Uncovering and improving upon the inherent deficiencies of radiology reporting through data mining

Degree of Uncertainty in Reporting Imaging Findings for Necrotizing Enterocolitis: A Secondary Analysis from a Pilot Randomized Diagnostic Trial

Diagnosis of necrotizing enterocolitis (NEC) relies heavily on imaging, but uncertainty in the language used in imaging reports can result in ambiguity, miscommunication, and potential diagnostic errors. To determine the degree of uncertainty in reporting imaging findings for NEC, we conducted a secondary analysis of the data from a previously completed pilot diagnostic randomized controlled trial (2019-2020). The study population comprised sixteen preterm infants with suspected NEC randomized to abdominal radiographs (AXRs) or AXR + bowel ultrasound (BUS). The level of uncertainty was determined using a four-point Likert scale. Overall, we reviewed radiology reports of 113 AXR and 24 BUS from sixteen preterm infants with NEC concern. The BUS reports showed less uncertainty for reporting pneumatosis, portal venous gas, and free air compared to AXR reports (pneumatosis: 1 [1-1.75) vs. 3 [2-3], p < 0.0001; portal venous gas: 1 [1-1] vs. 1 [1-1], p = 0.02; free air: 1 [1-1] vs. 2 [1-3], p < 0.0001). In conclusion, we found that BUS reports have a lower degree of uncertainty in reporting imaging findings of NEC compared to AXR reports. Whether the lower degree of uncertainty of BUS reports positively impacts clinical decision making in infants with possible NEC remains unknown.

Assessment of the Response to Abdominal and Pelvic Computed Tomography Report Recommendations: A Single-Center, Retrospective, Chart Review Study

Objectives

The radiology report is the primary form of communication between the radiologists and referring clinicians. It is a structured document containing several key components pertaining to the interpretation of radiological examinations and may require the addition of follow-up imaging recommendations to optimize patient outcomes. This study aims to determine whether follow-up imaging recommendations are being acknowledged and acted upon by referrers.

Methods

This retrospective study was conducted at a single tertiary hospital. Prerecorded BESTCare data of patients who underwent abdominal and pelvic computed tomography (CT) scans between October 1, 2017, and December 31, 2017, and received recommendations for further evaluation were collected after obtaining ethical approval from the local authority. Data of patients younger than 14 years old, patients who did not receive a recommendation, and patients who had CT scans that were uploaded to the BESTCare system but were performed outside the institution were excluded. The collected data were recorded in a password-protected Microsoft Excel file for further analysis.

Results

A total of 523 report recommendations from 422 abdominal and pelvic CT reports were analyzed. The most common organs indicated for CT scan evaluation were the breast (N = 54, 10.33%), kidney (N = 46, 8.80%), lymph node (N = 36, 6.88%), and colon (N = 33, 6.31%). The most common type of further evaluation recommended was further imaging (N = 410, 78.39%). A total of 278 (53.15%) recommendations were not performed, with 199 (71.58%) not having a documented rationale for noncompliance.

Conclusion

The majority of the follow-up imaging recommendations to ordering physicians were not carried out. This study highlights the need for notification and audit systems to monitor compliance with follow-up recommendations. Improving the communication between radiologists and referring physicians is key to optimizing patient healthcare.

Pediatric appendiceal ultrasound: maintaining accuracy, increasing determinacy and improving clinical outcomes following the introduction of a standardized reporting template

BACKGROUND

Pediatric patients who underwent appendiceal US and received an equivocal interpretation had poorer clinical outcomes and higher medical costs compared to those to whom a definitive interpretation was given, either positive or negative. In an effort to reduce equivocal interpretations, we educated our group on the importance of increasing determinacy and encouraged the use of a reporting template with a definitive impression.

OBJECTIVE

We hypothesized that educational sessions and implementation of an optional reporting template with only a definitive impression would reduce equivocal reporting and improve clinical outcomes without negatively impacting US diagnostic performance.

