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International Clinical Trials
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The search for patients with suitable profiles to participate in
clinical research drug trials has been compared to the proverbial search
for the needle in the haystack – and rightly so. Traditionally, the
call would go out from the pharmaceutical drug developer to their local
affiliates, who would contact their friendly local hospitals, where
diligent medical researchers would comb their paper-based patient
records for candidates who fit the long list of inclusion and exclusion
criteria – a laborious, time-consuming and costly process.
The
identification of trial-eligible patients represents a considerable and
costly bottleneck for the industry, impacting patients directly in the
form of slow progress and availability of new drugs on the market, as
well as elevated prices as a result of longer development time and
shorter patent protection. Each day a drug is delayed from reaching the
market, the pharma company loses up to $8 million (1). Recruitment
difficulties are the underlying reason for 30% of Phase 3 terminations
(2). Almost half the sites (48%) miss their enrolment targets for Phase 2
or 3 studies, with timelines nearly double their originally planned
duration to meet desired enrolment numbers (3). The level of
inefficiency and waste is, therefore, considerable.
Electronic Health Records
The
advent of electronic data capture and electronic health records (EHRs)
has only slightly improved the situation. A hospital’s health
information system (HIS) is not optimised for patient search and
identification within the context of a clinical trial, and there are
often no structured tools available. Even if there were, these systems
generally have limited search functionality; a user would search for 1-3
parameters, and then resort to a manual verification of the remaining
criteria.
Outside patient recruitment, information technology
has been applied in clinical research for over 50 years. Nowadays,
electronic data capture has become the de facto standard in trials.
Statistical randomisation technologies are used in planning studies
centrally, and allow the trial manager to minimise bias by creating
comparable cohorts across study sites. Meanwhile, drug shipments are
managed by software that calculates the optimal location of warehouses,
time spent in transit, supply expiry dates and site stock.
Clinical
trial management systems have been developed for the use of both pharma
companies who sponsor clinical trials, and CROs that often manage their
execution. These systems control data collection and the actions of
clinical research associates (CRAs) and doctors, calculate site invoices
to sponsors based on patient procedures, and allow the aggregation and
review of data on CRA and patient visits.
There have been some
attempts to use electronic systems for patient recruitment, but more in
the area of engagement – finding and approaching patients directly
through channels such as online groups and social media, and inviting
them to join trials. This approach works well for studies that rely on
volunteers, who are motivated to participate in clinical trials and are
more suited for chronic clinical conditions. However, directto- patient
marketing is labour-intensive and can result in high numbers of
irrelevant leads.
Patient recruitment based on EHRs is, to date,
a neglected and under-utilised area. Individual HISs have attempted to
conduct patient recruitment based on their EHRs, but multihospital
networks did not exist until very recently.
Brief History
Electronic
records for patient data came into use with the advent of digital
technologies. Hospitals and doctors’ practices recognised the value of
keeping patient records electronically for the documentation of patient
encounters in a standard and consistent way, reducing the problem of
illegible, handwritten notes and improving care by ensuring transition
of care and continuity of treatment. These records became known as
electronic medical records (EMRs). EMRs took root very easily because
they enabled data to be stored efficiently and retrieved quickly within a
practice, while maintaining specifiable standards of data security,
safety and retention.
At a higher level, clinical information
systems – which went across practices and allowed the integration of
patient information from multiple sources – arose in the 1960s. These
became more standardised in the 2000s, and became known as EHRs. A
patient’s complete medical history – including results from external
labs and diagnoses from specialists, for instance – EHRs compile an
overall picture of a subject’s health, while also supporting the
management of patient information within the healthcare ecosystem. As a
result, EMRs/EHRs enable doctors to offer a better standard of care for
their patients.
Feasibility
Representing a rich
source of patient information, a moderately structured EHR allows
complex queries to be made to a patient database – taking account of,
for example, medical history, medications taken, procedures and lab
values. A sponsor may, therefore, use such a query to evaluate the
possible number of patients at a specific hospital which fit a specific
set of criteria – and thus evaluate the feasibility of that site to run a
specific study. A mistaken feasibility assessment would represent a
potential source of problems, such as a trial not taking off, if the
patient enrolment numbers cannot be met; or an unnecessary number of
small studies in numerous locations, due to inefficient siting of
trials.
EHR use allows the standardisation of the feasibility
evaluation across multiple sites and, to a considerable extent, removes
the subjective element from the process. However, the employment of EHR
data across hospitals, healthcare networks and countries has a number of
associated challenges, including inconsistent coding practices and
semantic interoperability. These issues can be identified and mitigated,
though – something that is much harder to do with subjective
recruitment estimates by individual principal investigators, known to
provide patient enrolment estimates that may not meet the expectations.
