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  1. Article ; Online: The science of informatics and predictive analytics.

    Lenert, Leslie

    Journal of the American Medical Informatics Association : JAMIA

    2019  Volume 26, Issue 12, Page(s) 1425–1426

    Language English
    Publishing date 2019-11-15
    Publishing country England
    Document type Editorial
    ZDB-ID 1205156-1
    ISSN 1527-974X ; 1067-5027
    ISSN (online) 1527-974X
    ISSN 1067-5027
    DOI 10.1093/jamia/ocz202
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4128: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 312: Show issues

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  2. Article ; Online: Stressful life events in electronic health records: a scoping review.

    Scherbakov, Dmitry / Mollalo, Abolfazl / Lenert, Leslie

    Journal of the American Medical Informatics Association : JAMIA

    2024  Volume 31, Issue 4, Page(s) 1025–1035

    Abstract: Objectives: Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer 2 ... ...

    Abstract Objectives: Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer 2 major questions: how stressful life events are documented in EHR and how they are utilized in research and clinical care.
    Materials and methods: Three online databases (EBSCOhost platform, PubMed, and Scopus) were searched to identify papers that included information on stressful life events in EHR; paper titles and abstracts were reviewed for relevance by 2 independent reviewers.
    Results: Five hundred fifty-seven unique papers were retrieved, and of these 70 were eligible for data extraction. Most articles (n = 36, 51.4%) were focused on the statistical association between one or several stressful life events and health outcomes, followed by clinical utility (n = 15, 21.4%), extraction of events from free-text notes (n = 12, 17.1%), discussing privacy and other issues of storing life events (n = 5, 7.1%), and new EHR features related to life events (n = 4, 5.7%). The most frequently mentioned stressful life events in the publications were child abuse/neglect, arrest/legal issues, and divorce/relationship breakup. Almost half of the papers (n = 7, 46.7%) that analyzed clinical utility of stressful events were focused on decision support systems for child abuse, while others (n = 7, 46.7%) were discussing interventions related to social determinants of health in general.
    Discussion and conclusions: Few citations are available on the prevalence and use of stressful life events in EHR reflecting challenges in screening and storing of stressful life events.
    MeSH term(s) Humans ; Child ; Electronic Health Records
    Language English
    Publishing date 2024-02-13
    Publishing country England
    Document type Review ; Journal Article
    ZDB-ID 1205156-1
    ISSN 1527-974X ; 1067-5027
    ISSN (online) 1527-974X
    ISSN 1067-5027
    DOI 10.1093/jamia/ocae023
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4128: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 312: Show issues

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  3. Article: Application of Spatial Analysis for Electronic Health Records: Characterizing Patient Phenotypes and Emerging Trends.

    Mollalo, Abolfazl / Hamidi, Bashir / Lenert, Leslie / Alekseyenko, Alexander V

    Research square

    2024  

    Abstract: Background: Electronic health records (EHR) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread ... ...

    Abstract Background: Electronic health records (EHR) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHR in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.
    Objective: This study reviews advanced spatial analyses that employed individual-level health data from EHR within the US to characterize patient phenotypes.
    Methods: We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, study design, or specific health domains.
    Results: Only 49 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. A noteworthy surge (n = 42, 85.7%) in publications was observed post-2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized.
    Conclusions: This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future clinical decision support.
    Language English
    Publishing date 2024-01-15
    Publishing country United States
    Document type Preprint
    DOI 10.21203/rs.3.rs-3443865/v2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Could an artificial intelligence approach to prior authorization be more human?

    Lenert, Leslie A / Lane, Steven / Wehbe, Ramsey

    Journal of the American Medical Informatics Association : JAMIA

    2023  Volume 30, Issue 5, Page(s) 989–994

    Abstract: Prior authorization (PA) may be a necessary evil within the healthcare system, contributing to physician burnout and delaying necessary care, but also allowing payers to prevent wasting resources on redundant, expensive, and/or ineffective care. PA has ... ...

    Abstract Prior authorization (PA) may be a necessary evil within the healthcare system, contributing to physician burnout and delaying necessary care, but also allowing payers to prevent wasting resources on redundant, expensive, and/or ineffective care. PA has become an "informatics issue" with the rise of automated methods for PA review, championed in the Health Level 7 International's (HL7's) DaVinci Project. DaVinci proposes using rule-based methods to automate PA, a time-tested strategy with known limitations. This article proposes an alternative that may be more human-centric, using artificial intelligence (AI) methods for the computation of authorization decisions. We believe that by combining modern approaches for accessing and exchanging existing electronic health data with AI methods tailored to reflect the judgments of expert panels that include patient representatives, and refined with "few shot" learning approaches to prevent bias, we could create a just and efficient process that serves the interests of society as a whole. Efficient simulation of human appropriateness assessments from existing data using AI methods could eliminate burdens and bottlenecks while preserving PA's benefits as a tool to limit inappropriate care.
    MeSH term(s) Humans ; Artificial Intelligence ; Prior Authorization ; Delivery of Health Care ; Physicians
    Language English
    Publishing date 2023-02-21
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1205156-1
    ISSN 1527-974X ; 1067-5027
    ISSN (online) 1527-974X
    ISSN 1067-5027
    DOI 10.1093/jamia/ocad016
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4128: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 312: Show issues

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  5. Article: Stressful life events in electronic health records: a scoping review.

