Electronic Health Records Standards Research Articles

Validating claims-based definitions for deprescribing: Bridging the gap between clinical and administrative data.

Limited research has evaluated the validity of claims-based definitions for deprescribing. Evaluate the validity of claims-based definitions of deprescribing against electronic health records (EHRs) for deprescribing of benzodiazepines (BZDs) after a fall-related hospitalization. We used a novel data linkage between Medicare fee-for-service (FFS) and Part D with our health system's EHR. We identified patients aged ≥66 years with a fall-related hospitalization, continuous enrollment in Medicare FFS and Part D for 6 months pre- and post-hospitalization, and ≥2 BZD fills in the 6 months pre-hospitalization. Using a standardized EHR abstraction tool, we adjudicated deprescribing for a sub-sample with a fall-related hospitalization at UNC. We evaluated the validity of claims-based deprescribing definitions (e.g., gaps in supply, dosage reductions) versus chart review using sensitivity and specificity. Among 257 patients in the overall sample, 44% were aged 66-74 years, 35% had Medicare low-income subsidy, 79% were female. Among claims-based definitions using gaps in supply, the prevalence of BZD deprescribing ranged from 8.2% (no refills) to 36.6% (30-day gap). When incorporating dosage, the prevalence ranged from 55.3% to 65.8%. Among the validation sub-sample (n = 47), approximately one-third had BZDs deprescribed in the EHR. Compared to EHR, gaps in supply from claims had good sensitivity, but poor specificity. Incorporating dosage increased sensitivity, but worsened specificity. The sensitivity of claims-based definitions for deprescribing of BZDs was low; however, the specificity of a 90-day gap was >90%. Replication in other EHRs and for other low-value medications is needed to guide future deprescribing research.

Defining Incidental Versus Non-incidental COVID-19 Hospitalizations.

Background Rates of COVID-19 hospitalization are an important measure of the health system burden of severe COVID-19 disease and have been closely followed throughout the pandemic. The highly transmittable, but often less severe, Omicron COVID-19 variant has led to an increase in hospitalizations with incidental COVID-19 diagnoses where COVID-19 is not the primary reason for admission. There is a strong public health need for a measure that is implementable at low cost with standard electronic health record (EHR) datasetsthat can separate these incidental hospitalizations from non-incidental hospitalizations where COVID-19 is the primary cause or an important contributor. Two crude metrics are in common use. The first uses in-hospital administration of dexamethasone as a marker of non-incidental COVID-19 hospitalizations. The second, used by the United States (US) CDC, relies on a limited set of COVID-19-related diagnoses (i.e., respiratory failure, pneumonia). Both measures likely undercount non-incidental COVID-19 hospitalizations. We therefore developed an improved EHR-based measurethat is better able to capture the full range of COVID-19 hospitalizations. Methods We conducted a retrospective study of EDvisit data from a national emergency medicine group from April 2020 to August 2023. We assessed the CDC approach, the dexamethasone-based measure, and alternative approaches that rely on co-diagnoses likely to be related to COVID-19, to determine the proportion of non-incidental COVID-19 hospitalizations. Results Of the 153,325 patients diagnosed with COVID-19 at 112 general EDs in 17 US states, and admitted or transferred, our preferred measure classified 108,243 (70.6%) as non-incidental, compared to 71,066 (46.3%) using the dexamethasone measure and 77,399 (50.5%) using the CDC measure. Conclusions Identifying non-incidental COVID-19 hospitalizations using ED administration of dexamethasone or the CDC measure provides substantially lower estimates than our preferred measure.

Can OpenEHR, ISO 13606, and HL7 FHIR Work Together? An Agnostic Approach for the Selection and Application of Electronic Health Record Standards to the Next-Generation Health Data Spaces.

