Auto-segmentation of thoraco-abdominal organs in pediatric dynamic MRI.

Purpose: Analysis of the abnormal motion of thoraco-abdominal organs in respiratory disorders such as the Thoracic Insufficiency Syndrome (TIS) and scoliosis such as adolescent idiopathic scoliosis (AIS) or early onset scoliosis (EOS) can lead to better surgical plans. We can use healthy subjects to find out the normal architecture and motion of a rib cage and associated organs and attempt to modify the patient's deformed anatomy to match to it. Dynamic magnetic resonance imaging (dMRI) is a practical and preferred imaging modality for capturing dynamic images of healthy pediatric subjects. In this paper, we propose an auto-segmentation set-up for the lungs, kidneys, liver, spleen, and thoraco-abdominal skin in these dMRI images which have their own challenges such as poor contrast, image non-standardness, and similarity in texture amongst gas, bone, and connective tissue at several inter-object interfaces. Methods: The segmentation set-up has been implemented in two steps: recognition and delineation using two deep neural network (DL) architectures (say DL-R and DL-D) for the recognition step and delineation step, respectively. The encoder-decoder framework in DL-D utilizes features at four different resolution levels to counter the challenges involved in the segmentation. We have evaluated on dMRI sagittal acquisitions of 189 (near-)normal subjects. The spatial resolution in all dMRI acquisitions is 1.46 mm in a sagittal slice and 6.00 mm between sagittal slices. We utilized images of 89 (10) subjects at end inspiration for training (validation). For testing we experimented with three scenarios: utilizing (1) the images of 90 (=189-89-10) different (remaining) subjects at end inspiration for testing, (2) the images of the aforementioned 90 subjects at end expiration for testing, and (3) the images of the aforesaid 99 (=89+10) subjects but at end expiration for testing. In some situations, we can take advantage of already available ground truth (GT) of a subject at a particular respiratory phase to automatically segment the object in the image of the same subject at a different respiratory phase and then refining the segmentation to create the final GT. We anticipate that this process of creating GT would require minimal post hoc correction. In this spirit, we conducted separate experiments where we assume to have the ground truth of the test subjects at end expiration for scenario (1), end inspiration for (2), and end inspiration for (3). Results: Amongst these three scenarios of testing, for the DL-R, we achieve a best average location error (LE) of about 1 voxel for the lungs, kidneys, and spleen and 1.5 voxels for the liver and the thoraco- abdominal skin. The standard deviation (SD) of LE is about 1 or 2 voxels. For the delineation approach, we achieve an average Dice coefficient (DC) of about 0.92 to 0.94 for the lungs, 0.82 for the kidneys, 0.90 for the liver, 0.81 for the spleen, and 0.93 for the thoraco-abdominal skin. The SD of DC is lower for the lungs, liver, and the thoraco-abdominal skin, and slightly higher for the spleen and kidneys. Conclusions: Motivated by applications in surgical planning for disorders such as TIS, AIS, and EOS, we have shown an auto-segmentation system for thoraco-abdominal organs in dMRI acquisitions. This proposed setup copes with the challenges posed by low resolution, motion blur, inadequate contrast, and image intensity non-standardness quite well. We are in the process of testing its effectiveness on TIS patient dMRI data.

A qualitative study of patient perspectives of care management services in Comprehensive Primary Care Plus.

BACKGROUND: Care management has the potential to improve quality of care and health outcomes for chronic conditions, but questions remain about how patients perceive care management. Understanding patient perceptions is critical for ensuring care management can successfully engage patients and improve management of chronic conditions. OBJECTIVE: To understand high-risk patients' experiences and perceptions of care management. METHODS: We conducted 1-h phone interviews with 40 patients receiving care management at 12 practices participating in the Centers for Medicare & Medicaid Services Comprehensive Primary Care Plus model. Interviews were transcribed verbatim and analysed using a thematic approach. RESULTS: Most patients reported discussing health goals with their providers that aligned with their values and care preferences; a few reported that goal setting did not result in desired action steps. Most reported positive experiences receiving behavioural health support; a few reported unmet behavioural health needs that they had not expressed to their practice. Patients reported financial and transportation barriers to following care managers' recommendations. Care managers' active listening skills, accessibility, and caring personalities facilitated patient engagement. CONCLUSIONS: Practices should consider patient perspectives as they improve care management activities. Future research is needed to confirm our findings about patient perspectives regarding goal setting, behavioural health support, and barriers and facilitators to engagement.

