Harnessing the Electronic Health Record and Computerized Provider Order Entry Data for Resource Management During the COVID-19 Pandemic (Preprint)

BACKGROUND The COVID-19 pandemic has resulted in shortages of diagnostic tests, personal protective equipment, hospital beds, and other critical resources. OBJECTIVE We sought to improve the management of scarce resources by leveraging electronic health record (EHR) functionality, computerized provider order entry, clinical decision support (CDS), and data analytics. METHODS Due to the complex eligibility criteria for COVID-19 tests and the EHR implementation–related challenges of ordering these tests, care providers have faced obstacles in selecting the appropriate test modality. As test choice is dependent upon specific patient criteria, we built a decision tree within the EHR to automate the test selection process by using a branching series of questions that linked clinical criteria to the appropriate SARS-CoV-2 test and triggered an EHR flag for patients who met our institutional persons under investigation criteria. RESULTS The percentage of tests that had to be canceled and reordered due to errors in selecting the correct testing modality was 3.8% (23/608) before CDS implementation and 1% (262/26,643) after CDS implementation (P<.001). Patients for whom multiple tests were ordered during a 24-hour period accounted for 0.8% (5/608) and 0.3% (76/26,643) of pre- and post-CDS implementation orders, respectively (P=.03). Nasopharyngeal molecular assay results were positive in 3.4% (826/24,170) of patients who were classified as asymptomatic and 10.9% (1421/13,074) of symptomatic patients (P<.001). Positive tests were more frequent among asymptomatic patients with a history of exposure to COVID-19 (36/283, 12.7%) than among asymptomatic patients without such a history (790/23,887, 3.3%; P<.001). CONCLUSIONS The leveraging of EHRs and our CDS algorithm resulted in a decreased incidence of order entry errors and the appropriate flagging of persons under investigation. These interventions optimized reagent and personal protective equipment usage. Data regarding symptoms and COVID-19 exposure status that were collected by using the decision tree correlated with the likelihood of positive test results, suggesting that clinicians appropriately used the questions in the decision tree algorithm.

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Chapter 10: Electronic Data Management: Electronic Health Record Systems and Computerized Provider Order Entry Systems

Introduction to Acute & Ambulatory Care Pharmacy Practice ◽

10.37573/9781585285464.010 ◽

2017 ◽

pp. 195-212

Keyword(s):

Electronic Health Record ◽

Data Management ◽

Health Record ◽

Order Entry ◽

Electronic Data ◽

Computerized Provider Order Entry ◽

Electronic Health ◽

Order Entry Systems

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Leveraging electronic health record data to inform hospital resource management

Health Care Management Science ◽

10.1007/s10729-021-09554-4 ◽

2021 ◽

Author(s):

José Carlos Ferrão ◽

Mónica Duarte Oliveira ◽

Daniel Gartner ◽

Filipe Janela ◽

Henrique M. G. Martins

Keyword(s):

Resource Management ◽

Electronic Health Record ◽

Health Record ◽

Hospital Resource ◽

Electronic Health Record Data ◽

Record Data ◽

Electronic Health

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Association of Display of Patient Photographs in the Electronic Health Record With Wrong-Patient Order Entry Errors

JAMA Network Open ◽

10.1001/jamanetworkopen.2020.19652 ◽

2020 ◽

Vol 3 (11) ◽

pp. e2019652

Author(s):

Hojjat Salmasian ◽

Bonnie B. Blanchfield ◽

Kelley Joyce ◽

Kaila Centeio ◽

Gordon B. Schiff ◽

...

