Assess the effectiveness of providing Logical Observation Identifiers Names and Codes (LOINC®)-to-In Vitro Diagnostic (LIVD) coding specification, required by the United States Department of Health and Human Services for SARS-CoV-2 reporting, in medical center laboratories and utilize findings to inform future United States Food and Drug Administration policy on the use of real-world evidence in regulatory decisions.
We compared gaps and similarities between diagnostic test manufacturers\' recommended LOINC® codes and the LOINC® codes used in medical center laboratories for the same tests.
Five medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (chi-square 45.0, 4 df, P < .0001).
The five medical centers and three test manufacturers vary in how they organize, categorize, and store LIS catalog information. This variation impacts data quality and interoperability.
The results of the study indicate that providing the LIVD mappings was not sufficient to support laboratory data interoperability. National implementation of LIVD and further efforts to promote laboratory interoperability will require a more comprehensive effort and continuing evaluation and quality control.

This paper investigates the role of socioeconomic considerations in the formation of official COVID-19 reports. To this end, we employ a dataset that contains 1159 pre-processed indicators from the World Bank Group GovData360 and TCdata360 platforms and an additional 8 COVID-19 variables generated based on reports from 138 countries. During the analysis, a rank-correlation-based complex method is used to identify the time- and space-varying relations between pandemic variables and the main topics of World Bank Group platforms. The results not only draw attention to the importance of factors such as air traffic, tourism, and corruption in report formation but also support further discipline-specific research by mapping and monitoring a wide range of such relationships. To this end, a source code written in R language is attached that allows for the customization of the analysis and provides up-to-date results.

Importance: Medical device performance and safety databases can be analyzed for patterns and novel opportunities for improving patient safety and/or device design. Objective: The objective of this analysis was to use supervised machine learning to explore patterns in reported adverse events involving cochlear implants. Design: Adverse event reports for the top three CI manufacturers were acquired for the analysis. Four supervised machine learning algorithms were used to predict which adverse event description pattern corresponded with a specific cochlear implant manufacturer and adverse event type. Setting: U.S. government public database. Participants: Adult and pediatric cochlear patients. Exposure: Surgical placement of a cochlear implant. Main Outcome Measure: Classification prediction accuracy (% correct predictions). Results: Most adverse events involved patient injury (n = 16,736), followed by device malfunction (n = 10,760), and death (n = 16). The random forest, linear SVC, naïve Bayes and logistic algorithms were able to predict the specific CI manufacturer based on the adverse event narrative with an average accuracy of 74.8%, 86.0%, 88.5% and 88.6%, respectively. Conclusions & relevance: Using supervised machine learning algorithms, our classification models were able to predict the CI manufacturer and event type with high accuracy based on patterns in adverse event text descriptions.

OBJECTIVE: To demonstrate the influence and added value of a Standardized Assessment and Reporting System (StARS) upon the reporting of functioning outcomes for national rehabilitation quality reports. A StARS builds upon an ICF-based (International Classification of Functioning, Disability and Health) and interval-scaled common metric.
METHODS: Comparison of current ordinal-scaled Swiss national rehabilitation outcome reports including an expert-consensus-based transformation scale with StARS-based reports through descriptive statistical methods and content exploration of further development areas of the reports with relevant ICF Core Sets.
METHODS: Swiss national public rehabilitation outcome quality reports on the clinic level.
METHODS: A total of 29 Swiss rehabilitation clinics provided their quality report datasets including 18 047 patients.
METHODS: Neurological or musculoskeletal rehabilitation.
METHODS: Functional Independence Measure™ or Extended Barthel Index.
RESULTS: Outcomes reported with a StARS tended to be smaller but more precise than in the current ordinal-scaled reports, indicating an overestimation of achieved outcomes in the latter. The comparison of the common metric\'s content with ICF Core Sets suggests to include \'energy and drive functions\' or \'maintaining a basic body position\' to enhance the content of functioning as an indicator.
CONCLUSIONS: A StARS supports the comparison of outcomes assessed with different measures on the same interval-scaled ICF-based common metric. Careful consideration is needed whether an ordinal-scaled or interval-scaled reporting system is applied as the magnitude and precision of reported outcomes is influenced. The StARS\' ICF basis brings an added value by informing further development of functioning as a relevant indicator for national outcome quality reports in rehabilitation.

