Stability and compatibility study guidelines for AJHP.

In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

Assessing the readability and quality of online information on Bell's palsy.

OBJECTIVE: This study aimed to evaluate the readability and quality of current online information on Bell's palsy. METHOD: A Google search using the terms 'Bell's palsy' and 'facial palsy' was performed separately. The first three pages of results were analysed. Readability was assessed using Flesch Reading Ease Score, Flesch-Kincaid Grade Level, the Gunning-Fog Index and the Simple Measure of Gobbledygook. Quality was assessed using the Discern tool. Spearman's correlation between quality and readability was calculated. RESULTS: A total of 31 websites met the inclusion criteria. The mean Flesch Reading Ease Score, Flesch-Kincaid Grade Level, the Gunning Fox Index and the Simple Measure of Gobbledygook scores were 52.45 (95 per cent confidence interval = 47.01-57.86), 10.50 (95 per cent confidence interval = 9.42-11.58), 12.76 (95 per cent confidence interval = 11.68-13.85) and 9.36 (95 per cent confidence interval = 8.52-10.20), respectively. The average Discern score was 44 (95 per cent confidence interval = 40.88-47.12). A negligible correlation was noted between the Discern and Flesch Reading Ease Score (rs = -0.05, p = 0.80). CONCLUSION: Online information on Bell's palsy is generally of fair quality but is written above the recommended reading age guidance in the UK.

Blockchain Technology as a Solution to the Bullwhip Effect

The Covid-19 Pandemic has revealed the fragility and shortcomings of existing supply chains and the importance of digitalization and digital solutions for Supply chains. In this study, a solution proposal is presented to the problem of the Bullwhip effect, which is a serious problem for Supply Chain management performance. It will be emphasized that the operational and tactical capabilities provided by Blockchain technology, which is one of the digital solutions and offered as a solution proposal, may increase the performance of the Supply Chain by providing a solution to the bullwhip effect. In addition, in this study, the thematic analyses created as a result of expert opinions on this subject and other analyses and findings related to the study will be shared. The main purpose of the research is to provide a deep understanding of the ability of Blockchain Technology to provide a solution to the problem of the bullwhip effect, which is a serious problem for the successful management of the supply chain and constitutes a starting point for the studies to be done on this subject. © Peter Lang GmbH.

Transitioning EPMA applications in a large multisite teaching hospital.

INTRODUCTION: University Hospitals of Leicester (UHL) has co-developed and deployed a novel Electronic Prescribing and Medicines Administration (EPMA) application as part of the trust electronic patient record (EPR) programme that meets specific clinical demands and interoperability standards of the National Health Service (NHS) despite clinical pressures from the COVID-19 pandemic. METHODS: Following an initial limited pilot deployment, a big-bang whole site-based approach allowed transition of 1844 acute adult inpatients beds from an existing standalone EMPA to the new system. This project used a frontline driven and agile management strategy. Clinical risk was managed using a combination of standard risk logs, robust clinical prototyping and robust disaster recovery plans. Early engagement with clinical teams allowed for advanced product configuration before live deployment and reduced the need for sustained transition support for clinical staff. RESULTS: An iterative, well-governed approach, led by a combination of information technology (IT) and clinical staff with a responsive vendor, enabled a complex new EPMA system in a large acute NHS trust to be deployed with limited resources despite the ongoing COVID-19 pandemic. DISCUSSION: The development and deployment of EMPA and EPR systems across NHS trusts is a key enabler for better healthcare delivery. This case study shows it is possible to deploy a new clinical IT system at scale without interruption of clinical services and with a relatively modest deployment team. Sustainability of the project was also ensured through a clear clinically led governance structure to manage risk quickly and carry lessons learnt onto new developments.

Differences in Clinical Presentation With Long COVID After Community and Hospital Infection and Associations With All-Cause Mortality: English Sentinel Network Database Study.

