Trials with patient reported endpoints are reporting increased efficiencies when using electronic patient reported outcomes (ePRO), compared to paper diary data collection methods. To date, approximately 20% of all trials with patient reported endpoints are using ePRO solutions to collect efficacy data. As the adoption of electronic patient reported outcomes continues to increase, sponsors are finding new ways to justify this technology’s ROI, and identify the types of trials that are best suited to ePRO (versus paper). This article will describe how several market-leading sponsors have quantified the benefits of better data quality, with case examples from recent trials implemented by PHT Corporation (PHT). These analyses are provided with the intention to inform the clinical research community, and provide the frameworks for further ROI determinations.
Q&A: The Internet of Everything in Clinical TrialsCRF Health
The Internet of Everything (IoE) is being applied to more sectors, including clinical trials. IPT asks Rauha Tulkki-Wilke at CRF Health how this is happening and what the effects are.
Who needs fast data? - Journal for Clinical Studies KCR
How “no news” during the life of a trial is bad news, and what data management (among other things) can do to help when ensuring access to fast data? Get to know this and more about smart e-solutions in the newest article of Kaia Koppel, Associate Director, Biometrics & Clinical Trial Data Execution Systems at KCR, in the recent issue of Journal for Clinical Studies (p.40-21).
Comparative Study of Classification Method on Customer Candidate Data to Pred...IJECEIAES
Leasing vehicles are a company engaged in the field of vehicle loans. Purchase by way of credit becomes a mainstay because it can attract potential customers to generate more profit. But if there is a mistake in approving a customer candidate, the risk of stalled credit payments can happen. To minimize the risk, it can be applied the certain data mining technique to predict the future behavior of the customers. In this study, it is explored in some data mining techniques such as C4.5 and Naive Bayes for this purpose. The customer attributes used in this study are: salary, age, marital status, other installments and worthiness. The experiments are performed by using the Weka software. Based on evaluation criteria, i.e. accuracy, C4.5 algorithm outperforms compared to Naive Bayes. The percentage split experiment scenarios provide the precision value of 89.16% and the accuracy value of 83.33% wheres the cross validation experiment scenarios give the higher accuracy values of all used k-fold. The C4.5 experiment results also confirm that the most influential instant data attribute in this research is the salary.
Trials with patient reported endpoints are reporting increased efficiencies when using electronic patient reported outcomes (ePRO), compared to paper diary data collection methods. To date, approximately 20% of all trials with patient reported endpoints are using ePRO solutions to collect efficacy data. As the adoption of electronic patient reported outcomes continues to increase, sponsors are finding new ways to justify this technology’s ROI, and identify the types of trials that are best suited to ePRO (versus paper). This article will describe how several market-leading sponsors have quantified the benefits of better data quality, with case examples from recent trials implemented by PHT Corporation (PHT). These analyses are provided with the intention to inform the clinical research community, and provide the frameworks for further ROI determinations.
Q&A: The Internet of Everything in Clinical TrialsCRF Health
The Internet of Everything (IoE) is being applied to more sectors, including clinical trials. IPT asks Rauha Tulkki-Wilke at CRF Health how this is happening and what the effects are.
Who needs fast data? - Journal for Clinical Studies KCR
How “no news” during the life of a trial is bad news, and what data management (among other things) can do to help when ensuring access to fast data? Get to know this and more about smart e-solutions in the newest article of Kaia Koppel, Associate Director, Biometrics & Clinical Trial Data Execution Systems at KCR, in the recent issue of Journal for Clinical Studies (p.40-21).
Comparative Study of Classification Method on Customer Candidate Data to Pred...IJECEIAES
Leasing vehicles are a company engaged in the field of vehicle loans. Purchase by way of credit becomes a mainstay because it can attract potential customers to generate more profit. But if there is a mistake in approving a customer candidate, the risk of stalled credit payments can happen. To minimize the risk, it can be applied the certain data mining technique to predict the future behavior of the customers. In this study, it is explored in some data mining techniques such as C4.5 and Naive Bayes for this purpose. The customer attributes used in this study are: salary, age, marital status, other installments and worthiness. The experiments are performed by using the Weka software. Based on evaluation criteria, i.e. accuracy, C4.5 algorithm outperforms compared to Naive Bayes. The percentage split experiment scenarios provide the precision value of 89.16% and the accuracy value of 83.33% wheres the cross validation experiment scenarios give the higher accuracy values of all used k-fold. The C4.5 experiment results also confirm that the most influential instant data attribute in this research is the salary.
