Abstract
Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, ...containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies
Abstract
Background
Early detection and efficient management of sepsis are important for improving health care quality, effectiveness, and costs. Due to its high cost and prevalence, sepsis is a ...major focus area across institutions and many studies have emerged over the past years with different models or novel machine learning techniques in early detection of sepsis or potential mortality associated with sepsis.
Objective
To understand predictive analytics solutions for sepsis patients, either in early detection of onset or mortality.
Methods and Results
We performed a systematized narrative review and identified common and unique characteristics between their approaches and results in studies that used predictive analytics solutions for sepsis patients. After reviewing 148 retrieved papers, a total of 31 qualifying papers were analyzed with variances in model, including linear regression (
n
= 2), logistic regression (
n
= 5), support vector machines (
n
= 4), and Markov models (
n
= 4), as well as population (range: 24–198,833) and feature size (range: 2–285). Many of the studies used local data sets of varying sizes and locations while others used the publicly available Medical Information Mart for Intensive Care data. Additionally, vital signs or laboratory test results were commonly used as features for training and testing purposes; however, a few used more unique features including gene expression data from blood plasma and unstructured text and data from clinician notes.
Conclusion
Overall, we found variation in the domain of predictive analytics tools for septic patients, from feature and population size to choice of method or algorithm. There are still limitations in transferability and generalizability of the algorithms or methods used. However, it is evident that implementing predictive analytics tools are beneficial in the early detection of sepsis or death related to sepsis. Since most of these studies were retrospective, the translational value in the real-world setting in different wards should be further investigated.
Abstract
Objective
Academic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of clinical data. Enterprise data warehouses have emerged as ...central resources for these data, but often require an informatician to extract meaningful information, limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying clinical data from heterogeneous data models and sources.
Materials and Methods
Leaf utilizes a flexible biomedical concept system to define hierarchical concepts and ontologies. Each Leaf concept contains both textual representations and SQL query building blocks, exposed by a simple drag-and-drop user interface. Leaf generates abstract syntax trees which are compiled into dynamic SQL queries.
Results
Leaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an enterprise data warehouse with over 300 active users. Through the support of UW Medicine (https://uwmedicine.org), the Institute of Translational Health Sciences (https://www.iths.org), and the National Center for Data to Health (https://ctsa.ncats.nih.gov/cd2h/), Leaf source code has been released into the public domain at https://github.com/uwrit/leaf.
Discussion
Leaf allows the querying of single or multiple clinical databases simultaneously, even those of different data models. This enables fast installation without costly extraction or duplication.
Conclusions
Leaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly leverage existing user authentication systems and clinical databases in situ. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine biobanks, and clinical studies involving large patient cohorts.
Asthma affects a large proportion of the population and leads to many hospital encounters involving both hospitalizations and emergency department visits every year. To lower the number of such ...encounters, many health care systems and health plans deploy predictive models to prospectively identify patients at high risk and offer them care management services for preventive care. However, the previous models do not have sufficient accuracy for serving this purpose well. Embracing the modeling strategy of examining many candidate features, we built a new machine learning model to forecast future asthma hospital encounters of patients with asthma at Intermountain Healthcare, a nonacademic health care system. This model is more accurate than the previously published models. However, it is unclear how well our modeling strategy generalizes to academic health care systems, whose patient composition differs from that of Intermountain Healthcare.
This study aims to evaluate the generalizability of our modeling strategy to the University of Washington Medicine (UWM), an academic health care system.
All adult patients with asthma who visited UWM facilities between 2011 and 2018 served as the patient cohort. We considered 234 candidate features. Through a secondary analysis of 82,888 UWM data instances from 2011 to 2018, we built a machine learning model to forecast asthma hospital encounters of patients with asthma in the subsequent 12 months.
Our UWM model yielded an area under the receiver operating characteristic curve (AUC) of 0.902. When placing the cutoff point for making binary classification at the top 10% (1464/14,644) of patients with asthma with the largest forecasted risk, our UWM model yielded an accuracy of 90.6% (13,268/14,644), a sensitivity of 70.2% (153/218), and a specificity of 90.91% (13,115/14,426).
Our modeling strategy showed excellent generalizability to the UWM, leading to a model with an AUC that is higher than all of the AUCs previously reported in the literature for forecasting asthma hospital encounters. After further optimization, our model could be used to facilitate the efficient and effective allocation of asthma care management resources to improve outcomes.