Social distancing and shielding measures have been put in place to reduce social interaction and slow the transmission of the coronavirus disease (COVID-19). For older people, self-isolation presents particular challenges for mental health and social relationships. As time progresses, continued social distancing could have a compounding impact on these concerns.
This project aims to provide a tool for older people and their families and peers to improve their well-being and health during and after regulated social distancing. First, we will evaluate the tool\'s feasibility, acceptability, and usability to encourage positive nutrition, enhance physical activity, and enable virtual interaction while social distancing. Second, we will be implementing the app to provide an online community to assist families and peer groups in maintaining contact with older people using goal setting. Anonymized data from the app will be aggregated with other real-world data sources to develop a machine learning algorithm to improve the identification of patients with COVID-19 and track for real time use by health systems.
Development of this project is occurring at the time of publication, and therefore, a case study design was selected to provide a systematic means of capturing software engineering in progress. The app development framework for software design was based on agile methods. The evaluation of the app\'s feasibility, acceptability and usability shall be conducted using Public Health England\'s guidance on evaluating digital health products, Bandura\'s model of health promotion, the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework and the Nonadoption, Abandonment and Challenges to the Scale-up, Spread and Suitability (NASSS) framework.
Making use of a pre-existing software framework for health behavior change, a proof of concept was developed, and a multistage app development and deployment for the solution was created. Grant submissions to fund the project and study execution have been sought at the time of publication, and prediscovery iteration of the solution has begun. Ethical approval for a feasibility study design is being sought.
This case study lays the foundations for future app development to combat mental and societal issues arising from social distancing measures. The app will be tested and evaluated in future studies to allow continuous improvement of the app. This novel contribution will provide an evidence-based exemplar for future app development in the space of social isolation and loneliness.

Modern diagnostic methods (next-generation sequencing) are one of the current hopes with regard to a personalised medicine. By applying detailed genetic analysis, it is possible to not only improve the prediction of potential risks (as, e.g., concerning hereditary breast cancer) but also the precision of therapy by targeting it to a specific genetic variant. However, there is no international standard for creating, structuring and/or transferring the results of a genetic test report. This type of test report often contains large amounts of complex information, and a standardised and consistent structure would offer potential benefits to all. These include reduced expenditure of time (due to the elimination of information-conversion steps), improved safety (due to a reduction in the occurrence of transmission errors, misunderstanding or misinterpretation of content) and improved clinical information gathering (by the respective linkage to scientific data and literature). Especially in regard to secondary use, a standardised (electronic) format would improve the suitability of these data in retrospective studies and basic research. In this study, we analysed the format and content of 96 genetic testing reports (germline and somatic) from Germany, Switzerland and Austria. Based on these results, we summarised and discussed potentially critical data that were demonstrated to be reported inconsistently, and propose a baseline structure for reporting that would also ease future electronic conversion.

In an effort to increase price transparency, the Centers for Medicare and Medicaid Services (CMS) began reporting charges for Medicare inpatients treated in ∼3400 hospitals online in 2013. As of 2019, CMS began to require hospitals themselves to publicize a more comprehensive list of their underlying procedure charges.
The objective of this study was to assess the responses of hospitals to broad-scale public reporting of their charges for inpatient services.
We used descriptive analysis to examine the trend in CMS charge data for high charge hospitals before and after the 2013 intervention. We also applied difference-in-differences analysis to comprehensive inpatient charge data from New York and Florida for the years 2011-2016, defining the reported high-volume diagnosis-related groups (DRGs) as the intervention group.
At the national level, the CMS charge data showed relatively lower growth in high charge hospitals following the intervention. From the state data, we found that after 3 years, the growth in charges for reported DRGs in New York hospitals was 4%-9% lower than for unreported diagnosis-related groups. In Florida, it was 2%-8% lower.
Public reports of hospital inpatient charges by DRG appear to influence subsequent charges, slowing their growth.