In order to maximize the value of electronic health records (EHRs) for both health care and secondary use, it is necessary for the data to be interoperable and reusable without loss of the original meaning and context, in accordance with the findable, accessible, interoperable, and reusable (FAIR) principles. To achieve this, it is essential for health data platforms to incorporate standards that facilitate addressing needs such as formal modeling of clinical knowledge (health domain concepts) as well as the harmonized persistence, query, and exchange of data across different information systems and organizations. However, the selection of these specifications has not been consistent across the different health data initiatives, often applying standards to address needs for which they were not originally designed. This issue is essential in the current scenario of implementing the European Health Data Space, which advocates harmonization, interoperability, and reuse of data without regulating the specific standards to be applied for this purpose. Therefore, this viewpoint aims to establish a coherent, agnostic, and homogeneous framework for the use of the most impactful EHR standards in the new-generation health data spaces: OpenEHR, International Organization for Standardization (ISO) 13606, and Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR). Thus, a panel of EHR standards experts has discussed several critical points to reach a consensus that will serve decision-making teams in health data platform projects who may not be experts in these EHR standards. It was concluded that these specifications possess different capabilities related to modeling, flexibility, and implementation resources. Because of this, in the design of future data platforms, these standards must be applied based on the specific needs they were designed for, being likewise fully compatible with their combined functional and technical implementation.

Subspecialty Clinic Scheduling for Emergency Room Follow-ups: A Focus on Provider Satisfaction.

Inaccurate patient scheduling disrupts the continuity of care between patient and provider, diminishing satisfaction. One clinic had 19% of patients scheduled incorrectly after follow-up from emergency room visits. The purpose of this project was to reduce the number of incorrectly scheduled patients following up from emergency room visits that required orthopaedic consultations from advanced practice providers (APPs) and monitor the impact the change process had on APP job satisfaction. The design was nonexperimental, did not have a control group for comparison, and used standardized electronic health record documentation. A pretest and posttest design was used for the job satisfaction survey. Postimplementation data showed patients were correctly scheduled 97% of the time. Provider satisfaction improved from 2.375 to 3.125 on a 0- to 5-point scale as it pertains to clinic scheduling. Organizations can benefit from the use of standardized communication documentation to ensure proper follow-up for patients, improve continuity of care, and positively impact provider satisfaction.

Documentation of Disability Status and Accommodation Needs in the Electronic Health Record: A Qualitative Study of Health Care Organizations’ Current Practices

This qualitative study aimed to understand how early adopting health care organizations (HCOs) implement the documentation of patients' disability status and accommodation needs in the electronic health record (EHR). The authors conducted qualitative interviews with HCOs that had active or past initiatives to implement systematic collection of disability status in the EHR. The interviews elicited participants' current experiences, desired features of a standard EHR build, and challenges and successes. A team-based analysis approach was used to review and summarize quotations to identify themes and categorize text that exemplified identified themes. Themes identified from the interviews included "why" organizations collected disability status; of "what" their EHR build consisted, including who collected, how often data were collected, and what data were collected; and "how" organizations were implementing systematic collection. The main purpose for collection of disability status and accommodation needs was to prepare for patients with disabilities. Due to this priority, participants believed collection should (1) occur prior to patients' clinical encounters, (2) be conducted regularly, (3) use standardized language, and (4) be available in a highly visible location in the EHR. Leadership support to integrate collection into existing workflows was essential for success. Patients with disabilities experience significant disparities in the receipt of equitable health care services. To provide equitable care, HCOs need to systematically collect disability status and accommodation needs in the EHR to ensure that they are prepared to provide equitable care to all patients with disabilities.

Improving Seizure Frequency Documentation and Classification.