    Scherbakov, Dmitry / Mollalo, Abolfazl / Lenert, Leslie

    Research square

    2023  

    Abstract: Objective: Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer two ... ...

    Abstract Objective: Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer two major questions: how stressful life events are documented in EHR and how they are utilized in research and clinical care.
    Materials and methods: Three online databases (EBSCOhost platform, PubMed, and Scopus) were searched to identify papers that included information on stressful life events in EHR; paper titles and abstracts were reviewed for relevance by two independent reviewers.
    Results: 557 unique papers were retrieved, and of these 70 were eligible for data extraction. Most articles (n=36, 51.4%) were focused on the statistical association between one or several stressful life events and health outcomes, followed by clinical utility (n=15, 21.4%), extraction of events from free-text notes (n=12, 17.1%), discussing privacy and other issues of storing life events (n=5, 7.1%), and new EHR features related to life events (n=4, 5.7%). The most frequently mentioned stressful life events in the publications were child abuse/neglect, arrest/legal issues, and divorce/relationship breakup. Almost half of the papers (n=7, 46.7%) that analyzed clinical utility of stressful events were focused on decision support systems for child abuse, while others (n=7, 46.7%) were discussing interventions related to social determinants of health in general.
    Discussion and conclusions: Few citations are available on the prevalence and use of stressful life events in EHR reflecting challenges in screening and storing of stressful life events.
    Language English
    Publishing date 2023-12-13
    Publishing country United States
    Document type Preprint
    DOI 10.21203/rs.3.rs-3458708/v2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Toward Medical Documentation That Enhances Situational Awareness Learning.

    Lenert, Leslie A

    AMIA ... Annual Symposium proceedings. AMIA Symposium

    2017  Volume 2016, Page(s) 763–771

    Abstract: The purpose of writing medical notes in a computer system goes beyond documentation for medical-legal purposes or billing. The structure of documentation is a checklist that serves as a cognitive aid and a potential index to retrieve information for ... ...

    Abstract The purpose of writing medical notes in a computer system goes beyond documentation for medical-legal purposes or billing. The structure of documentation is a checklist that serves as a cognitive aid and a potential index to retrieve information for learning from the record. For the past 50 years, one of the primary organizing structures for physicians' clinical documentation have been the SOAP note (Subjective, Objective, Assessment, Plan). The cognitive check list is well-suited to differential diagnosis but may not support detection of changes in systems and/or learning from cases. We describe an alternative cognitive checklist called the OODA Loop (Observe, Orient, Decide, Act. Through incorporation of projections of anticipated course events with and without treatment and by making "Decisions" an explicit category of documentation in the medical record in the context of a variable temporal cycle for observations, OODA may enhance opportunities to learn from clinical care.
    MeSH term(s) Awareness ; Documentation/methods ; Humans ; Learning ; Medical Records ; Patient Care Planning ; Physicians/psychology
    Language English
    Publishing date 2017-02-10
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 1942-597X
    ISSN (online) 1942-597X
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Predicting Mammogram Screening Follow Through with Electronic Health Record and Geographically Linked Data.

    Davis, Matthew / Simpson, Kit / Lenert, Leslie A / Diaz, Vanessa / Alekseyenko, Alexander V

    Cancer research communications

    2023  Volume 3, Issue 10, Page(s) 2126–2132

    Abstract: Cancer is the second leading cause of death in the United States, and breast cancer is the fourth leading cause of cancer-related death, with 42,275 women dying of breast cancer in the United States in 2020. Screening is a key strategy for reducing ... ...

    Abstract Cancer is the second leading cause of death in the United States, and breast cancer is the fourth leading cause of cancer-related death, with 42,275 women dying of breast cancer in the United States in 2020. Screening is a key strategy for reducing mortality from breast cancer and is recommended by various national guidelines. This study applies machine learning classification methods to the task of predicting which patients will fail to complete a mammogram screening after having one ordered, as well as understanding the underlying features that influence predictions. The results show that a small group of patients can be identified that are very unlikely to complete mammogram screening, enabling care managers to focus resources.
    Significance: The motivation behind this study is to create an automated system that can identify a small group of individuals that are at elevated risk for not following through completing a mammogram screening. This will enable interventions to boost screening to be focused on patients least likely to complete screening.
    MeSH term(s) Female ; Humans ; United States/epidemiology ; Electronic Health Records ; Semantic Web ; Mass Screening/methods ; Mammography ; Breast Neoplasms/diagnosis
    Language English
    Publishing date 2023-10-02
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 2767-9764
    ISSN (online) 2767-9764
    DOI 10.1158/2767-9764.CRC-23-0263
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Informatics for public health and health system collaboration: Applications for the control of the current COVID-19 pandemic and the next one.