Identifying Patients
Performing
feasibility studies through EHR-networked hospitals allows sponsors to
identify the most suitable research hospitals for their trials, but
gives doctors no tool to find the patients. By itself, feasibility can
be a poor predictor of a study’s success, as the sites will still be
limited in their ability to identify the relevant patients in time – for
example, if the trial requires a patient profile where dozens of
criteria overlap, or where studies are for acute indications which
cannot be predicted from historical information, or especially if the
trial is time-sensitive.
An EHR-based recruitment system that
can identify patients allows a trial’s primary investigator (PI) to
start a study with a ready-to-use list of patients to screen,
potentially generated already from the feasibility query. This reduces
the PI’s workload dramatically and accelerates trial progress. It also
facilitates recruitment targets to be met more easily – particularly in
difficult cases, such as studies where patient consent is very low, or
rare diseases.
This is where the benefits of EHR-based patient
recruitment become apparent. Querying a complete database electronically
enables all potential candidates who fit the trial protocol criteria to
be found – exhaustively, and within a short time. The fact that the
candidates are pre-filtered according to matches with the protocol also
helps the subsequent processes of candidate validation and enrolment.
This reduces the time and resource effort from the beginning, allowing
the trial’s PI more flexibility in managing the trial and completing it
on time, or even earlier than expected.
The ability to run a
query against multiple overlapping recruitment criteria not only enables
a trial manager to come up with a better defined patient population
distributed across multiple sites, but also brings the capability to run
studies in populations which would normally be associated with
personalised medicine. Sometimes, these criteria are so specific that
eligible patients may only be found on a level similar to rare disease populations – for example, in fewer than 100 patients per million.
Patient Enrolment
Depending
on how they are configured, electronic recruitment systems may screen
for patients on a continuous basis and identify eligible candidates in
near-real time. This offers another important advantage where trials are
timesensitive. Recruiting a patient may depend heavily on certain
criteria which have a shelf life: for example, if a specific lab test
has to have been done within the past 24 hours, or if a specific
treatment has to have been initiated in the last seven days. Real time
systems allow such criteria to be queried, and a candidate population to
be assessed against completely up-to-date patient information.
But
the beauty of real time systems lies in their potential to support the
search for patients with acute indications, where historical data are of
no help. In these indications, the search is for patients where there
are no predictors for their condition, and relies on the patient
arriving at the site on occurrence. A good example would be trauma
studies. The time between the appearance of the patient and the start of
standard treatment would usually be very short, and it is a major
challenge for the PI to include the patient in a clinical trial without a
system that identifies the patient upon arrival and alerts the clinical
researcher immediately.
Real Time Recruitment
Bringing
everything together, a subset of studies can be identified which
require three distinct candidate search capabilities to arrive at
successful patient recruitment. They include the identification of sites
according to how their patient populations fit multi-search criteria,
the identification of patients which meet the study criteria, and the
ability to do both in the very limited amount of time available from the
moment the data appears in the system – ideally instantly (see full PDF
for Figure 1). The confluence of these three features is where the
strengths and benefits of real time recruitment become possible.
Examples
of indications that could benefit from real time recruitment include
trauma surgery, acute infectious diseases, pain relief and certain acute
cardiovascular conditions, which require time-sensitive treatment
initiation.
The implementation of a real time recruitment system
is not without challenges, however, as it heavily depends on the
ability and readiness of investigators to use it. Responding to an alert
requires quick, coordinated action within the research team. Acute
indications are normally treated with urgency, but using traditional
techniques means PIs may not be aware a suitable patient has come in
until the window of opportunity is gone. Therefore, PIs and study staff
need to have quick and ready access to the relevant information, and be
able to set up procedures for referral, enrolment and consent for
patients for acute indications studies.
Enrolling Patients Successfully
Multi-site
feasibility and patient identification in real time, with alert
generation, is an indispensable tool in modern clinical research,
allowing the successful enrolment of acute patients corresponding to
complex recruitment profiles.
References
1. The expanding web of clinical trial patient recruitment, ISR Reports, March 2014
2. Howes M, The pulse on global trials, CW Weekly: p6, March 2014
3.
Getz K and Lamberti MJ, 89% of trials meet enrollment, but timelines
slip, half of sites under-enroll, Tufts CSDD Impact Report 15(1),
January/February 2013
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