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: 2.95 - 3.05) to be followed-up, while explicit mentioning of the follow-up modality did not have a significant impact. Our findings can be incorporated into routine dictation macros so that the follow-up duration is explicitly mentioned whenever clinically applicable, and/or used as the basis for a quality improvement project focussed on improving adherence to follow-up imaging recommendations.

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 91.4% (95% CI 87.6-94.2%). Our results show that most critical findings are diagnosed on CT and MR exams and that intracranial hemorrhage and fluid collection are the most prevalent at our institution. 1.6% of the exams were found to have at least one of the ten critical findings we focused on. This methodology can enable detailed analysis of critical results reporting for research, workflow management, compliance, and quality assurance.

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. We selected imaging-reports containing 559 follow-up imaging recommendations and all subsequent reports from a multi-hospital academic practice. Three radiologists identified appropriate follow-up examinations among the subsequent reports for the same patient, if any, to establish a ground-truth dataset. We then trained an Extremely Randomized Trees that uses recommendation attributes, study meta-data and text similarity of the radiology reports to determine the most likely follow-up examination for a preceding recommendation. Pairwise inter-annotator F-score ranged from 0.853 to 0.868; the corresponding F-score of the classifier in identifying follow-up exams was 0.807. Our study describes a methodology to automatically determine the most likely follow-up exam after a follow-up imaging recommendation. The accuracy of the algorithm suggests that automated methods can be integrated into a follow-up management application to improve adherence to follow-up imaging recommendations. Radiology administrators could use such a system to monitor follow-up compliance rates and proactively send reminders to primary care providers and/or patients to improve adherence.

Automated Tracking of Follow-Up Imaging Recommendations.

OBJECTIVE. 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. MATERIALS AND METHODS. Radiology-related examination data, including report text, for examinations performed between June 1, 2015, and July 31, 2017, were extracted from the radiology departments at the University of Washington (UW) and Lahey Hospital and Medical Center (LHMC). The UW dataset contained 923,885 examinations, and the LHMC dataset contained 763,059 examinations. A 1-year period was used for detection of imaging recommendations and up to 14-months for the follow-up examination to be performed. RESULTS. On the basis of an algorithm with 97.9% detection accuracy, the follow-up imaging recommendation rate was 11.4% at UW and 20.9% at LHMC. Excluding mammography examinations, the overall follow-up imaging adherence rate was 51.9% at UW (range, 44.4% for nuclear medicine to 63.0% for MRI) and 52.0% at LHMC (range, 30.1% for fluoroscopy to 63.2% for ultrasound) using a matcher algorithm with 76.5% accuracy. CONCLUSION. This study suggests that follow-up imaging adherence rates vary by modality and between sites. Adherence rates can be influenced by various legitimate factors. Having the capability to identify patients who can benefit from patient engagement initiatives is important to improve overall adherence rates. Monitoring of follow-up adherence rates over time and critical evaluation of variation in recommendation patterns across the practice can inform measures to standardize and help mitigate risk.

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.

