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  1. Book: Clinical decision support

    Dighe, Anand S.

    tools, strategies, and emerging technologies

    (Clinics in laboratory medicine ; volume 39, number 2 (June 2019))

    2019  

    Author's details editor Anand S. Dighe
    Series title Clinics in laboratory medicine ; volume 39, number 2 (June 2019)
    Collection
    Language English
    Size x Seiten, Seite 198-331, Illustrationen
    Publisher Elsevier
    Publishing place Philadelphia, Pennsylvania
    Publishing country United States
    Document type Book
    HBZ-ID HT020113990
    ISBN 978-0-323-68115-5 ; 0-323-68115-8
    Database Catalogue ZB MED Medicine, Health

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    Zs A 1695 -39,2- not available for loan Zeitschriften-Lesesaal

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  2. Article ; Online: Electronic Health Record Optimization for Artificial Intelligence.

    Dighe, Anand S

    Clinics in laboratory medicine

    2022  Volume 43, Issue 1, Page(s) 17–28

    Abstract: Laboratory clinical decision support (CDS) typically relies on data from the electronic health record (EHR). The implementation of a sustainable, effective laboratory CDS program requires a commitment to standardization and harmonization of key EHR data ... ...

    Abstract Laboratory clinical decision support (CDS) typically relies on data from the electronic health record (EHR). The implementation of a sustainable, effective laboratory CDS program requires a commitment to standardization and harmonization of key EHR data elements that are the foundation of laboratory CDS. The direct use of artificial intelligence algorithms in CDS programs will be limited unless key elements of the EHR are structured. The identification, curation, maintenance, and preprocessing steps necessary to implement robust laboratory-based algorithms must account for the heterogeneity of data present in a typical EHR.
    MeSH term(s) Artificial Intelligence ; Electronic Health Records ; Laboratories ; Algorithms ; Decision Support Systems, Clinical
    Language English
    Publishing date 2022-12-14
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 604580-7
    ISSN 1557-9832 ; 0272-2712
    ISSN (online) 1557-9832
    ISSN 0272-2712
    DOI 10.1016/j.cll.2022.09.003
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  3. Article ; Online: Clinical decision support to improve CBC and differential ordering.

    Mahowald, Grace K / Lewandrowski, Kent B / Dighe, Anand S

    American journal of clinical pathology

    2024  

    Abstract: Objectives: Complete blood count and differential (CBC diff) is a common laboratory test that may be overused or misordered, particularly in an inpatient setting. We assessed the ability of a clinical decision support (CDS) alert to decrease unnecessary ...

    Abstract Objectives: Complete blood count and differential (CBC diff) is a common laboratory test that may be overused or misordered, particularly in an inpatient setting. We assessed the ability of a clinical decision support (CDS) alert to decrease unnecessary orders for CBC diff and analyzed its impact in the laboratory.
    Methods: We designed 3 CDS alerts to provide guidance to providers ordering CBC diff on inpatients at frequencies of daily, greater than once daily, or as needed.
    Results: The 3 alerts were highly effective in reducing orders for CBC diff at the frequencies targeted by the alert. Overall, test volume for CBC diff decreased by 32% (mean of 5257 tests per month) after implementation of the alerts, with a corresponding decrease of 22% in manual differentials performed (mean of 898 per month). Turnaround time for manual differentials decreased by a mean of 41.5 minutes, with a mean decrease of up to 90 minutes during peak morning hours.
    Conclusions: The 3 CDS alerts successfully decreased inpatient orders for CBC diff and improved the quality of patient care by decreasing turnaround time for manual differentials.
    Language English
    Publishing date 2024-03-20
    Publishing country England
    Document type Journal Article
    ZDB-ID 2944-0
    ISSN 1943-7722 ; 0002-9173
    ISSN (online) 1943-7722
    ISSN 0002-9173
    DOI 10.1093/ajcp/aqae024
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  4. Article ; Online: Enhancing the Value of the Laboratory with Clinical Decision Support.

    Dighe, Anand S

    Clinics in laboratory medicine

    2019  Volume 39, Issue 2, Page(s) ix–x

    MeSH term(s) Decision Support Systems, Clinical ; Electronic Health Records ; Humans ; Laboratories
    Language English
    Publishing date 2019-03-28
    Publishing country United States
    Document type Editorial ; Introductory Journal Article
    ZDB-ID 604580-7
    ISSN 1557-9832 ; 0272-2712
    ISSN (online) 1557-9832
    ISSN 0272-2712
    DOI 10.1016/j.cll.2019.02.001
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  5. Article ; Online: Using machine learning to develop smart reflex testing protocols.

    McDermott, Matthew / Dighe, Anand / Szolovits, Peter / Luo, Yuan / Baron, Jason

    Journal of the American Medical Informatics Association : JAMIA

    2023  Volume 31, Issue 2, Page(s) 416–425

    Abstract: Objective: Reflex testing protocols allow clinical laboratories to perform second line diagnostic tests on existing specimens based on the results of initially ordered tests. Reflex testing can support optimal clinical laboratory test ordering and ... ...

