RESUMEN
IMPORTANCE: Scales often arise from multi-item questionnaires, yet commonly face item non-response. Traditional solutions use weighted mean (WMean) from available responses, but potentially overlook missing data intricacies. Advanced methods like multiple imputation (MI) address broader missing data, but demand increased computational resources. Researchers frequently use survey data in the All of Us Research Program (All of Us), and it is imperative to determine if the increased computational burden of employing MI to handle non-response is justifiable. OBJECTIVES: Using the 5-item Physical Activity Neighborhood Environment Scale (PANES) in All of Us, this study assessed the tradeoff between efficacy and computational demands of WMean, MI, and inverse probability weighting (IPW) when dealing with item non-response. MATERIALS AND METHODS: Synthetic missingness, allowing 1 or more item non-response, was introduced into PANES across 3 missing mechanisms and various missing percentages (10%-50%). Each scenario compared WMean of complete questions, MI, and IPW on bias, variability, coverage probability, and computation time. RESULTS: All methods showed minimal biases (all <5.5%) for good internal consistency, with WMean suffered most with poor consistency. IPW showed considerable variability with increasing missing percentage. MI required significantly more computational resources, taking >8000 and >100 times longer than WMean and IPW in full data analysis, respectively. DISCUSSION AND CONCLUSION: The marginal performance advantages of MI for item non-response in highly reliable scales do not warrant its escalated cloud computational burden in All of Us, particularly when coupled with computationally demanding post-imputation analyses. Researchers using survey scales with low missingness could utilize WMean to reduce computing burden.
RESUMEN
PURPOSE: The specific aims of this paper are to (1) develop and operationalize an electronic health record (EHR) data quality framework, (2) apply the dimensions of the framework to the phenotype and treatment pathways of ductal carcinoma in situ (DCIS) using All of Us Research Program data, and (3) propose and apply a checklist to evaluate the application of the framework. METHODS: We developed a framework of five data quality dimensions (DQD; completeness, concordance, conformance, plausibility, and temporality). Participants signed a consent and Health Insurance Portability and Accountability Act authorization to share EHR data and responded to demographic questions in the Basics questionnaire. We evaluated the internal characteristics of the data and compared data with external benchmarks with descriptive and inferential statistics. We developed a DQD checklist to evaluate concept selection, internal verification, and external validity for each DQD. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) concept ID codes for DCIS were used to select a cohort of 2,209 females 18 years and older. RESULTS: Using the proposed DQD checklist criteria, (1) concepts were selected and internally verified for conformance; (2) concepts were selected and internally verified for completeness; (3) concepts were selected, internally verified, and externally validated for concordance; (4) concepts were selected, internally verified, and externally validated for plausibility; and (5) concepts were selected, internally verified, and externally validated for temporality. CONCLUSION: This assessment and evaluation provided insights into data quality for the DCIS phenotype using EHR data from the All of Us Research Program. The review demonstrates that salient clinical measures can be selected, applied, and operationalized within a conceptual framework and evaluated for fitness for use by applying a proposed checklist.
Asunto(s)
Neoplasias de la Mama , Carcinoma Intraductal no Infiltrante , Exactitud de los Datos , Registros Electrónicos de Salud , Humanos , Registros Electrónicos de Salud/normas , Femenino , Carcinoma Intraductal no Infiltrante/terapia , Carcinoma Intraductal no Infiltrante/patología , Carcinoma Intraductal no Infiltrante/epidemiología , Neoplasias de la Mama/terapia , Neoplasias de la Mama/epidemiología , Neoplasias de la Mama/diagnóstico , Estados Unidos , Persona de Mediana Edad , Adulto , AncianoRESUMEN
INTRODUCTION: Electronic Health Records (EHR) are a useful data source for research, but their usability is hindered by measurement errors. This study investigated an automatic error detection algorithm for adult height and weight measurements in EHR for the All of Us Research Program (All of Us). METHODS: We developed reference charts for adult heights and weights that were stratified on participant sex. Our analysis included 4,076,534 height and 5,207,328 wt measurements from â¼ 150,000 participants. Errors were identified using modified standard deviation scores, differences from their expected values, and significant changes between consecutive measurements. We evaluated our method with chart-reviewed heights (8,092) and weights (9,039) from 250 randomly selected participants and compared it with the current cleaning algorithm in All of Us. RESULTS: The proposed algorithm classified 1.4 % of height and 1.5 % of weight errors in the full cohort. Sensitivity was 90.4 % (95 % CI: 79.0-96.8 %) for heights and 65.9 % (95 % CI: 56.9-74.1 %) for weights. Precision was 73.4 % (95 % CI: 60.9-83.7 %) for heights and 62.9 (95 % CI: 54.0-71.1 %) for weights. In comparison, the current cleaning algorithm has inferior performance in sensitivity (55.8 %) and precision (16.5 %) for height errors while having higher precision (94.0 %) and lower sensitivity (61.9 %) for weight errors. DISCUSSION: Our proposed algorithm outperformed in detecting height errors compared to weights. It can serve as a valuable addition to the current All of Us cleaning algorithm for identifying erroneous height values.
