Impact of Follow-Up Imaging Recommendation Specificity on Adherence.

Radiology reports often contain follow-up imaging recommendations, but failure to comply with them in a timely manner can lead to delayed treatment, poor patient outcomes, complications, and legal liability. Using a dataset containing 2,972,164 exams for over 7 years, in this study we explored the association between recommendation specificity on follow-up rates. Our results suggest that explicitly mentioning the follow-up interval as part of a follow-up imaging recommendation has a significant impact on adherence making these recommendations 3 times more likely (95% CI: 2.

Framework for Extracting Critical Findings in Radiology Reports.

Critical results reporting guidelines demand that certain critical findings are communicated to the responsible provider within a specific period of time. In this paper, we discuss a generic report processing pipeline to extract critical findings within the dictated report to allow for automation of quality and compliance oversight using a production dataset containing 1,210,858 radiology exams. Algorithm accuracy on an annotated dataset having 327 sentences was 91.

Determining Follow-Up Imaging Study Using Radiology Reports.

Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to delayed treatment, poor patient outcomes, complications, unnecessary testing, lost revenue, and legal liability. The objective of this study was to develop a scalable approach to automatically identify the completion of a follow-up imaging study recommended by a radiologist in a preceding report.

Automated Tracking of Follow-Up Imaging Recommendations.

Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to poor patient outcomes, complications, and legal liability. As such, the primary objective of this research was to determine adherence rates to follow-up recommendations.

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.

Probabilistic Modeling of Exam Durations in Radiology Procedures.

In this paper, we model the statistical properties of imaging exam durations using parametric probability distributions such as the Gaussian, Gamma, Weibull, lognormal, and log-logistic. We establish that in a majority of radiology procedures, the underlying distribution of exam durations is best modeled by a log-logistic distribution, while the Gaussian has the poorest fit among the candidates. Further, through illustrative examples, we show how business insights and workflow analytics can be significantly impacted by making the correct (log-logistic) versus incorrect (Gaussian) model choices.

Improving Quality of Follow-Up Imaging Recommendations in Radiology.

Failure of timely follow-up imaging recommendations can result in suboptimal patient care. Evidence suggests that the use of conditional language in follow-up recommendations is associated with changes to follow-up compliance. Assuming that referring physicians prefer explicit guidance for follow-up recommendations, we develop algorithms to extract recommended modality and interval from follow-up imaging recommendations related to lung, thyroid and adrenal findings.

Determining Adherence to Follow-up Imaging Recommendations.

Purpose: Radiology reports often contain follow-up imaging recommendations. However, these recommendations are not always followed up by referring physicians and patients. Failure to comply in a timely manner can lead to delayed treatment, poor patient outcomes, unnecessary testing, lost revenue, and legal liability.

Extracting Follow-Up Recommendations and Associated Anatomy from Radiology Reports.

Adherence rates for timely imaging follow-up are usually low due to low rates of diligence by referring physicians and/or patients with following recommendations for follow-up imaging. This can lead to delayed treatment, poor patient outcomes, unnecessary testing, and legal liability. Existing follow-up recommendation detection methods are often disease- and modality-specific.

A model to determine payments associated with radiology procedures.

Objective: Across the United States, there is a growing number of patients in Accountable Care Organizations and under risk contracts with commercial insurance. This is due to proliferation of new value-based payment models and care delivery reform efforts. In this context, the business model of radiology within a hospital or health system context is shifting from a primary profit-center to a cost-center with a goal of cost savings.

Inpatient Complexity in Radiology-a Practical Application of the Case Mix Index Metric.

With ongoing healthcare payment reforms in the USA, radiology is moving from its current state of a revenue generating department to a new reality of a cost-center. Under bundled payment methods, radiology does not get reimbursed for each and every inpatient procedure, but rather, the hospital gets reimbursed for the entire hospital stay under an applicable diagnosis-related group code. The hospital case mix index (CMI) metric, as defined by the Centers for Medicare and Medicaid Services, has a significant impact on how much hospitals get reimbursed for an inpatient stay.

A context-sensitive image annotation recommendation engine for radiology.

In the typical radiology reading workflow, a radiologist would go through an imaging study and annotate specific regions of interest. The radiologist has the option to select a suitable description (e.g.

Using image references in radiology reports to support enhanced report-to-image navigation.

Radiology reports frequently contain references to image slices that are illustrative of described findings, for instance, "Neurofibroma in superior right extraconal space (series 5, image 104)". In the current workflow, if a report consumer wants to view a referenced image, he or she needs to (1) open prior study, (2) open the series of interest (series 5 in this example), and (3) navigate to the corresponding image slice (image 104). This research aims to improve this report-to-image navigation process by providing hyperlinks to images.

Mapping institution-specific study descriptions to RadLex Playbook entries.

The naming of imaging procedures is currently not standardized across institutions. As a result, it is a challenge to establish national registries, for instance, a national registry of dose to facilitate comparisons among different types of CT procedures. RSNA's RadLex Playbook is an effort towards addressing this gap (by introducing a unique Playbook identifier called an RPID for each procedure), and the current research focuses on semi-automatically mapping institution-specific procedure descriptions to Playbook entries to assist with this standardization effort.

