Crowdsourcing Electronic Health Record Improvements at Scale across an Integrated Health Care Delivery System.

BACKGROUND AND OBJECTIVE: Despite widespread adoption of electronic health records (EHRs), these systems have significant room for improved efficiency and efficacy. While the idea of crowdsourcing EHR improvement ideas has been reported, little is known about how this might work across an integrated health care delivery system in practice. METHODS: Our program solicited EHR improvement submissions during two timeframes across 10 hospitals and 60 clinics in an upper-Midwest integrated health care delivery system. Submissions were primarily collected via an EHR help feature. RESULTS: A total of 262 and 294 submissions were received in 2019 and 2022, with a majority initiated from physicians (73.5 and 46.9%, 2019 and 2022) specializing in family medicine (52.0 and 59.3%). In 2022, the program reached a larger variety of personnel than 2019, with 53.0% of submissions from advanced practice providers, nurses, administrative staff, and other roles (p < 0.0001). Many ideas (36.4 and 50.0% in 2019 and 2022) reflected a lack of user understanding of EHR features and were addressed through training/education. Significant (27.1 and 25.9%) or simple (24.0 and 14.7%) EHR optimizations were required to address most remaining suggestions, with a number part of planned EHR improvement projects already (16.3 and 17.6%). CONCLUSION: Our experience using a crowdsourcing approach for EHR improvement ideas provided clinicians and staff the opportunity to address frustrations with the EHR and offered concrete feedback and solutions. While previous studies have suggested EHR technology improvements as paramount, we observed large numbers of users having a misunderstanding of EHR features, highlighting the need for improved EHR user competency and training.

Validation and Refinement of a Pain Information Model from EHR Flowsheet Data.

BACKGROUND: Secondary use of electronic health record (EHR) data can reduce costs of research and quality reporting. However, EHR data must be consistent within and across organizations. Flowsheet data provide a rich source of interprofessional data and represents a high volume of documentation; however, content is not standardized. Health care organizations design and implement customized content for different care areas creating duplicative data that is noncomparable. In a prior study, 10 information models (IMs) were derived from an EHR that included 2.4 million patients. There was a need to evaluate the generalizability of the models across organizations. The pain IM was selected for evaluation and refinement because pain is a commonly occurring problem associated with high costs for pain management. OBJECTIVE: The purpose of our study was to validate and further refine a pain IM from EHR flowsheet data that standardizes pain concepts, definitions, and associated value sets for assessments, goals, interventions, and outcomes. METHODS: A retrospective observational study was conducted using an iterative consensus-based approach to map, analyze, and evaluate data from 10 organizations. RESULTS: The aggregated metadata from the EHRs of 8 large health care organizations and the design build in 2 additional organizations represented flowsheet data from 6.6 million patients, 27 million encounters, and 683 million observations. The final pain IM has 30 concepts, 4 panels (classes), and 396 value set items. Results are built on Logical Observation Identifiers Names and Codes (LOINC) pain assessment terms and extend the need for additional terms to support interoperability. CONCLUSION: The resulting pain IM is a consensus model based on actual EHR documentation in the participating health systems. The IM captures the most important concepts related to pain.

[Mental Disorders and Reasonable Accommodation at Work: The Potential of Quebec Law]. / Troubles mentaux et accommodement raisonnable au travail : les potentialités du droit québécois.

Objectives Being able to work constitutes a powerful social integration vector for those suffering from mental health disorders, while also providing therapeutic benefits for them. Yet, despite some advances in this regard, such persons are still denied employment. The obstacles to their full workforce integration reside in the prejudices surrounding the disorders affecting them and the need to adapt tasks to their needs and abilities. The International Convention on the Rights of Persons with Disabilities, adopted by the United Nations in 2006, points out that disability results from the interaction between persons with impairments and attitudinal and environmental barriers that hinder their full and effective participation in society on the basis of equality with others. It stresses the importance of the right to work of persons with disabilities and, in particular, the implementation of reasonable accommodation for these persons in all workplaces. From this perspective, this article aims to explore the impacts of the legal obligation of accommodation.Methods In Canada, the fundamental rights approach and, in particular, the obligation for reasonable accommodation with regard to handicapped workers, has been well established in case law since the mid 1980s. This obligation is of a constitutional nature and its extended range is limited only by the proof of undue hardship. It thus forcefully prompts labour markets to take into consideration the needs and capacities of disabled workers. Through a study of Quebec case law, this article looks at the potential of the duty to accommodate with regard to promoting access to and retention of employment among people with mental disorders.Results The study found that the duty to accommodate imposes an innovative approach that takes into account the particular situation of the disabled person and involves the participation of all stakeholders. Accommodation allows employees to maintain an employment relationship despite repeated absences caused by illness, thus enabling them to do their work. Employers must provide adjustments relating to working time and allow gradual return when necessary. Moreover, the accommodation may involve changing the work itself or its psychosocial aspects.Conclusion By implementing measures to change the psychosocial work environment, accommodation not only promotes access to and retention of employment among people with mental disorders, but also opens the way to first stage prevention for the benefit of all.

Modeling Flowsheet Data to Support Secondary Use.

