The role of compression in large scale data transfer and storage of typical biomedical signals at hospitals.

In modern hospitals, monitoring patients' vital signs and other biomedical signals is standard practice. With the advent of data-driven healthcare, Internet of medical things, wearable technologies, and machine learning, we expect this to accelerate and to be used in new and promising ways, including early warning systems and precision diagnostics. Hence, we see an ever-increasing need for retrieving, storing, and managing the large amount of biomedical signal data generated. The popularity of standards, such as HL7 FHIR for interoperability and data transfer, have also resulted in their use as a data storage model, which is inefficient. This article raises concern about the inefficiency of using FHIR for storage of biomedical signals and instead highlights the possibility of a sustainable storage based on data compression. Most reported efforts have focused on ECG signals; however, many other typical biomedical signals are understudied. In this article, we are considering arterial blood pressure, photoplethysmography, and respiration. We focus on simple lossless compression with low implementation complexity, low compression delay, and good compression ratios suitable for wide adoption. Our results show that it is easy to obtain a compression ratio of 2.7:1 for arterial blood pressure, 2.9:1 for photoplethysmography, and 4.1:1 for respiration.

Patient and process factors associated with opportunities for improvement in trauma care: a registry-based study.

BACKGROUND: Trauma is one of the leading causes of morbidity and mortality worldwide. Morbidity and mortality review of selected patient cases is used to improve the quality of trauma care by identifying opportunities for improvement (OFI). The aim of this study was to assess how patient and process factors are associated with OFI in trauma care. METHODS: We conducted a registry-based study using all patients between 2017 and 2021 from the Karolinska University Hospital who had been reviewed regarding the presence of OFI as defined by a morbidity and mortality conference. We used bi- and multivariable logistic regression to assess the associations between the following patient and process factors and OFI: age, sex, respiratory rate, systolic blood pressure, Glasgow Coma Scale (GCS), Injury Severity Score (ISS), survival at 30 days, highest hospital care level, arrival on working hours, arrival on weekends, intubation status and time to first computed tomography (CT). RESULTS: OFI was identified in 300 (5.8%) out of 5182 patients. Age, missing Glasgow Coma Scale, time to first CT, highest hospital care level and ISS were statistically significantly associated with OFI. CONCLUSION: Several patient and process factors were found to be associated with OFI, indicating that patients with moderate to severe trauma and those with delays to first CT are at the highest odds of OFI.

Risk Factors for the Absence of Diagnosis of Asthma Despite Disease Symptoms: Results from the Swedish GA2LEN Study.

BACKGROUND: Asthma is a common chronic disease presenting with airway symptoms such as wheezing, chest tightness and attacks of breathlessness. Underdiagnosis of asthma is common and correlates to negative outcomes such as a lower quality of life and reduced work capacity. PURPOSE: This study aims to identify factors for not being diagnosed with asthma if presenting with asthma symptoms. PATIENTS AND METHODS: A questionnaire was sent to 45,000 subjects (age 16-74 years) in Sweden. Subjects who reported both wheeze and breathlessness and wheeze when not having a cold were defined as having asthma-related symptoms. Data on demographics, educational level, smoking, physical activity, comorbidities, symptoms and asthma were collected. Logistic regression was used to identify risk factors for not being diagnosed with asthma. RESULTS: Of the 25,391 who responded to the survey, 6.2% reported asthma-related symptoms. Of these, 946 had been diagnosed with asthma previously, while 632 had not. Independent risk factors for not being diagnosed with asthma were higher age (OR (95% CI) (2.17 (1.39-3.40))), male sex (1.46 (1.17-1.81)), current smoking (2.92 (2.22-3.84)), low level of education (1.43 (1.01-2.01)), low physical activity (1.36 (1.06-1.74)), and hypertension (1.50 (1.06-2.12)). CONCLUSION: Men, smokers, older subjects, and those with low educational level or low physical activity are less likely to be diagnosed with asthma despite presenting asthma-related symptoms.