Determining scanned body part from DICOM study description for relevant prior study matching.

The typical radiology reporting workflow involves the radiologist first looking at one or more relevant prior studies before interpreting the current study. To improve workflow efficiency, PACS systems can display relevant prior imaging studies, typically based on a study's anatomy as indicated in the Body Part Examined field of the DICOM header. The content of the Body Part Examined field can be very generic.

An ontology-based similarity measure for biomedical data-application to radiology reports.

Background: Determining similarity between two individual concepts or two sets of concepts extracted from a free text document is important for various aspects of biomedicine, for instance, to find prior clinical reports for a patient that are relevant to the current clinical context. Using simple concept matching techniques, such as lexicon based comparisons, is typically not sufficient to determine an accurate measure of similarity.

Methods: In this study, we tested an enhancement to the standard document vector cosine similarity model in which ontological parent-child (is-a) relationships are exploited.

Using the general practice EMR for improving blood pressure medication adherence.

Purpose: Analysis of practice electronic medical records (EMRs) demonstrated widespread antihypertensive medication adherence problems in a Pacific-led general practice serving a predominantly Pacific (majority Samoan) caseload in suburban New Zealand. Adherence was quantified in terms of medication possession ratio (MPR, percent of days covered by medication supply) from the practice's prescribing data. We studied the effectiveness of general practice staff follow-up guided by EMR data to improve medication adherence.

Using primary care prescribing data to improve GP awareness of antidepressant adherence issues.

Background: Adherence to antidepressant therapy remains a major issue worldwide. Most people with depression are treated in a general practice setting, but many stop taking antidepressants before completing a six-month course as recommended by guidelines.

Objectives: To determine antidepressant adherence rates as indicated in primary care prescribing data and pharmacy dispensing data; to demonstrate commonly occurring patterns related to non-adherence, using a prescription visualisation tool we have developed; and to determine whether prescribing data is a good predictor of dispensing based adherence.

Prescribing history to identify candidates for chronic condition medication adherence promotion.

Poor adherence to long-term prescription medication is a frequent problem that undermines pharmacological control of important risk factors such as hypertension. A medication possession ratio (MPR) can be calculated from Practice Management System (PMS) data to provide a convenient indicator of adherence. We investigate how well prior MPR predicts later MPR, taking MPR<80% as indicative of 'non-adherence,' to assess the potential value of MPR calculation on PMS data for targeting adherence promotion activities by general practices.

Perspectives on adherence to blood pressure-lowering medications among Samoan patients: qualitative interviews.

Aim: To explore influences on adherence to taking long-term medications among Samoan patients in an Auckland general practice.

Methods: Twenty Samoan participants from an Auckland general practice were identified and interviewed about their views on adherence or non-adherence to taking blood pressure-lowering medications. One-to-one semi-structured interviews using open-ended questions were undertaken in Samoan and English, recorded, transcribed and translated into English.

Use of interval based quality indicators in blood pressure management to enhance quality of pay for performance incentives: comparison to two indicators from the Quality and Outcomes Framework.

Background: Pay for performance incentives are becoming increasingly popular, but are typically based on only a single point-in-time measurement as an indicator of chronic condition management.

Aims: To determine the association between three time-interval based indicators of suboptimal blood pressure (BP) control and two point-in-time indicators from the UK Quality and Outcomes Framework (QOF): BP5 (the percentage of patients with hypertension in whom the last BP in the previous nine months was < or = 150/90) and DM12 (the percentage of patients with diabetes in whom the last BP in the previous 15 months was < or = 145/85).

Methods: We extracted classification data and BP measurements from four New Zealand general practices with 4260 to 6130 enrolled patients.

Quality indicators to measure blood pressure management over a time interval.

Background: Quality indicators are an important part of the primary care landscape, but focus strongly on point-in-time measurements, such as a patient's last blood pressure (BP) measurement. There is a larger space of possible measurements, including ones that more explicitly consider management over an interval of time.

Objective: To determine the predictive abilities of five different quality indicators related to poor BP control.

A framework for assessing adherence and persistence to long-term medication.

Poor adherence and persistence to long-term medication is a growing concern worldwide. Despite their importance, tools that facilitate the identification of patients who show poor adherence and persistence rates are limited. Herein we present a framework we have developed to assist in assessing adherence and persistence rates.

Towards a framework for better management of patients with hypertension.

Management of patients with hypertension is poor and novel tools are needed to identify patients whose clinical outcomes can be improved. We herein present a framework we have developed that can be used to identify patients who meet specific audit criteria related to poor management of hypertension. Identified patients would benefit from an immediate follow-up by a practice nurse.

An ontology-based approach to enhance querying capabilities of general practice medicine for better management of hypertension.

Background: Hypertension is one of the most prevalent chronic conditions and is directly correlated to deadly risks; yet, despite the availability of effective treatment, there is still clear room for improving patient outcomes. Use of relational databases is widespread for storing patient data, but formulating queries to identify patients whose clinical management can be improved is challenging due to the temporal nature of chronic illness and the mismatch in levels of abstraction between key management concepts and coded clinical data.

Objective: The objective of this work is to develop a sharable and extensible analysis tool that can be used to identify hypertensive patients who satisfy any of a set of evidence-based criteria for quality improvement potential.