For UK doctors and medical students

Portfolio work you can stand behind.

Audit, quality improvement and systematic review are how you compete for a training post, and most people learn them by guessing. Regspire takes you through each one a stage at a time, using the methods the work is actually judged by, so you finish with a project you are glad to put your name to.

Create your accountor look around first ↓

The Founding Clinician Programme is open. Fifty places at any one time, £5 a month, held for as long as your subscription continues without interruption.

0Complete workflows
0Guided stages
0Founding places

One

Three workflows, each complete from question to write up.

Clinical audit

Included at launch

Turn a standard of care into measurable criteria drawn from published guidelines, collect data in a table built from those criteria, analyse compliance, then close the loop with an action plan, a second cycle and a report covering both. Regspire will not print an improvement your numbers do not support.

  1. 01Standards from guidelines
  2. 02Data collection
  3. 03Compliance analysis
  4. 04Action plan
  5. 05Second cycle
  6. 06Report and slides

Quality improvement

Included at launch

Set an aim with outcome, process and balancing measures. Track change on a run chart that separates a real signal from ordinary variation, work through PDSA cycles, and produce a report and poster structured to SQUIRE. Your baseline locks before you see the data, so a result cannot be adjusted afterwards.

  1. 01Aim and measures
  2. 02Baseline
  3. 03Run chart
  4. 04PDSA cycles
  5. 05Sustain
  6. 06SQUIRE report

Systematic review

Included at launch

Shape a question and test whether it is answerable, build the search, screen with a second reader, appraise risk of bias, pool a genuine meta-analysis, rate certainty with GRADE and write it up. The statistics run in the same software published reviews use, and every result can be reproduced outside the app.

  1. 01Question and feasibility
  2. 02Search
  3. 03Screening
  4. 04Risk of bias
  5. 05Meta-analysis
  6. 06GRADE
  7. 07Write up

Inside Regspire

What a project looks like while you are working on it.

Clinical audit workspaceSample project
AuditAnalysis · 100 cases

Statistics

Descriptive analysis of the collected data. Every value is computed by a stated method and is externally reproducible. No inference, and no AI in the numbers.

Methods: proportions shown with Wilson 95% CIs; spread as the sample standard deviation and IQR (quantile type 7). Percentages exclude blanks and N/A; each shows its n.

Dataset

120total records
100included
20excluded
6variables

Compliance

Risk assessed within 14 hours of admission82/100 · 82.0% (95% CI 73.3% to 88.3%)
Target 90%8.0 points below target
Assessment documented in the notes64/100 · 64.0% (95% CI 54.2% to 72.7%)
Target 90%26.0 points below target
Prophylaxis prescribed where indicated91/100 · 91.0% (95% CI 83.8% to 95.2%)
Target 90%1.0 point above target
Reassessed at 24 hours38/100 · 38.0% (95% CI 29.1% to 47.8%)
Target 90%52.0 points below target

Sample data. This is the analysis screen as the app renders it, including the Wilson confidence intervals and the target markers, so what you see here is what you get.

Two

How you know the numbers are right.

It is the only question worth asking of a clinical tool. Every claim below is referenced in the notes at the foot of this page, including the work still in progress.

Published methods only
Confidence intervals, run chart signal rules and pooled estimates follow methods published in the literature and used by the reviews these projects are compared against.1 Nothing statistical is invented for the product.
Every figure is traceable
Each number traces back to the inputs that produced it. Data exports as a table alongside the analysis script that generated the result, so a supervisor or a journal reviewer can rerun it without opening Regspire.2
Checked against known answers
The statistical engine is validated by reproducing published meta-analyses and comparing every figure against a second implementation written independently of the app.3 That work is ongoing, and each claim appears here only once it is supported.

Three

Who it is built for.

  • Medical studentsBuilding a portfolio before applications open, without a supervisor on hand to check the method.
  • Foundation doctorsEvidencing audit, quality improvement and research against a deadline that does not move.
  • Core and specialty traineesProducing work that has to hold up at ARCP and in an interview portfolio station.
  • Any NHS teamRunning improvement work that deserves to be done properly and written up once.

Four

The Founding Clinician Programme.

Fifty places at any one time, £5 a month, held at that price for as long as your subscription continues without interruption, whatever public pricing does later.

Create your account
  • Every workflow as it is released
  • Direct influence over what is built next
  • Your founding rate held on those terms

Not ready to join yet? Leave your email and we will let you know when the remaining places open.

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Notes

  1. Run chart interpretation uses the four standard signal rules for shift, trend, runs about the median and an astronomical point. Quality improvement reports follow SQUIRE 2.0 and systematic reviews follow PRISMA 2020. Pooled estimates use inverse variance and the Mantel and Haenszel method, with the DerSimonian and Laird and restricted maximum likelihood estimators available for random effects models, computed in R with metafor, the package behind a large share of published reviews. Return
  2. Audit and review data export as a table together with the analysis script that produced the figures, so a result can be recomputed from the same inputs by anyone with R. Return
  3. Validation to date includes the BCG vaccine and tuberculosis meta-analysis reported by Colditz and colleagues in 1994, reproduced across fixed effect, random effects and Mantel and Haenszel models, and a set of continuous outcome datasets covering mean difference and standardised mean difference. Every figure is cross checked against an independent implementation written separately from the application, and two defects found this way have been corrected. Return