MATERIALS AND METHODS

We retrospectively reviewed the charts of all patients <18 years old at Mayo Clinic Rochester whose initial evaluation for acute appendicitis was a US in the 3-year period following educational sessions and template implementation. All studies were interpreted by board-certified fellowship-trained pediatric radiologists. We performed statistical analysis to compare the pre- and post-implementation cohorts.

RESULTS

Following intervention, the rate of equivocal US interpretations was reduced from 23.7% to 9.3% (P<0.001). For studies with a definitive interpretation, measures of diagnostic performance of appendiceal US were similar for the pre- and post-implementation groups. US performance parameters were independent of appendiceal visualization. Follow-up CT utilization decreased from 18.7% to 8.9% (P<0.001). The negative laparotomy rate resulting from false-positive US interpretations remained low (6.8% vs. 5.0%, P=0.31).

CONCLUSION

Following education sessions and implementation of an appendiceal US reporting template encouraging definitive reporting, equivocation was reduced, excellent diagnostic performance was maintained, follow-up CT utilization was reduced, and a low negative laparotomy rate was preserved.

Quantitative Analysis of Uncertainty in Medical Reporting: Creating a Standardized and Objective Methodology

Uncertainty in text-based medical reports has long been recognized as problematic, frequently resulting in misunderstanding and miscommunication. One strategy for addressing the negative clinical ramifications of report uncertainty would be the creation of a standardized methodology for characterizing and quantifying uncertainty language, which could provide both the report author and reader with context related to the perceived level of diagnostic confidence and accuracy. A number of computerized strategies could be employed in the creation of this analysis including string search, natural language processing and understanding, histogram analysis, topic modeling, and machine learning. The derived uncertainty data offers the potential to objectively analyze report uncertainty in real time and correlate with outcomes analysis for the purpose of context and user-specific decision support at the point of care, where intervention would have the greatest clinical impact.

Impact of Radiology Report Wording on Care of Patients With Acute Epiploic Appendagitis

OBJECTIVE. The purpose of this study was to evaluate the association between the diagnostic certainty expressed by the wording of CT report impressions and subsequent use of standard treatment with analgesics versus nonstandard antibiotic administration in patients with acute epiploic appendagitis (EA). MATERIALS AND METHODS. Demographic, clinical, and radiologic data from a 10-year cohort of patients with acute EA were retrospectively analyzed and correlated with standard treatment with analgesics versus nonstandard treatment with antibiotics. A level of certainty was assigned to the CT report language based on the wording of the impression statements by two radiologists; their interreader agreement was assessed with kappa statistics. Bivariate analyses were performed to correlate all variables with antibiotic administration and to assess for collinearity. Multivariate logistic regression was performed to identify independent predictors of antibiotic use in patients with acute EA. RESULTS. Of 124 patients with CT-diagnosed acute EA, 22% (27/124) received antibiotic treatment. After the CT report impressions were evaluated, 27% (34/124) were categorized as low certainty and 73% (90/124) as high certainty (κ = 0.958, p < 0.001). Multivariate regression was significant (p < 0.001, Nagelkerke R² = 0.249) and found CT report impressions' level of certainty (odds ratio [OR] = 6.1, p < 0.001) and evaluation in an outpatient clinic rather than an emergency department (ED) (OR = 4.4, p = 0.003) to be independent predictors of antibiotic administration for patients with acute EA. Outpatient presentation was also correlated with age, abdominal pain duration, and left-colonic involvement in the bivariate analysis (all p ≤ 0.01). CONCLUSION. The diagnostic certainty conveyed by the wording of CT report impressions correlated with antibiotic treatment decisions for patients with acute EA. Patients whose report impressions expressed low rather than high certainty were six times more likely to receive antibiotic therapy; patients evaluated at outpatient clinics rather than EDs were four times more likely.

Detecting Technical Image Quality in Radiology Reports

Image interpretation accuracy is critical to ensure optimal care, yet many diagnostic reports contain expressions of uncertainty often due to shortcomings in technical quality among other factors. While radiologists will usually attempt to interpret images and render a diagnosis even if the imaging quality is suboptimal, often the details related to any quality concerns are dictated into the report. Despite imaging exam quality being an import factor for accurate image interpretation, there is a significant knowledge gap in terms of understanding the nature and frequency of technical limitations mentioned in radiology reports. To address some of these limitations, in this research we developed algorithms to automatically detect a broad spectrum of acquisition-related quality concerns using a dataset containing 1,210,858 exams. There was some type of a quality concern mentioned in 2.4% of exams with motion being the most frequent.

