Using patient-identifiable data for observational research and audit.
Ontology highlight
ABSTRACT:
SUBMITTER: Al-Shahi R
PROVIDER: S-EPMC1118832 | biostudies-literature | 2000 Oct
REPOSITORIES: biostudies-literature
Publications
Using patient-identifiable data for observational research and audit.
BMJ (Clinical research ed.) 20001001 7268
Similar Datasets
Project description:The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.
| S-EPMC6681681 | biostudies-literature
Project description:OBJECTIVE:Cardiac rehabilitation (CR) is an evidence-based intervention delivered by a wide range of high-volume and low-volume centres; however, the extent of volume-outcome relationship is yet to be studied. There is a lack of consensus about the effect of volume on outcomes, with evidence of mixed effects in acute and chronic care. The aim of this study is, to investigate the extent of association of outcomes in CR with patient volume. METHODS:Data was validated and extracted from the national audit from 2012 to 2013 for each CR centre. Volume was calculated as the total number of patients entering outpatient CR. Hierarchical multiple regression models were used to test for relationships between volume and outcomes. The outcomes included body mass index, blood pressure, psychosocial well-being, cholesterol, smoking cessation and physical activity. The analyses were adjusted for centre and patient characteristics and confounders. RESULTS:The number of patients included in the volume analysis was 48?476, derived from 178 CR centres. The average age per centre was 66?years with a 70% male distribution of patients enrolled. Regression analysis revealed no volume-outcome relationship, additionally no statistical significance existed. CONCLUSIONS:Unlike cardiac surgery this study, after accounting for staffing, age, gender and comorbidity, shows no effect of volume on outcome following CR delivered by high-volume and low-volume programmes. Based on our data there is no support for centralisation of services. Our findings and methodology can be used as a benchmark for future volume-outcome relationship studies in CR.
| S-EPMC4654095 | biostudies-other
Project description:Poor adherence to disease-modifying drugs is associated with an increased risk of relapse in patients with multiple sclerosis. However, adherence is difficult to assess objectively. RebiSmart(®) (Merck Serono SA, Geneva, Switzerland), a device for subcutaneous (sc) injection of interferon (IFN) ?-1a, features an electronic injection log that can assist in objective monitoring of adherence.To assess adherence to sc IFN ?-1a injections using data from RebiSmart(®).This was a single-group, observational, retrospective audit. Adherence data were collected from patients with relapsing multiple sclerosis in the United Kingdom and Ireland who had been prescribed sc IFN ?-1a and had been using RebiSmart(®) for a minimum of 24 months.In total, 225 patients were included in the full analysis set; 72% were in the United Kingdom, and 28% were in Ireland. Overall, the mean age was 44.1 years, and 73% were women. Patients received sc IFN ?-1a 44 µg (68%) or 22 µg (32%) three times per week. Mean adherence over the course of 24 months was 95.0% (median, 99.4%), and similar values were observed across all periods. The proportion of patients with 80% or higher adherence was 92.0% at 12 months and 91.1% at 24 months.High adherence to sc IFN ?-1a was observed across all patient groups using RebiSmart(®), according to 2-year treatment adherence data. This may be partly attributed to the expert support patients received, supplemented by routine and regular contact from the MySupport patient-support program, as well as the self-motivation of patients who persisted with treatment for 2 or more years.
| S-EPMC4062562 | biostudies-other
Project description:Sudden cardiac arrest (SCA) accounts for half of all cardiac deaths in Europe. In recent years, large-scale SCA registries have been set up to enable observational studies into risk factors and the effect of treatment approaches. The increasing scale and variety of data sources, coupled with the implementation of a new European data protection legal framework, causes researchers to struggle with how to handle these 'big data'. Data protection in the SCA setting is especially complex since patients become at least temporarily incapacitated, and are thus unable to provide prospective informed consent, and because the majority of patients do not survive. A narrative review employing a systematic literature search was conducted to thematically analyse ethical aspects of non-interventional emergency medicine and critical care research. Although the identified issues may apply to a wider patient population, we describe them within the context of SCA research. Potential harms were found to include: privacy breaches, genetic discrimination and issues associated with the disclosure of individual findings, study design and application of research results. Measures proposed to mitigate harms were: alternative informed consent models including deferred or waived consent and data governance approaches promoting data security, responsible sharing and public engagement. The themes identified in this study may serve as a basis for a much-needed ethical framework regarding research with data from patients with acute and critical illness such as SCA.