BACKGROUND: Most studies of long COVID (symptoms of COVID-19 infection beyond 4 weeks) have focused on people hospitalized in their initial illness. Long COVID is thought to be underrecorded in UK primary care electronic records. OBJECTIVE: We sought to determine which symptoms people present to primary care after COVID-19 infection and whether presentation differs in people who were not hospitalized, as well as post-long COVID mortality rates. METHODS: We used routine data from the nationally representative primary care sentinel cohort of the Oxford-Royal College of General Practitioners Research and Surveillance Centre (N=7,396,702), applying a predefined long COVID phenotype and grouped by whether the index infection occurred in hospital or in the community. We included COVID-19 infection cases from March 1, 2020, to April 1, 2021. We conducted a before-and-after analysis of long COVID symptoms prespecified by the Office of National Statistics, comparing symptoms presented between 1 and 6 months after the index infection matched with the same months 1 year previously. We conducted logistic regression analysis, quoting odds ratios (ORs) with 95% CIs. RESULTS: In total, 5.63% (416,505/7,396,702) and 1.83% (7623/416,505) of the patients had received a coded diagnosis of COVID-19 infection and diagnosis of, or referral for, long COVID, respectively. People with diagnosis or referral of long COVID had higher odds of presenting the prespecified symptoms after versus before COVID-19 infection (OR 2.66, 95% CI 2.46-2.88, for those with index community infection and OR 2.42, 95% CI 2.03-2.89, for those hospitalized). After an index community infection, patients were more likely to present with nonspecific symptoms (OR 3.44, 95% CI 3.00-3.95; P<.001) compared with after a hospital admission (OR 2.09, 95% CI 1.56-2.80; P<.001). Mental health sequelae were more strongly associated with index hospital infections (OR 2.21, 95% CI 1.64-2.96) than with index community infections (OR 1.36, 95% CI 1.21-1.53; P<.001). People presenting to primary care after hospital infection were more likely to be men (OR 1.43, 95% CI 1.25-1.64; P<.001), more socioeconomically deprived (OR 1.42, 95% CI 1.24-1.63; P<.001), and with higher multimorbidity scores (OR 1.41, 95% CI 1.26-1.57; P<.001) than those presenting after an index community infection. All-cause mortality in people with long COVID was associated with increasing age, male sex (OR 3.32, 95% CI 1.34-9.24; P=.01), and higher multimorbidity score (OR 2.11, 95% CI 1.34-3.29; P<.001). Vaccination was associated with reduced odds of mortality (OR 0.10, 95% CI 0.03-0.35; P<.001). CONCLUSIONS: The low percentage of people recorded as having long COVID after COVID-19 infection reflects either low prevalence or underrecording. The characteristics and comorbidities of those presenting with long COVID after a community infection are different from those hospitalized. This study provides insights into the presentation of long COVID in primary care and implications for workload.

Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis.

BACKGROUND: Following COVID-19, up to 40% of people have ongoing health problems, referred to as postacute COVID-19 or long COVID (LC). LC varies from a single persisting symptom to a complex multisystem disease. Research has flagged that this condition is underrecorded in primary care records, and seeks to better define its clinical characteristics and management. Phenotypes provide a standard method for case definition and identification from routine data and are usually machine-processable. An LC phenotype can underpin research into this condition. OBJECTIVE: This study aims to develop a phenotype for LC to inform the epidemiology and future research into this condition. We compared clinical symptoms in people with LC before and after their index infection, recorded from March 1, 2020, to April 1, 2021. We also compared people recorded as having acute infection with those with LC who were hospitalized and those who were not. METHODS: We used data from the Primary Care Sentinel Cohort (PCSC) of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database. This network was recruited to be nationally representative of the English population. We developed an LC phenotype using our established 3-step ontological method: (1) ontological step (defining the reasoning process underpinning the phenotype, (2) coding step (exploring what clinical terms are available, and (3) logical extract model (testing performance). We created a version of this phenotype using Protégé in the ontology web language for BioPortal and using PhenoFlow. Next, we used the phenotype to compare people with LC (1) with regard to their symptoms in the year prior to acquiring COVID-19 and (2) with people with acute COVID-19. We also compared hospitalized people with LC with those not hospitalized. We compared sociodemographic details, comorbidities, and Office of National Statistics-defined LC symptoms between groups. We used descriptive statistics and logistic regression. RESULTS: The long-COVID phenotype differentiated people hospitalized with LC from people who were not and where no index infection was identified. The PCSC (N=7.4 million) includes 428,479 patients with acute COVID-19 diagnosis confirmed by a laboratory test and 10,772 patients with clinically diagnosed COVID-19. A total of 7471 (1.74%, 95% CI 1.70-1.78) people were coded as having LC, 1009 (13.5%, 95% CI 12.7-14.3) had a hospital admission related to acute COVID-19, and 6462 (86.5%, 95% CI 85.7-87.3) were not hospitalized, of whom 2728 (42.2%) had no COVID-19 index date recorded. In addition, 1009 (13.5%, 95% CI 12.73-14.28) people with LC were hospitalized compared to 17,993 (4.5%, 95% CI 4.48-4.61; P<.001) with uncomplicated COVID-19. CONCLUSIONS: Our LC phenotype enables the identification of individuals with the condition in routine data sets, facilitating their comparison with unaffected people through retrospective research. This phenotype and study protocol to explore its face validity contributes to a better understanding of LC.

