Operating room analytics measures how allocated theatre time is used, categorises the delays that consume it, and reports utilisation, turnover, and start performance by service and room. It measures time and categorises cause. It does not evaluate surgical decisions, judge clinical necessity, or reallocate anyone’s block time.
Theatre analytics fails politically rather than technically. Block utilisation is arithmetic, and the moment it appears next to a surgeon’s name in a meeting about reallocation, every definitional weakness becomes a dispute. Case mix, scheduling accuracy, and clinically necessary delay all sit behind a low figure. Taction builds theatre analytics where those explanations are structurally attached, because a number without them will be argued rather than acted upon.
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It measures allocated versus used theatre time, in-room and procedure intervals, turnover between cases, and start performance, then categorises the delays behind each. Reporting rolls up by service, room, day, and surgeon with scheduling accuracy and case mix visible. It sits inside a wider healthcare data analytics practice, and the definitional agreement our analytics consulting practice establishes matters far more here than calculation ever does. Definitions are settled across surgical, anaesthesia, and nursing leadership before publication, because these figures enter an allocation conversation from the first committee appearance onward, whether or not anyone intended that.
Allocated block time compared against time used, with release rules, abandonment, and out-of-block cases all handled by an agreed definition. Definition choices change reported utilisation by wide margins. Rules are written down first.
Wheels in, procedure start, procedure finish, wheels out, and room ready produce the intervals every theatre conversation depends upon. Interval definitions are agreed with anaesthesia and nursing leadership explicitly. Capture during a list is imperfect.
Time between one case leaving and the next entering, separated into cleaning, setup, and waiting components rather than reported as a single figure. Component separation identifies the actual constraint. Waiting is usually the largest component.
Start performance measured against a defined target with delay cause recorded, treated as a team measure rather than an individual one. Team framing is essential because several roles must align.
Delays categorised by cause: patient readiness, anaesthesia, equipment, staffing, surgeon availability, or clinical necessity. Clinical necessity is a legitimate category rather than an excuse. Removing it from the taxonomy makes clinicians record nothing at all.
Booked duration compared against actual so scheduling error is visible separately from theatre performance. Scheduling accuracy frequently explains more variance than turnover does. Systematic booking error masquerades as poor theatre performance regularly.
It does not judge clinical necessity, evaluate surgical technique, reallocate block time, or determine anything about how a case should proceed. Those judgements belong to clinical and governance leadership. Governance decides allocation, not software.
Everything here depends on definitions and delay attribution being agreed before any figure is published, because theatre analytics is read in a political context from the first meeting onward. We settle utilisation rules, interval definitions, and the delay taxonomy with surgical, anaesthesia, and nursing leadership together. Underneath that, timestamp capture during a busy list determines what can honestly be measured, so our data quality practice is part of the build rather than assumed. Reporting is validated against lists your leadership remembers before anything reaches a committee agenda, because a disputed figure discredits the whole platform.
Block definition, release rules, turnover inclusion, and out-of-block handling agreed in writing with a version recorded against historical figures. Written rules end the recurring dispute about whether one period is comparable with another.
Interval calculation with turnover decomposed into cleaning, setup, and waiting components and reported by room and service. Decomposition identifies the person or equipment causing delay rather than reporting a long total.
A delay taxonomy agreed across roles with capture designed to take seconds at the point the delay occurs. Capture speed decides whether delay data exists, since anything designed for a quiet moment goes unused.
Booked against actual duration by surgeon, procedure, and service so scheduling error is separated from theatre efficiency. Separation directs the conversation accurately. Booking durations are frequently wrong in a consistent direction per surgeon.
Demand, allocated capacity, and used capacity compared to support allocation discussions, alongside our scheduling platform work. Allocation analysis informs a governance decision and supports the conversation rather than replacing it.
Utilisation, turnover, start, and delay reporting to case level through our data visualisation practice. Case-level drill-down is what makes a disputed figure checkable. A figure nobody can check will simply be disputed instead.
We publish no figures on utilisation improvement, turnover reduction, or additional cases accommodated, because those depend entirely on your case mix, your staffing, and decisions your governance committee makes. What we deliver is instrumentation so your team measures impact against its own data. The honest framing here is political as much as analytical: theatre figures are read in allocation discussions, so the value lies in figures that survive challenge rather than figures that look decisive. Read the items below as defensibility rather than as any claim about utilisation improvement or additional cases accommodated.
Definitions are written, versioned, and drillable to case level, so a disputed number can be checked rather than argued from recollection. Checkability is the whole value here. Recollection loses to a case list every time.
Cleaning, setup, and waiting are reported separately so the constraint is identified rather than the total merely observed. Decomposition points at the responsible process. Blame assigned broadly changes nothing operationally in a theatre.
