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Exposing exposure: enhancing patient safety through automated data mining of nuclear medicine reports for quality assurance and organ dose monitoring.

ABSTRACT: To develop and validate an open-source informatics toolkit capable of creating a radiation exposure data repository from existing nuclear medicine report archives and to demonstrate potential applications of such data for quality assurance and longitudinal patient-specific radiation dose monitoring.This study was institutional review board approved and HIPAA compliant. Informed consent was waived. An open-source toolkit designed to automate the extraction of data on radiopharmaceuticals and administered activities from nuclear medicine reports was developed. After iterative code training, manual validation was performed on 2359 nuclear medicine reports randomly selected from September 17, 1985, to February 28, 2011. Recall (sensitivity) and precision (positive predictive value) were calculated with 95% binomial confidence intervals. From the resultant institutional data repository, examples of usage in quality assurance efforts and patient-specific longitudinal radiation dose monitoring obtained by calculating organ doses from the administered activity and radiopharmaceutical of each examination were provided.Validation statistics yielded a combined recall of 97.6% ± 0.7 (95% confidence interval) and precision of 98.7% ± 0.5. Histograms of administered activity for fluorine 18 fluorodeoxyglucose and iodine 131 sodium iodide were generated. An organ dose heatmap which displays a sample patient's dose accumulation from multiple nuclear medicine examinations was created.Large-scale repositories of radiation exposure data can be extracted from institutional nuclear medicine report archives with high recall and precision. Such repositories enable new approaches in radiation exposure patient safety initiatives and patient-specific radiation dose monitoring.

SUBMITTER: Ikuta I

PROVIDER: S-EPMC3401350 | biostudies-other | 2012 Aug

REPOSITORIES: biostudies-other

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Project description:PurposeTo develop and validate an informatics toolkit that extracts anatomy-specific computed tomography (CT) radiation exposure metrics (volume CT dose index and dose-length product) from existing digital image archives through optical character recognition of CT dose report screen captures (dose screens) combined with Digital Imaging and Communications in Medicine attributes.Materials and methodsThis institutional review board-approved HIPAA-compliant study was performed in a large urban health care delivery network. Data were drawn from a random sample of CT encounters that occurred between 2000 and 2010; images from these encounters were contained within the enterprise image archive, which encompassed images obtained at an adult academic tertiary referral hospital and its affiliated sites, including a cancer center, a community hospital, and outpatient imaging centers, as well as images imported from other facilities. Software was validated by using 150 randomly selected encounters for each major CT scanner manufacturer, with outcome measures of dose screen retrieval rate (proportion of correctly located dose screens) and anatomic assignment precision (proportion of extracted exposure data with correctly assigned anatomic region, such as head, chest, or abdomen and pelvis). The 95% binomial confidence intervals (CIs) were calculated for discrete proportions, and CIs were derived from the standard error of the mean for continuous variables. After validation, the informatics toolkit was used to populate an exposure repository from a cohort of 54 549 CT encounters; of which 29 948 had available dose screens.ResultsValidation yielded a dose screen retrieval rate of 99% (597 of 605 CT encounters; 95% CI: 98%, 100%) and an anatomic assignment precision of 94% (summed DLP fraction correct 563 in 600 CT encounters; 95% CI: 92%, 96%). Patient safety applications of the resulting data repository include benchmarking between institutions, CT protocol quality control and optimization, and cumulative patient- and anatomy-specific radiation exposure monitoring.ConclusionLarge-scale anatomy-specific radiation exposure data repositories can be created with high fidelity from existing digital image archives by using open-source informatics tools.

Project description:BACKGROUND:The police attend numerous domestic violence events each year, recording details of these events as both structured (coded) data and unstructured free-text narratives. Abuse types (including physical, psychological, emotional, and financial) conducted by persons of interest (POIs) along with any injuries sustained by victims are typically recorded in long descriptive narratives. OBJECTIVE:We aimed to determine if an automated text mining method could identify abuse types and any injuries sustained by domestic violence victims in narratives contained in a large police dataset from the New South Wales Police Force. METHODS:We used a training set of 200 recorded domestic violence events to design a knowledge-driven approach based on syntactical patterns in the text and then applied this approach to a large set of police reports. RESULTS:Testing our approach on an evaluation set of 100 domestic violence events provided precision values of 90.2% and 85.0% for abuse type and victim injuries, respectively. In a set of 492,393 domestic violence reports, we found 71.32% (351,178) of events with mentions of the abuse type(s) and more than one-third (177,117 events; 35.97%) contained victim injuries. "Emotional/verbal abuse" (33.46%; 117,488) was the most common abuse type, followed by "punching" (86,322 events; 24.58%) and "property damage" (22.27%; 78,203 events). "Bruising" was the most common form of injury sustained (51,455 events; 29.03%), with "cut/abrasion" (28.93%; 51,284 events) and "red marks/signs" (23.71%; 42,038 events) ranking second and third, respectively. CONCLUSIONS:The results suggest that text mining can automatically extract information from police-recorded domestic violence events that can support further public health research into domestic violence, such as examining the relationship of abuse types with victim injuries and of gender and abuse types with risk escalation for victims of domestic violence. Potential also exists for this extracted information to be linked to information on the mental health status.

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Exposing exposure: enhancing patient safety through automated data mining of nuclear medicine reports for quality assurance and organ dose monitoring.

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