Asunto(s)
Algoritmos , Estatura , Peso Corporal , Registros Electrónicos de Salud , Humanos , Masculino , Adulto , Femenino , Persona de Mediana Edad , Estados Unidos , Valores de Referencia , Anciano , Adulto JovenRESUMEN
OBJECTIVE: With its size and diversity, the All of Us Research Program has the potential to power and improve representation in clinical trials through ancillary studies like Nutrition for Precision Health. We sought to characterize high-level trial opportunities for the diverse participants and sponsors of future trial investment. MATERIALS AND METHODS: We matched All of Us participants with available trials on ClinicalTrials.gov based on medical conditions, age, sex, and geographic location. Based on the number of matched trials, we (1) developed the Trial Opportunities Compass (TOC) to help sponsors assess trial investment portfolios, (2) characterized the landscape of trial opportunities in a phenome-wide association study (PheWAS), and (3) assessed the relationship between trial opportunities and social determinants of health (SDoH) to identify potential barriers to trial participation. RESULTS: Our study included 181 529 All of Us participants and 18 634 trials. The TOC identified opportunities for portfolio investment and gaps in currently available trials across federal, industrial, and academic sponsors. PheWAS results revealed an emphasis on mental disorder-related trials, with anxiety disorder having the highest adjusted increase in the number of matched trials (59% [95% CI, 57-62]; P < 1e-300). Participants from certain communities underrepresented in biomedical research, including self-reported racial and ethnic minorities, had more matched trials after adjusting for other factors. Living in a nonmetropolitan area was associated with up to 13.1 times fewer matched trials. DISCUSSION AND CONCLUSION: All of Us data are a valuable resource for identifying trial opportunities to inform trial portfolio planning. Characterizing these opportunities with consideration for SDoH can provide guidance on prioritizing the most pressing barriers to trial participation.
RESUMEN
Objective: The pandemic has pushed hospital system to re-evaluate the ways they provide care. West Tennessee Healthcare (WTH) developed a remote patient monitoring (RPM) program to monitor positive COVID-19 patients after being discharged from the hospital for any worsening symptomatology and preemptively mitigate the potential of readmission. Methods: We sought to compare the readmission rates of individuals placed on our remote monitoring protocol with individuals not included in the program. We selected remotely monitored individuals discharged from WTH from October 2020 to December 2020 and compared these data points with a control group. Results: We analyzed 1,351 patients with 241 patients receiving no RPM intervention, 969 patients receiving standard monitoring, and 141 patients enrolled in our 24-h remote monitoring. Our lowest all cause readmission rate was 4.96% (p = 0.37) in our 24-h remote monitoring group. We also collected 641 surveys from the monitored patients with two statistically significant answers. Discussion: The low readmission rate noted in our 24-h remotely monitored cohort signifies a potential opportunity that a program of this nature can create for a health care system struggling during a resource-limited time to continue to provide quality care. Conclusion: The program allowed the allocation of hospital resources for individuals with more acute states and monitored less critical patients without using personal protective equipment. The novel program was able to offer an avenue to improve resource utilization and provide care for a health system in a rural area. Further investigation is needed; however, significant opportunities can be seen with data obtained during the study.
