Click through and explore the likely impact of the eight key drivers.
Drivers of change in clinical development
Using the proprietary Clinical Development Productivity Index, the IQVIA Institute took a 10-year historical view of clinical trial complexity, success, and duration. With input from IQVIA therapy area experts and the IQVIA Clinical Development Trends Impact Assessment, the Institute researchers recast the data with a future perspective that identified and examined eight key drivers expected to impact clinical productivity through 2023.
90%
85%
80%
75%
70%
65%
2.5
3
3.5
4
Likelihood of Impact
Average Time Until Impact (Years)
Drug Types
Patient-Reported
Outcomes
Biomarkers
Digital Health
Real World Data
Patient Pools
Regulatory
Shifts
Predictive
Analytics
Drug Types
Shifts in drug types to targeted therapies and Next-Generation Biotherapeutics will improve trial efficacy and success rates and have accelerated development timelines, but may require longer-term patient follow up.
patient-reported outcomes
An increased focus on patient-reported outcomes will shed new light on patient experience, drug efficacy, and safety outside the clinical setting and lead to accelerated trial times.
Biomarkers
The increased availability and ease of biomarker testing will help narrow patient populations to those more likely to see an effect, resulting in improvements in trial efficacy, safety and success.
Digital Health
By enabling the capture of drug efficacy and safety data remotely, digital health technologies are expected to improve patient safety, support virtual trials and ease site work burden.
Real world Data
Real world data (RWD) will be used to optimize trial design and speed investigator and site selection. RWD could also enable new trial designs by acting as virtual control arms to support pragmatic, adaptive and real world evidence (RWE) registry trials.
Patient Pools
Availability of pools of pre-screened patients and direct-to-patient recruitment will facilitate enhanced trial enrollment, shorten trial duration and lead to faster market availability.
Regulatory Shifts
Changes in the regulatory landscape will further encourage the adoption of precision medicine approaches as well as novel trial designs and trial endpoints, while providing means for accelerated drug approvals and regulatory success.
Predictive Analytics
Artificial intelligence (AI), machine learning (ML) and other predictive technologies will be used to obtain value from big data in healthcare to identify new clinical hypotheses to test, reduce trial design risks and speed enrollment by identifying protocol-ready patients and predicting eligible patients with diseases.