| S-EPMC6136218 | biostudies-literature
Project description:BACKGROUND:Respecting patient privacy and confidentiality is critical for doctor-patient relationships and public trust in medical professionals. The frequency of potentially identifiable disclosures online during periods of active engagement is unknown. OBJECTIVE:The objective of this study was to quantify potentially identifiable content shared on social media by physicians and other health care providers using the hashtag #ShareAStoryInOneTweet. METHODS:We accessed and searched Twitter's API using Symplur software for tweets that included the hashtag #ShareAStoryInOneTweet. We identified 1206 tweets by doctors, nurses, and other health professionals out of 43,374 tweets shared in May 2018. Tweet content was evaluated in January 2019 to determine the incidence of instances where names or potentially identifiable information about patients were shared; content analysis of tweets in which information about others had been disclosed was performed. The study also evaluated whether participants raised concerns about privacy breaches and estimated the frequency of deleted tweets. The study used dual, blinded coding for a 10% sample to estimate intercoder reliability using Cohen ? statistic for identifying the potential identifiability of tweet content. RESULTS:Health care professionals (n=656) disclosing information about others included 486 doctors (74.1%) and 98 nurses (14.9%). Health care professionals sharing stories about patient care disclosed the time frame in 95 tweets (95/754, 12.6%) and included patient names in 15 tweets (15/754, 2.0%). It is estimated that friends or families could likely identify the clinical scenario described in 242 of the 754 tweets (32.1%). Among 348 tweets about potentially living patients, it was estimated that 162 (46.6%) were likely identifiable by patients. Intercoder reliability in rating the potential identifiability demonstrated 86.8% agreement, with a Cohen ? of 0.8 suggesting substantial agreement. We also identified 78 out of 754 tweets (6.5%) that had been deleted on the website but were still viewable in the analytics software data set. CONCLUSIONS:During periods of active sharing online, nurses, physicians, and other health professionals may sometimes share more information than patients or families might expect. More study is needed to determine whether similar events arise frequently and to understand how to best ensure that patients' rights are adequately respected.
| S-EPMC7492977 | biostudies-literature
Project description:BackgroundTo assess the completeness of reporting, research transparency practices, and risk of selection and immortal bias in observational studies using routinely collected data for comparative effectiveness research.MethodWe performed a meta-research study by searching PubMed for comparative effectiveness observational studies evaluating therapeutic interventions using routinely collected data published in high impact factor journals from 01/06/2018 to 30/06/2020. We assessed the reporting of the study design (i.e., eligibility, treatment assignment, and the start of follow-up). The risk of selection bias and immortal time bias was determined by assessing if the time of eligibility, the treatment assignment, and the start of follow-up were synchronized to mimic the randomization following the target trial emulation framework.ResultSeventy-seven articles were identified. Most studies evaluated pharmacological treatments (69%) with a median sample size of 24,000 individuals. In total, 20% of articles inadequately reported essential information of the study design. One-third of the articles (n = 25, 33%) raised some concerns because of unclear reporting (n = 6, 8%) or were at high risk of selection bias and/or immortal time bias (n = 19, 25%). Only five articles (25%) described a solution to mitigate these biases. Six articles (31%) discussed these biases in the limitations section.ConclusionReporting of essential information of study design in observational studies remained suboptimal. Selection bias and immortal time bias were common methodological issues that researchers and physicians should be aware of when interpreting the results of observational studies using routinely collected data.
| S-EPMC8608432 | biostudies-literature
Project description:BackgroundMany research studies fail to enroll enough research participants. Patient-facing electronic health record applications, known as patient portals, may be used to send research invitations to eligible patients.ObjectiveThe first aim was to determine if receipt of a patient portal research recruitment invitation was associated with enrollment in a large ongoing study of newborns (Early Check). The second aim was to determine if there were differences in opening the patient portal research recruitment invitation and study enrollment by race and ethnicity, age, or rural/urban home address.MethodsWe used a computable phenotype and queried the health care system's clinical data warehouse to identify women whose newborns would likely be eligible. Research recruitment invitations were sent through the women's patient portals. We conducted logistic regressions to test whether women enrolled their newborns after receipt of a patient portal invitation and whether there were differences by race and ethnicity, age, and rural/urban home address.ResultsResearch recruitment invitations were sent to 4510 women not yet enrolled through their patient portals between November 22, 2019, through March 5, 2020. Among women who received a patient portal invitation, 3.6% (161/4510) enrolled their newborns within 27 days. The odds of enrolling among women who opened the invitation was nearly 9 times the odds of enrolling among women who did not open their invitation (SE 3.24, OR 8.86, 95% CI 4.33-18.13; P<.001). On average, it took 3.92 days for women to enroll their newborn in the study, with 64% (97/161) enrolling their newborn within 1 day of opening the invitation. There were disparities by race and urbanicity in enrollment in the study after receipt of a patient portal research invitation but not by age. Black women were less likely to enroll their newborns than White women (SE 0.09, OR 0.29, 95% CI 0.16-0.55; P<.001), and women in urban zip codes were more likely to enroll their newborns than women in rural zip codes (SE 0.97, OR 3.03, 95% CI 1.62-5.67; P=.001). Black women (SE 0.05, OR 0.67, 95% CI 0.57-0.78; P<.001) and Hispanic women (SE 0.07, OR 0.73, 95% CI 0.60-0.89; P=.002) were less likely to open the research invitation compared to White women.ConclusionsPatient portals are an effective way to recruit participants for research studies, but there are substantial racial and ethnic disparities and disparities by urban/rural status in the use of patient portals, the opening of a patient portal invitation, and enrollment in the study.Trial registrationClinicalTrials.gov NCT03655223; https://clinicaltrials.gov/ct2/show/NCT03655223.