Booking accuracy is reported distinctly from theatre performance, which frequently reveals the larger of the two problems. Separation prevents misdirected improvement work. Booking accuracy is frequently the larger of the two problems.
Delay categories with clinical necessity as a legitimate option produce data clinicians will actually record honestly. Honest recording is the precondition for useful delay data. Dishonest delay data is worse than none at all.
Demand against allocated and used capacity supports a governance discussion with evidence rather than with anecdote and seniority. Evidence improves an inherently difficult conversation. Seniority and anecdote currently decide more of this than evidence.
Low utilisation frequently reflects allocation, case mix, or scheduling accuracy rather than a surgeon’s behaviour. That framing is built into the reporting rather than offered as a caveat. Surgeons dismiss figures lacking it.
We start with definitions and the delay taxonomy, because theatre analytics enters a political conversation immediately and undefined figures get discredited in their first committee appearance. Discovery is paid and time-boxed and produces an itemised fixed-scope estimate with a build or configure recommendation. Where your theatre management system already reports adequately and the gap is delay capture or scheduling accuracy, we scope only that. Delivery runs in short increments with surgical, anaesthesia, and nursing leadership reviewing real cases. Definitions that enter a committee undefined get discredited immediately, and recovering that credibility afterwards takes considerably longer than agreeing them.
Theatre timestamps assessed for how each is captured during a working list, with retrospective entries identified honestly. Capture reality constrains what can be reported. Retrospective entries are labelled rather than published as measured.
Utilisation rules, interval definitions, and delay categories agreed across surgical, anaesthesia, and nursing leadership in writing. Cross-role agreement is the precondition for acceptance. Three leadership groups must accept the rules for figures to hold.
Capture designed for the circumstance in which a delay actually occurs, which is rarely a moment anyone has spare. Design for pressure means seconds rather than minutes, at the point the delay occurs.
Calculation, decomposition, scheduling accuracy, and reporting built in increments with equipment and supply data via our inventory practice. Supply linkage explains equipment delays. Supply and equipment data explain a large share of delays.
Figures reviewed against lists your leadership remembers, with every discrepancy investigated to its cause before publication. Pre-publication validation protects credibility. Discrepancies are investigated to cause before any figure is published.
Reporting released with definition ownership transferred to your governance committee, then handover covering taxonomy maintenance. Committee ownership is the durable arrangement. Taxonomy maintenance passes to the governance committee with the reporting.
We build calculation and reporting on your data platform, with theatre management, scheduling, and supply data assembled through interfaces to your existing systems, including our clinical scheduling work where optimisation is in scope. Compliance covers HIPAA safeguards, access control appropriate to surgeon-level operational data, and audit sufficient to reconstruct any published figure with the definition version applied at the time. Every surgeon-level figure carries case mix, scheduling accuracy, and allocation pattern structurally rather than as a caveat that somebody may or may not actually read before they draw a conclusion from it.
Block allocation is a governance decision made by people who can explain it. We decline to build automated reallocation or systems that trigger block removal from a utilisation threshold. Accountability stays with people.
Case mix, scheduling accuracy, and allocation pattern are attached structurally to any surgeon-level figure. Structural context makes a naive comparison harder to produce. Producing a naive comparison requires deliberately stripping that context out.
Delays that were clinically necessary are recorded as such and never absorbed into a general inefficiency figure. Legitimate delay must have a home in the taxonomy. Its absence corrupts the whole delay dataset quickly.
Reporting is operational. We build nothing that pushes a surgeon or anaesthetist toward a decision about a case in order to improve a reported measure. Case decisions belong to the surgeon and anaesthetist involved.
Utilisation rules, intervals, and delay categories are versioned, with historical figures retaining their version. Version records explain period-to-period movement. Movement between periods becomes explicable rather than contested at a committee.
Inputs, definitions, delay records, and calculated figures are retained so any published number can be reproduced exactly. Reproducibility is what survives a committee challenge. A disputed figure is answered by reproduction rather than by argument.
We have been building healthcare software since 2013, which is over 12 years, and we have delivered more than 200 healthcare projects. We built our own EHR platform, Voyant Health, and FDA-registered applications, Revive Ease and PainKare, so procedural documentation and regulated build discipline are established practice here. We are ISO 27001 certified, our leadership brings more than 20 years of personal experience in the field, and we work from four US offices in Chicago, Cheyenne, Austin, and Sacramento. We will also tell you when your constraint is allocation rather than efficiency.
We settle utilisation rules and delay taxonomy across three clinical leadership groups before publishing anything. That sequence is slow and produces figures a committee cannot dismiss. Committees cannot dismiss what three leadership groups agreed.