Accurate and reliable seizure data are essential for evaluating treatment strategies and tracking the quality of care in epilepsy clinics. This quality improvement project aimed to increase seizure documentation (i.e., documentation of seizure frequency from 80% to 100%, date of last seizure from 35% to 50%, and International League Against Epilepsy (ILAE) seizure classification from 35% to at least 50%) over 6 months. We surveyed 7 epileptologists to determine their perceived seizure frequency, ILAE classification, and date of last seizure documentation habits. Baseline data were collected weekly from September to December 2021. Subsequently, we implemented a newly created flowsheet in our Electronic Health Record (EHR) based on the Epilepsy Learning Healthcare System (ELHS) Case Report Forms to increase seizure documentation in a standardized way. Two epileptologists tested this flowsheet tool in their epilepsy clinics between February 2022 and July 2022. Data were collected weekly and compared with documentation from other epileptologists within the same group. Epileptologists at our center believed they documented seizure frequency for 84%-87% of clinic visits, which aligned with baseline data collection, showing they recorded seizure frequency for 83% of clinic visits. Epileptologists believed they documented ILAE classification for 47%-52% of clinic visits, and baseline data showed this was documented in 33% of clinic visits. They also reported documenting the date of the last seizure for 52%-63% of clinic visits, but this occurred in only 35% of clinic visits. After implementing the new flowsheet, documentation increased to nearly 100% for all fields being completed by the providers who tested the flowsheet. We demonstrated that by implementing an easy-to-use standardized EHR documentation tool, our documentation of critical metrics, as defined by the ELHS, improved dramatically. This shows that simple and practical interventions can substantially improve clinically meaningful documentation.

Sustainability of an Operating Room to Pediatric Postanesthesia Care Unit Handoff Tool

The purpose of this quality improvement (QI) project was to reintroduce and assess the feasibility of a standardized, electronic health record (EHR) handoff tool and to evaluate the sustainability of a structured, team-based approach in a pediatric postanesthesia care unit (PACU). This QI project used an observational pre-post design using two separate convenience samples of handoffs and perianesthesia providers. A standardized EHR handoff tool was reintroduced for operating room to pediatric PACU handoff communication. Handoffs between anesthesia providers, surgery team members, and PACU nurses were observed pre- and postreintroduction of the EHR handoff tool. Anesthesia providers and PACU RNs received training for giving and receiving handoffs and were provided directions on locating the EHR handoff tool. A bedside audit of items communicated for the six handoff phases (introductions, situation, background, assessment, recommendations, and questions), handoff duration, team member participation, and handoff tool utilization were performed for 149 handoffs pre- and 146 handoffs postimplementation. To evaluate sustainability, the audits were compared to postimplementation data from the 2014 pilot handoff project. Following reintroduction, EHR handoff tool use increased from 4% to 19%. There was a statistically significant increase in items communicated for three of the six handoff phases when using the EHR tool (P < .05). There was no statistically significant increase in handoff duration (mean=3.66 minutes, SD=1.57 minutes) with the EHR handoff tool. Surgical team member presence for the team-based handoff increased from 90.7% pre to 95.9% post. Provider compliance with the team-based handoff approach, which includes a PACU RN, surgical team member, and anesthesia team member present for handoff, was sustained and increased 6 years postimplementation. Feedback from anesthesia providers and PACU RNs indicated mixed reports of satisfaction with the EHR tool, perceived handoff efficiency, and consistency in both giving and receiving handoff. Adherence to five of the six structured handoff phases, except introductions, was sustained and even improved 6 years following implementation. Evidence-based practice for handoff communication supports the use of a team approach and standardized EHR handoff tools. The reintroduction of a standardized EHR handoff tool improved the completeness of information transfer, yet did not lead to widespread adoption nor improved user satisfaction. There is an ongoing need to identify adoptable and sustainable perioperative handoff methods.

Improving Resident Hospital Discharge Communication by Changing Electronic Health Record Templates to Enhance Primary Care Provider Satisfaction.

Despite use of standardized electronic health record templates, the structure of discharge summaries may hinder communication from inpatient settings to primary care providers (PCPs). We developed an enhanced electronic discharge summary template to improve PCP satisfaction with written discharge summaries targeting diagnoses, medication reconciliation, laboratory test results, specialist follow-up, and recommendations. Resident template usage was measured using statistical process control charts. PCP reviewers' discharge summary satisfaction was surveyed using 5-point Likert scales analyzed using the Mann-Whitney U test. Residents were surveyed for satisfaction. Resident template usage increased from 61% initially to 72% of discharge summaries at 6 months. The PCP reviewers reported increased satisfaction for summaries using the template compared with those without (4.3 vs 3.9, P = .003). Surveyed residents desired template inclusion in the default electronic discharge summary (93%). This system-level resident-initiated quality improvement initiative created a novel discharge summary template that achieved widespread usage among residents and significantly increased outpatient PCP satisfaction.