"Off-the-shelf technology can be put to better use with bespoke integrated software as hospitals rethink efficient patient care"
--Published in The Times and on Raconteur
eConsent (electronic informed consent) adoption is on the rise!
100% of the Top 10 and 88% of the Top 25 Pharma have implemented eConsent - What's driving their adoption?
Learn more in our new infographic "14 Drivers of eConsent Adoption in Clinical Trials"
Epic EMR to OMOP CDM to Clinical Research Data Mart: an Unmaintained Road or ...Oksana Gologorskaya
Poster we presented at 2017 AMIA Joint Summits on Clinical and Translational Research Informatics.
In this research data delivery project, we explored a less traveled path of building a clinical data mart for a registry study on kidney transplant patients, based on the institutional instance of the EMR data, translated into the OMOP (Observational Medical Outcomes Partnership) common data model.
Machine learning is the field that focuses on how computers learn from data. Today, machine learning is playing an integral role in the medical industry. This is due to its ability to process huge datasets beyond the scope of human capability, and then convert the data analyzed into clinical insights that aid physicians in providing care. Machine learning is a powerful, relatively easy to implement tool with numerous possibilities to enhance medical practice. The applications of machine learning in medicine are advancing medicine into a new realm. Therefore, educating the next generation of medical professionals with machine learning is essential. This paper provides a brief introduction to applying machine learning in medicine. Matthew N. O Sadiku | Sarhan M. Musa | Adedamola Omotoso "Machine Learning in Medicine: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd20255.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/20255/machine-learning-in-medicine-a-primer/matthew-n-o-sadiku
Presentation Alliance of European Life Sciences Law Firms
(Julian Hitchcock and Sofie van der Meulen) on legal aspects of big data in pharma. Topics: privacy, IP, medical devices and IVD.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
With the promises of predictive analytics in big data, and the use of machine learning algorithms,
predicting future is no longer a difficult task, especially for health sector, that has witnessed a great
evolution following the development of new computer technologies that gave birth to multiple fields of
research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful
knowledge from it, predict diseases and anticipate the cure on the other hand. This prompted researchers
to apply all the technical innovations like big data analytics, predictive analytics, machine learning and
learning algorithms in order to extract useful knowledge and help in making decisions. In this paper, we
will present an overview on the evolution of big data in healthcare system, and we will apply three learning
algorithms on a set of medical data. The objective of this research work is to predict kidney disease by
using multiple machine learning algorithms that are Support Vector Machine (SVM), Decision Tree (C4.5),
and Bayesian Network (BN), and chose the most efficient one.
Get The Most Out Of Your Medical Imaging Equipment from Atlantis WorldwideAtlantis Worldwide LLC
Managing medical imaging equipment has always been challenging. You have to deal with staff shortages, the 4-28-21 Medical imaging bottom line, patient throughput and reducing exposure risks for staff and patients.
Frankie Rybicki slide set for Deep Learning in Radiology / MedicineFrank Rybicki
These are my #AI slides for medical deep learning using #radiology and medical imaging examples. Please use them & modify to teach your own group about medical AI.
The Complementary Roles of Computer-Aided Diagnosis and Quantitative Image A...Carestream
This presentation from RSNA explains how their similarities and differences have an impact on assessment, quality assurance and training in radiography. Read the blog at http://www.carestream.com/blog/2016/06/07/differences-between-computer-aided-diagnosis-and-quantitative-image-analysis/
"Off-the-shelf technology can be put to better use with bespoke integrated software as hospitals rethink efficient patient care"
--Published in The Times and on Raconteur
eConsent (electronic informed consent) adoption is on the rise!
100% of the Top 10 and 88% of the Top 25 Pharma have implemented eConsent - What's driving their adoption?
Learn more in our new infographic "14 Drivers of eConsent Adoption in Clinical Trials"
Epic EMR to OMOP CDM to Clinical Research Data Mart: an Unmaintained Road or ...Oksana Gologorskaya
Poster we presented at 2017 AMIA Joint Summits on Clinical and Translational Research Informatics.
