ASL expression in ALDH1A1+ neurons in the substantia nigra metabolically contributes to neurodegenerative phenotype.

Argininosuccinate lyase (ASL) is essential for the NO-dependent regulation of tyrosine hydroxylase (TH) and thus for catecholamine production. Using a conditional mouse model with loss of ASL in catecholamine neurons, we demonstrate that ASL is expressed in dopaminergic neurons in the substantia nigra pars compacta, including the ALDH1A1 + subpopulation that is pivotal for the pathogenesis of Parkinson disease (PD). Neuronal loss of ASL results in catecholamine deficiency, in accumulation and formation of tyrosine aggregates, in elevation of α-synuclein, and phenotypically in motor and cognitive deficits. NO supplementation rescues the formation of aggregates as well as the motor deficiencies. Our data point to a potential metabolic link between accumulations of tyrosine and seeding of pathological aggregates in neurons as initiators for the pathological processes involved in neurodegeneration. Hence, interventions in tyrosine metabolism via regulation of NO levels may be therapeutic beneficial for the treatment of catecholamine-related neurodegenerative disorders.

Optimization of Nursing-Specific Flu Alerts.

Clinical decision support interventions, such as alerts and reminders, can improve clinician compliance with practice guidelines and patient outcomes. Alerts that trigger at inappropriate times are often dismissed by clinicians, reducing desired actions rather than increasing them. A set of nursing-specific alerts related to influenza screening and vaccination were optimized so that they would "trigger" less often but function adequately to maintain institutional flu vaccination compliance. We analyzed the current triggering criteria for six flu vaccine-related alerts and asked nurse end users for suggestions to increase specificity. Using the "five rights" (of clinical decision support) as a framework, alerts were redesigned to address user needs. New alerts were tested and implemented and their activity compared in two different flu seasons, preoptimization and postoptimization. The redesigned alerts resulted in fewer alerts per encounter (P < .0001), less dismissals of alerts (P < .0001), and a 2.8% point improvement in compliance rates for flu vaccine screening, documentation, and administration. A focus group confirmed that the redesign improved workflow, but some nurses thought they still triggered too often. The five rights model can support improvements in alert design and outcomes.

Improving the Documentation Process for Referrals into Diabetes Education: A Quality Improvement Project.

The purpose of this project was to standardize the referral and documentation process for diabetes education in patients with type 2 diabetes. The goal was to refer all eligible patients with type 2 diabetes into diabetes education. A standardized template within the clinical note was created to capture if the patients had ever received diabetes education. Use of this template by the clinician improved documentation about diabetes education and increased referrals into diabetes education programs. The findings of this project can be applied to other primary care clinics to help increase the historically low referrals rates into diabetes education.

Big data science: A literature review of nursing research exemplars.

BACKGROUND: Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge. PURPOSE: The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals. METHODS: A literature review of studies published between 2009 and 2015. There were 650 journal articles identified in 17 key nursing informatics, general biomedical informatics, and nursing research journals in the Web of Science database. After screening for inclusion and exclusion criteria, 17 studies published in 18 articles were identified as big data nursing research applied to practice. DISCUSSION: Nurses clearly are beginning to conduct big data research applied to practice. These studies represent multiple data sources and settings. Although numerous analytic methods were used, the fundamental issue remains to define the types of analyses consistent with big data analytic methods. CONCLUSION: There are needs to increase the visibility of big data and data science research conducted by nurse scientists, further examine the use of state of the science in data analytics, and continue to expand the availability and use of a variety of scientific, governmental, and industry data resources. A major implication of this literature review is whether nursing faculty and preparation of future scientists (PhD programs) are prepared for big data and data science.

Clinical Decision Support for Nurses: A Fall Risk and Prevention Example.

Clinical decision support tools in electronic health records have demonstrated improvement with process measures and clinician performance, predominantly for providers. Clinical decision support tools could improve patient fall risk identification and prevention plans, a common concern for nursing. This quality-improvement project used clinical decision support to improve the rate of nurse compliance with documented fall risk assessments and, for patients at high risk, fall prevention plans of care in 16 adult inpatient units. Preintervention and postintervention data were compared using quarterly audits, retrospective chart review, safety reports, and falls and falls-with-injury rates. Documentation of fall risk assessments on the 16 units improved significantly according to quarterly audit data (P = .05), whereas documentation of the plans of care did not. Retrospective chart review on two units indicated improvement for admission fall risk assessment (P = .05) and a decrease in the documentation of the shift plan of care (P = .01); one unit had a statistically significant decrease in documentation of plans of care on admission (P = .00). Examination of safety reports for patients who fell showed all patients before and after clinical decision support had fall risk assessments documented. Falls and falls with injury did not change significantly before and after clinical decision support intervention.