ReDWINE: A clinical datamart with text analytical capabilities to facilitate rehabilitation research

Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients’ physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interventions face challenges, such as high cost and time commitment. Observational studies that use existing clinical data to observe the effect of an intervention have shown several advantages over RCTs. Electronic Health Records (EHRs) have become an increasingly important resource for conducting observational studies. To support these studies, we developed a clinical research datamart, called ReDWINE (Rehabilitation Datamart With Informatics iNfrastructure for rEsearch), that transforms the rehabilitation-related EHR data collected from the UPMC health care system to the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to facilitate rehabilitation research. The standardized EHR data stored in ReDWINE will further reduce the time and effort required by investigators to pool, harmonize, clean, and analyze data from multiple sources, leading to more robust and comprehensive research findings. ReDWINE also includes deployment of data visualization and data analytics tools to facilitate cohort definition and clinical data analysis. These include among others the Open Health Natural Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide text analytical capabilities at scale in ReDWINE. Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.

Lower Extremity Strains in the US National Football League, 2015-2019

Background: Lower extremity (LEX) strains, including hamstring, quadriceps, adductor, and calf strains, are among the most common injuries in sports. These injuries lead to high burden, resulting in significant missed participation time. Purpose: To describe the incidence of LEX strains in professional American football. Study Design: Descriptive epidemiology study. Methods: This study included all players who played in ≥1 National Football League (NFL) game or sustained a LEX strain during participation in the 2015-2019 seasons. LEX strain frequency was calculated by setting (game, practice, conditioning), timing in season (offseason, preseason, regular season, postseason), and roster position. Game incidence rates were calculated for season, roster position, and play type. LEX strains were identified in the standardized leaguewide electronic health record (n = 32 teams). Results: Across 5 years, 5780 LEX strains were reported among 2769 players (1-year risk, 26.7%; 95% CI, 26.0%-27.3%); 69% (n = 4015) resulted in time loss. Among all LEX strains, 54.7% were hamstring (n = 3163), 24.1% adductor (n = 1393), 12.6% calf (n = 728), 8.3% quadriceps (n = 477), and 0.3% multiple muscle groups (n = 19). Most were reported during preseason practices (n = 1076; 27%) and regular season games (n = 1060; 26%). The 2-week period of training camp practices comprised 19% of all time-loss strains. Among game injuries, preseason games had the highest rate of LEX strain (2.9/10,000 player-plays; 95% CI, 2.6-3.2). Defensive secondary players accounted for the highest proportion of time-loss LEX strains (27%; n = 1082). In games, punt plays had nearly twice the injury rate of kickoff plays (14.9/1000 plays [95% CI, 13.1-17.0] vs 7.5/1000 plays [95% CI, 6.2-8.9], respectively) and >3 times the rate of pass plays (4.3/1000 plays; 95% CI, 4.0-4.7) and run plays (2.6/1000 plays; 95% CI, 2.3-2.9). In aggregate, LEX strains led to an estimated 16,748 participation days missed each year and a median 12 days missed per injury. Conclusion: LEX strains affected 1 in 4 NFL players each year, resulting in a high burden of injury in terms of time lost from practice and competition. Safe return to the NFL season during training camp and reduction of injuries during regular season games are key focuses for future injury reduction.

Evolution of a Project to Improve Inpatient-to-Outpatient Dermatology Care Transitions: Mixed Methods Evaluation.