| S-EPMC8874929 | biostudies-literature
Project description:BackgroundPatient recruitment and data management are laborious, resource-intensive aspects of clinical research that often dictate whether the successful completion of studies is possible. Technological advances present opportunities for streamlining these processes, thus improving completion rates for clinical research studies.ObjectiveThis paper aims to demonstrate how technological adjuncts can enhance clinical research processes via automation and digital integration.MethodsUsing one clinical research study as an example, we highlighted the use of technological adjuncts to automate and streamline research processes across various digital platforms, including a centralized database of electronic medical records (enterprise data warehouse [EDW]); a clinical research data management tool (REDCap [Research Electronic Data Capture]); and a locally managed, Health Insurance Portability and Accountability Act-compliant server. Eligible participants were identified through automated queries in the EDW, after which they received personalized email invitations with digital consent forms. After digital consent, patient data were transferred to a single Health Insurance Portability and Accountability Act-compliant server where each participant was assigned a unique QR code to facilitate data collection and integration. After the research study visit, data obtained were associated with existing electronic medical record data for each participant via a QR code system that collated participant consent, imaging data, and associated clinical data according to a unique examination ID.ResultsOver a 19-month period, automated EDW queries identified 20,988 eligible patients, and 10,582 patients received personalized email invitations. In total, 1000 (9.45%) patients signed consents to participate in the study. Of the consented patients, 549 unique patients completed 779 study visits; some patients consented to the study at more than 1 time period during their pregnancy.ConclusionsTechnological adjuncts in clinical research decrease human labor while increasing participant reach and minimizing disruptions to clinic operations. Automating portions of the clinical research process benefits clinical research efforts by expanding and optimizing participant reach while reducing the limitations of labor and time in completing research studies.
| S-EPMC11822312 | biostudies-literature
Project description:Randomized controlled trials (RCTs) are the gold standard for making causal inferences, but RCTs are often not feasible in addiction research for ethical and logistic reasons. Observational data from real-world settings have been increasingly used to guide clinical decisions and public health policies. This paper introduces the potential outcomes framework for causal inference and summarizes well-established causal analysis methods for observational data, including matching, inverse probability treatment weighting, the instrumental variable method and interrupted time-series analysis with controls. It provides examples in addiction research and guidance and analysis codes for conducting these analyses with example data sets.
| S-EPMC9545953 | biostudies-literature
Project description:Mechanistic dynamical models allow us to study the behavior of complex biological systems. They can provide an objective and quantitative understanding that would be difficult to achieve through other means. However, the systematic development of these models is a non-trivial exercise and an open problem in computational biology. Currently, many research efforts are focused on model discovery, i.e. automating the development of interpretable models from data. One of the main frameworks is sparse regression, where the sparse identification of nonlinear dynamics (SINDy) algorithm and its variants have enjoyed great success. SINDy-PI is an extension which allows the discovery of rational nonlinear terms, thus enabling the identification of kinetic functions common in biochemical networks, such as Michaelis-Menten. SINDy-PI also pays special attention to the recovery of parsimonious models (Occam's razor). Here we focus on biological models composed of sets of deterministic nonlinear ordinary differential equations. We present a methodology that, combined with SINDy-PI, allows the automatic discovery of structurally identifiable and observable models which are also mechanistically interpretable. The lack of structural identifiability and observability makes it impossible to uniquely infer parameter and state variables, which can compromise the usefulness of a model by distorting its mechanistic significance and hampering its ability to produce biological insights. We illustrate the performance of our method with six case studies. We find that, despite enforcing sparsity, SINDy-PI sometimes yields models that are unidentifiable. In these cases we show how our method transforms their equations in order to obtain a structurally identifiable and observable model which is also interpretable.
| S-EPMC10615316 | biostudies-literature