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. Therefore, the primary objective of this research was to determine adherence rates to follow-up recommendations. METHODS: We extracted radiology examination-related data, including report text, for examinations performed between January 1, 2010, and February 28, 2017, from the radiology information system at an academic institution. The data set contained 2,972,164 examinations. The first 6 years were used as the period during which a follow-up recommendation was to be detected, allowing for a maximum of 14 months for a follow-up examination to be performed. RESULTS: At least one recommendation for follow-up imaging was present in 10.6% of radiology reports. Overall, the follow-up imaging adherence rate was 58.14%. Mammography had the highest follow-up adherence rate at 69.03%, followed by MRI at 67.54%. Of the modalities, nuclear medicine had the lowest adherence rate at 37.93%. CONCLUSIONS: This study confirms that follow-up imaging adherence rates are inherently low and vary by modality and that appropriate interventions may be needed to improve compliance to follow-up imaging 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. 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 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. Radiology departments need to increasingly understand how the transactional nature of the business relates to financial rewards. The main challenge with current reporting systems is that the information is presented only at an aggregated level, and often not broken down further, for instance, by type of exam. As such, the primary objective of this research is to provide better visibility into payments associated with individual radiology procedures in order to better calibrate expense/capital structure of the imaging enterprise to the actual revenue or value-add to the organization it belongs to. MATERIALS AND METHODS: We propose a methodology that can be used to determine technical payments at a procedure level. We use a proportion based model to allocate payments to individual radiology procedures based on total charges (which also includes non-radiology related charges). RESULTS: Using a production dataset containing 424,250 radiology exams we calculated the overall average technical charge for Radiology to be $873.08 per procedure and the corresponding average payment to be $326.43 (range: $48.27 for XR and $2750.11 for PET/CT) resulting in an average payment percentage of 37.39% across all exams. DISCUSSION: We describe how charges associated with a procedure can be used to approximate technical payments at a more granular level with a focus on Radiology. The methodology is generalizable to approximate payment for other services as well. Understanding payments associated with each procedure can be useful during strategic practice planning. CONCLUSIONS: Charge-to-total charge ratio can be used to approximate radiology payments at a procedure level.

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. Oftentimes, patients with the highest disease acuity are treated in tertiary care radiology departments. Therefore, the average hospital CMI based on the entire inpatient population may not be adequate to determine department-level resource utilization, such as the number of technologists and nurses, as case length and staffing intensity gets quite high for sicker patients. In this study, we determine CMI for the overall radiology department in a tertiary care setting based on inpatients undergoing radiology procedures. Between April and September 2015, CMI for radiology was 1.93. With an average of 2.81, interventional neuroradiology had the highest CMI out of the ten radiology sections. CMI was consistently higher across seven of the radiology sections than the average hospital CMI of 1.81. Our results suggest that inpatients undergoing radiology procedures were on average more complex in this hospital setting during the time period considered. This finding is relevant for accurate calculation of labor analytics and other predictive resource utilization tools.

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. Using a production dataset of 417,451 radiology reports, we observed that on average, follow-up interval was not mentioned in 79.4% of reports, and modality was missing in 47.4% of reports (4,819 reports contained a follow-up imaging recommendation for one of the three findings). We also developed an interactive dashboard to be used to monitor compliance rates. Recognizing the importance of increasing precision of follow-up recommendations, a quality improvement pilot study is underway with the goal of achieving a target where follow-up modality and interval are both explicitly specified.

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. To address some of these limitations, we present a generic radiology report processing pipeline that can be used to extract follow-up imaging recommendations by anatomy using an ontology-based approach. Using a large dataset from three hospitals, we discuss our methodology in the context of identifying follow-up imaging recommendations that are related to lung, adrenal and/or thyroid conditions. The algorithm has 99% accuracy (95% CI: 95.8-99%). We also present an interactive dashboard that can be used to understand trends related to follow-up recommendations.

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., "calcification") from a list of predefined descriptions, or input the description directly as free-text. However, this process is time-consuming and the descriptions are not standardized over time, even for the same patient or the same general finding. In this paper, we describe an approach that presents finding descriptions based on textual information extracted from a patient's prior reports. Using 133 finding descriptions obtained in routine oncology workflow, we demonstrate how the system can be used to reduce keystrokes by up to 86% in about 38% of the instances. We have integrated our solution into a PACS and discuss how the system can be used in a clinical setting to improve the image annotation workflow efficiency and promote standardization of finding descriptions.

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. We discuss an algorithm we have developed to facilitate the mapping process which first extracts RadLex codes from the procedure description and then uses the definition of an RPID to determine the most suitable RPID(s) for the extracted set of RadLex codes. We also developed a tool that has three modes of operations-a single procedure mapping mode that allows a user to map a single institution-specific procedure description to a Playbook entry, a bulk mode to process large number of descriptions, and an exploratory mode that assists a user to better understand how the selection of values for various Playbook attributes affects the resulting RPID. We validate our algorithms using 166 production CT procedure descriptions and discuss how the tool can be used by administrators to map institution-specific procedure descriptions to RPIDs.

