The quality of preventive and diagnostic medical care: why do southern states underperform?

As the cost of health care increases rapidly, the health care industry has turned its attention to methods of cost containment. However, concern exists that the drive to contain costs could lead to compromises in the quality of medical care. One practice that may slow the growth rate of health care expenditures and improve morbidity and mortality rates is the widespread use of preventive and diagnostic services. ; Using data compiled by the Centers for Medicare and Medicaid Services, this article evaluates the quality of care received by Medicare beneficiaries in each state. The authors examine states’ use of preventive services (influenza and pneumococcal immunizations) and diagnostic services (mammograms and diabetes screening tests) among Medicare beneficiaries. ; The analysis points out regional differences in preventive and diagnostic care across the United States. The West has higher levels of preventive care while the Northeast has higher scores for diagnostic care. But the South had the lowest average score for quality of care in both categories. The authors attribute differences among states’ levels of preventive and diagnostic care to their socioeconomic and demographic characteristics, noting in particular that the percentage of a state’s Medicare population that is black is inversely related to the quality of medical care. ; A better understanding of the causes behind racial disparities in the quality of medical care, the authors conclude, will promote the delivery of the highest quality of care to all Medicare beneficiaries and slow the growth rate of health care costs.

Delivering Diagnostic Quality Video over Mobile Wireless Networks for Telemedicine

In real-time remote diagnosis of emergency medical events, mobility can be enabled by wireless video communications. However, clinical use of this potential advance will depend on definitive and compelling demonstrations of the reliability of diagnostic quality video. Because the medical domain has its own fidelity criteria, it is important to incorporate diagnostic video quality criteria into any video compression system design. To this end, we used flexible algorithms for region-of-interest (ROI) video compression and obtained feedback from medical experts to develop criteria for diagnostically lossless (DL) quality. The design of the system occurred in three steps-measurement of bit rate at which DL quality is achieved through evaluation of videos by medical experts, incorporation of that information into a flexible video encoder through the notion of encoder states, and an encoder state update option based on a built-in quality criterion. Medical experts then evaluated our system for the diagnostic quality of the video, allowing us to verify that it is possible to realize DL quality in the ROI at practical communication data transfer rates, enabling mobile medical assessment over bit-rate limited wireless channels. This work lays the scientific foundation for additional validation through prototyped technology, field testing, and clinical trials

Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers.

Applying state-of-the-art machine learning techniques to medical images requires a thorough selection and normalization of input data. One of such steps in digital mammography screening for breast cancer is the labeling and removal of special diagnostic views, in which diagnostic tools or magnification are applied to assist in assessment of suspicious initial findings. As a common task in medical informatics is prediction of disease and its stage, these special diagnostic views, which are only enriched among the cohort of diseased cases, will bias machine learning disease predictions. In order to automate this process, here, we develop a machine learning pipeline that utilizes both DICOM headers and images to predict such views in an automatic manner, allowing for their removal and the generation of unbiased datasets. We achieve AUC of 99.72% in predicting special mammogram views when combining both types of models. Finally, we apply these models to clean up a dataset of about 772,000 images with expected sensitivity of 99.0%. The pipeline presented in this paper can be applied to other datasets to obtain high-quality image sets suitable to train algorithms for disease detection

Image-Capture Devices Extend Medicine's Reach

Johnson Space Center, Henry Ford Hospital in Detroit, and Houston-based Wyle Laboratories collaborated on NASA's Advanced Diagnostic Ultrasound in Microgravity (ADUM) experiment, which developed revolutionary medical ultrasound diagnostic techniques for long-distance use. Mediphan, a Canadian company with U.S. operations in Springfield, New Jersey drew on NASA expertise to create frame-grabber and data archiving technology that enables ultrasound users with minimal training to send diagnostic-quality ultrasound images and video to medical professionals via the Internet in near real time allowing patients as varied as professional athletes, Olympians, and mountain climbers to receive medical attention as soon as it is needed