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same "thing," but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use because of the existence of multiple semantically equivalent measures representing the same concepts. Ten information models were created in this study: five related to quality measures (falls, pressure ulcers, venous thromboembolism, genitourinary system including catheter-associated urinary tract infection, and pain management) and five high-volume physiological systems: cardiac, gastrointestinal, musculoskeletal, respiratory, and expanded vital signs/anthropometrics. The value of the information models is that flowsheet data can be extracted and mapped for semantically comparable flowsheet measures from a clinical data repository regardless of the time frame, discipline, or setting in which documentation occurred. The 10 information models simplify the representation of the content in flowsheet data, reducing 1552 source measures to 557 concepts. The amount of representational reduction ranges from 3% for falls to 78% for the respiratory system. The information models provide a foundation for including nursing and interprofessional assessments and interventions in common data models, to support research within and across health systems.

Expanding Interprofessional EHR Data in i2b2.

Emerging issues of team-based care, precision medicine, and big data science underscore the need for health information technology (HIT) tools for integrating complex data in consistent ways to achieve the triple aims of improving patient outcomes, patient experience, and cost reductions. The purpose of this study was to demonstrate the feasibility of creating a hierarchical flowsheet ontology in i2b2 using data-derived information models and determine the underlying informatics and technical issues. This study is the first of its kind to use information models that aggregate team-based care across time, disciplines, and settings into 14 information models that were integrated into i2b2 in a hierarchical model. In the process of successfully creating a hierarchical ontology for flowsheet data in i2b2, we uncovered a variety of informatics and technical issues described in this paper.

Modeling Flowsheet Data for Clinical Research.

Health care data included in clinical data repositories (CDRs) are increasingly used for quality reporting, business analytics and research; however, extended clinical data from interprofessional practice are seldom included. With the increasing emphasis on care coordination across settings, CDRs need to include data from all clinicians and be harmonized to understand the impact of their collaborative efforts on patient safety, effectiveness and efficiency. This study characterizes the extended clinical data derived from EHR flowsheet data that is available in the University of Minnesota's CDR and describes a process for creating an ontology that organizes that data so that it is more useful and accessible to researchers. The process is illustrated using a pressure ulcer ontology and compares ease of finding concepts in i2b2 for different data organization approaches. The challenges of the manual process and difficulties combining similar concepts are discussed.

Deletion of IL-4Rα in the BALB/c mouse is associated with altered lesion topography and susceptibility to experimental autoimmune encephalomyelitis.

The regulation of cytokine expression by immune deviation from a pro-inflammatory to anti-inflammatory or "regulatory" milieu is crucial to the prevention of permanent central nervous system (CNS) damage in neuroinflammation. Earlier studies in the murine experimental autoimmune encephalomyelitis (EAE) model pointed to an anti-inflammatory role for the Th2 cytokine, IL-4, which was not confirmed in IL-4Rα-deficient mice (IL-4Rα(-/-)). To examine the pathological consequences of loss of responsiveness to Th2 cytokines, we compared lesion evolution in IL-4Rα(-/-) and wild type (WT) BALB/c mice immunized with PLP180-199 and investigated how altering the magnitude of the antigen-specific autoimmune response in this model affected the pathology. We found that while changing the magnitude of the peripheral antigen-specific response differentially affected the incidence of clinical disease in WT BALB/c relative to IL-4Rα(-/-) mice, the differences in incidence did not correlate to differences in pro-inflammatory cytokine production. Additionally, although only approximately 75% of WT mice developed clinical disease, lesions were observed in 100% of the mice, principally in the cerebellum, mid-brain and cerebral hemispheres, and lesion load increased with increasing pro-inflammatory cytokine production. Despite being resistant to disease induction with increasing pro-inflammatory cytokine production, lesion incidence in IL-4Rα-deficient animals was equal to their WT counterparts. However, lesion severity in IL-4Rα-deficient animals was preferentially reduced in the mid-brain and cerebral hemispheres. From these studies, we conclude that signaling through IL-4Rα has little effect on regulating the peripheral pro-inflammatory cytokine profile in this EAE variant but has distinct effects on the determination of lesion topography.

Standardizing documentation for postoperative nausea and vomiting in the electronic health record.

Postoperative nausea and vomiting (PONV) remains a common postoperative complication that causes patient discomfort and increases health care costs. Clinicians use the American Society of PeriAnesthesia Nurses (ASPAN) guideline to help prevent and treat PONV. However, the lack of standardized terminology in the electronic health record (EHR) and the lack of clinical decision support tools make it difficult for clinicians to document guideline implementation and to determine the effects of nursing care on PONV. To address this, we created a concept map of the Perioperative Nursing Data Set (PNDS) that illustrates the relationship between elements of this standardized nursing terminology and the ASPAN guideline, using the Systematized Nomenclature of Medicine-Clinical Terms multidisciplinary terminology to fill any gaps. This mapping results in a standardized dataset specific to PONV for use in an EHR, which links nursing care to nursing diagnoses, interventions, and outcomes. The mapping and documentation in the EHR also allows standardized data collection for research, evaluation, and benchmarking, which makes perioperative nursing care of patients who are at risk for or experiencing PONV measureable and visible. Distributing this information to perioperative and perianesthesia nursing personnel, in addition to implementing risk assessment tools for PONV and clinical support alerts in electronic documentation systems, will help support implementation of the PONV clinical practice guideline in the EHR.