Asunto(s)
COVID-19 , Humanos , Cuidados Posteriores , COVID-19/epidemiología , Hospitales Rurales , Alta del Paciente , Estudios RetrospectivosRESUMEN
PURPOSE: To determine the distribution and quantity of ophthalmic care consumed on Affordable Care Act (ACA) plans, the demographics of the population utilizing these services, and the relationship between ACA insurance coverage plan tier, cost sharing, and total cost of ophthalmic care consumed. METHODS: This cross-sectional study analyzed ACA individual and small group market claims data from the Wakely Affordable Care Act (WACA) 2018 dataset, which contains detailed claims, enrollment, and premium data from Edge Servers for 3.9 million individual and small group market lives. We identified all enrollees with ophthalmology-specific billing, procedure, and national drug codes. We then analyzed the claims by plan type and calculated the total cost and out-of-pocket (OOP) cost. RESULTS: Among 3.9 million enrollees in the WACA 2018 dataset, 538,169 (13.7%) had claims related to ophthalmology procedures, medications, and/or diagnoses. A total of $203 million was generated in ophthalmology-related claims, with $54 million in general services, $42 million in medications, $20 million in diagnostics and imaging, and $86 million in procedures. Average annual OOP costs were $116 per member, or 30.9% of the total cost, and were lowest for members with platinum plans (16% OOP) and income-driven cost sharing reduction (ICSR) subsidies (17% OOP). Despite stable ocular disease distribution across plan types, beneficiaries with silver ICSR subsidies consumed more total care than any other plan, higher than platinum plan enrollees and almost 1.5× the cost of bronze plan enrollees. CONCLUSIONS: Ophthalmic care for enrollees on ACA plans generated substantial costs in 2018. Plans with higher OOP cost sharing may result in lower utilization of ophthalmic care.
Asunto(s)
Intercambios de Seguro Médico , Patient Protection and Affordable Care Act , Humanos , Seguro de Costos Compartidos , Estudios Transversales , Cobertura del Seguro , Seguro de Salud , Estados UnidosRESUMEN
OBJECTIVE: The All of Us Research Program (All of Us) aims to recruit over a million participants to further precision medicine. Essential to the verification of biobanks is a replication of known associations to establish validity. Here, we evaluated how well All of Us data replicated known cigarette smoking associations. MATERIALS AND METHODS: We defined smoking exposure as follows: (1) an EHR Smoking exposure that used International Classification of Disease codes; (2) participant provided information (PPI) Ever Smoking; and, (3) PPI Current Smoking, both from the lifestyle survey. We performed a phenome-wide association study (PheWAS) for each smoking exposure measurement type. For each, we compared the effect sizes derived from the PheWAS to published meta-analyses that studied cigarette smoking from PubMed. We defined two levels of replication of meta-analyses: (1) nominally replicated: which required agreement of direction of effect size, and (2) fully replicated: which required overlap of confidence intervals. RESULTS: PheWASes with EHR Smoking, PPI Ever Smoking, and PPI Current Smoking revealed 736, 492, and 639 phenome-wide significant associations, respectively. We identified 165 meta-analyses representing 99 distinct phenotypes that could be matched to EHR phenotypes. At P < .05, 74 were nominally replicated and 55 were fully replicated. At P < 2.68 × 10-5 (Bonferroni threshold), 58 were nominally replicated and 40 were fully replicated. DISCUSSION: Most phenotypes found in published meta-analyses associated with smoking were nominally replicated in All of Us. Both survey and EHR definitions for smoking produced similar results. CONCLUSION: This study demonstrated the feasibility of studying common exposures using All of Us data.
Asunto(s)
Estudio de Asociación del Genoma Completo , Salud Poblacional , Humanos , Estudio de Asociación del Genoma Completo/métodos , Fenotipo , Polimorfismo de Nucleótido Simple , FumarRESUMEN
The All of Us Research Program's Data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.
