Building the automation is the cheap part. The product is the evidence that it performs as intended, plus change control for as long as it runs. That inverts the usual calculation: a workflow you automate and then modify every quarter can cost more in revalidation than it ever saved in effort. The processes people most want automated are frequently the ones that keep changing, which is exactly what makes them painful and exactly what makes them the wrong first candidates.
We automate clinical trial workflows for sponsors, CROs and sites: study startup and site activation, essential document collection and processing, query routing, approval orchestration, task and notification management, and AI-assisted document work where the step is extraction rather than judgement. Every workflow comes with a designed exception path, an owner, and the documentation your quality function needs. Human approvals stay human, and nothing in what we build approves anything on a timeout.
Pick almost any clinical trial workflow and measure it properly and the same shape appears. A site contract goes out, comes back with a comment, waits for legal, waits for the site, waits for a signature. A regulatory submission waits for one missing document. An investigator CV is requested, chased three times, received in the wrong format, and refiled. The sum of the actual work in any of those is small. The elapsed time is weeks, and nearly all of it is handoffs, queues, chasing and rework from information arriving incomplete.
This matters because it decides what automation is worth building. Automating a task that takes ten minutes and then sits in somebody’s queue for six days saves ten minutes. The interventions that change the elapsed time are different in kind: removing a handoff, running two steps in parallel that were sequential for no reason, pre-populating a form so it comes back complete the first time, notifying the right person automatically rather than when somebody remembers, and making the current state of every item visible so nothing sits unnoticed. Those are automation too, and they are rarely what gets specified when a team is asked which tasks to automate.
Teams automate the task and the queue stays
Ten minutes saved on an item that waits six days for a handoff.
The painful processes are the unstable ones
And in a validated environment instability is what makes automation expensive.
The same information is entered in three systems
Because the systems were bought separately and integrated never.
Approvals wait on somebody noticing
Nothing is blocked, nobody is informed, and the item simply sits.
Every study rebuilds the workflow slightly differently
So nothing accumulates and the next study starts from a template nobody trusts.
Exceptions have no designed path
An automated workflow with an undesigned failure mode creates an invisible backlog.
Reminders got mistaken for automation
A notification tells somebody to act. It does not establish that anybody did.
Nobody can say where the time goes
Because elapsed time is reported in total and never broken into wait and work.
Measure one workflow as wait time and work time before automating it. Take a workflow everybody agrees is painful, instrument it end to end, and split the elapsed time into time somebody was actively working on an item and time the item was waiting. The ratio is usually somewhere between one to ten and one to fifty. Then ask which automation addresses the larger number. In most cases the answer is routing, parallelisation, pre-population and visibility rather than task automation, and in a few cases the answer is that the process has a single bottleneck person and no automation fixes that. Either finding is worth more than the automation you would otherwise have built.
Standardize comes before prioritize and both come before automate, which is the opposite of how most automation programmes run. Automating a process that five countries perform five ways produces five automations, five validation packages and a maintenance problem that grows.
Step 1
Assess the process and its intended use
Step 2
Map it as wait and work, with owners
Step 3
Standardize before automating
Step 4
Prioritize on volume, stability and validation cost
Step 5
Automate the handoffs, the routing and the chase first
Step 6
Integrate through APIs where they exist
Step 7
Validate the whole workflow, not the components
Step 8
Monitor completion, exceptions and the backlog
Step 9
Optimize and retire deliberately
Rank your automation candidates by validation cost, not by complaint volume. List the processes under consideration and score each on four things: how many times it runs a year, how stable it has been over the last two, what its intended use implies for validation, and whether it has a single accountable owner. Then look at what the ranking says. It will not match the list the organization would produce from frustration, and the mismatch is the point. High-volume stable processes with clear owners are where automation compounds. Unstable high-profile processes are where automation programmes go to die, and the honest first move on those is to simplify the process rather than encode it.
Grouped by what the automation actually removes. Most engagements start with one workflow, instrumented and rebuilt, because that produces the measurement that makes the business case for the next one.
