Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study.

BACKGROUND: Through our work, we have demonstrated how clinical decision support (CDS) tools integrated into the electronic health record (EHR) assist providers in adopting evidence-based practices. This requires confronting technical challenges that result from relying on the EHR as the foundation for tool development; for example, the individual CDS tools need to be built independently for each different EHR. OBJECTIVE: The objective of our research was to build and implement an EHR-agnostic platform for integrating CDS tools, which would remove the technical constraints inherent in relying on the EHR as the foundation and enable a single set of CDS tools that can work with any EHR. METHODS: We developed EvidencePoint, a novel, cloud-based, EHR-agnostic CDS platform, and we will describe the development of EvidencePoint and the deployment of its initial CDS tools, which include EHR-integrated applications for clinical use cases such as prediction of hospitalization survival for patients with COVID-19, venous thromboembolism prophylaxis, and pulmonary embolism diagnosis. RESULTS: The results below highlight the adoption of the CDS tools, the International Medical Prevention Registry on Venous Thromboembolism-D-Dimer, the Wells' criteria, and the Northwell COVID-19 Survival (NOCOS), following development, usability testing, and implementation. The International Medical Prevention Registry on Venous Thromboembolism-D-Dimer CDS was used in 5249 patients at the 2 clinical intervention sites. The intervention group tool adoption was 77.8% (4083/5249 possible uses). For the NOCOS tool, which was designed to assist with triaging patients with COVID-19 for hospital admission in the event of constrained hospital resources, the worst-case resourcing scenario never materialized and triaging was never required. As a result, the NOCOS tool was not frequently used, though the EvidencePoint platform's flexibility and customizability enabled the tool to be developed and deployed rapidly under the emergency conditions of the pandemic. Adoption rates for the Wells' criteria tool will be reported in a future publication. CONCLUSIONS: The EvidencePoint system successfully demonstrated that a flexible, user-friendly platform for hosting CDS tools outside of a specific EHR is feasible. The forthcoming results of our outcomes analyses will demonstrate the adoption rate of EvidencePoint tools as well as the impact of behavioral economics "nudges" on the adoption rate. Due to the EHR-agnostic nature of EvidencePoint, the development process for additional forms of CDS will be simpler than traditional and cumbersome IT integration approaches and will benefit from the capabilities provided by the core system of EvidencePoint.

Nudging Health Care Providers' Adoption of Clinical Decision Support: Protocol for the User-Centered Development of a Behavioral Economics-Inspired Electronic Health Record Tool.

BACKGROUND: The improvements in care resulting from clinical decision support (CDS) have been significantly limited by consistently low health care provider adoption. Health care provider attitudes toward CDS, specifically psychological and behavioral barriers, are not typically addressed during any stage of CDS development, although they represent an important barrier to adoption. Emerging evidence has shown the surprising power of using insights from the field of behavioral economics to address psychological and behavioral barriers. Nudges are formal applications of behavioral economics, defined as positive reinforcement and indirect suggestions that have a nonforced effect on decision-making. OBJECTIVE: Our goal is to employ a user-centered design process to develop a CDS tool-the pulmonary embolism (PE) risk calculator-for PE risk stratification in the emergency department that incorporates a behavior theory-informed nudge to address identified behavioral barriers to use. METHODS: All study activities took place at a large academic health system in the New York City metropolitan area. Our study used a user-centered and behavior theory-based approach to achieve the following two aims: (1) use mixed methods to identify health care provider barriers to the use of an active CDS tool for PE risk stratification and (2) develop a new CDS tool-the PE risk calculator-that addresses behavioral barriers to health care providers' adoption of CDS by incorporating nudges into the user interface. These aims were guided by the revised Observational Research Behavioral Information Technology model. A total of 50 clinicians who used the original version of the tool were surveyed with a quantitative instrument that we developed based on a behavior theory framework-the Capability-Opportunity-Motivation-Behavior framework. A semistructured interview guide was developed based on the survey responses. Inductive methods were used to analyze interview session notes and audio recordings from 12 interviews. Revised versions of the tool were developed that incorporated nudges. RESULTS: Functional prototypes were developed by using Axure PRO (Axure Software Solutions) software and usability tested with end users in an iterative agile process (n=10). The tool was redesigned to address 4 identified major barriers to tool use; we included 2 nudges and a default. The 6-month pilot trial for the tool was launched on October 1, 2021. CONCLUSIONS: Clinicians highlighted several important psychological and behavioral barriers to CDS use. Addressing these barriers, along with conducting traditional usability testing, facilitated the development of a tool with greater potential to transform clinical care. The tool will be tested in a prospective pilot trial. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42653.

