Development Of Decision Support Tool Research Articles

Towards the development of an impact-based decision support tool for surface-transportation hazards. Part II: An Hourly Winter Storm Severity Index

Towards the development of an impact-based decision support tool for surface-transportation hazards. Part II: An Hourly Winter Storm Severity Index

Towards the development of an impact-based decision support tool for surface-transportation hazards. Part I: Tying Weather Variables to Road Hazards and Quantifying Impacts

Towards the development of an impact-based decision support tool for surface-transportation hazards. Part I: Tying Weather Variables to Road Hazards and Quantifying Impacts

Clinical evaluation of a machine learning-based dysphagia risk prediction tool.

The rise of digitization promotes the development of screening and decision support tools. We sought to validate the results from a machine learning based dysphagia risk prediction tool with clinical evaluation. 149 inpatients in the ENT department were evaluated in real time by the risk prediction tool, as well as clinically over a 3-week period. Patients were classified by both as patients at risk/no risk. The AUROC, reflecting the discrimination capability of the algorithm, was 0.97. The accuracy achieved 92.6% given an excellent specificity as well as sensitivity of 98% and 82.4% resp. Higher age, as well as male sex and the diagnosis of oropharyngeal malignancies were found more often in patients at risk of dysphagia. The proposed dysphagia risk prediction tool proved to have an outstanding performance in discriminating risk from no risk patients in a prospective clinical setting. It is likely to be particularly useful in settings where there is a lower incidence of patients with dysphagia and less awareness among staff.

Epidemiological Risk Factors and Modelling Approaches for Risk Assessment of Lumpy Skin Disease Virus Introduction and Spread: Methodological Review and Implications for Risk-Based Surveillance in Australia

Lumpy skin disease (LSD) is a vector-borne infection caused by the poxvirus lumpy skin disease virus (LSDV) and is a serious disease of cattle, water buffalo, and banteng. While the disease has never occurred in Australia, it is regarded as a growing threat to the Australian cattle industry as there is on-going spread of the disease throughout Asia. The development of geospatial decision support tools, such as spatial epidemiological modelling, may assist in assessing areas at greater risk of this threat. To guide the design of disease modelling approaches to support future risk-based surveillance, existing LSDV epidemiological models need to be evaluated. In this study, we performed a literature review to evaluate existing LSDV epidemiological models, identify key risk factors for introduction and spread of LSDV, and consider previously adopted control strategies. The PRISMA guidelines were used to establish the processes for article selection and information extraction, and the PICO process was used to formulate search terms. From studies that met our inclusion criteria, we extracted information on LSDV epidemiological model structure and parameterisation, risk factors for LSDV transmission and spread, and biosecurity control strategies. The literature search retrieved a total of 402 articles from four databases, of which 68 were identified for inclusion in this review following screening. Of the 68 articles reviewed, 47 explored risk factors associated with LSDV transmission and spread, four explored risk factors of LSDV introduction, four explored existing surveillance strategies in LSD-free countries, and 14 presented epidemiological models. Our findings indicate that there are various risk factors for LSDV transmission in LSD endemic countries, including long-distance airborne movement of infected vectors such as stable flies and cattle movement between countries over land borders. Key risk factors for LSDV spread in LSD endemic countries include physical environmental characteristics, weather conditions, and population distributions of livestock and vectors. Our results indicate that while a variety of modelling studies have been conducted, the majority of studies experimentally explored LSD transmission mechanisms in vectors and cattle. Spatial and spatio-temporal models have primarily been developed for LSD endemic countries and focus on the spread of the disease in terms of environmental factors in relation to previous LSD events. There were very few studies on LSD-free countries, and these only focussed on risk of LSD introduction through specific entry pathways. This review did not identify any literature exploring the risk of spread of LSDV following introduction in LSD-free countries or geospatial modelling of the suitability of LSD-free countries for LSDV incursions. In conjunction with the risk parameters and models described in the identified literature, there is need to consider a wide range of risk factors specific to Australia to inform the design of risk-based surveillance for LSD in Australia.