In-hospital dermatological care has shifted from dedicated dermatology wards to consultation services, and some consulted patients may require postdischarge follow-up in outpatient dermatology. Safe and timely care transitions from inpatient-to-outpatient specialty care are critical for patient health, but communication around these transitions can be disjointed, and workflows can be complex. In this 3-phase quality improvement effort, we developed and evaluated an intervention that leveraged an electronic health record (EHR) feature, known as SmartPhrase, to enable a new workflow to improve transitions from inpatient care to outpatient dermatology. Phase 1 (February-March 2021) included interviews with patients and process mapping with key stakeholders to identify gaps and inform an intervention: a SmartPhrase table and associated workflow to promote collection of patient information needed for scheduling follow-up and closed-loop communication between dermatology and scheduling teams. In phase 2 (April-May 2021), semistructured interviews-with dermatologists (n=5), dermatology residents (n=5), and schedulers (n=6)-identified pain points and refinements. In phase 3, the intervention was evaluated by triangulating data from these interviews with measured changes in scheduling efficiency, visit completion, and messaging volume preimplementation (January-February 2021) and postimplementation (April-May 2021). Preintervention pain points included unclear workflow for care transitions, limited patient input in follow-up planning, multiple messaging channels (eg, EHR based, email, and phone messages), and time-inefficient patient tracking. The intervention addressed most pain points; interviewees reported the intervention was easy to adopt and improved scheduling efficiency, workload, and patient involvement. More visits were completed within the desired timeframe of 14 days after discharge during the postimplementation period (21/47, 45%) than the preimplementation period (28/41, 68%; P=.03). The messaging workload also decreased from 88 scheduling-related messages sent for 25 patients before implementation to 30 messages for 8 patients after implementation. Inpatient-to-outpatient specialty care transitions are complex and involve multiple stakeholders, thus requiring multifaceted solutions. With deliberate evaluation, broad stakeholder input, and iteration, we designed and implemented a successful solution using a standard EHR feature, SmartPhrase, integrated into a standardized workflow to improve the timeliness of posthospital specialty care and reduce workload.

Improving Outcomes of Pediatric Lupus Care Delivery With Provider Goal-Setting Activities and Multidisciplinary Care Models.

The present study was undertaken to evaluate high-quality care delivery in the context of provider goal-setting activities and a multidisciplinary care model using an electronic health record (EHR)-enabled pediatric lupus registry. We then determined associations between care quality and prednisone use among youth with systemic lupus erythematosus (SLE). We implemented standardized EHR documentation tools to autopopulate a SLE registry. We compared pediatric Lupus Care Index (pLCI) performance (range 0.0-1.0; 1.0 representing perfect metric adherence) and timely follow-up 1) before versus during provider goal-setting activities and population management, and 2) in a multidisciplinary lupus nephritis versus rheumatology clinic. We estimated associations between pLCI and subsequent prednisone use adjusted for time, current medication, disease activity, clinical features, and social determinants of health. We analyzed 830 visits by 110 patients (median 7 visits per patient [interquartile range 4-10]) over 3.5 years. The provider-directed activity was associated with improved pLCI performance (adjusted β 0.05 [95% confidence interval (95% CI) 0.01, 0.09]; mean 0.74 versus 0.69). Patients with nephritis in multidisciplinary clinic had higher pLCI scores (adjusted β 0.06 [95% CI 0.02, 0.10]) and likelihood of timely follow-up than those in rheumatology (adjusted relative risk [RR] 1.27 [95% CI 1.02, 1.57]). A pLCI score of ≥0.50 was associated with 0.72-fold lower adjusted risk of subsequent prednisone use (95% CI 0.53, 0.93). Minoritized race, public insurance, and living in areas with greater social vulnerability were not associated with reduced care quality or follow-up, but public insurance was associated with higher risk of prednisone use. Greater attention to quality metrics is associated with better outcomes in childhood SLE. Multidisciplinary care models with population management may additionally facilitate equitable care delivery.