In this research data delivery project, we explored a less traveled path of building a clinical data mart for a registry study on kidney transplant patients, based on the institutional instance of the EMR data, translated into the OMOP (Observational Medical Outcomes Partnership) common data model.
Machine learning is the field that focuses on how computers learn from data. Today, machine learning is playing an integral role in the medical industry. This is due to its ability to process huge datasets beyond the scope of human capability, and then convert the data analyzed into clinical insights that aid physicians in providing care. Machine learning is a powerful, relatively easy to implement tool with numerous possibilities to enhance medical practice. The applications of machine learning in medicine are advancing medicine into a new realm. Therefore, educating the next generation of medical professionals with machine learning is essential. This paper provides a brief introduction to applying machine learning in medicine. Matthew N. O Sadiku | Sarhan M. Musa | Adedamola Omotoso "Machine Learning in Medicine: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd20255.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/20255/machine-learning-in-medicine-a-primer/matthew-n-o-sadiku
Presentation Alliance of European Life Sciences Law Firms
(Julian Hitchcock and Sofie van der Meulen) on legal aspects of big data in pharma. Topics: privacy, IP, medical devices and IVD.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
With the promises of predictive analytics in big data, and the use of machine learning algorithms,
predicting future is no longer a difficult task, especially for health sector, that has witnessed a great
evolution following the development of new computer technologies that gave birth to multiple fields of
research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful
knowledge from it, predict diseases and anticipate the cure on the other hand. This prompted researchers
to apply all the technical innovations like big data analytics, predictive analytics, machine learning and
learning algorithms in order to extract useful knowledge and help in making decisions. In this paper, we
will present an overview on the evolution of big data in healthcare system, and we will apply three learning
algorithms on a set of medical data. The objective of this research work is to predict kidney disease by
using multiple machine learning algorithms that are Support Vector Machine (SVM), Decision Tree (C4.5),
and Bayesian Network (BN), and chose the most efficient one.
Get The Most Out Of Your Medical Imaging Equipment from Atlantis WorldwideAtlantis Worldwide LLC
Managing medical imaging equipment has always been challenging. You have to deal with staff shortages, the 4-28-21 Medical imaging bottom line, patient throughput and reducing exposure risks for staff and patients.
Frankie Rybicki slide set for Deep Learning in Radiology / MedicineFrank Rybicki
These are my #AI slides for medical deep learning using #radiology and medical imaging examples. Please use them & modify to teach your own group about medical AI.
The Complementary Roles of Computer-Aided Diagnosis and Quantitative Image A...Carestream
This presentation from RSNA explains how their similarities and differences have an impact on assessment, quality assurance and training in radiography. Read the blog at http://www.carestream.com/blog/2016/06/07/differences-between-computer-aided-diagnosis-and-quantitative-image-analysis/
Mouth Breathing During Exercise May Increase Asthma Riskyogisri001
Trikarana Transcendental Meditation (TTM)
Yogi Sriji is an enlightened spiritual leader; founder of Trikarana Yoga Charitable Trust and Trikarana Transcendental Meditation (TTM) Center, a unit of the Trust, started in the year 2009 in Coimbatore, an Industrial City in Tamil Nadu, India.
Phrasal or better known as "Phasal Verbs" verbs are expressions of the English language made up of two or three words. These verbs are formed by a common verb, an adverb or a preposition which are to become new verbs.
Flyktningkrise, økende arbeidsledighet og økonomiske nedgangstider øver press på den norske modellen. Hvilke utviklingstrekk gjør seg gjeldende og hvilke konsekvenser kan de få for velferdsstaten?
Yngvar Åsholt, kunnskapsdirektør i NAV, gir innblikk i utviklingstrekk, utfordringer og mulige veivalg.
Capturing Patient-Reported Outcome (PRO) Data Electronically: The Past, Prese...CRF Health
Patient-reported outcomes (PROs) are an
important means of evaluating the treatment benefit of
new medical products. It is recognized that PRO measures
should be used when assessing concepts best
known by the patient or best measured from the patient’s
perspective. As a result, there is growing emphasis on
well defined and reliable PRO measures. In addition,
advances in technology have significantly increased
electronic PRO (ePRO) data collection capabilities and
options in clinical trials. The movement from paperbased
to ePRO data capture has enhanced the integrity
and accuracy of clinical trial data and is encouraged by
regulators. A primary distinction in the types of ePRO
platforms is between telephone-based interactive voice
response systems and screen-based systems. Handheld
touchscreen-based devices have become the mainstay for
remote (i.e., off-site, unsupervised) PRO data collection
in clinical trials. The conventional approach is to provide
study subjects with a handheld device with a devicebased
proprietary software program. However, an
emerging alternative for clinical trials is called bring
your own device (BYOD). Leveraging study subjects’
own Internet-enabled mobile devices for remote PRO
data collection (via a downloadable app or a Web-based
data collection portal) has become possible due to the
widespread use of personal smartphones and tablets.