Study startup and activation
Study startup orchestration with parallel tracks where sequencing was habit rather than dependency
Site activation workflows with current state visible to everybody waiting on it
Site onboarding and essential document collection, with requests pre-populated and chased automatically
Regulatory and ethics submission packet assembly against a derived expected document set
Contract and budget workflow routing with turnaround measured per step and per owner
Documents and records
eTMF and eISF workflow automation, filing to the expected structure rather than to a free-form drop
Document AI for extraction, classification and completeness checking, with human verification
Version and currency monitoring, so an expired document is flagged before an inspection finds it
Protocol amendment distribution, acknowledgement tracking and downstream impact routing
Study closeout workflows including reconciliation of what must exist before archive
Study conduct
Patient screening coordination and enrolment workflow handoffs between site roles
Clinical data query routing to the right role, with aging and escalation visible
Monitoring visit scheduling, report routing and follow-up item tracking
Safety case intake routing to the qualified function, within your pharmacovigilance procedures
Task management and notification across sponsor, CRO and site boundaries
How it is built to last
API-first integration with CTMS, EDC, eTMF, eISF, IRT and eCOA where interfaces exist
Interface automation where they do not, documented and monitored as a known fragility
Designed exception paths with a named owner and a visible backlog for every workflow
Validation evidence produced as a build deliverable, scoped to intended use
Electronic signature and approval steps implemented to the requirements your quality function sets
The table scores each process the way we would before building anything: how often it runs, how stable it has been, and what its intended use implies for validation. The scoring is the recommendation.
| Process | Volume and stability | Our view |
|---|---|---|
| Investigator document collection | Very high volume, very stable, clear owner | The best first candidate in this estate, almost always |
| Site onboarding | High volume, mostly stable, some country variation | Strong candidate once the country variants are documented |
| eTMF and eISF filing workflows | Continuous, stable structure, inspection relevance | Strong candidate, and the completeness checking is where the value is |
| Regulatory and ethics packet assembly | Moderate volume, stable per jurisdiction | Good candidate per jurisdiction, poor candidate as one workflow |
| Contract and budget routing | Moderate volume, stable process, variable content | Automate the routing and the visibility, not the negotiation |
| Clinical data query routing | Very high volume, stable, high value | Strong candidate, and it removes real elapsed time |
| Monitoring visit logistics and follow-up | High volume, stable, many handoffs | Strong candidate, and the follow-up tracking matters more than scheduling |
| Patient recruitment coordination | Variable, site dependent, consent constrained | Automate handoffs between site roles, nothing patient facing |
| Safety case intake routing | Variable volume, strict obligations | Automate intake and routing only. Assessment stays with your PV function |
| Protocol amendment distribution | Episodic, and the content changes by definition | Automate distribution and acknowledgement. Do not encode the amendment logic |
| Study closeout | Low frequency per study, high volume across a portfolio | Good candidate at portfolio level, poor candidate study by study |
| Sponsor and CRO operations handoffs | Continuous, and contractually specific per sponsor | Automate where a sponsor relationship is long-lived, not for a single study |
Improve clinical trial visibility and streamline clinical development through connected systems, data-driven insights, and automated workflows. From clinical trial data solutions and clinical operations analytics to site and patient analytics, trial workflow automation, and clinical systems integration, help life sciences organizations improve trial oversight, operational efficiency, and decision-making.
The architectural failure in workflow automation is distributing the process. A piece of logic in a platform flow, a piece in a script, a piece in a system configuration, a piece in a rule somebody set in a mailbox. Each works. Together they mean nobody can describe the process, which also means nobody can validate it or change it safely.
| Layer | What belongs there | What does not |
|---|---|---|
| Orchestration | The process definition: steps, sequence, branches, owners, timers | Data transformation, and business rules that belong to a system of record |
| Integration | Moving data and triggering actions in CTMS, EDC, eTMF, eISF, IRT, eCOA | Process decisions, which belong in orchestration where they are visible |
| Document processing | Extraction, classification, completeness checking, with human verification | Approval of a document, or any determination of regulatory adequacy |
| Interface automation | Acting on systems with no usable API, documented and monitored | Anything where an API exists, and anything irreversible without a check |
| Task and notification | Who is told what, when, and what the escalation is | The record itself, which lives in the system of record |
| Exception handling | Where a failed item goes, who owns it, and how it is visible | Silence, which is the default when this layer is not designed |
| Audit and evidence | Attributable record of every automated and human action | Anything that makes a system action look like a person acted |
Ask somebody to describe one automated workflow end to end from the system. Pick an automated process and ask a person who did not build it to explain exactly what happens, step by step, reading only from the tooling. If they have to open four systems, a script repository and somebody’s inbox rules to assemble the answer, the process is distributed rather than defined, and three things follow: it cannot be validated coherently, a change cannot be assessed reliably, and the person who built it is now a dependency. Consolidating the definition into one readable place is unglamorous and is the difference between an automation estate you can maintain and one you are afraid of.