Blockchain-Based Framework for Interoperable Electronic Health Records for an Improved Healthcare System

The healthcare industry has been transitioning from paper-based medical records to electronic health records (EHRs) in most healthcare facilities. However, the current EHR frameworks face challenges in secure data storage, credibility, and management. Interoperability and user control of personal data are also significant concerns in the healthcare sector. Although block chain technology has emerged as a powerful solution that can offer the properties of immutability, security, and user control on stored records, its potential application in EHR frameworks is not yet fully understood. To address this gap in knowledge, this research aims to provide an interoperable blockchain-based EHR framework that can fulfill the requirements defined by various national and international EHR standards such as HIPAA and HL7. The research method employed is a systematic literature review to explore the current state of the art in the field of EHRs, including blockchain-based implementations of EHRs. The study defines the interoperability issues in the existing blockchain-based EHR frameworks, reviews various national and international standards of EHR, and further defines the interoperability requirements based on these standards. The proposed framework can offer safer methods to interchange health information for the healthcare sector and can provide the properties of immutability, security, and user control on stored records without the need for centralized storage. The contributions of this work include enhancing the understanding of the potential application of blockchain technology in EHR frameworks and proposing an interoperable blockchain-based EHR framework that can fulfill the requirements defined by various national and international EHR standards. Overall, this study has significant implications for the healthcare sector, as it can enhance the secure sharing and storage of electronic health data while ensuring the confidentiality, privacy, and integrity of medical records.

A Comprehensive Health Electronic Record System with MySQL RDMS, QGIS Database and Mongo DB

The advancement in data storage systems and novel data types has made organizations to stop relying on the use of simple client/server I.T infrastructure and leverage more on multiple categories of database systems to keep heterogeneous data. The study exploits the benefits around deploying hybrid relational database management systems and NoSQL systems while developing better electronic health records (EHR) systems within health facilities alongside facility decision support system (FDSS). More particularly, GIS, MySQL and Mongo DB databases were integrated to enhance EHR systems alongside offering improve clinical decision support. The study adopted experimental design to develop the Electronic Health Records System using GIS, MySQL and Mongo DB software to create the database. Findings revealed that the atomicity, consistency, isolation and durability feature typical of relational database management systems guaranteed data security, integrity, ease of access and efficient transaction processing. Mongo database offered the system a more precise internal data structure and solid scalability along with simplified mapping of application objects to the underlying database design. The GIS database enabled a clear visualization of patients’ geographical locations, medical facilities, and the physical location of the physicians. Integrating these database systems within the health care arena was instrumental in compelling application systems to adhere to the HIPAA EHR standards without compromising performance and scalability.

Development of an electronic medical record provider evaluation template for children with attention deficit hyperactivity disorder.

Attention deficit hyperactivity disorder (ADHD) is the most common neurobehavioral condition in children. Current guidelines recommend that Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-V) criteria be met before diagnosing ADHD. However, 14-49% of providers are not documenting the use of DSM-V criteria in screening and diagnosis. The use of a standardized electronic health record (EHR) ADHD template is a potential option to assist providers with documenting the DSM-V criteria. The aim for this project was to provide a 1-hour educational in-service on the use of the DSM-V criteria, Vanderbilt rating scale, and a revised EHR ADHD template, and to assess provider use pre and post intervention. Results showed that few providers (n = 1; 2%) document using DSM-V criteria preintervention and post intervention with only a modest increase in template use (n = 3; 5%). However, there were statistical differences between provider type with advanced practice registered nurses and physician assistants screening more frequently using the EHR DSM-V ADHD template compared with physicians (p = .009). Future studies are needed to evaluate barriers to use, including practice preference, openness to change, and other factors that may affect provider workflow.

