Weak real-world evidence and registries

Connect hospital records across countries so that questions about how treatments work in real patients can be answered in weeks without moving the data, to a standard regulators accept.

Instead of asking twenty hospitals for permission, a researcher would apply once to a single national body that can grant access to all cancer records under one set of rules.

We know surprisingly little about what happens to cancer survivors twenty years on. Linking their treatment records to later health records would show which treatments cause which problems and who needs watching.

After surgery or curative treatment, everyone gets regular blood tests for leftover cancer DNA, and the pooled results power forecasts of who will relapse and trials of acting early.

When researchers use hospital records to ask 'would drug A have beaten drug B in a trial', they should follow a published recipe and register their plan first, so the answer can be trusted.

Sometimes a trial cannot randomise, so the new drug is compared with past patients' records. Clear published rules on when that is allowed, and how it must be done, would replace case-by-case guesswork.

Measure and publish, for every practice-changing result, how long it takes before most eligible patients in each country and hospital actually receive it.

Trials tell us a drug works but not where it fits among the others. Commit to answering 'which order' from hospital data within a year of each approval.

Record, for every patient, the order of treatments and what happened, so that the most common sequences can be compared and the worst ones flagged.

Doctors often use cancer drugs outside their approved use based on a hunch or a small study. Record what happens every time so the hunches become evidence.

Scans find unexpected lumps in the adrenal, thyroid, pancreas and lung. Nobody knows how many matter. A national cohort following them for ten years would tell us whom to watch.

Give every tumour board a tool that pulls up the relevant trials and guideline lines for each case with citations, records what was decided, and later shows how the patient did.

Surgeons' skill affects whether cancer comes back, but nobody measures it. Recording operations and rating them, increasingly with AI, then linking ratings to outcomes, would make surgical quality visible and improvable.

Drugs are approved on trials of fit, younger patients and then given to everyone. A required follow-on trial in older, sicker and more diverse patients would show whether the benefit holds in real life.

Millions of people take common drugs and some get cancer. Running standardised analyses across whole-country records could rank which old drugs deserve a real trial.

Childhood cancers are rare, so no one country sees enough cases. Pool the treatment and outcome of every child treated anywhere into one governed dataset.

Build a public collection where every major cancer trial has a matching analysis done on hospital data, so we learn exactly when real-world evidence can be trusted and when it cannot.

Different cancers favour different organs, and so do different patients. A model that predicts which organ is at risk could target surveillance and prevention.

Connect the cancer registry to death records, pharmacy records and scan reports automatically every week, so we always know what happened to every patient without anyone filling in a form.

Trials often stop following patients once the main result is in, so we never learn whether the drug extended life. Linking participants to national death and cancer registries costs almost nothing and would answer that question.

Half of patients take antioxidant vitamins during chemotherapy. One good observational study suggests they raise recurrence by 40%. Patients deserve a definitive answer, and it can be obtained cheaply by adding supplement tracking to trials already running.

Radiotherapy machines that adapt to the tumour each day cost far more than standard ones and their benefit is unproven. Payers would fund them only within registries and trials that measure whether they help.

Many combinations are already used off-label. Careful analysis of what happened to those patients can rule out the pairs that clearly do not help before spending money on trials.

Before spending millions on a randomised trial, analyse existing patient records as if the trial had already happened. If the answer is obvious or the question is unanswerable, skip or redesign the trial.

Scotland and Wales put a floor under the price of alcohol. Deaths from liver disease have already fallen. Cancer takes longer to show, so someone has to keep measuring for a decade.

Drugs approved early on promising but unproven results would have every treated patient followed in a registry, so we know within two years whether the promise held.

Genetic test results for tumours are mostly PDFs. Require labs to also send a computer-readable version to a national store, so variants can be linked to what treatments worked.

Trials stop following patients after a few years, so late side effects and late relapses are missed. Link trial participants to national records so follow-up continues automatically for decades.

Trials are inspected to check the data are real and traceable. Do the same for the hospital databases used to make regulatory decisions.

Millions of people have had weight-loss surgery or now take weight-loss drugs. Linking those records to cancer registries would show, cancer by cancer, how much reversing obesity prevents, for almost no cost.

Join the national list of who got cancer to the genetic profile of each tumour, so we can see for the whole population which mutations matter and which drugs work for them.

Use joined-up pharmacy and hospital records to spot when a common everyday medicine makes a cancer drug work worse or cause more harm.

You cannot fix what you cannot count. Every donor-funded cancer programme should fund and require a population-based cancer registry so results can be measured over time.