A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining

The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However, the lack of a comprehensive and systematic narrative motivated us to construct a literature review on this topic. In this paper, we present a review of the literature on healthcare analytics using data mining and big data. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a database search between 2005 and 2016. Critical elements of the selected studies—healthcare sub-areas, data mining techniques, types of analytics, data, and data sources—were extracted to provide a systematic view of development in this field and possible future directions. We found that the existing literature mostly examines analytics in clinical and administrative decision-making. Use of human-generated data is predominant considering the wide adoption of Electronic Medical Record in clinical care. However, analytics based on website and social media data has been increasing in recent years. Lack of prescriptive analytics in practice and integration of domain expert knowledge in the decision-making process emphasizes the necessity of future research.

Redefining the Practice of Peer Review Through Intelligent Automation Part 2: Data-Driven Peer Review Selection and Assignment

In conventional radiology peer review practice, a small number of exams (routinely 5% of the total volume) is randomly selected, which may significantly underestimate the true error rate within a given radiology practice. An alternative and preferable approach would be to create a data-driven model which mathematically quantifies a peer review risk score for each individual exam and uses this data to identify high risk exams and readers, and selectively target these exams for peer review. An analogous model can also be created to assist in the assignment of these peer review cases in keeping with specific priorities of the service provider. An additional option to enhance the peer review process would be to assign the peer review cases in a truly blinded fashion. In addition to eliminating traditional peer review bias, this approach has the potential to better define exam-specific standard of care, particularly when multiple readers participate in the peer review process.

Redefining the Practice of Peer Review Through Intelligent Automation-Part 3: Automated Report Analysis and Data Reconciliation

One method for addressing existing peer review limitations is the assignment of peer review cases on a completely blinded basis, in which the peer reviewer would create an independent report which can then be cross-referenced with the primary reader report of record. By leveraging existing computerized data mining techniques, one could in theory automate and objectify the process of report data extraction, classification, and analysis, while reducing time and resource requirements intrinsic to manual peer review report analysis. Once inter-report analysis has been performed, resulting inter-report discrepancies can be presented to the radiologist of record for review, along with the option to directly communicate with the peer reviewer through an electronic data reconciliation tool aimed at collaboratively resolving inter-report discrepancies and improving report accuracy. All associated report and reconciled data could in turn be recorded in a referenceable peer review database, which provides opportunity for context and user-specific education and decision support.

Association Between Confidence Level of Acute Pulmonary Embolism Diagnosis on CTPA images and Clinical Outcomes

RATIONALE AND OBJECTIVES

The purpose was to evaluate clinical characteristics associated with low confidence in diagnosis of acute pulmonary embolism (PE) as expressed in computed tomography pulmonary angiography (CTPA) reports and to evaluate the effect of confidence level in PE diagnosis on patient clinical outcomes.

MATERIALS AND METHODS

This study included radiology reports from 1664 consecutive CTPA considered positive for acute PE (8/2003-5/2010). All reports were retrospectively assessed for the level of confidence in diagnosis. Baseline characteristics and outcomes (therapies related to PE and short-term mortality) were compared between high and low confidence groups. Multivariable logistic and Cox regression analyses were used to analyze the relationship between the confidence level and outcomes.

RESULTS

One-hundred sixty of 1664 (9.6%) reports had language that reflected a low confidence in PE diagnosis. The low confidence group had smaller (segmental and subsegmental) suspected emboli (prevalence, 72.5% vs. 50.7%; P < .001) and more comorbidities. The low confidence group had a lower likelihood of receiving PE-related therapies (adjusted odds ratio [OR], 0.18; 95% confidence interval, 0.10-031, P < .001), but there was no change in the all-cause and PE-related 30-day and/or 90-day mortality (OR of death for low confidence, 0.81-1.13, P values > .5).