The informational needs of patients with ANCA-associated vasculitis-development of an informational needs questionnaire.

OBJECTIVE: The aim of the study was to compare the informational needs of patients with ANCA-associated vasculitis (AAV). METHODS: We developed a Vasculitis Informational Needs Questionnaire that was distributed to members of Vasculitis UK (VUK) by mail and registrants of the Vasculitis Clinical Research Consortium (VCRC) online registry with self-reported AAV. Patients were asked to use a 5-point scale (1 = not important, 5 = extremely important) to rank aspects of information in the following domains: disease, investigations, medication, disease management and psychosocial care. The source and preferred method of educational delivery were recorded. RESULTS: There were 314 VUK and 273 VCRC respondents. Respondents rated information on diagnosis, prognosis, investigations, treatment and side effects as extremely important. Information on patient support groups and psychosocial care was less important. There was no difference in the ratings of needs based on group, sex, age, disease duration, disease or method of questionnaire delivery. The most-preferred methods of providing information for both groups were by a doctor (with or without written material) or web based; educational courses and compact disc/digital video disc (CD/DVD) were the least-preferred methods. CONCLUSION: This study demonstrates that people with AAV seek specific information concerning their disease, treatment regimes and side effects and the results of investigations. Individuals preferred to receive this information from a doctor. Patients with AAV should be treated in a similar manner to patients with other chronic illnesses in which patient education is a fundamental part of care.

An informatics framework for the standardized collection and analysis of medication data in networked research.

Medication exposure is an important variable in virtually all clinical research, yet there is great variation in how the data are collected, coded, and analyzed. Coding and classification systems for medication data are heterogeneous in structure, and there is little guidance for implementing them, especially in large research networks and multi-site trials. Current practices for handling medication data in clinical trials have emerged from the requirements and limitations of paper-based data collection, but there are now many electronic tools to enable the collection and analysis of medication data. This paper reviews approaches to coding medication data in multi-site research contexts, and proposes a framework for the classification, reporting, and analysis of medication data. The framework can be used to develop tools for classifying medications in coded data sets to support context appropriate, explicit, and reproducible data analyses by researchers and secondary users in virtually all clinical research domains.

Collecting patient-reported outcome measures in the electronic health record: Lessons from the NIH pragmatic trials Collaboratory.

The NIH Pragmatic Trials Collaboratory supports the design and conduct of 27 embedded pragmatic clinical trials, and many of the studies collect patient reported outcome measures as primary or secondary outcomes. Study teams have encountered challenges in the collection of these measures, including challenges related to competing health care system priorities, clinician's buy-in for adoption of patient-reported outcome measures, low adoption and reach of technology in low resource settings, and lack of consensus and standardization of patient-reported outcome measure selection and administration in the electronic health record. In this article, we share case examples and lessons learned, and suggest that, when using patient-reported outcome measures for embedded pragmatic clinical trials, investigators must make important decisions about whether to use data collected from the participating health system's electronic health record, integrate externally collected patient-reported outcome data into the electronic health record, or collect these data in separate systems for their studies.

Evolving a national clinical trials learning health system.

Clinical trials generate key evidence to inform decision making, and also benefit participants directly. However, clinical trials frequently fail, often struggle to enroll participants, and are expensive. Part of the problem with trial conduct may be the disconnected nature of clinical trials, preventing rapid data sharing, generation of insights and targeted improvement interventions, and identification of knowledge gaps. In other areas of healthcare, a learning health system (LHS) has been proposed as a model to facilitate continuous learning and improvement. We propose that an LHS approach could greatly benefit clinical trials, allowing for continuous improvements to trial conduct and efficiency. A robust trial data sharing system, continuous analysis of trial enrollment and other success metrics, and development of targeted trial improvement interventions are potentially key components of a Trials LHS reflecting the learning cycle and allowing for continuous trial improvement. Through the development and use of a Trials LHS, clinical trials could be treated as a system, producing benefits to patients, advancing care, and decreasing costs for stakeholders.

Guiding principles for technical infrastructure to support computable biomedical knowledge.

Over the past 4 years, the authors have participated as members of the Mobilizing Computable Biomedical Knowledge Technical Infrastructure working group and focused on conceptualizing the infrastructure required to use computable biomedical knowledge. Here, we summarize our thoughts and lay the foundation for future work in the development of CBK infrastructure, including: explaining the difference between computable knowledge and data, and contextualizing the conversation with the Learning Health Systems and the FAIR principles. Specifically, we provide three guiding principles to advance the development of CBK infrastructure: (a) Promote interoperable systems for data and knowledge to be findable, accessible, interoperable, and reusable. (b) Enable stable, trustworthy knowledge representations that are human and machine readable. (c) Computable knowledge resources should, when possible, be open. Standards supporting computable knowledge infrastructures must be open.