Trial registration is widely endorsed as it is considered not only to enhance transparency and quality of reporting but also to help safeguard against outcome reporting bias and probably spin, known as specific reporting that could distort the interpretation of results thus mislead readers. We planned to investigate the current registration status of recently published randomized controlled trials (RCTs) of acupuncture, outcome reporting bias in the prospectively registered trials, and the association between trial registration and presence of spin and methodological factors in acupuncture RCTs.
Acupuncture RCTs published in English in recent 5 years (January 2013 to December 2017) were searched in PubMed, Cochrane Central Register of Controlled Trials, and EMBASE. Trial registration records identified in the publications and trial registries were classified into prospectively registered, retrospectively registered, or unregistered. Primary outcomes were identified and the direction of the results was judged as statistically significant (positive) or statistically nonsignificant (negative). We compared registered and published primary outcomes to assess outcome reporting bias and assessed whether discrepancies favored statistically significant outcomes. Frequency and strategies of spin in published reports with statistically nonsignificant results for primary outcomes were then identified. We also analyzed whether the trial registration status was associated with spin and quality of methodological factors.
Of the 322 included RCTs, 41.9% (n = 135) were prospectively registered. Among 64 studies that were prospectively registered and specified primary outcomes, 25 trials had the discrepancies between the registered and published primary outcomes and 60% of them (15 trials) favored the statistically significant findings. Among 169 studies that specified primary outcomes, trial registration status was not associated with the direction of results, i.e., statistically significant or not. Spin was identified in 56.4% out of 78 studies with statistically nonsignificant primary outcomes and claiming efficacy with no consideration of statistically nonsignificant primary outcomes was the most common strategy for spin. Trial registration status was not statistically different between studies with and without spin.
While trial registration seemed to have improved over time, primary outcomes in registered records and publications were often inconsistent, tending to favor statistically significant findings and spin was common in studies with statistically nonsignificant primary outcomes. Journal editors and researchers in this field should be alerted to still prevalent reporting bias and spin.

Report cards on provider performance are intended to improve consumer decision-making and address information gaps in the market for quality. However, inadequate risk adjustment of report-card measures often biases comparisons across providers. We test whether going to a skilled nursing facility (SNF) with a higher star rating leads to better quality outcomes for a patient. We exploit variation over time in the distance from a patient\'s residential ZIP code to SNFs with different ratings to estimate the causal effect of admission to a higher-rated SNF on health care outcomes, including mortality. We found that patients who go to higher-rated SNFs achieved better outcomes, supporting the validity of the SNF report card ratings.

Professional autonomy has come under greater scrutiny due to managerialism, consumerism, information and communication technologies (ICT), and the changing composition of professions themselves. This scrutiny is often portrayed as a tension between professional and managerial logics. Recently, medical autonomy has increasingly been shaped in terms of transparency, where publication of clinical performance (via ICT) might be a more pervasive form of surveillance. Such transparency may have the potential for a more explicit managerial logic but is contested by clinicians. This paper applies notions of surveillance to public reporting of cardiac surgery, involving the online publication of mortality rates of named surgeons. It draws on qualitative data from a case-study of cardiac surgeons in one hospital, incorporating interviews with health care managers and national policymakers in England. We examine how managerial logics are mediated by professional autonomy, generating patterns of enrolment, resistance and reactivity to public reporting. The managerial \'gaze\' of public reporting is becoming widespread but the surgical specialty is accommodating it, leading to a re-assertion of knowledge, based on professional definitions. The paper assesses whether this form of surveillance is challenging to or being assimilated by the medical profession, thereby recasting the profession itself.