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Demand for clinical nutrition increases with rising number of chronic diseases

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A multidisciplinary approach coupled with AI-backed solutions can be the answer to management of morbidities.

Clinical nutrition in medicine has a pivotal role, helping physicians diagnose and treat disorders that affect dietary constituent intake, absorption, and metabolism. As clinical nutrition is a part of a multidisciplinary approach in treatment, it also promotes good health via the prevention of diet- related diseases such as obesity, with its co-morbidities of hypertension, diabetes, dyslipidaemias, increased risks of cardiovascular disease, some cancers and pulmonary failure. In addition, intestinal disorders related to inadequate nutrient absorption; eating disorders; and malnutrition associated with chronic illness and surgical trauma are among the most common causes of mortality.

Scientific breakthroughs on the association between dietary components and illness cellular mechanisms emerge on a regular basis. A high frequency of nutritional diseases in clinical medicine and mounting scientific data supporting the importance of dietary modification in disease prevention is growing, along with the standardisation of curricula for nutrition education of medical students and trainees, is becoming increasingly important for the cost-effective integration of nutritional concepts into medical practice.

Use of digital technology in clinical nutrition is also gaining prevalence, with AI being used namely in diet optimisation, food image recognition, risk prediction and diet pattern analysis. In an interview with Lina Shibib, Clinical Nutritionist, Medcare Hospitals and Medical Centres, we dive into how innovation and tech advancements are being integrated in clinical nutrition to support treatment plans become personalised in combatting chronic illnesses. Excerpts:

 

How does clinical nutrition serve as an integral component of chronic disease management in multidisciplinary approaches?

Chronic disease represents one of the most important challenges facing healthcare systems. According to the WHO, tobacco use, physical inactivity, and unhealthy diet are the three most common modifiable risk factors of chronic disease. Most approaches to “fix the damage” would have to go back to these three factors – all which can be dealt with by a clinical nutritionist. As per the CDC, adults who eat a healthy diet live longer and have a lower risk of obesity, heart disease, type 2 diabetes, and certain cancers – the top causes of mortality. So basically, good nutrition can help reduce the risk and even treat some diseases, including heart disease, diabetes, stroke, some cancers, and osteoporosis.

Comment on the relationship between clinical nutritionists and physicians (for example, when diagnosing patients with irritable bowel syndrome) and working hand-in-hand to create personalised treatments.

As chronic disease numbers are rising, so is the demand for a multidisciplinary approach to target the villain – which many times is diet and lifestyle. A doctor will identify the cause, whereas the nutritionist will educate the patient further on a more personal level. Nutritionist literally “befriend” the patient to find out as much as possible about the patient’s diet, lifestyle, sleep, exercise, as well as psychology. Once all these families are discovered, the nutritionist will then report back (if necessary) to the doctor to account for all those aspects in the treatment plan, and together come up with a personalised approach for the patient. In the case of irritable bowel syndrome (IBS), for example, a doctor will diagnose the condition, but the nutritionist will work with the patient closer to discover which foods and habits are causing the symptoms and teach them healthy eating methods to avoid future discomfort.

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Lina Shibib, Clinical Nutritionist, Medcare Hospitals and Medical Centres
 

As healthcare pivots towards digitalisation, how has this affected the clinical nutrition discipline?

Artificial Intelligence (AI) is human intelligence displayed by machines, and it is a technology that allows computers to conduct cognition, learning, inference, and actions in the same way that people can. To accomplish this, a machine must learn an algorithm based on a vast amount of big, scientific data acquired using a deep learning technique. Nevertheless, the real scientists will always be the nutritionists. An app can easily help a patient calculate calories, identify “hidden ingredients”, calculate blood pressure, blood sugar, and even exercise but it cannot go out of its “way”, in this case algorithm, to personalise a diet plan.

Just as technology is evolving, so is the F&B world. New foods require proper clinical-based research before being introduced into apps – most of which these apps lack. For a proper clinical nutrition plan, a one-size-fits-all diet through apps is not going to target the medical issue on a personal level. In some cases, a simple wrong data entry might even result in the wrong “advice”, which, especially in case of a medical condition, can have a serious impact on health.

Does AI play a part in automating the requirements of certain patients based on data? How can this prevent the prevalence of chronic illnesses and help manage it?

AI is generally regarded as positive by practitioners but also brings with it many challenges in medical ethics and patient-clinician relationships. AI systems can analyse unstructured clinical notes on patients, prepare reports (for example, on radiology examinations), transcribe patient interactions and conduct conversational AI, but the final translation of the data needs to always be approved by an actual doctor.

Healthcare is also being tackled by many tech companies and start-ups. For example, Google is working with health delivery networks to develop big data prediction algorithms that will alert clinicians to high-risk illnesses like sepsis and heart failure. AI-based image interpretation algorithms are being developed by Google, Enlitic, and several other firms. Jvion provides a 'clinical success machine’ that identifies patients who are most at risk and most likely to react to treatment programmes. Each of these could help professionals determine the optimal diagnosis and therapy for their patients by providing decision support, but not actually “do” the doctors job of prescribing and operating.

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