Characterizing variability of electronic health record-driven phenotype definitions.

OBJECTIVE: The aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used. MATERIALS AND METHODS: A sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries. RESULTS: Most of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27. DISCUSSION: Despite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints. CONCLUSIONS: The phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.

Equity and bias in electronic health records data.

Embedded pragmatic clinical trials (ePCTs) are conducted during routine clinical care and have the potential to increase knowledge about the effectiveness of interventions under real world conditions. However, many pragmatic trials rely on data from the electronic health record (EHR) data, which are subject to bias from incomplete data, poor data quality, lack of representation from people who are medically underserved, and implicit bias in EHR design. This commentary examines how the use of EHR data might exacerbate bias and potentially increase health inequities. We offer recommendations for how to increase generalizability of ePCT results and begin to mitigate bias to promote health equity.

Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory.

Embedded pragmatic clinical trials (ePCTs) play a vital role in addressing current population health problems, and their use of electronic health record (EHR) systems promises efficiencies that will increase the speed and volume of relevant and generalizable research. However, as the number of ePCTs using EHR-derived data grows, so does the risk that research will become more vulnerable to biases due to differences in data capture and access to care for different subsets of the population, thereby propagating inequities in health and the healthcare system. We identify 3 challenges-incomplete and variable capture of data on social determinants of health, lack of representation of vulnerable populations that do not access or receive treatment, and data loss due to variable use of technology-that exacerbate bias when working with EHR data and offer recommendations and examples of ways to actively mitigate bias.

Development of a standards-based phenotype model for gross motor function to support learning health systems in pediatric rehabilitation.

INTRODUCTION: Research and continuous quality improvement in pediatric rehabilitation settings require standardized data and a systematic approach to use these data. METHODS: We systematically examined pediatric data concepts from a pediatric learning network to determine capacity for capturing gross motor function (GMF) for children with Cerebral Palsy (CP) as a demonstration for enabling infrastructure for research and quality improvement activities of an LHS. We used an iterative approach to construct phenotype models of GMF from standardized data element concepts based on case definitions from the Gross Motor Function Classification System (GMFCS). Data concepts were selected using a theory and expert-informed process and resulted in the construction of four phenotype models of GMF: an overall model and three classes corresponding to deviations in GMF for CP populations. RESULTS: Sixty five data element concepts were identified for the overall GMF phenotype model. The 65 data elements correspond to 20 variables and logic statements that instantiate membership into one of three clinically meaningful classes of GMF. Data element concepts and variables are organized into five domains relevant to modeling GMF: Neurologic Function, Mobility Performance, Activity Performance, Motor Performance, and Device Use. CONCLUSION: Our experience provides an approach for organizations to leverage existing data for care improvement and research in other conditions. This is the first consensus-based and theory-driven specification of data elements and logic to support identification and labeling of GMF in patients for measuring improvements in care or the impact of new treatments. More research is needed to validate this phenotype model and the extent that these data differentiate between classes of GMF to support various LHS activities.

Evaluating Completeness of Discrete Data on Physical Functioning for Children With Cerebral Palsy in a Pediatric Rehabilitation Learning Health System.

OBJECTIVE: The purpose of this study was to determine the extent that physical function discrete data elements (DDE) documented in electronic health records (EHR) are complete within pediatric rehabilitation settings. METHODS: A descriptive analysis on completeness of EHR-based DDEs detailing physical functioning for children with cerebral palsy was conducted. Data from an existing pediatric rehabilitation research learning health system data network, consisting of EHR data from 20 care sites in a pediatric specialty health care system, were leveraged. Completeness was calculated for unique data elements, unique outpatient visits, and unique outpatient records. RESULTS: Completeness of physical function DDEs was low across 5766 outpatient records (10.5%, approximately 2 DDEs documented). The DDE for Gross Motor Function Classification System level was available for 21% (n = 3746) outpatient visits and 38% of patient records. Ambulation level was the most frequently documented DDE. Intercept only mixed effects models demonstrated that 21.4% and 45% of the variance in completeness for DDEs and the Gross Motor Function Classification System, respectively, across unique patient records could be attributed to factors at the individual care site level. CONCLUSION: Values of physical function DDEs are missing in designated fields of the EHR infrastructure for pediatric rehabilitation providers. Although completeness appears limited for these DDEs, our observations indicate that data are not missing at random and may be influenced by system-level standards in clinical documentation practices between providers and factors specific to individual care sites. The extent of missing data has significant implications for pediatric rehabilitation quality measurement. More research is needed to understand why discrete data are missing in EHRs and to further elucidate the professional and system-level factors that influence completeness and missingness. IMPACT: Completeness of DDEs reported in this study is limited and presents a significant opportunity to improve documentation and standards to optimize EHR data for learning health system research and quality measurement in pediatric rehabilitation settings.