Asunto(s)
Investigación Biomédica , Salud Poblacional , Humanos , Ecosistema , Medicina de PrecisiónRESUMEN
OBJECTIVE: The All of Us Research Program collects data from multiple information sources, including health surveys, to build a national longitudinal research repository that researchers can use to advance precision medicine. Missing survey responses pose challenges to study conclusions. We describe missingness in All of Us baseline surveys. STUDY DESIGN AND SETTING: We extracted survey responses between May 31, 2017, to September 30, 2020. Missing percentages for groups historically underrepresented in biomedical research were compared to represented groups. Associations of missing percentages with age, health literacy score, and survey completion date were evaluated. We used negative binomial regression to evaluate participant characteristics on the number of missed questions out of the total eligible questions for each participant. RESULTS: The dataset analyzed contained data for 334,183 participants who submitted at least one baseline survey. Almost all (97.0%) of the participants completed all baseline surveys, and only 541 (0.2%) participants skipped all questions in at least one of the baseline surveys. The median skip rate was 5.0% of the questions, with an interquartile range (IQR) of 2.5% to 7.9%. Historically underrepresented groups were associated with higher missingness (incidence rate ratio (IRR) [95% CI]: 1.26 [1.25, 1.27] for Black/African American compared to White). Missing percentages were similar by survey completion date, participant age, and health literacy score. Skipping specific questions were associated with higher missingness (IRRs [95% CI]: 1.39 [1.38, 1.40] for skipping income, 1.92 [1.89, 1.95] for skipping education, 2.19 [2.09-2.30] for skipping sexual and gender questions). CONCLUSION: Surveys in the All of Us Research Program will form an essential component of the data researchers can use to perform their analyses. Missingness was low in All of Us baseline surveys, but group differences exist. Additional statistical methods and careful analysis of surveys could help mitigate challenges to the validity of conclusions.
Asunto(s)
Salud Poblacional , Humanos , Encuestas y Cuestionarios , Encuestas Epidemiológicas , Conducta SexualRESUMEN
The Affordable Care Act (ACA) mandated coverage of common preventive services with zero patient cost sharing. However, patients may still experience high same-day costs when receiving these "zero-dollar" preventive services. Our analysis of on- and off-exchange individual-market health plans during 2016-18 revealed that 21-61 percent of enrollees experienced same-day cost exposure greater than $0 when accessing ACA-mandated free preventive services.
Asunto(s)
Cobertura del Seguro , Patient Protection and Affordable Care Act , Estados Unidos , Humanos , Seguro de Costos Compartidos , Servicios Preventivos de Salud , Cooperación del Paciente , Seguro de SaludRESUMEN
Background: Many clinical trials leverage real-world data. Typically, these data are manually abstracted from electronic health records (EHRs) and entered into electronic case report forms (CRFs), a time and labor-intensive process that is also error-prone and may miss information. Automated transfer of data from EHRs to eCRFs has the potential to reduce data abstraction and entry burden as well as improve data quality and safety. Methods: We conducted a test of automated EHR-to-CRF data transfer for 40 participants in a clinical trial of hospitalized COVID-19 patients. We determined which coordinator-entered data could be automated from the EHR (coverage), and the frequency with which the values from the automated EHR feed and values entered by study personnel for the actual study matched exactly (concordance). Results: The automated EHR feed populated 10,081/11,952 (84%) coordinator-completed values. For fields where both the automation and study personnel provided data, the values matched exactly 89% of the time. Highest concordance was for daily lab results (94%), which also required the most personnel resources (30 minutes per participant). In a detailed analysis of 196 instances where personnel and automation entered values differed, both a study coordinator and a data analyst agreed that 152 (78%) instances were a result of data entry error. Conclusions: An automated EHR feed has the potential to significantly decrease study personnel effort while improving the accuracy of CRF data.
RESUMEN
PURPOSE: Patient portal secure messages are not always authored by the patient account holder. Understanding who authored the message is particularly important in an oncology setting where symptom reporting is crucial to patient treatment. Natural language processing has the potential to detect messages not authored by the patient automatically. METHODS: Patient portal secure messages from the Memorial Sloan Kettering Cancer Center were retrieved and manually annotated as a predicted unregistered proxy (ie, not written by the patient) or a presumed patient. After randomly splitting the annotated messages into training and test sets in a 70:30 ratio, a bag-of-words approach was used to extract features and then a Least Absolute Shrinkage and Selection Operator (LASSO) model was trained and used for classification. RESULTS: Portal secure messages (n = 2,000) were randomly selected from unique patient accounts and manually annotated. We excluded 335 messages from the data set as the annotators could not determine if they were written by a patient or proxy. Using the remaining 1,665 messages, a LASSO model was developed that achieved an area under the curve of 0.932 and an area under the precision recall curve of 0.748. The sensitivity and specificity related to classifying true-positive cases (predicted unregistered proxy-authored messages) and true negatives (presumed patient-authored messages) were 0.681 and 0.960, respectively. CONCLUSION: Our work demonstrates the feasibility of using unstructured, heterogenous patient portal secure messages to determine portal secure message authorship. Identifying patient authorship in real time can improve patient portal account security and can be used to improve the quality of the information extracted from the patient portal, such as patient-reported outcomes.
