A decision-analytic approach to define poor prognosis patients: a case study for non-seminomatous germ cell cancer patients

Dijk, M. R. van; Steyerberg, Ewout W.; Habbema, J. Dik F.

doi:10.1186/1472-6947-8-1

Cited by 48 publications

(34 citation statements)

References 39 publications

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“…1031219-2). Subsequent to this certification, free-text fields were scanned for personal information using a previously published rule-based approach 10 . Briefly, this approach scans text for known patterns indicating presence of PHI (e.g.…”

Section: Methodsmentioning

confidence: 99%

The eICU Collaborative Research Database, a freely available multi-center database for critical care research

et al. 2018

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Critical care patients are monitored closely through the course of their illness. As a result of this monitoring, large amounts of data are routinely collected for these patients. Philips Healthcare has developed a telehealth system, the eICU Program, which leverages these data to support management of critically ill patients. Here we describe the eICU Collaborative Research Database, a multi-center intensive care unit (ICU)database with high granularity data for over 200,000 admissions to ICUs monitored by eICU Programs across the United States. The database is deidentified, and includes vital sign measurements, care plan documentation, severity of illness measures, diagnosis information, treatment information, and more. Data are publicly available after registration, including completion of a training course in research with human subjects and signing of a data use agreement mandating responsible handling of the data and adhering to the principle of collaborative research. The freely available nature of the data will support a number of applications including the development of machine learning algorithms, decision support tools, and clinical research.

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Section: Methodsmentioning

confidence: 99%

The eICU Collaborative Research Database, a freely available multi-center database for critical care research

et al. 2018

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“…due to toxicity of treatment) is assumed to be constant at 4%. Net benefit occurs only when the cancer-specific mortality given standard treatment is above the threshold of 11% 451 (2) Check assumptions of relative benefit and absolute harm: subgroup effects of treatment may exist, both for benefit and harm, which invalidate the simple decision-analytic model in Fig. 2.3 .…”

Section: Intensity Of Treatmentmentioning

confidence: 99%

“…451 Several non-randomized trials reported a higher survival for patients treated with HD-CT as first line treatment (including etoposide, ifosfamide, cisplatin) with autologous stem cell support, compared to standard-dose (SD) chemotherapy (including bleomycin, etoposide, cisplatin). However, HD-CT is related to a higher toxicity, both during treatment (e.g.…”

Section: *236 Example: Defining a Poor Prognosis Subgroup In Cancermentioning

confidence: 99%

Clinical Prediction Models

Steyerberg¹

2009

Statistics for Biology and Health

1,317

720

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“… 1 This phenomenon of information overload has been observed in several care settings, from outpatient clinics to hospital admissions and the emergency department (ED). 2–4 With the advent of health information exchange, review of patient data will only become more complex and time consuming. 5 , 6 One of the promises of the electronic health record (EHR) is to support clinicians at the point of patient care.…”

Section: Background and Significancementioning

confidence: 99%

HARVEST, a longitudinal patient record summarizer

Hirsch

Tanenbaum

Gorman

et al. 2014

Journal of the American Medical Informatics Association

111

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Objective To describe HARVEST, a novel point-of-care patient summarization and visualization tool, and to conduct a formative evaluation study to assess its effectiveness and gather feedback for iterative improvements.Materials and methods HARVEST is a problem-based, interactive, temporal visualization of longitudinal patient records. Using scalable, distributed natural language processing and problem salience computation, the system extracts content from the patient notes and aggregates and presents information from multiple care settings. Clinical usability was assessed with physician participants using a timed, task-based chart review and questionnaire, with performance differences recorded between conditions (standard data review system and HARVEST).Results HARVEST displays patient information longitudinally using a timeline, a problem cloud as extracted from notes, and focused access to clinical documentation. Despite lack of familiarity with HARVEST, when using a task-based evaluation, performance and time-to-task completion was maintained in patient review scenarios using HARVEST alone or the standard clinical information system at our institution. Subjects reported very high satisfaction with HARVEST and interest in using the system in their daily practice.Discussion HARVEST is available for wide deployment at our institution. Evaluation provided informative feedback and directions for future improvements.Conclusions HARVEST was designed to address the unmet need for clinicians at the point of care, facilitating review of essential patient information. The deployment of HARVEST in our institution allows us to study patient record summarization as an informatics intervention in a real-world setting. It also provides an opportunity to learn how clinicians use the summarizer, enabling informed interface and content iteration and optimization to improve patient care.

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A decision-analytic approach to define poor prognosis patients: a case study for non-seminomatous germ cell cancer patients

Cited by 48 publications

References 39 publications

The eICU Collaborative Research Database, a freely available multi-center database for critical care research

The eICU Collaborative Research Database, a freely available multi-center database for critical care research

Clinical Prediction Models

HARVEST, a longitudinal patient record summarizer

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