    Abstract Objective: Reflex testing protocols allow clinical laboratories to perform second line diagnostic tests on existing specimens based on the results of initially ordered tests. Reflex testing can support optimal clinical laboratory test ordering and diagnosis. In current clinical practice, reflex testing typically relies on simple "if-then" rules; however, this limits the opportunities for reflex testing since most test ordering decisions involve more complexity than traditional rule-based approaches would allow. Here, using the analyte ferritin as an example, we propose an alternative machine learning-based approach to "smart" reflex testing.
    Methods: Using deidentified patient data, we developed a machine learning model to predict whether a patient getting CBC testing will also have ferritin testing ordered. We evaluate applications of this model to reflex testing by assessing its performance in comparison to possible rule-based approaches.
    Results: Our underlying machine learning models performed moderately well in predicting ferritin test ordering (AUC=0.731 in reference to actual ordering) and demonstrated promising potential to underlie key clinical applications. In contrast, none of the many traditionally framed, rule-based, hypothetical reflex protocols we evaluated offered sufficient agreement with actual ordering to be clinically feasible. Using chart review, we further demonstrated that the strategic deployment of our model could avoid important ferritin test ordering errors.
    Conclusions: Machine learning may provide a foundation for new types of reflex testing with enhanced benefits for clinical diagnosis.
    MeSH term(s) Humans ; Reflex ; Machine Learning ; Ferritins
    Chemical Substances Ferritins (9007-73-2)
    Language English
    Publishing date 2023-10-09
    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/ocad187
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  6. Article ; Online: Using Machine Learning to Develop Smart Reflex Testing Protocols.

    McDermott, Matthew / Dighe, Anand / Szolovits, Peter / Luo, Yuan / Baron, Jason

    ArXiv

    2023  

    Abstract: Objective: Reflex testing protocols allow clinical laboratories to perform second line diagnostic tests on existing specimens based on the results of initially ordered tests. Reflex testing can support optimal clinical laboratory test ordering and ... ...

    Abstract Objective: Reflex testing protocols allow clinical laboratories to perform second line diagnostic tests on existing specimens based on the results of initially ordered tests. Reflex testing can support optimal clinical laboratory test ordering and diagnosis. In current clinical practice, reflex testing typically relies on simple "if-then" rules; however, this limits their scope since most test ordering decisions involve more complexity than a simple rule will allow. Here, using the analyte ferritin as an example, we propose an alternative machine learning-based approach to "smart" reflex testing with a wider scope and greater impact than traditional rule-based approaches.
    Methods: Using patient data, we developed a machine learning model to predict whether a patient getting CBC testing will also have ferritin testing ordered, consider applications of this model to "smart" reflex testing, and evaluate the model by comparing its performance to possible rule-based approaches.
    Results: Our underlying machine learning models performed moderately well in predicting ferritin test ordering and demonstrated greater suitability to reflex testing than rule-based approaches. Using chart review, we demonstrate that our model may improve ferritin test ordering. Finally, as a secondary goal, we demonstrate that ferritin test results are missing not at random (MNAR), a finding with implications for unbiased imputation of missing test results.
    Conclusions: Machine learning may provide a foundation for new types of reflex testing with enhanced benefits for clinical diagnosis and laboratory utilization management.
    Language English
    Publishing date 2023-02-01
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Use of Clinical Decision Support to Improve the Laboratory Evaluation of Monoclonal Gammopathies.

    Pearson, Daniel S / McEvoy, Dustin S / Murali, Mandakolathur R / Dighe, Anand S

    American journal of clinical pathology

    2023  Volume 159, Issue 2, Page(s) 192–204

    Abstract: Objectives: There is considerable variation in ordering practices for the initial laboratory evaluation of monoclonal gammopathies (MGs) despite clear society guidelines to include serum free light chain (sFLC) testing. We assessed the ability of a ... ...