Impact of implementing an electronic radiation oncology-specific in-house incident learning system

Purpose or Objective Incident learning systems (ILS) provide a formalised framework for incident reporting, analysis, data visualisation, feedback, and learning. Robust ILS can identify quality improvement (QI) areas and strengthen quality assurance (QA) pathways. A QI project to develop a digital in-house radiation oncology (RO) ILS was undertaken, with success demonstrated in the first 12 months of use. Materials and Methods A needs assessment was performed in 2019, including an in-house survey on staff knowledge and understanding of current incident reporting methods, ILS and the safety culture climate. Additionally, relevant literature was reviewed. From this, the QI team designed and implemented an electronic reporting system to suit departmental needs and tested its impact at 12 months via a follow-up survey. Results The needs assessment identified that the paper-based ILS in use required improvement. Barriers to reporting were perceived by 67% of respondents and most staff (75%) preferred an electronic in-house system. The state-wide hospitallevel reporting system did not meet the detailed needs of RO. Therefore, a customised electronic departmental-level reporting system was developed on the Varian AriaTM oncology information system platform. It supported actual incident reporting and lower level reporting (e.g., near miss, protocol non-compliance) to increase capacity for learning and QA/QI guidance. It works in parallel with the state-wide system to ensure clinical governance of higher-level reports being reported correctly. The new ILS includes a dedicated triage team, ensuring accurate data capture and rapid coordination of further analysis/escalation when required. Increased data accuracy has been demonstrated in the new ILS, with easy access for all staff to see reports. Clear data visualisation tools are used in Microsoft ExcelTM and Power BITM. The triage team provides increased communication and rapid feedback to staff and management when needed for urgent QI, education or reminders. Monthly meetings to discuss learning opportunities and potential QI ideas are now open to all, rather than the previous separate senior staff meetings. Follow up survey results after 12-months of system use showed: decreased perception of barriers (from 67% to 57%);increased participation in reporting (48% to 70% of respondents having been involved);increased perception of a no-blame culture (49% to 58%);and increased ability to learn from reported incidents (49% to 86%). Conclusion The creation of a customised electronic ILS, suited to RO department needs, addressed issues with the previous system. Overall, the new ILS had a positive impact and adapted rapidly when Covid-19 impacted the standard hospital workflow. Increased feedback loops to the RO team are well integrated into the new ILS. The move to electronic an ILS has enabled easy access to data that highlight weaknesses in processes and protocols and has supported continuing QI initiatives.

Inaccurate recording of routinely collected data items influences identification of COVID-19 patients.

BACKGROUND: During the Coronavirus disease 2019 (COVID-19) pandemic it became apparent that it is difficult to extract standardized Electronic Health Record (EHR) data for secondary purposes like public health decision-making. Accurate recording of, for example, standardized diagnosis codes and test results is required to identify all COVID-19 patients. This study aimed to investigate if specific combinations of routinely collected data items for COVID-19 can be used to identify an accurate set of intensive care unit (ICU)-admitted COVID-19 patients. METHODS: The following routinely collected EHR data items to identify COVID-19 patients were evaluated: positive reverse transcription polymerase chain reaction (RT-PCR) test results; problem list codes for COVID-19 registered by healthcare professionals and COVID-19 infection labels. COVID-19 codes registered by clinical coders retrospectively after discharge were also evaluated. A gold standard dataset was created by evaluating two datasets of suspected and confirmed COVID-19-patients admitted to the ICU at a Dutch university hospital between February 2020 and December 2020, of which one set was manually maintained by intensivists and one set was extracted from the EHR by a research data management department. Patients were labeled 'COVID-19' if their EHR record showed diagnosing COVID-19 during or right before an ICU-admission. Patients were labeled 'non-COVID-19' if the record indicated no COVID-19, exclusion or only suspicion during or right before an ICU-admission or if COVID-19 was diagnosed and cured during non-ICU episodes of the hospitalization in which an ICU-admission took place. Performance was determined for 37 queries including real-time and retrospective data items. We used the F1 score, which is the harmonic mean between precision and recall. The gold standard dataset was split into one subset including admissions between February and April and one subset including admissions between May and December to determine accuracy differences. RESULTS: The total dataset consisted of 402 patients: 196 'COVID-19' and 206 'non-COVID-19' patients. F1 scores of search queries including EHR data items that can be extracted real-time ranged between 0.68 and 0.97 and for search queries including the data item that was retrospectively registered by clinical coders F1 scores ranged between 0.73 and 0.99. F1 scores showed no clear pattern in variability between the two time periods. CONCLUSIONS: Our study showed that one cannot rely on individual routinely collected data items such as coded COVID-19 on problem lists to identify all COVID-19 patients. If information is not required real-time, medical coding from clinical coders is most reliable. Researchers should be transparent about their methods used to extract data. To maximize the ability to completely identify all COVID-19 cases alerts for inconsistent data and policies for standardized data capture could enable reliable data reuse.