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. For instance, an imaging study to exclude pancreatitis and another one to exclude renal stones will both have "abdomen" in their body part field, making it difficult to differentiate them. To improve prior study matching and support better study filtering, in this paper, we present a rule-based approach to determine specific body parts contained in the free-text DICOM Study Description field. Algorithms were trained using a production dataset of 1200 randomly selected unique study descriptions and validated against a test dataset of 404 study descriptions. Our validation resulted in 99.94% accuracy. The proposed technique suggests that a rule-based approach can be used for domain specific body part extraction from DICOM headers.

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. For a given concept, we define a semantic vector consisting of all parent concepts and their corresponding weights as determined by the shortest distance between the concept and parent after accounting for all possible paths. Similarity between the two concepts is then determined by taking the cosine angle between the two corresponding vectors. To test the improvement over the non-semantic document vector cosine similarity model, we measured the similarity between groups of reports arising from similar clinical contexts, including anatomy and imaging procedure. We further applied the similarity metrics within a k-nearest-neighbor (k-NN) algorithm to classify reports based on their anatomical and procedure based groups. 2150 production CT radiology reports (952 abdomen reports and 1128 neuro reports) were used in testing with SNOMED CT, restricted to Body structure, Clinical finding and Procedure branches, as the reference ontology. RESULTS: The semantic algorithm preferentially increased the intra-class similarity over the inter-class similarity, with a 0.07 and 0.08 mean increase in the neuro-neuro and abdomen-abdomen pairs versus a 0.04 mean increase in the neuro-abdomen pairs. Using leave-one-out cross-validation in which each document was iteratively used as a test sample while excluding it from the training data, the k-NN based classification accuracy was shown in all cases to be consistently higher with the semantics based measure compared with the non-semantic case. Moreover, the accuracy remained steady even as k value was increased - for the two anatomy related classes accuracy for k=41 was 93.1% with semantics compared to 86.7% without semantics. Similarly, for the eight imaging procedures related classes, accuracy (for k=41) with semantics was 63.8% compared to 60.2% without semantics. At the same k, accuracy improved significantly to 82.8% and 77.4% respectively when procedures were logically grouped together into four classes (such as ignoring contrast information in the imaging procedure description). Similar results were seen at other k-values. CONCLUSIONS: The addition of semantic context into the document vector space model improves the ability of the cosine similarity to differentiate between radiology reports of different anatomical and image procedure-based classes. This effect can be leveraged for document classification tasks, which suggests its potential applicability for biomedical information retrieval.

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. We develop and evaluate a regular expressions-based algorithm that recognizes image references at a sentence level. Validation on 314 image references from general radiology reports shows precision of 99.35%, recall of 98.08% and F-measure of 98.71%, suggesting this is a viable approach for image reference extraction. We demonstrate how recognized image references can be hyperlinked in a PACS report viewer allowing one-click access to the images.

