Considering the Artifact Ecology when Supporting the Evolution of Practices - Analyzing the Parallel Journeys of Two Teleconsultation Software in a General Hospital

The growing use of teleconsultation, especially since the start of the Covid-19 pandemic, changes physicians' work at the hospital. In this paper, we set out to study how physicians have integrated teleconsultation into their healthcare practices. Moreover, we are interested in how teleconsultation software contributes to developing new medical practices and how the design of teleconsultation software can better support them. Based on 16 months of fieldwork in a general hospital that offers two different teleconsultation software, we have investigated teleconsultation practices through interviews and observations involving ten physicians doing teleconsultation and a telemedicine secretary. Unlike the existing informal remote care by phone, we observe that teleconsultation supports new formal healthcare practices, particularly for patient care management and inter-organizational cooperation. While analyzing the integration of teleconsultation in physicians' practices, we highlight that both pieces of software do not support those practices on equal terms according to their design. We argue that teleconsultation software design can limit the spread of these new healthcare practices and that the artifact ecology of physicians should be considered during the design process.

A Clinical Case of COVID-19-Associated Pulmonary Aspergillosis (CAPA), Illustrating the Challenges in Diagnosis (Despite Overwhelming Mycological Evidence)

The COVID-19 pandemic has resulted in large numbers of patients requiring critical care management. With the established association between severe respiratory virus infection and invasive pulmonary aspergillosis (7.6% for COVID-19-associated pulmonary aspergillosis (CAPA)), the pandemic places a significant number of patients at potential risk from secondary invasive fungal disease. We described a case of CAPA with substantial supporting mycological evidence, highlighting the need to employ strategic diagnostic algorithms and weighted definitions to improve the accuracy in diagnosing CAPA.

Chagas Disease in Pregnant Women from Endemic Regions Attending the Hospital General de Mexico, Mexico City

Trypanosoma cruzi infection leads to Chagas disease (CD), a neglected tropical infection of significant public health importance in South and Central America and other, non-endemic, countries. Pregnant women and their children are of particular importance to screen as T. cruzi can be transmitted vertically. The objective of this study was to screen for T. cruzi infection among pregnant women from endemic areas seen at the Hospital General de Mexico for prenatal care, so that they and their children may be quickly connected to CD treatment. Pregnant women were recruited through the hospital prenatal clinic and screened for T. cruzi infection using a series of serological and molecular tests. Of 150 screened patients, mean age 26.8 (SD 6.4), 30 (20.0%) were positive by at least one diagnostic test. Of these, only nine (6%) were positive as determined by PCR. Diagnosis of chronic CD is difficult in endemic places like Mexico due to the limitations of current commercially available diagnostic tests. Further evaluation of diagnostic performance of various assays could improve current CD diagnostic algorithms and proper care management in these regions. Genetic variability in the parasite may also play a role in the differing assay performances seen in this study, and this may be a valuable avenue of further research.

Prediction Models for Future High-Need High-Cost Healthcare Use: a Systematic Review

Background In an effort to improve both quality of care and cost-effectiveness, various care-management programmes have been developed for high-need high-cost (HNHC) patients. Early identification of patients at risk of becoming HNHC (i.e. case finding) is crucial to a programme’s success. We aim to systematically identify prediction models predicting future HNHC healthcare use in adults, to describe their predictive performance and to assess their applicability. Methods Ovid MEDLINE® All, EMBASE, CINAHL, Web of Science and Google Scholar were systematically searched from inception through January 31, 2021. Risk of bias and methodological quality assessment was performed through the Prediction model Risk Of Bias Assessment Tool (PROBAST). Results Of 5890 studies, 60 studies met inclusion criteria. Within these studies, 313 unique models were presented using a median development cohort size of 20,248 patients (IQR 5601–174,242). Predictors were derived from a combination of data sources, most often claims data (n = 37; 62%) and patient survey data (n = 29; 48%). Most studies (n = 36; 60%) estimated patients’ risk to become part of some top percentage of the cost distribution (top-1–20%) within a mean time horizon of 16 months (range 12–60). Five studies (8%) predicted HNHC persistence over multiple years. Model validation was performed in 45 studies (76%). Model performance in terms of both calibration and discrimination was reported in 14 studies (23%). Overall risk of bias was rated as ‘high’ in 40 studies (67%), mostly due to a ‘high’ risk of bias in the subdomain ‘Analysis’ (n = 37; 62%). Discussion This is the first systematic review (PROSPERO CRD42020164734) of non-proprietary prognostic models predicting HNHC healthcare use. Meta-analysis was not possible due to heterogeneity. Most identified models estimated a patient’s risk to incur high healthcare expenditure during the subsequent year. However, case-finding strategies for HNHC care-management programmes are best informed by a model predicting HNHC persistence. Therefore, future studies should not only focus on validating and extending existing models, but also concentrate on clinical usefulness.

Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Background Chronic obstructive pulmonary disease (COPD) poses a large burden on health care. Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes. Objective The aim of this study is to develop a more accurate model to predict severe COPD exacerbations. Methods We examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD. Results The final model had an area under the receiver operating characteristic curve of 0.866. When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347). Conclusions Our model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes. International Registered Report Identifier (IRRID) RR2-10.2196/13783