g., name), and action items (e.g., to-do list) and significantly less discussion of imaging (e.g., X-rays) than the usual tool. RRT was also associated with fewer questions (t = 3.1, p = 0.03), and correspondingly, fewer responses (t = 3.2, p = 0.02). Communication breakdowns related to incorrect responses was fewer during the use of RRT (t = 0.5, p = 0.01). There were no statistically significant differences in the time spent for rounding between the two tools. CONCLUSIONS: Our findings showed that RRT impacted rounding workflow: during pre-rounding, by saving time and effort in gathering information from multiple sources; during rounding, by streamlining content of the conversations using the structured RRT template; and during post-rounding, by supporting explicit discussion of patient tasks and action items for patient care planning and management.

Embedding a Medical Search Engine Within an Electronic Health Record.

This study investigates an information retrieval tool embedded in an electronic health record (EHR). 1-Search provides a single search for retrieving information from a variety of content sources. 1-Search's usefulness and impact were determined by measuring the extent of physicians' information needs, pre- and post-implementation user satisfaction, and the impact of 1-Search on clinical decision-making. Results support incorporation of 1-Search into the EHR, the continued use of 1-Search, and further development.

Simulation of ICD-9 to ICD-10-CM Transition for Family Medicine: Simple or Convoluted?

OBJECTIVE: The objective of this study was to examine the impact of the transition from International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), to Interactional Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), on family medicine and to identify areas where additional training might be required. METHODS: Family medicine ICD-9-CM codes were obtained from an Illinois Medicaid data set (113,000 patient visits and $5.5 million in claims). Using the science of networks, we evaluated each ICD-9-CM code used by family medicine physicians to determine whether the transition was simple or convoluted. A simple transition is defined as 1 ICD-9-CM code mapping to 1 ICD-10-CM code, or 1 ICD-9-CM code mapping to multiple ICD-10-CM codes. A convoluted transition is where the transitions between coding systems is nonreciprocal and complex, with multiple codes for which definitions become intertwined. Three family medicine physicians evaluated the most frequently encountered complex mappings for clinical accuracy. RESULTS: Of the 1635 diagnosis codes used by family medicine physicians, 70% of the codes were categorized as simple, 27% of codes were convoluted, and 3% had no mapping. For the visits, 75%, 24%, and 1% corresponded with simple, convoluted, and no mapping, respectively. Payment for submitted claims was similarly aligned. Of the frequently encountered convoluted codes, 3 diagnosis codes were clinically incorrect, but they represent only <0.1% of the overall diagnosis codes. CONCLUSIONS: The transition to ICD-10-CM is simple for 70% or more of diagnosis codes, visits, and reimbursement for a family medicine physician. However, some frequently used codes for disease management are convoluted and incorrect, and for which additional resources need to be invested to ensure a successful transition to ICD-10-CM.

A diabetes dashboard and physician efficiency and accuracy in accessing data needed for high-quality diabetes care.

PURPOSE: We compared use of a new diabetes dashboard screen with use of a conventional approach of viewing multiple electronic health record (EHR) screens to find data needed for ambulatory diabetes care. METHODS: We performed a usability study, including a quantitative time study and qualitative analysis of information-seeking behaviors. While being recorded with Morae Recorder software and "think-aloud" interview methods, 10 primary care physicians first searched their EHR for 10 diabetes data elements using a conventional approach for a simulated patient, and then using a new diabetes dashboard for another. We measured time, number of mouse clicks, and accuracy. Two coders analyzed think-aloud and interview data using grounded theory methodology. RESULTS: The mean time needed to find all data elements was 5.5 minutes using the conventional approach vs 1.3 minutes using the diabetes dashboard (P <.001). Physicians correctly identified 94% of the data requested using the conventional method, vs 100% with the dashboard (P <.01). The mean number of mouse clicks was 60 for conventional searching vs 3 clicks with the diabetes dashboard (P <.001). A common theme was that in everyday practice, if physicians had to spend too much time searching for data, they would either continue without it or order a test again. CONCLUSIONS: Using a patient-specific diabetes dashboard improves both the efficiency and accuracy of acquiring data needed for high-quality diabetes care. Usability analysis tools can provide important insights into the value of optimizing physician use of health information technologies.

Attending and resident physician perceptions of an EMR-generated rounding report for adult inpatient services.

BACKGROUND: With limited work hours, efficient rounding and effective hand-offs have become essential. We created a completely electronic medical record (EMR)-generated rounding report for use during pre-rounding, team rounds, and sign-out/hand-offs. We hypothesized that this would reduce workloads. METHODS: We used a pre- and post-implementation survey of the residents and faculty members of the Departments of Family and Community Medicine and Internal Medicine. RESULTS: After 5 months of use, residents and attending physicians reported a daily time savings of 44 minutes. Seventy-six percent of users also agreed that the rounding report improved patient safety. Rounding report users were more satisfied with the rounding process, spent less time updating other lists or documents, and less time pre-rounding. In addition, there were trends toward spending more time with patients, adherence to work-hour rules, increased accuracy of information during sign-out, improved satisfaction, confidence while cross-covering, and decreased clinically relevant errors. CONCLUSIONS: Utilization of well-designed, EMR-generated reports for the use of patient transfer, sign-out, and rounding should become more commonplace considering the improved efficiency, satisfaction, and potential for improved patient care.

Issues and questions to consider in implementing secure electronic patient-provider web portal communications systems.

PURPOSE: Patients are increasingly interested in using Internet-based technologies to communicate with their providers, schedule clinic visits, request medication refills, and view their medical records electronically. However, healthcare organizations face significant challenges in providing such highly personal and sensitive communication in an effective and user-friendly manner. METHODS: Based on the literature and our experience in providing a secure web-based patient-provider communication portal in primary care clinics, a framework was developed that identifies key issues and questions to consider in implementing secure electronic patient-provider communications systems. RESULTS: The framework serves to categorize the many lessons learned from our implementation process and the specific issues and questions healthcare organizations need to consider in implementing such systems related to seven areas: strategic fit and priority; selection process & implementation team; integration into communications and workflows; HIPAA issues & clinic policies; systems implementation & training; marketing & enrollment; on-going performance monitoring. CONCLUSION: The framework provides a useful guide for organizations looking to implement secure electronic patient-provider communication systems.