The Quality of Medical Care, Behavioral Risk Factors, and Longevity Growth

The rate of increase of longevity has varied considerably across U.S. states since 1991. This paper examines the effect of the quality of medical care, behavioral risk factors (obesity, smoking, and AIDS incidence), and other variables (education, income, and health insurance coverage) on life expectancy and medical expenditure using longitudinal state-level data. We examine the effects of three different measures of the quality of medical care. The first is the average quality of diagnostic imaging procedures, defined as the fraction of procedures that are advanced procedures. The second is the average quality of practicing physicians, defined as the fraction of physicians that were trained at top-ranked medical schools. The third is the mean vintage (FDA approval year) of outpatient and inpatient prescription drugs. Life expectancy increased more rapidly in states where (1) the fraction of Medicare diagnostic imaging procedures that were advanced procedures increased more rapidly; (2) the vintage of self- and provider-administered drugs increased more rapidly; and (3) the quality of medical schools previously attended by physicians increased more rapidly. States with larger increases in the quality of diagnostic procedures, drugs, and physicians did not have larger increases in per capita medical expenditure.

The effect of non-medical factors on variations in the performance of colonoscopy among different health care settings

Background: Previous studies in the literature have shown significant variations in colonoscopy performance, even when medical factors are taken into account. This study aimed to examine the role of non-medical factors (i.e. embodied in health care system design) as possible contributors to variations in colonoscopy performance. Methods: We used patient data from a multicenter observational study conducted between 2000 and 2002 in 21 centers across 11 western countries. Variability was captured through two performance outcomes (diagnostic yield and colonoscopy withdrawal time), jointly studied as dependent variables using a multilevel two-equation system. Results: Results showed that open-access systems and high-volume colonoscopy centers were independently associated with a higher likelihood of detecting significant lesions and higher withdrawal durations. Fee for service (FFS) payment was associated with shorter withdrawal durations, and had an indirect negative impact on the diagnostic yield. Teaching centers exhibited lower detection rates and higher withdrawal times. Conclusions: Our results suggest that gate-keeping colonoscopy is likely to miss patients with significant lesions and that developing specialized colonoscopy units is important to improve performance. Results also suggest that FFS may result in a lower quality of care in colonoscopy practice and highlight that longer withdrawal times do not necessarily mean higher quality in teaching-centers.Medical Practice Variation (MPV), performance, non-medical factors, panel two-equation linear-probit model, colonoscopy

Update and Acceleration of Health Care Using Artificial Intelligence in Medical Treatments and Diagnostics

Researchers support the growth of artificial intelligence and similar methods in health and medical care for the purpose of continuously improving processes. By focusing on the growth on data analytics, statistics, applied mathematics, and computer methods including machine learning, the future of health-care methods will change. The development of computerized methods and the growth of data systems produce ample materials for artificial intelligence to develop and to bring physician assistance programs to enable continuous improvement resulting in superior health and medical care. This includes applications in intensive care as well as diagnostic therapies. The focus is on examples in the use of the promising developments in data science methods, the accumulation of medical and research data. With quality and continuous improvement in process control applications where one determines the usefulness of data analytics, there are great possibilities of change in the improvement in medical applications as well as the management of medical and health-care treatment and diagnostic facilities

AutoML Systems For Medical Imaging

The integration of machine learning in medical image analysis can greatly enhance the quality of healthcare provided by physicians. The combination of human expertise and computerized systems can result in improved diagnostic accuracy. An automated machine learning approach simplifies the creation of custom image recognition models by utilizing neural architecture search and transfer learning techniques. Medical imaging techniques are used to non-invasively create images of internal organs and body parts for diagnostic and procedural purposes. This article aims to highlight the potential applications, strategies, and techniques of AutoML in medical imaging through theoretical and empirical evidence.Comment: 11 pages, 4 figures; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging