• Dr Rohith Govindraj

A.I. – the escape from Time Warp of Cancer Treatment Pathways

Every disease ever known to the mankind has challenged us humans to find a cure. Research has helped us understand the patterns of them diseases but the early diagnosis and treatment is still a race against time. Failure to do so leads to significant morbidity and mortality.

Patients put their trust in doctors and the system to help solve their problems. The human capabilities can only be pushed to a certain limit under constraints of budgets and the targets set by the system which is backed only by outdated research.

The healthcare systems across the globe are under immense pressures to beat the clock especially when it comes to cancer. Efficiency of hospitals are measured by achievements against the cancer treatment waiting times.

As an example, the current 62-day cancer treatment target introduced in 2013 has pushed the NHS, UK to treat all cancer in this set target time and is achieved in 72.5% of the cases. However, the published results are far from reality because of the underreported patient experiences in this journey. Patients now are using the Internet blogs to express their views and concerns about the true waiting periods they face whilst being allocated slots for investigations, to get the results of tests performed and appointments in specialist review clinics. However, this internet savvy group only represents the tip of the iceberg of a much deeper problem.

A national review of radiology reporting within the NHS in England by the Care Quality Commission(CQC) published in 2018 describes the key performance indicators (KPI) that tended to be longest were for routine, outpatients and GP requests. Most hospitals aimed for urgent/fastracked to be done at 2-5 days and routine requests in 7-21 days. However, this is not always achievable due to

the work load, staffing and IT issues.

The recognisable delays that can be quantified are the waiting times to access clinic time slots in GP clinics (2-4days) limited consultation times (10-13mins) investigations scheduling/reporting (2weeks) MDT slots (2weeks) and the wait for definitive treatment of cancer(4weeks). The unquantifiable time which could be in several weeks or months is lost whilst the patient actually recognises the symptoms thus adding a bigger challenge for doctors in treating a much more advanced disease.

The Cancer MDT does help deliver a structure to management of cancer pathway. However, the current frequency of MDT meetings trigger actions in once-a-week only fashion. It is an impossible task to increase the frequency of these meetings with the current level of funding and staffing in the healthcare systems across the globe. Thus, needing an intelligent AI system that can generate customised plans for cancer patients which will be evidence based, prioritised and person centred.

Artificial Intelligence when applied to various steps of the patient journey will double the efficiency of the healthcare systems. Self-referral of patients with worrying symptoms for their initial investigations, that could rule out sinister conditions, could be a possibility with the help of symptom analysing AI. This not just saves several days of waiting for GP appointments but also filter out unwanted tests for patients.

Image analysing AI can be used to improve the pick up rate of small cancers on radiological imaging and also early reporting. Natural Language processing (NLP) will help interpret letters, reports and suggest the next action to be taken in the management of cancer patients.

The future of AI in Healthcare will see its application into human lives at the very beginning of their journey. It will study every lifestyle choice made, environmental factors and even the data from social media feeds to decipher it’s correlation to human diseases. Thus, helping detect diseases in its earliest stages and limit its progression since we all do know that Prevention is definitely better than Cure.

Dr Rohith Govindraj

United Kingdom

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