Utilizing Computerized Provider Order Entry (CPOE) to Reduce the Garbage in Garbage out Effect in the Cytology Laboratory

Cuda, Jacqueline; Seigh, Lindsey; Clark, Katherine; Pantanowitz, Liron

doi:10.1016/j.jasc.2016.07.195

Cited by 5 publications

(5 citation statements)

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“…Electronic ordering can save staff time in trying to decipher handwriting; facilitate faster transfer of information; reduce duplication; and improve accessibility of information 2. There is also evidence that requests submitted by CPOE are significantly more likely than paper-based requests to contain useful information, including clinical questions and information on prior diagnoses 4 15 17 26 41. Furthermore, computerised ordering systems have been shown to reduce healthcare expenditure; decrease the number of tests per admission46; and improve test turnaround time 26…”

Section: Discussionmentioning

confidence: 99%

“…EHRs support multiple healthcare functions, including creation and storage of patient notes, managing electronic prescriptions and patient-facing web portals 3. Widening access to EHRs is also driving greater adoption of computerised provider order entry (CPOE) systems, which are used by healthcare workers (HCWs) to submit diverse electronic requests, ranging from laboratory investigations,4–14 to imaging studies and radiological examinations,15–22 to health education videos,3 to drugs and medications 1 2 23–25. The proportion of hospitals using CPOE is not known 5.…”

Section: Introductionmentioning

confidence: 99%

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Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis

et al. 2023

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IntroductionRelevant clinical information is vital to inform the analytical and interpretative phases of most investigations. The aim of this study is to evaluate the impact of implementation of computerised provider order entry (CPOE), featuring order-specific electronic order entry forms (eOEFs), on the quality and quantity of clinical information included with investigation requests.MethodsThe CPOE module of a commercially available electronic health record (Cerner Millennium) was implemented at a large, tertiary care centre. The laboratory information management system was interrogated to collect data on specimens sent for microbiological culture 1 year before implementation of CPOE (2018), immediately post implementation (2019) and 6 months post implementation (2020). An interrupted time series analysis was performed, using text mining, to evaluate the quality and quantity of free-text clinical information.ResultsIn total, 39 919 specimens were collected from 16 458 patients. eOEFs were used to place 10 071 out of 13 735 orders in 2019 (73.3%), and 9155 out of 12 229 orders in 2020 (74.9%). No clinical details were included with 653 out of 39 919 specimens (1.6%), of which 22 (3.4%) were ordered using eOEFs. The median character count increased from 14 in 2018, to 41 in 2019, and 38 in 2020. An anti-infective agent was specified in 581 out of 13 955 requests (4.2%) in 2018; 5545 out of 13 735 requests (40.4%) in 2019; and 5215 out of 12 229 requests (42.6%) in 2020. Ciprofloxacin or piperacillin-tazobactam (Tazocin) were mentioned in the clinical details included with 421 out of 15 335 urine culture requests (2.7%), of which 406 (96.3%) were ordered using eOEFs. Subsequent detection of in vitro non-susceptibility led to a change in anti-infective therapy for five patients.ConclusionsImplementation of CPOE, featuring order-specific eOEFs, significantly and sustainably improves the quality and quantity of clinical information included with investigation requests, resulting in changes to patient management that would not otherwise have occurred.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis

et al. 2023

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“…The quality of data has a significant impact on the ability of a model to learn and generalize effectively. It is essential to gather representative data for training the model, adhering to the well-known principle of “garbage in, garbage out” [ 3 ]. Traditionally, many of these preprocessing tasks have been performed manually, but powerful data science applications have now emerged, accelerating these processes through automatization [ 4 ].…”

Section: Machine Learningmentioning

confidence: 99%

Application of Machine Learning in Chronic Kidney Disease: Current Status and Future Prospects

Delrue,

De Bruyne,

Speeckaert

2024

Biomedicines

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The emergence of artificial intelligence and machine learning (ML) has revolutionized the landscape of clinical medicine, offering opportunities to improve medical practice and research. This narrative review explores the current status and prospects of applying ML to chronic kidney disease (CKD). ML, at the intersection of statistics and computer science, enables computers to derive insights from extensive datasets, thereby presenting an interesting landscape for constructing statistical models and improving data interpretation. The integration of ML into clinical algorithms aims to increase efficiency and promote its adoption as a standard approach to data interpretation in nephrology. As the field of ML continues to evolve, collaboration between clinicians and data scientists is essential for defining data-sharing and usage policies, ultimately contributing to the advancement of precision diagnostics and personalized medicine in the context of CKD.

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“…When a cytology report is electronically signed out in the LIS, an outbound message via a results interface is transmitted back into the EMR to close the data loop. Using electronic order entry enables automation, can help with appropriate test use, and minimizes errors introduced with manual tasks and illegible handwriting on paper requisitions …”

Section: Introductionmentioning

confidence: 99%

The role of informatics in patient‐centered care and personalized medicine

Hanna

Pantanowitz

2017

Cancer Cytopathology

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The practice of cytopathology has dramatically changed due to advances in genomics and information technology. Cytology laboratories have accordingly become increasingly dependent on pathology informatics support to meet the emerging demands of precision medicine. Pathology informatics deals with information technology in the laboratory, and the impact of this technology on workflow processes and staff who interact with these tools. This article covers the critical role that laboratory information systems, electronic medical records, and digital imaging plays in patient-centered personalized medicine. The value of integrated diagnostic reports, clinical decision support, and the use of whole-slide imaging to better evaluate cytology samples destined for molecular testing is discussed. Image analysis that offers more precise and quantitative measurements in cytology is addressed, as well as the role of bioinformatics tools to cope with Big Data from next-generation sequencing. This article also highlights the barriers to the widespread adoption of these disruptive technologies due to regulatory obstacles, limited commercial solutions, poor interoperability, and lack of standardization.Cancer Cytopathol 2017;125(6 suppl):494

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Utilizing Computerized Provider Order Entry (CPOE) to Reduce the Garbage in Garbage out Effect in the Cytology Laboratory

Cited by 5 publications

References 0 publications

Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis

Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis

Application of Machine Learning in Chronic Kidney Disease: Current Status and Future Prospects

The role of informatics in patient‐centered care and personalized medicine

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