However, there are a number of scientific and operational
issues that must be addressed before BYOD can be
routinely considered as a practical alternative to conventional
ePRO data collection methods. Nevertheless,
the future for ePRO data collection is bright and the
promise of BYOD opens a new chapter in its evolution.
Electronic clinical outcomes assessment (eCOA) clinical trials are becoming increasingly popular in the medical industry. By using these trials, pharmaceutical companies and research institutions can measure the effectiveness of their treatments more accurately and quickly. eCOA trials also allow for real-time data collection, which helps reduce costs and improve the accuracy of results. Additionally, eCOA trials provide more flexibility for researchers in terms of how they design their studies, allowing them to use different types of assessments to best suit their needs.
With almost half of oncology studies failing due to a lack of patient retention, there is a critical need to develop more efficient and patient focused strategies. Jessica Thilaganathan at CRF Health sits down with International Clinical Trials to explain why electronic clinical outcome solutions could be the answer. (Published with permission of International Clinical Trials).
Electronic Health Record Software Is A Productivity Tool.pdfssuserbed838
Electronic Health Record Software can analyze the data deeply and assist doctors technically in making clinical decisions for patients. It collects data from all the sources and varies the entry.
The post-pandemic clinical trial landscape has paved way for the integration of technology, data analytics, and digitization to facilitate all aspects of trial management from patient identification and recruitment to data collection and statistical analysis
Using eConsent in Clinical Research to Support Patient Understanding and WelfareCRF Health
Deciding to participate in a clinical trial is not risk-free for any participant. Some studies are designed for healthy volunteers and some are designed for participants with certain medical conditions.
Care Beyond Walls and Wires: Using Remote Monitoring to Enhance Patient CareCRF Health
About half of all adults in the US—117 million people—have one or more chronic health conditions, and the number is on the rise.1
In order to address this growing challenge, the Care Beyond Walls and Wires (CBWW) program was created to increase access to care and improve outcomes for patients suffering from chronic disease.
A collaboration between Qualcomm® Wireless Reach™, Northern Arizona Healthcare (NAH), Qualcomm Life, Inc., and Entra Health Systems, CBWW began as a congestive heart failure remote monitoring program.
Started in 2011, CBWW utilizes 3G/4G mobile broadband with connected home-use medical devices to enable nursing care coordinators to remotely monitor patients. As of 2017 the program is expanding to cover more patients with other chronic or high-risk conditions such as sepsis (blood poisoning from infection). The program aims to extend the delivery of care beyond the walls of hospitals by giving patients the tools to stay connected to providers – anytime, anywhere.
eConsent: Five (5) Key Areas of Preparation CRF Health
Site recruitment and retention of an adequate study population remains a major determinant of the success of a clinical trial. A recent survey to gather opinions on informed consent related to participant understanding, subject recruitment, and retention from 105 respondents' across site-based roles revealed 5 key areas that all sites should consider when preparing an eConsent.
Drivers of Remote Patient Monitoring (RPM)CRF Health
Adoption of Remote Patient Monitoring technology is growing increasingly amongst healthcare providers for its ability to save time, money, and lives.
Download the "Drivers of Remote Patient Monitoring (RPM)" infographic to see real-life examples of how RPM is gaining traction for so many in the industry.
Recruitment & Retention: Breaking Down the Barriers to eConsent AdoptionCRF Health
Provided by ACRP's Clinical Researcher Magazine. Original at https://www.acrpnet.org/2017/04/01/recruitment-retention-breaking-barriers-econsent-adoption/
For more information on electronic informed consent visit www.crfhealth.com
Case Study: World's Largest COPD eCOA Trial Requires Reliability and Global S...CRF Health
CRF Health and its TrialMax® eCOA platform were adopted by a top 50 global pharmaceutical manufacturer to support one of the largest recorded phase III studies in chronic obstructive pulmonary disease (COPD). The trial, involving more than 19,000 patients in 35 countries, leveraged TrialMax® to deliver electronic versions of key COPD instruments, enabling investigators to monitor exacerbations while making compliance as easy as possible for patients.
Equivalence of Electronic and Paper Administration of Patient-Reported Outcom...CRF Health
Objective
To conduct a systematic review and meta-analysis of the equivalence between electronic and paper administration of patient reported outcome measures (PROMs) in studies conducted subsequent to those included in Gwaltney et al’s 2008 review.
Methods
A systematic literature review of PROM equivalence studies conducted between 2007 and 2013 identified 1,997 records from which 72 studies met pre-defined inclusion/exclusion criteria. PRO data from each study were extracted, in terms of both correlation coefficients (ICCs, Spearman and Pearson correlations, Kappa statistics) and mean differences (standardized by the standard deviation, SD, and the response scale range). Pooled estimates of correlation and mean difference were estimated. The modifying effects of mode of administration, year of publication, study design, time interval between administrations, mean age of participants and publication type were examined.
Recommendations on Evidence Needed to Support Measurement Equivalence between...CRF Health
Patient-reported outcomes (PROs) are the consequences of disease and/or its treatment
as reported by the patient. The importance of PRO measures in clinical trials for new drugs, biologic
agents, and devices was underscored by the release of the US Food and Drug Administration’s draft
guidance for industry titled "Patient-Reported Outcome Measures: Use in Medical Product Development
to Support Labeling Claims." The intent of the guidance was to describe how the FDA will evaluate the
appropriateness and adequacy of PRO measures used as effectiveness endpoints in clinical trials. In
response to the expressed need of ISPOR members for further clarification of several aspects of the draft
guidance, ISPOR’s Health Science Policy Council created three task forces, one of which was charged
with addressing the implications of the draft guidance for the collection of PRO data using electronic data
capture modes of administration (ePRO). The objective of this report is to present recommendations from
ISPOR’s ePRO Good Research Practices Task Force regarding the evidence necessary to support the
comparability, or measurement equivalence, of ePROs to the paper-based PRO measures from which
they were adapted.
Guidance for Industry Electronic Source Data in Clinical InvestigationsCRF Health
This guidance provides recommendations to sponsors, Contract Research Organizations (CROs), clinical investigators, and others involved in the capture, review, and retention of electronic source data in FDA-regulated clinical investigations.2 In an effort to streamline and modernize clinical investigations this guidance promotes capturing source data in electronic form, and it is intended to assist in ensuring the reliability, quality, integrity, and traceability of data from electronic source to electronic regulatory submission.
Guidance for Industry Patient-Reported Outcome Measures: Use in Medical Produ...CRF Health
This guidance describes how the Food and Drug Administration (FDA) reviews and evaluates existing, modified, or newly created patient-reported outcome (PRO) instruments used to support claims in approved medical product labeling.2 A PRO instrument (i.e., a questionnaire plus the information and documentation that support its use) is a means to capture PRO data used to measure treatment benefit or risk in medical product clinical trials. This guidance does not address the use of PRO instruments for purposes beyond evaluation of claims made about a medical product in labeling. This guidance also does not address disease-specific issues. Guidance on clinical trial endpoints for specific diseases can be found on various FDA Web sites.3
By explicitly addressing the review issues identified in this guidance, sponsors can increase the efficiency of their discussions with the FDA during the medical product development process, streamline the FDA’s review of PRO instrument adequacy and resultant PRO data collected during a clinical trial, and provide optimal information about the patient perspective for use in making conclusions about treatment effect at the time of medical product approval. PRO instrument development is an iterative process and we recognize there is no single correct way to develop a PRO instrument. Different strategies and methods can be used to address FDA review issues.
Guidance for Industry and Food and Drug Administration Staff: Mobile Medical ...CRF Health
The Food and Drug Administration (FDA) recognizes the extensive variety of actual and
potential functions of mobile apps, the rapid pace of innovation in mobile apps, and the potential
benefits and risks to public health represented by these apps. The FDA is issuing this guidance
document to inform manufacturers, distributors, and other entities about how the FDA intends to
apply its regulatory authorities to select software applications intended for use on mobile
platforms (mobile applications or “mobile apps”). Given the rapid expansion and broad
applicability of mobile apps, the FDA is issuing this guidance document to clarify the subset of mobile apps to which the FDA intends to apply its authority.