Keyword(s):

Electronic Health Record ◽

Health Record ◽

Order Entry ◽

Electronic Health

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Evaluating Incorporation of Drug Restrictions into Computerized Drug Order Entries after Transition to an Electronic Health Record

Hospital Pharmacy ◽

10.1310/hpj4807-568 ◽

2013 ◽

Vol 48 (7) ◽

pp. 568-573 ◽

Cited By ~ 1

Author(s):

Ryan Rodriguez ◽

Benjamin Staley ◽

Randy C. Hatton

Keyword(s):

Electronic Health Record ◽

Implementation Strategies ◽

Health Record ◽

Order Entry ◽

Task List ◽

Restriction Policy ◽

Electronic Health ◽

Computerized Prescriber Order Entry ◽

Formulary Restriction ◽

Drug Order

Background Incorporation of drug restriction policy into electronic drug order entries (DOEs) can promote responsible medication use and resource utilization when implemented systematically. Objective To identify drugs that require further incorporation of formulary restriction policy into their DOEs after migration to an electronic health record with computerized prescriber order entry (CPOE). Methods After transition to CPOE, test orders for formulary restricted drugs were entered in the CPOE environment. Data were collected about rationale for drug restriction, type of formulary restriction, presence of incorporation of restriction policy into the DOE, and whether incorporation was consistent with a recommended method. Restricted drugs requiring revision of policy incorporation into their DOEs were analyzed to create a prioritized task list based on rationale for the restriction. Results Of all restricted drugs, 63.6% (287/451) did not have restriction policy incorporated into their DOEs consistent with the recommended method and therefore required revision. Eighteen percent (81/451) of restricted drugs had no incorporation of restriction policy in their DOEs. Safety was the rationale for restriction in 21% (17/81) of these, which received highest priority for revision. When drugs were orderable but restricted, 61.9% (78/126) lacked optimal incorporation of policy in DOEs to promote adherence. When drugs were not orderable, 64% (206/322) did not provide guidance to formulary alternatives in DOEs when they should have. Conclusion After transition to CPOE, almost two-thirds of all analyzed restricted drugs lacked optimal incorporation of formulary restriction policies in their DOEs. DOEs with restrictions related to safety reasons were among those most frequently requiring revision. Some DOEs can better promote adherence and provide guidance to prescribers through revision. Predefined, systematic implementation strategies should be used during changes in computerized drug use processes.

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A multifaceted approach in leveraging electronic health record tools for safe inpatient use of concentrated U-500 insulin

American Journal of Health-System Pharmacy ◽

10.1093/ajhp/zxab408 ◽

2021 ◽

Author(s):

Marc A Willner ◽

Jeffrey Ketz ◽

Ramona A Davis ◽

Angela W Yaniv ◽

Alina Bulgar-Grozav ◽

...

Keyword(s):

Electronic Health Record ◽

Cleveland Clinic ◽

Patient Specific ◽

Health Record ◽

Order Entry ◽

Measuring Device ◽

Multifaceted Approach ◽

Patient Reported ◽

Electronic Health ◽

Insulin Use

Abstract Disclaimer In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Purpose The Institute for Safe Medication Practices classifies subcutaneous insulin as a high-risk medication. Concentrated U-500 insulin carries additional risks in comparison to conventional U-100 insulin, as the 5-fold more concentrated nature of this product, limitations to insulin pen dosing, and various devices for dose measurement may lead to miscommunication of patient-reported doses, resulting in downstream errors in ordering, verification, or administration. We describe a multifaceted approach to leveraging technical tools within the electronic health record (EHR) for U-500 insulin use. Summary At Cleveland Clinic, the U-500 insulin use process evolved in a number of phases using EHR tools. Phase 1 included new clinical decision support and documentation tools during order entry, including a customized alert that fired during order entry recommending that the prescriber order a consult with endocrinology and requiring the prescriber to provide the patient’s home insulin measuring device and the source of the patient’s reported home dose. In order verification, a customized alert fired directing the pharmacist to contact the patient or patient’s nurse and validate the information provided by the prescriber. Phase 2 involved transitioning dispensing of patient-specific doses from tuberculin syringes to U-500 insulin syringes. Phase 3 transitioned to use of U-500 insulin pens and included automatic dose rounding of ordered doses down to the nearest 5 units, and an additional customized pharmacist alert intended for cost conservation was added to fire if the patient had a recent administration of U-500 insulin documented, directing the pharmacist to determine whether the nurse needed a new pen dispensed. Conclusion Cleveland Clinic successfully implemented customized tools and processes within the EHR pertaining to the prescribing, verification, dispensing, and administration of U-500 insulin.

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