Characterizing the Spatiotemporal Heterogeneity of the COVID-19 Vaccination Landscape.

As variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged throughout 2021-2022, the need to maximize vaccination coverage across the United States to minimize severe outcomes of coronavirus disease 2019 (COVID-19) has been critical. Maximizing vaccination requires that we track vaccination patterns to measure the progress of the vaccination campaign and target locations that may be undervaccinated. To improve efforts to track and characterize COVID-19 vaccination progress in the United States, we integrated Centers for Disease Control and Prevention and state-provided vaccination data, identifying and rectifying discrepancies between these data sources. We found that COVID-19 vaccination coverage in the United States exhibits significant spatial heterogeneity at the county level, and we statistically identified spatial clusters of undervaccination, all with foci in the southern United States. We also identified vaccination progress at the county level as variable through summer 2021; the progress of vaccination in many counties stalled in June 2021, and few had recovered by July, with transmission of the SARS-CoV-2 delta variant rapidly rising. Using a comparison with a mechanistic growth model fitted to our integrated data, we classified vaccination dynamics across time at the county scale. Our findings underline the importance of curating accurate, fine-scale vaccination data and the continued need for widespread vaccination in the United States, especially with the continued emergence of highly transmissible SARS-CoV-2 variants.

Nanoantibodies applications in medicine

Introduction: Nanoantibodies are composed solely of the variable region of the heavy chain and are obtained from some species of camelids and sharks. They have high binding capacity, high specificity, small size, high accessibility, and high tissue penetration, so they could potentially be used to treat, diagnose, and prevent several diseases. Method: A bibliographic review of the medical applications of nanoantibodies was carried out. Scientific articles were examined, published in English and Spanish from 2015 to 2021 in Google Academic, Elsevier, PubMed, Clinical trials, Annual Reviews, and ScienceDirect databases. Studies that showed greater value according to language, information accuracy, and publication date were preferred. Results and discussion: 21 articles were selected to be evaluated and analyzed, of which 20 were preclinical studies and one clinical study. Nanoantibodies stand out as therapeutic, diagnostic, and preventive alternatives against cancer, hepatitis C, Alzheimer’s disease, Parkinson’s disease, diarrhea caused by rotavirus, and COVID-19. Conclusions: Nanoantibodies can be very useful for the prevention, diagnosis, and treatment of different diseases;however, it is necessary to continue developing clinical and preclinical studies that support the safety and efficacy of these drugs.

Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset.

OBJECTIVE: The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing. MATERIALS AND METHODS: The National COVID Cohort Collaborative (N3C) table of laboratory measurement data-over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test. RESULTS: Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors' records lacked units). DISCUSSION: The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference. CONCLUSION: The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.

Comparison of Adolescent Vaccination Data Accuracy by Urban Village in DKI Jakarta Province in July 2021 Using Several Data Mining Methods

Since 2020, the outbreak of the Coronavirus disease has begun to enter the territory of Indonesia. For a year and a half, various efforts have been made to reduce the number of deaths caused by this pandemic. One of the efforts made by the government is the provision of vaccinations for the community, especially for adolescents. This is one way to attract people's interest to vaccinate and also make it easier for the government and the system to process vaccination data, especially for youth vaccination. The purpose of this study is to determine the accuracy of the data on adolescents who have been vaccinated in the DKI Jakarta province in July 2021 by using several methods of data mining. Of the three data mining methods used in this study, the JRip method produces the highest percentage of accuracy, which is 100%. © 2021 IEEE.

Validity of range of motion, muscle strength, sensitivity, and Tinel sign tele-assessment in adults with traumatic brachial plexus injury.