Care management, which involves providing additional support to people with chronic and mental health conditions, has the potential to improve the quality of health care people receive and to improve their overall health. Care management can involve doctors, nurses, and other staff at doctors' offices working with patients to set goals for their health and working with them to manage their physical and mental health. Despite the promise of care management to improve health, the way that patients think about and experience care management is not well known. In our study, we conducted interviews with 40 patients to understand their experiences and thoughts about care management. We found that most patients talk about health goals with their doctor or nurse, and that their health goals were consistent with their values and care preferences. Most patients reported positive experiences receiving support for mental health. Some patients explained that they had difficulty following through on appointments or other services recommended by their doctor or nurse because they could not afford the costs or because they did not have transportation. Nurses' caring personalities and availability outside of appointments helped patients to take actions to improve their health.

Automatic upper airway segmentation in static and dynamic MRI via anatomy-guided convolutional neural networks.

PURPOSE: Upper airway segmentation on MR images is a prerequisite step for quantitatively studying the anatomical structure and function of the upper airway and surrounding tissues. However, the complex variability of intensity and shape of anatomical structures and different modes of image acquisition commonly used in this application makes automatic upper airway segmentation challenging. In this paper, we develop and test a comprehensive deep learning-based segmentation system for use on MR images to address this problem. MATERIALS AND METHODS: In our study, both static and dynamic MRI data sets are utilized, including 58 axial static 3D MRI studies, 22 mid-retropalatal dynamic 2D MRI studies, 21 mid-retroglossal dynamic 2D MRI studies, 36 mid-sagittal dynamic 2D MRI studies, and 23 isotropic dynamic 3D MRI studies, involving a total of 160 subjects and over 20 000 MRI slices. Samples of static and 2D dynamic MRI data sets were randomly divided into training, validation, and test sets by an approximate ratio of 5:2:3. Considering that the variability of annotation data among 3D dynamic MRIs was greater than for other MRI data sets, we increased the ratio of training data for these data to improve the robustness of the model. We designed a unified framework consisting of the following procedures. For static MRI, a generalized region-of-interest (GROI) strategy is applied to localize the partitions of nasal cavity and other portions of upper airway in axial data sets as two separate subobjects. Subsequently, the two subobjects are segmented by two separate 2D U-Nets. The two segmentation results are combined as the whole upper airway structure. The GROI strategy is also applied to other MRI modes. To minimize false-positive and false-negative rates in the segmentation results, we employed a novel loss function based explicitly on these rates to train the segmentation networks. An inter-reader study is conducted to test the performance of our system in comparison to human variability in ground truth (GT) segmentation of these challenging structures. RESULTS: The proposed approach yielded mean Dice coefficients of 0.84±0.03, 0.89±0.13, 0.84±0.07, and 0.86±0.05 for static 3D MRI, mid-retropalatal/mid-retroglossal 2D dynamic MRI, mid-sagittal 2D dynamic MRI, and isotropic dynamic 3D MRI, respectively. The quantitative results show excellent agreement with manual delineation results. The inter-reader study results demonstrate that the segmentation performance of our approach is statistically indistinguishable from manual segmentations considering the inter-reader variability in GT. CONCLUSIONS: The proposed method can be utilized for routine upper airway segmentation from static and dynamic MR images with high accuracy and efficiency. The proposed approach has the potential to be employed in other dynamic MRI-related applications, such as lung or heart segmentation.