    Lenert, Leslie A / Ding, Wei / Jacobs, Jeff

    Journal of the American Medical Informatics Association : JAMIA

    2021  Volume 28, Issue 8, Page(s) 1807–1811

    Abstract: Public health faces unprecedented challenges in its efforts to control COVID-19 through a national vaccination campaign. Addressing these challenges will require fundamental changes to public health data systems. For example, of the core data systems for ...

    Abstract Public health faces unprecedented challenges in its efforts to control COVID-19 through a national vaccination campaign. Addressing these challenges will require fundamental changes to public health data systems. For example, of the core data systems for immunization campaigns is the immunization information system (IIS); however, IISs were designed for tracking the vaccinated, not finding the patients who are high risk and need to be vaccinated. Health systems have this data in their electronic health records (EHR) systems and often have a greater capacity for outreach. Clearly, a partnership is needed. However, successful collaborations will require public health to change from its historical hierarchical information supply chain model to an ecosystem model with a peer-to-peer exchange with population health providers. Examples of the types of informatics innovations necessary to support such an ecosystem include a national patient identifier, population-level data exchange for immunization data, and computable electronic quality measures. Rather than think of these components individually, a comprehensive approach to rapidly adaptable tools for collaboration is needed.
    MeSH term(s) COVID-19/prevention & control ; Delivery of Health Care/organization & administration ; Health Information Interoperability ; Humans ; Information Dissemination ; Intersectoral Collaboration ; Patient Identification Systems ; Public Health Administration ; Public Health Informatics
    Language English
    Publishing date 2021-04-23
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1205156-1
    ISSN 1527-974X ; 1067-5027
    ISSN (online) 1527-974X
    ISSN 1067-5027
    DOI 10.1093/jamia/ocab066
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4128: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    Zs.MO 312: Show issues

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  9. Article ; Online: Shared Decision Making: From Decision Science to Data Science.

    Shaoibi, Azza / Neelon, Brian / Lenert, Leslie A

    Medical decision making : an international journal of the Society for Medical Decision Making

    2020  Volume 40, Issue 3, Page(s) 254–265

    Abstract: Background. ...

    Abstract Background.
    MeSH term(s) Adult ; Bayes Theorem ; Data Science/methods ; Data Science/trends ; Decision Making, Shared ; Female ; Humans ; Male ; Patient Participation/methods ; Patient Participation/psychology ; Patient Preference/psychology
    Language English
    Publishing date 2020-02-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 604497-9
    ISSN 1552-681X ; 0272-989X
    ISSN (online) 1552-681X
    ISSN 0272-989X
    DOI 10.1177/0272989X20903267
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1665: Show issues Location:
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  10. Article ; Online: VACtrac: enhancing access immunization registry data for population outreach using the Bulk Fast Healthcare Interoperable Resource (FHIR) protocol.

    Lenert, Leslie / Jacobs, Jeff / Agnew, James / Ding, Wei / Kirchoff, Katie / Weatherston, Duncan / Deans, Kenneth

    Journal of the American Medical Informatics Association : JAMIA

    2022  

    Abstract: COVID-19 vaccination uptake has been suboptimal, even in high-risk populations. New approaches are needed to bring vaccination data to the groups leading outreach efforts. This article describes work to make state-level vaccination data more accessible ... ...

    Abstract COVID-19 vaccination uptake has been suboptimal, even in high-risk populations. New approaches are needed to bring vaccination data to the groups leading outreach efforts. This article describes work to make state-level vaccination data more accessible by extending the Bulk Fast Healthcare Interoperability Resource (FHIR) standard to better support the repeated retrieval of vaccination data for coordinated outreach efforts. We also describe a corresponding low-foot-print software for population outreach that automates repeated checks of state-level immunization data and prioritizes outreach by social determinants of health. Together this software offers an integrated approach to addressing vaccination gaps. Several extensions to the Bulk FHIR protocol were needed to support bulk query of immunization records. These are described in detail. The results of a pilot study, using the outreach tool to target a population of 1500 patients are also described. The results confirmed the limitations of current patient-by-patient approach for querying state immunizations systems for population data and the feasibility of a Bulk FHIR approach.
    Language English
    Publishing date 2022-12-06
    Publishing country England
    Document type Journal Article
    ZDB-ID 1205156-1
    ISSN 1527-974X ; 1067-5027
    ISSN (online) 1527-974X
    ISSN 1067-5027
    DOI 10.1093/jamia/ocac237
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.MO 312: Show issues

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