Evaluating patient-reported outcome measures in Peru: a cross-sectional study of satisfaction and net promoter score using the 2016 EnSuSalud survey.

BACKGROUND: Patient-reported measures attempt to quantify the value health services provide to users. Satisfaction is a common summative measure, but often has limited utility in identifying poor quality care. We compared satisfaction and the net promoter score (NPS), which was developed to help businesses quantify consumer sentiment, in a nationally representative survey in Peru. We aimed to compare NPS and satisfaction as individual ratings of care, assess the relationship of patient-reported experience ratings to these outcome measures and consider the utility of these measures as indicators of facility performance based on reliability within facilities and capacity to discriminate between facilities. METHODS: We analysed the 2016 National Survey on User Satisfaction of Health Services, a cross-sectional outpatient exit survey. We assessed ratings by patient characteristics and compared the distributions of satisfaction and NPS categories. We tested the association of patient-reported experience measures with each outcome using multilevel ordinal logistic regression. We used intraclass correlation (ICC) from these models to predict minimum sample for reliable assessment and compared patient-reported experience measures in facilities with average satisfaction but below or above average NPS. RESULTS: 13 434 individuals rated services at 184 facilities. Satisfaction (74% satisfied) and NPS (17% reported at least 9 out of 10) were largely concordant within individuals but weakly correlated (0.37). Ratings varied by individual factors such as age and visit purpose. Most domains of patient-reported experience were associated with both outcomes. Adjusted ICC was higher for NPS (0.26 vs 0.11), requiring a minimum of 7 (vs 20) respondents for adequate reliability. Within the 70% of facilities classified as average based on satisfaction, NPS-based classification revealed systematic differences in patient-reported experience measures. CONCLUSION: While satisfaction and NPS were broadly similar at an individual level, this evidence suggests NPS may be useful for benchmarking facility performance as part of national efforts in Peru and throughout Latin America to identify deficits in health service quality.

Mobility restrictions were associated with reductions in COVID-19 incidence early in the pandemic: evidence from a real-time evaluation in 34 countries.

Most countries have implemented restrictions on mobility to prevent the spread of Coronavirus disease-19 (COVID-19), entailing considerable societal costs but, at least initially, based on limited evidence of effectiveness. We asked whether mobility restrictions were associated with changes in the occurrence of COVID-19 in 34 OECD countries plus Singapore and Taiwan. Our data sources were the Google Global Mobility Data Source, which reports different types of mobility, and COVID-19 cases retrieved from the dataset curated by Our World in Data. Beginning at each country's 100th case, and incorporating a 14-day lag to account for the delay between exposure and illness, we examined the association between changes in mobility (with January 3 to February 6, 2020 as baseline) and the ratio of the number of newly confirmed cases on a given day to the total number of cases over the past 14 days from the index day (the potentially infective 'pool' in that population), per million population, using LOESS regression and logit regression. In two-thirds of examined countries, reductions of up to 40% in commuting mobility (to workplaces, transit stations, retailers, and recreation) were associated with decreased cases, especially early in the pandemic. Once both mobility and incidence had been brought down, further restrictions provided little additional benefit. These findings point to the importance of acting early and decisively in a pandemic.

CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning

The value of pulmonary function test (PFT) data is increasing due to the advent of the Coronavirus Infectious Disease 19 (COVID-19) and increased respiratory disease. However, these PFT data cannot be directly used in clinical studies, because PFT results are stored in raw image files. In this study, the classification and itemization medical image (CIMI) system generates valuable data from raw PFT images by automatically classifying various PFT results, extracting texts, and storing them in the PFT database and Excel files. The deep-learning-based optical character recognition (OCR) technology was mainly used in CIMI to classify and itemize PFT images in St. Mary’s Hospital. CIMI classified seven types and itemized 913,059 texts from 14,720 PFT image sheets, which cannot be done by humans. The number, type, and location of texts that can be extracted by PFT type are all different, but CIMI solves this issue by classifying the PFT image sheets by type, allowing researchers to analyze the data. To demonstrate the superiority of CIMI, the validation results of CIMI were compared to the results of the other four algorithms. A total of 70 randomly selected sheets (ten sheets from each type) and 33,550 texts were used for the validation. The accuracy of CIMI was 95%, which was the highest accuracy among the other four algorithms.