Asunto(s)
Procesamiento de Lenguaje Natural , Portales del Paciente , Humanos , Prueba de Estudio ConceptualRESUMEN
The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools.
RESUMEN
The All of Us (AoU) Research Program aggregates electronic health records (EHR) data from 300,00+ participants spanning 50+ distinct data sites. The diversity and size of AoU's data network result in multifaceted obstacles to data integration that may undermine the usability of patient EHR. Consequently, the AoU team implemented data quality tools to regularly evaluate and communicate EHR data quality issues at scale. The use of systematic feedback and educational tools ultimately increased site engagement and led to quantitative improvements in EHR quality as measured by program- and externally-defined metrics. These improvements enabled the AoU team to save time on troubleshooting EHR and focus on the development of alternate mechanisms to improve the quality of future EHR submissions. While this framework has proven effective, further efforts to automate and centralize communication channels are needed to deepen the program's efforts while retaining its scalability.
RESUMEN
OBJECTIVE: A participant's medical history is important in clinical research and can be captured from electronic health records (EHRs) and self-reported surveys. Both can be incomplete, EHR due to documentation gaps or lack of interoperability and surveys due to recall bias or limited health literacy. This analysis compares medical history collected in the All of Us Research Program through both surveys and EHRs. MATERIALS AND METHODS: The All of Us medical history survey includes self-report questionnaire that asks about diagnoses to over 150 medical conditions organized into 12 disease categories. In each category, we identified the 3 most and least frequent self-reported diagnoses and retrieved their analogues from EHRs. We calculated agreement scores and extracted participant demographic characteristics for each comparison set. RESULTS: The 4th All of Us dataset release includes data from 314 994 participants; 28.3% of whom completed medical history surveys, and 65.5% of whom had EHR data. Hearing and vision category within the survey had the highest number of responses, but the second lowest positive agreement with the EHR (0.21). The Infectious disease category had the lowest positive agreement (0.12). Cancer conditions had the highest positive agreement (0.45) between the 2 data sources. DISCUSSION AND CONCLUSION: Our study quantified the agreement of medical history between 2 sources-EHRs and self-reported surveys. Conditions that are usually undocumented in EHRs had low agreement scores, demonstrating that survey data can supplement EHR data. Disagreement between EHR and survey can help identify possible missing records and guide researchers to adjust for biases.
Asunto(s)
Registros Electrónicos de Salud , Salud Poblacional , Documentación , Humanos , Almacenamiento y Recuperación de la Información , Encuestas y CuestionariosRESUMEN
Differences in obesity and body fat distribution across gender and race/ethnicity have been extensively described. We sought to replicate these differences and evaluate newly emerging data from the All of Us Research Program (AoU). We compared body mass index (BMI), waist circumference, and waist-to-hip ratio from the baseline physical examination, and alanine aminotransferase (ALT) from the electronic health record in up to 88,195 Non-Hispanic White (NHW), 40,770 Non-Hispanic Black (NHB), 35,640 Hispanic, and 5,648 Asian participants. We compared AoU sociodemographic variable distribution to National Health and Nutrition Examination Survey (NHANES) data and applied the pseudo-weighting method for adjusting selection biases of AoU recruitment. Our findings replicate previous observations with respect to gender differences in BMI. In particular, we replicate the large gender disparity in obesity rates among NHB participants, in which obesity and mean BMI are much higher in NHB women than NHB men (33.34 kg/m2 versus 28.40 kg/m2 respectively; p<2.22x10-308). The overall age-adjusted obesity prevalence in AoU participants is similar overall but lower than the prevalence found in NHANES for NHW participants. ALT was higher in men than women, and lower among NHB participants compared to other racial/ethnic groups, consistent with previous findings. Our data suggest consistency of AoU with national averages related to obesity and suggest this resource is likely to be a major source of scientific inquiry and discovery in diverse populations.