    Abstract Objectives: There is considerable variation in ordering practices for the initial laboratory evaluation of monoclonal gammopathies (MGs) despite clear society guidelines to include serum free light chain (sFLC) testing. We assessed the ability of a clinical decision support (CDS) alert to improve guideline compliance and analyzed its clinical impact.
    Methods: We designed and deployed a targeted CDS alert to educate and prompt providers to order an sFLC assay when ordering serum protein electrophoresis (SPEP) testing.
    Results: The alert was highly effective at increasing the co-ordering of SPEP and sFLC testing. Preimplementation, 62.8% of all SPEP evaluations included sFLC testing, while nearly 90% of evaluations included an sFLC assay postimplementation. In patients with no prior sFLC testing, analysis of sFLC orders prompted by the alert led to the determination that 28.9% (800/2,769) of these patients had an abnormal κ/λ ratio. In 452 of these patients, the sFLC assay provided the only laboratory evidence of a monoclonal protein. Moreover, within this population, there were numerous instances of new diagnoses of multiple myeloma and other MGs.
    Conclusions: The CDS alert increased compliance with society guidelines and improved the diagnostic evaluation of patients with suspected MGs.
    MeSH term(s) Humans ; Decision Support Systems, Clinical ; Paraproteinemias/diagnosis ; Immunoglobulin Light Chains ; Multiple Myeloma
    Chemical Substances Immunoglobulin Light Chains
    Language English
    Publishing date 2023-01-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 2944-0
    ISSN 1943-7722 ; 0002-9173
    ISSN (online) 1943-7722
    ISSN 0002-9173
    DOI 10.1093/ajcp/aqac151
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  8. Article ; Online: Clinical Decision Support in Laboratory Medicine.

    Choy, Kay Weng / Cornu, Pieter / Dighe, Anand S / Georgiou, Andrew / Peters, Lindsay / Sikaris, Kenneth A / Loh, Tze Ping

    Clinical chemistry

    2024  Volume 70, Issue 3, Page(s) 474–481

    MeSH term(s) Humans ; Decision Support Systems, Clinical
    Language English
    Publishing date 2024-02-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 80102-1
    ISSN 1530-8561 ; 0009-9147
    ISSN (online) 1530-8561
    ISSN 0009-9147
    DOI 10.1093/clinchem/hvae002
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  9. Article ; Online: Decision Support Tools within the Electronic Health Record.

    Rudolf, Joseph W / Dighe, Anand S

    Clinics in laboratory medicine

    2019  Volume 39, Issue 2, Page(s) 197–213

    Abstract: Laboratory tests are an integral part of the electronic health record (EHR). Providing clinical decision support (CDS) for the ordering, collection, reporting, viewing, and interpretation of laboratory testing is a fundamental function of the EHR. The ... ...

    Abstract Laboratory tests are an integral part of the electronic health record (EHR). Providing clinical decision support (CDS) for the ordering, collection, reporting, viewing, and interpretation of laboratory testing is a fundamental function of the EHR. The implementation of a sustainable, effective laboratory CDS program requires a commitment to standardization and harmonization of the laboratory dictionaries that are the foundation of laboratory-based CDS. In this review, the authors provide an overview of the tools available within the EHR to improve decision making throughout the entire laboratory testing process, from test order to clinical action.
    MeSH term(s) Decision Support Systems, Clinical ; Electronic Health Records ; Humans ; Laboratories/standards ; Medical Order Entry Systems ; Quality Assurance, Health Care
    Language English
    Publishing date 2019-03-28
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 604580-7
    ISSN 1557-9832 ; 0272-2712
    ISSN (online) 1557-9832
    ISSN 0272-2712
    DOI 10.1016/j.cll.2019.01.001
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  10. Article ; Online: Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts.

    Baron, Jason M / Huang, Richard / McEvoy, Dustin / Dighe, Anand S

    JAMIA open

    2021  Volume 4, Issue 1, Page(s) ooab006

    Abstract: Objectives: While well-designed clinical decision support (CDS) alerts can improve patient care, utilization management, and population health, excessive alerting may be counterproductive, leading to clinician burden and alert fatigue. We sought to ... ...

    Abstract Objectives: While well-designed clinical decision support (CDS) alerts can improve patient care, utilization management, and population health, excessive alerting may be counterproductive, leading to clinician burden and alert fatigue. We sought to develop machine learning models to predict whether a clinician will accept the advice provided by a CDS alert. Such models could reduce alert burden by targeting CDS alerts to specific cases where they are most likely to be effective.
    Materials and methods: We focused on a set of laboratory test ordering alerts, deployed at 8 hospitals within the Partners Healthcare System. The alerts notified clinicians of duplicate laboratory test orders and advised discontinuation. We captured key attributes surrounding 60 399 alert firings, including clinician and patient variables, and whether the clinician complied with the alert. Using these data, we developed logistic regression models to predict alert compliance.
    Results: We identified key factors that predicted alert compliance; for example, clinicians were less likely to comply with duplicate test alerts triggered in patients with a prior abnormal result for the test or in the context of a nonvisit-based encounter (eg, phone call). Likewise, differences in practice patterns between clinicians appeared to impact alert compliance. Our best-performing predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.82. Incorporating this model into the alerting logic could have averted more than 1900 alerts at a cost of fewer than 200 additional duplicate tests.
    Conclusions: Deploying predictive models to target CDS alerts may substantially reduce clinician alert burden while maintaining most or all the CDS benefit.
    Language English
    Publishing date 2021-03-01
    Publishing country United States
    Document type Journal Article
    ISSN 2574-2531
    ISSN (online) 2574-2531
    DOI 10.1093/jamiaopen/ooab006
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