Discriminating Acute Respiratory Distress Syndrome from other forms of respiratory failure via iterative machine learning.

Acute Respiratory Distress Syndrome (ARDS) is associated with high morbidity and mortality. Identification of ARDS enables lung protective strategies, quality improvement interventions, and clinical trial enrolment, but remains challenging particularly in the first 24 hours of mechanical ventilation. To address this we built an algorithm capable of discriminating ARDS from other similarly presenting disorders immediately following mechanical ventilation. Specifically, a clinical team examined medical records from 1263 ICU-admitted, mechanically ventilated patients, retrospectively assigning each patient a diagnosis of "ARDS" or "non-ARDS" (e.g., pulmonary edema). Exploiting data readily available in the clinical setting, including patient demographics, laboratory test results from before the initiation of mechanical ventilation, and features extracted by natural language processing of radiology reports, we applied an iterative pre-processing and machine learning framework. The resulting model successfully discriminated ARDS from non-ARDS causes of respiratory failure (AUC = 0.85) among patients meeting Berlin criteria for severe hypoxia. This analysis also highlighted novel patient variables that were informative for identifying ARDS in ICU settings.

Atrial fibrillation is an independent predictor for in-hospital mortality in patients admitted with SARS-CoV-2 infection.

BACKGROUND: Atrial fibrillation (AF) is the most encountered arrhythmia and has been associated with worse in-hospital outcomes. OBJECTIVE: This study was to determine the incidence of AF in patients hospitalized with coronavirus disease 2019 (COVID-19) as well as its impact on in-hospital mortality. METHODS: Patients hospitalized with a positive COVID-19 polymerase chain reaction test between March 1 and April 27, 2020, were identified from the common medical record system of 13 Northwell Health hospitals. Natural language processing search algorithms were used to identify and classify AF. Patients were classified as having AF or not. AF was further classified as new-onset AF vs history of AF. RESULTS: AF occurred in 1687 of 9564 patients (17.6%). Of those, 1109 patients (65.7%) had new-onset AF. Propensity score matching of 1238 pairs of patients with AF and without AF showed higher in-hospital mortality in the AF group (54.3% vs 37.2%; P < .0001). Within the AF group, propensity score matching of 500 pairs showed higher in-hospital mortality in patients with new-onset AF as compared with those with a history of AF (55.2% vs 46.8%; P = .009). The risk ratio of in-hospital mortality for new-onset AF in patients with sinus rhythm was 1.56 (95% confidence interval 1.42-1.71; P < .0001). The presence of cardiac disease was not associated with a higher risk of in-hospital mortality in patients with AF (P = .1). CONCLUSION: In patients hospitalized with COVID-19, 17.6% experienced AF. AF, particularly new-onset, was an independent predictor of in-hospital mortality.

Eruptions and related clinical course among 296 hospitalized adults with confirmed COVID-19.

BACKGROUND: Limited information exists on mucocutaneous disease and its relation to course of COVID-19. OBJECTIVE: To estimate prevalence of mucocutaneous findings, characterize morphologic patterns, and describe relationship to course in hospitalized adults with COVID-19. METHODS: Prospective cohort study at 2 tertiary hospitals (Northwell Health) between May 11, 2020 and June 15, 2020. RESULTS: Among 296 hospitalized adults with COVID-19, 35 (11.8%) had at least 1 disease-related eruption. Patterns included ulcer (13/35, 37.1%), purpura (9/35, 25.7%), necrosis (5/35, 14.3%), nonspecific erythema (4/35, 11.4%), morbilliform eruption (4/35, 11.4%), pernio-like lesions (4/35, 11.4%), and vesicles (1/35, 2.9%). Patterns also showed anatomic site specificity. A greater proportion of patients with mucocutaneous findings used mechanical ventilation (61% vs 30%), used vasopressors (77% vs 33%), initiated dialysis (31% vs 9%), had thrombosis (17% vs 11%), and had in-hospital mortality (34% vs 12%) compared with those without mucocutaneous findings. Patients with mucocutaneous disease were more likely to use mechanical ventilation (adjusted prevalence ratio, 1.98; 95% confidence interval, 1.37-2.86); P < .001). Differences for other outcomes were attenuated after covariate adjustment and did not reach statistical significance. LIMITATIONS: Skin biopsies were not performed. CONCLUSIONS: Distinct mucocutaneous patterns were identified in hospitalized adults with COVID-19. Mucocutaneous disease may be linked to more severe clinical course.

Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area.