COVID-19 PCR tests remain a critical component of safe and responsible travel in 2024. They ensure compliance with international travel regulations, help detect and control the spread of new variants, protect vulnerable populations, and provide peace of mind. As we continue to navigate the complexities of global travel during the pandemic, PCR testing stands as a key measure to keep everyone safe and healthy. Whether you are planning a business trip, a family vacation, or an international adventure, incorporating PCR testing into your travel plans is a prudent and necessary step. Visit us at https://www.globaltravelclinics.com/
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More Info:- https://www.imarcgroup.com/veterinary-diagnostics-market
Rate Controlled Drug Delivery Systems, Activation Modulated Drug Delivery Systems, Mechanically activated, pH activated, Enzyme activated, Osmotic activated Drug Delivery Systems, Feedback regulated Drug Delivery Systems systems are discussed here.
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TEST BANK For Accounting Information Systems, 3rd Edition by Vernon Richardson, Verified Chapters 1 - 18, Complete Newest Version
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Embracing Electronic PRO
1. March 2009 Supplement to
appliedclinicaltrialsonline.com
How ePRO Empowers
Patients and Research
PRESENTED BY
2. I
n today’s technology focused world, paper-based administration is quickly becoming
outdated. Despite the obvious benefits of adopting a paper free approach to data cap-
ture, the clinical trials industry continues to be apprehensive about using electronic
methods. Patient reports can be the only way to reliably collect assessments pertain-
ing to subjective information, such as the perception of pain and fatigue. It is essential
that this data be captured accurately, using the most efficient instrument medium.
Approximately half of the 20-year patent protection period is spent on the drug develop-
ment process. One of the largest cost and time commitments during this time is clinical trial
testing and the associated data collection and reporting from the patients in the trial. On av-
erage, approximately $6 million to $11 million is spent on paper-based patient reported out-
comes (PRO) capture. With regulatory costs and government levies on the rise, the industry
may need to adopt a more streamlined and cost effective method of patient reporting.
The industry is familiar with the three main methods used in clinical trials to collect
the important information from patients as they progress through a trial. Interactive
Voice Response (IVR), electronic PRO (ePRO), and paper-based instruments are the pri-
mary ways to capture patient reported data. Each method has its own advantages for the
clinical study management team, but it is fair to argue that in terms of accuracy, compli-
ance, and efficiency, ePRO is outperforming its main rival: the paper questionnaire.
Paper vs. ePRO
Last year CRF Inc. conducted a study into compliance rates when using ePRO methods.
The results were unequivocal: patient compliance when using electronic diaries is more
than three times that for paper-based trials. As we know, paper-based trials usually have
a patient compliance rate of around 30%. Compare this to the ePRO rate of 89.5% and this
alone makes the argument for using electronic capture extremely persuasive.
So why is paper still proving more popular? The traditional argument against ePRO has
been cost. Investing money into the purchase of eDiary devices from the outset can be off-
putting for some. However, more and more clinical trials are moving to ePRO as sponsors
begin to recognize the savings that come down the line through improved efficiency, speed,
and accuracy of the data. In addition, the eDiary devices themselves can be reused for sub-
sequent studies, lowering the effective cost of the hardware over time.
Risk is a major factor for the clinical trial team when deciding on the medium to use for
PRO instruments. The traditional reluctance in using electronic data capture (EDC) meth-
ods has been attributed to an estimate of risk, with doubt over the security and stability of
using an electronic device to capture the patient data. However, the risks associated with
paper are far greater, with the potential for loss, damage, and inaccuracies.
ePRO reports can be sent to the study team in real time, removing the risk of losses in
transit. All manner of accidents can happen, leading to the paper being damaged or even
lost before ending up back in the hands of the study management team. ePRO also provides
substantially more discretion and privacy for the patient; there is no possibility of a curious
friend or relative inadvertently or intentionally browsing through a diary left lying out.
The FDA’s ALCOA (Attributable, Legible, Contemporaneous, Original, Accurate) crite-
ria for patient data in labelling claims have helped to substantiate the argument for using
ePRO, as patient data is far more accurate when collected electronically.