CONCLUSIONS

Roughly 10% of positive CTPA reports had uncertainty in PE findings, and patients with reports categorized as low confidence had smaller emboli and more comorbidities. Although the low confidence group was less likely to receive PE-related therapies, patients in this group were not associated with higher probability of short-term mortality.

Impact of an Information Technology-Enabled Initiative on the Quality of Prostate Multiparametric MRI Reports

RATIONALE AND OBJECTIVES

Assess the impact of implementing a structured report template and a computer-aided diagnosis (CAD) tool on the quality of prostate multiparametric magnetic resonance imaging (mp-MRI) reports.

MATERIALS AND METHODS

Institutional Review Board approval was obtained for this Health Insurance Portability and Accountability Act-compliant study performed at an academic medical center. The study cohort included all prostate mp-MRI reports (n = 385) finalized 6 months before and after implementation of a structured report template and a CAD tool (collectively the information technology [IT] tools) integrated into the picture archiving and communication system workstation. Primary outcome measure was quality of prostate mp-MRI reports. An expert panel of our institution's subspecialty-trained abdominal radiologists defined prostate mp-MRI report quality as optimal, satisfactory, or unsatisfactory based on documentation of nine variables. Reports were reviewed to extract the predefined quality variables and determine whether the IT tools were used to create each report. Chi-square and Student's t tests were used to compare report quality before and after implementation of IT tools.

RESULTS

The overall proportion of optimal or satisfactory reports increased from 29.8% (47/158) to 53.3% (121/227) (P < .001) after implementing the IT tools. Although the proportion of optimal or satisfactory reports increased among reports generated using at least one of the IT tools (47/158 = [29.8%] vs. 105/161 = [65.2%]; P < .001), there was no change in quality among reports generated without use of the IT tools (47/158 = [29.8%] vs. 16/66 = [24.2%]; P = .404).

CONCLUSIONS

The use of a structured template and CAD tool improved the quality of prostate mp-MRI reports compared to free-text report format and subjective measurement of contrast enhancement kinetic curve.

Strategies for medical data extraction and presentation part 1: current limitations and deficiencies

Data overload is a burgeoning challenge for the medical imaging community; with resulting technical, clinical, and economic ramifications. A primary concern for radiologists is the timely, efficient, and accurate extraction of imaging and clinical data, which collectively are essential in determining accurate diagnosis. In current practice, imaging data retrieval is limited by the fact that imaging and report data are de-coupled from one another, along with the non-standardized and often ambiguous free text data contained within narrative radiology reports. Clinical data retrieval is equally challenging and flawed by the lack of information system integration, paucity of clinical order entry data, and diminished role of the technologist in providing clinical data. These combined factors have the potential to adversely affect radiologist performance and clinical outcomes by diminishing workflow, report accuracy, and diagnostic confidence. New and innovative strategies are required to improve and automate data extraction and presentation, in a context- and user-specific fashion.

Quality of chest radiograph reports

BACKGROUND

Examination requests and imaging reports are the most important communication instruments between clinicians and radiologists. An accurate and clear report helps referring physicians make care decisions for their patients.

PURPOSE

To evaluate the contents of initial and re-reported chest reports, assess the inter-observer agreement, and evaluate the clarity of the report contents from the viewpoint of the referring physicians.

MATERIAL AND METHODS

The content and agreement of the reports were analyzed by comparing the initial reports with re-reports prepared by a chest radiologist. The referring physicians evaluated the contents of 50 reports regarding their medical facts, clarity, and intelligibility. The results were analyzed using cross-over tables, the Pearson Chi-Square, and kappa statistics.

RESULTS

Radiologists mostly addressed the questions posed by the referring physicians. General radiologists included separate conclusions in their reports more frequently (22%) than the chest radiologist in her re-reports. Reports prepared by the chest radiologist contained nearly 50% more findings than the general radiologists' reports. Inter-observer agreement between the initial and specialist re-reported reports was 66%, but the kappa value was 0.31. The reports were considered clear/intelligible by the referring physicians in 68% of the initial reports by the general radiologists and in 94% of the re-reported studies by the chest radiologist.