BACKGROUND: The COVID-19 pandemic and the need for social distancing created challenges for accessing and providing health services. Telemedicine enables prompt evaluation of patients with traumatic brachial plexus injury, even at a distance, without prejudice to the prognosis. The present study aimed to verify the validity of range of motion, muscle strength, sensitivity, and Tinel sign tele-assessment in adults with traumatic brachial plexus injury (TBPI). METHODS: A cross-sectional study of twenty-one men and women with TBPI admitted for treatment at a Rehabilitation Hospital Network was conducted. The participants were assessed for range of motion, muscle strength, sensitivity, and Tinel sign at two moments: in-person assessment (IPA) and tele-assessment (TA). RESULTS: The TA muscle strength tests presented significant and excellent correlations with the IPA (the intra-rater intraclass correlation coefficient, ICC ranged between 0.79 and 1.00 depending on the muscle tested). The agreement between the TA and IPA range of motion tests ranged from substantial to moderate (weighted kappa coefficient of 0.47-0.76 (p < 0.05) depending on the joint), and the kappa coefficient did not indicate a statistically significant agreement in the range of motion tests of supination, wrist flexors, shoulder flexors, and shoulder external rotators. The agreement between the IPA andTA sensitivity tests of all innervations ranged from substantial to almost perfect (weighted kappa coefficient 0.61-0.83, p < 0.05) except for the C5 innervation, where the kappa coefficient did not indicate a statistically significant agreement. The IPA versus TA Tinel sign test showed a moderate agreement (weighted kappa coefficient of 0.57, p < 0.05). CONCLUSIONS: The present study demonstrated that muscle strength tele-assessment is valid in adults with TBPI and presented a strong agreement for many components of TA range of motion, sensitivity, and Tinel sign tests.

The Importance of Data Accuracy and Transparency for Policymaking during a Public Health Crisis: A Case Study in the State of Iowa

Background. High-quality data are necessary for decision-making during the SARS-CoV-2 pandemic. Lack of transparency and accuracy in data reporting can erode public confidence, mislead policymakers, and endanger safety. Two major data errors in Iowa impacted critical state- and county-level decision-making. Methods. The Iowa Department of Public Health (IDPH) publishes daily COVID-19 data. Authors independently tracked daily data from IDPH and other publicly available sources (i.e., county health departments, news media, and social networks). Data include: number and type of tests, results, hospitalizations, intensive care unit admissions, and deaths at state/county levels. Results. Discrepancies were identified between IDPH and non-IDPH data, with at least two confirmed by IDPH: (1) The backdating of test results identified on May 28, 2020. IDPH labeled results as occurring up to four months before the actual test date. IDPH confirmed that if a person previously tested for SARS-CoV-2, a new test result was attributed to the initial test's date. Corrections on August 19, 2020 increased positivity rates in 31 counties, but decreased the state's overall rate (9.1% to 7.5%). (2) The selective exclusion of antigen test results noted on August 20, 2020. Antigen testing was included in the total number of tests reported in metric denominators, but their results were being excluded from their respective numerators. Thus, positive antigen results were interpreted as de facto negative tests, artificially lowering positivity rates. Corrections increased Iowa's positivity rate (5.0% to 14.2%). In July 2020, the Iowa Department of Education mandated in-person K-12 learning for counties with < 15% positivity. These data changes occurred during critical decision-making, altering return-to-learn plans in seven counties. The Center for Medicare and Medicaid Services' requirements also caused nursing homes to urgently revise testing strategies. Timeline of changes to Iowa state COVID-19 testing through the end of August 2020. Change in positive and overall test results due to IDPH data corrections. These graphs represent the difference in cumulative total reported test results when pulled from the IDPH website on September 29, 2020 compared to data for the same dates when pulled on August 19, 2020 before the announced adjustment. The adjustment and subsequent daily changes in reported data amount to a dramatic change in the number of reported positive cases (A) with an increase of nearly 3,000 cases by April 25, as well as the loss of tens of thousands of data points when tracking total resulted tests (B). Conclusion. Data availability, quality, and transparency vary widely across the US, hindering science-based policymaking. Independent audit and curations of data can contribute to better public health policies. We urge all states to increase the availability and transparency of public health data.

