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The impact of PACS on clinician work practices in the intensive care unit: a systematic review of the literature.

ABSTRACT: OBJECTIVE: To assess evidence of the impact of Picture Archiving and Communication Systems (PACS) on clinicians' work practices in the intensive care unit (ICU). METHODS: We searched Medline, Pre-Medline, CINAHL, Embase, and the SPIE Digital Library databases for English-language publications between 1980 and September 2010 using Medical Subject Headings terms and keywords. RESULTS: Eleven studies from the USA and UK were included. All studies measured aspects of time associated with the introduction of PACS, namely the availability of images, the time a physician took to review an image, and changes in viewing patterns. Seven studies examined the impact on clinical decision-making, with the majority measuring the time to image-based clinical action. The effect of PACS on communication modes was reported in five studies. DISCUSSION: PACS can impact on clinician work practices in three main areas. Most of the evidence suggests an improvement in the efficiency of work practices. Quick image availability can impact on work associated with clinical decision-making, although the results were inconsistent. PACS can change communication practices, particularly between the ICU and radiology; however, the evidence base is insufficient to draw firm conclusions in this area. CONCLUSION: The potential for PACS to impact positively on clinician work practices in the ICU and improve patient care is great. However, the evidence base is limited and does not reflect aspects of contemporary PACS technology. Performance measures developed in previous studies remain relevant, with much left to investigate to understand how PACS can support new and improved ways of delivering care in the intensive care setting.

SUBMITTER: Hains IM

PROVIDER: S-EPMC3384105 | biostudies-literature | 2012 Jul-Aug

REPOSITORIES: biostudies-literature

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The impact of PACS on clinician work practices in the intensive care unit: a systematic review of the literature.

Hains Isla M IM Georgiou Andrew A Westbrook Johanna I JI

Journal of the American Medical Informatics Association : JAMIA 20120209 4

<h4>Objective</h4>To assess evidence of the impact of Picture Archiving and Communication Systems (PACS) on clinicians' work practices in the intensive care unit (ICU).<h4>Methods</h4>We searched Medline, Pre-Medline, CINAHL, Embase, and the SPIE Digital Library databases for English-language publications between 1980 and September 2010 using Medical Subject Headings terms and keywords.<h4>Results</h4>Eleven studies from the USA and UK were included. All studies measured aspects of time associat ...[more]

PMID: 22323392

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Project description:BackgroundIn the intensive care unit (ICU), delirium is a common, acute, confusional state associated with high risk for short- and long-term morbidity and mortality. Machine learning (ML) has promise to address research priorities and improve delirium outcomes. However, due to clinical and billing conventions, delirium is often inconsistently or incompletely labeled in electronic health record (EHR) datasets. Here, we identify clinical actions abstracted from clinical guidelines in electronic health records (EHR) data that indicate risk of delirium among intensive care unit (ICU) patients. We develop a novel prediction model to label patients with delirium based on a large data set and assess model performance.MethodsEHR data on 48,451 admissions from 2001 to 2012, available through Medical Information Mart for Intensive Care-III database (MIMIC-III), was used to identify features to develop our prediction models. Five binary ML classification models (Logistic Regression; Classification and Regression Trees; Random Forests; Naïve Bayes; and Support Vector Machines) were fit and ranked by Area Under the Curve (AUC) scores. We compared our best model with two models previously proposed in the literature for goodness of fit, precision, and through biological validation.ResultsOur best performing model with threshold reclassification for predicting delirium was based on a multiple logistic regression using the 31 clinical actions (AUC 0.83). Our model out performed other proposed models by biological validation on clinically meaningful, delirium-associated outcomes.ConclusionsHurdles in identifying accurate labels in large-scale datasets limit clinical applications of ML in delirium. We developed a novel labeling model for delirium in the ICU using a large, public data set. By using guideline-directed clinical actions independent from risk factors, treatments, and outcomes as model predictors, our classifier could be used as a delirium label for future clinically targeted models.

Project description:Strained intensive care unit (ICU) capacity represents a fundamental supply-demand mismatch in ICU resources. Strain is likely to be influenced by a range of factors; however, there has been no systematic evaluation of the spectrum of measures that may indicate strain on ICU capacity.We performed a systematic review to identify indicators of strained capacity. A comprehensive peer-reviewed search of MEDLINE, EMBASE, CINAHL, Cochrane Library, and Web of Science Core Collection was performed along with selected grey literature sources. We included studies published in English after 1990. We included studies that: (1) focused on ICU settings; (2) included description of a quality or performance measure; and (3) described strained capacity. Retrieved studies were screened, selected and extracted in duplicate. Quality was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Analysis was descriptive.Of 5297 studies identified in our search; 51 fulfilled eligibility. Most were cohort studies (n = 39; 76.5%), five (9.8%) were case-control, three (5.8%) were cross-sectional, two (3.9%) were modeling studies, one (2%) was a correlational study, and one (2%) was a quality improvement project. Most observational studies were high quality. Sixteen measures designed to indicate strain were identified 110 times, and classified as structure (n = 4, 25%), process (n = 7, 44%) and outcome (n = 5, 31%) indicators, respectively. The most commonly identified indicators of strain were ICU acuity (n = 21; 19.1% [process]), ICU readmission (n = 18; 16.4% [outcome]), after-hours discharge (n = 15; 13.6% [process]) and ICU census (n = 13; 11.8% [structure]). There was substantial heterogeneity in the operational definitions used to define strain indicators across studies.We identified and characterized 16 indicators of strained ICU capacity across the spectrum of healthcare quality domains. Future work should aim to evaluate their implementation into practice and assess their value for evaluating strategies to mitigate strain.This systematic review was registered at PROSPERO (March 27, 2015; CRD42015017931 ).

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The impact of PACS on clinician work practices in the intensive care unit: a systematic review of the literature.

Publications

The impact of PACS on clinician work practices in the intensive care unit: a systematic review of the literature.

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