Informatics education for translational research teams: An unrealized opportunity to strengthen the national research infrastructure.

Objective: To identify the informatics educational needs of clinical and translational research professionals whose primary focus is not informatics. Introduction: Informatics and data science skills are essential for the full spectrum of translational research, and an increased understanding of informatics issues on the part of translational researchers can alleviate the demand for informaticians and enable more productive collaborations when informaticians are involved. Identifying the level of interest in different topics among various types of of translational researchers will help set priorities for development and dissemination of informatics education. Methods: We surveyed clinical and translational science researchers in Clinical and Translational Science Award (CTSA) programs about their educational needs and preferences. Results: Researchers from 23 out of the 62 CTSA hubs responded to the survey. 67% of respondents across roles and topics expressed interest in learning about informatics topics. There was high interest in all 30 topics included in the survey, with some variation in interest depending on the role of the respondents. Discussion: Our data support the need to advance training in clinical and biomedical informatics. As the complexity and use of information technology and data science in research studies grows, informaticians will continue to be a limited resource for research collaboration, education, and training. An increased understanding of informatics issues across translational research teams can alleviate this burden and allow for more productive collaborations. To inform a roadmap for informatics education for research professionals, we suggest strategies to use the results of this needs assessment to develop future informatics education.

Summary of fourth annual MCBK public meeting: Mobilizing computable biomedical knowledge-metadata and trust.

The exponential growth of biomedical knowledge in computable formats challenges organizations to consider mobilizing artifacts in findable, accessible, interoperable, reusable, and trustable (FAIR+T) ways1. There is a growing need to apply biomedical knowledge artifacts to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations lack the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are insufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Fourth Annual MCBK public meeting, which was held virtually July 20 to July 21, 2021 and convened over 100 participants spanning diverse domains to frame and address important dimensions for mobilizing CBK.

An interactive voice response diary for patients with non-dystrophic myotonia.

INTRODUCTION: Non-dystrophic myotonia (NDM) is caused by mutations in muscle chloride and sodium channels. Currently, there is no standardized instrument for documenting symptom frequency and severity in NDM. METHODS: Subjects used an automated, interactive, telephone-based voice response diary (IVR) to record frequency and severity of stiffness, weakness, pain, and tiredness once a week for 8 weeks, after their baseline visits. RESULTS: We describe the IVR and report data on 76 subjects for a total of 385 person-weeks. Overall there were 5.1 calls per subject. Forty-eight subjects called in 5 or more times, and 14 called in 8 times. Stiffness was both the most frequent and severe symptom. Warm-up and handgrip myotonia were associated with higher severity scores for stiffness. CONCLUSIONS: IVR is a convenient technology to allow patient reporting of repeated and real-time symptom frequency and severity, and it is presently being used in a trial of mexiletine in NDM.

Association between evidence-based training and clinician proficiency in electronic health record use.

OBJECTIVES: The purpose of the study was to determine if association exists between evidence-based provider training and clinician proficiency in electronic health record (EHR) use and if so, which EHR use metrics and vendor-defined indices exhibited association. MATERIALS AND METHODS: We studied ambulatory clinicians' EHR use data published in the Epic Systems Signal report to assess proficiency between training participants (n = 133) and nonparticipants (n = 14). Data were collected in May 2019 and November 2019 on nonsurgeon clinicians from 6 primary care, 7 urgent care, and 27 specialty care clinics. EHR use training occurred from August 5 to August 15, 2019, prior to EHR upgrade and organizational instance alignment. Analytics performed were descriptive statistics, paired t-tests, multivariate correlations, and hierarchal multiple regression. RESULTS: For number of appointments per 30-day reporting period, trained clinicians sustained an average increase of 16 appointments (P < .05), whereas nontrained clinicians incurred a decrease of 8 appointments. Only the trained clinician group achieved postevent improvement in the vendor-defined Proficiency score with an effect size characterized as moderate to large (dCohen = 0.625). DISCUSSION: Controversies exist on the return of investment from formal EHR training for clinician users. Previously published literature has mostly focused on qualitative data indicators of EHR training success. The findings of our EHR use training study identified EHR use metrics and vendor-defined indices with the capacity for translation into productivity and generated revenue measurements. CONCLUSIONS: One EHR use metric and 1 vendor-defined index indicated improved proficiency among trained clinicians.