Performance Accuracy of a Laboratory-Developed Real-Time RT-PCR Method for Detection of SARS-CoV-2 in Self-Collected Saliva Specimens

OBJECTIVE: The ongoing COVID-19 pandemic caused by SARS-CoV-2 has challenged diagnostic laboratories to re-examine traditional methods for collecting specimens and sample types used in molecular testing. Our goal was to demonstrate that saliva can be used for detecting SARS-CoV-2 and correlates well with established molecular methods using nasopharyngeal (NP) swabs. METHODS: We examined use of a saliva collection device in conjunction with a laboratory-developed real-time reverse transcription-polymerase chain reaction (LDPCR) method for detecting SARS-CoV-2 in a symptomatic population and compared results with 2 US Food and Drug Administration (FDA)-approved methods (emergency use authorization [EUA]) that use specimens from NP swabs. RESULTS: The sensitivity of LDPCR compared with the reference methods was 75.0% (21/28);specificity, 98.1% (104/106). When cycle threshold values were compared between paired specimens using the LDPCR and a EUA reverse transcription PCR method, both targeting the open-reading frame gene, the mean value for saliva was 4.66 cycles higher than for NP specimens. CONCLUSION: Use of self-collected saliva in conjunction with an LDPCR for SARS-CoV-2 compared favorably with 2 FDA EUA methods using NP swabs. The use of an alternative sample type and assay method will aid in expanding the availability of testing during the ongoing COVID-19 pandemic.

Automated, customized, patientcentric performance feedback in clinical trials to improve data accuracyand patient compliance

Ensuring the accuracy and reliability of clinical trial data is a cornerstone of producing reliable study results. Variability in key performance tasks at a subject level can introduce noise and increase the risk of trial failure. In the age of COVID-19, these issues are even more important as many patients are being monitored remotely. An automated, customizable, patientcentric feedback system was created to allow patients to be more involved in their own performance and progress throughout the clinical trial. This system provides feedback to patients on key performance tasks such as adherence to study medication, compliance with eDiary completion, and accuracy of symptom reporting. The reports are generated on a weekly or bi-weekly period throughout the study and provided to subjects on a tablet or hand-held device. In a 30-min usability session, four patients assessed the usability and understanding of the reports. Overall, patients reported with enthusiasm for the design of the report as well as appreciation for the effort to connect more with patients by providing them with feedback on their performance. Participants were generally able to use the report to find details of their performance and determine if it was in an acceptable range or needed improvement. This performance feedback system is currently being implemented in an ongoing clinical trial. Future research will provide information on the effectiveness of the performance system in improving data accuracy and patient compliance.

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.

OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

Analytical evaluation of the novel VITROS BRAHMS procalcitonin immunoassay.

To determine the analytical performance of Novel VITROS BRAHMS Procalcitonin Immunoassay on VITROS 3600 and correlation with BRAHMS PCT sensitive KRYPTOR reference method. Analytical performances including imprecision studies, linearity, limit of detection (LoD) and determination of hemolysis index were performed for VITROS BRAHMS PCT assay. Imprecision was assessed on plasma pool and internal control with 2 levels. The method comparison was performed using 162 plasma obtained from clinical departments. The total imprecision was acceptable and all CV were <5%. The LoD was in accordance with manufacturer's claims. The equation of linearity in the lower range was found to be y = 1.0014x - 0.0091, with r2 = 1. No interference to hemoglobin up to 11 g/L was observed. Correlation studies showed a good correlation between PCT measurements using VITROS BRAHMS PCT assay against KRYPTOR system including for values lower than 2 µg/L. The novel VITROS BRAHMS PCT assay from OrthoClinical Diagnostics shows analytical performances acceptable for clinical use. In addition, the concordance with KRYPTOR method was fine at all clinical cut-offs.

Mobile apps for travel medicine and ethical considerations: A systematic review.

BACKGROUND: The advent of mobile applications for health and medicine will revolutionize travel medicine. Despite their many benefits, such as access to real-time data, mobile apps for travel medicine are accompanied by many ethical issues, including questions about security and privacy. METHODS: A systematic literature review as conducted following PRISMA guidelines. Database screening yielded 1795 results and seven papers satisfied the criteria for inclusion. Through a mix of inductive and deductive data extraction, this systematic review examined both the benefits and challenges, as well as ethical considerations, of mobile apps for travel medicine. RESULTS: Ethical considerations were discussed with varying depth across the included articles, with privacy and data protection mentioned most frequently, highlighting concerns over sensitive information and a lack of guidelines in the digital sphere. Additionally, technical concerns about data quality and bias were predominant issues for researchers and developers alike. Some ethical issues were not discussed at all, including equity, and user involvement. CONCLUSION: This paper highlights the scarcity of discussion around ethical issues. Both researchers and developers need to better integrate ethical reflection at each step of the development and use of health apps. More effective oversight mechanisms and clearer ethical guidance are needed to guide the stakeholders in this endeavour.