Asunto(s)
Distribución de la Grasa Corporal , Índice de Masa Corporal , Etnicidad/estadística & datos numéricos , Obesidad/fisiopatología , Planificación de Atención al Paciente/organización & administración , Grupos Raciales/estadística & datos numéricos , Adolescente , Adulto , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , Encuestas Nutricionales , Obesidad/epidemiología , Factores Sexuales , Estados Unidos/epidemiología , Circunferencia de la Cintura , Adulto JovenRESUMEN
OBJECTIVE: A growing research literature has highlighted the work of managing and triaging clinical messages as a major contributor to professional exhaustion and burnout. The goal of this study was to discover and quantify the distribution of message content sent among care team members treating patients with breast cancer. MATERIALS AND METHODS: We analyzed nearly two years of communication data from the electronic health record (EHR) between care team members at Vanderbilt University Medical Center. We applied natural language processing to perform sentence-level annotation into one of five information types: clinical, medical logistics, nonmedical logistics, social, and other. We combined sentence-level annotations for each respective message. We evaluated message content by team member role and clinic activity. RESULTS: Our dataset included 81 857 messages containing 613 877 sentences. Across all roles, 63.4% and 21.8% of messages contained logistical information and clinical information, respectively. Individuals in administrative or clinical staff roles sent 81% of all messages containing logistical information. There were 33.2% of messages sent by physicians containing clinical information-the most of any role. DISCUSSION AND CONCLUSION: Our results demonstrate that EHR-based asynchronous communication is integral to coordinate care for patients with breast cancer. By understanding the content of messages sent by care team members, we can devise informatics initiatives to improve physicians' clerical burden and reduce unnecessary interruptions.
RESUMEN
OBJECTIVE: Family health history is important to clinical care and precision medicine. Prior studies show gaps in data collected from patient surveys and electronic health records (EHRs). The All of Us Research Program collects family history from participants via surveys and EHRs. This Demonstration Project aims to evaluate availability of family health history information within the publicly available data from All of Us and to characterize the data from both sources. MATERIALS AND METHODS: Surveys were completed by participants on an electronic portal. EHR data was mapped to the Observational Medical Outcomes Partnership data model. We used descriptive statistics to perform exploratory analysis of the data, including evaluating a list of medically actionable genetic disorders. We performed a subanalysis on participants who had both survey and EHR data. RESULTS: There were 54 872 participants with family history data. Of those, 26% had EHR data only, 63% had survey only, and 10.5% had data from both sources. There were 35 217 participants with reported family history of a medically actionable genetic disorder (9% from EHR only, 89% from surveys, and 2% from both). In the subanalysis, we found inconsistencies between the surveys and EHRs. More details came from surveys. When both mentioned a similar disease, the source of truth was unclear. CONCLUSIONS: Compiling data from both surveys and EHR can provide a more comprehensive source for family health history, but informatics challenges and opportunities exist. Access to more complete understanding of a person's family health history may provide opportunities for precision medicine.
Asunto(s)
Registros Electrónicos de Salud , Encuestas Epidemiológicas , Anamnesis , Investigación Biomédica , Enfermedades Genéticas Congénitas/epidemiología , Humanos , Internet , Medicina de PrecisiónRESUMEN
PURPOSE: Our objective was to demonstrate the efficacy of a telehealth training course on high-dose-rate (HDR) brachytherapy for gynecologic cancer treatment for clinicians in low- and middle-income countries (LMICs). METHODS: A 12-week course consisting of 16 live video sessions was offered to 10 cancer centers in the Middle East, Africa, and Nepal. A total of 46 participants joined the course, and 22 participants, on average, attended each session. Radiation oncologists and medical physicists from 11 US and international institutions prepared and provided lectures for each topic covered in the course. Confidence surveys of 15 practical competencies were administered to participants before and after the course. Competencies focused on HDR commissioning, shielding, treatment planning, radiobiology, and applicators. Pre- and post-program surveys of provider confidence, measured by 5-point Likert scale, were administered and compared. RESULTS: Forty-six participants, including seven chief medical physicists, 16 senior medical physicists, five radiation oncologists, and three dosimetrists, representing nine countries attended education sessions. Reported confidence scores, both aggregate and paired, demonstrated increases in confidence in all 15 competencies. Post-curriculum score improvement was statistically significant (P < .05) for paired respondents in 11 of 15 domains. Absolute improvements were largest for confidence in applicator commissioning (2.3 to 3.8, P = .009), treatment planning system commissioning (2.2 to 3.9, P = .0055), and commissioning an HDR machine (2.2 to 4.0, P = .0031). Overall confidence in providing HDR brachytherapy services safely and teaching other providers increased from 3.1 to 3.8 and 3.0 to 3.5, respectively. CONCLUSION: A 12-week, low-cost telehealth training program on HDR brachytherapy improved confidence in treatment delivery and teaching for clinicians in 10 participating LMICs.