Emergency department care coordination strategies and perceived impact under Maryland's hospital payment reforms.

BACKGROUND: Emergency department (ED) care coordination plays an important role in facilitating care transitions across settings. We studied ED care coordination processes and their perceived effectiveness in Maryland (MD) hospitals, which face strong incentives to reduce hospital-based care through global budgets. METHODS: We conducted a qualitative study using semi-structured interviews to examine ED care coordination processes and perceptions of effectiveness. Interviews were conducted from January through October 2019 across MD hospital-based EDs. Results were reviewed to assign analytic domains and identify emerging themes. Descriptive statistics of ED care coordination staffing and processes were also calculated. RESULTS: A total of 25 in-depth interviews across 18 different EDs were conducted with ED physician leadership (n = 14) and care coordination staff (CCS) (n = 11). Across all EDs, there was significant variation in the hours and types of CCS coverage and the number of initiatives implemented to improve care coordination. Participants perceived ED care coordination as effective in facilitating safer discharges and addressing social determinants of health; however, adequate access to outpatient providers was a significant barrier. The majority of ED physician leaders perceived MD's policy reform as having a mixed impact, with improved care transitions and overall patient care as benefits, but increased physician workloads and worsened ED throughput as negative effects. CONCLUSIONS: EDs have responded to the value-based care incentives of MD's global budgeting program with investments to enhance care coordination staffing and a variety of initiatives targeting specific patient populations. Although the observed care coordination initiatives were broadly perceived to produce positive results, MD's global budgeting policies were also perceived to produce barriers to optimizing ED care. Further research is needed to determine the association of the various strategies to improve ED care coordination with patient outcomes to inform practice leaders and policymakers on the efficacy of the various approaches.

Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition.

Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition. The approach presented here is based on the analysis of brightness distribution patterns present in rectangular segments (here called "characteristic vectors") from the ultrasound digital images. In a first step we identified and eliminated the skin and subcutaneous tissue (fat and muscle) in lung ultrasound frames, and the "characteristic vectors"were analyzed using standard neural networks using artificial intelligence methods. We analyzed 60 lung ultrasound frames corresponding to 21 children under age 5 years (15 children with confirmed pneumonia by clinical examination and X-rays, and 6 children with no pulmonary disease) from a hospital based population in Lima, Peru. Lung ultrasound images were obtained using an Ultrasonix ultrasound device. A total of 1450 positive (pneumonia) and 1605 negative (normal lung) vectors were analyzed with standard neural networks, and used to create an algorithm to differentiate lung infiltrates from healthy lung. A neural network was trained using the algorithm and it was able to correctly identify pneumonia infiltrates, with 90.9% sensitivity and 100% specificity. This approach may be used to develop operator-independent computer algorithms for pneumonia diagnosis using ultrasound in young children.