CONCLUSION

Radiology report quality was rather high despite their contents varying depending on the radiologist. Inter-observer agreement of the chest radiographs was low due to the non-structured reports containing different quantities of information, thus complicating the comparison. Referring physicians considered both short and long radiology reports to be clear.

Hidden costs of poor image quality: a radiologist's perspective

Although image quality is a well-recognized component in the successful delivery of medical imaging services, it has arguably declined over the past decade owing to several technical, economic, cultural, and geographic factors. To improve quality, the radiologist community must take a more proactive role in image quality analysis and optimization; these require analysis of not just the single step of image acquisition but the entire imaging chain. Radiologists can benefit through improved report accuracy, diagnostic confidence, and workflow efficiency. The derived data-driven analyses offer an objective means for provider performance analysis, which can help combat commoditization trends and self-referral by nonradiologist providers.

Digitization of medicine: how radiology can take advantage of the digital revolution

In the era of medical cost containment, radiologists must continually maintain their actual and perceived value to patients, payers, and referring providers. Exploitation of current and future digital technologies may be the key to defining and promoting radiology's "brand" and assure our continued relevance in providing predictive, preventive, personalized, and participatory medicine. The Association of University of Radiologists Radiology Research Alliance Digitization of Medicine Task Force was formed to explore the opportunities and challenges of the digitization of medicine that are relevant to radiologists, which include the reporting paradigm, computational biology, and imaging informatics. In addition to discussing these opportunities and challenges, we consider how change occurs in medicine, and how change may be effected in medical imaging community. This review article is a summary of the research of the task force and hopefully can be used as a stimulus for further discussions and development of action plans by radiology leaders.

The future of the radiology information system

OBJECTIVE

Today in the hospital setting, several functions of the radiology information system (RIS), including order entry, patient registration, report repository, and the physician directory, have moved to enterprise electronic medical records. Some observers might conclude that the RIS is going away. In this article, we contend that because of the maturity of the RIS market compared with other areas of the health care enterprise, radiology has a unique opportunity to innovate.

CONCLUSION

While most of the hospital enterprise spends the next several years going through the digital transformation converting from paper to a digital format, radiology can leap ahead in its use of analytics and information technology. This article presents a summary of new RIS functions still maturing and open to innovation in the RIS market.

Automatically correlating clinical findings and body locations in radiology reports using MedLEE

In this paper, we describe and evaluate a system that extracts clinical findings and body locations from radiology reports and correlates them. The system uses Medical Language Extraction and Encoding System (MedLEE) to map the reports' free text to structured semantic representations of their content. A lightweight reasoning engine extracts the clinical findings and body locations from MedLEE's semantic representation and correlates them. Our study is illustrative for research in which existing natural language processing software is embedded in a larger system. We manually created a standard reference based on a corpus of neuro and breast radiology reports. The standard reference was used to evaluate the precision and recall of the proposed system and its modules. Our results indicate that the precision of our system is considerably better than its recall (82.32-91.37% vs. 35.67-45.91%). We conducted an error analysis and discuss here the practical usability of the system given its recall and precision performance.

Bridging the text-image gap: a decision support tool for real-time PACS browsing

In this paper, we introduce an ontology-based technology that bridges the gap between MR images on the one hand and knowledge sources on the other hand. The proposed technology allows the user to express interest in a body region by selecting this region on the MR image he or she is viewing with a mouse device. The proposed technology infers the intended body structure from the manual selection and searches the external knowledge source for pertinent information. This technology can be used to bridge the gap between image data in the clinical workflow and (external) knowledge sources that help to assess the case with increased certainty, accuracy, and efficiency. We evaluate an instance of the proposed technology in the neurodomain by means of a user study in which three neuroradiologists participated. The user study shows that the technology has high recall (>95%) when it comes to inferring the intended brain region from the participant's manual selection. We are confident that this helps to increase the experience of browsing external knowledge sources.

A novel report generation approach for medical applications: the SISDS methodology and its applications

BACKGROUND

Despite exciting innovation in information system technologies, the medical reporting has remained static for a long time. Structured reporting was established to address the deficiencies in report content but has largely failed in its adoption due to concerns over workflow and productivity. The methods used in medical reporting are insufficient in providing with information for statistical processing and medical decision making as well as high quality healthcare.

OBJECTIVE

The aim of this study is to introduce a novel method that enables professionals to efficiently produce medical reports that are less error-prone and can be used in decision support systems without extensive post-processing.

METHODOLOGY

We first present the formal definition of the proposed method, called SISDS, that provides a clear separation between the data, logic and presentation layers. It allows free-text like structured data entry in a structured form, and reduces the cognitive effort by inline editing and dynamically controlling the information flow based on the entered data. Then, we validate the usability and reliability of the method on a real-world testbed in the field of radiology. For this purpose, a sample esophagus report was constructed by a focus group of radiologists and real patient data have been collected using a web-based prototype; these data are then used to build a decision support system with off-the-shelf tools. The usability of the method is assessed by evaluating its acceptability by the users and the accuracy of the resulting decision support system. For reliability, we conducted a controlled experiment comparing the performance of the method to that of transcriptionist-oriented systems in terms of the rate of successful diagnosis and the total time required to enter the data.

RESULT

The most noticeable observation in the evaluation is that the rate of successful diagnosis improves significantly with the proposed method; in our case study, a success rate of 81.25% has been achieved by using the SISDS method compared to 43.75% for the transcriptionist-oriented system. In addition, the average time required to obtain the final approved reports decreased from 29 min to 14 min. Based on questionnaire responses, the acceptance rate of the SISDS methodology by users is also found to outperform the rates of the current methods.

CONCLUSION

The empirical results show that the method can effectively help to reduce medical errors, increase data quality, and lead to more accurate decision support. In addition, the dynamic hierarchical data entry model proves to provide a good balance between cognitive load and structured data collection.

Consensus versus disagreement in imaging research: a case study using the LIDC database

Traditionally, image studies evaluating the effectiveness of computer-aided diagnosis (CAD) use a single label from a medical expert compared with a single label produced by CAD. The purpose of this research is to present a CAD system based on Belief Decision Tree classification algorithm, capable of learning from probabilistic input (based on intra-reader variability) and providing probabilistic output. We compared our approach against a traditional decision tree approach with respect to a traditional performance metric (accuracy) and a probabilistic one (area under the distance-threshold curve-AuC(dt)). The probabilistic classification technique showed notable performance improvement in comparison with the traditional one with respect to both evaluation metrics. Specifically, when applying cross-validation technique on the training subset of instances, boosts of 28.26% and 30.28% were noted for the probabilistic approach with respect to accuracy and AuC(dt), respectively. Furthermore, on the validation subset of instances, boosts of 20.64% and 23.21% were noted again for the probabilistic approach with respect to the same two metrics. In addition, we compared our CAD system results with diagnostic data available for a small subset of the Lung Image Database Consortium database. We discovered that when our CAD system errs, it generally does so with low confidence. Predictions produced by the system also agree with diagnoses of truly benign nodules more often than radiologists, offering the possibility of reducing the false positives.

Medical imaging data reconciliation, part 3: reconciliation of historical and current radiology report data

Correlation of historical imaging and radiology report data with the current imaging data set is a critical step in the radiologic interpretation process and, if incomplete, can adversely affect diagnostic accuracy. In its current form, the extraction and analysis of historical imaging and report data is limited by manual workflow, inefficient data organization, and a lack of imaging and report data integration. The reconciliation of historical and contemporaneous radiology report data provides an opportunity to improve the consistency, completeness, and accuracy of radiology report data, while providing opportunities to automate workflow related to data extraction, interpretation, and peer review. The derived data analytics can in turn be used to facilitate physician consultations, education and training, and proactive intervention in the event of report discrepancies.

Informatics in radiology: Measuring and improving quality in radiology: meeting the challenge with informatics

Quality is becoming a critical issue for radiology. Measuring and improving quality is essential not only to ensure optimum effectiveness of care and comply with increasing regulatory requirements, but also to combat current trends leading to commoditization of radiology services. A key challenge to implementing quality improvement programs is to develop methods to collect knowledge related to quality care and to deliver that knowledge to practitioners at the point of care. There are many dimensions to quality in radiology that need to be measured, monitored, and improved, including examination appropriateness, procedure protocol, accuracy of interpretation, communication of imaging results, and measuring and monitoring performance improvement in quality, safety, and efficiency. Informatics provides the key technologies that can enable radiologists to measure and improve quality. However, few institutions recognize the opportunities that informatics methods provide to improve safety and quality. The information technology infrastructure in most hospitals is limited, and they have suboptimal adoption of informatics techniques. Institutions can tackle the challenges of assessing and improving quality in radiology by means of informatics.

Creation and storage of standards-based pre-scanning patient questionnaires in PACS as DICOM objects

Radiology departments around the country have completed the first evolution to digital imaging by becoming filmless. The next step in this evolution is to become truly paperless. Both patient and non-patient paperwork has to be eliminated in order for this transition to occur. A paper-based set of patient pre-scanning questionnaires were replaced with web-based forms for use in an outpatient imaging center. We discuss this process by which questionnaire elements are converted into SNOMED-CT terminology concepts, stored for future use, and sent to PACS in Digital Imaging and Communications in Medicine (DICOM) format to be permanently stored with the relevant study in the DICOM image database.

Automatic classification of mammography reports by BI-RADS breast tissue composition class

Because breast tissue composition partially predicts breast cancer risk, classification of mammography reports by breast tissue composition is important from both a scientific and clinical perspective. A method is presented for using the unstructured text of mammography reports to classify them into BI-RADS breast tissue composition categories. An algorithm that uses regular expressions to automatically determine BI-RADS breast tissue composition classes for unstructured mammography reports was developed. The algorithm assigns each report to a single BI-RADS composition class: 'fatty', 'fibroglandular', 'heterogeneously dense', 'dense', or 'unspecified'. We evaluated its performance on mammography reports from two different institutions. The method achieves >99% classification accuracy on a test set of reports from the Marshfield Clinic (Wisconsin) and Stanford University. Since large-scale studies of breast cancer rely heavily on breast tissue composition information, this method could facilitate this research by helping mine large datasets to correlate breast composition with other covariates.

Intersectional innovation in biomarker development for patient-centric medicine

The pharmaceutical and healthcare industries are being revolutionized by the use of genomics, proteomics, metabolomics, bioinformatics and molecular imaging. Patient friendly diagnosis, treatment and disease management options that utilize the combination of these technologies are currently in development. New innovations in pharmaceutical advancement are taking place at the intersection of these technologies, and will be coupled with societal changes as we move to a fully networked and individual-centric consumer base. Numerous examples of the combinations of molecular characterization technologies aimed at better preclinical and clinical disease understanding, diagnosis and treatment are highlighted that are ideally situated to generate the intersectional innovation that drives healthcare advancement. The true value in patient-centric medicine will only be realized as the improved molecular characterization of disease provided by these technologies is integrated across platforms that operate directly in the patient and care provider space to provide a comprehensive view of health. Molecular profiling and imaging technologies must become fully integrated and amenable for patient and physician use in a networked environment that can provide a personal health avatar approach to medicine.

Customization of medical report data

Structured reporting offers a number of theoretical advantages, perhaps the most important of which is creation of standardized report databases. The standardized data created can in turn be used to customize data display, report content, historical data retrieval, interpretation analysis, and results communication in both a context and user-specific manner. In addition, these referenceable report databases can be used to facilitate the practice of evidence based medicine, through data-driven meta-analysis and determination of best practice guidelines. This concept will only be realized if the customized data delivery technology provides real and tangible value to end users, accentuates workflow, can be seamlessly integrated into existing information system technologies, and be shown to yield reproducibility of the evidence domain. The time is here for the medical imaging and clinical communities to embrace this vision in order to improve clinical outcomes and patient safety.