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. METHODS: A framework for identification of suboptimal long-term condition management from routinely-collected EMR data, the ChronoMedIt (Chronological Medical Audit) tool, was applied to data of two Pacific-led general practices to identify patients with low MPR. One practice undertook intervention, the other provided usual care. A cohort was based on MPR<80% for antihypertensive medication in a baseline 6-month period. At the intervention practice a team was established to provide reminders and motivation for these patients and discuss their specific needs for assistance to improve adherence for 12 months. MPR and systolic blood pressure (SBP) was collected at baseline and for last six months of intervention based on practice EMRs; national claims data provided assessment of MPR based on dispensing. Nursing notes were analysed, and patient and provider focus groups were conducted. RESULTS: Of the 252 intervention patients with MPR<80% initially, MPR improved 12.0% (p=0.0002) and systolic blood pressure was 3.5mmHg lower (p=0.07) as compared to the control cohort. MPR from national claims data improved by 11.5% (p=0.0001) as compared to the control. Patients welcomed the approach as caring and useful. Providers felt the approach worthy of wider deployment but that it required dedicated staffing. DISCUSSION AND CONCLUSIONS: Systematic follow-up of patients with demonstrated poor medication possession appears effective in the context of a Pacific-led general practice serving a largely Pacific caseload. It was possible to exploit the EMR database to identify patients with low antihypertensive medication possession and to raise their level of medication possession significantly. The measured effect on systolic BP was only marginally significant, leaving open the question of the precise value of the intervention in terms of morbidity and mortality. The intervention was found to be feasible and was met with good acceptance from the intervention patients, who appreciated the concern reflected in the follow-up effort. The intervention practice is continuing use of ChronoMedIt to guide long-term condition management with extension to cholesterol and blood sugar.

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. METHODS: We analysed general practice electronic prescribing data for the year ending 31 December 2006 and linked pharmacy dispensing records by National Health Index. We calculated medication adherence for patients starting antidepressants using a six-month evaluation period and a gap-based adherence measure. Patients with a gap of more than 15 days in antidepressant therapy were considered non-adherent. Using a prescription visualisation tool, we described common modes of non-adherence. RESULTS: Out of 2713 patients, 153 satisfied our inclusion criteria. Thirty-nine percent of patients showed poor adherence based on prescribing and 68% showed poor adherence on dispensing. Prescribing based non-adherence had a positive predictive value of 98% (95% CI 92%-99%) and negative predictive value of 51% (CI 47%-52%) for dispensing based non-adherence. Three broad categories of non-adherence were identified: 1) failure to return for re-prescription, 2) failure to maintain adherence despite initial attempts and 3) failure to return for re-prescription in a timely manner. CONCLUSIONS: Prescribing data identifies substantial adherence issues in antidepressant therapy. Clinicians should consider adherence issues as part of the overall treatment regime and discuss such issues during consultations.

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. We examine PMS data for two New Zealand metropolitan general practices, one with a predominantly Pacific caseload, across 2008 and 2009. We find prevalence of non-adherence in 2009 to be 51.63% (95% confidence interval [CI] 47.9-55.3) for patients at the Pacific practice and 28.09% (95% CI 25.0-31.1) at the other practice for patients who are demonstrably active with the practice in 2009. The positive predictive value (PPV) of 2008 non-adherence for 2009 non-adherence is 71.80% (95% CI, 66.5-77.1) and negative predictive value (NPV) 61.52% (95% CI 56.9-66.1) for the Pacific practice; PPV is 61.38% (95% CI 54.6-68.2) and NPV is 82.19% (95% CI 79.2-85.2) for the other practice. The results indicate good potential for decision support tools to target adherence promotion.

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. Transcriptions were examined by two researchers to identify themes. FINDINGS: Patients with 'high' and 'lower' rates of adherence to taking usual medication were identified using medication possession ratio cut-offs from medical records of timely prescribing. Ten participants with 'high' and 10 with 'lower' rates of adherence were interviewed, including 11 women and nine men. Themes identified for those with lower adherence included 'lack of transport', 'family commitments', 'forgetfulness', 'church activities', 'feeling well' and 'priorities'. Themes identified for those with high rates of adherence included 'prioritising health', 'previous event', 'time management', 'supportive family members' and 'relationship with GP (language and trust)'. A theme common to both was 'coping with the stress of multiple comorbidities'. CONCLUSION: Reasons for adherence and non-adherence to taking blood pressure-lowering medications among the Samoan patients interviewed were multifactorial and encompass personal, social, cultural and environmental factors. Interdisciplinary teams to support treatment decisions (including Pacific health professionals or community health workers), systematic identification of those with low rates of adherence, phone or text follow-up, use of church or family networks, provision of transport where needed and better tools and resources may help address this problem.