1076: QUALITY IMPROVEMENT INITIATIVE TO PREVENT FLUID OVERLOAD IN PEDIATRIC INTENSIVE CARE UNIT PATIENTS

Introduction: Fluid administration for critically ill children frequently exceeds hydration requirements and is associated with increased morbidity and mortality. The global aim of this ongoing single-center quality improvement (QI) initiative is to standardize and implement process improvement tools to detect and prevent fluid overload (FO) in patients admitted to the Pediatric Intensive Care Unit (PICU). Our SMART aim is to reduce the incidence of percent fluid overload (%FO) >/= 10% at 72 hours of PICU admission by 20% in the first 6 months. Methods: In collaboration with key stakeholders, we used Plan-Do-Study Act (PDSA) QI methodology to develop electronic health record (EHR)-based tools and education sessions to help providers detect and prevent FO in PICU patients. We conducted a retrospective chart review with a convenience sample to obtain baseline metrics. We assessed current FO understanding and perceptions of proposed interventions with anonymous surveys distributed to PICU attendings, fellows, residents, advanced practice providers and nurses. In April 2022, we implemented a single screen “Fluid Management accordion” within our EHR that displays all clinical markers used to evaluate a patient’s fluid status, and an EHR-based %FO calculator. In July 2022, we added PICU admission order set modifications to include a total fluid goal. Outcome metrics are collected monthly. Quarterly follow up surveys began in July 2022 and will trend intervention impact and perceptions. Results: Chart review (n=562) revealed a 40% baseline incidence of %FO >/= 10% at 72 hours of PICU admission. Pre-implementation, most respondents (n=115) agreed that we could improve our awareness to patients’ fluid status (92%) and that using standardized EHR orders and tools would be helpful (76%). Few (27%) reported knowing how to calculate %FO. Three months after initial EHR tool launch, many survey respondents (n=49) reported using the fluid management accordion at least once (40%) and 43% reported knowing how to calculate %FO. Six-month implementation data will be available in October 2022. Conclusions: To date we have standardized EHR-based tools for FO detection. We suspect that with continued education and feedback we will likely see a reduction in %FO incidence in our PICU population.

Developing a universal phenotyping algorithm to identify patients with clinically diagnosed and probable Alzheimer’s disease using electronic health record data

AbstractBackgroundDespite significant advancements in our understanding of Alzheimer’s disease (AD) over the last decade, no disease‐modifying treatments exist for it. Rich clinical information on patients with AD is currently available in electronic health records (EHR) worldwide. Our aim is to develop a portable phenotyping algorithm to mine EHRs and identify patients with clinical and probable preclinical AD. Identified patients can subsequently be used as cohorts in future biomedical research studies.MethodWe developed an algorithm using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). This enables other institutes using this standardized EHR data representation to avail the algorithm with minimum effort for local implementation. The algorithm’s backbone is based on ubiquitous information readily derived from EHRs such as ICD‐10 billing codes, medication history, cognitive test scores (Mini‐Mental State Exam), imaging reports and lab results. The model classifies identified patients into three categories based on high (category 1), intermediate (category 2) and low (category 3) probability with AD. The algorithm was applied to a deidentified EHR database at the Vanderbilt University Medical Center (VUMC) with clinical information from over 3 million individuals. We performed an manual chart review on 100 randomly selected samples of each category’s AD cases to assess its predictive value.ResultImplementation of the phenotyping algorithm yielded 274,000 patients with probable Alzheimer’s disease. 4169 patients were identified based on category 1 criteria, 13270 patients were identified using category 2 and 256,302 patients were identified in category 3. Independent chart review resulted in a PPV of 94% for category 1, 86% for category 2 and 8% for category 3 patients. Majority of false positive cases occurred because of mild cognitive impairment (MCI) patients in category 3 who were selected through broad billing codes and medication history.ConclusionA portable phenotyping algorithm can help identify large cohorts of patients with diagnosed and probable Alzheimer’s disease for research use by mining EHRs. Based on preliminary results, our algorithm is a promising attempt to achieve this goal. We aim to improve on it till it selects AD patients with an even higher predictive value (&gt;95%).

Skeletal Outcomes in Children and Young Adults with Glomerular Disease.

