Requests for startups

Generic imatinib and abiraterone can cost patients hundreds of dollars a month through insurance yet be sold at cost plus a fixed markup by transparent pharmacies; making that the default channel for oral cancer generics would save patients and plans money.

Whenever an AI tool gives a result about a patient, the hospital system would permanently record what it saw, which version it was, what it said and what the doctor did with it.

Each trial would set its target mix of patients from cancer registry data on who gets that cancer, by age, sex and ethnicity, and show a public running tally so gaps are visible while there is still time to fix them.

Having had another cancer years ago, or living with controlled HIV or treated hepatitis, still keeps patients out of trials for no scientific reason. Condition-specific rules, as FDA guidance recommended in 2020, would replace blanket bans and widen access in communities where these conditions are more common.

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.

Frailty is the strongest predictor of who will be harmed by treatment, but it is rarely measured. Software can estimate it automatically from existing records and flag patients who need a closer look.

People should not have to pay to be in a trial. Sponsors would routinely cover travel, hotel and food costs, paid up front rather than claimed back, which is already accepted by regulators as fair rather than coercive.

You cannot manage what you do not measure quickly. Publishing stage at diagnosis by cancer and region every quarter, not years later, would show whether detection efforts are working.

Publish a fake but realistic copy of each secure cancer dataset so researchers can write and test their code at home, then run the finished code on the real data.

Trials sometimes quietly swap the outcome they promised to measure for one that looks better. Software can compare the registered plan with the published paper and flag the switch.

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.

Build and certify a single free tool that strips names and identifying marks from cancer scans and pathology slides, so every hospital stops writing its own.

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.

Secure online workrooms where approved researchers can analyse cancer records without downloading them, with the data already cleaned and organised for cancer questions.

Most cancer drug approvals depend on a handful of facts already in the chart, such as the diagnosis, biomarker and line of therapy; sending those automatically in a standard format would return most decisions before the patient leaves the room.

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.

In much of Africa and South Asia there is about one pathologist per million people, so the pathologist, not the tissue sample, is the diagnostic bottleneck. A slide scanner in each district lab, routing images to a pooled roster of pathologists including diaspora volunteers, could give a diagnosis in days instead of months; the gap is scale and financing.

Patients moving between hospitals often carry paper folders or nothing. A standard electronic summary of diagnosis, treatments, and doses that any system can read would stop repeated tests and dangerous gaps.

Thousands of patients have received standard treatment in the control arms of past trials. Pooling their anonymised data would let new trials borrow from them and randomise fewer patients to old treatments.

Every cancer trial's anonymised patient-level data would go into one trusted repository within eighteen months of completion, with a single access committee, so researchers can re-analyse, pool and learn from trials that today stay locked up.

Hospitals rarely know what fraction of their patients got the recommended treatment. Software reading the electronic record can show each team, every month, where care deviated from guidelines.

Cancer treatment guidance changes constantly and takes years to reach many clinics. Make guidelines live documents that software can read, updated as evidence arrives and adapted to what each country can afford.

Risk-based follow-up guidelines key surveillance to cumulative dose and radiotherapy field. Survivors frequently cannot obtain either, so the guidelines are unusable even where someone is willing to follow them.

Give each patient a scrambled code that is the same across hospitals, labs and registries, so records can be joined without anyone seeing names.

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

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.

Trial staff still retype data from the hospital record into the trial database, and monitors then check every entry by hand. Piping data directly and checking by risk would cut cost and errors.

Instead of opening a trial at fifty hospitals and waiting for patients, keep a network of pre-vetted clinics ready and switch a trial on where a matching patient is found.

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.

If one lab suddenly reports twice the rate of 'positive' biomarker results that other labs report, its assay has probably drifted. Pooling anonymised positivity rates by laboratory, assay and version, with automated outlier detection and case-mix adjustment, would catch reagent lot problems and protocol drift within weeks rather than at occasional proficiency runs.

At each point where treatment is chosen, software checks the patient's record against open trials and the clinician must note which were discussed, so trials stop being something only some people hear about.

Oncologists spend hours a day typing notes. Software that listens to the consultation and drafts the note, the letter and the orders could return that time to seeing patients.

Phase 3 trials are often launched on a small single-arm response-rate study with no comparison group, and most then fail. Requiring a randomised phase 2 with a concurrent control first, with exceptions only for large effects in refractory settings, would filter out weak drugs earlier.

For each type of laboratory model, keep a public record of how often its predictions came true in patients, so that researchers know which models to trust for which question.

Cancer units in poorer countries lose treatment days, spoil drugs and damage machines during power cuts. Solar panels with batteries sized for the cancer unit would remove that failure point.

India's 2012 compulsory licence on sorafenib cut its price by about 97% and its 2019 cap on trade margins lowered the shelf price of 42 cancer drugs; voluntary licences through a patent pool would achieve the same without a fight.

Combine registry cancer incidence by district with open trial site locations from ClinicalTrials.gov to map the regions where patients live more than an hour from any trial. Sponsors and funders would use it to decide where to open sites and justify site selection in diversity plans.

Companies with drugs that might work together rarely test them because the legal negotiation takes longer than the trial. A pre-written standard contract would fix that.

Set common rules for how hospitals check that an AI tool still works as the scanners, patients and practices around it change, and when it must be switched off.

For each drug target, show how many programmes have been tried against it and how many failed, so new teams know what they are up against.

No one knows exactly how many oncologists, nurses, physicists and pathologists each country has or needs. A public, regularly updated model would let governments plan training and spot shortfalls years ahead.

Drugs that reach brain tissue may still fail to reach the fluid where cancer spreads along the linings. A lab model of that second barrier would let us screen for drugs that cross it.

When a computer suggests a treatment, it should show the doctor the specific trial result and guideline sentence behind the suggestion, so it can be checked and trusted.

A drug approved in the US may take five years to reach a patient in Poland or never reach Nigeria. A live public tracker would show exactly where and why it is stuck.

Every funder that spends more than $50 million a year on cancer research would publish what it funds in a shared, coded database, so gaps and duplication can be seen across the whole system.

Medical physicists, who keep radiotherapy machines accurate and safe, are in even shorter supply than oncologists in under-resourced systems. Routine linac quality assurance is now largely automated and log-file based, so one physicist could review it remotely while trained radiation therapists take the measurements, covering three to five machines once regulators define the supervision standard.

Before a trial is finalised, a paid panel of patients and community members from the groups the trial needs would review it and could require changes to visit schedules, procedures and materials that would deter people like them.

Several big projects have sequenced the same tumours at different times and places, but their data sit apart. Bringing them together with common analysis would show general rules of how cancers evolve.

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

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.

Every cancer consultation is recorded with consent and the patient receives the audio plus a checked written summary of what was said and decided.

Hospitals without enough physicists could upload scans to a shared planning centre, where AI drafts the treatment plan and remote experts finish and check it within a day.

Software can already spot impossible statistics, mismatched p-values and duplicated images in a paper. Journals should run these checks on every submission, as spell-check runs on every document.

A platelet count that is normal but rising year on year, or haemoglobin drifting down, can signal cancer. Records already hold these trends; software could use them.

Nobody publishes how much cancer isotope is made, where, or when supply will fall short. A public observatory would let hospitals and investors plan.

Labs usually use whichever tumour models they already have. A searchable index that finds the model closest to a specific patient's tumour would make experiments more relevant.