Automation introduces a specific integrity question that manual processes do not have: when a step is performed by software, the record has to say so, attributably. Which frameworks apply to a given workflow, how it is classified and what validation it requires are determined by your quality and regulatory functions. We build so their determination is satisfiable and the evidence exists.
Frameworks where they apply
- ICH E6 Good Clinical Practice principles for the conduct of clinical research
- GxP and GAMP 5 risk-based lifecycle approaches where your quality function has adopted them
- FDA 21 CFR Part 11 and EU Annex 11 for electronic records and signatures where applicable
- Computerized system validation scoped to the intended use your quality function assigns
Approvals and signatures
- Electronic signatures implemented to the requirements your quality function specifies, including signer identity verification and the association of each signature to the specific record signed
- Human approval preserved as a deliberate, attributable act with a recorded reason
- Delegation and authority rules resolved against current roles, not a maintained list
- No auto-approval, no default approval, and no timeout treated as consent
Privacy and access
- HIPAA where protected health information passes through a workflow
- GDPR and equivalent regimes, including personal data about site and investigator staff
- Role-based access resolved per user, with sponsor and study separation where required
- Least privilege for every automated identity, scoped to the actions it needs
Evidence
- Audit trail distinguishing human actions from automated ones, both attributable
- Requirements, test evidence and traceability produced as build deliverables
- Change control covering workflow definitions, integrations, agent action boundaries and the prompts and models behind any AI-assisted step, because a prompt change is a change to a validated system
- Periodic review, because a workflow validated two years ago has had two years of change around it
Automation reporting leads with success rate, which is the least interesting number available. The question that matters is what happened to the failures: whether anybody knows about them, whether anybody owns them, and whether they are being worked or accumulating.
| Failure | How it presents |
|---|---|
| An item fails silently | Success rate looks healthy and a growing set of items is simply nowhere |
| The exception queue has no owner | Failures are visible, nobody is accountable, and the queue only grows |
| A vendor update breaks interface automation | Steps fail, or worse, act on the wrong field and report success |
| A workflow succeeds on incomplete input | The process completes and the output is missing something nobody checked |
| A retry re-performs a completed step | Duplicate records, duplicate filings or duplicate notifications somebody has to unpick |
| Approval turnaround is reported as an average | The mean looks acceptable while a tail of items waits weeks |
| Role changes break routing | Items route to somebody who left, and sit there |
| A workflow drifted from the validated version | Small changes accumulated outside change control |
| Nobody retired the manual process | Both run, people use the one they trust, and effort went up |
Automation programmes report hours saved, which is hard to verify and easy to overstate. The two numbers that are checkable are how long things now take end to end, and whether anything is sitting in a queue nobody owns.
| Outcome | How to tell |
|---|---|
| Administrative workload reduced | The same team handles more studies without the overtime pattern returning |
| Site activation accelerated | Elapsed time per startup step fell, and the binding queue is known by name |
| Workflow consistency improved | One process with documented variants, rather than one per country or sponsor |
| Manual errors reduced | Rework from incomplete submissions fell because forms arrive complete |
| Compliance controls strengthened | Audit trail distinguishes human from automated action, and approvals are deliberate |
| Operational visibility increased | Anybody waiting on an item can see where it is without asking |
| Delivery more efficient overall | Exception backlogs are owned and stable, and nothing runs in parallel with a manual twin |
We work with sponsors, CROs and sites on clinical workflow automation: processes instrumented as wait and work before anything is built, candidates ranked on volume, stability and validation cost, orchestration kept in one readable place, exception paths designed with owners, and validation evidence produced as a deliverable. Human approvals stay human and nothing approves on a timeout.
CaliberFocus automates clinical trial workflows for sponsors, CROs and sites in validated environments. We measure each process as wait time and work time before anything is built, rank candidates on volume, stability and validation cost, keep orchestration in one readable place, design every exception path with an owner, and produce validation evidence as a deliverable. Human approvals stay human.
- Workflows chosen on volume, stability and validation cost
- Handoffs, routing and chasing automated before tasks
- Designed exception paths with a named owner and visible backlog
- Audit trails that separate automated actions from human ones
Security & Compliance