We separate cleaning, setup, and waiting rather than reporting a single turnover number. Decomposition identifies the constraint instead of assigning blame broadly. Blame assigned broadly changes nothing operationally in a theatre.
Building Voyant Health means we understand which theatre timestamps are captured reliably and which depend on somebody recording them mid-list. Mid-list recording is unreliable and we report accordingly rather than optimistically.
Taction is ISO 27001 certified, with documented access control, encryption, and change control that stands up to a customer security review without improvisation. Surgeon-level operational data carries role-based access with full logging.
Block decisions stay with your governance committee. That refusal removes a feature occasionally requested and keeps accountability where it belongs. Committees make allocation decisions and explain them to the services affected.
Four US offices in Chicago, Cheyenne, Austin, and Sacramento, with delivery overlapping your working hours through definition workshops and validation. Escalation reaches a named delivery lead rather than a shared support queue.
Pricing turns on timestamp capture quality, whether delay capture is in scope, and how many rooms and services participate. The tiers below cover engineering. Third-party licensing, cloud infrastructure, data subscriptions, and hardware are separate from engineering cost and itemised clearly. Benchmark datasets and theatre management licensing are held directly by your organisation and sit outside our engineering estimate entirely at every tier. Where the audit shows theatre timestamps are entered after the list, we narrow the reporting scope honestly rather than quoting a platform that would publish approximations as though they were measurements.
$40,000 to $80,000 for block utilisation and interval reporting with agreed definitions and case-level drill-down for one facility. Delay capture and scheduling accuracy analytics can follow in a later phase.
$80,000 to $200,000 for utilisation, turnover decomposition, start performance, delay capture and taxonomy, scheduling accuracy, and capacity analysis. This tier covers most single-site surgical services that we are asked to scope.
Starting at $200,000 for multi-site surgical services with consistent definitions, several source systems, supply linkage, and consolidated governance reporting. Room count and definition governance drive the figure more than case volume.
A paid, time-boxed discovery phase produces a timestamp and capture audit, draft definitions across roles, a delay taxonomy, a build recommendation, and an itemised estimate. The agreed definitions are yours whether or not we build.
Room count, delay capture scope, source system quality, and definition disagreement across roles. Cross-role definition work takes longer than the engineering does. Agreeing definitions across three leadership groups is the real schedule risk.
Budget annually for definition review, taxonomy maintenance, capture monitoring, and source system change handling. Governance changes drive most definition revisions each year. Allocation policy changes are what trigger most definition revisions.
If your block utilisation figures get disputed every committee meeting, start with definitions. A paid discovery phase gives you a timestamp and capture audit across your theatres, draft utilisation rules and interval definitions agreed across surgical, anaesthesia, and nursing leadership, a delay taxonomy that includes clinical necessity as a category, a build or configure recommendation, and an itemised fixed-scope estimate. You keep the definitions and the audit regardless of what you decide to build.
These are the questions perioperative directors, block committees, and surgical service leaders raise before scoping this work. Most concern definitions, because in this category definitions are the substance rather than the preamble. One concerns something we refuse to build. Where an answer depends on your capture quality, the discovery audit settles it quickly and is worth having on its own regardless of build. We would rather spend the first weeks settling definitions than hand a committee figures that the affected service dismantles in its first meeting on the subject, which we have watched happen.
Because the definition determines the number and most organisations have never written theirs down. Whether turnover counts, how releases are handled, and what happens to out-of-block cases each move utilisation by wide margins. We settle those rules in writing across surgical, anaesthesia, and nursing leadership before publishing a single figure.
No. It reports utilisation, demand, scheduling accuracy, and case mix so your governance committee can make that decision with evidence and explain it. We decline to build automated reallocation or threshold-triggered block removal, because those decisions require judgement and accountability that software cannot carry. Evidence supports that decision rather than making it.
Frequently not. Allocation that does not match demand, scheduling durations that are systematically wrong, staffing constraints, and case mix all produce low utilisation independently of any surgeon’s behaviour. That is why scheduling accuracy and case mix are attached structurally to every surgeon-level figure we report.
As a legitimate delay category recorded as such, never absorbed into a general inefficiency figure. If clinical necessity is not an available category, clinicians record something else or nothing at all, and the delay dataset becomes worthless. Making it available is what keeps the recording honest.
No. A single turnover figure tells you it is long without telling you why. We decompose it into cleaning, setup, and waiting components so the constraint is identifiable, which usually turns out to be waiting on a person or a piece of equipment rather than cleaning time.
Then we say so and narrow the reporting scope accordingly. Intervals built on times reconstructed at the end of a list look precise and are approximate. We grade each timestamp by how it is captured and label any figure resting on retrospective entry rather than publishing it as though it were measured.
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