A rapid review of machine learning approaches for telemedicine in the scope of COVID-19.

The COVID-19 pandemic has rapidly spread around the world. The rapid transmission of the virus is a threat that hinders the ability to contain the disease propagation. The pandemic forced widespread conversion of in-person to virtual care delivery through telemedicine. Given this gap, this article aims at providing a literature review of machine learning-based telemedicine applications to mitigate COVID-19. A rapid review of the literature was conducted in six electronic databases published from 2015 through 2020. The process of data extraction was documented using a PRISMA flowchart for inclusion and exclusion of studies. As a result, the literature search identified 1.733 articles, from which 16 articles were included in the review. We developed an updated taxonomy and identified challenges, open questions, and current data types. Our taxonomy and discussion contribute with a significant degree of coverage from subjects related to the use of machine learning to improve telemedicine in response to the COVID-19 pandemic. The evidence identified by this rapid review suggests that machine learning, in combination with telemedicine, can provide a strategy to control outbreaks by providing smart triage of patients and remote monitoring. Also, the use of telemedicine during future outbreaks could be further explored and refined.

Semantic interoperability in health records standards: a systematic literature review.

The integration and exchange of information among health organizations and system providers are currently regarded as a challenge. Each organization usually has an internal ecosystem and a proprietary way to store electronic health records of the patient's history. Recent research explores the advantages of an integrated ecosystem by exchanging information between the different inpatient care actors. Many efforts seek quality in health care, economy, and sustainability in process management. Some examples are reducing medical errors, disease control and monitoring, individualized patient care, and avoiding duplicate and fragmented entries in the electronic medical record. Likewise, some studies showed technologies to achieve this goal effectively and efficiently, with the ability to interoperate data, allowing the interpretation and use of health information. To that end, semantic interoperability aims to share data among all the sectors in the organization, clinicians, nurses, lab, the entire hospital. Therefore, avoiding data silos and keep data regardless of vendors, to exchange the information across organizational boundaries. This study presents a comprehensive systematic literature review of semantic interoperability in electronic health records. We searched seven databases of articles published between 2010 to September 2020. We showed the most chosen scenarios, technologies, and tools employed to solve interoperability problems, and we propose a taxonomy around semantic interoperability in health records. Also, we presented the main approaches to solve the exchange problem of legacy and heterogeneous data across healthcare organizations.

First and second COVID-19 waves in Brazil: A cross-sectional study of patients' characteristics related to hospitalization and in-hospital mortality.

BACKGROUND: Background The second wave of the COVID-19 pandemic was more aggressive in Brazil compared to other countries around the globe. Considering the Brazilian peculiarities, we analyze the in-hospital mortality concerning socio-epidemiological characteristics of patients and the health system of all states during the first and second waves of COVID-19. METHODS: We performed a cross-sectional study of hospitalized patients with positive RT-PCR for SARS-CoV-2 in Brazil. Data was obtained from the Influenza Epidemiological Surveillance Information System (SIVEP-Gripe) and comprised the period from February 25, 2020, to April 30, 2021, separated in two waves on November 5, 2020. We performed a descriptive study of patients analyzing socio-demographic characteristics, symptoms, comorbidities, and risk factors stratified by age. In addition, we analyzed in-hospital and intensive care unit (ICU) mortality in both waves and how it varies in each Brazilian state. FINDINGS: Between February 25, 2020 and April 30, 2021, 678 235 patients were admitted with a positive RT-PCR for SARS-CoV-2, with 325 903 and 352 332 patients for the first and second wave, respectively. The mean age of patients was 59 · 65 (IQR 48 · 0 - 72 · 0). In total, 379 817 (56 · 00%) patients had a risk factor or comorbidity. In-hospital mortality increased from 34 · 81% in the first to 39 · 30% in the second wave. In the second wave, there were more ICU admissions, use of non-invasive and invasive ventilation, and increased mortality for younger age groups. The southern and southeastern regions of Brazil had the highest hospitalization rates per 100 000 inhabitants. However, the in-hospital mortality rate was higher in the northern and northeastern states of the country. Racial differences were observed in clinical outcomes, with White being the most prevalent hospitalized population, but with Blacks/Browns (Pardos) having higher mortality rates. Younger age groups had more considerable differences in mortality as compared to groups with and without comorbidities in both waves. INTERPRETATION: We observed a more considerable burden on the Brazilian hospital system throughout the second wave. Furthermore, the north and northeast of Brazil, which present lower Human Development Indexes, concentrated the worst in-hospital mortality rates. The highest mortality rates are also shown among vulnerable social groups. Finally, we believe that the results can help to understand the behavior of the COVID-19 pandemic in Brazil, helping to define public policies, allocate resources, and improve strategies for vaccination of priority groups. FUNDING: Coordinating Agency for Advanced Training of Graduate Personnel (CAPES) (C.F. 001), and National Council for Scientific and Technological Development (CNPq) (No. 309537/2020-7).

Toward a Model for Personal Health Record Interoperability.

Health information technology, applied to electronic health record (EHR), has evolved with the adoption of standards for defining patient health records. However, there are many standards for defining such data, hindering communication between different healthcare providers. Even with adopted standards, patients often need to repeatedly provide their health information when they are taken care of at different locations. This problem hinders the adoption of personal health record (PHR), with the patients' health records under their own control. Therefore, the purpose of this paper is to propose an interoperability model for PHR use. The methodology consisted prototyping an application model named OmniPHR, to evaluate the structuring of semantic interoperability and integration of different health standards, using a real database from anonymized patients. We evaluated health data from a hospital database with 38 645 adult patients' medical records processed using different standards, represented by openEHR, HL7 FHIR, and MIMIC-III reference models. OmniPHR demonstrated the feasibility to provide interoperability through a standard ontology and artificial intelligence with natural language processing (NLP). Although the first executions reached a 76.39% F1-score and required retraining of the machine-learning process, the final score was 87.9%, presenting a way to obtain the original data from different standards on a single format. Unlike other models, OmniPHR presents a unified, structural semantic and up-to-date vision of PHR for patients and healthcare providers. The results were promising and demonstrated the possibility of subsidizing the creation of inferences rules about possible patient health problems or preventing future problems.

A Hospital Bed Allocation Hybrid Model Based on Situation Awareness.

Bed management is an important area of planning and control for hospitals, as it has the important role of maintaining the balance between patients from the emergency department, patients who have elective surgery or scheduled treatment, and patients who are discharged from the hospital, while maintaining high bed occupancy rates. Effective management of these resources has always been a challenge for managers. In the 1980s and 1990s, thousands of patients had operations canceled due to nonmedical reasons. Due to the constant uncertainty experienced by hospitals today, use of the cognitive model known as situation awareness has been increasing in healthcare. Situation awareness seeks to understand environmental context to design the future, using artificial intelligence techniques. In this context, this article contributes the use of situation awareness in bed management using a hybrid system that combines known techniques of artificial neural networks and multiattribute value theory for decision-making by automating the process of bed allocation. The system was evaluated in a hospital in Porto Alegre, Brazil, yielding a result of 93.5% similarity between the beds determined by the proposed model and those chosen by the hospital manager.