Leveraging artificial intelligence and software engineering methods in epidemiology for the co-creation of decision-support tools based on mechanistic models

Epidemiological modelling is a key lever for infectious disease control and prevention on farms. It makes it possible to understand the spread of pathogens, but also to compare intervention scenarios even in counterfactual situations. However, the actual capability of decision makers to use mechanistic models to support timely interventions is limited. This study demonstrates how artificial intelligence (AI) techniques can make mechanistic epidemiological models more accessible to farmers and veterinarians, and how to transform such models into user-friendly decision-support tools (DST). By leveraging knowledge representation methods, such as the textual formalization of model components through a domain-specific language (DSL), the co-design of mechanistic models and DST becomes more efficient and collaborative. This facilitates the integration of explicit expert knowledge and practical insights into the modelling process. Furthermore, the utilization of AI and software engineering enables the automation of web application generation based on existing mechanistic models. This automation simplifies the development of DST, as tool designers can focus on identifying users' needs and specifying expected features and meaningful presentations of outcomes, instead of wasting time in writing code to wrap models into web apps.To illustrate the practical application of this approach, we consider the example of Bovine Respiratory Disease (BRD), a tough challenge in fattening farms where young beef bulls often develop BRD shortly after being allocated into pens. BRD is a multi-factorial, multi-pathogen disease that is difficult to anticipate and control, often resulting in the massive use of antimicrobials to mitigate its impact on animal health, welfare, and economic losses. The DST developed from an existing mechanistic BRD model empowers users, including farmers and veterinarians, to customize scenarios based on their specific farm conditions. It enables them to anticipate the effects of various pathogens, compare the epidemiological and economic outcomes associated with different farming practices, and decide how to balance the reduction of disease impact and the reduction of antimicrobial usage (AMU).The generic method presented in this article illustrates the potential of artificial intelligence (AI) and software engineering methods to enhance the co-creation of DST based on mechanistic models in veterinary epidemiology. The corresponding pipeline is distributed as an open-source software. By leveraging these advancements, this research aims to bridge the gap between theoretical models and the practical usage of their outcomes on the field.

Whole-cycle management of women with epilepsy of child-bearing age: ontology construction and application.

The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively. The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology's scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers. WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system's utility and effectiveness. Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions.

Optimising decision support tools for the agricultural sector

AbstractSeveral challenges threaten the viability of agriculture in the Mediterranean region, for instance, climate change and variability, land degradation and desertification, environmental and social pressures in rural areas, and the arrangement and extent of properties. These challenges require the attention of farm managers and effective decision-making that can safeguard the natural resource base, enhance resilience and food security, and promote sustainable production landscapes. Decision Support Tools (DSTs) offer valuable assistance in addressing these challenges by improving the decision-making process for both farmers and advisors. They enable data-informed decisions that can enhance the sustainability performance of agricultural businesses in the region. A crucial component of designing a proficient DST is the prompt involvement of stakeholders using a participatory approach to define the needs and requirements of end users. In this study, we engaged twenty-nine stakeholders, including farmers, advisors, extension officers, policy makers, and industry representatives from the Argolida regional unit and the Greek National Ministry of Rural Development and Food. This engagement was aimed at conducting a comprehensive analysis of user needs. To achieve this, we employed the Q-methodology approach to gain a thorough comprehension of the viewpoints and requirements of these diverse stakeholder groups. The results illustrated factors such as the need for user-friendly interfaces, the importance of data accuracy and reliability, the benefit of flexibility and adaptability, and the need for appropriate training and support. These findings can aid the effective development of DSTs so that emerging challenges can be framed in a manner that will facilitate solutions.

Evaluation of an Impulse-Response Emulator for Groundwater Contaminant Transport Modeling.

There is a significant need to develop decision support tools capable of delivering accurate representations of environmental conditions, such as ground and surface water solute concentrations, in a timely and computationally efficient manner. Such tools can be leveraged to assess a large number of potential management strategies for mitigating non-point source pollutants. Here, we assess the effectiveness of the impulse-response emulation approach to approximate process-based groundwater model estimates of solute transport from MODFLOW and MT3D over a wide range of model inputs and parameters, with the goal of assessing where in parameter space the assumptions underlying this emulation approach are valid. The impulse-response emulator was developed using the sensitivity analysis utilities in the PEST++ software suite and is capable of approximating MODFLOW/MT3D estimates of solute transport over a large portion of the parameter space tested, except in cases where the Courant number is above 0.5. Across all runs tested, the highest percent errors were at the plume fronts. These results suggest that the impulse-response approach may be suitable for emulation of solute transport models for a wide range of cases, except when high-resolution outputs are needed, or when very low concentrations at plume edges are of particular interest.

Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance

Rental assistance programs provide individuals with financial assistance to prevent housing instabilities caused by evictions and avert homelessness. Since these programs operate under resource constraints, they must decide who to prioritize. Typically, funding is distributed by a reactive allocation process that does not systematically consider risk of future homelessness. We partnered with Anonymous County (PA) to explore a proactive and preventative allocation approach that prioritizes individuals facing eviction based on their risk of future homelessness. Our ML models, trained on state and county administrative data accurately identify at-risk individuals, outperforming simpler prioritization approaches by at least 20% while meeting our equity and fairness goals across race and gender. Furthermore, our approach would reach 28% of individuals who are overlooked by the current process and end up homeless. Beyond improvements to the rental assistance program in Anonymous County, this study can inform the development of evidence-based decision support tools in similar contexts, including lessons about data needs, model design, evaluation, and field validation.

Design and calibration of a nitrate decision support tool for groundwater wells in Wisconsin, USA

This paper describes development of a nitrate decision support tool for groundwater wells (GW-NDST) that combines nitrate leaching and groundwater lag-times to compute well concentrations. The GW-NDST uses output from support models that simulate leached nitrate, groundwater age distributions, and nitrate reduction rates. The support models are linked through convolution to simulate nitrate transport to wells. Spatially distributed parameters were adjusted through calibration to 34,255 nitrate sample targets. Prediction uncertainty is illustrated via Monte Carlo realizations informed during calibration. Over 78% of target concentrations were within the simulated range of results from 450 realizations. An example forecasting scenario illustrates that a range of feasible outcomes exist and should be considered when interpreting forecasts for decision making. Uncertainty in forecasting is unavoidable; the intent of characterizing uncertainty in the GW-NDST is to facilitate decision making by increasing insight into the response of nitrate contamination to physical and chemical processes.

Analysis of mobile clinic deployments in conflict zones

PurposeThis study aims to deepen the understanding of the challenges and implications entailed by deploying mobile clinics in conflict zones to reach populations affected by violence and cut off from health-care services.Design/methodology/approachThis research combines an integrated literature review and an instrumental case study. The literature review comprises two targeted reviews to provide insights: one on conflict zones and one on mobile clinics. The case study describes the process and challenges faced throughout a mobile clinic deployment during and after the Iraq War. The data was gathered using mixed methods over a two-year period (2017–2018).FindingsArmed conflicts directly impact the populations’ health and access to health care. Mobile clinic deployments are often used and recommended to provide health-care access to vulnerable populations cut off from health-care services. However, there is a dearth of peer-reviewed literature documenting decision support tools for mobile clinic deployments.Originality/valueThis study highlights the gaps in the literature and provides direction for future research to support the development of valuable insights and decision support tools for practitioners.

U.S. cereal rye winter cover crop growth database

Winter cover crop performance metrics (i.e., vegetative biomass quantity and quality) affect ecosystem services provisions, but they vary widely due to differences in agronomic practices, soil properties, and climate. Cereal rye (Secale cereale) is the most common winter cover crop in the United States due to its winter hardiness, low seed cost, and high biomass production. We compiled data on cereal rye winter cover crop performance metrics, agronomic practices, and soil properties across the eastern half of the United States. The dataset includes a total of 5,695 cereal rye biomass observations across 208 site-years between 2001–2022 and encompasses a wide range of agronomic, soils, and climate conditions. Cereal rye biomass values had a mean of 3,428 kg ha−1, a median of 2,458 kg ha−1, and a standard deviation of 3,163 kg ha−1. The data can be used for empirical analyses, to calibrate, validate, and evaluate process-based models, and to develop decision support tools for management and policy decisions.

P624 Development and Validation of a Clinical Decision Support Tool to Predict Outcomes of ustekinumab Treatment in Patients with Crohn’s Disease

Abstract Background Ustekinumab (UST) has been demonstrated for the treatment of Crohn's disease (CD) with a high efficacy and safety profile. There is limited data concerning disease progression after UST treatment. This study aims to establish a clinical decision support tool (CDST) to identify Chinese patients with CD who are less likely to experience disease progression under UST treatment. Methods A multicentre, retrospective observational study was conducted among Chinese patients with CD initiating UST between 1 May 2020 and 20 October 2022. Disease progression (including CD-related surgery, hospitalization and complications) was evaluated at week 24 (±4w). Logistic regression analysis was performed to identify factors associated with disease progression. The CDST, established based on these factors, was both internally and externally validated. The performance of this tool was tested using receiver operating characteristic (ROC) area under the curve (AUC) analyses. Results A total of 519 patients were enrolled in this study. In the derivation analysis, four factors including prior biologics usage, prior immunosuppressant usage, baseline faecal calprotectin, and baseline haemoglobin were identified to correlate with progression. A good predictive ability of this model was confirmed by AUC as high as 0.76 for the internal validation cohort and 0.72 for the external validation cohort, respectively. The CDST stratified patients into low, intermediate, or high risk of progression with cut-off values of 6 and 16. Conclusion We developed and validated a scoring system to identify CD patients less likely to experience progression at week 24 with UST therapy. This CDST will facilitate therapy decision-making and precision medicine.

Assessing Decision Support Tools for Mitigating Tail Biting in Pork Production: Current Progress and Future Directions.

Tail biting (TB) in pigs is a complex issue that can be caused by multiple factors, making it difficult to determine the exact etiology on a case-by-case basis. As such, it is often difficult to pinpoint the reason, or set of reasons, for TB events, Decision Support Tools (DSTs) can be used to identify possible risk factors of TB on farms and provide suitable courses of action. The aim of this review was to identify DSTs that could be used to predict the risk of TB behavior. Additionally, technologies that can be used to support DSTs, with monitoring and tracking the prevalence of TB behaviors, are reviewed. Using the PRISMA methodology to identify sources, the applied selection process found nine DSTs related to TB in pigs. All support tools relied on secondary information, either by way of the scientific literature or expert opinions, to determine risk factors for TB predictions. Only one DST was validated by external sources, seven were self-assessed by original developers, and one presented no evidence of validation. This analysis better understands the limitations of DSTs and highlights an opportunity for the development of DSTs that rely on objective data derived from the environment, animals, and humans simultaneously to predict TB risks. Moreover, an opportunity exists for the incorporation of monitoring technologies for TB detection into a DST.

Patient factors associated with survival after critical care interhospital transfer

ObjectiveTo identify the factors that predict mortality post-transfer and develop a comprehensive mortality prediction model capable of supporting pre-transfer decision making.Materials and methodsElectronic health record data from the Medical Transport Data Repository of a large health system hospital in Northeast Ohio that consists of a main campus and 11 affiliated medical centers. We retrospectively analyzed patient data from the referring hospital encounter prior to interhospital transfer. All patient data including diagnoses, laboratory results, medication, and medical and social history were analyzed to predict in-hospital mortality post-transfer. We employed a multi-method approach including logistic regression, gradient boosting, and multiple correspondence analysis to identify significant predictors of mortality as well as variables that are clinically useful to inform clinical decision support development. We identified all patients aged 21 and older that underwent critical care transfer in the health system between 2010 and 2017.ResultsWe found that age, laboratory results (albumin, INR, platelets, BUN, leukocyte, hemoglobin, glucose), vital signs (temperature, respirations, pulse, systolic blood pressure, pulse oximetry), and ventilator usage are the most predictive variables of post-interhospital transfer mortality. Using structured data from the EHR we achieved the same performance as APACHE IV within our health system (0.85 vs. 0.85). Lastly, mode of transport alone was not a significant predictor for the general population in any of the outcome models.ConclusionsOur findings provide a foundation for the development of decision support tools to guide transport referrals and identified the need for further inquiry to discern the role of mode of transport to enable future inclusion in decision support approaches. Further inquiry is needed to identify factors that differentiate patients not triaged as time-sensitive transfers but still require helicopter intervention to maintain or improve post-interhospital transfer morbidity and mortality.

Application of a user experience design approach for an EHR-based clinical decision support system.

We applied a user experience (UX) design approach to clinical decision support (CDS) tool development for the specific use case of pediatric asthma. Our objective was to understand physicians' workflows, decision-making processes, barriers (ie, pain points), and facilitators to increase usability of the tool. We used a mixed-methods approach with semi-structured interviews and surveys. The coded interviews were synthesized into physician-user journey maps (ie, visualization of a process to accomplish goals) and personas (ie, user types). Interviews were conducted via video. We developed physician journey maps and user personas informed by their goals, systems interactions, and experiences with pediatric asthma management. The physician end-user personas identified were: efficiency, relationship, and learning. Features of a potential asthma CDS tool sought varied by physician practice type and persona. It was important to the physician end-user that the asthma CDS tool demonstrate value by lowering workflow friction (ie, difficulty or obstacles), improving the environment surrounding physicians and patients, and using it as a teaching tool. Customizability versus standardization were important considerations for uptake. Different values and motivations of physicians influence their use and interaction with the EHR and CDS tools. These different perspectives can be captured by applying a UX design approach to the development process. For example, with the importance of customizability, one approach may be to build a core module with variations depending on end-user preference. A UX approach can drive design to help understand physician-users and meet their needs; ultimately with the goal of increased uptake.