RR2-10.2196/resprot.5039.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Large amounts of personal health data are being collected and made available through existing and emerging technological media and tools. While use of these data has significant potential to ...facilitate research, improve quality of care for individuals and populations, and reduce healthcare costs, many policy-related issues must be addressed before their full value can be realized. These include the need for widely agreed-on data stewardship principles and effective approaches to reduce or eliminate data silos and protect patient privacy. AMIA's 2012 Health Policy Meeting brought together healthcare academics, policy makers, and system stakeholders (including representatives of patient groups) to consider these topics and formulate recommendations. A review of a set of Proposed Principles of Health Data Use led to a set of findings and recommendations, including the assertions that the use of health data should be viewed as a public good and that achieving the broad benefits of this use will require understanding and support from patients.
To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to ...electronic health record data on large populations, and required under the 21st Century Cures Act Rule.
We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR.
Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12 000 resources/min.
The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases.
To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.
Abstract Objective To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app “listener” that accesses ...standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). Methods We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and artificial intelligence (AI) for processing unstructured text. Results Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization’s security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across 5 healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. Discussion and Conclusion Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs, (2) increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.
Background: Primary data collection is a critical activity in clinical research. Even with significant advances in technical capabilities, clear benefits of use, and even user preferences for using ...electronic systems for collecting primary data, paper-based data collection is still common in clinical research settings. However, with recent developments in both clinical research and tablet computer technology, the comparative advantages and disadvantages of data collection methods should be determined. Objective: To describe case studies using multiple methods of data collection, including next-generation tablets, and consider their various advantages and disadvantages. Materials and Methods: We reviewed 5 modern case studies using primary data collection, using methods ranging from paper to next-generation tablet computers. We performed semistructured telephone interviews with each project, which considered factors relevant to data collection. We address specific issues with workflow, implementation and security for these different methods, and identify differences in implementation that led to different technology considerations for each case study. Results and Discussion: There remain multiple methods for primary data collection, each with its own strengths and weaknesses. Two recent methods are electronic health record templates and next-generation tablet computers. Electronic health record templates can link data directly to medical records, but are notably difficult to use. Current tablet computers are substantially different from previous technologies with regard to user familiarity and software cost. The use of cloud-based storage for tablet computers, however, creates a specific challenge for clinical research that must be considered but can be overcome.
OBJECTIVES: To explore changes in mortality and hospital usage for chronically ill seniors enrolled in a multidisease care management program, Care Management Plus (CMP).
DESIGN: Controlled clinical ...trial with seven intervention and six control clinics with additional patient‐level matching.
SETTING: Intermountain Health Care, a large health system in Utah; seven intervention and six control clinics.
PARTICIPANTS: Three thousand four hundred thirty‐two senior patients (≥65) enrolled in Medicare.
INTERVENTION: The intervention employed nurse care managers supported by specialized information technology in primary care to manage chronically ill patients (2002–2005).
MEASUREMENTS: Mortality and hospitalization data were collected from clinical records and Medicare billing.
RESULTS: One thousand one hundred forty‐four intervention patients were matched to 2,288 controls. Average age was 76.2; average comorbidity score was 2.3±1.1; 75% of patients had two or more chronic diseases. Survival analyses showed lower mortality and slightly more emergency department visits for care managed patients than for controls. In patients with diabetes mellitus, the intervention resulted in significantly lower mortality at 1 year (6.2%, vs 10.6% for controls) and at 2 years (12.9% vs 18.2%). Hospitalization rate was lower (21.0%, vs. 24.2% for controls) at 1 year and substantially more so at the 2‐year follow‐up.
CONCLUSION: CMP was successful in reducing death for all patients. For complex patients with diabetes mellitus in the intervention group, death and hospital usage were lower. Per clinic, hypothesized savings from decreased hospitalizations was $17,384 to $70,349.
We measured the prevalence (or rate) of patient-note mismatches (clinical notes judged to pertain to another patient) in the electronic medical record. The rate ranged from 0.5% (95% CI 0.2% to 1.7%) ...before a pop-up window intervention to 0.3% (95% CI 0.1% to 1.1%) after the intervention. Clinicians discovered patient-note mismatches in 0.05-0.03% of notes, or about 10% of actual mismatches. The reduction in rates after the intervention was statistically significant. Therefore, while the patient-note mismatch rate is low compared to published rates of other documentation errors, it can be further reduced by the design of the user interface.
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