Children with glomerular disease have unique risk factors for compromised bone health. Studies addressing skeletal complications in this population are lacking. This retrospective cohort study utilized data from PEDSnet, a national network of pediatric health systems with standardized electronic health record data for more than 6.5 million patients from 2009 to 2021. Incidence rates (per 10,000 person-years) of fracture, slipped capital femoral epiphysis (SCFE), and avascular necrosis/osteonecrosis (AVN) in 4598 children and young adults with glomerular disease were compared with those among 553,624 general pediatric patients using Poisson regression analysis. The glomerular disease cohort was identified using a published computable phenotype. Inclusion criteria for the general pediatric cohort were two or more primary care visits 1 year or more apart between 1 and 21 years of age, one visit or more every 18 months if followed >3 years, and no chronic progressive conditions defined by the Pediatric Medical Complexity Algorithm. Fracture, SCFE, and AVN were identified using SNOMED-CT diagnosis codes; fracture required an associated x-ray or splinting/casting procedure within 48 hours. We found a higher risk of fracture for the glomerular disease cohort compared with the general pediatric cohort in girls only (incidence rate ratio [IRR], 1.6; 95% CI, 1.3 to 1.9). Hip/femur and vertebral fracture risk were increased in the glomerular disease cohort: adjusted IRR was 2.2 (95% CI, 1.3 to 3.7) and 5 (95% CI, 3.2 to 7.6), respectively. For SCFE, the adjusted IRR was 3.4 (95% CI, 1.9 to 5.9). For AVN, the adjusted IRR was 56.2 (95% CI, 40.7 to 77.5). Children and young adults with glomerular disease have significantly higher burden of skeletal complications than the general pediatric population.

Systematic Assessment of the Interoperability Requirements and Challenges of Secure Blockchain-Based Electronic Health Records

A peer-to-peer (P2P) decentralized information-sharing network is used to share data and maintain security, privacy, and integrity standards called blockchain. In this case, information sharing and updating require regular simplification. The presented systematic review mainly focuses on the interoperability of electronic health records (EHRs) using blockchain. Correspondingly, 18 blockchain-based solutions were selected to address the interoperability challenges of EHRs. The limitation of solutions includes reliability, privacy, integrity, sharing, and standards. This systematic review contains six phase’s research question, research phase, article selection, abstract-based keyword, data extraction, and progress tracking. Various Web resources such as Google Scholar, Web of Science, and IEEE are used to extract the relevant manuscripts. Primarily, 18 articles were selected to present the interoperable requirements of EHRs using blockchain, standards of blockchain-based EHRs, and solutions for interoperability of EHRs using blockchain. The conducted study explains the best available interoperable blockchain-based EHR standards, implementations, applications, and challenges.

Electronic Health Record-Based Deep Learning Prediction of Death or Severe Decompensation in Heart Failure Patients

BackgroundSurgical mechanical ventricular assistance and cardiac replacement therapies, although life-saving in many heart failure (HF) patients, remain high-risk. Despite this, the difficulty in timely identification of medical therapy nonresponders and the dire consequences of nonresponse have fueled early, less selective surgical referral. Patients who would have ultimately responded to medical therapy are therefore subjected to the risk and life disruption of surgical therapy. ObjectivesThe purpose of this study was to develop deep learning models based upon commonly-available electronic health record (EHR) variables to assist clinicians in the timely and accurate identification of HF medical therapy nonresponders. MethodsThe study cohort consisted of all patients (age 18 to 90 years) admitted to a single tertiary care institution from January 2009 through December 2018, with International Classification of Disease HF diagnostic coding. Ensemble deep learning models employing time-series and densely-connected networks were developed from standard EHR data. The positive class included all observations resulting in severe progression (death from any cause or referral for HF surgical intervention) within 1 year. ResultsA total of 79,850 distinct admissions from 52,265 HF patients met observation criteria and contributed >350 million EHR datapoints for model training, validation, and testing. A total of 20% of model observations fit positive class criteria. The model C-statistic was 0.91. ConclusionsThe demonstrated accuracy of EHR-based deep learning model prediction of 1-year all-cause death or referral for HF surgical therapy supports clinical relevance. EHR-based deep learning models have considerable potential to assist HF clinicians in improving the application of advanced HF surgical therapy in medical therapy nonresponders.