Importance: There is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19). Objective: To describe the clinical characteristics and outcomes of patients with COVID-19 hospitalized in a US health care system. Design, Setting, and Participants: Case series of patients with COVID-19 admitted to 12 hospitals in New York City, Long Island, and Westchester County, New York, within the Northwell Health system. The study included all sequentially hospitalized patients between March 1, 2020, and April 4, 2020, inclusive of these dates. Exposures: Confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by positive result on polymerase chain reaction testing of a nasopharyngeal sample among patients requiring admission. Main Outcomes and Measures: Clinical outcomes during hospitalization, such as invasive mechanical ventilation, kidney replacement therapy, and death. Demographics, baseline comorbidities, presenting vital signs, and test results were also collected. Results: A total of 5700 patients were included (median age, 63 years [interquartile range {IQR}, 52-75; range, 0-107 years]; 39.7% female). The most common comorbidities were hypertension (3026; 56.6%), obesity (1737; 41.7%), and diabetes (1808; 33.8%). At triage, 30.7% of patients were febrile, 17.3% had a respiratory rate greater than 24 breaths/min, and 27.8% received supplemental oxygen. The rate of respiratory virus co-infection was 2.1%. Outcomes were assessed for 2634 patients who were discharged or had died at the study end point. During hospitalization, 373 patients (14.2%) (median age, 68 years [IQR, 56-78]; 33.5% female) were treated in the intensive care unit care, 320 (12.2%) received invasive mechanical ventilation, 81 (3.2%) were treated with kidney replacement therapy, and 553 (21%) died. As of April 4, 2020, for patients requiring mechanical ventilation (n = 1151, 20.2%), 38 (3.3%) were discharged alive, 282 (24.5%) died, and 831 (72.2%) remained in hospital. The median postdischarge follow-up time was 4.4 days (IQR, 2.2-9.3). A total of 45 patients (2.2%) were readmitted during the study period. The median time to readmission was 3 days (IQR, 1.0-4.5) for readmitted patients. Among the 3066 patients who remained hospitalized at the final study follow-up date (median age, 65 years [IQR, 54-75]), the median follow-up at time of censoring was 4.5 days (IQR, 2.4-8.1). Conclusions and Relevance: This case series provides characteristics and early outcomes of sequentially hospitalized patients with confirmed COVID-19 in the New York City area.

Designing a Clinical Data Warehouse Architecture to Support Quality Improvement Initiatives.

Clinical data warehouses, initially directed towards clinical research or financial analyses, are evolving to support quality improvement efforts, and must now address the quality improvement life cycle. In addition, data that are needed for quality improvement often do not reside in a single database, requiring easier methods to query data across multiple disparate sources. We created a virtual data warehouse at NewYork Presbyterian Hospital that allowed us to bring together data from several source systems throughout the organization. We also created a framework to match the maturity of a data request in the quality improvement life cycle to proper tools needed for each request. As projects progress in the Define, Measure, Analyze, Improve, Control stages of quality improvement, there is a proper matching of resources the data needs at each step. We describe the analysis and design creating a robust model for applying clinical data warehousing to quality improvement.

A competency-based curriculum to acculturate biomedical informatics students to the practice of medicine.

Biomedical informatics students who choose to study clinical information systems may not have significant clinical experience. A course was designed to "acculturate" these students to the practice of medicine through case-based presentations that span three competency areas: biomedicine, clinical workflow and practice, and applications in clinical informatics.

Redesigning electronic health record systems to support public health.

Current electronic health record systems are primarily clinical in focus, designed to provide patient-level data and provider-level decision support. Adapting EHR systems to serve public health needs provides the possibility of enormous advances for public health practice and policy. In this review, we evaluate EHR functionality and map it to the three core functions of public health: assessment, policy development, and assurance. In doing so, we identify and discuss important design, implementation, and methodological issues with current systems. For example, in order to support public health's traditional focus on preventive health and socio-behavioral factors, EHR data models would need to be expanded to incorporate environmental, psychosocial, and other non-medical data elements, and workflow would have to be examined to determine the optimal way of collecting these data. We also argue that redesigning EHR systems to support public health offers benefits not only to the public health system but also to consumers, health-care institutions, and individual providers.

Architectural design of a data warehouse to support operational and analytical queries across disparate clinical databases.

As the clinical data warehouse of the New York Presbyterian Hospital has evolved innovative methods of integrating new data sources and providing more effective and efficient data reporting and analysis need to be explored. We designed and implemented a new clinical data warehouse architecture to handle the integration of disparate clinical databases in the institution. By examining the way downstream systems are populated and streamlining the way data is stored we create a virtual clinical data warehouse that is adaptable to future needs of the organization.