Which Legal Approaches Help Limit Harms to Patients From Clinicians' Conscience-Based Refusals?

This article canvasses laws protecting clinicians' conscience and focuses on dilemmas that occur when a clinician refuses to perform a procedure consistent with the standard of care. In particular, the article focuses on patients' experience with a conscientiously objecting clinician at a secular institution, where patients are least likely to expect conscience-based care restrictions. After reviewing existing laws that protect clinicians' conscience, the article discusses limited legal remedies available to patients.

Factors associated with evidence-based decision-making among patients and providers.

Aim: For comparative effectiveness research to achieve its purpose, providers and patients must use research evidence to make medical decisions. Therefore, this study examined factors associated with evidence-based decision-making by patients and providers. Methods: Data were collected via cross-sectional online surveys of patients (n = 603) and providers (n = 628) between November 2011 and January 2012. Results: For both patients and providers, evidence-based medical decision-making is associated with perceptions, that is, some combination of self efficacy, attitudes and opinions. However, whereas knowledge is the most consistent factor associated with decision-making for providers, it is not associated at all for patients. Conclusion: Efforts to promote evidence-based medical decision-making among patients and providers should focus on skills training to improve self efficacy, and messages that highlight the benefits of patient engagement in medical decisions.

Measuring Changes in the Economics of Medical Practice.

For the latter third of the twentieth century, researchers have estimated production and cost functions for physician practices. Today, those attempting to measure the inputs and outputs of physician practice must account for many recent changes in models of care delivery. In this paper, we review practice inputs and outputs as typically described in research on the economics of medical practice, and consider the implications of the changing organization of medical practice and nature of physician work. This evolving environment has created conceptual challenges in what are the appropriate measures of output from physician work, as well as what inputs should be measured. Likewise, the increasing complexity of physician practice organizations has introduced challenges to finding the appropriate data sources for measuring these constructs. Both these conceptual and data challenges pose measurement issues that must be overcome to study the economics of modern medical practice. Despite these challenges, there are several promising initiatives involving data sharing at the organizational level that could provide a starting point for developing the needed new data sources and metrics for physician inputs and outputs. However, additional efforts will be required to establish data collection approaches and measurements applicable to smaller and single specialty practices. Overcoming these measurement and data challenges will be key to supporting policy-relevant research on the changing economics of medical practice.

The Results Are Only as Good as the Sample: Assessing Three National Physician Sampling Frames.

BACKGROUND: Databases of practicing physicians are important for studies that require sampling physicians or counting the physician population in a given area. However, little is known about how the three main sampling frames differ from each other. OBJECTIVE: Our purpose was to compare the National Provider and Plan Enumeration System (NPPES), the American Medical Association Masterfile and the SK&A physician file. METHODS: We randomly sampled 3000 physicians from the NPPES (500 in six specialties). We conducted two- and three-way comparisons across three databases to determine the extent to which they matched on address and specialty. In addition, we randomly selected 1200 physicians (200 per specialty) for telephone verification. KEY RESULTS: One thousand, six hundred and fifty-five physicians (55 %) were found in all three data files. The SK&A data file had the highest rate of missing physicians when compared to the NPPES, and varied by specialty (50 % in radiology vs. 28 % in cardiology). NPPES and SK&A had the highest rates of matching mailing address information, while the AMA Masterfile had low rates compared with the NPPES. We were able to confirm 65 % of physicians' address information by phone. The NPPES and SK&A had similar rates of correct address information in phone verification (72-94 % and 79-92 %, respectively, across specialties), while the AMA Masterfile had significantly lower rates of correct address information across all specialties (32-54 % across specialties). CONCLUSIONS: None of the data files in this study were perfect; the fact that we were unable to reach one-third of our telephone verification sample is troubling. However, the study offers some encouragement for researchers conducting physician surveys. The NPPES and to a lesser extent, the SK&A file, appear to provide reasonably accurate, up-to-date address information for physicians billing public and provider insurers.