When a screening test misses a cancer, we should know. Linking every negative result to the cancer registry and publishing what was missed, by stage, should be a condition of use.

Patients and doctors cannot see how long each CAR-T maker takes or how often manufacturing fails. Publishing this would create pressure to get faster and more reliable.

Older, frailer and sicker patients are usually kept out of trials but make up most of those treated. Require companies to report how these patients do in practice.

New operations and surgical devices spread by enthusiasm, not evidence. Every new technique would have to be entered in a registry that tracks patients through defined stages before it can be widely used and paid for.

Trials and hospital records describe the same things in different languages. Publish the translation so trial patients can be followed for life in routine data and trial results compared with routine care.

Hospitals would only be paid for cancer treatment if they record a small, standard set of facts (diagnosis, stage, biomarkers, treatment, outcome) in a shared format that any computer can read.

When a drug is approved early, each country often demands its own follow-up study. A single shared registry would answer the safety questions faster and better.

Companies and charities give or discount cancer drugs in poorer countries, but nobody records whether the patients did well. Make a simple outcome record part of every programme.

Publish the exact rules used to work out from messy hospital records which treatment a patient was on and when it stopped working, and test them all on the same data.

Leftover-cancer blood tests are being sold faster than evidence that acting on them helps. Paying for them only when the result is recorded would generate the missing evidence.

When a health system pays for a new, uncertain cancer drug, it would require that every patient's outcome is recorded and that a pre-agreed analysis decides whether payment continues.

Once a drug is in wide use, real-world records could be checked routinely for whether side effects differ by ancestry or sex, since trials were too small in those groups to notice. Findings would go into the label.

Just as clinical trials must be registered before they start, studies using hospital data should be registered too, so the failed or unwelcome ones cannot quietly disappear.

The WHO set three simple goals for breast cancer: most cancers found early, diagnosis within 60 days, and most patients finishing treatment. Every country should publish how it is doing on each, every year.

Publish a simple report card showing how complete, timely and standard each hospital's cancer data are, so poor recording becomes visible and fixable.

National cancer registries already collect the outcome data. Adding a randomisation button lets doctors compare two standard treatments across thousands of patients at a fraction of the usual cost.

Rare cancer patients are already tracked in registries. Offering randomisation inside the registry makes trials far cheaper and lets almost anyone take part.

Track how much of each cancer drug patients actually receive and how often they need to reduce it, and use that to change the official dose when the label is too high.

Set the price of a new cancer drug provisionally, then adjust it up or down after three years depending on how well patients actually did.

How long people wait between first noticing something wrong and being diagnosed is barely measured. Recording it routinely and publishing it by hospital would expose where the system loses time.

Cancer drugs are often prescribed outside their approved use based on hope or small studies. Capturing outcomes of these uses would reveal which ones fail so they can be stopped.

Use the cancer registry itself as the trial machine: randomise patients at diagnosis, then let the registry collect the outcomes for a fraction of the usual cost.

When two approved drugs are both reasonable next steps and nobody knows which should come first, let the clinic flip a coin and record what happens.

The big metformin cancer trial failed after years and millions, despite strong observational hints. Cheaper checks on causality should be passed before funding the next one.

New cancer drugs are approved on trials of younger, fitter patients, then given mostly to older ones. Regulators should require real-world safety and benefit data in the over-75s and put it on the label.

Hospitals keep their records at home; researchers send in a programme that runs at each hospital and only the summary results come back.

Before a trial is finalised, run its entry rules against records of real patients with that cancer and report what fraction would qualify. If it is under half, explain why.

When a trial has no comparison group, the comparison is sometimes built from old patient records. Set rules for how that is done so the answer is not rigged.

Radiologists would record tumour measurements and response in tick-box, coded form rather than prose, so progression is machine-readable across every scan.

Countries track how bacteria become resistant to antibiotics and publish it. Doing the same for cancer drugs would show which escape routes are becoming common and where.

Sequence every cancer at diagnosis, along with the patient's inherited genes, and pool the results with treatments and outcomes so every patient teaches the system how to treat the next.

Real-world studies say a drug 'stopped working' based on clinic notes. Check how often that matches a proper scan review, and fix the definitions so the two agree.

Pragmatic trials want to use the progression dates recorded in ordinary clinic notes instead of expensive protocol scans. Checking how well those routine records match formal trial measurements would show when that shortcut is safe.

Some everyday medicines, such as antibiotics or steroids, seem to blunt immunotherapy. Automatically scanning health records for such harmful pairs would catch them years earlier.

Step counts and sleep from a wristband could show whether a cancer treatment is helping or harming daily life. Prove they track survival and quality of life, then use them in real-world studies.