Rachael King
Embracing Electronic PRO
Why ePRO
edges out
paper as the
most reliable
data source.
EDC & Information Technology V i e wpoi n t
Rachael King is chief
executive officer of CRF Inc.
3. Electronic benefits
The common issues raised when collecting data from paper-
based instruments are that of legibility and human error
when interpreting handwritten answers. The margin for er-
ror is heavily increased when the study management team
must decipher someone else’s writing, without also taking
into account that they could be physically unwell, in pain or
have a disability that affects their coordination.
Spelling can also affect the accuracy of the data, as this
can lead the team to make a possibly serious misinterpre-
tation. With the administrative burden of typing up the
handwritten responses removed, the clinical study team is
free to spend this time on other aspects of the trial, such as
monitoring patient safety or increasing enrollment.
Data accuracy is called into question when patients are
asked to recall complex and sensitive information retrospec-
tively. When using a paper-based instrument, the onus is on
the patient to effectively recall their details—no easy task
when you consider that the paper questionnaires can be
filled in some time after the symptom or pain has occurred.
With an ePRO instrument, patients have the freedom to
complete the information at the time they need to. The intro-
duction of mobile devices means that patients now also have
the flexibility to fill in their information from wherever they
are, even when they’re on the move. This allows the clinical
team to get a real sense of the patient experience and as the
patient data is captured in real time, allowing for more real-
istic and ultimately more accurate answers.
Paper impossibilities
Real-time access to data has even greater value for the
clinical trial team. Clinical managers can get a snapshot
view of their trial data, with instant visualizations of patient
enrollment, compliance rates, and overall progress. When
considering patient safety, this is invaluable, as potential
issues can be spotted easily at any stage of the trial and the
appropriate interventions can be instigated.
This also means an end to the frustrating wait for paper-
based instruments to be collected, data to be prepared for
analysis, and inaccuracies and extra information to be re-
solved. The electronic capture allows for accurate date and
time stamps on each response, something that is impossible
with paper. This information ensures that the team can see
their patients’ responses and know that they adhere to the
criteria needed for timely data collection.
The design of the instrument on an electronic device
puts the control back into the hands of the clinical trial
team, as patients cannot add to their answers extraneous
information. All too often a patient alters data when com-
pleting their paper instrument, adding what they feel is
useful extra information, or even raising questions. These
additional comments—however well intended—can actu-
ally result in a high query volume.
The additional time spent recording and interpreting this
data can be significant, further escalating costs down the
line. Each query raised must be investigated and has the
potential to affect the data that is issued. With an electronic
capture method, the instrument can be formatted so that
patients choose from a list of options, with no space to add
extra and possibly confusing information.
Designing ePRO
Clearly, the formatting and design of the electronic instru-
ment requires time and planning at the start of the trial.
Although initially this may seem to be a burden, a carefully
planned electronic instrument will facilitate a smooth trial,
with little administration needed at completion, as the data
is captured, available, and ready to be analyzed.
The best ePRO companies are experienced in working
with the trial team during the design phase and make it a col-
laborative process. Trial managers need not worry that they
will be left floundering with incomprehensible technology.
The use of ePRO enables the team to design compre-
hensive patient questionnaires that are easy for a patient to
complete. With technology infiltrating so many aspects of
modern life, from mobile phones to the Internet, the world
at large is much more familiar with handling and under-
standing electronic devices. Plus, images can be used to
make the questions more accessible. Graphical and visual
representations add to the instrument and also give the abil-
ity to incorporate examples of what information is needed.
Touch screen functionality allows patients to easily enter
their data in a more natural way, for example, by using the
stylus or a finger to indicate on a body map where they feel
pain. Navigating through an electronic questionnaire is logi-
cal, with a touch point at the end of every question, allowing
patients to see just one question at a time, rather than feel-
ing potentially overwhelmed by sheets of paper full of detail.
Any changes that occur midstudy can be attended to when
using EDC. If a change to an instrument question is needed
as a result of an amendment to the study, electronic methods
are better suited to cascading the updated information.
Validation considerations
One of the key issues when comparing electronic instru-
ments to paper instruments is the measurement of valid-
EDC & Information TechnologyV i e wpoi n t
The design of the instrument on an
electronic device puts the control
back into the hands of the trial team,
as patients cannot add extraneous
information to their answers.