Identifying Key Drivers for a National Transition to Low Carbon Energy using Agent-based Supply Chain Models

Understanding robust pathways to achieve affordable, reliable, and equitable energy transitions to a decarbonized society has received growing attention. While existing energy transition literature emphasizes the role of government policy and technological innovation, little has been written to integrate “top-down” approaches encompassing government policy and “bottom-up” individual enterprise-level decisions. Therefore, here we develop a hybrid top-down, bottom-up production consumption model towards intra-national energy transition planning using a decentralized agent-based approach, including a detailed geographic coverage utilizing GIS to map individual energy units. This enables considering SC-level dynamics of end-users pursuing economic objectives subject to regulatory constraints. Two different case studies are reported. The first study reveals that green hydrogen consumption in the Indian state of Tamil Nadu may grow only up to 3% by 2042 from the current nil usage, even with aggressive technological cost reductions and a fixed carbon price of $20/ton CO2, but demand could grow more rapidly if stringent criteria pollutant emissions limits are imposed. In the second case study, which considers natural gas usage across India, the power and city gas sectors are found to be the consistent demand drivers, with their share rising from ∼40% of the total in 2022 to ∼60% by 2032. This is consistent with IEA and IRENA projections. The proposed agent-based model thus provides multiple benefits: It enables the identification of key sectors driving the energy transition; it helps analyze critical factors like resource availability, supply chain infrastructure, and economics; and it can be used to develop decision support tools to investigate clean energy policies.

INNOVATING HOME-BASED CARE THROUGH HOME HEALTH DISCHARGE DECISION SUPPORT (HEADS-UP)

Abstract Medicare and private payers rely upon home health (HH) nurses and physicians to evaluate the needs of older adults and decide when ready for discharge. However, there are no national, empirically derived clinical decision support tools to assist clinicians in making these frequent and important discharge decisions. The purpose of this work was to begin development of a clinical decision support tool to support HH clinician decision-making grounded in the evidence and based in real time. Clinician experts, in teams of three: two nurses and one physician reviewed and judged 500 case studies via a secure, dedicated website, making the decision if the older adults should be discharged from services versus recertified for additional care. Our sample matches the national older adult HH population by gender, race and primary diagnosis. The clinician experts recommended over 44.6% of older adults be recertified for more care when in reality, HH clinicians recertified only 22% for additional care. Regardless of insurance status, Blacks were 1.75 times more likely than Whites to be discharged by HH clinicians even though experts recommended more care (p=0.031). Hispanics trended toward being more likely to be discharged by HH clinicians compared to the experts’ recommendation for more care (p=0.094). Our findings indicate that when provided with organized clinical information and ample time to accurately identify the needs of older adults, expert clinicians refer for additional care significantly more often than real-world HH clinicians and that racial disparities exist in current HH discharge decision making.

Microalgae Production on Biogas Digestate in Sub-Alpine Region of Europe—Development of Simple Management Decision Support Tool

In a one-and-a-half-year study conducted in the ALS6 region in Europe (Ljubljana, Slovenia), the cultivation of microalgae in anaerobic digestate from food waste, mainly Scenedesmus dimorphus and Scenedesmus quadricauda, was investigated in three ponds (1260 L each) under a greenhouse. The effects of changing digestate quality and quantity as well as seasonal fluctuations on the productivity of the microalgae were investigated in three stages: Learning/Design (SI), Testing (SII), and Verification/Calibration (SIII). A decision support tool (DST) was developed using easy-to-measure parameters such as pH, temperature, electrical conductivity, mineral nitrogen forms and physical, biological parameters (OD, delayed fluorescence intensity). To control optimal pond operation, we proposed the photosynthetic culture index (PCI) as an early indicator for necessary interventions. Flocculation and nitrite levels (above 3 mg NO2-N L−1) were signals for the immediate remediation of the algae culture. Under optimal conditions in summer SIII, an average algal biomass production of 11 ± 1.5 g m−2 day−1 and a nitrogen use efficiency of 28 ± 2.6 g biomass/g N-input were achieved with the developed DST. The developed DST tool was, in this study, successfully implemented and used for the cultivation of microalgae consortia predominated by Scenedesmus dimorphus and S. quadricauda with biogas digestate. DST offers the possibility to be modified according to producers’ specific needs, facility, digestate and climate conditions, and as such, could be used for different microalgae cultivation processes with biogas digestate as a food source.

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review.

Electronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research. The objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences. We used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results. We present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized. The Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.